path: root/gnqa/paper1_eval/src/data/datasets/human/dataset_citizenscientist_general_1.json

{
  "question": [
    "What is the most cited environmental factor for the onset of asthma?",
    "How would one extract the DNA, from say, flora or fauna?",
    "genetics",
    "what is bioinformatics",
    "Explain the process of finding a genetic marker followed by a quantitative trait loci."
  ],
  "answer": [
    "The most cited environmental factor for the onset of asthma is living in an urban area, particularly in low-income settings. Other factors include exposure to air pollution, toxins in food and drink, and aerosols, especially during the rainy season.",
    "DNA extraction from flora or fauna involves several steps. For flora, a common method is the mixed alkyl trimethyl ammonium bromide (MATAB) procedure. Plant material is ground and incubated in a pre-warmed extraction buffer, then purified with chloroform:isoamylalcohol, and DNA extracts are precipitated with isopropanol. For fauna, DNA can be extracted from various samples such as blood, tissue, or even stool. A common method involves cell rupture in the presence of EDTA to prevent DNA fragmentation, followed by enzymatic digestion of cell walls, solubilization of the cell membrane, and purification using phenol-chloroform extraction and ethanol precipitation. In both cases, the extracted DNA is usually stored at -20 C until use.",
    "Genetics plays a significant role in various aspects of human life and health, including aging, weight loss, bone traits, physical activity levels, and susceptibility to diseases. Genetic factors can influence lifespan, with certain genes linked to conditions like Alzheimer's disease, heart disease, and cancer. Studies have also shown that genetics can affect the magnitude of weight loss and physical activity levels. In addition, genetics can impact bone traits and the response to different diets. Research on animal models has further demonstrated the influence of genetics on exercise capacity and responses to training regimens.",
    "Bioinformatics is a field that deals with the application of computer systems to the understanding and organization of biological data. It involves the use of computational tools to gather, store, analyze and integrate biological and genetic information. This can then be used for gene-based drug discovery and development, prediction of protein function from sequence and structural information, and analysis of genomic data.",
    "The process of finding a genetic marker followed by a quantitative trait loci (QTL) involves several steps. First, a population is developed for genetic mapping. This population can be a segregating population or a permanent population. The population is then genotyped using molecular markers. Next, the population is phenotyped for an interested trait. QTL analysis is then conducted using statistical procedures to find markers linked to the QTL. This involves single-marker regression across all chromosomes, where a hypothetical QTL is evaluated at the location of each marker locus. The significance of that QTL is estimated from a likelihood ratio statistic. A permutation test is then conducted to establish genome-wide significance criteria for the trait. The result is a list of marker loci that show a significant association with the trait. These loci are most likely to be near QTLs. The goal of QTL mapping is to identify regions of the genome that harbor genes relevant to a specified trait."
  ],
  "contexts": [
    [
      "INTRODUCTIONAsthma is a chronic disease of the airways defined by its symptoms, which include reversible airflow obstruction, inflammation, and bronchial hyperresponsiveness.The ancient Egyptians, Greeks, and Romans made reference to the symptoms of asthma, and today the disease is estimated to affect 235-334 million people worldwide (44,53).The atopic triad.Perhaps the most widely recognized pattern of co-occurrence is the one of asthma, atopic dermatitis (eczema), and allergic rhinitis (hay fever), which together are referred to as the atopic triad and characteristically present clinically in a temporal sequence known as the atopic march.Within this sequence, atopic dermatitis is typically the first component to manifest, with approximately 20-30% of individuals with mild disease and 70% of those with severe disease going on to develop asthma.Individuals who undergo this distinctive sequence of disease progression frequently exhibit a more severe and persistent phenotype, with increased risk of allergen sensitization.",
      "Clinically, asthma is characterized by episodes of coughing, chest tightness, wheezing, dyspnea, or sputum production.Often, asthma sufferers experience a combination of these symptoms, or some symptoms more than others.Pulmonary breathing tests typically demonstrate variable airway obstruction and hyperreactivity, but may be normal, even in patients with severe and uncontrolled disease [8].Thus, the diagnosis of asthma, which is based on general clinical symptoms and variable lung function testing, is non-specific and heavily dependent on clinical history.Within the \"umbrella\" diagnosis of asthma there exists a diverse array of differing clinical phenotypes [9].For example, childhood asthma is often associated with personal and parental atopic diseases (i.e., atopic dermatitis, food allergy, eosinophilic esophagitis, allergic rhinitis), viral infections, and tobacco smoke exposure [10].Alternatively, adult-onset asthma is less associated with atopic disease [11,12], but more associated with female sex [13], sinus disease [14], and preceding respiratory infections such as pneumonia [15].In addition, adult-onset disease is often of higher severity [12,16] with a faster and more persistent decline in lung function [17].Moreover, although severe patients are found in every demographic and age group, the most common phenotype is an adult female that is older and obese [18].IntroductionAn estimated 9% of children and 6% of adults in the United States have asthma [1].The total number of asthma sufferers worldwide is estimated to be over 300 million, with an additional 100 million expected to develop asthma by 2025 [2][3][4][5].Developed countries are the most affected, with some of the highest rates found in the United Kingdom, Australia, New Zealand and the Republic of Ireland [3].Asthma prevalence is rising significantly in developing countries in transition to a more Western lifestyle [3].In 2007, the cost of disease in the United States was estimated to be $56 billion in relation to medical expenses, missed days of work, and early deaths [1].The rate of asthma deaths has likely plateaued, but is still as high as 250,000 per year worldwide [6].Morbidity and mortality are particularly high in ethnic minorities living below or near the poverty line, and African American children had a death rate 10 times that of non-Hispanic white children in 2015 [7].Thus, asthma is a costly, growing health problem associated with high morbidity and mortality.",
      "Getting accurate estimatesof exposures is difficult, whether this is air pollution or toxins in our food anddrink, but these are important questions. Rutter: That is an important point. From the twin study data it is clear thatenvironmental effects account for quite a lot of the variance on all the multifactorial disorders. Yet the kinds of measures that are used arent terribly solid. Theyinclude broad thing such as socio-economic status (SES). Even where there aregood measures the care taken in testing for environmental mediation is usuallypoor.Bronchiolitis, a diseasethat happens in the first year of life in many infants, is strongly associated withsubsequent asthma. We ascertained it in the first years of life and have been following these people to age 25 now. For the people who had bronchiolitis and nowhave asthma, their parents recall much better that they had bronchiolitis than thosewho dont have asthma now. It is at least twice more. Extraordinarily, some ofthese latter parents dont recall that they took their child to the doctor in the fi rstyear of life.If you arrive in the USA whenyou are young you have almost the same prevalence of asthma as an adult as thosewho are born in the USA and who are not Mexican. But if you arrive at older agesyou have less asthma. If you arrive at the age of 20 you have the same asthma riskas those born in Mexico (Eldeirawi et al 2005). Kotb: This is extremely interesting. There is a relationship between depressionand the immune system. This especially applies to natural killer (NK) cells, whichare the main cells that fight cancers.A more constructive approach is the use of refined measures of environment: an interviewthat quantifies the level of independence of stressful life events (Brown & Harris1978) or objectively recorded events in natural experiments (Kilpatrick et al2007). Factors that are considered as environmental, e.g. smoking, are strongly determined by personality and genetic factors. Personality-related factors and stressfullife events also influence detection of physical health outcomes including abdominal pain, appendectomy, peptic ulcer or diabetes control (Creed 2000).",
      "; Guffey, S.E. Investigation into pedestrian exposure to near-vehicle exhaust emissions. Environ. Health2009, 8, 13. [CrossRef] [PubMed]Our World in Data.org. 2017. Available online: https://ourworldindata.org/data-review-air-pollution-deaths (accessed on10 January 2022). Pope, C.A. , III. Respiratory disease associated with community air pollution and a steel mill, Utah Valley. Am. J. Public Health1989, 79, 623628. [CrossRef] [PubMed]Pope, C.A. , III. What do epidemiologic findings tell us about the health effects of environmental aerosols? J. Aerosol. Med. 2000,13, 335354. [CrossRef] [PubMed]Pope, C.A. , III.",
      "Case for Support BBSRC Grant Application September 2005Integrative Analysis of the Genetic Factors behind Asthma and Atopic DermatitisPart I: Research ProposalBackgroundAIntroduction of topic of research and its academic and wider contextAsthma is the most common disease of childhood, and affects one child in seven in the UnitedKingdom. Atopic Dermatitis (AD, eczema) affects similar numbers of children. About 60% of children withsevere AD will have concomitant asthma. Treatments for both diseases are unsatisfactory. Abandonment oforthodox medical therapy for AD is common in many families who have children with the disease.",
      "This is most common during the rainyseason when aerosols are created, which results in repeated inhalation of Bp [43, 44]. Environmental sampling studies reveal there is a positive association between theprevalence of disease and the degree of environmental contamination [7]. In addition toenvironmental factors, data suggests that host factors play an important role in mountingan immune response against infectious diseases [45] such as melioidosis. While healthypersons can contract melioidosis, most patients in endemic regions have an underlyingpredisposition [28], which suggests that the immunological status of the patient caninfluence disease initiation and progression [15].",
      "Sensitivity analysisWe did two sets of post-hoc sensitivity analyses to assess the effects of potential poor recall of age of onset among individuals with adult-onset asthma, and the effects of misclassification of COPD as asthma among the adultonset cases, even with exclusion of cases with a reported diagnosis of COPD, emphysema, or chronic bronchitis.First, to assure that the adult-onset cases did not include a significant proportion of childhood-onset asthma in which symptoms remitted in early life but then relapsed in adulthood, we replaced adult-onset cases with increasing proportions of randomly selected childhood-onset cases, and then tested for association at the two most significant childhood onset-specific loci.This procedure was repeated 20 times for each proportion to quantify the sampling variability (appendix pp 7-8).Second, we did two analyses in which we removed either individuals with ages of asthma onset between 46 and 65 years or adult-onset cases and controls with FEV/FVC <070.For each, we compared p values and ORs with the GWAS including all adult-onset cases (appendix pp 8-9).We used data for British white individuals from UK Biobank data release July 19, 2017. 8We extracted disease status (asthma, allergic rhinitis, atopic dermatitis, food allergy, chronic obstructive pulmonary disease (COPD), emphysema, and chronic bronchitis), age of on set of asthma, and sex from self-reported question naires and hospital records (International Classification of Diseases 10th revision [ICD-10] codes) by querying our in-house protected UK Biobank database server. 9For our main case analysis, we included individuals who self-reported that they had doctor-diagnosed asthma.Further details of our research approach are provided in the appendix (pp 4-7).",
      "; Guffey, S.E. Investigation into pedestrian exposure to near-vehicle exhaust emissions. Environ. Health2009, 8, 13. [CrossRef] [PubMed]Our World in Data.org. 2017. Available online: https://ourworldindata.org/data-review-air-pollution-deaths (accessed on10 January 2022). Pope, C.A. , III. Respiratory disease associated with community air pollution and a steel mill, Utah Valley. Am. J. Public Health1989, 79, 623628. [CrossRef] [PubMed]Pope, C.A. , III. What do epidemiologic findings tell us about the health effects of environmental aerosols? J. Aerosol. Med. 2000,13, 335354. [CrossRef] [PubMed]Pope, C.A. , III.",
      "8 Thesocio-ecologic framework posits that various aspects of a childs environment directly and indirectly impact thechilds health and development.9 Drawing on this framework, Beck and colleagues10 examined several biologic,social and ecologic variables to provide a greater understanding of factors influencing asthma-related hospitalreadmissions for black children compared to their white counterparts. The study revealed that black childrenwere over two times as likely to be readmitted for an asthma-related illness compared to white children; thisresulted from significant differences in almost every socio-ecologic variable measured, including diseasemanagement practices and access to primary care.Specific AimsAsthma is the most common chronic pediatric medical condition in the United States, with a prevalenceover 9.6% in children under 18 years of age.1, 2 Low-income, urban children incur a disproportionate share ofasthma prevalence and morbidity;2-4 13% of children living below the poverty threshold are diagnosed withasthma compared to 8% of non-poor (>200% poverty),3 and poverty is associated with higher rates of asthmaattacks.1 Living in an urban area confers additional risk for asthma and increased ED utilization.4, 5Implementation of the National Asthma Education and Prevention Programs (NAEPP) Guidelines hascontributed to reductions in asthma morbidity and mortality rates, and these guidelines emphasize establishinga partnership between healthcare providers and patients/families to promote effective asthma management.6The NAEPP expert panel states, building a partnership requires that clinicians promote opencommunication and ensure that patients have a basic and accurate foundation of knowledge about asthma(p.124),6 yet care partnerships also require that the patient/parent effectively communicate issues such asemerging symptoms or response to medications.Vital & health statistics Series 3, Analytical and epidemiological studies. 2012(35):1-58. CDC. Current Asthma Prevalence. https://www.cdc.gov/asthma/most_recent_data.htm. 2015. UpdatedJune 2017. Accessed March 9, 2018. Northridge J, Ramirez OF, Stingone JA, Claudio L. The role of housing type and housing quality inurban children with asthma. Journal of urban health : bulletin of the New York Academy of Medicine. 2010;87(2):211-224. Flores G, Snowden-Bridon C, Torres S, et al. Urban minority children with asthma: substantialmorbidity, compromised quality and access to specialists, and the importance of poverty and specialtycare.Asthma Prevalence and DisparitiesAsthma is the most common chronic pediatric medical condition in the United States,1 affecting anestimated 6.2 million children annually.2 Poorly controlled pediatric asthma contributes to over 700,000 visits ayear to emergency departments (ED).1 Children living in impoverished, urban settings are disproportionatelyaffected by asthma,3 and the disparate impact of asthma is even worse among black and Latino children, andchildren whose parents have limited English proficiency (LEP) in these urban low-income areas.4-6 A 2017longitudinal study revealed that black race and Latino ethnicity are significantly associated with worse asthmaoutcomes including 1) asthma knowledge, 2) asthma-related quality of life, 3) asthma severity, and4) asthma control.The Journal of asthma : official journal of the Association for the Care of Asthma. 2017:16. Inkelas M, Garro N, McQuaid EL, Ortega AN. Race/ethnicity, language, and asthma care: findings froma 4-state survey. Annals of allergy, asthma & immunology : official publication of the American Collegeof Allergy, Asthma, & Immunology. 2008;100(2):120-127. National Asthma Education and Prevention Program. Expert Panel Report 3: Guidelines for theDiagnosis and Management of Asthma Bethesda, MD: National Institutes of Health, National Heart,Lung, and Blood Institute; 2007. Publication no. 08-045.1. NIH Consensus Group. Video report: What is mHealth?Contact PD/PI: Coker, Tumaini RuckerINTRODUCTION TO APPLICATIONResearch Plan OverviewChildhood asthma is the most common pediatric medical condition in the United States, anddisproportionately affects children living in low-income, urban settings. Many low-income, urban families rely onemergency department (ED) services as their source for sick care for their child. This is often due to not havinga primary care provider or sufficient access to their primary care provider for asthma management."
    ],
    [
      "Taxon Sampling and DNA ExtractionsWe extracted DNA from 72 pinned specimens from the National Museum of Natural History (NMNH) Entomology collection for this study.We plucked middle legs from the pinned bees using a pair of sterilized forceps and washed the tissue in 95% ethanol to remove dust, pollen, and other forms of accumulated debris on the bee legs.After evaporation of the ethanol (by drying the tissue on a clean Kimwipe  ), the samples were placed in a freezer for several hours.DNA was then extracted destructively by grinding the frozen tissue with a sterile pestle, using a DNeasy Blood and TissueKit (Qiagen, Valencia, CA, USA) and following the manufacturer's protocol, except the DNA was eluted in 130L ddH 2 O instead of the supplied buffer.We ran 10L of each extract for 60 min at 100 volt on 1.5% agarose SB (sodium borate) gels, to estimate size of the genomic DNA.",
      "Extraction of biomolecular fractions from faecal samples.Biomolecular fractions were extracted from unthawed, frozen faecal subsamples (150 mg) after pretreatment of the weighed subsamples with 1.5 ml RNAlater ICE (LifeTechnologies) overnight.The faeces-RNAlater ICE mixture was homogenized by bead-beating, as previously described 53 .Differential centrifugation and extraction using the All-In-One kit (Norgen Biotek) to recover DNA and proteins were carried out as previously described 53 .DNA fractions were supplemented with DNA extracted from 200 mg subsamples using the MOBIO Power Soil Kit.",
      "Bulk DNA Extraction.Total DNA was collected from the cell pellets remaining after Ficoll density centrifugation for B lymphocyte isolation using the DNeasy Blood & Tissue Kit (Qiagen) following the manufacturer's specifications.The concentrations of DNA were quantified using the Qubit High-Sensitivity dsDNA Kit, and the qualities of DNA were evaluated with 1% agarose gel electrophoresis.",
      "MethodsLaboratory procedures.We initially screened 107 ancient samples (Supplementary Data 1) in dedicated clean facilities at the ancient DNA lab of Jilin University, China, following published protocols for DNA extraction and library preparation 36,37 .Prior to sampling, we wiped all skeletal elements with 5% bleach and irradiated with UV-light for 30 min from each side.We drilled teeth to obtain fine powder using a dental drill (Dremel, USA).We sampled the dense part of petrous bones around the cochlea by first removing the outer part using the sandblaster (Renfert, Germany), and then grinding the clean inner part into fine powder with the mixer mill (Retsch, Germany).We digested the powder (50-100 mg) in 900 l 0.5 M EDTA (Sigma-Aldrich), 16.7 l of Proteinase K (Sigma-Aldrich), and 83.3 l ddH 2 O (Thermo Fisher, USA) at 37 C for 18 h.Then we transferred the supernatant to a MinElute silica spin column (QIAGEN, Germany) after fully mixed with the 13 ml custom binding buffer [5 M guanidine hydrochloride (MW 95.53), 40% Isopropanol, 90 mM Sodium Acetate (3 M), and 0.05% Tween-20] followed by two washes with PE buffer (80% ethanol).Then we eluted the DNA with 100 l TET buffer (QIAGEN, Germany).",
      "Genomic DNA extractionLeukocytes were isolated from 5-ml peripheral blood samples.DNA was prepared by phenol extraction and chloroform extraction followed by isopropanol precipitation, washed with ethanol, and air-dried.Tris-EDTA buffer pH 8.0 was used to dissolve the final genomic DNA product.",
      "The pulled down DNA fragments were extracted and purified using phenolchloroform extraction/ethanol precipitation.The samples were stored at -20 C until use.",
      "DNA and RNA extractionFor nucleic acid extraction, pellets containing 2,000 to 5,000 nematodes were ground into fine powder with a liquid nitrogen-cooled mortar and pestle [88] and then extracted using either an RNeasy kit (Qiagen, Valencia, CA, USA) or a Genomic Tips kit (Qiagen; following the protocol for extraction of genomic DNA from cells in culture).Alternatives to the liquid nitrogen grinding procedure were attempted for DNA extraction (including homogenization, bead beating, three rounds of freeze-thaw, and simple incubation with the Genomic Tips digestion buffer from Qiagen, proteinase K and RNase A), but all resulted in the extraction of degraded genomic DNA.The integrity of genomic DNA after different extraction methods was evaluated by examination of highmolecular-weight genomic DNA using agarose gel electrophoresis and comparison of amplification of long PCR products from equal amounts of template (QPCR; described below).RNA was quantified with a NanoDrop Fluorospectrometer (NanoDrop Technologies, Wilmington, DE, USA) and analyzed for integrity with a BioAnalyzer (Agilent Technologies, Santa Clara, CA, USA).DNA quantity was measured before QPCR using PicoGreen dye (Invitrogen Corporation, Carlsbad, CA, USA), as described previously [21].",
      "Bacterial DNA extractionDNA was extracted from the freeze-dried luminal content of the 4 sections of the intestine using the method described by Salonen et al. [28].In short, approximately 0.1 g was used for mechanical and chemical lysis using 0.5 ml buffer (500 mM NaCl, 50 mM Tris-HCl (pH 8), 50 mM EDTA, 4% SDS) and 0.25 g of 0.1 mm zirconia beads and 3 mm glass beads.Nucleic acids were precipitated by addition of 130 l, 10 M ammonium acetate, using one volume of isopropanol.Subsequently, DNA pellets were washed with 70% ethanol.Further purification of DNA was performed using the QiaAmp DNA Mini Stool Kit (Qiagen, Hilden, Germany).Finally, DNA was dissolved in 200 l Tris/EDTA buffer and its purity and quantity were checked spectrophotometrically (ND-1000, nanoDrop technologies, Wilmington, USA).DNA isolation from scrapings of the small intestine and the colon Genomic DNA was isolated from the crushed scraping by using DNeasy W Blood and Tissue Kit (Qiagen, Venlo, the Netherlands) according to the manufacturer's instructions.The DNA was treated with RNase and eluted in Tris/EDTA buffer (pH 9.0).DNA purity and quantity were checked spectrophotometricaly (ND-1000, nanoDrop technologies, Wilmington, USA).",
      "DNA extractionIn a strictly controlled, separate and sterile workplace, approximately 0.2 mL saliva and 50 mL PBS containing the plaque sample were mixed with Qiagen's AL buffer by pulse vortexing for 30 s (Qiagen, Valencia, CA).Total DNA was extracted from the suspension of each sample using a QIAamp DNA Mini Kit (Qiagen, Valencia, CA).Isolated DNA was eluted in 50 mL distilled water.",
      "Most typically, DNA is extracted from blood samples, dried blood spots, buccal swabs, saliva, tissue and even urine and stool samples.In forensic science, other sources have been validated e.g.bone, tooth pulp, dandruff and others.",
      "Blood samples were collected by jugular venipuncture from each animal into 6-ml EDTA vacutainer tubes (Greiner Bio-One, GmbH).The collected blood samples were kept in iceboxes until refrigerated at 4 C.Genomic DNA was extracted using the DNeasy  Blood and Tissue Kit (Qiagen), as per the manufacturer's instructions with a slight modification of increased lysis time to 90 min.DNA quality and quantity were determined using 1% agarose gel electrophoresis (Merck) and Qubit  3.0 Fluorometer (Life Technologies) respectively.",
      "Genomic DNA extractionDNA from MEF cultures or mouse liver was isolated by phenol/chloroform extraction, as described [11].",
      "DNA isusually recovered from cells by methods that include cell rupture but thatprevent the DNA from fragmenting by mechanical shearing. This is generally undertaken in the presence of EDTA, which chelates the magnesium ionsneeded as cofactors for enzymes that degrade DNA, termed DNase. Ideally,cell walls, if present, should be digested enzymatically (e.g. , lysozyme in thebacteria or bacterial cell). In addition the cell membrane should be solubilizedusing detergent.In specific cases, such as insects,contamination can be reduced by hypochlorite treatment before extractionto avoid contact with foreign DNA (15). DNA preparation includes thedigestion of samples using different lysis buffers, which contain proteinaseK at several concentrations. DNA purification has been performed bythe classical phenol-chloroform extraction and ethanol precipitation (16). Further treatment with RNAse and a further round of extraction and precipitation has been recommended (5,17). Negative controls using distilled waterinstead of a DNA sample can detect possible environmental or reagentcontaminants.DNA solutions can be stored frozen,although repeated freezing and thawing tends to damage long DNA moleculesby shearing. A flow diagram summarizing the extraction of DNA is given inFig. 1.2. The above-described procedure is suitable for total cellular DNA. If the DNA from a specific organelle or viral particle is needed, it is best toisolate the organelle or virus before extracting its DNA, because the recoveryof a particular type of DNA from a mixture is usually rather difficult.",
      "Isolation of Total DNA from Tissues.Total DNA was isolated as described (19) with slight modifications.Briefly, 0.1-g samples of tissue were frozen in liquid nitrogen, and DNA was extracted from the frozen tissues by the proteinase KSDSphenol method.",
      "Genomic DNA extractionGenomic DNA was extracted by the mixed alkyl trimethyl ammonium bromide (MATAB) procedure.Briefly, 250 mg of plant material was ground in liquid nitrogen and immediately incubated in 2 ml of pre-warmed extraction buffer (100 mM Tris-HCl, pH 8, containing 20 mM EDTA, 1.4 M NaCl, 2% (w/v) MATAB, 1% (w/v) PEG6000 (polyethylene glycol), 0.5% (w/v) sodium sulfite, 20% (w/v) Igepal CA630, 20% (w/v) lithium dodecyl sulfate, and 20% (w/v) sodium deoxycholate) at 74 C for 20 min.After purification with 2 ml of chloroform:isoamylalcohol (24:1, v/v), DNA extracts were precipitated with 1.6 ml of isopropanol then resuspended in 1 ml of buffer (50 mM Tris-HCl, pH 8, containing 10 mM EDTA and 0.7 M NaCl).The extracts were purified on anion exchange columns (QIAGEN-tip 20) following the manufacturer's instructions (QIAGEN, Valencia, CA).",
      "After three washes withice-cold phosphate buffer saline (PBS), DNA was extracted from 100-150mg of cecal contentsusing the QIAmp DNA stool Mini Kit (Qiagen) following mechanical cell lysis as describedpreviously [10]. The supernatant from the first wash, which was 10 times volume per weight ofcecal contents, was stored at -80C for sIgA measurements. Extracted DNA was initially amplified using universal primers for the V5-V6 region of the 16S rRNA gene and containing barcoded adapters. The forward primer used was 784F (5-RGGATTAGATACCC-3) and thereverse primer was 1064R (5-CGACRRCCATGCANCACCT-3).",
      "The conventional DNA extraction procedure involved the homogenization of single D. magna in 400 l of sperm lysis buffer (100 mM Tris-HCl, pH 8; 500 mM NaCl; 10 mM ethylenediaminetetraacetic acid [EDTA], pH 8; 1% SDS; 2% mercaptoethanol) followed by RNase treatment (40 g, 37C for 1.5 h).The DNA was then extracted in phenol (pH 8) and chloroform:isoamyl alcohol (1:1).The DNA was finally precipitated by two volumes of ice-cold ethanol in the presence of 3 M sodium acetate (1/10 of the DNA volume) and was incubated at 80C overnight.Precipitated DNA was harvested by centrifugation, dried in air, and the final pellet dissolved in sterile analytic grade water."
    ],
    [
      "Recent developments on the genetics of aging can be seen as several streams of effort.In general, humans show a relatively modest (<50%) heritability of life spans (results obtained from twin studies discussed below).The apoE polymorphisms are remarkable for their influence on both cardiovascular disease and Alzheimer disease.In contrast, rare mutant genes with high penetrance cause these same diseases but with early onset and a major shortening of the life span.Shortlived laboratory models (fruit flies, nematodes, mice) are yielding rapid advances, with the discovery of mutants that increase life spans in association with altered metabolism, which leads to questions on the physiological organization of aging processes.Although these early findings do not show that a conserved genetic program actually controls aging processes across animal phylogeny, it is striking how frequently findings of metabolic rate, insulin signaling, and free radicals have emerged from very different approaches to aging in nematodes and mammals, for example.These findings hint that the genetic control of life span was already developed in the common ancestor of modern animals so that subsequent evolution of life spans was mediated by quantitative changes in the control of metabolism through insulin and the production of free radicals.",
      "In orderto accomplish this task, we looked for possible novel genetic factors that regulatephysical activity levels. We used behavioral genetics methodology combined with atranslational genetics approach in order to propose genetic candidate regions as wellas candidate genes for this complex phenotype in humans (Chapter 2 and 3) andmice (Chapters 2, 3, and 4).",
      "Since that time, observations across species have shown that life span can be extended by genetic factors.One of the first demonstrations of this entailed the study of recombinant inbred populations of the nematode worm Caenorhabditis elegans by Thomas E. Johnson.Then a postdoc in William (Bill) Wood's lab at the University of Colorado Boulder, Tom and Bill demonstrated that crosses of C. elegans strains did not display the heterosis effect that interfered with many other studies, \"As predicted, we found significant genetic effects on life span as well as other life history traits. \"This finding established a method for evaluating genetic factors that influenced life-span variation.In fact, their measurements of life span of the recombinant inbred strains demonstrated the heritability of life span to be 19%-51% (1).Consistent with theories of the 1970s and 1980s, it was concluded that these genetic factors were a collection of small influences across many genes.This finding was one of the first steps in demonstrating that genetic factors influence aging.As genetic analysis was making great progress in understanding other biological processes, such as developmental programming, the realization that aging could be investigated using the same tools was highly significant.GeneticsAging is influenced by genetic factors.It may be surprising to know that as recently as the 1970s and 1980s, the concept of modulating",
      "Previous unbiased systemsgenetics approaches relying on the use of mouse genetic reference populations (GRPs) have been successful in identifying theunderlying mechanisms in complex metabolic traits, such asmitochondrial function (Chella Krishnan et al. , 2018; Norheimet al. , 2019; Williams et al. , 2016), lipid metabolism (Jha et al. ,2018a, 2018b; Linke et al. , 2020; Parker et al. , 2019), atherosclerosis (Bennett et al. , 2015; Smallwood et al. , 2014), and liver diseases (Chella Krishnan et al. , 2018; Hui et al. , 2018).",
      "This population geneticmechanism also can maintain genetic variability for aging, like antagonistic pleiotropy. LARGE-EFFECT MUTANTS AND THE GENETICS OF AGINGOne approach that has become increasingly common in the characterization of the genetics of aging is to isolate aging mutants, usually from mutagenesis experiments, andthen to determine the mechanistic basis for the unusual life span in the mutants. Thisapproach has led to the discovery of genes that can enhance (e.g. , Maynard Smith 1958;Lin et al. 1988; reviewed in Guarente and Kenyon 2000, Kim 2007) or reduce life span(e.g. , Pearl and Parker 1922).Research with animal modelshas established that genetic factors explain a significant amount of variation in both exercise capacity in an untrained state (Koch and Britton 2001) and in the physiological responses to training regimens (Troxell et al. 2003). Bunger et al. (1994) reported the results of sixty generations of selecting laboratorymice for an index combining high body weight and high stress resistance, where the308L E V E L S O F O B S E R VAT I O Nlatter denoted the distance to exhaustion on a treadmill.",
      "The DNA of over 500,000 people was read to reveal the specific 'genetic fingerprints' of each participant.Then, after asking each of the participants how long both of their parents had lived, Timmers et al. pinpointed 12 DNA regions that affect lifespan.Five of these regions were new and had not been linked to lifespan before.Across the twelve as a whole several were known to be involved in Alzheimer's disease, smoking-related cancer or heart disease.Looking at the entire genome, Timmers et al. could then predict a lifespan score for each individual, and when they sorted participants into ten groups based on these scores they found that top group lived five years longer than the bottom, on average.",
      "NATurE GENETicSadjustments, using a matched meta-analysis conducted on the same subset of 28 studies:",
      "GENETIC ANALYSIS OF LONGEVITY, OF AGING, AND OF AGE-SENSITIVE TRAITS IN MICEBiogerontology has just begun to benefit from the attention and skills of professional geneticists.Geneticists can attack problems of aging from several related but fundamentally distinct directions.Studies of rare mutations at individual loci, such as the Werner's syndrome locus WRN, whose mutant form produces, in middle-aged people, several of the diseases typically not seen until old age, can give attractive points of entry into the pathophysiology of age-related diseases.In mice there are now four reports of mutations-two naturally occurring and two artificially produced-that lead to impressive increases in mean and maximal longevity (Miskin and Masos, 1997;Brown-Borg et al., 1996;Miller, 1999;Migliaccio et al., 1999), and thus provide extremely valuable models for testing mechanistic ideas and the control of aging.Some of these, such as the dw/dw and df/df dwarfing mutations that affect levels of growth hormone and thyroid hormone, provide clues to endocrine-dependent pathways that could regulate age effects in multiple cells and tissues.The recent report (Migliaccio et al., 1999) that mouse life span can be extended by an induced mutation that diminishes cell susceptibility to apoptotic death after injury should stimulate new inquiries into the effects of altered cell turnover on age-dependent changes.Each of these mutations, however, is exceptionally rare in natural populations; despite their effect on longevity, perhaps mediated by a direct effect on aging, each of the mutations is likely to have, overall, a negative effect on reproductive success and thus fail to become fixed in natural mouse populations.",
      "Genetics had a strong impact on femoral traits (eg, bone volume fraction [BV/TV] basal Ca, h2 = 0.60) as well as their RCR (eg, BV/TV,h2 = 0.32). Quantitative trait locus (QTL) mapping identied up to six loci affecting each bone trait. A subset of loci was detected inboth diet groups, providing replication of environmentally robust genetic effects. Several loci control multiple bone phenotypes suggesting the existence of genetic pleiotropy. QTL controlling the bone RCR did not overlap with basal diet QTL, demonstrating geneticindependence of those traits.",
      "This population geneticmechanism also can maintain genetic variability for aging, like antagonistic pleiotropy. LARGE-EFFECT MUTANTS AND THE GENETICS OF AGINGOne approach that has become increasingly common in the characterization of the genetics of aging is to isolate aging mutants, usually from mutagenesis experiments, andthen to determine the mechanistic basis for the unusual life span in the mutants. Thisapproach has led to the discovery of genes that can enhance (e.g. , Maynard Smith 1958;Lin et al. 1988; reviewed in Guarente and Kenyon 2000, Kim 2007) or reduce life span(e.g. , Pearl and Parker 1922).",
      "(17) The role ofgenetics in bone was first suggested by early twin studies(18,19) and family studies. (20-23) Forexample, Krall and Dawson-Hughes(22) measured familial resemblance of bone density of femaleand male members of 40 families. They reported that 46-62% of variance in bone density wasattributable to heredity. However, the fact that genetics does not explain all of the variation in bone18mass suggests that bone mass is also influenced by other environmental factors as well as theinteraction between genetics and extrinsic factors.",
      "when examining the role that genetics may play in howchildren form attachments, as other studies have observedthat parenting particularly affected children with variouspolymorphisms of genes that regulate the DA system (i.e. , DAT19- and 10-repeat and Dopamine Receptor D4 7-repeat) andreward sensitivity (Bakermans-Kranenburg et al. , 2008; Bosmanset al. , 2020). Our findings further support the notion thatmultiple genes may make a child more or less susceptibleto their caregiving environment (Belsky and Beaver, 2011;Roisman et al.",
      "when examining the role that genetics may play in howchildren form attachments, as other studies have observedthat parenting particularly affected children with variouspolymorphisms of genes that regulate the DA system (i.e. , DAT19- and 10-repeat and Dopamine Receptor D4 7-repeat) andreward sensitivity (Bakermans-Kranenburg et al. , 2008; Bosmanset al. , 2020). Our findings further support the notion thatmultiple genes may make a child more or less susceptibleto their caregiving environment (Belsky and Beaver, 2011;Roisman et al.",
      "Previous unbiased systemsgenetics approaches relying on the use of mouse genetic reference populations (GRPs) have been successful in identifying theunderlying mechanisms in complex metabolic traits, such asmitochondrial function (Chella Krishnan et al. , 2018; Norheimet al. , 2019; Williams et al. , 2016), lipid metabolism (Jha et al. ,2018a, 2018b; Linke et al. , 2020; Parker et al. , 2019), atherosclerosis (Bennett et al. , 2015; Smallwood et al. , 2014), and liver diseases (Chella Krishnan et al. , 2018; Hui et al. , 2018).",
      "TranslationalA LTHOUGH there is much debate about the processes driving human aging, there is little doubt that genetic influences play a significant role (1).Humans clearly live very much longer than the currently favored laboratory models of aging, and such interspecies differences in reproductively 'fit' life span must have an inherited genetic foundation.Within human populations, environmental and behavioral exposures are important but at least a quarter of life expectancy variation in twin or family studies is attributable to inherited genetic or epigenetic factors (2).Age-related conditions such as type 2 diabetes, myocardial infarction, common cancers, and Alzheimer's disease (AD) typically have onsets after the fourth decade of life; \"successful\" agers delay these onsets until relatively late in life (3).Many aging traits and diseases show moderate heritability, including cardiovascular disease (CVD) (4) and impaired physical functioning (5), independent of known environmental risk factors.",
      "Genetics of weight loss.A necessary condition for tailoring weight loss protocols to genetics or genomics is identifying reliable and meaningful genetic or genomic predictors.The heritability, or genetic variance, of weight loss first was documented in a careful laboratory study of identical twins.Bouchard and colleagues (C. Bouchard et al., 1994) induced weight loss in identical twin pairs through supervised exercise designed to produce of daily energy balance deficits of 500 kcals.Strong similarity between co-twins as compared to non-related individuals provided some of the first evidence of genetic involvement in magnitude of weight loss with intervention.",
      "lifestyle and changes in diet, a significant proportion of heritable factors also contribute to individual susceptibility (Hu 2011).",
      "Genetics had a strong impact on femoral traits (eg, bone volume fraction [BV/TV] basal Ca, h2 = 0.60) as well as their RCR (eg, BV/TV,h2 = 0.32). Quantitative trait locus (QTL) mapping identied up to six loci affecting each bone trait. A subset of loci was detected inboth diet groups, providing replication of environmentally robust genetic effects. Several loci control multiple bone phenotypes suggesting the existence of genetic pleiotropy. QTL controlling the bone RCR did not overlap with basal diet QTL, demonstrating geneticindependence of those traits."
    ],
    [
      "At a high level, the Research and Development Space of Bioinformatics canbe viewed as a set of non-orthogonal vectors (Figure 1) that describeBioinformatic ActivitiesBiological Data TypesBiological SpeciesComputing InfrastructureDevelopment EffortBioinformatic activities (acquisition, storage, retrieval, integration, analysis,visualization, modeling) need to be developed for multiple biological data typesArchitectures for Integration of Data and Applications33(nucleic and amino acid sequences, physical and linkage maps, RNA, protein andmetabolite expression arrays and clinical and eld assays) derived from multiple biological species using multiple biotechnology platforms.As Bioinformaticsemerges as a discipline, however, it is likely that both research and developmentcan and will be accommodated in large programmatic grants. 7. REFERENCESBenton, D., 2000, Standards to Enable Bioinformatics Data and Information Integration, In BarnettInternationals 2nd Annual Bioinformatics and Data Integration Conference, Philadelphia, PA.Boyle, J., 1998, Building Component Software for the Biological Sciences, CCP11 Newsletter, 4:2214. Dowell, R., Jokerst, A., Day, S., Eddy, L., and Stein, L., 2001, The distributed annotation system, BMCBioinformatics 2(7). This article is available at http://www.biomedcentral.com/1471-2105/2/7.3132William D. BeavisClinical AssaysBiologicalData TypesCellular NetworksMolecular NetworksProtein ExpressionInfrastructureRNA ExpressionMapsDNA SequenceBioinformaticActivitiesFlyAcquire DataStoreIntegrateQueryAnalyzeVisualizeModelYeast A.thalianaH.sapiensCow Pig corn soyBiologicalSpeciesCDevelopmentEffortFIGURE 1. Representation of the research and development space spanned by various aspects ofBioinformatics. to understanding the structure and evolution of whole genomes. Even the morefocused and applied bioinformatics goals, e.g.",
      "The Bioinformatics (Modeling core) analyzed biological data (responseto infection by a pathogen) from projects using Bayesian network analysis and created aBayesian Network Webserver (BNW - http://compbio.uthsc.edu/BNW). We have obtained significant results for all projects supported by this grant funding. We aretherefore very enthusiastic to follow up on the data we have obtained. We are applying forfunding from different sources to continue these studies either as separate projects for thedifferent DoD priority pathogens, or as a big program project that will involve pathogens andsupporting cores to do omics studies.",
      "Ball Department ofBiochemistry, Stanford University MedicalSchool, Stanford, CA, USAJames R. Brown Bioinformatics,GlaxoSmithKline Pharmaceuticals, UpperProvidence, PA, USAAruna Bansal Discovery and PipelineGenetics, GlaxoSmithKlinePharmaceuticals, Third Avenue, Harlow,Essex, UKElissa J. Chesler Oak Ridge NationalLaboratory, Biosciences Division, OakRidge, TN, USAMichael R. Barnes Bioinformatics,GlaxoSmithKline Pharmaceuticals, ThirdAvenue, Harlow, Essex, UKBryan J. Barratt Research andDevelopment Genetics, AstraZeneca,Alderley Park, Macclesfield, Cheshire, UKMatthew J. Betts Structural andComputational Biology Programme, EMBL,Meyerhofstrasse 1, 69117 Heidelberg,GermanyDiana Blaydon Centre for CutaneousResearch, Institute of Cell and MolecularScience, Queen Marys School of Medicineand Dentistry, Whitechapel, London, UKKarl W. Broman Department ofBiostatistics, Johns Hopkins University,Baltimore, MD, USAEllen M. Brown Discovery Informatics,AstraZeneca, Alderley Park, Macclesfield,Cheshire, UKRichard R. Copley Wellcome Trust Centrefor Human Genetics, University of Oxford,Oxford, UKBarry Dancis Bioinformatics,GlaxoSmithKline Pharmaceuticals UpperProvidence, PA, USASteve Deharo Bioinformatics,GlaxoSmithKline Pharmaceuticals, ThirdAvenue, Harlow, Essex, UKPaul S. Derwent Bioinformatics,GlaxoSmithKline Pharmaceuticals, ThirdAvenue, Harlow, Essex, UKIan C. Gray Paradigm Therapeutics (S) PteLtd, 10 Biopolis Way, Singapore 138670Joel Greshock Translational Medicine,Clinical Pharmacology Division,GlaxoSmithKline Pharmaceuticals, UpperMerion, PA, USASimon C. Heath Centre National deGenotypage, Evry Cedex, FrancexviiiCONTRIBUTORSDavid P. Kelsell Centre for CutaneousResearch, Institute of Cell and MolecularScience, Queen Marys School of Medicineand Dentistry, Whitechapel, London,UKRalph McGinnis Wellcome Trust SangerInstitute, Hinxton, Cambridge, UKCharles A. Mein Genome Centre, QueenMarys School of Medicine and Dentistry,Charterhouse Square, London, UKMary Plumpton Bioinformatics,GlaxoSmithKline Pharmaceuticals,Stevenage, Hertfordshire, UKRobert B. Russell Structural andComputational Biology Programme, EMBL,Meyerhofstrasse 1, 69117 Heidelberg,GermanyPhilippe Sanseau Bioinformatics,GlaxoSmithKline Pharmaceuticals,Stevenage, Hertfordshire, UKColin A. M. Semple Bioinformatics, MRCHuman Genetics Unit, Edinburgh EH4 2XU,UKGavin Sherlock Department of Genetics,Stanford University Medical School,Stanford, CA, USAChristopher Southan Global CompoundSciences, AstraZeneca R&D, Molndal,SwedenMartin S. Taylor Wellcome Trust Centrefor Human Genetics, University of Oxford,Oxford, UKMagnus Ulvsback MolecularPharmacology, AstraZeneca R&D, Molndal,SwedenCharlotte Vignal Discovery and PipelineGenetics, GlaxoSmithKlinePharmaceuticals, Third Avenue, Harlow,Essex, UKChaolin Zhang Department of BiomedicalEngineering, State University of New Yorkat Stony Brook, NY, USAMichael Q. Zhang Cold Spring HarborLaboratory, Cold Spring Harbor, NY, USAXiaoyue Zhao Cold Spring HarborLaboratory, Cold Spring Harbor, NY, USAGlossary of BioinformaticsBLAST (Basic Local Alignment Search Tool) A tool for identifying sequences in adatabase that match a given query sequence.",
      "TheNCBI creates automated systems for storing and analyzing knowledge about molecular biology, biochemistry, andgenetics; facilitating the use of such databases and software by the research and medical community; coordinatingefforts to gather biotechnology information both nationallyand internationally; and performing research into advancedmethods of computer-based information processing for analyzing the structure and function of biologically importantmolecules. NCBI bioinformatics-related resources may beaccessed through its home page at: www.ncbi.nlm.nih.gov. The NCBI has three principal branches:1. Computational Biology Branch (http://www.ncbi.nlm. nih.gov/CBBresearch/)2. Information Engineering Branch (http://www.ncbi.nlm. nih.gov/IEB/)3.",
      "Bioinformatics 18(Suppl 1):S136S144. doi: 10.1093/bioinformatics/18.suppl_1.S136.",
      "CBELife Sciences EducationVol. 9, 98 107, Summer 2010ArticleTeaching Bioinformatics and Neuroinformatics by UsingFree Web-based ToolsWilliam Grisham,* Natalie A. Schottler,* Joanne Valli-Marill, Lisa Beck,and Jackson Beatty**Department of Psychology and Office of Instructional Development, University of California, Los Angeles,Los Angeles, CA 90095; and Department of Psychology, Bryn Mawr College, Bryn Mawr, PA 19010Submitted November 9, 2009; Revised February 25, 2010; Accepted March 2, 2010Monitoring Editor: Mary Lee LedbetterThis completely computer-based modules purpose is to introduce students to bioinformaticsresources.We present an easy-to-adopt module that weaves together several important bioinformatic tools so students can grasp how these tools are used in answering research questions. Students integrate information gathered from websites dealing with anatomy (Mouse BrainLibrary), quantitative trait locus analysis (WebQTL from GeneNetwork), bioinformatics and geneexpression analyses (University of California, Santa Cruz Genome Browser, National Center forBiotechnology Informations Entrez Gene, and the Allen Brain Atlas), and information resources(PubMed).",
      "TheNCBI creates automated systems for storing and analyzing knowledge about molecular biology, biochemistry, andgenetics; facilitating the use of such databases and software by the research and medical community; coordinatingefforts to gather biotechnology information both nationallyand internationally; and performing research into advancedmethods of computer-based information processing for analyzing the structure and function of biologically importantmolecules. NCBI bioinformatics-related resources may beaccessed through its home page at: www.ncbi.nlm.nih.gov. The NCBI has three principal branches:1. Computational Biology Branch (http://www.ncbi.nlm. nih.gov/CBBresearch/)2. Information Engineering Branch (http://www.ncbi.nlm. nih.gov/IEB/)3.",
      "CONCLUSIONNIH-PA Author ManuscriptBioinformatics is fundamentally about the information of biology. Information, in turn, isburied within a cacophony of data produced by a wide swath of molecular techniques. Inneuroscience, the breadth of data is exceptionally large as it spans genomics, proteomics,metabolomics, image analysis, and behavioral science, among other protocols, and requiresresearchers to store data with due diligence based on the data types, data scope and depth,and underlying querying requirements.",
      "As David Searls, director of bioinformatics at SmithKline Beecham (King of Prussia, Pennsylvania), points out, bioinformatics is supported by theory; an increasing number of journals and scientific meetings are devoted to it; and it now has its own society, the International Society for Computational Biology (associated with the conference series Intelligent Systems for Molecular Biology), whose president is Larry Hunter of the National Library of Medicine.A case in point is Structural Bioinformatics (San Diego, California), a start-up company that, as its name suggests, is particularly interested in structural information about gene products.The company has been look-ing for a vice-president of bioinformatics since December -someone who takes a systems approach to structure-function issues, has a strong grounding in biology, cell biology and biochemistry and who knows how to use computational systems to solve these problems, but who is not necessarily a computational scientist.",
      "Ball Department ofBiochemistry, Stanford University MedicalSchool, Stanford, CA, USAJames R. Brown Bioinformatics,GlaxoSmithKline Pharmaceuticals, UpperProvidence, PA, USAAruna Bansal Discovery and PipelineGenetics, GlaxoSmithKlinePharmaceuticals, Third Avenue, Harlow,Essex, UKElissa J. Chesler Oak Ridge NationalLaboratory, Biosciences Division, OakRidge, TN, USAMichael R. Barnes Bioinformatics,GlaxoSmithKline Pharmaceuticals, ThirdAvenue, Harlow, Essex, UKBryan J. Barratt Research andDevelopment Genetics, AstraZeneca,Alderley Park, Macclesfield, Cheshire, UKMatthew J. Betts Structural andComputational Biology Programme, EMBL,Meyerhofstrasse 1, 69117 Heidelberg,GermanyDiana Blaydon Centre for CutaneousResearch, Institute of Cell and MolecularScience, Queen Marys School of Medicineand Dentistry, Whitechapel, London, UKKarl W. Broman Department ofBiostatistics, Johns Hopkins University,Baltimore, MD, USAEllen M. Brown Discovery Informatics,AstraZeneca, Alderley Park, Macclesfield,Cheshire, UKRichard R. Copley Wellcome Trust Centrefor Human Genetics, University of Oxford,Oxford, UKBarry Dancis Bioinformatics,GlaxoSmithKline Pharmaceuticals UpperProvidence, PA, USASteve Deharo Bioinformatics,GlaxoSmithKline Pharmaceuticals, ThirdAvenue, Harlow, Essex, UKPaul S. Derwent Bioinformatics,GlaxoSmithKline Pharmaceuticals, ThirdAvenue, Harlow, Essex, UKIan C. Gray Paradigm Therapeutics (S) PteLtd, 10 Biopolis Way, Singapore 138670Joel Greshock Translational Medicine,Clinical Pharmacology Division,GlaxoSmithKline Pharmaceuticals, UpperMerion, PA, USASimon C. Heath Centre National deGenotypage, Evry Cedex, FrancexviiiCONTRIBUTORSDavid P. Kelsell Centre for CutaneousResearch, Institute of Cell and MolecularScience, Queen Marys School of Medicineand Dentistry, Whitechapel, London,UKRalph McGinnis Wellcome Trust SangerInstitute, Hinxton, Cambridge, UKCharles A. Mein Genome Centre, QueenMarys School of Medicine and Dentistry,Charterhouse Square, London, UKMary Plumpton Bioinformatics,GlaxoSmithKline Pharmaceuticals,Stevenage, Hertfordshire, UKRobert B. Russell Structural andComputational Biology Programme, EMBL,Meyerhofstrasse 1, 69117 Heidelberg,GermanyPhilippe Sanseau Bioinformatics,GlaxoSmithKline Pharmaceuticals,Stevenage, Hertfordshire, UKColin A. M. Semple Bioinformatics, MRCHuman Genetics Unit, Edinburgh EH4 2XU,UKGavin Sherlock Department of Genetics,Stanford University Medical School,Stanford, CA, USAChristopher Southan Global CompoundSciences, AstraZeneca R&D, Molndal,SwedenMartin S. Taylor Wellcome Trust Centrefor Human Genetics, University of Oxford,Oxford, UKMagnus Ulvsback MolecularPharmacology, AstraZeneca R&D, Molndal,SwedenCharlotte Vignal Discovery and PipelineGenetics, GlaxoSmithKlinePharmaceuticals, Third Avenue, Harlow,Essex, UKChaolin Zhang Department of BiomedicalEngineering, State University of New Yorkat Stony Brook, NY, USAMichael Q. Zhang Cold Spring HarborLaboratory, Cold Spring Harbor, NY, USAXiaoyue Zhao Cold Spring HarborLaboratory, Cold Spring Harbor, NY, USAGlossary of BioinformaticsBLAST (Basic Local Alignment Search Tool) A tool for identifying sequences in adatabase that match a given query sequence.",
      "The large number of bioinformatic tools that have beenmade available to scientists during the last few years has presented theproblem of which to use and how best to obtain scientifically valid answers(3). In this chapter, we will provide a guide for the most efficient way toanalyze a given sequence or to collect information regarding a gene, protein,structure, or interaction of interest by applying current publicly available software and databases that mainly use the World Wide Web.",
      "At a high level, the Research and Development Space of Bioinformatics canbe viewed as a set of non-orthogonal vectors (Figure 1) that describeBioinformatic ActivitiesBiological Data TypesBiological SpeciesComputing InfrastructureDevelopment EffortBioinformatic activities (acquisition, storage, retrieval, integration, analysis,visualization, modeling) need to be developed for multiple biological data typesArchitectures for Integration of Data and Applications33(nucleic and amino acid sequences, physical and linkage maps, RNA, protein andmetabolite expression arrays and clinical and eld assays) derived from multiple biological species using multiple biotechnology platforms.As Bioinformaticsemerges as a discipline, however, it is likely that both research and developmentcan and will be accommodated in large programmatic grants. 7. REFERENCESBenton, D., 2000, Standards to Enable Bioinformatics Data and Information Integration, In BarnettInternationals 2nd Annual Bioinformatics and Data Integration Conference, Philadelphia, PA.Boyle, J., 1998, Building Component Software for the Biological Sciences, CCP11 Newsletter, 4:2214. Dowell, R., Jokerst, A., Day, S., Eddy, L., and Stein, L., 2001, The distributed annotation system, BMCBioinformatics 2(7). This article is available at http://www.biomedcentral.com/1471-2105/2/7.",
      "Ball Department ofBiochemistry, Stanford University MedicalSchool, Stanford, CA, USAJames R. Brown Bioinformatics,GlaxoSmithKline Pharmaceuticals, UpperProvidence, PA, USAAruna Bansal Discovery and PipelineGenetics, GlaxoSmithKlinePharmaceuticals, Third Avenue, Harlow,Essex, UKElissa J. Chesler Oak Ridge NationalLaboratory, Biosciences Division, OakRidge, TN, USAMichael R. Barnes Bioinformatics,GlaxoSmithKline Pharmaceuticals, ThirdAvenue, Harlow, Essex, UKBryan J. Barratt Research andDevelopment Genetics, AstraZeneca,Alderley Park, Macclesfield, Cheshire, UKMatthew J. Betts Structural andComputational Biology Programme, EMBL,Meyerhofstrasse 1, 69117 Heidelberg,GermanyDiana Blaydon Centre for CutaneousResearch, Institute of Cell and MolecularScience, Queen Marys School of Medicineand Dentistry, Whitechapel, London, UKKarl W. Broman Department ofBiostatistics, Johns Hopkins University,Baltimore, MD, USAEllen M. Brown Discovery Informatics,AstraZeneca, Alderley Park, Macclesfield,Cheshire, UKRichard R. Copley Wellcome Trust Centrefor Human Genetics, University of Oxford,Oxford, UKBarry Dancis Bioinformatics,GlaxoSmithKline Pharmaceuticals UpperProvidence, PA, USASteve Deharo Bioinformatics,GlaxoSmithKline Pharmaceuticals, ThirdAvenue, Harlow, Essex, UKPaul S. Derwent Bioinformatics,GlaxoSmithKline Pharmaceuticals, ThirdAvenue, Harlow, Essex, UKIan C. Gray Paradigm Therapeutics (S) PteLtd, 10 Biopolis Way, Singapore 138670Joel Greshock Translational Medicine,Clinical Pharmacology Division,GlaxoSmithKline Pharmaceuticals, UpperMerion, PA, USASimon C. Heath Centre National deGenotypage, Evry Cedex, FrancexviiiCONTRIBUTORSDavid P. Kelsell Centre for CutaneousResearch, Institute of Cell and MolecularScience, Queen Marys School of Medicineand Dentistry, Whitechapel, London,UKRalph McGinnis Wellcome Trust SangerInstitute, Hinxton, Cambridge, UKCharles A. Mein Genome Centre, QueenMarys School of Medicine and Dentistry,Charterhouse Square, London, UKMary Plumpton Bioinformatics,GlaxoSmithKline Pharmaceuticals,Stevenage, Hertfordshire, UKRobert B. Russell Structural andComputational Biology Programme, EMBL,Meyerhofstrasse 1, 69117 Heidelberg,GermanyPhilippe Sanseau Bioinformatics,GlaxoSmithKline Pharmaceuticals,Stevenage, Hertfordshire, UKColin A. M. Semple Bioinformatics, MRCHuman Genetics Unit, Edinburgh EH4 2XU,UKGavin Sherlock Department of Genetics,Stanford University Medical School,Stanford, CA, USAChristopher Southan Global CompoundSciences, AstraZeneca R&D, Molndal,SwedenMartin S. Taylor Wellcome Trust Centrefor Human Genetics, University of Oxford,Oxford, UKMagnus Ulvsback MolecularPharmacology, AstraZeneca R&D, Molndal,SwedenCharlotte Vignal Discovery and PipelineGenetics, GlaxoSmithKlinePharmaceuticals, Third Avenue, Harlow,Essex, UKChaolin Zhang Department of BiomedicalEngineering, State University of New Yorkat Stony Brook, NY, USAMichael Q. Zhang Cold Spring HarborLaboratory, Cold Spring Harbor, NY, USAXiaoyue Zhao Cold Spring HarborLaboratory, Cold Spring Harbor, NY, USAGlossary of BioinformaticsBLAST (Basic Local Alignment Search Tool) A tool for identifying sequences in adatabase that match a given query sequence.",
      "There are online bioinformatics resources from which this type of information may be sourced.",
      "Ball Department ofBiochemistry, Stanford University MedicalSchool, Stanford, CA, USAJames R. Brown Bioinformatics,GlaxoSmithKline Pharmaceuticals, UpperProvidence, PA, USAAruna Bansal Discovery and PipelineGenetics, GlaxoSmithKlinePharmaceuticals, Third Avenue, Harlow,Essex, UKElissa J. Chesler Oak Ridge NationalLaboratory, Biosciences Division, OakRidge, TN, USAMichael R. Barnes Bioinformatics,GlaxoSmithKline Pharmaceuticals, ThirdAvenue, Harlow, Essex, UKBryan J. Barratt Research andDevelopment Genetics, AstraZeneca,Alderley Park, Macclesfield, Cheshire, UKMatthew J. Betts Structural andComputational Biology Programme, EMBL,Meyerhofstrasse 1, 69117 Heidelberg,GermanyDiana Blaydon Centre for CutaneousResearch, Institute of Cell and MolecularScience, Queen Marys School of Medicineand Dentistry, Whitechapel, London, UKKarl W. Broman Department ofBiostatistics, Johns Hopkins University,Baltimore, MD, USAEllen M. Brown Discovery Informatics,AstraZeneca, Alderley Park, Macclesfield,Cheshire, UKRichard R. Copley Wellcome Trust Centrefor Human Genetics, University of Oxford,Oxford, UKBarry Dancis Bioinformatics,GlaxoSmithKline Pharmaceuticals UpperProvidence, PA, USASteve Deharo Bioinformatics,GlaxoSmithKline Pharmaceuticals, ThirdAvenue, Harlow, Essex, UKPaul S. Derwent Bioinformatics,GlaxoSmithKline Pharmaceuticals, ThirdAvenue, Harlow, Essex, UKIan C. Gray Paradigm Therapeutics (S) PteLtd, 10 Biopolis Way, Singapore 138670Joel Greshock Translational Medicine,Clinical Pharmacology Division,GlaxoSmithKline Pharmaceuticals, UpperMerion, PA, USASimon C. Heath Centre National deGenotypage, Evry Cedex, FrancexviiiCONTRIBUTORSDavid P. Kelsell Centre for CutaneousResearch, Institute of Cell and MolecularScience, Queen Marys School of Medicineand Dentistry, Whitechapel, London,UKRalph McGinnis Wellcome Trust SangerInstitute, Hinxton, Cambridge, UKCharles A. Mein Genome Centre, QueenMarys School of Medicine and Dentistry,Charterhouse Square, London, UKMary Plumpton Bioinformatics,GlaxoSmithKline Pharmaceuticals,Stevenage, Hertfordshire, UKRobert B. Russell Structural andComputational Biology Programme, EMBL,Meyerhofstrasse 1, 69117 Heidelberg,GermanyPhilippe Sanseau Bioinformatics,GlaxoSmithKline Pharmaceuticals,Stevenage, Hertfordshire, UKColin A. M. Semple Bioinformatics, MRCHuman Genetics Unit, Edinburgh EH4 2XU,UKGavin Sherlock Department of Genetics,Stanford University Medical School,Stanford, CA, USAChristopher Southan Global CompoundSciences, AstraZeneca R&D, Molndal,SwedenMartin S. Taylor Wellcome Trust Centrefor Human Genetics, University of Oxford,Oxford, UKMagnus Ulvsback MolecularPharmacology, AstraZeneca R&D, Molndal,SwedenCharlotte Vignal Discovery and PipelineGenetics, GlaxoSmithKlinePharmaceuticals, Third Avenue, Harlow,Essex, UKChaolin Zhang Department of BiomedicalEngineering, State University of New Yorkat Stony Brook, NY, USAMichael Q. Zhang Cold Spring HarborLaboratory, Cold Spring Harbor, NY, USAXiaoyue Zhao Cold Spring HarborLaboratory, Cold Spring Harbor, NY, USAGlossary of BioinformaticsBLAST (Basic Local Alignment Search Tool) A tool for identifying sequences in adatabase that match a given query sequence."
    ],
    [
      "This is an open access article distributed under the Creative Commons Attribution License,which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. 1. IntroductionThe association between a complex phenotypic trait andgenetic markers on the chromosomes can be detectedthrough statistical analysis, leading to the identification ofquantitative trait loci (QTL)regions of the chromosomesthat appear to be associated with the phenotype. Quantitativetrait loci (QTL) are expected to be associated with the genescontrolling some aspects of the phenotype.",
      "Nowadays manydifferent cost-efficient genotyping solutions (including sequencing and SingleNucleotide Polymorphisms arrays) have opened the way to systematic genome-widefine mapping of quantitative traits (Quantitative Trait Locus or QTL mapping). The process of QTL mapping (Figure 1) consists in searching for genome regions that influence the value of a given trait. For example, identifying a QTL forplant height means finding a DNA region at which the plants that carry a certainallele tend to be significantly higher or lower than those carrying another allele.",
      "QTLs are regions within thegenome whose genetic variation modulates quantitatively a phenotype characteristic ofthe particular trait under study (Lynch and Walsh, 1998). Determining the associationbetween variations in specific disease phenotypes or a trait, with variations in genotypesof a reference population can be used to locate a QTL. One of the methods used formapping QTLs associated with complex traits is genetic markers-trait association. Genetic markers associated with certain loci can be inherited in linkage disequilibrium. Generating populations with linked loci in disequilibrium is achieved though eithercrosses between inbred lines, or use of the out-bred populations.",
      "Often, the first step in analysis of new traitdata is single-marker regression across all chromosomes. A hypothetical QTL is evaluated atthe location of each marker locus, and the significance of that QTL is estimated from a likelihood ratio statistic (LRS) (Haley and Knott,1992). For this analysis, WebQTL automatically does a permutation test to establish genomewide significance criteria for the trait (Churchilland Doerge, 1994).",
      "One possible approach to facilitate this endeavor is to identify quantitative trait loci(QTL) that contribute to the phenotype and consequently unravel the candidategenes within these loci. Each proposed candidate locus contains multiple genes and,therefore, further analysis is required to choose plausible candidate genes. One ofsuch methods is to use comparative genomics in order to narrow down the QTL to aregion containing only a few genes. We illustrate this strategy by applying it togenetic findings regarding physical activity (PA) in mice and human.",
      "Elucidation of the molecular basis of these traits has provendifficult as they are under the control of multiple genes andgenetic loci. The standard approach to gene identificationinvolves mapping by linkage analysis in experimental crosses,and this has led to the localization in the rat genome ofhundreds of quantitative trait loci (QTLs) underlying traitvariation (68). We refer to these loci as physiological quantitative trait loci (pQTLs).",
      "Often, the first step in analysis of new trait data is single-marker regression across all chromosomes.A hypothetical QTL is evaluated at the location of each marker locus, and the significance of that QTL is estimated from a likelihood ratio statistic (LRS) (Haley and Knott, 1992).For this analysis, WebQTL automatically does a permutation test to establish genomewide significance criteria for the trait (Churchill and Doerge, 1994).By default, it returns a list of marker loci that show greater than sugges-tive association with the trait according to standard criteria (Lander and Kruglyak, 1995), but it will also accept user-defined criteria.Local maxima in the LRS in this list identify loci that are most likely to be near QTLs.WebQTL provides this list within a few seconds.",
      "QTLs can be identified through their geneticlinkage to visible marker loci with genotypes that can be readily classified [94, 97]. Assuch, markers that are genetically linked quantitative trait will segregate more often withtrait values, whereas unlinked markers will lack an association with the phenotype [94,98]. The principal goal of a QTL analysis is to identify all QTLs linked to a trait anddiscern whether phenotypic differences are mainly due to a few loci with large effects, ormany loci with small effects [98].",
      "This is an open access article distributed under the Creative Commons Attribution License,which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. 1. IntroductionThe association between a complex phenotypic trait andgenetic markers on the chromosomes can be detectedthrough statistical analysis, leading to the identification ofquantitative trait loci (QTL)regions of the chromosomesthat appear to be associated with the phenotype. Quantitativetrait loci (QTL) are expected to be associated with the genescontrolling some aspects of the phenotype.",
      "The basic principle of classic QTL is trait segregation along with themarkers and necessitated the availability of two or more genetically differentlines corresponding with the phenotypic trait. Markers like single nucleotidepolymorphisms (SNPs) and microsatellites are used for genotypic distinctions(Vignal et al. , 2002). QTL mapping is achieved in four basic steps; the first one is the measurementof variation for a trait in the individuals. It is a prerequisite to have the traitsthat show phenotypic variability among the individuals (inbred strains).",
      "Often, the first step in analysis of new trait data is single-marker regression across all chromosomes.A hypothetical QTL is evaluated at the location of each marker locus, and the significance of that QTL is estimated from a likelihood ratio statistic (LRS) (Haley and Knott, 1992).For this analysis, WebQTL automatically does a permutation test to establish genomewide significance criteria for the trait (Churchill and Doerge, 1994).By default, it returns a list of marker loci that show greater than sugges-tive association with the trait according to standard criteria (Lander and Kruglyak, 1995), but it will also accept user-defined criteria.Local maxima in the LRS in this list identify loci that are most likely to be near QTLs.WebQTL provides this list within a few seconds.",
      "Often, the first step in analysis of new traitdata is single-marker regression across all chromosomes. A hypothetical QTL is evaluated atthe location of each marker locus, and the significance of that QTL is estimated from a likelihood ratio statistic (LRS) (Haley and Knott,1992). For this analysis, WebQTL automatically does a permutation test to establish genomewide significance criteria for the trait (Churchilland Doerge, 1994).",
      "Quantitative Trait Locus (QTL) mappingTo map QTL, we used 934 AXB/BXA genetic informative markers obtained from http://www. genenetwork.org. For all the in vitro measurements and gene expression linkage analysis, agenome-wide scan was performed using R/qtl [57]. Significance of QTL logarithm-of-odds(LOD) scores was assessed using 1000 permutations of the phenotype data [114] and the corresponding p-values reported. For the cellular phenotypes, QTL significance was reported at agenome-wide threshold corresponding to p < 0.05.",
      "Typically one may obtain a location known to derive from only one of the twoparent strains that contains a chromosomal region that correlates with a trait of interest. Since the actual gene and gene product will frequently remain unknown, the region isreferred to as quantitative trait locus (QTL), and is simply named for the trait itself(Alberts & Schughart, 2010). Growing sets of strain-dependent marker locations inestablished RI strains are continually updated in online repositories.",
      "By definition, aquantitative trait locus is a chromosomal region that contains a gene, or genes, thatregulate a portion of the genetic variation for a particular phenotype (Wehner et al. 2001). The goal of QTL mapping is to identify regions of the genome that harbourgenes relevant to a specified trait. QTL map locations are commonly determined byinitial screening of mice with specific genetic characteristics, such as recombinantinbred strains, the F2 of two inbred strains, or recombinant congenic strains (Flint2003).",
      "Often, the first step in analysis of new traitdata is single-marker regression across all chromosomes. A hypothetical QTL is evaluated atthe location of each marker locus, and the significance of that QTL is estimated from a likelihood ratio statistic (LRS) (Haley and Knott,1992). For this analysis, WebQTL automatically does a permutation test to establish genomewide significance criteria for the trait (Churchilland Doerge, 1994).",
      "QTL linkage studies are conducted in order to map a region or regions of the genome whichaffect a continuous or quantitative trait. In agriculture, as soon as markers linked to QTL arefound for economically important traits, these markers can be used for selecting individualsin breeding programmes. In human studies, the aim is often to identify markers indicatingdisease susceptibility. Current techniques for measuring markers are usually relatively slowand laborious. Newer DNA technology, such as SNP or single nucleotide polymorphisms(Kwok, 2001b; Patil et al.",
      "Genomic regions linked to complex traits can be identified by genetic mappingand quantitative trait locus (QTL) analysis (Shehzad and Okuno 2014). 7QTL mappingQTL mapping with molecular markers is the first strategy in genetic studies. In plantbreeding, QTL mapping is an essential step required for marker-assisted selection(Mohan et al. 1997; Shehzad and Okuno 2014). The fundamental idea underlying QTLanalysis is to associate genotype and phenotype in a population exhibiting a geneticvariation (Broman and Sen 2009).Four steps of QTL mapping are (1) development aWpopulation, (2) genotyping the population using molecular markers, (3) phenotyping thepopulation for an interested trait, and (4) QTL analysis using statistical procedures to findIEmarkers linked to the QTL (Bernardo 2002). PREVPopulations used for genetic mapping can be a segregating population (F2 andbackcross) or a permanent population (double haploids or recombinant inbred lines). Recombinant inbred lines (RILs) are developed by selfing of individual progenies of theF2 plants until homozygosity is achieved (F7-F8).",
      "Thistool allows systems genetic analysis of single genes or small sets of genes using a bottom-upapproach. relations define quantitative trait loci (QTLs). Because the marker is not typically theactual site of the polymorphism, interpolative methods have been developed to estimatethe distance of the QTL from the marker and the strength of the association. Usingmultiple-regression and model-fitting methods, the true complexity of the phenotypicvariation can be modeled through the consideration of multiple loci and environmentalfactors as predictors [13]."
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