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              "content": "### Potential Benefits of CRISPR-Cas9:\n\n1. **Accelerated Genomic Research**: CRISPR-Cas9 tools have significantly accelerated the pace of genomic research by allowing highly efficient knockouts or edits of virtually any gene in cells or model organisms [1].\n2. **Clinical Trials and Stability**: Multiple CRISPR-Cas9-based clinical trials are in progress, and early trial results suggest that CRISPR-engineered cells are stable and do not cause acute adverse reactions in humans [1].\n3. **Functional Genomics**: CRISPR is set to make a significant impact on genomic screening and scientific discovery, with recent demonstrations showing great promise [2].\n4. **Targeted Gene Regulation and Epigenome Modifications**: CRISPR-Cas9 has been linked to various effector proteins to enable targeted gene regulation and epigenome modifications [3].\n5. **Precision Medicine**: CRISPR-Cas9 holds significant promise for the development of new disease models and precision-targeted therapies, particularly in molecular medicine [4].\n6. **Versatility in Applications**: CRISPR-Cas9 can induce genome edits by creating targeted DNA breaks that trigger site-specific DNA repair. It can also control the transcriptional output of genes or alter genome sequences using nucleotide base editing, which does not require DNA break repair [8].\n\n### Potential Risks of CRISPR-Cas9:\n\n1. **Off-Target Effects**: CRISPR-Cas9 is known to generate off-target alterations, which can result in unwanted mutations and potentially cytotoxic effects [6].\n2. **Technical Challenges**: There are several technical challenges that need to be addressed to maximize the benefits of CRISPR technology [2].\n3. **Long-Term Safety**: While early trial results are promising, the long-term safety of CRISPR-engineered cells is yet to be determined [1].\n4. **Mismatch Tolerance**: The CRISPR-Cas9 system can tolerate certain mismatches to the DNA target, which could be a disadvantage as it may lead to unintended edits [7].\n\nIn summary, while CRISPR-Cas9 offers numerous benefits such as accelerating genomic research, enabling precision medicine, and providing versatile applications, it also poses risks like off-target effects, technical challenges, and concerns about long-term safety.",
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              "text": "neered nucleases, CRISPR-Cas9 \ntools have accelerated the pace of \ngenomic research by permitting \nhighly efficient knockouts or \nedits of virtually any gene in cells \nor model organisms. Multiple CRISPR-Cas9based clinical trials \nare in progress or are expected \nto begin soon. Although Cas9-\nengineered cells havent yet dem -\nonstrated efficacy at scale, early \ntrial results suggest that such \ncells are stable and dont cause \nacute adverse reactions in humans. \nLong-term safety is yet to be de -",
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              "text": "stageissetforCRISPRtomakeanenormousimpactongenomic\nscreening and thus scientic discovery in the coming years, and\nrecent demonstrations of this system have shown great promise\n(Shalem etal., 2015 ).However,a number of technical challenges\nmust be addressed in order to maximize the benet of this\ntechnology. In this review, we will discuss current applications\nof CRISPR in functional genomics and provide a perspective on\nfuturedevelopmentsinthisarea.\nCRISPR/Cas9 Genome Editing",
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              "text": "heralding the age of genome editing. Furthermore, Cas9 or guide RNAs have been linked to various effector proteins to enable targeted gene regulation\n12,13 and epigenome modifications14,15. \nIt is worth noting, however, that many of these feats had been demonstrated previously using other nucleases or DNA-binding proteins\n1,16. In this Perspective, I shed light on early genome \nediting platforms that laid the groundwork for the widespread use of CRISPRCas9 in research and medicine (Fig. 1 ).",
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              "text": "CRISPR/CAS9 HOLDS SIGNIFICANT\nPROMISE FOR THE DEVELOPMENT OFNEW AD MODELS AND PRECISIONTARGETED AD THERAPY\nClustered regularly interspaced short palindromic\nrepeat (CRISPR)-Cas nucleases have revolutionizedthe eld of gene editing and have tremendous appli-cation in the eld of molecular medicine [98102].Despite a signicant surge in CRISPR/Cas9-mediated genome editing in various disease models,the progress in the eld of AD has lagged behindsubstantially. We believe that genome editing can sig-",
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              "text": "81.\nApplications for CRISPRCas9 beyond genome editing",
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              "text": "cline- or Tet-regulated Cas9 system. Current CRISPR/Cas systems arefrom Streptococcus pyogenes ,Streptococcus thermophilus ,Neisseria\nmeningitides and Treponema denticola .2.5. Caveats of advanced genome editing tools\nOff-target effects . The DNA-binding domains of ZFNs and TALENs\nneed to be very speci c for the target site to avoid off-target cleavage,\nwhich results in unwanted mutations and potentially cytotoxic effects\n[27]. CRISPR/Cas9 is also known to generate off-target alterations,",
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              "text": "on transcriptional interfere nce (CRISPRi) and activation\n(CRISPRa) have also harnessed Cas9-based technologies for\nuse in genome-wide studies ( 59,174). In addition, recent\nimprovements in lentiviral library generation and propagation,as well as large-scale DNA and RNA synthesis, have allowedCRISPR-Cas9 technology to be exploited across multiple modelplatforms ( 59,175178).\nnCas9\nThe CRISPR-Cas9 system can tolerate certain mismatches to the\nDNA target since the required gRNAs are short. A disadvantage,",
              "title": "2020 - Functional Genomics in Pancreatic \u03b2 Cells Recent Advances in Gene Deletion and Genome Editing Technologies for Diabetes Research.pdf",
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              "text": "CRISPR-Cas9 can be used to in -\nduce genome edits by creating \ntargeted DNA breaks that trigger \nsite-specific DNA repair. In next-\ngeneration formats, it can also \ncontrol the transcriptional output \nof genes or alter genome se -\nquences using a process of nu -\ncleotide base editing \nthat does not require \nrepair of DNA breaks. \nAs these technolo -\ngies continue to mature, it will \nbecome increasingly possible to \nalter cellular genomes efficiently \nand accurately.\nComing on the heels of engi -",
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              "text": "S.P . Raikwar et al. / Alzheimers Disease: New Therapeutic Horizons 333\ngene editing efciency of the CRISPR/Cas9 systems.",
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              "text": "13. Kleinstiver BP, etal. High-fidelity CRISPRCas9 nucleases with no detectable genome-wide \noff-target effects. Nature. 2016;529:4905.\n 14. Brane A, Tollefsbol T.Targeting telomeres and telomerase: studies in aging and disease uti-\nlizing CRISPR/Cas9 technology. Cells. 2019;8:186.\n 15. Wang H, etal. One-step generation of mice carrying mutations in multiple genes by CRISPR/\nCas-mediated genome engineering. Cell. 2013;153:9108.",
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              "content": "Epigenetics influences gene expression without changing the underlying DNA sequence through several mechanisms. These include:\n\n1. **DNA Methylation**: This involves the addition of methyl groups to DNA, primarily at CpG sites, which can affect gene transcription by converting cytosine to 5-methylcytosine, thereby repressing gene activity [1], [4].\n2. **Histone Modifications**: Chemical modifications to histone proteins, such as methylation, acetylation, phosphorylation, and ubiquitination, can alter the chromatin structure, making genes more or less accessible for transcription [1], [6].\n3. **Non-coding RNAs (ncRNAs)**: These RNAs, including microRNAs (miRNAs), can regulate gene expression at the post-transcriptional level by binding to messenger RNAs (mRNAs) and preventing their translation into proteins [1], [6].\n\nThese epigenetic modifications are influenced by both genetic variants and environmental factors such as pollution, tobacco smoking, obesity, lack of physical activity, and alcohol consumption [2], [6]. These changes are heritable and can be passed from one generation to the next without altering the DNA sequence itself [4], [5].",
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              "text": "to regulate lifetime and aging processes. In fact, epigenetics modulate gene expression\nwithout altering the DNA sequence. This is possible by means of different kinds of\nepigenetic modifications, including DNA methylation and histone modifications\n(which might affect gene transcription), and noncoding (nc)RNAs (which might\nchange gene expression at the post-transcriptional level)[59]. Given the crucial role of\nepigenetics in the modulation of gene expression, its alteration can contribute to",
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              "text": "can regulate gene expression while the underlying DNA\nsequence remains the same. The epigenome is influenced\nboth by underlying genetic variants as well as by environ-\nmental factors including the social environment, health\nbehaviors, and environmental pollutants [ 11]. Methylation\nof CpG dinucleotides, the best understood epigenetic\nmechanism, is also dynamic over the life course. It is well\nestablished that epigenomic patterns of DNA methylation\nchange with age [ 12]. A recent study in lymphocytes",
              "title": "2018 - DNA methylation in the APOE genomic.pdf",
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              "text": "Epigenetics Changes arising from alterations in gene expression \nlevels that are caused by reversible chemical \nmodification of DNA, but not changes to the DNA \nsequence passed on from parents to offspring.",
              "title": "2016 - Te-Mata-Ira-Genome-Research-Guidelines.pdf",
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              "text": "Epigenetic changes refer to heritable changes in gene expression\nwhich do not involve changes in DNA sequences. Several epigenetic\nmechanisms have been found to regulate gene expression. Whilst\nthe most studied mechanism relates to DNA methylation, other\nchanges, including histone modi cations and non-coding RNAs,\nalso play an important role, and can be transmitted from one\ngeneration to the next. DNA methylation involves the addition of\nmethyl groups to DNA, mainly at CpG sites, which converts cytosine",
              "title": "2015 - Maternal diabetes, gestational diabetes and the role of epigenetics in their long term effects on offspring.pdf",
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              "text": "EPIGENETIC STUDIES \n An epigenetic mechanism is a biochemical alteration to the DNA molecule that \ndoes not change the sequence of the DNA but does in  uence gene expression. \n Epigenetics   is often de  ned as the study of mitotically and/or meiotically heri-\ntable changes in gene function that cannot be explained by changes in DNA sequence (Russo, Martienssen, & Riggs, 1996, p. 1). \n The epigenetic/epigenomic approach shares many advantages and disad-",
              "title": "2011 - Molecular Genomic Research Designs.pdf",
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              "text": "ity and expression of genes without changing their DNA sequence [ 4]. These modications\nare: DNA methylation, histone modications, and ncRNAs including miRNA [4]. The en-\nvironment and lifestyle can induce epigenetic changes, such as pollution, tobacco smoking,\nobesity, lack of physical activity, and alcohol consumption [ 108]. Furthermore, exposure\nto such environmental factors can have a buttery effect: epigenetic modications may",
              "title": "2022 - Genomics and Epigenomics of Gestational Diabetes Mellitus Understanding the Molecular Pathways of the Disease Pathogenesis.pdf",
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              "text": "epigenetics is the study of mitotically heritable alterations in gene expression potential that are not caused by changes in DNA sequence (Jaenisch and Bird,  2003 ). Hence, rather than encompassing all of developmental biology, modern epigenetics is focused on understanding the spe-ci c molecular mechanisms that convey cellular memory. \n Within the nucleus, the mammalian genome is wrapped",
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              "text": "gene expression can also occur by  trans-epigenetics ( Bonasio\net al., 2010 ), in which proteins and RNAs inuence gene expres-sion and repression. Stable transcription factor networks are anexample of trans -epigenetics ( Young, 2011 ). Clearly, enzymes\nthat modify DNA and histones (methyltransferases, demethy-lases, acetyltransferases, deacetylases) are central epigeneticregulatory mechanisms ( Rando and Chang, 2009 ).\nThe essence of epigenetics is not only the establishment, but",
              "title": "2012 - Aging, Rejuvenation, and Epigenetic.pdf",
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              "text": "pay attention to epigenetic effects on gene expressionmeaning changes that are heritable but that do not involve any change in DNA sequence (see Rutter 2006). Three key points are relevant. First, genes only have effects when they are expressed. Many genes are expressed in only some body tissues and only at certain phases in development. Second, there are multiple inherited DNA elements that do not code for proteins but yet which have important effects through their in  uence on gene expression. We need to",
              "title": "2008 - Genetic Effects on Environmental Vulnerability to Disease Novartis Foundation Symposium 293.pdf",
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              "text": "genetics of gene expression (i.e. regular genetical genomics) and the genetics of epigenetics could be studied simultaneously, thus revealing genes that directly or indirectly affect epigenetic gene states. An additional issue that could be addressed by such an approach is to estimate the percentage of variation in gene expression that can be explained by different epigenetic conformations.",
              "title": "2011 - EXPLOITING NATURAL AND INDUCED GENETIC VARIATION TO STUDY HEMATOPOIESIS.pdf",
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              "content": "Mitochondrial DNA (mtDNA) plays a unique role in heredity and differs significantly from nuclear DNA (nDNA) in several ways:\n\n1. **Inheritance Pattern**: Mitochondrial DNA is inherited exclusively from the mother, a phenomenon known as maternal or extranuclear inheritance [4], [6], [7]. This contrasts with nuclear DNA, which is inherited from both parents.\n\n2. **Replication and Segregation**: mtDNA replicates autonomously and independently of the cell cycle, unlike nuclear DNA which replicates during cell division [1], [5]. Mitochondria are semiautonomous organelles with their own replication, transcription, and translation systems [2].\n\n3. **Genetic Variation and Mutation Rate**: Mitochondrial DNA has a higher mutation rate compared to nuclear DNA, approximately tenfold higher [2]. This can lead to a condition called heteroplasmy, where both mutated and normal mitochondria coexist within the same cell [1], [2].\n\n4. **Structure and Size**: The mitochondrial genome is a short circular molecule, significantly smaller than the nuclear genome. The human mitochondrial genome is only 16,569 base pairs long and encodes 37 genes, whereas the nuclear genome consists of approximately 1500 genes related to mitochondrial function [8], [9].\n\n5. **Functional Role**: mtDNA carries essential genetic information for cellular metabolism and energy generation, which is crucial for the functioning of the cell [10]. This is distinct from the broader range of functions encoded by nuclear DNA.\n\nIn summary, mitochondrial DNA is maternally inherited, replicates independently, has a higher mutation rate, and is structurally distinct from nuclear DNA. These differences underscore the unique role of mtDNA in heredity and cellular function.",
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              "text": "drial DNA sequence variation seems impossible withoutan understanding of some important differences betweennuclear and mitochondrial genetics (Table I). Mitochon-drial DNA replicates autonomously and is inherited viathe cytoplasm of the parent cell with the individualmitochondrion being the segregating unit (Attardi et al.,1995). Thus, in the case of mitochondrial mutations bothmutated as well as normal mitochondria may be presentwithin the same cell. This situation has been termedheteroplasmy and can",
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              "text": "cMitochondria are semiautonomous organelles; possess their own\nreplication-, transcription- and translation system\ncExclusively maternal inheritance of mitochondrial DNA\ncMitotic segregation of mitochondrial DNAcan lead to hetero-\nplasmy, i.e., the proportion of genetically different populations ofmitochondria differs between generations of mitotically activecells\ncApproximately tenfold higher mutation rate compared with nuclear",
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              "text": "DIFFERENCES BETWEEN MITOCHONDRIAL\nAND NUCLEAR GENETICS\nArealisticassessmentoftherelevanceofmitochon-",
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              "text": "In the fifth mode of inheritance, the disease mutation lies not on a chromosome in the nucleus but rather in mitochondrial DNA outside the nucleus. Mitochondria are inherited exclu-\nsively from an offsprings mother; because of this phenome-\nnon, the mutation and thus the disease can be passed only from a mother to her offspring. This is maternal inheritance, also known as extranuclear inheritance (Figure 11). Representative disorders include various mitochondrial myopathies.",
              "title": "2015 - Basic Concepts and Potential Applications of Genetics and Genomics for Cardiovascular and Stroke Clinicians.pdf",
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              "text": "The regulation of the mitochondrial genome also reflects \nits prokaryotic ancestry. While nuclear DNA undergoes replication during cell division, mtDNA replication occurs independently of cell cycle. The majority of the compo-nents for mtDNA replication are imported nuclear-encoded proteins, including the catalytic subunit of mtDNA poly -",
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              "text": "Unlike the nuclear genome, which requires both paternal and maternal contributions, mtDNA is inherited solely from the maternal lineage. It is unclear what advantage a uniparental mtDNA transmission confers, but one possibil-ity is to minimize the number of distinct genomes to maxi-mize the efficiency of a multi-genomic system (Hill etal. 2019). In fact, humans have developed complex, redundant mechanisms to ensure uniparental inheritance of mtDNA (DeLuca and OFarrell 2012; Rojansky etal. 2016). Paternal",
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              "text": "mitochondria and sperm are not, mitochondrial DNAis usually inherited from the mother. Therefore, mito-chondrial genes  and diseases due to DNA-sequencevariants in them  are transmitted in a matrilineal pat-tern that is distinctly different from the pattern of in-heritance of nuclear genes. \nMONOGENIC CONDITIONS \nOver the course of the 20th century, a combination",
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              "text": "2. Mitochondrial DNA structure and properties\nMitochondrial genomes (mt-genomes) are short\ncircular molecules that, with the exception of viruses,represent the most economically packed forms ofDNA in the whole biosphere. The human mt-genomeis only 16,569bp long [9]; within this extension, wend the coding sequences for seven subunits of theNADH-ubiquitone reductase (respiratory complex I),the apocytochrome bof the ubiquitone cytochrome\ncreductase (respiratory complex III), three subunits",
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              "text": "Abstract\nThe human mitochondrial genome consists of approximately 1500 genes, 37 encoded by the maternally inherited mitochondrial DNA\n(mtDNA) and the remainder encoded in the nuclear DNA (nDNA). The mtDNA is present in thousands of copies per cell and encodes",
              "title": "2005 - The mitochondrial genome in human adaptive radiation and disease.pdf",
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              "text": "(mtDNA). MtDNA carries important genetic information concerning cellular metabolismand the generation of energy. It has been suggested that mitochondria and mtDNA could be\nof significance during early embryo development. Our work confirms this hypothesis. Specif-\nically, our findings implicate mitochondria and their genome in female reproductive agingand the generation of embryonic chromosome abnormalities. Importantly, we describe a di-",
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              "content": "The ethical considerations surrounding prenatal genetic testing and the selective termination of pregnancies based on genetic factors are multifaceted and complex. Key considerations include:\n\n1. **Parental Beliefs and Attitudes**: Decisions about prenatal testing and subsequent actions, such as termination, are heavily influenced by parental beliefs about abortion, attitudes regarding disability, and their perceptions of the usefulness of the information revealed by genetic tests [2]. These personal beliefs and attitudes play a crucial role in shaping the ethical landscape of prenatal genetic testing.\n\n2. **Informed Consent**: Ensuring that parents are fully informed about the implications of genetic testing is critical. This includes understanding the potential outcomes, the accuracy of the tests, and the possible decisions they may face based on the results [4]. Informed consent is a cornerstone of ethical medical practice, particularly in the context of genetic testing.\n\n3. **Respect for Parental Decision-Making**: Ethical considerations must balance clinical benefits and risks with respect for parental decision-making. Parents should generally have the final authority in making decisions about their children's health care, including whether to undergo genetic testing and how to respond to the results [5].\n\n4. **Social and Psychological Implications**: The ability to predict future diseases through genetic testing, coupled with limited options for prevention or treatment, has significant social and psychological implications. These must be addressed to ensure that parents are not unduly burdened by the information provided by genetic tests [7].\n\n5. **Access and Equity**: Ethical issues also arise from the accessibility of genetic testing and the opportunities it creates. There are concerns about equitable access to these technologies and the potential for disparities in who can benefit from them [1].\n\n6. **Family Communication Challenges**: Genetic testing results can create communication challenges within families, as they navigate the complex information and make decisions that affect their future [1].\n\nIn summary, the ethical considerations surrounding prenatal genetic testing and selective termination involve respecting parental beliefs and decision-making, ensuring informed consent, addressing social and psychological impacts, and promoting equitable access to genetic testing technologies. These considerations must be carefully balanced to navigate the ethical complexities of prenatal genetic testing.",
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              "text": "1999) raises practical and ethical issues of access to resulting opportunities and creates family communication challenges. Currently, prenatal testing for chromosomal diseases has become increasingly common (Moyer et al., 1999). Options such as pre-implantation genetic diagnosis (PGD) can identify over 1,250 disease-related mutations creating an opportunity for parents to select unaffected embryos for implantation in the womb (R. M. Green, 2008). Test results provide potential parents with information",
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              "text": "undergo prenatal testing have determined that partners base their decision upon several factors, including, but not limited to: parental beliefs about abor-tion, attitudes regarding disability and their perceptions of the usefulness of having the information revealed by genetic tests (Moyer et al., 1999, p. 522). Abortion beliefs constitute a key issue in the decision-making process. Even though a majority of parents receiving abnormal prenatal test results terminate their pregnancies (Redlinger-Grosse,",
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              "text": "Hum Genet 1995;57:12331241.\n24. Committee on Bioethics. Ethical and policy issues in genetic testing and \nscreening of children. Pediatrics 2013;131:620622.\n25. Ross LF, Saal HM, David KL, Anderson RR. Technical report: ethical and policy issues in genetic testing and screening of children. Genet Med 2013;15:  \n234245.\n26. Wilfond B, Ross LF. From genetics to genomics: ethics, policy, and parental decision-making. J Pediatr Psychol 2009;34:639647.",
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              "text": "Informed Consent and Genetic Testing   \nGenetic testing is increasingly used across the life continuum \nfor screening, diagnosis, and de termining the best treatment \nof diseases. Obstetric and pediat ric nurses have traditionally \nbeen involved in the genetic testing process with prenatal \nscreening for genetic conditions such as spina bifida and Down \nsyndrome, and newborn screening for genetic conditions such",
              "title": "2008 - Genetic and Genomic Healthcare Ethical Issues of Importance to Nurses.pdf",
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              "text": "Objective Ethical evaluation of genetic testing in children is traditionally based on balancing clinical\nbenefits and risks. However, this focus can be inconsistent with the general practice of respecting parentaldecision-making about their childrens health care. We argue that respect for parental decision-making\nshould play a larger role in shaping pediatric genetic testing practices, and play a similar role regarding decisions",
              "title": "2009 - From Genetics to Genomics Ethics, Policy, and Parental.pdf",
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              "text": "prenatal decisions. Further research needs to investigate how different families engage in such discussions and decision-making pro-cesses, especially as prenatal testing becomes more common and better able to predict or prevent a wider range of genetic conditions.",
              "title": "2009 - Opening Up the Conversation on Genetics.pdf",
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              "text": "all of the complex ethical and legal issues rel-\nevant to genetic testing would disappear if\nthere were effective preventions or treatments\navailable for genetic conditions. The ability\nto predict future disease in conjunction with\na limited ability to do much about it has im-\nportant social and psychological implications\nthat must be addressed in conducting genetic\nresearch.\nOne final factor worth consideration in un-\nderstandingthesensitivitytogeneticmedicine",
              "title": "2020 - Informed Consent for Genetic and Genomics Research.pdf",
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              "text": "Newborn screening by tandem mass spec-trometry: ethical and social issues. Can J Public Health 2007; \n  98:   284286. \n 65 Belle-Isle L: Genetic testing for late onset dis-\neases: a population and public health per-spective. Health Policy Res Bull 2001; \n  1:   11\n12. \n 66 Williams-Jones B: Private genetic testing in \nCanada: a summary. Health Law Rev 2001;   9:  \n 1013. \n 67 Begleiter ML: Training for genetic counsel-\nlors. Nat Rev Genet 2002;   3:   557561. \n 68 Carroll JC, Reid AJ, Woodward CA, Per-",
              "title": "2008 - Canada Public Health Genomics.pdf",
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              "text": "Although risk-based genetic testing for common diseases raise similar ethical issues to more \ntraditional genetic testing for rare diseases, new challenges are raised due to the type of information \nrevealed and access to tests. With thoughtful deliberation with health professionals, patients and \nfamilies, test developers and laboratories, insurers and other stakeholders, these issues can be \naddressed to ensure the safe and appropriate use of these promising new clinical applications. \n  \nREFERENCES",
              "title": "2018 - Ethical_Social_and_Legal_Consequences.pdf",
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              "text": "against testing, parents should generally be given final\ndecision-making authority.\nEthical Considerations in Developing Policy\nfor Comprehensive Genomic Testing\nIn the near future, genomic testing is likely to become\nmore accessible and will provide both information aboutthe risks of common conditions such as heart disease,\ndiabetes, and hypertension as well as predictions aboutindividual responses to specific pharmaceuticals and\nother medical therapies (Aspinall & Hamermesh, 2007).",
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              "content": "### How-to Guide for Genetic Sequencing\n\nGenetic sequencing is a powerful tool used to determine the order of nucleotides in DNA. This guide will walk you through the basic steps involved in genetic sequencing, referencing key resources for further information.\n\n#### Step 1: Sample Preparation\n1. **Collect DNA Sample**: Obtain a sample from the organism or tissue of interest. This could be blood, saliva, or tissue biopsy.\n2. **Extract DNA**: Use a DNA extraction kit to isolate DNA from the sample. This involves cell lysis, removal of proteins and other contaminants, and purification of DNA.\n\n#### Step 2: Library Preparation\n1. **Fragment DNA**: Break the DNA into smaller fragments. This can be done using mechanical shearing or enzymatic digestion.\n2. **Add Adapters**: Ligate specific adapter sequences to the ends of the DNA fragments. These adapters are necessary for the fragments to bind to the sequencing platform.\n\n#### Step 3: Amplification\n1. **PCR Amplification**: Amplify the DNA fragments using Polymerase Chain Reaction (PCR). This step increases the quantity of DNA to ensure there is enough material for sequencing.\n\n#### Step 4: Sequencing\n1. **Choose a Sequencing Platform**: Select an appropriate sequencing technology based on your needs. Common platforms include Illumina, Ion Torrent, and Pacific Biosciences [6], [9].\n2. **Load DNA onto the Sequencer**: Follow the manufacturer's instructions to load your prepared DNA library onto the sequencing machine.\n3. **Run the Sequencer**: Initiate the sequencing run. The machine will read the nucleotide sequences of the DNA fragments and generate raw sequence data.\n\n#### Step 5: Data Analysis\n1. **Base Calling**: Use software like Phred to convert raw data into nucleotide sequences [10].\n2. **Sequence Assembly**: Assemble the short DNA sequences into longer contiguous sequences (contigs) using tools like Phrap or CAP3 [10].\n3. **Variant Calling**: Identify genetic variants by comparing the assembled sequences to a reference genome. This can be done using various bioinformatics tools.\n\n#### Step 6: Interpretation\n1. **Annotation**: Annotate the sequences to identify genes, regulatory elements, and other functional regions.\n2. **Data Management**: Store and manage the high-throughput sequence data efficiently. This is crucial for handling large datasets generated by next-generation sequencing technologies [3], [4].\n\n#### Additional Resources\n- **Next-Generation Sequencing (NGS)**: For a detailed understanding of NGS technologies and their applications, refer to Shendure and Ji's review on next-generation DNA sequencing [1], [5].\n- **Sequencing Platforms**: For comparisons of different sequencing platforms, see the work by Quail et al. [6].\n- **Bioinformatics Tools**: Explore various bioinformatics tools and software for sequence analysis and data management [10].\n\nBy following these steps and utilizing the referenced resources, you can successfully perform genetic sequencing and analyze the resulting data.",
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              "text": "36. Sequencing, H.G. Finishing the euchromatic sequence of the human genome. Nature 2004 ,431, 931945.\n37. Heather, J.M.; Chain, B. The sequence of sequencers: The history of sequencing DNA. Genomics 2016 ,107,\n18. [CrossRef]\n38. Rothberg, J.M.; Leamon, J.H. The development and impact of 454 sequencing. Nat. Biotechnol. 2008 ,26,\n11171124. [CrossRef] [PubMed]\n39. Shendure, J.; Ji, H. Next-generation DNA sequencing. Nat. Biotechnol. 2008 ,26, 11351145. [CrossRef]\n[PubMed]",
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              "text": "11 BIOINFORMATIC CHALLENGES FOR GENOMIC MEDICINE \n \n \nProcessing and managing of high-throughput sequence data \n \n High throughput sequencing offers severa l advantages relative to array-based \ngenotyping or expression assays. First, unlike genotyping arrays, whole genome sequencing is not limited to interrogating onl y known sequence variants. Similarly, RNA-\nsequencing (RNA-seq) enables expression quanti fication of novel transcripts that are not",
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              "text": "High-throughput bacterial genome sequencing: an embarrassment of choice,\naworldof opportunity.NatRevMicrobiol2012;10:599-606.\n11.CroucherNJ,DidelotX.Theapplicationof genomicstotracingbacterialpathogen\ntransmission.CurrOpinMicrobiol2015;23:62-7.\n12.ShendureJ,JiH.Next-generationDNAsequencing.NatBiotechnol2008;26:1135-\n45.\n13.MillerJR,KorenS,SuttonG.Assemblyalgorithmsfornext-generationsequencing\ndata.Genomics2010;95:315-27.\n14.OlsonND,LundSP,ColmanRE,FosterJT,SahlJW,SchuppJM,etal.Bestpractices",
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              "text": "sequencing. Genome Res. 20, 11651173 (2010).\n64. English,A.C. etal.  Assessing structural variation in a \npersonal genome-towards a human reference diploid \ngenome. BMC Genomics 16, 286 (2015).\n65. Carneiro,M.O. etal.  Pacific Biosciences sequencing \ntechnology for genotyping and variation discovery in \nhuman data. BMC Genomics 13, 375 (2012).\n66. Quail,M.A. etal.  A tale of three next generation \nsequencing platforms: comparison of Ion T orrent, \nPacific Biosciences and Illumina MiSeq sequencers.",
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              "text": "Nat. Biotechnol. 30, 10331036 (2012).\n111. Chrystoja,C.C. & Diamandis,E.P . Whole genome \nsequencing as a diagnostic test: challenges and \nopportunities. Clin. Chem. 60, 724733 (2014).\n112. McGuire,A.L. etal.  Point-counterpoint. Ethics and \ngenomic incidental findings. Science 340, 10471048 \n(2013).\n113. Bowers,J. etal.  Virtual terminator nucleotides for \nnext-generation DNA sequencing. Nat. Methods 6, \n593595 (2009).\n114. Heger,M. Chinas Direct Genomics unveils new",
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              "text": "sequencing. Bioinformatics 31, 20402042 (2015).\n46. Qiagen.  Oncology insights enabled by knowledge base-\nguided panel design and the seamless workflow of the \nGeneReader NGS system  Press Release. Qiagen  \n[online], http://www.genereaderngs.com/PROM-9192-\n001_1100403_WP_GeneReader_NGS_0116_NA.pdf  \n(2016).\n47. Forgetta,V. etal.  Sequencing of the Dutch elm disease \nfungus genome using the Roche/454 GS-FLX Titanium \nSystem in a comparison of multiple genomics core",
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              "text": "FURTHER INFORMATION\n10X Genomics: http://www.10xgenomics.com\n454 Sequencing: http://www.454.com\nAdvances in Genome Biology and Technology (AGBT): \nhttp://www.agbt.org\nBGISEQ500: http://seq500.com/en/portal/Sequencer.shtml\nIllumina: http://www.illumina.com\nIon Torrent: https://www.thermofisher.com/us/en/home/\nbrands/ion-torrent.html\nOxford Nanopore Technologies: https://www.nanoporetech.\ncom\nPacific Biosciences: http://www.pacb.com\nPersonal Genome Project: http://www.personalgenomes.org",
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              "text": "DNA), and provide the means to link sequences containing applications. First, \nbase- callers like Phred (4,5) extract raw sequences from raw data. There are \nalso contig assemblers like Phrap (University of Washington, http://bozeman.\nmbt.washington.edu/phrap.docs/phrap.html ) or CAP3 (6) that assemble frag-\nments to contigs and packages like consed (7) or GAP4 (8), which are used to \nfinish sequencing projects. These programs are not explained in detail here.",
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              "content": "The genes that give a predisposition to developing Type 1 Diabetes (T1D) include:\n\n1. **Major Histocompatibility Complex (MHC)** genes located in the HLA region of chromosome 6 [1], [2], [6].\n2. **Insulin gene (INS)** located in the IDDM2 locus on chromosome 11 [3].\n3. **CTLA-4 (Cytotoxic T-Lymphocyte Associated 4)** [2].\n4. **PTPN22 (Protein Tyrosine Phosphatase, Non-Receptor Type 22)** [2].\n5. **IL2RA/CD25 (Interleukin 2 Receptor Alpha)** [2].\n6. **IFIH1/MDA5 (Interferon-Induced Helicase 1)** [2].\n\nThese genes are strongly associated with T1D and contribute to the genetic susceptibility to the disease.",
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              "text": "are involved in the development of the disease [127 ].  There is evidence that more \nthan twenty regions of the genome are involved in t he genetic susceptibility to T1D.  \nThe genes most strongly associated with T1D are loc ated in the HLA region of \nchromosome 6 [128].  Similar to T1D, T2D has a stro ng genetic component.  To date, \nmore than 50 candidate genes for T2D have been inve stigated in various populations \nworldwide.  Candidate genes are selected due to the ir interference with pancreatic",
              "title": "2017 - Machine Learning and Data Mining Methods in Diabetes Research.pdf",
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              "text": "pre-existing statistical support for a role in T1D-susceptibility: these\nare the major histocompatibility complex (MHC), the genes encod-\ning insulin, CTLA-4 (cytotoxic T-lymphocyte associated 4) and\nPTPN22 (protein tyrosine phosphatase, non-receptor type 22), and\nthe regions around the interleukin 2 receptor alpha ( IL2RA/CD25 )\nand interferon-induced helicase 1 genes ( IFIH1 /MDA5)94. However,\nthese signals can explain only part of the familial aggregation of T1D.",
              "title": "2018 - Genome-wide association study of 14,000 cases of seven common diseases and 3,000 shared controls.pdf",
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              "text": "C. The Insulin Gene\nA lesser genetic predisposition to T1D is conferred by\nthe IDDM2 locus on chromosome 11 containing the insu-lin gene region. A polymorphic region located 5 =of the\ninsulin gene was rst reported in 1984 to be associatedwith T1D in caucasoids (39). Now established as a pri-\nTYPE 1 DIABETES: FROM CAUSE TO CURE 81\nPhysiol Rev VOL 91 JANUARY 2011 www.prv.org\nDownloaded from journals.physiology.org/journal/physrev (041.090.188.152) on July 14, 2023.",
              "title": "2011 - Type 1 Diabetes Etiology, Immunology.pdf",
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              "text": "ception of the insulin gene (434). The genetic susceptibil-ity component of T1D allows some targeting of primarypreventive care to family members of diagnosed T1Dpatients, but there is no complete inheritance of the dis-ease. Nevertheless, the risk for developing T1D comparedwith people with no family history is /H110111015 times\ngreater. Although /H1101170% of individuals with T1D carry",
              "title": "2011 - Type 1 Diabetes Etiology, Immunology.pdf",
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              "text": "Genes signifying increased risk for both type 1 and type 2 dia-betes have been identified. Genomewide association studies have identified over 50 loci associated with an increased genetic risk of type 1 diabetes. Several T1D candidate genes for increased risk of developing type 1 diabetes have been sug-gested or identified within these regions, but the molecular basis by which they contribute to islet cell inflammation and beta cell destruction is not fully understood.\n12 Also, several",
              "title": "2015 -precision-medicine-for-managing-diabetes.pdf",
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              "text": "14 \n carried out on large cohorts including collections of families with  affected sibling pairs (Pociot  \net al., 2010). These studies have provided evidence for over forty T1D susceptibility regions , \nbut the  exact mechanisms by which the variation found in these regions  confer susceptibility to \nT1D is still not clear (Noble and Erlich, 2012). The most important genes contributing to T1D \nsusceptibility are located in the MHC class II region , also  referred to as t he Human Leukocyte",
              "title": "2016 - The Genomics of Type 1 Diabetes.pdf",
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              "text": "The ultimate proof of an inherited contribution to disease pathogenesis comes\nfrom the identication of susceptibility genes. As described below, an increasing\nnumber of T2D susceptibility genes have been discovered in the past decade, especially,but not exclusively, in monogenic subtypes. Collectively, these probably account for294 A. L. Gloyn and M. I. McCarthy",
              "title": "2001 - The genetics of type 2 diabetes.pdf",
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              "text": "loci contribute to Type 1 Diabetes (T1D) susceptibility and age at T1D onset. Hum.\nImmunol. 66,301313 (2005).\n9. Aly, T. A. et al. Extreme genetic risk for type 1A diabetes. Proc. Natl Acad. Sci. USA\n103, 14074 14079 (2006).\n10. Noble, J. A. et al. The HLA class I A locus affects susceptibility to type 1 diabetes.\nHum. Immunol. 63,657664 (2002).\n11. Honeyman, M. C., Harrison, L. C., Drummond, B., Colman, P. G. & Tait, B. D.\nAnalysis of families at risk for insulin-dependent diabetes mellitus reveals that",
              "title": "2007 - Localization of type 1 diabetes susceptibility to the MHC Class 1 Genes.pdf",
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              "text": "failure linked to T2D genetic risk and pathophysiology. Single celltranscriptome analysis of human islet cells indicate that multiplemonogenic diabetes genes are highly expressed in beta cells (e.g.,\nPDX1, PAX4, INS, HNF1A, andGCK)[27]. However, other non-beta cell\ntypes express genes mutated in monogenic diabetes (such as PAX6\nand RFX6 ), congenital hyperinsulinemia ( HADH, UCP2 ) and those\nimplicated as T2D GWAS target/effector genes [28].",
              "title": "2019 - (Epi)genomic heterogeneity of pancreatic islet function and failure in type 2 diabetes.pdf",
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              "text": "chain promoter (Serreze and Leiter 2001). This observation, alongwith human genetic studies, suggests that increased T1D risk in\nhumans may also result from the combination of rare and common\nvariants within the human population (Concannon et al. 2009b).\nDespite the identification of several Iddgenes to date, this\nlimited collection does not fully explain T1D pathogenesis or the\nunderlying genetic architecture for T1D risk. One of the many Idd",
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              "text": "comprehensive, powerful, flexible and interactive gene set enrichment analysis toolkit. Nucleic\nAcids Research ,45(W1), W130W137.\n[44] Zhang, B., Kirov, S., & Snoddy, J. (2005). WebGestalt: an integrated system for exploring\ngene sets in various biological contexts. Nucleic Acids Research ,33(Web Server issue), W741-8.\n[45] McLaren, W., Gil, L., Hunt, S. E., Riat, H. S., Ritchie, G. R. S., Thormann, A., Flicek, P ., et al.\n(2016). The ensembl variant effect predictor. Genome Biology ,17(1), 122.",
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              "content": "You can use the Rat Genome Database (RGD) for genetic, genomics, phenotype, and disease-related data generated from rat research. The RGD is specifically mentioned as a platform for genetic and genomic research, including disease portals and phenotypic data [2], [5]. Additionally, it serves as a repository for various types of data such as QTLs, strains used as disease models, expression data, and more [5]. The database can be accessed at http://www.rgd.mcw.edu [1], [5].",
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              "text": "417 Use of Rat Genomics for Investigating the Metabolic Syndrome\nand phenotypic traits are available to the scientific community \nin databases, such as Ensembl ( http://www.ensembl.or g), the \nRat Genome Database ( http://www.rgd.mcw.ed u), eQTL \nExplorer ( http://www. web.bioinformatics.ic.ac.uk/eqtlexplore r) \nor GeneNetwork ( http://www.genenetwork.or g). Additional \nonline rat genetic resources have been recently reviewed by \nTwigger et  al. (11).",
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              "text": "Howard Jacob (Medical College of Wisconsin)\ndiscussed the Rat Genome Database disease portals,\na platform for genetic and genomic research. Thereare 845 strains of rats, 573 of which are inbred,including substrains. Historically, biologists usingthe rat as a model have been disease focused,studying diseases, related phenotypes, pathways, and\nbiological processes. The Rat Genome Database",
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              "text": "10. Consortium STAR, Saar K, Beck A, Bihoreau \nMT, Birney E, Brocklebank D, Chen Y et  al \n(2008) SNP and haplotype mapping for \ngenetic analysis in the rat. Nat Genet \n40:560566\n 11. Twigger SN, Pruitt KD, Fernndez-Surez \nXM, Karolchik D, Worley KC, Maglott DR \net al (2008) What everybody should know about the rat genome and its online resources. \nNat Genet 40:523527\n 12. Butcher LM, Beck S (2008) Future impact of \nintegrated high-throughput methylome anal-\nyses on human health and disease. J Genet",
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              "text": "for linkage analyses using new methods of efficient genotyping \nbased on genechip microarrays (10). In addition, over 800,000 \nESTs and 5,000 annotated rat gene sequences are available for \nfunctional analyses of candidate genes. Development of new \nmethodologies for high throughput phenotyping, such as expres-\nsion profiling, are becoming routinely used. Most of these genetic 2. Recent \nAdvances in Rat \nGenetics and \nGenomics",
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              "text": "serves as a repository of all rat QTLs related to thedisease area as well as associated mouse and humanQTLs, strains used as disease models, phenotypedata, related references, expression data, genome-wide views of disease genes, and QLS via GViewer,comparative maps of disease-related regions, cus-tomization of data sets and download options, and\nanalysis and visualization of function and cellular\nlocalization makeup of gene sets (http://www.rgd.mcw.edu/).\nENU mutagenesis is now being done with rats.",
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              "text": "3. Can data sharing in rodent phenotyping help with replicability?\nLaboratory mice and rats are the main mammalian models currently\nused for high-throughput genomic and behavior genetic research, and\nare employed primarily to explore and test gene function. This is con-\nsidered by some to be the great challenge facing biologists today \n(Collins et al., 2007 ). Rodent models are used extensively as part of\npreclinical development and testing of treatments for disease in hu-",
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              "text": "Bioinformatics and Statistical Analysis\nR was used for basic analysis of phenotypic data. GeneNetwork\n(www.genenetwork.org) was used for correlation and genetic\nanalyses. The original phenotypes published in this paper and all\nmicroarray data generated in these cohorts are available for public\nanalysis or download using the GeneNetwork database (Species:\nMouse, Group: BXD, Type: Adipose mRNA, Liver mRNA, or\nMuscle mRNA, then select the EPFL datasets). The three",
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              "text": "[23]. Shimoyama M, De Pons J, Hayman GT, Laulederkind SJ, Liu W, Nigam R, Petri V , Smith JR, \nTutaj M, Wang S-J, The Rat Genome Database 2015: genomic, phenotypic and environmental \nvariations and disease, Nucleic acids research 43(D1) (2014) D743D750. [PubMed: 25355511] \n[24]. Dickinson ME, Flenniken AM, Ji X, Teboul L, Wong MD, White JK, Meehan TF, Weninger WJ, \nWesterberg H, Adissu H, High-throughput discovery of novel developmental phenotypes, Nature \n537(7621) (2016) 508. [PubMed: 27626380]",
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              "text": "database (dbSNP) build 130 to identify genes located inthe vicinity of selected SNPs. Homologues of the genes formouse and rat were identified using the NCBI's Homolo-Gene release 64. We included only those genes that wereevolutionarily conserved in three different species namelyhuman, mouse and rat.\nAnalysis of microarray data",
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              "text": "(data not shown). Therefore, it seems\nlogical to position the rat field so themechanistic, disease-based research canbe integrated into the awesome power ofthe human and mouse genome projects.\nProgress of the Rat Genome Project\nRecognizing the usefulness of the rat as amodel system, NIH, led by the NationalHeart, Lung, and Blood Institute(NHLBI), has funded the Rat GenomeProject (RGP), the Rat Expressed Se-quence Tag (RGP\nEST) Project, and the Rat",
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              "text": "(http://www.cbil.upenn.edu/PaGE/). All microarray platforms and image-analysis\nsoftware are supported. In addition, RAD is being used for CGH, ChIP , and SAGE\ndata. RAD can produce MAGE-ML les for export of data to other databases or\nsoftware packages. RAD is part of a more general Genomics Unied Schema, which\nprovides a platform to integrate gene and transcript data from a variety of organisms.\nAdvantages\nRAD is a scalable, Web-accessible database that can accommodate data from sev-",
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              "text": "differentiallysusceptibletodeath,withalpha-RGCsandintrinsicallyphotosensitiveRGCs\n(ipRGCs) being less sensitive to cell death than other RGC subtypes in a mouse model\nof glaucoma.\nKeywo\nrds: retinal ganglion cells, gene regulatory networks, transcription factors, recombinant inbred strain,\nsubtypes\nINTRODUCTION\nTheretinalganglioncell(RGC)isthenaloutputneuronoftheretina,projectingthroughtheoptic\nnerve to the brain, where it targets a number of functionally distinct areas: for visual perception,",
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              "text": "AG18245 (DG), NIAAA U01AA014425 (LL), and P20 DA021131 (RW). We thank Derek Rains, Gurjit Rai,\nMeifen Lu, Richard Cushing, Erich Brauer, and Alan Weatherford for their invaluable technical assistance.\nAbbreviations\nBrdU bromodeoxyuridine\nCV cresyl violet\nGF growth fraction\nLOD likelihood of the odds\nLRS likelihood ratio statistic\nNSCs neural stem cells\nOB olfactory bulb\nDG dentate gyrus\nQTL quantitative trait locus\nRI recombinant inbred\nRMS rostral migratory stream\nSGZ subgranular zone",
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              "text": "Rdh10, Lrat,) whose biology functions are directly associated w ith the metabolism of  retinoid. \nRGR (retinal G protein-coupled receptor, protein of  Rgr ) is a protein that structurally resembles \nvisual pigments and other G protein-coupled recepto rs. Light isomerizes 11- cis - into all-trans -\nretinal, triggering a conformational transition of the opsin molecule that initiates phototransduction . \nAfter bleaching all- trans -retinal leaves the opsin, and light sensitivity mu st be restored by",
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              "text": "GeneNetwork system, we were able to define robust expression covariance signatures for\nRGCs and confirmed membership of Chrna6 within the RGC cell type of the retina using\nnew array data sets and RT-PCR tracking through a progressive RGC loss mouse line.\nChrna6  can be added as reliable biomarker for RGCs and RGC loss secondary to glaucoma.\nIt is important to note that in addition to providing evidence for Chrna6 expression as a",
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              "content": "For pathway analyses, you can use the following resources:\n\n1. **KEGG (Kyoto Encyclopedia of Genes and Genomes)**: This database allows you to cross-reference a list of genes with biological functions and genetic networks, including metabolic, signaling, or other regulation pathways [1], [4], [9], [10].\n\n2. **Reactome**: A cross-referenced, manually curated, and peer-reviewed pathway database that provides enhanced pathway visualization [2], [3], [5], [7], [9], [10].\n\n3. **WikiPathways**: A multifaceted pathway database that bridges metabolomics to other omics research [2], [3], [9], [10].\n\n4. **HumanCyc**: Primarily a database of metabolic pathways [4].\n\n5. **Pathway Commons**: A web resource for biological pathway data [6].\n\n6. **BioCarta**: Provides pathway maps [8].\n\n7. **BioGRID**: A genetic and protein interaction database [8].\n\n8. **DAVID (Database for Annotation, Visualization, and Integrated Discovery)**: Offers gene ontology and pathway analysis [8].\n\n9. **WebGestalt**: Provides gene ontology and pathway analysis [8].\n\nThese resources can help you determine whether a pathway is over-represented in your gene list and whether it is over-activated or under-activated [1].",
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              "text": "[3] and KEGG [4] all allow a list of genes to be crossed\nwith biological functions and genetic networks, including\nmetabolic, signalling or other regulation pathways. Basic\nstatistical analysis (e.g., [5,6]) can then determine\nwhether a pathway is over-represented in the list, and\nwhether it is over-activated or under-activated. However,\none can argue that introducing information on the path-\nway at this point in the analysis process sacrifices some\nstatistical power to the simplicity of the approach. For",
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              "text": "Sidiropoulos, K., Viteri, G., Sevilla, C., Jupe, S., Webber, M., Orlic -Milacic, M., et al. (2017). \nReactome enhanced pathway visualization. Bioinformatics  33, 3461 3467. \ndoi:10.1093/bioinformatics/btx441.  \nSlenter, D. N., Kutmon, M., Hanspers, K., Riutta, A., Windsor, J., Nunes, N., et al. (2018). \nWikiPathways: a multifaceted pathway database bri dging metabolomics to other omics \nresearch. Nucleic Acids Res.  46, D661 D667. doi:10.1093/nar/gkx1064.",
              "title": "2020 - Gene network a continuously updated tool for systems genetics analyses.pdf",
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              "text": "Sidiropoulos, K., Viteri, G., Sevilla, C., Jupe, S., Webber, M., Orlic -Milacic, M., et al. (2017). \nReactome enhanced pathway visualization. Bioinformatics  33, 3461 3467. \ndoi:10.1093/bioinformatics/btx441.  \nSlenter, D. N., Kutmon, M., Hanspers, K., Riutta, A., Windsor, J., Nunes, N., et al. (2018). \nWikiPathways: a multifaceted pathway database bri dging metabolomics to other omics \nresearch. Nucleic Acids Res.  46, D661 D667. doi:10.1093/nar/gkx1064.",
              "title": "2020 - Gene network a completely updated tool for systems genetics analyses.pdf",
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              "text": "analysis, we restrict the analysis to curated, peer-reviewedpathways based on experimental evidence, and pathways inferred\nvia gene homology. We draw candidate pathways from the\ncollections listed in Figure 6 (see also Supplementary Materials).\nKEGG [146] and HumanCyc [147] are primarily databases of\nmetabolic pathways, and are unlikely to be relevant to someJoint Analysis of Variants and Pathways in Disease\nPLOS Genetics | www.plosgenetics.org 11 October 2013 | Volume 9 | Issue 10 | e1003770",
              "title": "2013 - Integrated Enrichment Analysis of Variants.pdf",
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              "text": "textual interface, also linking out to the original articles.\nAnalysing participating pathways is an important aspect\nof any gene s functional analysis strategy. In this view,\nREACTOME (http://www.reactome.org) [13] is a cross\nreferenced, manually curated and peer reviewed pathway\ndatabase. LitInspector (http://www.litinspector.org) [14]and NetPath (http://www.netpath.org/index.html) [15]\nallow one to access curated signal transduction related lit-",
              "title": "2013 - Candidate gene association studies a comprehensive guide to useful in silicotools.pdf",
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              "text": "I, Babur O, Anwar N, Schultz N, Bader GD, Sander C (2011) Pathway Commons, a web resource for biological pathway data. Nucleic Acids Res 39(Database issue):D685D690. doi: \n 10.1093/nar/gkq1039      \n    6.    Baker EJ, Jay JJ, Bubier JA, Langston MA, \nChesler EJ (2012) GeneWeaver: a web-based system for integrative functional genomics. Nucleic Acids Res 40(Database issue):D1067D1076. doi: \n 10.1093/nar/gkr968      \n     7.    Bubier JA, Phillips CA, Langston MA, Baker",
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              "text": "67. Krmer, A., Green, J., Pollard, J. Jr. & Tugendreich, S. Causal analysis approaches in ingenuity pathway analysis. Bioinformatics  \n30, 523530 (2014).\n68. Jassal, B. et al. The reactome pathway knowledgebase. Nucleic Acids Res. 48, D498D503 (2020).\n69. Okonechnikov, K., Conesa, A. & Garca-Alcalde, F. Qualimap 2: advanced multi-sample quality control for high-throughput sequencing data. Bioinformatics 32, 292294 (2016).",
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              "text": "Biocarta\n   pathway  maps  www.biocarta.com\nBioGRID\n   genetic  and  protein  interaction  database  thebiogrid.org\nAnalysisPLINK  processing  and  QC  of  genetic  data  sets  pngu.mgh.harvard.edu/ purcell/plink\nBioconductor\n   processing  and  QC  of  expression  data  sets  www.bioconductor.org\nDAVID\n  gene  ontology,  pathway  analysis  david.abcc.ncifcrf.gov\nWebGestalt\n   gene  ontology,  pathway  analysis  bioinfo.vanderbilt.edu/webgestalt\nSage",
              "title": "2011 - The age of the \u201come\u201d Genome, transcriptome and proteome data set collection and analysis.pdf",
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              "text": "2004; Gene Ontology Consortium, 2015; The Gene Ontology Consortium, 2019) , KEGG \npathways  (Kanehisa and Goto, 2000; Kanehisa et al., 2012) , Panther pathways  (Mi et al., 2019a, \n2019b) , Reactome pathways  (Sidiropoulos et al., 2017; Jassal et al., 2020) , and Wikipathway \npathways  (Pico et al., 2008; Slenter et al., 2018)  (Figure 31). As many different annotations as \nwanted can be chosen by clicking on the + icon ( Figure 31). Also note, that the user can",
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              "content": "Once a sperm combines with an egg, the traits passed onto the resulting lifeform are determined by several genetic and epigenetic factors:\n\n1. **Chromosomal Segments**: Individuals inherit a patchwork of chromosomal segments from maternal and paternal chromosomes, which are combined during fertilization to form the zygote [2].\n   \n2. **Mendelian Laws**: The Mendel laws of segregation and independent assortment play a crucial role. Each parent passes a randomly selected allele for a given trait to the offspring, ensuring genetic diversity [5].\n\n3. **Gene Activation and Regulation**: The process of gene activation and regulation is complex and involves several mechanisms:\n   - **Imprinting**: Genes can be marked with small chemical tags that determine whether they are activated or remain inactive after fertilization, depending on whether the modification was made by the father (in the sperm) or the mother (in the egg) [3], [4].\n   - **Maternal Effect**: The mother can alter gene activity in her offspring via the placenta [4].\n   - **Cis-regulation**: Instructions encoded within the embryo's DNA can directly control if and when a nearby gene becomes activated [4].\n\n4. **Epigenetic Marks**: Epigenetic marks, which are less constrained and can be maintained, affect gene expression and phenotype later in development [6].\n\nThese factors collectively determine how traits are passed onto the resulting lifeform after the sperm combines with the egg.",
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              "text": "the egg and the sperm. Such a process would result in genetic changes that will be copied into every cell of the future adult, including reproductive cells (Stock & Campbell, 2000), opening the door to irreversibly alter the human species. Inevitably, signifi  cant self-disclosure and discussion challenges await families",
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              "text": "phenomena such as mutations and gene conversion events) occur in relevant meioses \nleading up to the formation of the gametes (i.e., egg and sperm) which are combined \nduring fertilization and the formation of zygotes. Thus, individuals inherit a patch-\nwork of chromosomal segments from maternal and paternal chromosomes.",
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              "text": "a fertilized egg is a complicated process that relies on controlling: which genes are active; whenthese genes activate; and for how long they are active. In broad terms, there are four ways that thiscontrol can be achieved:\nFirst, inside the sperm or egg, genes can be marked with small chemical tags that flag these genes",
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              "text": "to be activated (or remain inactive) after fertilization, depending on whether the modification wasmade by the father (in the sperm) or the mother (in the egg); this process is known as imprinting.\nSecond, the mother can alter the gene activity in her offspring via the placenta; this process is known\nas maternal effect. Third, instructions encoded within the embryos DNA can directly control if, andwhen, a nearby gene becomes activated; this is known as  cis-regulation. Finally, similar instructions",
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              "text": "(Figures 8 and 9). Two gametes (egg and sperm) ultimately \njoin into a single cell, the zygote, which has the full comple-ment of 23 chromosome pairs restored. If all goes well, the zygote gives rise to a live offspring.\nThe Mendel Laws: Segregation and Independent \nAssortment\nBoth of the Mendel laws pertain directly to the process of \nmeiosis. The first Mendel law, the law of segregation, states \nthat each parent passes a randomly selected allele for a given",
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              "text": "sex chromosome effects. (B)Soon after fertilization, male and female cells have sex-specic transcriptomes, epigenomes, and phenotypes (for example, male\nembryos grow faster than female embryos). At implantation, lineage determination begins and gene expression differences are reduced. Epigenetic marks, however,\nare less constrained and some are maintained, affecting gene expression, and phenotype later in development. Once specic lineages are established, differences in",
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              "text": "the subset of that genetic information that is active.  But how does the differentiation process \nbegin?  The key insight in resolving this conundrum came from fly genetics and was the \nrealization that the egg is not a homogenous sack of protoplasm.  The maternally-derived genes \nactive in the fertilized egg are asymmetrically distributed such that at the first cell division each \ndaughter cell receives a different complement of factors.  Development continues as a",
              "title": "2008 - Genotype-phenotype relationships and the patterning of complex traits as exemplified in the mammalian dentition.pdf",
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              "text": "genes.  An altered gene may be passed on to every cell that develops from it.  The resulting features my help, harm, or have little or no effect on the offsprings success in its environment. (AAAS, pg. 109, 5B:9-12#4 ) 6. Heritable material: The information passed from parents to offspring is coded in DNA molecules (AAAS, pg 108, 5B:9-12#3) 7. Mutagens: Gene mutations can be caused by such things as radiation and chemicals.  When they occur in sex cells, the mutations can be passed onto offspring; if they",
              "title": "2007 - Promoting_Student_Scientific_Literacy_of_Molecular Genetics and Genomics.pdf",
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              "text": "or father (sperm cell). Each gamete has a set of 23 unpaired chromosomes. Two human gametes (egg and sperm) combine \nto create a cell (zygote) that contains the full human genome \nof 23 paired chromosomes.Genetic Information Nondiscrimination Act (GINA)\nUS federal legislation that makes it unlawful to discriminate \nagainst individuals on the basis of their genetic profiles in regard to health insurance and employment. These protections \nare intended to encourage Americans to take advantage of",
              "title": "2015 - Basic Concepts and Potential Applications of Genetics and Genomics for Cardiovascular and Stroke Clinicians.pdf",
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              "text": "spermatozoa: more than the sum of its parts? DNA, histones, pro -\ntamines and epigenetics. Reproduction 139:287301\nNilsson EE, Sadler-Riggleman I, Skinner MK (2018) Environmentally \ninduced epigenetic transgenerational inheritance of disease. Envi-ron Epigenet 4:dvy016Pembrey M, Saffery R, Bygren LO, Network in Epigenetic Epide-",
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              "content": "Genetic tracing is often matrilineal rather than patrilineal primarily because mitochondrial DNA (mtDNA) is inherited solely from the maternal lineage. Unlike nuclear DNA, which is a combination of both paternal and maternal contributions, mtDNA is passed down from mothers to all their children without any admixture from the father [4], [5]. This uniparental inheritance of mtDNA simplifies the tracing of maternal lineage as it remains relatively unchanged through generations, making it a reliable marker for genetic studies [4], [5]. Additionally, humans have developed complex mechanisms to ensure this uniparental inheritance of mtDNA, which further supports its use in genetic tracing [4].",
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              "text": "variation with cultural practices around lineage. In certain societies, individuals place greater\nimportance on (and have greater knowledge about) one side of the family than another\n(unilineal descent). Thus, individuals in patrilineal groups trace relationships through males\nonly so that your fathers brothers children are members of your family, but not your fathers\nsisters (Kottak, 2007 ). They are members of their husbands group or family. Efforts to create",
              "title": "2009 - When Family Means More (or Less) Than Genetics.pdf",
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              "text": "maternal lineage membership with those who weredirectly genotyped. Based on these pedigree (matrilineal) relation-",
              "title": "2012 - Mitochondrial Genomic Analysis of Late Onset.pdf",
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              "text": "in three-generation families, and read pair tracing DNMs with phased variants.\nIn the former approach, we determined the parent of origin as in our previous \nanalysis4. For example, if an offspring of the proband was a carrier of the DNM \nallele and had haplotype sharing to paternal chromosome of the proband, we \nassigned the mutation to the father. Meanwhile, if the offspring was not a DNM \nallele carrier, we would assign it to the maternal germline. We restricted the haplo -",
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              "text": "Unlike the nuclear genome, which requires both paternal and maternal contributions, mtDNA is inherited solely from the maternal lineage. It is unclear what advantage a uniparental mtDNA transmission confers, but one possibil-ity is to minimize the number of distinct genomes to maxi-mize the efficiency of a multi-genomic system (Hill etal. 2019). In fact, humans have developed complex, redundant mechanisms to ensure uniparental inheritance of mtDNA (DeLuca and OFarrell 2012; Rojansky etal. 2016). Paternal",
              "title": "2020 - Mitonuclear genomics and aging.pdf",
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              "text": "c) Mitochondrial DNA (maternal line testing) markers:\nmitochondrial DNA or mtDNA haploid is the\nmaternally inherited mitochondrial genome\n(mtDNA) [ 44]. All children inherit mtDNA from\ntheir mother, with no admixture from the father.\nLike Y-line DNA, mtDNA is passed intact from one\ngeneration to the next but through maternal line.\nMitochondrial DNA does not follow any surname.\nIn fact, the surname changes in every generation\nwhen women marry. Polymorphisms of mtDNA",
              "title": "2015 - Self-reported race or ethnicity in the age of genomic.pdf",
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              "text": "a family pedigree may be hampered if the participant is not familiar with her mothers relatives,\nbut her mothers brothers children (her cousins) may be able to supplement her overall family\nhistory. Knowledge about the cultural system of unilineal descent avoids assuming the\nuniversality of bilateral descent. Cultural beliefs such as these also have implications in the\nconduct of genetic research in terms of confidentiality and autonomy (Benkendorf et al.,",
              "title": "2009 - When Family Means More (or Less) Than Genetics.pdf",
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              "text": "225 three-generation families using haplotype sharing (Fig. 1c and \nMethods), 80.4% were found to be of paternal origin (Extended Data \nFig. 1). Figure 1e shows a strong relationship between the number of \npaternal DNMs and the fathers age at conception (1.47 per year, 95% \nCI 1.341.59) and a weaker impact of the mothers age on the number \nof maternal DNMs (0.37 per year, 95% CI 0.300.45).\nThe parental origin of all DNMs was also assessed by read pair",
              "title": "2017 - Parental influence on human germline de novo.pdf",
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              "text": "sistent with a maternal imprinting effect in familiesfrom France [18], the USA[10, 18, 21] (Figure 2; Table3) and Canada [27]. However, in a large family dataset from the UK, and in smaller data sets fromDenmark and Sardinia, the transmission of VNTRsusceptibility alleles is more pronounced frommothersthanfromfathers,andnowsignicantlysoinUK families (Figure 2; Table 3). Comparison of theresults from the USAwith those from the UK suggestthat unexplained inter-population differences in thisparent-of-origin",
              "title": "1996 - IDDM2-VNTR-encoded Susceptibility to Type 1 Diabetes.pdf",
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              "text": "started with the largest matrilineage and worked down the list. Theparticipants selected for mtDNA sequencing were selected inde-pendent of their cognitive or dementia status. 274 matrilineageswere represented by this dataset. As a result, the sequencedmitochondrial genomes also represent as many different majormitochondrial haplogroups and clusters as possible (Table 1).Selection was made blind to case-control status. 287 samples weresent to Family Tree DNA (www.familytreedna.com) for Sangersequencing of",
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              "text": "genetics-based population divergence studies. Am J Phys Anthropol 128(2):415 423.22. Helgason A, Hrafnkelsson B, Gulcher JR, War d R, Stefnsson K (2003) A populationwide\ncoalescent analysis of Icelandic matrilineal and patrilineal genealogies: Evidence for a\nfaster evolutionary rate of mtDNA lineages than Y chromosomes. Am J Hum Genet 72(6):\n1370 1388.\n23. Amster G, Sella G (2015) Life history effects on the molecular clock of autosomes and\nsex chromosomes. Proc Natl Acad Sci USA 113(6):1588 1593.",
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              "content": "DNA replication is a critical process that ensures the accurate copying of genetic information during cell division. Here is an explanation of the process and how it maintains accuracy:\n\n1. **Separation of DNA Strands**: During replication, the two strands of the mother cell DNA are separated [1], [2]. This separation is essential to expose the nucleotide sequences of each strand, which will serve as templates for the synthesis of new complementary strands.\n\n2. **Formation of New Strands**: New nucleotides are assembled to form two double helices that are identical to the original DNA molecule [1], [2]. Each original strand pairs with a newly synthesized complementary strand, resulting in two DNA molecules, each consisting of one old and one new strand. This method is known as semi-conservative replication.\n\n3. **Initiation at Replication Origins**: Replication begins at specific locations on the DNA molecule called replication origins. During the S phase of the cell cycle, replication starts from these origins [3].\n\n4. **Replication Fork and Enzyme Activity**: The replication process involves the formation of a replication fork, where enzymes like DNA helicase unwind the DNA double helix. DNA polymerase then adds new nucleotides to the growing DNA strand by matching them with the complementary nucleotides on the template strand.\n\n5. **Error Minimization and Repair Mechanisms**: Cells have mechanisms to monitor replication and minimize errors. For instance, cell-cycle checkpoints ensure coordination of DNA synthesis and repair with cell division [6]. Additionally, specific repair proteins can revert certain DNA injuries, ensuring the fidelity of the replication process [4].\n\n6. **Postreplicative Repair**: If replication encounters obstacles such as DNA adducts, cells can induce DNA damage tolerance mechanisms. These mechanisms allow the completion of replication through error-free template switching using the sister chromatid or through translesion polymerases [9].\n\nBy following these steps and employing various error-checking and repair mechanisms, DNA replication ensures that genetic information is accurately copied and transmitted to daughter cells during cell division.",
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              "text": "the DNA, i.e. the whole genome. During replication the two strands of themother cell DNA are separated, and new nucleotides are put together to maketwo double helices identical to the original one, see Figure 2.1.\nTAAGACCG\nAT\nT\nCTGGCCCGTGGC. . . .\n. . .. .\nATTCTGGCTAAGACCG. . . . . . . .\nFigure 2.1: A DNA chain consists of two strands of complementary nucleotides. When\nDNA is replicated, two double chains identical to the original one are created.",
              "title": "2005 - Numerical Algorithms for Mapping of Multiple Quantitative Trait Loci in Experimental Populations.pdf",
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              "text": "the DNA, i.e. the whole genome. During replication the two strands of themother cell DNA are separated, and new nucleotides are put together to maketwo double helices identical to the original one, see Figure 2.1.\nTAAGACCG\nAT\nT\nCTGGCCCGTGGC. . . .\n. . .. .\nATTCTGGCTAAGACCG. . . . . . . .\nFigure 2.1: A DNA chain consists of two strands of complementary nucleotides. When\nDNA is replicated, two double chains identical to the original one are created.",
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              "text": "The mechanism to maintain the rDNA copy number\nThe gene amplication mechanism that counteracts\nrecombination-mediated loss of rDNA copies is well\nstudied in budding yeast [ 6,11]. During the S phase of the\ncell cycle, replication starts from replication origins, and isinhibited at the replication fork barrier site (RFB) by the\nfunction of the fork blocking protein, Fob1 (Fig. 3)[12].\nThis inhibition works as a recombinational hotspot toinduce amplication for copy number recovery as follow;",
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              "text": "S and G2 when the DNA is replicated, providing a pristine secondcopy of the sequence (sister chromatid) for aligning the breaks. Incontrast, the less-accurate end joining is most relevant in the G1phase of the cell cycle, when a second copy is not available\n14. \nFinally, some single repair proteins directly revert certain injuries,\nsuch as O6-methylguanine methyltransferase, which removes \nO6-methyl guanine. This highly mutagenic lesion permits base",
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              "text": "Replication",
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              "text": "genotoxic agents and to guarantee faithfulchromosome duplication and transmission to\nthe offspring. In addition to DNA damage\nrepair, cells monitor replication to minimize er-rors of DNA synthesis. In eukaryotes, cell-cycle\ncheckpoints guarantee coordination of DNA\nsynthesis and DNA repair with cell division.Genome instability is mainly due to sporadic\nreplication or repair errors but can also take\nplace in response to developmental or environ-mental signals, as occurs in meiosis, and antigen",
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              "text": "This section will explain how cells normally divide. It will also desc ribe how an unexpected change in \nthe structure of DNA can sometimes cause harm to th e body. New tools to study genetic variations of \ncommon diseases and to identify genetic variatio ns common to specific diseases will also be \npresented. \nCell Division  \nHumans grow and develop as a result of a process called cell \ndivision. There are two types of cell division  mitosis and meiosis.",
              "title": "2009 - Basic Genetics and Genomics A Primer for Nurses.pdf",
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              "text": "and replicated (by a templating mechanism).  Each DNA molecule in a cell forms a single chromosome. (NRC, pg. 185, 9-12:C2#1) 4. Genes as information for building proteins: The genetic information in DNA molecules provide the instructions on assembling protein molecules. The code is virtually the same for all life forms. (AAAS, pg. 114, 5C:9-12#4 ) 5. Molecular nature of genes and mutations: Genes are segments of DNA molecules.  Inserting, deleting, or substituting DNA segments can alter genes.  An altered",
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              "text": "When a replication fork encounters a DNA\nadduct, cells induce DNA damage toler-ance mechanisms that allow completion of\nreplication. Adducts can be bypassed by\npostreplicative repair via translesion poly-merases (either faithful or error-prone) or\nvia error-free template switching using the\nsister chromatid (64, 105). Postreplicativerepair guarantees genome stability by allowing\ncompletion of replication (albeit at the expense",
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              "text": "genome instability in part because of the unique structureof replicating DNA molecules (Figure 2). When single-strand lesions occur in non-replicating molecules of DNA,the overall integrity of chromosomes is maintained byhydrogen bond base pairing on either side of these lesionsuntil they are repaired (Figure 2A). In contrast to non-replicating DNA, replicating DNA at replication forkscontains unwound, highly recombinogenic single-strandedtemplate DNA before this DNA is converted to double-strand DNA by",
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              "content": "### Potential Benefits of CRISPR-Cas9:\n\n1. **Accelerated Genomic Research**: CRISPR-Cas9 tools have significantly accelerated the pace of genomic research by enabling highly efficient knockouts or edits of virtually any gene in cells or model organisms [1].\n   \n2. **Clinical Trials and Stability**: Multiple CRISPR-Cas9-based clinical trials are in progress, and early trial results suggest that CRISPR-engineered cells are stable and do not cause acute adverse reactions in humans [1].\n\n3. **Functional Genomics**: CRISPR-Cas9 is set to make a significant impact on genomic screening and scientific discovery, with recent demonstrations showing great promise [2].\n\n4. **Targeted Gene Regulation and Epigenome Modifications**: CRISPR-Cas9 has been linked to various effector proteins to enable targeted gene regulation and epigenome modifications [3].\n\n5. **Precision Medicine**: CRISPR-Cas9 holds significant promise for the development of new models and precision-targeted therapies for diseases such as Alzheimer's Disease (AD) [5].\n\n6. **Versatility and Efficiency**: CRISPR-Cas9 provides a highly versatile platform that allows fast and efficient genome editing in an ever-growing list of organisms [10].\n\n### Potential Risks of CRISPR-Cas9:\n\n1. **Off-Target Effects**: CRISPR-Cas9 is known to generate off-target alterations, which can result in unwanted mutations and potentially cytotoxic effects [4].\n\n2. **Technical Challenges**: There are several technical challenges that need to be addressed to maximize the benefits of CRISPR-Cas9 technology [2].\n\n3. **Long-Term Safety**: While early trial results are promising, the long-term safety of CRISPR-Cas9-engineered cells is yet to be determined [1].\n\n4. **Mismatch Tolerance**: The CRISPR-Cas9 system can tolerate certain mismatches to the DNA target, which could potentially lead to unintended edits [8].\n\nBy considering these benefits and risks, researchers and clinicians can better navigate the development and application of CRISPR-Cas9 technologies.",
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              "text": "neered nucleases, CRISPR-Cas9 \ntools have accelerated the pace of \ngenomic research by permitting \nhighly efficient knockouts or \nedits of virtually any gene in cells \nor model organisms. Multiple CRISPR-Cas9based clinical trials \nare in progress or are expected \nto begin soon. Although Cas9-\nengineered cells havent yet dem -\nonstrated efficacy at scale, early \ntrial results suggest that such \ncells are stable and dont cause \nacute adverse reactions in humans. \nLong-term safety is yet to be de -",
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              "text": "stageissetforCRISPRtomakeanenormousimpactongenomic\nscreening and thus scientic discovery in the coming years, and\nrecent demonstrations of this system have shown great promise\n(Shalem etal., 2015 ).However,a number of technical challenges\nmust be addressed in order to maximize the benet of this\ntechnology. In this review, we will discuss current applications\nof CRISPR in functional genomics and provide a perspective on\nfuturedevelopmentsinthisarea.\nCRISPR/Cas9 Genome Editing",
              "title": "2015 - A new age in functional genomics using CRISPR Cas9 in arrayed library screening.pdf",
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              "text": "heralding the age of genome editing. Furthermore, Cas9 or guide RNAs have been linked to various effector proteins to enable targeted gene regulation\n12,13 and epigenome modifications14,15. \nIt is worth noting, however, that many of these feats had been demonstrated previously using other nucleases or DNA-binding proteins\n1,16. In this Perspective, I shed light on early genome \nediting platforms that laid the groundwork for the widespread use of CRISPRCas9 in research and medicine (Fig. 1 ).",
              "title": "2016 - Genome editing comes of age.pdf",
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              "text": "cline- or Tet-regulated Cas9 system. Current CRISPR/Cas systems arefrom Streptococcus pyogenes ,Streptococcus thermophilus ,Neisseria\nmeningitides and Treponema denticola .2.5. Caveats of advanced genome editing tools\nOff-target effects . The DNA-binding domains of ZFNs and TALENs\nneed to be very speci c for the target site to avoid off-target cleavage,\nwhich results in unwanted mutations and potentially cytotoxic effects\n[27]. CRISPR/Cas9 is also known to generate off-target alterations,",
              "title": "2016 - Dissecting diabetes metabolic disease.pdf",
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              "text": "CRISPR/CAS9 HOLDS SIGNIFICANT\nPROMISE FOR THE DEVELOPMENT OFNEW AD MODELS AND PRECISIONTARGETED AD THERAPY\nClustered regularly interspaced short palindromic\nrepeat (CRISPR)-Cas nucleases have revolutionizedthe eld of gene editing and have tremendous appli-cation in the eld of molecular medicine [98102].Despite a signicant surge in CRISPR/Cas9-mediated genome editing in various disease models,the progress in the eld of AD has lagged behindsubstantially. We believe that genome editing can sig-",
              "title": "2018 - Neuro-Immuno-Gene- and GenomeEditing-Therapy for Alzheimer\u2019s.pdf",
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              "text": "81.\nApplications for CRISPRCas9 beyond genome editing",
              "title": "2016 - Genome editing comes of age.pdf",
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              "text": "CRISPR-Cas9 can be used to in -\nduce genome edits by creating \ntargeted DNA breaks that trigger \nsite-specific DNA repair. In next-\ngeneration formats, it can also \ncontrol the transcriptional output \nof genes or alter genome se -\nquences using a process of nu -\ncleotide base editing \nthat does not require \nrepair of DNA breaks. \nAs these technolo -\ngies continue to mature, it will \nbecome increasingly possible to \nalter cellular genomes efficiently \nand accurately.\nComing on the heels of engi -",
              "title": "2021 - Human Molecular Genetics and Genomics.pdf",
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              "text": "on transcriptional interfere nce (CRISPRi) and activation\n(CRISPRa) have also harnessed Cas9-based technologies for\nuse in genome-wide studies ( 59,174). In addition, recent\nimprovements in lentiviral library generation and propagation,as well as large-scale DNA and RNA synthesis, have allowedCRISPR-Cas9 technology to be exploited across multiple modelplatforms ( 59,175178).\nnCas9\nThe CRISPR-Cas9 system can tolerate certain mismatches to the\nDNA target since the required gRNAs are short. A disadvantage,",
              "title": "2020 - Functional Genomics in Pancreatic \u03b2 Cells Recent Advances in Gene Deletion and Genome Editing Technologies for Diabetes Research.pdf",
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              "text": "13. Kleinstiver BP, etal. High-fidelity CRISPRCas9 nucleases with no detectable genome-wide \noff-target effects. Nature. 2016;529:4905.\n 14. Brane A, Tollefsbol T.Targeting telomeres and telomerase: studies in aging and disease uti-\nlizing CRISPR/Cas9 technology. Cells. 2019;8:186.\n 15. Wang H, etal. One-step generation of mice carrying mutations in multiple genes by CRISPR/\nCas-mediated genome engineering. Cell. 2013;153:9108.",
              "title": "2020 - Clinical Genetics and Genomics of Aging.pdf",
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              "text": "Since its discovery, CRISPR-Cas technology has ignited a biological revolu-\ntion by providing a highly versatile platform that allows fast and efficient genome \nediting in an ever-growing list of organisms. In this chapter we will first describe \nthe most recent advances in the development and application of the CRISPR-Cas \nplatform in biomedical research. Then we will discuss the most recent and notable \nbasic research applications of this technology in the study of the molecular causes",
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              "content": "To differentiate between X and Y DNA in the context of DNA tracing and determining quantitative trait loci (QTLs), one must consider the unique characteristics of each chromosome:\n\n1. **Recombination Rates**: The Y chromosome largely does not undergo recombination, whereas the X chromosome has a slower recombination rate compared to autosomes. This difference in recombination rates has significant implications for the detection of QTLs [1].\n\n2. **QTL Mapping**: QTLs are stretches of DNA closely linked to genes that influence specific phenotypes. The process of QTL mapping involves measuring phenotypes within a population and genotyping at numerous marker loci [2], [4]. Given the unique recombination characteristics of the X and Y chromosomes, the strategies for mapping QTLs on these chromosomes may differ.\n\n3. **Genetic Markers and Phenotypic Traits**: When measuring correlations between genetic markers and phenotypic traits, the lack of recombination on the Y chromosome and the slower recombination on the X chromosome must be taken into account. This affects how QTLs linked to these chromosomes are identified and analyzed [10].\n\nIn summary, the primary differences between X and Y DNA in the context of DNA tracing and QTL determination lie in their recombination rates and the subsequent impact on QTL detection and mapping strategies.",
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              "text": "While most of the Y chromosome does not undergo\nrecombination, the recombination rate of the X chromosomeis slower than that of the autosomes. This has important\nconsequences on the detection of significant QTLs. For a\ncomprehensive view of these issues, see(43).\n9.Probe hybridization artifacts\nWhen several probes are available for the same gene, it is\nnot uncommon to observe a difference in the mapping results",
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              "text": "8 QTL Mapping  \n \nAllelic variation exists among natural populations and inbred strains, and this is \nreflective of the segregation of quantitative tr ait loci (QTLs) [96]. QTLs are stretches of \nDNA that are closely linked to genes that underlie a phenotype of interest. QTL analysis has been proven to be an invaluable tool to  help unravel heritable traits, by enabling \nresearchers to map different quantitative traits back to the genomic location involved in the regulation of these phenotypes.",
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              "text": "8 QTL Mapping  \n \nAllelic variation exists among natural populations and inbred strains, and this is \nreflective of the segregation of quantitative tr ait loci (QTLs) [96]. QTLs are stretches of \nDNA that are closely linked to genes that underlie a phenotype of interest. QTL analysis has been proven to be an invaluable tool to  help unravel heritable traits, by enabling \nresearchers to map different quantitative traits back to the genomic location involved in the regulation of these phenotypes.",
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              "text": "The basic  pr emise  of QTL  an alysis  is simple  (Ph illips  and Belknap,\n2002 ) . First,  one must  meas  ure a speci  c phen  otype  within  a popul  ation.\nNext, the population must be genotyped at a hundred or more marker loci186 Boehm II et al.",
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              "text": "genes underlying QTLs in animals and plants (see for example Shirley et al 2004,Korstanje & Paigen 2002, Fridman et al 2004). I should also point out, though,\nthat even in a single QTL region isolated in a congenic strain, it is possible that\nthere is more than one allele that aects the phenotype. So, you have a fair pointabout the challenges and complexities of QTL analysis.\nKoolhaas: There are dierent questions underlying both approaches. The QTL",
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              "text": "genes underlying QTLs in animals and plants (see for example Shirley et al 2004,Korstanje & Paigen 2002, Fridman et al 2004). I should also point out, though,\nthat even in a single QTL region isolated in a congenic strain, it is possible that\nthere is more than one allele that aects the phenotype. So, you have a fair pointabout the challenges and complexities of QTL analysis.\nKoolhaas: There are dierent questions underlying both approaches. The QTL",
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              "text": "through analysis of line crosses, quantitative trait loci (QTL) mapping, and verification\nof candidate genes with quantitative complementation tests or genetic engineering (e.g.,McGuire and Tully 1987; Chandra et al. 2001; Dierick and Greenspan 2006; Edwardset al. 2006). They can also be used to study the underlying physiological, neural, andmolecular mechanisms of the differences in behavior between selected and controllines, or between divergently selected lines.",
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              "text": "through analysis of line crosses, quantitative trait loci (QTL) mapping, and verification\nof candidate genes with quantitative complementation tests or genetic engineering (e.g.,McGuire and Tully 1987; Chandra et al. 2001; Dierick and Greenspan 2006; Edwardset al. 2006). They can also be used to study the underlying physiological, neural, andmolecular mechanisms of the differences in behavior between selected and controllines, or between divergently selected lines.",
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              "text": "genetic background.\nGene identification of QTL should be distinguished from identification of the quanti-\ntative trait nucleotide (QTN). The latter is a daunting task, since SNPs are so frequent.\nFinal proof for a QTN in mice would require placing a genomic segment containing theputative QTN from a donor mouse strain on the background of another strain using\nhomologous recombination and reproducing the phenotype of the donor strain.",
              "title": "2005 - quantitative-trait-analysis-in-the-investigation-of-function-and.pdf",
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              "text": "measuring correlations between genetic markers and phenotypic \ntraits in a population. Individuals are scored for their  phenotype      for \na particular trait, and their genotype at a marker. If there is a differ-\nence in mean phenotype between those individuals with one geno-\ntype at a particular locus compared with the other, than we can infer \nthat there is a QTL linked to that marker [ 40 ,  153 ]. 2.3  Analysis and QTL \nMappingDavid G. Ashbrook and Reinmar Hager",
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              "text": "for people to exchange data easily over the Web. Two other notable developments are\nBioMart and GBrowse. The BioMart project (http://www.biomart.org/), originally a\nspin-off from Ensembl, offers a generic data management system that allows complex\nsearches of biological data such as sequence annotation. The GBrowse project (Stein\net al. , 2002; http://www.gmod.org/) has produced a generic genome browser that can\nbe customized to organize, display and query a new genome scale data set. These",
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              "text": "for people to exchange data easily over the Web. Two other notable developments are\nBioMart and GBrowse. The BioMart project (http://www.biomart.org/), originally a\nspin-off from Ensembl, offers a generic data management system that allows complex\nsearches of biological data such as sequence annotation. The GBrowse project (Stein\net al. , 2002; http://www.gmod.org/) has produced a generic genome browser that can\nbe customized to organize, display and query a new genome scale data set. These",
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              "text": "for people to exchange data easily over the Web. Two other notable developments are\nBioMart and GBrowse. The BioMart project (http://www.biomart.org/), originally a\nspin-off from Ensembl, offers a generic data management system that allows complex\nsearches of biological data such as sequence annotation. The GBrowse project (Stein\net al. , 2002; http://www.gmod.org/) has produced a generic genome browser that can\nbe customized to organize, display and query a new genome scale data set. These",
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              "text": "(http://ensembl.org/ ) and the National Center for Biotechnology Information\n(NCBI) (http://www.ncbi.nlm.nih.gov/ ) all provide portals to the most current, and\narchived public assemblies. These sites also provide means of searching the assem-\nblies, such as BLAST (Altschul et al. , 1997), BLAT (Kent, 2002) and SSAHA (Ning\net al. , 2001) as well as precomputed annotation for the genome assemblies that can\nbe readily incorporated into comparative genomic analyses.",
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              "text": "(http://ensembl.org/ ) and the National Center for Biotechnology Information\n(NCBI) (http://www.ncbi.nlm.nih.gov/ ) all provide portals to the most current, and\narchived public assemblies. These sites also provide means of searching the assem-\nblies, such as BLAST (Altschul et al. , 1997), BLAT (Kent, 2002) and SSAHA (Ning\net al. , 2001) as well as precomputed annotation for the genome assemblies that can\nbe readily incorporated into comparative genomic analyses.",
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              "text": "(http://ensembl.org/ ) and the National Center for Biotechnology Information\n(NCBI) (http://www.ncbi.nlm.nih.gov/ ) all provide portals to the most current, and\narchived public assemblies. These sites also provide means of searching the assem-\nblies, such as BLAST (Altschul et al. , 1997), BLAT (Kent, 2002) and SSAHA (Ning\net al. , 2001) as well as precomputed annotation for the genome assemblies that can\nbe readily incorporated into comparative genomic analyses.",
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              "text": "resources. We present an easy-to-adopt module that weaves together several important bioin-formatic 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",
              "title": "2010 - Teaching Bioinformatics and Neuroinformatics by using Free Web-Based Tools.pdf",
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            "id": "df5e9619-d45e-5958-a88d-d33ecc59387d",
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              "text": "References\nAltman RB. Building successful biological databases. Briefings in Bioinformatics. 2004; 5:45. \n[PubMed: 15153301] \nAshburner M, Ball CA, Blake JA, Botstein D, Butler H, Cherry JM, et al. Gene ontology: Tool for the \nunification of biology. The Gene Ontology Consortium. Nature Genetics. 2000; 25:2529. \n[PubMed: 10802651] \nAshish N, Ambite JL, Muslea M, Turner JA. Neuroscience data integration through mediation: an",
              "title": "2012 - Biological Databases for Behavioral Neurobiology.pdf",
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              "text": "Sequences, Protein Structures, Complete Genomes, Tax-\nonomy, Medical Genetics resources (see later), and others (see   http://www.ncbi.nlm.nih.gov/Database/index.html     for a complete listing of databases).  Entrez   PubMed provides \naccess to full-text articles at journal websites and other related web resources, some of which are free to the \npublic. This site also provides links to other molecular biology resources.  \n   The National Center for Biotechnology Information (  http://",
              "title": "2008 - (Infectious Disease) Karl A. Western (auth.), Vassil St. Georgiev PhD, Karl A. Western MD, John J. McGowan PhD (eds.) - National Institute of Allergy and Infectious Diseases, NIH_ Frontiers in Researc (3).pdf",
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              "text": "Sequences, Protein Structures, Complete Genomes, Tax-\nonomy, Medical Genetics resources (see later), and others (see   http://www.ncbi.nlm.nih.gov/Database/index.html     for a complete listing of databases).  Entrez   PubMed provides \naccess to full-text articles at journal websites and other related web resources, some of which are free to the \npublic. This site also provides links to other molecular biology resources.  \n   The National Center for Biotechnology Information (  http://",
              "title": "2008 - Biotools for Determining the Genetics of Susceptibility to Infectious Diseases.pdf",
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              "text": "supported by a signicant BLAST match to one or more expressed sequences or\nproteins. Ensembl also identies the positions of known human genes from public\nsequence database entries, usually using GENEWISE to predict their exon structures.\nThe total set of Ensembl genes should therefore be a much more accurate reection\nof reality than ab initio predictions alone, but it is clear that some novel genes are\nmissed (Hogenesch et al. , 2001). Of the many novel genes that are detected, some are",
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              "text": "Ostell/Spidey/\nSSAHA at Sanger Institute http://www.sanger.ac.uk/Software/analysis/SSAHA/\nhuman and mouse genomes, where there are large full-length cDNA collections to\nguide the hunt for genes, Ensembl should be very reliable.\nFrom the beginning, many genomic features other than predicted genes were\nincluded in Ensembl: different repeat classes, cytological bands, CpG island predic-\ntions, tRNA gene predictions, expressed sequence clusters from the UniGene database",
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              "content": "The primary difference between QTL mapping and GWAS lies in their approaches and resolutions for identifying genetic associations with traits.\n\n1. **QTL Mapping**:\n   - **Approach**: QTL mapping involves identifying segments of the genome (quantitative trait loci) that are associated with a particular phenotype. It typically uses linkage analysis to find statistically significant linkages between phenotypes and genotypes [7], [8].\n   - **Resolution**: Historically, QTL mapping has had low resolution, meaning it identifies larger genomic regions associated with traits rather than pinpointing specific genetic variants [6].\n\n2. **GWAS (Genome-Wide Association Studies)**:\n   - **Approach**: GWAS is a holistic, whole-genome approach that analyzes the association between DNA polymorphisms and phenotypic traits across the entire genome. It establishes correlations between changes in DNA at specific loci and changes in disease traits within populations [2], [4].\n   - **Resolution**: GWAS allows for much finer mapping of QTLs compared to traditional linkage analysis, providing higher resolution in identifying specific genetic variants associated with traits [3].\n\nIn summary, while QTL mapping identifies broader genomic regions linked to traits with lower resolution, GWAS provides a finer, more detailed mapping of genetic associations across the entire genome [2], [3], [4], [6], [7], [8].",
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              "text": "traditional QTL mapping and GWASsapproaches can benefit from systems-biological approaches by filling in criticalinformation about the molecular phenotypes that stand between DNAvariation and complex disease (figure5). The incorporation of data fromhigh-throughput molecular profilingtechnologies, such as gene expressionmicroarrays, can better define a diseaseby identifying groups of genes thatrespond to or covary with disease-associated traits. Network analysis ofdisease-associated genes allows",
              "title": "2012 - Identifying Gene Networks Underlying the Neurobiology of Ethanol and Alcoholism.pdf",
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              "text": "knowledge of the true QTL location (Doss et al. 2005 ),\nwhich can be used to empirically estimate the power of aGWAS performed at a similar scale (Hao et al. 2008 ;\nSchadt et al. 2008 ). A GWAS on its own does little more\nthan establish correlations between changes in DNA at agiven locus and changes in a disease trait of interest, with\nrespect to populations of interest. Further, these studies on",
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              "text": "genotypes. Since association studies allow for a mu ch finer mapping of the QTL \nthan that obtained with linkage analysis, there is a trade-off to consider between \npower and resolution when choosing the mapping stra tegy. Genome-wide associa- \ntion studies (GWAS) have naturally been used to per form genetical genomics \nstudies in humans [18, 24-27] and are emerging in m odel organisms studies using \noutbred populations [28].  \n8.2.2  Combining studies",
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              "text": "genetically also mapped to the same genomic\nlocation. In order to locate the positions of\ngenes that are responsible for a certain trait,\nGWAS can be conducted. GWAS is a quan-\ntitative approach to analyze the association of\nwhole genome DNA polymorphisms and a phe-\nnotypic trait, thereby localizing the genes un-\nderlining the trait.\nGenome-Wide Association\nStudies (GWAS)\nGWAS is a holistic whole-genome approach\nto robustly determine the association of DNA\npolymorphisms with correlated phenotypic",
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              "text": "(PHMs) use principles of MR embedded within a Bayesian hierarchical model to detect interac-tions between regulatory elements [ 98].\nFurthermore, GWAS is often integrated with the QTL analysis despite the fact that many GWAS\nloci are not strong eQTL loci [ 56]. GWAS-eQTL colocalization methods, including RTC [ 145],\nQTLMacth [ 158], Sherlock [ 159], and coloc [ 160], are based on the concept that disease-",
              "title": "2020 - A Multi-Omics Perspective of Quantitative Trait Loci in Precision Medicine.pdf",
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              "text": "association studies (GWAS) or linkage studies (Enoch 2013).\nQTL mapping studies historically had very low resolution,and many have been performed using populations for whichlimited genetic data exist. Publications of gene expressionstudies typically highlight a few interesting gene centered\nresults, but the bulk of information is rejected due to concern",
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              "text": "pairs that include many genes within the seg-\nment. On the other hand, GWAS may point to\nseveral or even many genomic locations for the\ntrait of interest, complicating further functional\nanalysis.\nAnalysis of Quantitative Trait Loci (QTL)\nQTL analysis reveals statistically signicant\nlinkage between phenotypes and genotypes,\nthereby providing explanation for the genetic\nbasis of variation in complex traits (Falconer\nand Mackay, 1996; Lynch and Walsh, 1998). In\na sense, QTL analysis can be viewed as incom-",
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              "text": "QTL mapping\nQTL mapping using GeneNetwork has been described in detail elsewhere ( Mulligan et al., 2017 ). However, in brief, quantitative trait\nloci (QTLs) are segments of the genome affecting a particular phenotype ( Falconer and Mackay, 1996 ). QTL mapping, identifying",
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              "text": "3. Genetic Mapping Methods\nSeveral statistical approaches have been developed for genome-wide linkage analysis of \ntraditional phenotypes. The same approaches can be used to map eQTLs. These approaches \nrange from single marker tests ( t-test, ANOVA, and simple regression analysis) to multiple \nlocus mapping methods. The only major difference is that eQTL studies involve tens of \nthousands of expression traits and require fast algorithms. Since an eQTL study tests for",
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              "text": "plete GWAS analysis with limited number of\nmarkers that does not cover the entire genome.\nAs such, if one or few QTLs are found, there\nmay be more QTLs in the genome to be dis-\ncovered. More importantly, in the absence of\nclosely linked markers in the genomic regions\ncontaining signicant QTLs for the trait, the\nmost signicant genes responsible for the trait\ncan be missed. However, because of historical\nreasons such as the lack of genome-wide mark-\ners, or the lack of funding, QTL analysis is still",
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              "content": "To determine which gene in your QTL is causal for the trait, you can follow these steps:\n\n1. **Identify Candidate Genes**: Start by identifying candidate genes within the QTL interval. This can be done by looking at genes that are expressed in tissues likely to influence the traits of interest [6].\n\n2. **Sequence Candidate Genes**: Sequence the cDNA of strong candidate genes to identify amino acid polymorphisms. This helps in detecting sequence differences in coding or regulatory regions between the two parental inbred strains [1], [6].\n\n3. **Expression Studies**: Test for mRNA and protein expression differences in target tissues of the original strains used to detect the QTL. This will help refine the list of candidate genes [1].\n\n4. **Fine Mapping**: Perform fine mapping of the QTL interval to shorten the list of plausible candidate polymorphisms [6].\n\n5. **Correlation with Trait**: Use gene expression data to screen for genes within the QTL interval whose expression correlates with the trait of interest [7].\n\n6. **Proof of Causality**: After refining the list of candidate genes, test them rigorously for proof of cause and effect. This involves proving definitively which nucleotide polymorphism underlies the QTL [1], [6].\n\nBy following these steps, you can systematically narrow down and identify the causal gene within your QTL.",
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              "text": "candidate genes. These candidate genes must then betested for a causal link to the phenotype. A good starting\npoint would be sequencing the cDNA of strong candidate\ngenes to identify amino acid polymorphisms and testingfor mRNA and protein expression differences in target\ntissues of the original strains used to detect the QTL.\nSequencing and expression studies will rene the list ofcandidate genes that can then be tested rigorously for\nproof of cause and effect. The nal proof of a causal gene",
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              "text": "candidate genes. These candidate genes must then betested for a causal link to the phenotype. A good starting\npoint would be sequencing the cDNA of strong candidate\ngenes to identify amino acid polymorphisms and testingfor mRNA and protein expression differences in target\ntissues of the original strains used to detect the QTL.\nSequencing and expression studies will rene the list ofcandidate genes that can then be tested rigorously for\nproof of cause and effect. The nal proof of a causal gene",
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              "text": "do you identify the responsible gene within a QTL that you have identified? \nGenerally, one starts by performing a strain survey to find two parental inbred \nstrains that have a markedly different trait. One can now look up many different \ntraits of inbred mice online at the Mouse Phenome Database ( http://phenome.\njax.org/pub-cgi/phenome/mpdcgi?rtn=docs/home ). However, the trait you may \nwant to study may not be present in wild type mice, so you may want to cross",
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              "text": "used to test the hypothesis at locus-specific sig-nificance (LRS 12). In doing so, an additional\n7 cQTLs are observed as consistent in both diets(Fig. 2I, red number).\nSolving QTLs: Finding the quantitative\ntrait gene\nFor cis-QTLs, the causal factors can be quickly\nidentified: With few exceptions, they will be driv-en by variants within the gene itself or imme-diately adjacent. For trans-QTLs, mQTLs, and\ncQTLs, the identification of the causal quanti-",
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              "text": "data is to find a quantitative trait locus, or QTL. A QTL \n(http://gn1.genenetwork.org/glossary.html#Q ) is an area on a chromosome that can contain \none or many genes, that is linked to a change in phenotype. After a  QTL that is responsible for \nthe apparent variation  in phenotype  has been identified , one can  start stu dying the  genes \nwithin that locus  to identify  the likely causal gene .  \n \nOnce the data  is normalized appropriately  (in our case, no normalization was required) , the QTL",
              "title": "2020 - Gene network a completely updated tool for systems genetics analyses.pdf",
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              "text": "candidate genes that are expressed in tissues likely to inuence the traits of interest(Su et al 2004). These candidate genes are then sequenced in the two parental inbred\nstrains looking for sequence dierences in coding or regulatory regions.\nAfter ne mapping the QTL interval and shortening the list of plausible\ncandidate polymorphisms, the major challenge remains /C246 proving denitively\nwhich nucleotide polymorphism underlies the QTL. The most direct proof",
              "title": "2005 - quantitative-trait-locus-analysis-of-aggressive-behaviours-in-mi.pdf",
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              "text": "because these strains have been genotyped at more than 14,000 markers, including single\nnucleotide polymorphisms (SNP). Hundreds of genes may lie within a QTL interval, so\nidentifying the underlying genes requires complementary methods. One method is to use\nBXD gene expression data (a public resource at www.genenetwork.org) to screen for genes\nwithin the QTL interval whose expression correlates with the trait of interest [23].",
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              "text": "candidate genes that are expressed in tissues likely to inuence the traits of interest(Su et al 2004). These candidate genes are then sequenced in the two parental inbred\nstrains looking for sequence dierences in coding or regulatory regions.\nAfter ne mapping the QTL interval and shortening the list of plausible\ncandidate polymorphisms, the major challenge remains /C246 proving denitively\nwhich nucleotide polymorphism underlies the QTL. The most direct proof",
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              "text": "curate approaches to identify various types of QTL according to their molecular features, in par-\nticular to control various confounding factors, such as dietary habit and population structure.\nFine Mapping of Causal Variants and Causal Genes\nDespite the identi cation of large numbers of QTLs, it remains challenging to establish causal",
              "title": "2020 - A Multi-Omics Perspective of Quantitative Trait Loci in Precision Medicine.pdf",
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              "text": "to date, only a small handful of genes have been definitively identified for complex traits. \nOur own efforts to identify a causal gene were stymied by the compound nature of QTLs \nand the high gene density  in Qrr1 , and in Vol8a . Furthermore, it is now becoming clear \nthat in addition to the canonical candidate genes, there are multiple spliced variants, \nmicroRNAs, and epigenetic factors to be considered.  \n \nWith what appears to be an increasingly complex genom ic landscape, it is now all",
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              "content": "The query appears to be based on a misunderstanding. Males do not have two Y chromosomes; they have one X and one Y chromosome, while females have two X chromosomes. This is clearly explained in the provided context:\n\n- Males have an XY genotype, meaning they have one X chromosome and one Y chromosome [3].\n- Females have an XX genotype, meaning they have two X chromosomes [3].\n\nAdditionally, it is noted that for each chromosome pair, one chromosome is inherited from the mother and one from the father [2]. Therefore, males inherit their Y chromosome from their father and their X chromosome from their mother, while females inherit one X chromosome from each parent.",
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              "text": "that accounts for the significant difference. One explanationis a contribution of the Y chromosome from the B strain. Sincethe cross was non-reciprocal all F2 mice carried the B strain Ychromosome. Thus, males carrying Chr X B QTL alleles andthe B Y chromosome differ in two ways from females carry-ing Chr X A alleles (or AB but B alleles are recessive) and noY chromosome, but in only one way from males carrying ChrX A/J QTL alleles because they share the B Y chromosome.However, pursuit of the identity of",
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              "text": "women comprises 2 X chromosomes and in men 1 X and 1 Y chromosome (Figure 2). For each chromosome pair, 1 chro-\nmosome was inherited from the mother and 1 from the father. The full set of chromosomes is collectively called the genome. \nThe human genome is largely contained within the nucleus \nof each cell, where it is separated from the rest of the cell functions. However, a small amount of DNA exists outside \nthe nucleus in the mitochondria and is considered to be part of \nthe human genome.",
              "title": "2015 - Basic Concepts and Potential Applications of Genetics and Genomics for Cardiovascular and Stroke Clinicians.pdf",
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              "text": "betweenmalesandfemalesisthesexchromosomes.MaleshaveanXYgenotypeand\nfemaleshaveanXXgenotype.TheXisamuchlargerchromosome,165.5x106bpsvs.\n16.0x106bps,withapproximately30timesmoregenesthantheYchromosome.To\ncompensateforthelargernumberofgenes,andtoensurefemalesdonothaveover\nexpressionofgenesresidingontheXchromosome,oneoftheXchromosomesis\ninactivated(7).TheXinactivationoccursearlyindevelopmentandisarandomprocess.\nOnlyasmallportionoftheinactivatedchromosomeretainstranscriptionalability.This",
              "title": "2007 - Prenatal nicotine exposure alters gene expression in a sexually dimorphic manner.pdf",
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              "text": "mammals. Instead of a dominant gene for maleness on the Y chromosome, it is the ratioof X chromosomes to autosomes that determines gender. The 2:2 ratio of XX femalesand the 1:2 ratio in XY males produce different ratios of regulatory proteins encoded byX-linked and autosomal genes. Those regulatory genes in turn cause transcripts of theregulatory Sex-lethal (Sxl) gene to be spliced differently in males and females, which be-",
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              "text": "mammals. Instead of a dominant gene for maleness on the Y chromosome, it is the ratioof X chromosomes to autosomes that determines gender. The 2:2 ratio of XX femalesand the 1:2 ratio in XY males produce different ratios of regulatory proteins encoded byX-linked and autosomal genes. Those regulatory genes in turn cause transcripts of theregulatory Sex-lethal (Sxl) gene to be spliced differently in males and females, which be-",
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              "text": "gins the process of sexual differentiation. A fly with two X chromosomes can thereforecarry a Y and still be a fertile female, leading to a paradoxical sex chromosome system inwhich males inherit X chromosomes from their fathers (figure 16.13). \nRice and Chippindale (2001) used a combination of these genetic techniques to test",
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              "text": "gins the process of sexual differentiation. A fly with two X chromosomes can thereforecarry a Y and still be a fertile female, leading to a paradoxical sex chromosome system inwhich males inherit X chromosomes from their fathers (figure 16.13). \nRice and Chippindale (2001) used a combination of these genetic techniques to test",
              "title": "2009 - Garland_and_Rose_Experimental_Evolution.pdf",
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              "text": "ity on the X chromosome compared to the other five strains(Figure 2B ). Compared to females, males had a deficiency of\nheterozygous X-linked SNP loci ( Supplementary Figure S2 ),\nwhich was expected because males are hemizygous. The resid-ual X-linked heterozygous SNPs in males could be due to mis-assembled autosomal contigs on the X chromosome, multiplecopies on the X, or homology between X and autosomalsequences.\nChromosome XAutosomesProportion of SNP lociHomozygous SNPs\nHeterozygous SNPs",
              "title": "2022 - Genetic and genomic architecture in eight strains of the laboratory opossum.pdf",
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              "text": "sex chromosome  \nY chromosome:  One of the two sex chromosomes, X and Y. See also; X chromosome, \nsex chromosome",
              "title": "2012 - Needs Analysis of Genetics and Genomics in Communication Sciences and Disorders.pdf",
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              "text": "one Y chromosome.\nHuman chromosomes are typically displayed pictorially in\na karyotype, as shown in Figure 9, arranged according to\nlength and position of the centromere (i.e., the most con-stricted area of a chromosome). The ends of the chromosomesare called telomeres. Most human karyotypes look identicalbecause they are constructed from cells arrested in the phaseof the cell cycle when chromosomes are most condensed.\nDuring this phase of the cell cyc le, allelic differences cannot\nbe detected.",
              "title": "2017 - Primer in Genetics and Genomics, Article 1 DNA, Genes, and Chromosomes.pdf",
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              "content": "To differentiate between X and Y DNA with respect to DNA tracing and determining quantitative trait loci (QTLs), consider the following points:\n\n1. **Recombination Rates**:\n   - The Y chromosome largely does not undergo recombination, which means it is passed down relatively unchanged from father to son. This makes it useful for tracing paternal lineage [1].\n   - The X chromosome, on the other hand, does undergo recombination, but at a slower rate compared to autosomes. This slower recombination rate has significant implications for the detection of QTLs on the X chromosome [1].\n\n2. **QTL Mapping**:\n   - QTLs are stretches of DNA closely linked to genes that underlie specific phenotypes. The process of QTL mapping involves identifying these regions to understand the genetic basis of heritable traits [2].\n   - The detection of QTLs on the X chromosome may be influenced by its slower recombination rate, which can affect the resolution and accuracy of QTL mapping compared to autosomes [1].\n\nIn summary, the primary differences between X and Y DNA in the context of DNA tracing and QTL determination lie in their recombination behaviors and how these behaviors impact the detection and mapping of QTLs. The Y chromosome's lack of recombination makes it a stable marker for paternal lineage tracing, while the X chromosome's slower recombination rate affects the detection of significant QTLs [1], [2].",
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              "text": "While most of the Y chromosome does not undergo\nrecombination, the recombination rate of the X chromosomeis slower than that of the autosomes. This has important\nconsequences on the detection of significant QTLs. For a\ncomprehensive view of these issues, see(43).\n9.Probe hybridization artifacts\nWhen several probes are available for the same gene, it is\nnot uncommon to observe a difference in the mapping results",
              "title": "2009 - eQTL analysis in mice and rats.pdf",
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              "text": "8 QTL Mapping  \n \nAllelic variation exists among natural populations and inbred strains, and this is \nreflective of the segregation of quantitative tr ait loci (QTLs) [96]. QTLs are stretches of \nDNA that are closely linked to genes that underlie a phenotype of interest. QTL analysis has been proven to be an invaluable tool to  help unravel heritable traits, by enabling \nresearchers to map different quantitative traits back to the genomic location involved in the regulation of these phenotypes.",
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              "text": "8 QTL Mapping  \n \nAllelic variation exists among natural populations and inbred strains, and this is \nreflective of the segregation of quantitative tr ait loci (QTLs) [96]. QTLs are stretches of \nDNA that are closely linked to genes that underlie a phenotype of interest. QTL analysis has been proven to be an invaluable tool to  help unravel heritable traits, by enabling \nresearchers to map different quantitative traits back to the genomic location involved in the regulation of these phenotypes.",
              "title": "2015 -Emery- Genetic Control of Survival and Weight Loss during Pneumonic Burk.pdf",
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              "text": "genetic background.\nGene identification of QTL should be distinguished from identification of the quanti-\ntative trait nucleotide (QTN). The latter is a daunting task, since SNPs are so frequent.\nFinal proof for a QTN in mice would require placing a genomic segment containing theputative QTN from a donor mouse strain on the background of another strain using\nhomologous recombination and reproducing the phenotype of the donor strain.",
              "title": "2005 - quantitative-trait-analysis-in-the-investigation-of-function-and.pdf",
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              "text": "The basic  pr emise  of QTL  an alysis  is simple  (Ph illips  and Belknap,\n2002 ) . First,  one must  meas  ure a speci  c phen  otype  within  a popul  ation.\nNext, the population must be genotyped at a hundred or more marker loci186 Boehm II et al.",
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              "text": "verify the difference, and the data were then ana-lyzed by the QTL detection method of Belknap et al.(1997) based on allele frequency differences betweenthe two lines. When a difference was confirmed,individual genotypes and individual behavioral re-sponses to MA were used to estimate the position ofthe bQTL using the interval mapping methods as\nimplemented in R/qtl (Broman et al. 2003). The lat-",
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              "text": "X axis depicts 19 autosomes and X chromoso me. The Y axis is the likelihood ratio statistic from a single QTL model. Two \nQTLs, on chromosomes 1 and 11, are significant at a mu ltiple test corrected permut ation threshold as shown.\nChromosome 1 and 11 likeli hood ratio statistic plots Figure 2\nChromosome 1 and 11 likelih ood ratio statistic plots . Interval mapping plots of chromosomes 1 and 11, showing more \ndetail of Figure 1. 2 LOD support inte rvals are shown in Mb on the X axis.",
              "title": "2008 - Using gene expression databases for classical trait QTL candidate gene discovery in the BXD recombinant inbred genetic reference population Mouse forebrain weight.pdf",
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              "text": "genes underlying QTLs in animals and plants (see for example Shirley et al 2004,Korstanje & Paigen 2002, Fridman et al 2004). I should also point out, though,\nthat even in a single QTL region isolated in a congenic strain, it is possible that\nthere is more than one allele that aects the phenotype. So, you have a fair pointabout the challenges and complexities of QTL analysis.\nKoolhaas: There are dierent questions underlying both approaches. The QTL",
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              "text": "genes underlying QTLs in animals and plants (see for example Shirley et al 2004,Korstanje & Paigen 2002, Fridman et al 2004). I should also point out, though,\nthat even in a single QTL region isolated in a congenic strain, it is possible that\nthere is more than one allele that aects the phenotype. So, you have a fair pointabout the challenges and complexities of QTL analysis.\nKoolhaas: There are dierent questions underlying both approaches. The QTL",
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              "text": "model at the QTL assumes that the original lines arexed for different alleles although genes can besegregating elsewhere. Hence, it is possible to combineinformation about the QTL across families. The\nassumption of xation at the QTL can be tested by",
              "title": "2005 -Knott- Regression based QTL mapping.pdf",
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              "content": "Once a sperm combines with an egg, the traits passed onto the resulting lifeform are determined by several factors:\n\n1. **Chromosomal Segments from Parents**: Individuals inherit a patchwork of chromosomal segments from both maternal and paternal chromosomes [1].\n2. **Mendelian Laws**: The Mendel laws of segregation and independent assortment play a crucial role. The law of segregation states that each parent passes a randomly selected allele for a given trait to their offspring [5].\n3. **Gene Activation and Regulation**: The process involves controlling which genes are active, when they activate, and for how long. This control can be achieved through mechanisms such as imprinting, maternal effect, cis-regulation, and other instructions encoded within the embryo's DNA [3], [4].\n4. **Epigenetic Marks**: Epigenetic marks, which are chemical modifications to DNA or histones, can affect gene expression and phenotype later in development [7].\n\nThese combined genetic and epigenetic factors determine how traits are passed onto and expressed in the resulting lifeform.",
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              "text": "phenomena such as mutations and gene conversion events) occur in relevant meioses \nleading up to the formation of the gametes (i.e., egg and sperm) which are combined \nduring fertilization and the formation of zygotes. Thus, individuals inherit a patch-\nwork of chromosomal segments from maternal and paternal chromosomes.",
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              "text": "the egg and the sperm. Such a process would result in genetic changes that will be copied into every cell of the future adult, including reproductive cells (Stock & Campbell, 2000), opening the door to irreversibly alter the human species. Inevitably, signifi  cant self-disclosure and discussion challenges await families",
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              "text": "a fertilized egg is a complicated process that relies on controlling: which genes are active; whenthese genes activate; and for how long they are active. In broad terms, there are four ways that thiscontrol can be achieved:\nFirst, inside the sperm or egg, genes can be marked with small chemical tags that flag these genes",
              "title": "2015 - Constraint and divergence of global gene expression in the mammalian embryo.pdf",
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              "text": "to be activated (or remain inactive) after fertilization, depending on whether the modification wasmade by the father (in the sperm) or the mother (in the egg); this process is known as imprinting.\nSecond, the mother can alter the gene activity in her offspring via the placenta; this process is known\nas maternal effect. Third, instructions encoded within the embryos DNA can directly control if, andwhen, a nearby gene becomes activated; this is known as  cis-regulation. Finally, similar instructions",
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              "text": "(Figures 8 and 9). Two gametes (egg and sperm) ultimately \njoin into a single cell, the zygote, which has the full comple-ment of 23 chromosome pairs restored. If all goes well, the zygote gives rise to a live offspring.\nThe Mendel Laws: Segregation and Independent \nAssortment\nBoth of the Mendel laws pertain directly to the process of \nmeiosis. The first Mendel law, the law of segregation, states \nthat each parent passes a randomly selected allele for a given",
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              "text": "the subset of that genetic information that is active.  But how does the differentiation process \nbegin?  The key insight in resolving this conundrum came from fly genetics and was the \nrealization that the egg is not a homogenous sack of protoplasm.  The maternally-derived genes \nactive in the fertilized egg are asymmetrically distributed such that at the first cell division each \ndaughter cell receives a different complement of factors.  Development continues as a",
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              "text": "sex chromosome effects. (B)Soon after fertilization, male and female cells have sex-specic transcriptomes, epigenomes, and phenotypes (for example, male\nembryos grow faster than female embryos). At implantation, lineage determination begins and gene expression differences are reduced. Epigenetic marks, however,\nare less constrained and some are maintained, affecting gene expression, and phenotype later in development. Once specic lineages are established, differences in",
              "title": "2019 - Sexual Dimorphism in the Age of Genomics How, When, Where.pdf",
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              "text": "genes.  An altered gene may be passed on to every cell that develops from it.  The resulting features my help, harm, or have little or no effect on the offsprings success in its environment. (AAAS, pg. 109, 5B:9-12#4 ) 6. Heritable material: The information passed from parents to offspring is coded in DNA molecules (AAAS, pg 108, 5B:9-12#3) 7. Mutagens: Gene mutations can be caused by such things as radiation and chemicals.  When they occur in sex cells, the mutations can be passed onto offspring; if they",
              "title": "2007 - Promoting_Student_Scientific_Literacy_of_Molecular Genetics and Genomics.pdf",
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              "text": "or father (sperm cell). Each gamete has a set of 23 unpaired chromosomes. Two human gametes (egg and sperm) combine \nto create a cell (zygote) that contains the full human genome \nof 23 paired chromosomes.Genetic Information Nondiscrimination Act (GINA)\nUS federal legislation that makes it unlawful to discriminate \nagainst individuals on the basis of their genetic profiles in regard to health insurance and employment. These protections \nare intended to encourage Americans to take advantage of",
              "title": "2015 - Basic Concepts and Potential Applications of Genetics and Genomics for Cardiovascular and Stroke Clinicians.pdf",
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              "text": "spermatozoa: more than the sum of its parts? DNA, histones, pro -\ntamines and epigenetics. Reproduction 139:287301\nNilsson EE, Sadler-Riggleman I, Skinner MK (2018) Environmentally \ninduced epigenetic transgenerational inheritance of disease. Envi-ron Epigenet 4:dvy016Pembrey M, Saffery R, Bygren LO, Network in Epigenetic Epide-",
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              "content": "The provided context does not include specific instructions on how to add a new species to the GeneNetwork database. However, it does mention that data from multiple species have been integrated into GeneNetwork [1], [6]. This suggests that the process is possible, but the exact steps are not detailed in the given excerpts.\n\nTo add a new species to the GeneNetwork database, you may need to refer to the official GeneNetwork website (www.genenetwork.org) [5], [7], or contact their support team for detailed guidance. Additionally, reviewing the materials and methods information page linked to datasets in GeneNetwork [2] might provide further insights into the process.",
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              "text": "GeneNetwork have reinvigorated it, including the addition  of data from  10 species, multi -omics \nanalysis, updated code, and new tools. The new GeneNetwork is now an exciting resource for \npredictive medicine and systems genetics, which is constantly being maintained and improved.   \nHere, we give a brief overview of the process  for carrying out some of the most common \nfunctions on GeneNetwork, as a gateway to deeper analyses , demonstrating how a small",
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              "text": "addition to this, GeneNetwork can be used to study correlations between traits and to\nperform data mining in genomic regions containing candidates for quantitative trait genes\n(Hoffman et al., 2011). All datasets in GeneNetwork are linked to a materials and methods\ninformation page that summarizes experimental details relating to the dataset.\nDatabases within GeneNetwork include the transcriptome database, the BXD published",
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              "text": "publication, and links to the dataset database and to the published paper (4C). There is also an option \nto add this trait to your collection by pressing the Add button (4D), or to view this trait in an ear lier \nversion of GeneNetwork, GN1 (4E).",
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              "text": "Bayesian inference of species networks from multilocus sequence data. Mol. Biol. Evol. 35, 504517 (2018).\n167. Flouri, T ., Jiao, X., Rannala, B. & Yang, Z. A Bayesian \nimplementation of the multispecies coalescent  \nmodel with introgression for phylogenomic analysis. Mol. Biol. Evol. 37, 12111223 (2020).\n168. Kubatko, L. in Handbook of Statistical Genomics   \n(eds Balding, D., Moltke, I. & Marioni, J.) 219245 \n(Wiley, 2019).\n169. Rannala, B., Edwards, S., Leach, A. D. & Yang, Z.",
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              "text": "subnetworks \n GeneNetwork (www.genenetwork.org) is a depository of data-\nsets and tools for use in complex systems biology approaches in \norder to generate or predict higher order gene function ( 23, 24 ).",
              "title": "2011 - Peroxisomal L-bifunctional enzyme (Ehhadh) is essential for the production of medium-chain dicarboxylic acids.pdf",
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              "text": "on different cross types, such as F 2crosses (B6BTBRF2,\nB6D2F2, BH/HB F2, CastB6/B6Cast F2, B6JxB6N F2), butalso on more complex outbred crosses such as the HS, the\nCC, and the Hybrid Mouse Diversity Panel. Recently, data\nfrom other species has also been integrated into GeneNet-\nwork (human, rat, monkey, fruit ies, and others) to facilitate\nthe translational research of results into other species. To this\nend, GeneNetwork provides many tools for the analysis of",
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              "content": "In QTL (Quantitative Trait Loci) analyses, several genes have been identified as being associated with diabetes. Specifically:\n\n1. **RASA1, Nnt, and PSK1** on chromosome 13 have been identified as candidate genes underlying diabetes-related traits [4].\n2. **PAX4** near the locus 7q32 has been identified in a genome-wide association study in a Chinese population as a susceptibility locus for type 2 diabetes [6].\n3. **Genes on chromosomes 8q24 and 7q11** have been associated with insulin sensitivity and insulin secretion, which are critical factors in the development of type 2 diabetes [2].\n4. **Genes on 5q11-q13 and 7q32** have been identified as significant susceptibility factors for type 1 diabetes [9].\n\nThese genes and loci are typically associated with diabetes in QTL analyses, highlighting the complex genetic architecture of the disease.",
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              "text": "genes that are responsible for obesity-associated diabetes.\nBy the generation of subcongenic lines of a QTL, if pos-\nsible starting with chromosome substitution strains, thensmall critical regions that harbor the gene(s) in question\ncan be identied with certainty. Sequence analysis and\nmRNA proling together with gene targeting in-vitro andin-vivo may lead to a solid chain of evidence linking\nsequence differences with altered molecular, cellular, and",
              "title": "2014 - The genetic basis of obesity-associated type 2 diabetes (diabesity) in polygenic mouse models.pdf",
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              "text": "tensive nondiabetic families, the QTLs on chromosomes\n8q24 and 7q11, which are located in regions previouslyidentied as harboring type 2 diabetesassociated genes,may govern insulin sensitivity and insulin secretion in thepresence of insulin resistance before development of overttype 2 diabetes. Follow-up ne-scale mapping aroundthese loci and well-designed candidate gene studies, inparticular, are strongly encouraged.\nACKNOWLEDGMENTS",
              "title": "2006 - Quantitative Trait Loci on Chromosome 8q24.pdf",
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              "text": "studies used the QTL approach for statistical analysis of genotypes\nand phenotypes measured in the crosses. The concept of genetic\ndissection of diabetes into quantitative endophenotypes was\nintroduced and resulted in the detection of genetic loci responsible\nfor the control of fasting glycemia [39,42] , fasting insulinemia\n[39,43] , glucose tolerance [39,41,42] , insulin secretion induced by\nglucose or arginine [39], body weight [39,41,44] , adiposity [39],\nb-",
              "title": "2017 - Genomic regulation of type 2 diabetes endophenotypes Contribution.pdf",
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              "text": "indicating that risk factors exist on both genetic back-\ngrounds [ 29]. QTL mapping studies indicate that these\nmurine metabolic traits have a complex genetic architec-\nture that is not dominated by any single allele [ 2931],\nmuch like humans [ 32,33].\nPrior work identied candidate genes on Chr 13 that might\nunderlie diabetes-related traits, including RASA1, Nnt, andPSK1. RASA1 show strong sequence differences between\nB6 and D2 strains [ 34]. Rasche et al. [ 35] reported that",
              "title": "2015 - A Chromosome 13 locus is associated with male-specific mortality in mice.pdf",
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              "text": "genetic background [4]. Linkage analyses have shown that\nseveral quantitative trait loci interact with each other and\nwith the environment to elicit obesity syndromes that are\npotentially diabetic. Several recent genome-wide associa-\ntion studies have identified novel candidate genes for\nT2DM but the effect of these variants on disease suscepti-\nbility is generally low, with odds ratios mostly around 1.5\n[5-11].\nMultiple studies on the transcriptome level have been per-",
              "title": "2008 - Meta-Analysis Approach identifies Candidate Genes and associated Molecular Networks for Type-2 Diabetes Mellitus.pdf",
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              "text": "(2011).\n7. Steinthorsdottir, V. et al. Identification of low-frequency and rare sequence variants associated with elevated or reduced risk of type 2 diabetes. Nat. Genet.  \n46, 294298 (2014).8. Ma, R. C. et al. Genome-wide association study in a Chinese population \nidentifies a susceptibility locus for type 2 diabetes at 7q32 near PAX4. \nDiabetologia 56, 12911305 (2013).\n9. Huyghe, J. R. et al. Exome array analysis identifies new loci and low-frequency",
              "title": "2016 - The genetic architecture of type 2 diabetes.pdf",
              "version": "v0",
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              "text": "nificant QTL, strongly associated with body weight (Galli et al.1996; Gauguier et al. 1996). Moreover, Gauguier and colleagues(1996) mapped a QTL linked to postprandial insulin secretion intheregionofChr4wherewedetectedasuggestiveQTL.DifferentNIDDM models (obese OLETF rats and lean GK rats) may carryalleles conferring NIDDM susceptibility in the same genes. Thecombined results imply the possibility of common genetic factorsunderlyingNIDDMinhumans,notwithstandingthehighdegreeofgenetic heterogeneity in human",
              "title": "1998 - Genetic dissection of ``OLETF_, a rat model for non-insulin-dependent diabetes mellitus.pdf",
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              "text": "data indicates that variants regulating islet gene transcription influence type 2 diabetes(T2D) predisposition and glucose homeostasis. However, the specific genes through whichthese regulatory variants act remain poorly characterized. We generated expression quanti-tative trait locus (eQTL) data in 118 human islet samples using RNA-sequencing and high-density genotyping. We identified fourteen loci at which cis-exon-eQTL signals overlapped",
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              "text": "linkage analysis assists in the identication of possiblegene-gene interactions and that 5q11-q13 and 7q32together constitute a signicant susceptibility factorfor type 1 diabetes. Diabetes 53:15841591, 2004Type 1 diabetes is a common multifactorial dis-\nease characterized by autoimmune destructionof the insulin-producing /H9252-cells in the endocrine\npancreas, resulting in deranged metabolic ho-",
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              "text": "model for common forms of NIDDM in humans associ-ated with obesity. This study identies the location of amajor QTL and additional independent QTLs contrib-uting to development of hyperglycemia in TH malemice. We have also elucidated genegene interactionsbetween QTLs in the development of NIDDM, detect-ing new QTLs that reveal their signicant effects onlywhen they interact with other QTLs. This complexinheritance pattern associated with genegene inter-actions may be of prime importance in",
              "title": "2001 - Genetic Analysis of a New Mouse Model for Non-InsulinDependent Diabetes.pdf",
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              "content": "The gene TCF7L2 is primarily involved in Type 2 diabetes. This association is well-documented across multiple sources:\n\n1. Single nucleotide polymorphisms in TCF7L2 are reproducibly associated with Type 2 diabetes and reduce the insulin response to glucose in nondiabetic individuals [2].\n2. The TCF7L2 locus represents the strongest known genetic determinant of Type 2 diabetes, with risk allele carriers showing impaired insulin production and beta-cell dysfunction [3].\n3. Variants of the TCF7L2 gene confer risk of Type 2 diabetes [4], [5].\n4. TCF7L2 is considered the strongest genetic determinant for the risk of developing Type 2 diabetes [7].\n5. Overexpression of TCF7L2 protein decreases the sensitivity of beta islet cells to secrete insulin, which is involved in the regulation of insulin secretion [9].\n\nThus, the gene TCF7L2 is significantly involved in Type 2 diabetes.",
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              "text": "T. I., de Bakker, P . I. et al (2006). TCF7L2",
              "title": "2010 - Candidate Gene and Genome-Wide Association Studies in Behavioral Medicine.pdf",
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              "text": "single nucleotide polymorphisms in TCF7L2 are reproduc-ibly associated with type 2 diabetes and reduce the insulinresponse to glucose in nondiabetic individuals. Diabetes55:28902895\n135. Cauchi S, Meyre D, Dina C, Choquet H, Samson C,\nGallina S, Balkau B, Charpentier G, Pattou F, StetsyukV, Scharfmann R, Staels B, Fru  hbeck G, Froguel P 2006\nTranscription factor TCF7L2 genetic study in the Frenchpopulation: expression in human\n/H9252-cells and adipose tissue",
              "title": "2009 - Pathomechanisms of Type 2 Diabetes Genes.pdf",
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              "text": "rs7903146 and rs12255372 in intron 3 of the TCF7L2 gene\n[20], associated with a ~45% increase in Type 2 diabetes\nrisk per allele. As such, the TCF7L2 locus presently repre-\nsents the strongest known genetic determinant of Type 2diabetes. Risk allele carriers show impaired insulin produc-tion [21] and b-cell dysfunction in vitro [22].\nTCF7L2 (previously referred to as TCF-4) is a\nhigh-mobility group box-containing transcription factor\ninvolved in Wingless-type MMTV integration site (Wnt)",
              "title": "2014  - Dorothy Hodgkin Lecture 2014 Understanding genes identified by genome\u2010wide association.pdf",
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              "text": "et al. Variant of transcription factor 7-like 2 (TCF7L2) gene confers risk of type 2 \ndiabetes. Nat Genet . 2006;38:320-23. \n Sladek R, Rocheleau G, Rung J, Dina C, Shen L, Serre D, et al. A genome- [9]\nwide association study identifies novel risk loci for type 2 diabetes. Nature . \n2007;445:881-85.\n Kirchhoff K, Machicao F, Haupt A, Schafer SA, Tschritter O, Staiger H, et al. [10]\nPolymorphisms in the TCF7L2, CDKAL1 and SLC30A8 genes are associated",
              "title": "2015 - Type 2 Diabetes Mellitus and the Association of Candidate Genes.pdf",
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              "text": "transcription factor 7-like 2 ( TCF7L2 ) gene confers risk of type 2 diabetes. Nat Genet. 2006;\n38:320323. [PubMed: 16415884]\n172. Gloyn AL, Noordam K, Willemsen MA, Ellard S, Lam WW, et al. Insights into the biochemical\nand genetic basis of glucokinase activation from naturally occurring hypoglycemia mutations.\nDiabetes. 2003; 52:24332440. [PubMed: 12941786]\n173. Pearson ER, Donnelly LA, Kimber C, Whitley A, Doney AS, et al. Variation in TCF7L2",
              "title": "2012 - Type 2 Diabetes Genetics Beyond GWAS.pdf",
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              "text": "L. Mechanisms by which common variants in the TCF7L2 gene \nincrease risk of type 2 diabetes. J Clin Invest  2007; 117: 2155-2163 \n[PMID: 17671651 DOI: 10.1172/JCI30706]\n164 Gloyn AL , Braun M, Rorsman P. Type 2 diabetes susceptibility \ngene TCF7L2 and its role in beta-cell function. Diabetes  2009; 58: \n800-802 [PMID: 19336690 DOI: 10.2337/db09-0099]\n165 da Silva Xavier G , Loder MK, McDonald A, Tarasov AI, Carzaniga \nR, Kronenberger K, Barg S, Rutter GA. TCF7L2 regulates late",
              "title": "2015 - Diabetes mellitus The epidemic of the century.pdf",
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              "text": "tion. Although the disease progression results from aninterplay of environmental factors and genetic predisposi-\ntion, in recent years TCF7L2 gene has been considered the\nstrongest genetic determinant for the risk of developingT2DM [ 24,19,20]. The gene encodes a transcription\nfactor of the canonical Wnt signaling pathway, expressed\nin several tissues, known to have developmental roles indetermining cell fate, survival, proliferation and movement\n[9]. Wnt signaling plays an important role also in B-cell",
              "title": "2013 - TCF7L2 gene polymorphisms and type 2 diabetes association with diabetic retinopathy and cardiovascular autonomic neuropathy.pdf",
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              "text": "transcription factor 7-like 2 (TCF7L2) gene confers risk of type 2diabetes. Nat Genet 38:320 3231422 Diabetologia (2007) 50:1418 1422",
              "title": "2007 - A German genome-wide linkage scan for type 2 diabetes supports the existence of a metabolic syndrome locus on chromosome 1p36.13 and a type 2 diabetes locus on chromosome 16p12.pdf",
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              "text": "genes which also play a significant role in the risk and \npathogenesis of the disease[158,159]. The association \nof TCF7L2  gene variants with type 2 diabetes and \nits mechanism of action received special attention \nby several investigators[161,162]. Over expression of the protein was shown to decrease the sensitivity of \nbeta islet cells to secrete insulin[163,164] and was more \nprecisely involved in the regulation of secretary granule \nfusion that constitute a late event in insulin secretion",
              "title": "2015 - Diabetes mellitus The epidemic of the century.pdf",
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              "text": "Muggeo M, Stoico V, Negri C, Pignatti PF, Bonora E, Bonadonna\nRC (2011) Variants and haplotypes of TCF7L2 are associatedwithb-cell function in patients with newly diagnosed type 2\ndiabetes: the Verona Newly Diagnosed Type 2 Diabetes Study\n(VNDS) 1. J Clin Endocrinol Metab 96(2):E389E393\n13. Grundy SM, Cleeman JI, Merz CN, Brewer HB Jr, Clark LT,\nHunninghake DB, Pasternak RC, Smith SC Jr, Stone NJ, National\nHeart, Lung, and Blood Institute, American College of Cardiol-",
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              "content": "Once a sperm combines with an egg, the traits passed onto the resulting lifeform are determined by several factors:\n\n1. **Chromosomal Segments from Parents**: Individuals inherit a patchwork of chromosomal segments from both maternal and paternal chromosomes [1].\n   \n2. **Mendelian Laws**: The Mendel laws of segregation and independent assortment play a crucial role. The law of segregation states that each parent passes a randomly selected allele for a given trait to their offspring [4].\n\n3. **Gene Activation and Regulation**: The process involves controlling which genes are active, when they activate, and for how long. This control can be achieved through mechanisms such as imprinting, maternal effect, cis-regulation, and other instructions encoded within the embryo's DNA [3], [5].\n\n4. **Epigenetic Marks**: Soon after fertilization, epigenetic marks can affect gene expression and phenotype later in development [8].\n\n5. **Heritable Material**: The information passed from parents to offspring is coded in DNA molecules [7].\n\nThese factors collectively determine how traits are inherited and expressed in the resulting lifeform.",
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              "text": "phenomena such as mutations and gene conversion events) occur in relevant meioses \nleading up to the formation of the gametes (i.e., egg and sperm) which are combined \nduring fertilization and the formation of zygotes. Thus, individuals inherit a patch-\nwork of chromosomal segments from maternal and paternal chromosomes.",
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              "text": "the egg and the sperm. Such a process would result in genetic changes that will be copied into every cell of the future adult, including reproductive cells (Stock & Campbell, 2000), opening the door to irreversibly alter the human species. Inevitably, signifi  cant self-disclosure and discussion challenges await families",
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              "text": "a fertilized egg is a complicated process that relies on controlling: which genes are active; whenthese genes activate; and for how long they are active. In broad terms, there are four ways that thiscontrol can be achieved:\nFirst, inside the sperm or egg, genes can be marked with small chemical tags that flag these genes",
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              "text": "(Figures 8 and 9). Two gametes (egg and sperm) ultimately \njoin into a single cell, the zygote, which has the full comple-ment of 23 chromosome pairs restored. If all goes well, the zygote gives rise to a live offspring.\nThe Mendel Laws: Segregation and Independent \nAssortment\nBoth of the Mendel laws pertain directly to the process of \nmeiosis. The first Mendel law, the law of segregation, states \nthat each parent passes a randomly selected allele for a given",
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              "text": "to be activated (or remain inactive) after fertilization, depending on whether the modification wasmade by the father (in the sperm) or the mother (in the egg); this process is known as imprinting.\nSecond, the mother can alter the gene activity in her offspring via the placenta; this process is known\nas maternal effect. Third, instructions encoded within the embryos DNA can directly control if, andwhen, a nearby gene becomes activated; this is known as  cis-regulation. Finally, similar instructions",
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              "text": "the subset of that genetic information that is active.  But how does the differentiation process \nbegin?  The key insight in resolving this conundrum came from fly genetics and was the \nrealization that the egg is not a homogenous sack of protoplasm.  The maternally-derived genes \nactive in the fertilized egg are asymmetrically distributed such that at the first cell division each \ndaughter cell receives a different complement of factors.  Development continues as a",
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              "text": "genes.  An altered gene may be passed on to every cell that develops from it.  The resulting features my help, harm, or have little or no effect on the offsprings success in its environment. (AAAS, pg. 109, 5B:9-12#4 ) 6. Heritable material: The information passed from parents to offspring is coded in DNA molecules (AAAS, pg 108, 5B:9-12#3) 7. Mutagens: Gene mutations can be caused by such things as radiation and chemicals.  When they occur in sex cells, the mutations can be passed onto offspring; if they",
              "title": "2007 - Promoting_Student_Scientific_Literacy_of_Molecular Genetics and Genomics.pdf",
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              "text": "sex chromosome effects. (B)Soon after fertilization, male and female cells have sex-specic transcriptomes, epigenomes, and phenotypes (for example, male\nembryos grow faster than female embryos). At implantation, lineage determination begins and gene expression differences are reduced. Epigenetic marks, however,\nare less constrained and some are maintained, affecting gene expression, and phenotype later in development. Once specic lineages are established, differences in",
              "title": "2019 - Sexual Dimorphism in the Age of Genomics How, When, Where.pdf",
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              "text": "or father (sperm cell). Each gamete has a set of 23 unpaired chromosomes. Two human gametes (egg and sperm) combine \nto create a cell (zygote) that contains the full human genome \nof 23 paired chromosomes.Genetic Information Nondiscrimination Act (GINA)\nUS federal legislation that makes it unlawful to discriminate \nagainst individuals on the basis of their genetic profiles in regard to health insurance and employment. These protections \nare intended to encourage Americans to take advantage of",
              "title": "2015 - Basic Concepts and Potential Applications of Genetics and Genomics for Cardiovascular and Stroke Clinicians.pdf",
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              "text": "Proponents of the evo-devo view rightly point out that evolution occurs through changes in the \ndevelopment of traits, which may or may not have changes in DNA as their root cause.  The \nprocesses that produce traits occur during development and involve more than just genes.   \nAll animals begin life as a fertilized egg, a single cell containing mitochondria and other \norganelles, and enough maternally derived RNA and proteins to kick start development and",
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              "content": "A ribosomal binding site (RBS) is a specific sequence of nucleotides in messenger RNA (mRNA) that is crucial for the initiation of protein synthesis. At a high level, you can think of it as a \"landing pad\" for the ribosome, which is the molecular machine that reads the mRNA and translates it into a protein.\n\nHere's a simple analogy: Imagine the ribosome as a train and the mRNA as a track. The ribosomal binding site is like a train station where the train (ribosome) stops to pick up passengers (the instructions for making a protein). Without this station, the train wouldn't know where to start its journey.\n\nIn more technical terms, the RBS helps the ribosome to correctly position itself on the mRNA so that it can start translating the genetic code into a protein at the right spot. This process is essential for the proper expression of genes and the production of proteins that perform various functions in the cell [1], [2], [3].",
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              "text": "173. Griffey, R. H.; Greig, M. J.; Haoyun, A.; Sasmor, H.; Manalili, S.\nTargeted Site-Specific Gas-Phase Cleavage of Oligoribonucleotides.\nApplication in Mass Spectrometry-Based Identification of Ligand\nBinding Sites. J. Am. Chem. Soc. 1999, 121, 474475.\n174. Hanson, C. L.; Fucini, P.; Ilag, L. L.; Nierhaus, K. H.; Robinson, C. V.\nDissociation of Intact Escherichia coli Ribosomes in a Mass Spectrome-\nterEvidence for Conformational Change in a Ribosome Elongation",
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              "text": "270 G.L. Sutphin e t a l.\ngene (Hinneb usch 2005 ). The m echanism of re gulation i s t hought to in v o lv e r el-\nati v e a v a ilability of the l ar ge and small r ibosome s ub units. Specically , w hen 60Sribosomal sub unit l e v els a re lo w , ternary comple x e s containing initiation f actors\nand 40S ribosomal sub units are p roposed to more frequently scan through the",
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              "text": "then used to develop synthetic gene networks with defined outputs, without significant post-hoc adjustments\n22,4751. Alternatively, syn-\nthetic ribosome binding site (RBS) sequences can be used to optimize protein expression levels. Recently, Salis et al.\n52 have developed a \nthermodynamic model for predicting the relative translational ini -",
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              "text": "Philips, R.M., 2017 How Many Ribosomes Are in a Cell? [WWW Document]. URL http://\nbook.bionumbers.org/how-many-ribosomes-are-in-a-cell/ ((accessed 7.24.16) n.d.).\nR Core Team, 2014. R: a Language and Environment for Statistical Computing. R Founda-\ntion for Statistical Computing, Vienna, Austria.\nSigurdson, A.J., Ha, M., Hauptmann, M., Bhatti, P., Sram, R.J., Beskid, O., Tawn, E.J.,",
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              "text": "structure, and to find sites that are likely to be cleaved or modified; interac-\ntion or catalytic mechanisms can be simulated. Bioinformatic resources on \nthe WWW range from the determination of the molecular weight to complex \nthreading and three-dimensional (3D) prediction algorithms. A huge list of \ntools can be found on the ExPASy proteomic tools homepage (65). Because \nof the great variety of programs available, several of these single tools have",
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              "content": "Once a sperm combines with an egg, the traits passed onto the resulting lifeform are determined by several factors:\n\n1. **Chromosomal Segments**: Individuals inherit a patchwork of chromosomal segments from maternal and paternal chromosomes due to phenomena such as mutations and gene conversion events during meiosis [1].\n\n2. **Mendelian Laws**: The Mendel laws of segregation and independent assortment play a crucial role. The law of segregation states that each parent passes a randomly selected allele for a given trait to their offspring [4].\n\n3. **Gene Activation and Regulation**: The process of trait determination also involves controlling which genes are active, when they activate, and for how long. This control can be achieved through mechanisms such as imprinting, maternal effect, and cis-regulation [3], [5].\n\n4. **Epigenetic Marks**: Epigenetic marks, which are chemical modifications to DNA that do not change the DNA sequence but affect gene expression, also play a role. These marks can be inherited and influence gene expression and phenotype later in development [8].\n\n5. **Heritable Material**: The information passed from parents to offspring is coded in DNA molecules, and any mutations in these genes can be inherited by the offspring [7].\n\nIn summary, the combination of chromosomal inheritance, Mendelian genetic laws, gene activation and regulation mechanisms, epigenetic marks, and the heritable DNA material all contribute to how traits are passed onto the resulting lifeform after fertilization.",
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              "text": "phenomena such as mutations and gene conversion events) occur in relevant meioses \nleading up to the formation of the gametes (i.e., egg and sperm) which are combined \nduring fertilization and the formation of zygotes. Thus, individuals inherit a patch-\nwork of chromosomal segments from maternal and paternal chromosomes.",
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              "text": "the egg and the sperm. Such a process would result in genetic changes that will be copied into every cell of the future adult, including reproductive cells (Stock & Campbell, 2000), opening the door to irreversibly alter the human species. Inevitably, signifi  cant self-disclosure and discussion challenges await families",
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              "text": "a fertilized egg is a complicated process that relies on controlling: which genes are active; whenthese genes activate; and for how long they are active. In broad terms, there are four ways that thiscontrol can be achieved:\nFirst, inside the sperm or egg, genes can be marked with small chemical tags that flag these genes",
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              "text": "(Figures 8 and 9). Two gametes (egg and sperm) ultimately \njoin into a single cell, the zygote, which has the full comple-ment of 23 chromosome pairs restored. If all goes well, the zygote gives rise to a live offspring.\nThe Mendel Laws: Segregation and Independent \nAssortment\nBoth of the Mendel laws pertain directly to the process of \nmeiosis. The first Mendel law, the law of segregation, states \nthat each parent passes a randomly selected allele for a given",
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              "text": "to be activated (or remain inactive) after fertilization, depending on whether the modification wasmade by the father (in the sperm) or the mother (in the egg); this process is known as imprinting.\nSecond, the mother can alter the gene activity in her offspring via the placenta; this process is known\nas maternal effect. Third, instructions encoded within the embryos DNA can directly control if, andwhen, a nearby gene becomes activated; this is known as  cis-regulation. Finally, similar instructions",
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              "text": "the subset of that genetic information that is active.  But how does the differentiation process \nbegin?  The key insight in resolving this conundrum came from fly genetics and was the \nrealization that the egg is not a homogenous sack of protoplasm.  The maternally-derived genes \nactive in the fertilized egg are asymmetrically distributed such that at the first cell division each \ndaughter cell receives a different complement of factors.  Development continues as a",
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              "text": "genes.  An altered gene may be passed on to every cell that develops from it.  The resulting features my help, harm, or have little or no effect on the offsprings success in its environment. (AAAS, pg. 109, 5B:9-12#4 ) 6. Heritable material: The information passed from parents to offspring is coded in DNA molecules (AAAS, pg 108, 5B:9-12#3) 7. Mutagens: Gene mutations can be caused by such things as radiation and chemicals.  When they occur in sex cells, the mutations can be passed onto offspring; if they",
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              "text": "sex chromosome effects. (B)Soon after fertilization, male and female cells have sex-specic transcriptomes, epigenomes, and phenotypes (for example, male\nembryos grow faster than female embryos). At implantation, lineage determination begins and gene expression differences are reduced. Epigenetic marks, however,\nare less constrained and some are maintained, affecting gene expression, and phenotype later in development. Once specic lineages are established, differences in",
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              "text": "or father (sperm cell). Each gamete has a set of 23 unpaired chromosomes. Two human gametes (egg and sperm) combine \nto create a cell (zygote) that contains the full human genome \nof 23 paired chromosomes.Genetic Information Nondiscrimination Act (GINA)\nUS federal legislation that makes it unlawful to discriminate \nagainst individuals on the basis of their genetic profiles in regard to health insurance and employment. These protections \nare intended to encourage Americans to take advantage of",
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              "content": "Sure! Let's break down the difference between short-read and long-read sequencing in a way that's easy to understand.\n\n### Short-Read Sequencing:\n1. **Read Length**: Short-read sequencing generates small fragments of DNA, typically around 50-300 base pairs long [4].\n2. **Technology**: This method is commonly used by platforms like Illumina [3].\n3. **Advantages**: \n   - **High Throughput**: It can produce a large amount of data quickly and is relatively cost-effective [4].\n   - **Accuracy**: Generally, short-read sequencing has a lower error rate per base compared to long-read sequencing [4].\n4. **Limitations**:\n   - **Complex Regions**: It struggles to accurately sequence complex regions of the genome, such as those with long repetitive elements or structural variations [6].\n   - **Assembly**: Because the reads are short, assembling them into a complete genome can be challenging and may leave gaps or errors [6].\n\n### Long-Read Sequencing:\n1. **Read Length**: Long-read sequencing produces much longer fragments, often several kilobases (thousands of base pairs) in length [6].\n2. **Technology**: There are two main types:\n   - **Single-Molecule Real-Time Sequencing**: This method reads long DNA fragments directly without needing to amplify them [8].\n   - **Synthetic Long-Reads**: This method uses short-read technology to create long reads in a computational process [2].\n3. **Advantages**:\n   - **Complex Regions**: It can span complex or repetitive regions in a single read, making it easier to resolve these areas accurately [7].\n   - **Transcriptomics**: Long reads can cover entire mRNA transcripts, helping researchers understand gene structures and variations better [7].\n4. **Limitations**:\n   - **Cost and Throughput**: Long-read sequencing is generally more expensive and produces less data per run compared to short-read sequencing [4].\n   - **Error Rate**: The error rate per base can be higher than that of short-read sequencing, although this is improving with new technologies [4].\n\nIn summary, short-read sequencing is like reading a book by looking at many small snippets of text, which is fast and accurate but can be tricky if the text is very repetitive or complex. Long-read sequencing, on the other hand, is like reading longer passages at a time, which helps to understand the context better but might be slower and more expensive.",
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              "text": "for sequencing on existing short-read instrumentation, \nafter which data are split by barcode and reassembled \nwith the knowledge that fragments sharing barcodes Barcodes\nA series of known bases \naddedto a template molecule \neither through ligation or \namplification. After \nsequencing, these barcodes \ncan be used to identify which \nsample a particular read is \nderived from.\nFigure 5 | Real-time and synthetic long-read sequencing approaches.",
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              "text": "sequence 2D read.\nSynthetic long-reads.  Unlike true sequencing platforms, \nsynthetic long-read technology relies on a system of \nbarcoding to associate fragments that are sequenced on \nexisting short-read sequencers61. These approaches par -\ntition large DNA fragments into either microtitre wells \nor an emulsion such that very few molecules exist in \neach partition. Within each partition the template frag -\nments are sheared and barcoded. This approach allows",
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              "text": "sequencing. This platform is used by the Illumina \nsuite of platforms.\n36. Dohm,J.C., Lottaz,C., Borodina,T . & \nHimmelbauer,H. Substantial biases in ultra-short read \ndata sets from high-throughput DNA sequencing. \nNucleic Acids Res. 36, e105 (2008).\n37. Nakamura,K. etal.  Sequence-specific error profile \nofIllumina sequencers. Nucleic Acids Res. 39, e90 \n(2011).\n38. Minoche,A.E., Dohm,J.C. & Himmelbauer,H. \nEvaluation of genomic high-throughput sequencing \ndata generated on Illumina HiSeq and genome",
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              "text": "Comparison of short-read platforms.  Individual short-\nread sequencing platforms vary with respect to through -\nput, cost, error profile and read structure (TABLE1 ). \nDespite the existence of several NGS technology pro -\nviders, NGS research is increasingly being conducted \nwithin the Illumina suite of instruments21. Although \nthis implies high confidence in their data, it also raises \nconcerns about systemic biases derived from using a \nsingle sequencing approach2628. As a consequence, new",
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              "text": "short-read sequencing. arXiv, arXiv:1203.3907v2, https://arxiv.org/abs/\n12073907 .\nGarrison, E., Sire n, J., Novak, A.M., Hickey, G., Eizenga, J.M., Dawson, E.T.,\nJones, W., Garg, S., Markello, C., Lin, M.F., et al. (2018). Variation graph toolkit\nimproves read mapping by representing genetic variation in the reference. Nat.\nBiotechnol. 36, 875879 .\nGiambartolomei, C., Vukcevic, D., Schadt, E.E., Franke, L., Hingorani, A.D.,",
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              "text": "or  transcriptomic structure53.\nLong-read sequencing\nOverview.  It has become apparent that genomes are \nhighly complex with many long repetitive elements, \ncopy number alterations and structural variations that \nare relevant to evolution, adaptation and disease5456. \nHowever, many of these complex elements are so long \nthat short-read paired-end technologies are insufficient \nto resolve them. Long-read sequencing delivers reads in \nexcess of several kilobases, allowing for the resolution of",
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              "text": "these large structural features. Such long reads can span \ncomplex or repetitive regions with a single continuous \nread, thus eliminating ambiguity in the positions or size \nof genomic elements. Long reads can also be useful for \ntranscriptomic research, as they are capable of span -\nning entire mRNA transcripts, allowing researchers to \nidentify the precise connectivity of exons and discern \ngeneisoforms.\nCurrently, there are two main types of long-read tech -",
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              "text": "nologies: single-molecule real-time sequencing  approaches \nand synthetic approaches that rely on existing short-\nread technologies to construct long reads  insilico . The \nsingle-molecule approaches differ from short-read \napproaches in that they do not rely on a clonal popula -\ntion of amplified DNA fragments to generate detectable Figure 2 | Sequencing by ligation methods. a | SOLiD sequencing. Following cluster \ngeneration or bead deposition onto a slide, fragments are sequenced by ligation, in",
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              "text": "Tools for alignment-free analyses of sequencing data\nThe vast majority of next-generation sequencing experiments in mouse have read alignment \nto a reference genome as their first step. However, the primary data from any sequencing \nexperiment are the reads themselves. Recognition that the raw reads are information-rich has \nled to the development of alignment-free algorithms for error correction (among many \nothers, Chaisson and Pevzner 2008 ), abundance estimation ( Patro et al. 2014 ), and de novo",
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              "text": "(right). Sequencing adaptors (depicted by short red bars and short purple bars) are subsequently ligated to\neach cDNA fragment (green lines) and short sequence reads (single end or paired ends) from each cDNA are\ngenerated using high-throughput sequencing technology. The resulting sequence reads [short lines beneath\nthe genome sequence with three genes shown (fat blue bars)] are aligned with the reference genome to",
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              "content": "Using a pangenome-based reference can be more useful than using a single linear reference for several reasons:\n\n1. **Capturing Genetic Diversity**: A single linear reference genome represents just one version of a species' DNA, which might not include all the genetic variations found in different individuals. A pangenome, on the other hand, includes multiple versions of genes and sequences from various individuals, capturing a broader spectrum of genetic diversity [3].\n\n2. **Reducing Reference Bias**: When using a single linear reference, there's a risk of reference bias, which means that genetic variations not present in the reference genome might be missed or misinterpreted. A pangenome reduces this bias by including multiple genetic variants, making it more likely to accurately represent the genetic makeup of different individuals [1].\n\n3. **Improving Disease Research**: By including a wider range of genetic variations, a pangenome can help researchers better understand the genetic basis of diseases. This comprehensive approach allows for more accurate identification of disease-associated genetic variants, which can lead to better diagnostics and treatments [6].\n\n4. **Enhanced Analysis Tools**: The use of a pangenome can improve the performance of genetic analysis tools. For example, it can provide a more accurate benchmark for evaluating new sequencing technologies and analysis methods, leading to more reliable results [3].\n\n5. **Overcoming Limitations of Single References**: A single reference genome assumes that the order and structure of genes are the same across all individuals, which is not always true. This assumption can lead to errors in genetic studies. A pangenome, by including multiple sequences, can account for these structural variations and provide a more accurate representation of the genome [2].\n\nIn summary, a pangenome-based reference is more useful because it captures the genetic diversity of a species, reduces reference bias, improves disease research, enhances analysis tools, and overcomes the limitations of using a single linear reference. This makes it a more comprehensive and accurate tool for genetic studies.",
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              "text": "When reliable prior knowledge exists about the variant composition in a pan-genome (typi-\ncally obtained via read-to-reference mapping), there are computational tools that can transform\na linear reference sequence and a set of variant calls into graphs (18).This approach bypasses the\ncomputationallyexpensiveall-versus-allalignmentstepalongwiththeuncertaintiesofsubsequent\ngraph construction, but the trade-off is increased reference bias and a potentially incomplete",
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              "text": "(Karolchik et al. 2014 )] and Ensembl ( Flicek et al. 2013 ). Use of a single haploid reference \nsequence as an anchor for all studies of genetic variation in mouse offers many practical \nadvantages. But the dependency on a reference genome requires several assumptions about \nthe nature of genetic variation which may be violated in practicethe strongest of which is \nthat of genomic collinearity (i.e., conserved marker order) between strains. We consider the",
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              "text": "for at least 500 ancestrally diverse humans. This resource willalso provide a set of highly accurate genomes that can be\nused as a benchmarking dataset to improve short-read analysis\ntools. Even more importantly, these genomes allow completelynew designs for more effective short-read analysis strategiesthat overcome many of the limitations described above.\nTransitioning to a pan-genome reference will require develop-",
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              "text": "2018;562(7726):203-209. http://doi.org/10.1038/s41586-018-0579-z\n110. Li R, Li Y, Zheng H, et al. Building the sequence map of the human\npan-genome. Nat Biotechnol . 2010;28(1):57-63. http://doi.org/10.\n1038/nbt.1596\n111. Vernikos G, Medini D, Riley DR, Tettelin H. Ten years of pan-\ngenome analyses. Curr Opin Microbiol . 2015;23:148-154. http://\ndoi.org/10.1016/j.mib.2014.11.016\n112. Miga KH, Wang T. The need for a human pangenome reference\nsequence. Annu Rev Genomics Hum Genet . 2021;22:81-102.\nhttp://",
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              "text": "Whilemostpan-genomesconstructedtodateareprimarilygene-basedbecauseoftherelative\neaseofcomparingandcategorizingdiscreteunitsdefinedbytranscriptionandtranslation,theim-\nportanceofnoncodingandrepetitivesequencesisunquestionable.Itwouldthereforebeextremely\npowerfultodefineacomprehensivesequence-basedpan-genomethatincludesinformationabout\ntherelativepositionofallsequences.Unfortunately,interpretingnoncodingsequencevariationischallenging.Indeed,evenforclassesofnoncodingsequencesofknownimportance,e.g.,promot-",
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              "text": "assessment  will improve our understanding of the reference to  better assemble and interpret future \ngenome sequences.  \n \nWe have previously developed a method to assess the risk of a patient for 55 diseases using a \nquantitative human disease -SNP association database, and showed that we could suggest useful \nand clinical relevant information using his personal genome sequence  (16). Here, we queried  the \nreference genome sequence against our databa se and  identified  3,556 disease -susceptib ility",
              "title": "2011 - The Reference Human Genome High Risk of Type 1 Diabetes and Other Disorder.pdf",
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              "text": "The shortcomings of a single, linear reference genome\nper species are well appreciated, and richer reference datastructures are an active area of research (Church et al.\n2015 ). An alternative is de novo assembly of the genomes\nof commonly used strains. The Sanger Mouse GenomesProject is using a combination of long-insert jumping\nlibraries and optical mapping to build de novo assemblies",
              "title": "2015 - Informatics resources for the Collaborative Cross and related mouse populations.pdf",
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              "text": "undertake comprehensive and \npowerful explorations rather than \nbeing confined to testing hypoth -\neses focused on candidate path -\nways. With the completion of the \nfirst reference sequence of the \nhuman genome,3 attention shifted \nfrom searching for genes to dis -\ncovering their functions. System -\natic genetic mapping in families \nand populations helped scientists \npinpoint the genetic variants that \ncontribute to human disease.",
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              "text": "points, nding statistical associations, modeling and run ning predic-\ntors, or constructing and pruning networks of detected rela tions. In the\nfollowing paragraphs I will explore these opportunities in detail.\n1.4.1 Population reference genomes\nGenomes are relatively similar between individuals, there fore, instead of\nassembling the complete sequence for each person, we only de termine\npoints of DNA variation compared to a reference genome. Subs equently,",
              "title": "2009 - Detection and interpretation of expression quantitative trait loci (eQTL).pdf",
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              "text": "having a reference genome for a related specie s certainly makes the process easier. The \navailability of long-read sequences vastly improves our ability to assemble new genomes, and new \ntechnologies, such as PacBio  and Nanopore, are now able to give reads between 100-1000 \nkilobases, an order of m agnitude longer than current Illumina sequencing (Shendure et al.  2017). \nCombining these new technologies with traditional short read NGS will greatly improve our ability",
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              "content": "Not all genetic regulation is done solely through DNA elements like promoters, repressors, and activators. There are other forms of genetic regulation that play crucial roles in controlling gene expression. Here are some key points to consider:\n\n1. **Non-Coding DNA**: Recent research has shown that much of the non-coding part of the genome, which does not code for proteins, is still active in regulating gene expression. This includes various regulatory activities that are not directly related to the traditional DNA elements like promoters and repressors [1].\n\n2. **Chromatin Structure**: The structure of chromatin, which is the complex of DNA and proteins in the cell nucleus, plays a significant role in gene regulation. For example, histone acetylation, which involves adding acetyl groups to histone proteins, can decondense chromosomal structure and make DNA more accessible for transcription [9].\n\n3. **Epigenetic Regulation**: Epigenetics involves changes in gene expression that do not alter the DNA sequence itself. This can include modifications like DNA methylation and histone modification, which affect how tightly DNA is wound around histones and thus its accessibility for transcription [6], [7].\n\n4. **Post-Transcriptional Regulation**: After DNA is transcribed into RNA, there are additional layers of regulation. This includes processes like RNA splicing, editing, and degradation, which can influence how much of the RNA is available to be translated into protein [7].\n\n5. **Translational and Post-Translational Regulation**: Even after RNA is translated into protein, there are mechanisms that regulate the activity, stability, and localization of proteins. These include modifications like phosphorylation and ubiquitination, which can alter protein function and lifespan [7].\n\nIn summary, while DNA elements like promoters, repressors, and activators are important for genetic regulation, there are multiple other layers of regulation involving chromatin structure, epigenetic modifications, and post-transcriptional and post-translational processes that also play critical roles in controlling gene expression.",
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              "text": "al., 2012 ; Hindhorff, 2009; Barrett et al ., 2007 ). Recent efforts by the Encyclopedia of DNA \nelements (ENCODE) consortium, to characterise the human genome, have revealed that most \nof the non -coding part of the genome is not inactive but is associated with  different forms of \nregulatory activity (ENCODE, 2012 ; Thurman, 2012 ). One important regulatory process that \ntakes place within the genome is the (in-) activation  of gene expression through the interaction",
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              "text": "network of transcriptional regulators. Nature 403, \n335338 (2000).\n18. Gardner,T ., Cantor,C. & Collins,J. Construction of a \ngenetic toggle switch in Escherichia coli. Nature 403, \n339342 (2000).\n19. Kauffman,S.A. Metabolic stability and epigenesis in \nrandomly constructed genetic nets. J.Theor. Biol. 22, \n437467 (1969).\n20. Thomas,R. Boolean formalization of genetic control \ncircuits. J.Theor. Biol. 42, 563585 (1973).\nREVIEWS\nNATURE REVIEWS | GENETICS   ADV ANCE ONLINE PUBLICATION | 11",
              "title": "2015 - Quantitative and logic modelling of molecular and gene networks.pdf",
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              "text": "25 \n 2.8 REGULATION OF GENE EXPRESSION  \n \nApart  from the protein coding sequences, there are other biologically relevant nucleic acid \nsequences that play other important roles in the genome such as regulation of gene expression \nand maintenance of the chromatin structure (Pique -Regis  et al., 2011). Regu lation of gene \nexpression involves  a process that leads to increase or decrease in the production of specific",
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              "text": "expression is regulated at many levels, but gene transcription \nrepresents an essential and, in many cases, dominant point of control. Protein-coding genes are transcribed from promoters, \nwhich represent genomic regions that recruit basal transcrip-\ntion factors and RNA polymerase II. Physiological levels of gene expression and responses to internal and external signals require the actions of additional sequence-specific transcrip-\ntion factors that recruit nucleosome-remodeling complexes,",
              "title": "2013 - Genetic and Genomic Approaches to Understanding Macrophage Identity and Function.pdf",
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              "text": "regulatory elements  and variants thereof that may affect gene expression particularly through \nthe binding of transcription factors (TFs) to DNA.  \nThe suggestion that the  genetic determinants of complex diseases are perh aps better sought in \nproblems associated with gene regulation is due to findings that many of the disease associated \nvariants occur in non -coding DNA sequences within the genome  (ENCODE, 2012; Schuab et",
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              "text": "through multiple cell divisions at the transcriptio nal and epigenetic level need to be more 204 \ncarefully examined and have evolved as an exciting area of research. 205 \n 206 \nEpigenetics and transcriptional regulation  207 \nRegulation of gene expression relies on the ac cessibility of DNA to various transcription 208 \nfactors, co-activators/co-repressors, and the transcriptional machinery. DNA is first wrapped 209",
              "title": "2010 - The Role of Epigenetics in the Pathology of Diabetic Complications.pdf",
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              "text": "post-translationally, translationally, transcriptionally, or epigenetically  (Lempradl  \net al, 2015; Zong  et al, 2017) . It seems likely that these different layers of \nregulation can operate cooperatively on different time- scales . More permanent  \nadaptations might be expected following persistent regulation on a more transient \nlevelfor example,  lowered transcriptional activity of a gene might follow  a \nperiod of low functional  activity of its protein. Elucidating the means of such",
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              "text": "important  component in the regulation of gene expression with\nbetween  10  and  20%  of  the  transcriptome  being  regulated  by  DNA\nvariation.\n2. Technologies\nThe  study  of  DNA  and  its  downstream  effects  is  very  much\na  technology  driven  process.  Most  of  the  rst  screens  looking  at\nDNA  changes in disease involved looking at segregation in fam-\nilies  because  there  were  no  reasonable  technologies  at  the  time",
              "title": "2011 - The age of the \u201come\u201d Genome, transcriptome and proteome data set collection and analysis.pdf",
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              "text": "the cytosine and adenine nucleotides[31]. In addition, the c hromosomal\nstructure of DNA can be decondensated by histone acetylatio n (trans-\nfer of acetyl groups to DNA organizational elements), makin g it more\naccessible for transcription[87]. The transcriptional ex pression of genes\nis further regulated by genetic variants themselves[7]. Fi nally, proteins\nform a complex network of interactions[265] that, in turn, a lso regulate\ngene expression[331].",
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              "text": "eterogeneity and common, small effect genetic variants will be assessed.  h \nD (c) Regulatory Signals: \nCo-regulation of genes via shared transcriptional networks provides the basis for context-dependent gene \nexpression, an understanding of which is vital to the understanding of disease etiology and disease progression. In \nparticular, transcription factors (TF) and their transcription factor binding sites (TFBS) provide a key component in the understanding of how co-regulation is achieved.",
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              "content": "The different relationships between traits can be categorized into several types based on the provided context:\n\n1. **Correlation Among Traits in a Pair**: This refers to how traits within a pair are related to each other in terms of their correlation [1], [2], [3].\n\n2. **Correlation Between a Trait Pair and Other Factors**: This involves examining how a pair of traits correlates with other external factors or conditions [1], [2], [3].\n\n3. **High-Order Organization of Traits**:\n   - **Groups of Tightly Related Traits**: These are traits that share the same transcript mechanisms and are highly correlated with each other (modules 1, 2, 6, 7, 8) [6], [7], [8].\n   - **Groups of Distinct Traits with Shared Mechanisms**: These traits share the same transcript mechanisms but do not necessarily have high correlations among themselves (modules 3, 4, 5) [6], [7], [8].\n   - **Overlapping Traits in Different Groups**: Different groups of traits may have overlapping traits but typically differ in their underlying mechanisms [6], [7], [8].\n\nThese relationships highlight the complexity and interconnectedness of traits, showing that they can be related through direct correlations, shared mechanisms, or overlapping characteristics.",
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              "text": "3, 4 and 5 suggest previously unknown connections between traits.\nWe next characterized pairs of traits within each group of traits (trait pairs) to show that the\nquality of these pairs is not lower than in existing methods. We focused on three main properties of\ntrait pairs: the correlation among traits in a pair; the correlation between a trait pair and the",
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              "text": "3, 4 and 5 suggest previously unknown connections between traits.\nWe next characterized pairs of traits within each group of traits (trait pairs) to show that the\nquality of these pairs is not lower than in existing methods. We focused on three main properties of\ntrait pairs: the correlation among traits in a pair; the correlation between a trait pair and the",
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              "text": "taxonomy of traits is that it allows researchers to turn theirattention to the ways temperament and personality traitsexpress themselves in daily life and to the fundamental pro-cesses underlying variations in these traits. In this section, we rst describe the traits and then review some of the mostinteresting current work on the psychological and evolutionaryunderpinnings of each trait. A more detailed description of thecomponents of these traits is found in Caspi and Shiner (2006).Because relatively less",
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              "text": "ditions and related totraits ofinter est,often bycomparing two groups differing forthetrait.\nDarvasi (2003) states that thereisanundeclar eddispute among resear chers who study\ncomplex traits :::Onone side areclassical geneticists :::ontheother areproponents ofgene\nexpr ession analysis :::.Darvasi goes ontooutline thepossible advantages ofcombining\nthese techniques over and above either technique alone. Inaddition tobetter correlating ge-",
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              "text": "three types of high-order organization of traits. (i) Groups of tightly related traits that share thesame transcripts mechanisms (modules 1, 2, 6, 7, 8, e.g., Figure 3 ). (ii) Groups of distinct traits that\nshare the same transcripts mechanism, but not necessarily high correlations among them (modules 3,\n4, 5, e.g., Figure 4 ). (iii) Different groups commonly have overlapping traits, but typically differ in their\nunderlying mechanisms ( Figure 2B ).",
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              "text": "three types of high-order organization of traits. (i) Groups of tightly related traits that share thesame transcripts mechanisms (modules 1, 2, 6, 7, 8, e.g., Figure 3 ). (ii) Groups of distinct traits that\nshare the same transcripts mechanism, but not necessarily high correlations among them (modules 3,\n4, 5, e.g., Figure 4 ). (iii) Different groups commonly have overlapping traits, but typically differ in their\nunderlying mechanisms ( Figure 2B ).",
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              "text": "three types of high-order organization of traits. (i) Groups of tightly related traits that share thesame transcripts mechanisms (modules 1, 2, 6, 7, 8, e.g., Figure 3 ). (ii) Groups of distinct traits that\nshare the same transcripts mechanism, but not necessarily high correlations among them (modules 3,\n4, 5, e.g., Figure 4 ). (iii) Different groups commonly have overlapping traits, but typically differ in their\nunderlying mechanisms ( Figure 2B ).",
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              "text": "of varying effect sizes (small to moderate), interact with each other across time to manifest as individual genotypic and phenotypic traits. These traits contribute to normal variation in human behavior. Yet, these trait variants also increase the susceptibility of a disorder or a condition for many others.",
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              "text": "action will open a Correlation Plot page in which you can examine the\nrelationship between the two traits. Look for linearity and outliers.\n3.3.1. Selection and Saving Multiple Traits\nThe list of traits on the Correlation Results page represents traits that may\nbe related in some way. You may want to select a group of them for further\nanalysis. For example, use the checkboxes to the left of each entry to check\nentries 1, 9, 10, 14, 16, 18, traits related to brain size. Click the Add to collection",
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              "content": "Yes, the landscape of QTL (Quantitative Trait Loci) and GWAS (Genome-Wide Association Studies) hits can be used to find relationships between traits. This can be achieved through several methods:\n\n1. **Correlated Traits in Different Environments**: Multiple GWAS for the same trait in different environments can be treated as correlated traits, which helps in exploring the genetic and phenotypic basis of local adaptation [1].\n\n2. **Mapping Pleiotropy**: Newer approaches map pleiotropy by simultaneously associating genomic loci with multiple traits, which can reveal relationships between traits [2].\n\n3. **QTL-Trait-Trait Triads**: Causal inference in GWAS and QTL studies involves identifying pairs of traits with a common QTL and determining whether the QTL directly affects each of the two traits independently or if it affects only one trait, which then influences the other [4].\n\n4. **Colocalization and Integration of Data**: Methods such as Bayesian tests for colocalization between pairs of genetic association studies using summary statistics, and Mendelian randomization integrating GWAS and eQTL data, can reveal genetic determinants of complex and clinical traits, thereby identifying relationships between traits [5].\n\nThese methods collectively demonstrate that the landscape of QTL and GWAS hits can indeed be used to find relationships between traits.",
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              "text": "ST, see [40,120122]). Such tools may also offer a\nway of incorporating GxE interactions, as multiple GWAS for the\nsame trait in different environments can be treated as correlatedtraits [123].\nAs association data for a greater variety of populations, species,\nand traits becomes available, we view the methods described outhere as a productive way forward in developing a quantitativeframework to explore the genetic and phenotypic basis of local\nadaptation.\nMaterials and Methods",
              "title": "2014 - A Population Genetic Signal of Polygenic Adaptation.pdf",
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              "text": "has been achieved by quantitative trait loci mapping, admixture \nmapping and GW AS131, which have limited power to detect \nsmall-effect-size genes. Newer approaches map pleiotropy by simultaneously associating genomic loci with multiple traits\n54 \nand can also detect epistatic interactions using machine learning algorithms\n132.Detecting the genomic signatures of correlational selectionCorrelational selection could potentially be inferred from \nsignatures of selective sweeps at loci under strong selection",
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              "text": "pairs that include many genes within the seg-\nment. On the other hand, GWAS may point to\nseveral or even many genomic locations for the\ntrait of interest, complicating further functional\nanalysis.\nAnalysis of Quantitative Trait Loci (QTL)\nQTL analysis reveals statistically signicant\nlinkage between phenotypes and genotypes,\nthereby providing explanation for the genetic\nbasis of variation in complex traits (Falconer\nand Mackay, 1996; Lynch and Walsh, 1998). In\na sense, QTL analysis can be viewed as incom-",
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              "text": "studies.   \nThere are  many possible causal networks even in a simple syst em consisting of \na genomic locus (QTL) and two traits, T1 and T2 ( Figure 1 ). Causal inference in \nGWLS and GWAS involves, in its simplest form, the i dentification of pairs of traits \nwith a common QTL (QTL-trait-trait triads) and dete rmining whether the QTL \ndirectly affects each of two traits (independent), or if the QTL affects only one trait",
              "title": "2011 - Genetical genomics approaches for systems genetics.pdf",
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              "text": "tions by matching patterns of expression QTL and GWAS.\nAm. J. Hum. Genet. 92, 92\n160. Giambartolomei, C. et al. (2014) Bayesian test for colocalisation\nbetween pairs of genetic association studies using summary\nstatistics. PLoS Genet. 10, e1004383\n161. Porcu, E. et al. (2019) Mendelian randomization integrating\nGWAS and eQTL data reveals genetic determinants of com-plex and clinical traits. Nat. Commun. 10, 3300\n162. Zhu, Z. et al. (2016) Integration of summary data from GWAS",
              "title": "2020 - A Multi-Omics Perspective of Quantitative Trait Loci in Precision Medicine.pdf",
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              "text": "knowledge of the true QTL location (Doss et al. 2005 ),\nwhich can be used to empirically estimate the power of aGWAS performed at a similar scale (Hao et al. 2008 ;\nSchadt et al. 2008 ). A GWAS on its own does little more\nthan establish correlations between changes in DNA at agiven locus and changes in a disease trait of interest, with\nrespect to populations of interest. Further, these studies on",
              "title": "2010 - Systems genetics, bioinformatics and eQTL mapping.pdf",
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              "text": "Another method to identify candidate genes is to leverage data generated in another\npopulation or species. Phenome-wide association studies (PheWAS) take a gene or variant\nof interest and nd all reported associations in GWAS datasets. A number of these GWAS\ntools exist, using either different methods, or different human cohorts (https://atlas.ctglab.\nnl/PheWAS, http://pheweb.sph.umich.edu/, accessed on 2 February 2022).\nMouse QTL mapping has high power but low precision (i.e., we can detect a QTL, but",
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              "text": "Another method to identify candidate genes is to leverage data generated in another\npopulation or species. Phenome-wide association studies (PheWAS) take a gene or variant\nof interest and nd all reported associations in GWAS datasets. A number of these GWAS\ntools exist, using either different methods, or different human cohorts (https://atlas.ctglab.\nnl/PheWAS, http://pheweb.sph.umich.edu/, accessed on 2 February 2022).\nMouse QTL mapping has high power but low precision (i.e., we can detect a QTL, but",
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              "text": "Another method to identify candidate genes is to leverage data generated in another\npopulation or species. Phenome-wide association studies (PheWAS) take a gene or variant\nof interest and nd all reported associations in GWAS datasets. A number of these GWAS\ntools exist, using either different methods, or different human cohorts (https://atlas.ctglab.\nnl/PheWAS, http://pheweb.sph.umich.edu/, accessed on 2 February 2022).\nMouse QTL mapping has high power but low precision (i.e., we can detect a QTL, but",
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              "text": "narrow regions ofthegenome harboring trait associated genetic variants. Itisstill, however, a\nchallenge toidentify causal genes and several approaches have been developed that canassist\ninbridging thisgap. Specifically, systems genetics approaches involving theintegration of\nother types of-omics data have proven useful [25]. Two systems genetics approaches for\ninforming GWAS areexpression quantitative trait loci(eQTL) discovery and co-expression",
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