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              "text": "GeneNetwork is an open-access database that collates genomic\ninformation of diverse experimental crosses and reference panels\nas well as phenotypic data from miscellaneous research groups\n[26].\nStatistics\nData generation, statistical analysis and graph creation were\nperformed with SPSS Statistics 21 (IBM, Ehningen, Germany). As\nappropriate, mean and median values were further used for QTLanalysis. Phenotypic robustness for each strain was assessed by the",
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              "text": "users can take advantage of a systems genetics approach (Rosen et al., 2003, 2007). While\nthe candidate gene approach asks which one gene mutation causes a particular disease, the\nsystems genetics approach explores which phenotypes and diseases result from diverse sets\nof genetic and molecular markers (Rosen et al., 2003, 2007). The majority of data sets in\nGeneNetwork are collected from GRPs consisting of hundreds of diverse, inbred strains of",
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              "text": "Based on this, Goh et al. created networks using data from the Online\nMendelian Inheritance in Man (OMIM) [18]database that houses lists of\ndisease gene links. Two networks emerged: the human disease network inwhich disease nodes were connected if they were caused by mutations in\nthe same gene, and the disease gene network where gene nodes were",
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              "text": "atic way. Users begin by selecting one or more human diseases and\nclicking on Compare. The genes associated with the selected disease\nare tested for enrichment against all sets of known associat ed genes for\nworm phenotypes. The result reveals functionally coherent , evolution-\narily conserved gene networks.\nAlternatively, users can also start by selecting worm pheno types,\nwhich are tested against human diseases. In addition to cross -species",
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              "text": "is tackling this immense challenge bystudying networks of genes, proteins,metabolites, and other biomarkers thatrepresent models of genuine biologicalpathways. Studying complex diseasesin terms of gene networks rather thanindividual genes or genomic loci shouldaid in uncovering disease genes. Withthis approach, the effects of multiplegenes in the network are combined,producing a stronger signal and reducingthe number of statistical tests of associ-ation that must be performed.",
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              "content": "GeneNetwork.org provides several tools and resources to explore how genes interact with each other. It is a bioinformatics tool that allows users to explore systems genetics data, which is crucial for defining biological networks and predicting molecular interactions [1]. GeneNetwork offers correlation and mapping strategies to assess associations among multiple genes and quantitative trait loci (QTLs), facilitating the study of complex traits [3]. Additionally, it provides access to a wide variety of data, including genotypes and phenotypes, which can illuminate relationships between different levels of a biological system, such as the genome, transcriptome, and phenome [6]. GeneNetwork also computes gene-gene co-expression correlations, which are essential for understanding gene interactions [9].",
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              "text": "Molecular Genetics and Genomics \n1 3\nas overexpression, knockdown, knockout and mutation \n(Online Resource 1).\nGene network construction\nGenegene interaction data were extracted from the STRING database (http://strin g-db.org/) (Christian etal. 2003), a web resource that includes comprehensively predicted and known interaction information. Then, the genegene interaction pairs were imported into Cytoscape software (Version 3.5.1) (http://cytos  cape.org/ ) (Smoot etal. 2011 ) to construct a",
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              "text": "of links to external resources for tracing the interrelationships of a gene among multiple\nWeb-based resources. GeneNetwork also offers a number of correlation and mapping\nstrategies for assessing associations among multiple genes and QTLs. GeneNetwork aims to\nmake the study of complex traits through the use of systems genetics widely available to the\nscientific community. A powerful tool that can be integrated with GeneNetwork or used on",
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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": "is shown in Figure 1A. Associations between transcript\nabundance, phenotypic traits and genotype can be estab-\nlished either using correlation or genetic linkage mapping\nfunctions [29,30]. The main page of GeneNetwork at\nhttp://www.genenetwork.org  provides access to subsets of\ndata through pull-down menus that allow specific data\nsets to be queried. The datasets can be further restricted\nusing a single text box for specific database entries to\nquery probe set or trait ID, or annotations associated with",
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              "text": "genetics approaches can not only provide insights into the roles of \nindividual genes or developmental pathways but also illuminate \nrelationships between different levels of a biologic system, such as \nthe genome, transcriptome, and phenome [ 10]. One such resource \nof systems genetics is the GeneNetwork website and resource \n(www.genenetwork.org ) that provides access to a wide variety of \ndata such as genotypes (e.g., SNPs), phenotypes that are obtained",
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              "text": "occurrence; GN, gene neighbor; GT, genetic interaction; LC, literature-curated protein interactions; MS, affinity purification/mass spectrome try; PG, phy-\nlogenetic profiles; PI, fly protein interactions; TS, tertiary structure; and YH, yeast two-hybrid). Detailed descriptions are listed in Suppleme ntal Table S1. ( B)\nEssential genes were highly interconnected in HumanNet, and thus predictable from the network, as shown by ROC analysis. Genes were ranked by their sum",
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              "text": "from co-regulation patterns found within tens of thousands of samples\nfor which gene expression was measured. GeneNetwork provid es un-\nprecedented resolution and predictive power across multip le cell types\nand tissues. Analogous to discovering patterns in expressi on data, the\nnetwork of protein-protein interactions can also be comput ationally pre-\ndicted using various methods[381].\nThe combined current knowledge of how cells control functio ns",
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              "text": "(http://string-db.org/ ). STRING creates networks representing the best available knowledge of gene\ninterconnections. Each protein-protein interaction is annotated with scores indicating how likely an\ninteraction should be true. Scores rank from 0 to 1, with one being the highest confidence. A score\nof 0.5 indicates roughly every second interaction might be erroneous. Gene-gene co-expression cor-\nrelations were computed as Pearson product-moment correlations (r) in Genenetwork.org after\nremoving outliers.",
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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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              "content": "GeneNetwork.org aids in finding the genetic causes of common diseases by utilizing a systems genetics approach. This approach allows researchers to explore which phenotypes and diseases result from diverse sets of genetic and molecular markers, rather than focusing on single gene mutations [1]. GeneNetwork provides access to a wide variety of data, including genotypes (e.g., SNPs) and phenotypes, which can be used to study complex traits [7]. The platform integrates data on gene expression, protein interactions, and DNA-protein binding to construct biological networks that are predictive of disease [4]. Additionally, GeneNetwork offers correlation and mapping strategies for assessing associations among multiple genes and quantitative trait loci (QTLs), facilitating the study of complex traits [5]. This makes GeneNetwork a powerful tool for predictive medicine and systems genetics, helping researchers understand the genetic underpinnings of common diseases [6].",
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              "text": "users can take advantage of a systems genetics approach (Rosen et al., 2003, 2007). While\nthe candidate gene approach asks which one gene mutation causes a particular disease, the\nsystems genetics approach explores which phenotypes and diseases result from diverse sets\nof genetic and molecular markers (Rosen et al., 2003, 2007). The majority of data sets in\nGeneNetwork are collected from GRPs consisting of hundreds of diverse, inbred strains of",
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              "text": "Based on this, Goh et al. created networks using data from the Online\nMendelian Inheritance in Man (OMIM) [18]database that houses lists of\ndisease gene links. Two networks emerged: the human disease network inwhich disease nodes were connected if they were caused by mutations in\nthe same gene, and the disease gene network where gene nodes were",
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              "text": "Genetics Home Reference - Genetics Home Reference provides consumer-friendly \ninformation about the effects of genetic variations on human health. \nhttp://ghr.nlm.nih.gov/  \nGene Reviews  Features expert-authored, peer-reviewed, current disease descriptions \nthat apply genetic testing to the diagnosis, management, and genetic counseling of \npatients and families with specific inherited conditions. www.genetests.org/servlet/access?",
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              "text": "genetics approaches can not only provide insights into the roles of \nindividual genes or developmental pathways but also illuminate \nrelationships between different levels of a biologic system, such as \nthe genome, transcriptome, and phenome [ 10]. One such resource \nof systems genetics is the GeneNetwork website and resource \n(www.genenetwork.org ) that provides access to a wide variety of \ndata such as genotypes (e.g., SNPs), phenotypes that are obtained",
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              "text": "eron Genetics Center ( https://www.regeneron.com/ge -\nnetics-center ), and aims to identify rare loss-of-function \nmutations in founder populations to delineate further the \ngenetic factors that underpin health and disease. This ini -\ntiative is also addressed at developing countries and those \nin resource-limiting environments, under the coordina -\ntion of the Genomic Medicine Alliance ( http://www.ge -\nnomicmedicinealliance.org ), a founding partner of the",
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              "text": "GeneNetwork.org is also a valuable\nteaching tool. While mainly designed for\nresearchers interested in testing gene-to-\nphenotype relationships, GeneNetwork.\norghas been adapted for dry-lab teaching\nin neuroscience and genetics ( Grisham et\nal., 2017 ). A useful approach is to assign\nsets of vetted questions, such as the exam-\nples discussed above, and to help students\nwork toward answers, solutions, or novelquestions. Several examples relating to the",
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              "text": "Letters NATure GeNeTicsIn our testing dataset, 19.8% of participants were at   threefold \nincreased risk for at least 1 of the 5 diseases studied (Table 2).\nThe potential to identify individuals at significantly higher genetic \nrisk, across a wide range of common diseases and at any age, poses a number of opportunities and challenges for clinical medicine.\nWhere effective prevention or early detection strategies are \navailable, key issues will include the allocation of attention and",
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              "text": "genetic risks of disease on risk-reducing health behaviour: Systematic \nreview with meta-analysis. BMJ. 2016;352:i1102.\n57. Vernarelli JA. Impact of genetic risk assessment on nutrition-related life-\nstyle behaviours. Proc Nutr Soc . 2013;72(1):153159.\n58. Marteau TM, French DP , Griffin SJ, et  al. Effects of communicating DNA-\nbased disease risk estimates on risk-reducing behaviours. Cochrane \nDatabase Syst Rev . 2010;(10). \n59. National Human Genome Research Institute. All about The Human",
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              "text": "personalized screening based on age and \npolygenic risk profile.\n12 Pashayan N, Pharoah P. Translating genomics \ninto improved population screening: hype or \nhope? Hum. Genet.  130(1), 1921 (2011).\n13 Pharoah PD, Antoniou A, Bobrow M, \nZimmern RL, Easton DF, Ponder BA. Polygenic susceptibility to breast cancer and \nimplications for prevention. Nat. Genet.  31(1), \n3336 (2002).\nnn\t Examines the potential for prediction of \nrisk based on common genetic variation and \ncompares this with the prediction that",
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              "text": "Eur J Hum Genet.\n12. Janssens AC, van Duijn CM (2008) Genome-based prediction of common\ndiseases: advances and prospects. Hum Mol Genet 17: R166173.\n13. Wray NR, Goddard ME, Visscher PM (2007) Prediction of individual genetic\nrisk to disease from genome-wide association studies. Genome Res 17:15201528.\n14. Wray NR, Goddard ME, Visscher PM (2008) Prediction of individual genetic\nrisk of complex disease. Curr Opin Genet Dev 18: 257263.\n15. Jakobsdottir J, Gorin MB, Conley YP, Ferrell RE, Weeks DE (2009)",
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              "text": "deposited in the GeneNetwork website (http://www.genenetwork.org) so that other\ninvestigators can look for correlations between gene expression patterns and phenotypic traits.\nThe GeneNetwork is an open resource and consists of a set of linked resources for systems\ngenetics. It has been designed for integration of networks of genes, transcripts, and traits such\nas toxicity, cancer susceptibility, and behavior for several species. Phenotypic QTLs using the",
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              "text": "genetics approaches can not only provide insights into the roles of \nindividual genes or developmental pathways but also illuminate \nrelationships between different levels of a biologic system, such as \nthe genome, transcriptome, and phenome [ 10]. One such resource \nof systems genetics is the GeneNetwork website and resource \n(www.genenetwork.org ) that provides access to a wide variety of \ndata such as genotypes (e.g., SNPs), phenotypes that are obtained",
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              "content": "Scientists use GeneNetwork.org to study differences in gene expression by leveraging a variety of analytical tools and datasets available on the platform. GeneNetwork provides access to large published phenotype and genome datasets from several species, allowing for exploratory and statistical analysis [2]. The platform includes microarray data of gene expression in the brain and other phenotypes, which can be used to compare traits across different datasets [1]. \n\nGeneNetwork also facilitates the comparison of gene expression patterns with drug responses and other phenotypic data, making it practical for identifying candidate genes for complex traits through QTL analyses [2], [4]. The platform supports correlation and network analysis to compare associations between tissues and across rodent or human datasets, which is useful for systems genetics mapping [5]. \n\nAdditionally, bioinformatic analyses on GeneNetwork.org include tools for gene ontology, presence of cis-regulation or polymorphisms, phenotype correlations, and principal component analyses, which help in evaluating differentially expressed genes and understanding distinct biological processes [10].",
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              "text": "GeneNetwork provides users with an array of analyticaltools to compare a given trait with a number of data setsavailable from other experimenters. Microarray data ofgene expression in the brain and data of other phenotypes\nare two such examples of possible tools. For this study, we",
              "title": "2008 - Genetic Analysis of Posterior Medial Barrel Subfield Size.pdf",
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              "text": "al., 2005). GeneNetwork is designed primarily as a web service for exploratory and\nstatistical analysis of large published phenotype and genome datasets, and includes data\nfrom several species (see Supplementary Discussion). GeneNetwork includes extensive\nphenotype data extracted from the literature and submitted by users, which makes it\npractical to compare data on drug responses with gene expression patterns. Gene expression",
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              "text": "data are entered into GeneNetwork after they have been shepherded through a system like\nPhenoGen that has extensive capabilities for normalization and quality control. A\ncomparison of the brain gene expression datasets and some of the tools for data analysis\navailable on PhenoGen and GeneNetwork is shown in Table 3, and more detailed\ninformation on features provided by each site is outlined in the Supplementary DiscussionHoffman et al. Page 5\nAddict Biol . Author manuscript; available in PMC 2012 July 1.",
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              "text": "(description of GeneNetwork provided by Dr. Robert W. Williams). Both of these websites\nfocus to a large extent on correlations of behavioral phenotype with gene expression levels\nin recombinant inbred and inbred panels of mice and rats, and on QTL analyses, as a means\nto identify candidate genes for complex traits. What distinguishes PhenoGen, in addition to\nthe tools for raw expression data analysis described above, is that the user can not only",
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              "text": "including correlation and network analysis to compare associations \nbetween tissues and between other rodent or human data sets[32]\nMany of the Data Sets are amenable to systems genetics mapping and other methods and are accessible at GeneNetwork. The Description and Usage column provides details about the data set and potential",
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              "text": "including correlation and network analysis to compare associations \nbetween tissues and between other rodent or human data sets[32]\nMany of the Data Sets are amenable to systems genetics mapping and other methods and are accessible at GeneNetwork. The Description and Usage column provides details about the data set and potential",
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              "text": "by example in the Supplementary Methods, and in the Users Manual that can be\ndownloaded from the website.\nThere are a number of databases that investigators can use to assist in various aspects of\ngene expression data storage and mining (e.g., (Chesler et al., 2005; Galperin and Cochrane,\n2009; Gentleman et al., 2004; Mailman et al., 2007; Saal et al., 2002; Swertz et al., 2010)).\nOne relatively well-known database is GeneNetwork (www.genenetwork.org) (Chesler et",
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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": "from co-regulation patterns found within tens of thousands of samples\nfor which gene expression was measured. GeneNetwork provid es un-\nprecedented resolution and predictive power across multip le cell types\nand tissues. Analogous to discovering patterns in expressi on data, the\nnetwork of protein-protein interactions can also be comput ationally pre-\ndicted using various methods[381].\nThe combined current knowledge of how cells control functio ns",
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              "text": "differentially expressed were further evaluated. Bioinformatic analyses were predominantly \nperformed using tools available at GeneNetwork. org, and included gene ontology, presence of cis-\nregulation or polymorphisms, phenotype correlations, and principal component analyses. \nComparisons of differential gene expression between groups showed little overlap. Gene Ontology \ndemonstrated distinct biological processes in each group with the combined exposure (RSE) being",
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              "content": "Yes, GeneNetwork.org can be used to learn about genetic influences on behavior. It is a comprehensive resource equipped with tools and features for studying genetic correlates to neurobehavioral phenotypes [5]. The platform includes a phenotype database with data on behavioral traits, among others, which can be used for correlation and network analyses to identify relationships with genetic data [4]. Additionally, GeneNetwork focuses on correlations of behavioral phenotypes with gene expression levels in recombinant inbred and inbred panels of mice and rats, which helps in identifying candidate genes for complex traits [6]. The resource is designed for the multivariate genetic analysis of complex traits, including behavior, in genetic reference populations [9].",
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              "text": "GeneNetwork.org is also a valuable\nteaching tool. While mainly designed for\nresearchers interested in testing gene-to-\nphenotype relationships, GeneNetwork.\norghas been adapted for dry-lab teaching\nin neuroscience and genetics ( Grisham et\nal., 2017 ). A useful approach is to assign\nsets of vetted questions, such as the exam-\nples discussed above, and to help students\nwork toward answers, solutions, or novelquestions. Several examples relating to the",
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              "text": "GeneNetwork.org is also a valuable\nteaching tool. While mainly designed for\nresearchers interested in testing gene-to-\nphenotype relationships, GeneNetwork.\norghas been adapted for dry-lab teaching\nin neuroscience and genetics ( Grisham et\nal., 2017 ). A useful approach is to assign\nsets of vetted questions, such as the exam-\nples discussed above, and to help students\nwork toward answers, solutions, or novelquestions. Several examples relating to the",
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              "text": "Category 1: Web Resources for Online Analysis\nof the Genetics of Alcoholism and More\nGeneNetwork \n(www.genenetwork.org): This is a comprehensive\nresource for learning about genetics, but users may",
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              "text": "GeneNetwork also features a phenotype database, a public repository of data from over 700\ntraits previously measured across several laboratories in BXD RI (and other) strains. These\ninclude behavioral, biochemical, and anatomical traits. The data consist of strain means, not\nraw data from individual mice, and so we use the term genetic correlation. Using this\ndatabase, we performed correlation and network analyses to identify relationships with",
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              "text": "biological function of the new gene list. As mentioned previously, GeneNetwork\n(www.genenetwork.org) is a collaborative Web-based resource equipped with tools and\nfeatures for studying gene/gene and exploring genetic correlates to neurobehavioral\nphenotypes (Chesler et al., 2003, 2004). The Web site is home to a growing collection of\ngene expression and phenotypic data from a variety of species and brain regions, with a host",
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              "text": "(description of GeneNetwork provided by Dr. Robert W. Williams). Both of these websites\nfocus to a large extent on correlations of behavioral phenotype with gene expression levels\nin recombinant inbred and inbred panels of mice and rats, and on QTL analyses, as a means\nto identify candidate genes for complex traits. What distinguishes PhenoGen, in addition to\nthe tools for raw expression data analysis described above, is that the user can not only",
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              "text": "with another database, GeneNetwork, correlating behavioral phenotypes with geneO'Brien et al. Page 11\nInt Rev Neurobiol . Author manuscript; available in PMC 2014 July 21.\nNIH-PA Author Manuscript NIH-PA Author Manuscript NIH-PA Author Manuscript",
              "title": "2012 - Using Genome-Wide Expression Profiling to Define Gene Networks Relevant to the Study of Complex Traits From RNA Integrity to Network Topology.pdf",
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              "text": "interested in behavioral variation and in ways to exploit bioinformatic resources and \nmethods to dissect and (we hope) reassemble and model behavior. You do not need to be a \nstatistician or geneticist to use these tools.\nIn order to use GeneNetwork, we have to start with some ground rules and assumptions. The \nfirst is that behavioral traits must vary significantly. This is a chapter about behavioral \nvariation  with an equal emphasis on both words. If a behavior is a \"fixed action pattern\" that",
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              "text": "facilitated through the development of GeneNetwork(www.genenetwork.org), an Inte rnet resource for the multi-\nvariate genetic analysis of complex traits in genetic reference\npopulations (Chesler\net al. 2003, 2004; Wang et al. 2003).\nGeneNetwork aids in identication of candidate genesand bio-molecular mechanisms underlying addiction-relatedphenotypes and includes a wealth of data on mRNAexpression proles from various tissues of the centralnervous system (Chesler\net al. 2005; Peirce et al. 2006;",
              "title": "2009 - High\u2010throughput behavioral phenotyping in the expanded panel of BXD recombinant inbred strains.pdf",
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              "text": "deposited in the GeneNetwork website (http://www.genenetwork.org) so that other\ninvestigators can look for correlations between gene expression patterns and phenotypic traits.\nThe GeneNetwork is an open resource and consists of a set of linked resources for systems\ngenetics. It has been designed for integration of networks of genes, transcripts, and traits such\nas toxicity, cancer susceptibility, and behavior for several species. Phenotypic QTLs using the",
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              "content": "GeneNetwork.org plays a significant role in personalized medicine by serving as an open-access, online data analysis resource for systems biology and systems genetics [1]. It is a tool for systems genetics and predictive medicine, which aims to predict and potentially avoid phenotypic outcomes such as diseases [2]. The platform supports the integration of networks of genes, transcripts, and traits, which is crucial for understanding complex genetic interactions and their implications for personalized medicine [10]. Additionally, GeneNetwork.org facilitates the comparison of data on drug responses with gene expression patterns, which is essential for tailoring therapeutic strategies to individual genetic profiles [9].",
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              "text": "is tackling this immense challenge bystudying networks of genes, proteins,metabolites, and other biomarkers thatrepresent models of genuine biologicalpathways. Studying complex diseasesin terms of gene networks rather thanindividual genes or genomic loci shouldaid in uncovering disease genes. Withthis approach, the effects of multiplegenes in the network are combined,producing a stronger signal and reducingthe number of statistical tests of associ-ation that must be performed.",
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              "text": "genotypes and phenotypes, geneticists hope to discover and interpret the network of causal\ngenotype-phenotype relationships that determine a trait of interest.\nSystems genetics research often follows a workow of nding a gene network, nding\nregulators of that network, and then performing a focused ge ne perturbation experiment\nto determine the role of the associated network on gene expre ssion or function. To be-\ngin, a large gene correlation graph must be sifted through , to nd a highly connected",
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              "text": "genetics approaches can not only provide insights into the roles of \nindividual genes or developmental pathways but also illuminate \nrelationships between different levels of a biologic system, such as \nthe genome, transcriptome, and phenome [ 10]. One such resource \nof systems genetics is the GeneNetwork website and resource \n(www.genenetwork.org ) that provides access to a wide variety of \ndata such as genotypes (e.g., SNPs), phenotypes that are obtained",
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              "text": "the risk of missing important biological phenomena [43].  \n \n8.4  Defining gene and QTL networks \nIn addition to the genetic dissection of phenotypic  variation using QTL mapping \ntechniques, systems geneticists are interested in r econstructing the biological net- \nworks that connect genes, proteins and other traits  based on their observed genetic \n(co-)variation. In this context, biological network s are often defined by graphical",
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              "text": "GeneNetwork http://www.genenetwork.org is anexample of a bioinformatics tool that can be used to\nexplore systems genetics data.\nThe importance of defining biological networks and\npredicting molecular interactions has been emphasized\nby several reports [1,2]. Such studies emphasize that\nwhen knowledge about DNA variation within popula-\ntions is interfaced with data on gene expression, protein\ninteractions and DNA-protein binding, biological\nnetworks can be constructed that are predictive of the",
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              "text": "It is important to integrate the gene variants and environmental factors to the trait to understand the network controlling that trait.  In systems genetics approach, different trait networks are related to different networks of gene and environmental variants to find global genetic modulation of the complex phenotype. The availability of genetic reference panels makes it easy to acquire diverse phenotypic data and advanced computational models make it possible to analyse their relationship.   2.2.1.",
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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": "genetic variants (SNPs, insertions, deletions, duplications, etc.) that segregate in the family [ 13]. The\nstrains are appropriate for systems genetics /systems biology analysis [ 14], genetic mapping and genetic\ncorrelations of parameter means, and thus constitute an ideal platform for toxicogenomic research [ 15].\nAll data are available at www.genenetwork.org. GeneNetwork exists in two forms, GN1 and GN2 [ 16].\nGN2 is an expansion and renement of the features of GN1. A tutorial of how to use GN1 may be",
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              "content": "Researchers identify important genes for certain traits using GeneNetwork.org through a series of steps and tools provided by the platform:\n\n1. **Data Selection and Trait Mining**: Researchers begin by selecting a data set and mining it for traits of interest based on user search queries [1]. This involves using the main search page to query specific data sets and identify traits that are relevant to their study.\n\n2. **Trait Collection and Analysis**: Once traits are identified, they are selected and placed in a collection for further inspection and quantitative analysis [1]. This allows researchers to organize and focus on specific traits for deeper investigation.\n\n3. **Advanced Search Options**: GeneNetwork offers advanced search options that enable researchers to query data sets for specific genomic intervals and locate traits with the highest likelihood ratio statistic (LRS) values, which are indicative of strong genetic associations [4].\n\n4. **Correlation and Genetic Linkage Mapping**: Researchers can establish associations between transcript abundance, phenotypic traits, and genotype using correlation or genetic linkage mapping functions [5]. This helps in identifying candidate genes linked to specific traits.\n\n5. **QTL Analysis and Network Graphs**: The platform allows for the generation of quantitative trait loci (QTL) analyses, network graphs, and correlation matrices, which are essential for understanding the genetic architecture of complex traits [3].\n\nBy utilizing these tools and processes, researchers can effectively identify and analyze genes that are important for specific traits using GeneNetwork.org.",
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              "text": "Fig. 2. \nGeneNetwork main search page and organization. Most analyses in GeneNetwork will \nfollow the steps shown in panels A  through D. In this workfl ow, a data set is selected ( A) \nand mined for traits of interest based on user search queries ( B). Traits are then selected \nfrom the search ( C) and placed in a collection for further inspection and quantitative analysis \n(D). The banner menu contains additional search options and helpful resources under the",
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              "text": "Fig. 2. \nGeneNetwork main search page and organization. Most analyses in GeneNetwork will \nfollow the steps shown in panels A  through D. In this workfl ow, a data set is selected ( A) \nand mined for traits of interest based on user search queries ( B). Traits are then selected \nfrom the search ( C) and placed in a collection for further inspection and quantitative analysis \n(D). The banner menu contains additional search options and helpful resources under the",
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              "text": "Another powerful feature of GeneNetwork is the ability to create and analyze whole collections \nof data. In Figure 3  there are boxes within the table that can be selected in order to form a trait \ncollection. To do this, select the boxes in the table that su it the interests of the study, and press \nAdd. This function allows groups of traits to be saved for later analysis such as the generation of \na QTL, a network graph, and correlation matrix, some of which will be investigated further in",
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              "text": "analysis in GeneNetwork, but there is an even more direct way to answer the same question. \nIt is possible to query data sets in GeneNetwork from the Select and Search  page using \nadvanced options to locate the highest trait LRS values for any genomic interval, in this case \nthe region within 2 Mb of Comt . (Note: You can explore this and other search options \nfurther by clicking the Advanced Search  button and reading the section Advanced",
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              "text": "is shown in Figure 1A. Associations between transcript\nabundance, phenotypic traits and genotype can be estab-\nlished either using correlation or genetic linkage mapping\nfunctions [29,30]. The main page of GeneNetwork at\nhttp://www.genenetwork.org  provides access to subsets of\ndata through pull-down menus that allow specific data\nsets to be queried. The datasets can be further restricted\nusing a single text box for specific database entries to\nquery probe set or trait ID, or annotations associated with",
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              "text": "genetic mapping, and correlation of quantitative traits such as gene expression data and behavioral parameters (Wang  et al, 2003) . GeneNetwork employs \ngenotype data from 3809 markers, selected based on their being informative (i.e., different between progenitor strains). GeneNetwork outputs peak likelihood ratio statistic (LRS) locations for each trait, whic h can be directly converted to",
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              "text": "GeneNetwork provides users with an array of analyticaltools to compare a given trait with a number of data setsavailable from other experimenters. Microarray data ofgene expression in the brain and data of other phenotypes\nare two such examples of possible tools. For this study, we",
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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": "(description of GeneNetwork provided by Dr. Robert W. Williams). Both of these websites\nfocus to a large extent on correlations of behavioral phenotype with gene expression levels\nin recombinant inbred and inbred panels of mice and rats, and on QTL analyses, as a means\nto identify candidate genes for complex traits. What distinguishes PhenoGen, in addition to\nthe tools for raw expression data analysis described above, is that the user can not only",
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              "text": "of links to external resources for tracing the interrelationships of a gene among multiple\nWeb-based resources. GeneNetwork also offers a number of correlation and mapping\nstrategies for assessing associations among multiple genes and QTLs. GeneNetwork aims to\nmake the study of complex traits through the use of systems genetics widely available to the\nscientific community. A powerful tool that can be integrated with GeneNetwork or used on",
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              "content": "GeneNetwork.org can assist in understanding complex traits like height or intelligence through several key features:\n\n1. **Analytical Tools and Data Sets**: GeneNetwork provides a variety of analytical tools that allow users to compare traits with numerous datasets available from other researchers. This includes microarray data of gene expression in the brain and other phenotypic data, which can be crucial for studying complex traits [1].\n\n2. **Systems Genetics Approach**: The platform offers a systems genetics approach, which helps illuminate the relationships between different biological system levels, such as the genome, transcriptome, and phenome. This comprehensive view can provide insights into the roles of individual genes and developmental pathways involved in complex traits [2].\n\n3. **Correlation and Genetic Linkage Mapping**: GeneNetwork allows for the establishment of associations between transcript abundance, phenotypic traits, and genotype using correlation or genetic linkage mapping functions. This can help identify genetic factors contributing to complex traits like height or intelligence [6].\n\n4. **Data Mining and Trait Correlations**: The platform can be used to study correlations between traits and perform data mining in genomic regions containing candidates for quantitative trait genes. This feature is particularly useful for identifying genetic components of complex traits [4].\n\n5. **Multi-Omics Analysis**: GeneNetwork has been updated to include multi-omics analysis, which integrates various types of biological data. This holistic approach can enhance the understanding of complex traits by considering multiple layers of biological information [7].\n\nOverall, GeneNetwork.org provides a comprehensive suite of tools and data that can facilitate the exploration and understanding of complex traits like height and intelligence through a systems genetics framework.",
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              "text": "genetics approaches can not only provide insights into the roles of \nindividual genes or developmental pathways but also illuminate \nrelationships between different levels of a biologic system, such as \nthe genome, transcriptome, and phenome [ 10]. One such resource \nof systems genetics is the GeneNetwork website and resource \n(www.genenetwork.org ) that provides access to a wide variety of \ndata such as genotypes (e.g., SNPs), phenotypes that are obtained",
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              "text": "201 5Nature America, Inc.  All rights reserved.\n6 ADVANCE ONLINE PUBLICATION  Nature Ge Neticsa n a ly s i s\n11. Yang, J. et al. Common SNPs explain a large proportion of the heritability for human \nheight. Nat. Genet.  42, 565569 (2010).\n12. Yang, J., Lee, S.H., Goddard, M.E. & Visscher, P.M. GCTA: a tool for genome-wide \ncomplex trait analysis. Am. J. Hum. Genet.  88, 7682 (2011).\n13. Lee, S.H., Yang, J., Goddard, M.E., Visscher, P.M. & Wray, N.R. Estimation of",
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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": "medicine.\nGeneNetwork.org is a tool for quantitative genetics that started in 2001 as WebQTL [38]. It\nevolved from analyses of forward genetics in the BXD mouse family, to phenome-wide\nassociation studies and reverse genetics in a variety of species. Although GeneNetwork contains\ndata for many species and populations, it most prominently contains data for the BXD family.\nOver 10,000 classical phenotypes, measured under a variety of environmental conditions, and",
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              "content": "Yes, there are known genetic mutations that cause premature aging. Some specific genetic syndromes associated with premature aging include:\n\n1. Hutchinson-Gilford Progeria Syndrome, which is caused by mutations in the LMNA gene [4].\n2. Rothmund-Thomson syndrome and related disorders, which are associated with mutations in the RECQL4 gene [4].\n3. Ataxia-telangiectasia, which is another genetic disorder linked to premature aging [4].\n\nAdditionally, Martin (1978) listed 162 genetic syndromes in humans that exhibit some or many signs of premature aging [1]. These conditions highlight the connection between genetic mutations and premature aging.",
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              "text": "logical phenomena is often facilitated by the \nstudy of genetic mutants, and, in the case of \nhumans, genetic disorders. Accordingly, a search \nwas made, over the years, for genetic disorders \ncharacterized by premature aging. If DNA dam- \nage and repair has anything to do with aging it \nshould be evidenced in such individuals. Martin \n(1978) listed 162 genetic syndromes in humans with some or many signs of premature aging. \nAbout 21 feahares are considered as markers for",
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              "text": "[315] Szilard, L. On the nature of the aging process. Proc. Natl. Acad. Sci. USA\n45:3545; 1959.\n[316] Vijg, J.; Dolle, M. E. Large genome rearrangements as a primary cause of\naging. Mech. Ageing Dev. 123:907915; 2002.\n[317] Vijg, J. Somatic mutations and aging: a re-evaluation. Mutat. Res.\n447:117135; 2000.\n[318] Martin, G. M. Genetic syndromes in Man with potential relevance to the\npathobiology of aging. Birth Defects Orig. Artic. Ser. 14:539; 1978.",
              "title": "2007 - Trends in oxidative aging theories.pdf",
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              "text": "19\n 6. Milholland B, Suh Y , Vijg J.Mutation and catastrophe in the aging genome. Exp Gerontol. \n2017;94:3440.\n 7. Maslov AY , Ganapathi S, Westerhof M, Quispe-Tintaya W, White RR, Van Houten B, etal. \nDNA damage in normally and prematurely aged mice. Aging Cell. 2013;12:46777.\n 8. Blokzijl F, de Ligt J, Jager M, Sasselli V , Roerink S, Sasaki N, etal. Tissue-specific mutation \naccumulation in human adult stem cells during life. Nature. 2016;538:2604.",
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              "text": "143 Gonzalo S, Kreienkamp R & Askjaer P (2017) Hutchinson -Gilford Progeria \nSyndrome: A premature aging disease caused by LMNA gene mutations. \nAgeing Res. Rev.  33, 1829. \n144 Lu L, Jin W & Wang LL (2017) Aging in Ro thmund -Thomson syndrome and \nrelated RECQL4 genetic disorders. Ageing Res. Rev.  33, 3035. \n145 de Renty C & Ellis NA (2017) Blooms syndrome: Why not premature aging? \nAgeing Res. Rev.  33, 3651. \n146 Shiloh Y & Lederman HM (2017) Ataxia -telangiectasia (A -T): An emerging",
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              "text": "genetic disease model of premature aging, In: Harrison,D.E., eds, Genetic Effects on Aging II (Telford Press, Caldwell,NJ), pp. 521542.\n[2] Djawdan, M., Sugiyama, T., Schlaeger, L., Bradley, T.J. and\nRose, M.R. (1996) Metabolic aspects of the trade-off between\nfecundity and longevity in Drosophila melanogaster ,Physiol.\nZool. 69, 11751195.\n[3] Fleming, J.E., Spicer, G.S., Garrison, R.C. and Rose, M.R.",
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              "text": "genes of a whole chromosome ineffective, couldbe a main causal factor in aging (Szilard, 1959).According to Maynard Smith, such types of mu-tations do not seem likely to be common enoughto be the main cause of aging. However, at thetime quantitative information on the possible age-related accumulation of different types of muta-tions in various tissues of mammals wascompletely lacking. The question, therefore,whether somatic mutations are a cause of aging,has not been resolved, more than four decadesafter",
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              "text": "features of premature aging (16, 17). Subsequent experiments conrmed that mitochondrial DNA\nmutations and deletions were the driving force behind the observed accelerated aging phenotypes(18).\nTHE LINK BETWEEN NUCLEAR GENOME INTEGRITY\nAND PREMATURE AGING\nThe notion that the majority of currently identied progeria syndromes originate from defects\nin genome maintenance highlights the importance of the condition of DNA in the process of",
              "title": "2016 - Genome Integrity in Aging.pdf",
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              "text": "Tryggvason K,ZhouZ.Genomicinstability inlaminopathy based\npremature aging,NatMed. 2005;11:780 785.\n13.MisteliT,ScaffidiP.Genomeinstability inprogeria:when\nrepairgetsold,NatMed. 2005;11:718 719.\n14.PereiraS,Bourgeois P,NavarroC,EstevesVieiraV,CauP,De\nSandreGiovannoli A,LvyN.HGPSandrelatedpremature aging\ndisorders: Fromgenomicidentification tothefirsttherapeutic \napproaches, MechAgeingDev.2008;129:449 459.\n15.SmithED,Kudlow BA,FrockRL,KennedyBK.Atypenuclear",
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              "text": "Nature Genetics | Volume 55 | February 2023 | 268279 278\nArticle https://doi.org/10.1038/s41588-022-01279-621. Tiwari, V. & Wilson, D. M. 3rd. DNA damage and associated DNA \nrepair defects in disease and premature aging. Am. J. Hum. Genet.  \n105, 237257 (2019).\n22. Tamae, D., Lim, P., Wuenschell, G. E. & Termini, J. Mutagenesis and repair induced by the DNA advanced glycation end product N2-1-(carboxyethyl)-2-deoxyguanosine in human cells. Biochemistry  \n50, 23212329 (2011).",
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              "text": "resources, gene expression pro les, and gene network\nconstructions, methods for the analysis of gene function\nhave been revolutionised in the past few years. One great\nresource for the analysis of gene networks is the databaseGeneNetwork, which consists of a set of linked resources for\nsystems genetics (Andreux et al., 2012). It has been designed\nfor multiple scale integration of networks of genes,transcripts in multiple tissues. GeneNetwork is an interac-",
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              "text": "files on GeneNetwork) will also reduce the energy barrier of adopting powerful systems \ngenetics and systems behavioral approaches. Web services such as GeneNetwork and its \ncompanionsGeneWeaver ( Baker et al., 2012 ), WebGestalt ( Zhang et al., 2005 ), DAVID \n(Huang et al., 2009a ; Huang et al., 2009b ), and the Allen Brain Atlas ( Lein et al., 2007 )\ncan now be used as virtual and free laboratories to test specific biological hypothesis, or they \ncan be used to generate new ideas ab initio .",
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              "text": "Its use is centred upon user-specied genes and can identify novel\npotential master regulatory genes for further investigation. We are\nworking to increase the functionality and power of the GeneNet-\nwork and systems genetics further in a number of areas. In partic-\nular, increasing the number of strains studied can increase the\nmapping resolution. By increasing the genetic diversity of the\nfounders of an RI set, the potential for observing regulatory poly-",
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              "content": "To access and use the data available on GeneNetwork.org, you can follow these steps:\n\n1. **Navigating to the Website**: Start by visiting the GeneNetwork website at www.genenetwork.org [8].\n\n2. **Searching for Data**: There are two primary ways to search for data on GeneNetwork:\n   - Use the global search bar located at the top of the page. This feature allows you to search for genes, mRNAs, or proteins across all datasets, providing data across various species, groups, and types of data [5].\n   - Alternatively, you can follow the main search workflow, which involves selecting a dataset, mining it for traits of interest based on user search queries, selecting traits from the search, and placing them in a collection for further inspection and quantitative analysis [3].\n\n3. **Analyzing Data**: Once you have selected the data, GeneNetwork provides an analytical environment where you can perform correlation analysis and linkage mapping. This environment helps identify and substantiate gene targets for further research [7].\n\n4. **Accessing Genotype Files**: If you need genotype files, they can be accessed directly via a specific URL: http://www.genenetwork.org/webqtl/main.py?FormID=sharinginfo&GN_AccessionId=600 [2].\n\n5. **Using Additional Resources**: The banner menu on the main search page contains additional search options and helpful resources to assist with your analyses [3].\n\nBy following these steps, you can effectively access and utilize the data available on GeneNetwork.org for your research needs.",
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              "text": "Fig. 2. \nGeneNetwork main search page and organization. Most analyses in GeneNetwork will \nfollow the steps shown in panels A  through D. In this workfl ow, a data set is selected ( A) \nand mined for traits of interest based on user search queries ( B). Traits are then selected \nfrom the search ( C) and placed in a collection for further inspection and quantitative analysis \n(D). The banner menu contains additional search options and helpful resources under the",
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              "text": "Fig. 2. \nGeneNetwork main search page and organization. Most analyses in GeneNetwork will \nfollow the steps shown in panels A  through D. In this workfl ow, a data set is selected ( A) \nand mined for traits of interest based on user search queries ( B). Traits are then selected \nfrom the search ( C) and placed in a collection for further inspection and quantitative analysis \n(D). The banner menu contains additional search options and helpful resources under the",
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              "text": "1. Data  \nOnce you have navigated to genenetwork.org, t here are two ways to search for data in GN. The \nfirst is to use the global search bar located at the top of the page  (Figure 1 ). This is a new \nfeature in GN that allows researchers to search for genes, mRNAs, or proteins across all of the \ndatasets. This will give the user data for that search term  across many different species, groups, \nand types of data. Because of this, the global search bar is a good area to start ones searches if",
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              "text": "data are entered into GeneNetwork after they have been shepherded through a system like\nPhenoGen that has extensive capabilities for normalization and quality control. A\ncomparison of the brain gene expression datasets and some of the tools for data analysis\navailable on PhenoGen and GeneNetwork is shown in Table 3, and more detailed\ninformation on features provided by each site is outlined in the Supplementary DiscussionHoffman et al. Page 5\nAddict Biol . Author manuscript; available in PMC 2012 July 1.",
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              "text": "abundance data sets directly within GeneNetwork's ana-\nlytical environment we provide simple web access to the\ndata for the research community. In this environment, a\ncombination of correlation analysis and linkage mapping\nprovides the potential to identify and substantiate gene\ntargets for saturation mapping and positional cloning. By\nintegrating datasets from an unsequenced crop plant (bar-\nley) in a database that has been designed for an animal\nmodel species (mouse) with well established genome",
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              "text": "need to read the help files, FAQs, or one of the references(Chesler et al., 2003; Grisham et al., 2010, www.lifescied.org/content/9/2/98.full.pdf). GeneNetwork is one ofan interlinked trio of sites built up by NIAAA (GeneWeaverand WebGestalt are the other two) to house extensivedata for human, monkey, rat, mouse, and fruit fly. Itincludes hundreds of data sets on responsesto alcohol,particularly in a family of mice called the BXDs. Dataare linked with powerful gene analysis and mappingtools. Think of it as",
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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": "data are entered into GeneNetwork after they have been shepherded through a system like\nPhenoGen that has extensive capabilities for normalization and quality control. A\ncomparison of the brain gene expression datasets and some of the tools for data analysis\navailable on PhenoGen and GeneNetwork is shown in Table 3, and more detailed\ninformation on features provided by each site is outlined in the Supplementary DiscussionHoffman et al. Page 5\nAddict Biol . Author manuscript; available in PMC 2012 July 1.",
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              "text": "thank the members of the GeneNetwork.org team for their assistance, excellent data curation, and\ninformatics support.\nConicts of Interest: The authors declare no conict of interest.\nReferences\n1. Wilkinson, M.D.; Dumontier, M.; Aalbersberg, I.J.; Appleton, G.; Axton, M.; Baak, A.; Blomberg, N.; Boiten, J.W.; da Silva Santos,\nL.B.; Bourne, P .E.; et al. The FAIR Guiding Principles for scientic data management and stewardship. Sci. Data 2016 ,3, 160018.\n[CrossRef]",
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              "text": "thank the members of the GeneNetwork.org team for their assistance, excellent data curation, and\ninformatics support.\nConicts of Interest: The authors declare no conict of interest.\nReferences\n1. Wilkinson, M.D.; Dumontier, M.; Aalbersberg, I.J.; Appleton, G.; Axton, M.; Baak, A.; Blomberg, N.; Boiten, J.W.; da Silva Santos,\nL.B.; Bourne, P .E.; et al. The FAIR Guiding Principles for scientic data management and stewardship. Sci. Data 2016 ,3, 160018.\n[CrossRef]",
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              "text": "1.    Phenotypic data should be quality checked and preprocessed \nbefore being uploaded to GeneNetwork. This includes nor-\nmalization of data, removal of outliers or windsorization, even-\ntually transformation of data to obtain normal distribution.   \n   2.    When uploading data to GeneNetwork for permanent and \npublic storage, make sure to follow the GeneNetwork naming \nguide for phenotypes.   \n   3.    When uploading your own data make sure that for any pheno-",
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              "text": "analysis of behavior and for neurologic diseases are provided in\nthe study by\nMulligan et al. (2017) .\nGeneNetwork.org is committed to data and code workflows\nthat are FAIR compliant, ensuring that those who generate data\nand key ideas get the deserved credit. To further ensure effective\nand secure dissemination of data and ideas, as well as improved\nreproducibility, the GeneNetwork.org infrastructure is currently\nbeing redesigned using more modular structures and APIs that",
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              "content": "The difference between looking at one gene and studying a whole gene network lies in the scope and context of the analysis. When examining a single gene, the focus is on understanding the role and function of that specific gene, often in isolation. This approach can be limited because it does not consider the interactions and relationships that gene may have with others.\n\nIn contrast, studying a whole gene network involves analyzing a system of interconnected genes, which provides a broader context. A gene network is a graphical model where nodes represent genes or gene products, and edges represent interactions between them [1]. This approach allows researchers to explore how multiple genes interact within biological pathways, potentially uncovering complex relationships and combined effects that a single-gene analysis might miss [2]. By studying gene networks, researchers can gain insights into the collective behavior of genes, which can be crucial for understanding complex diseases and biological functions [2]. Additionally, gene networks can help identify highly connected subgraphs that correspond to biologically relevant networks, aiding in the identification of causative genes and their regulatory roles [5].",
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              "text": "is tackling this immense challenge bystudying networks of genes, proteins,metabolites, and other biomarkers thatrepresent models of genuine biologicalpathways. Studying complex diseasesin terms of gene networks rather thanindividual genes or genomic loci shouldaid in uncovering disease genes. Withthis approach, the effects of multiplegenes in the network are combined,producing a stronger signal and reducingthe number of statistical tests of associ-ation that must be performed.",
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              "text": "traditional genetical genomics approaches. It should also be noted that our approach is\ndifferent from studying gene-gene regulation within a pathway, which focuses on the\ninteractive activities of individual gene pairs genes within a pathway.\nA biological pathway is defined as a series of molecular interactions and reactions. If there\nare subtle changes in the expression level of a few genes located in the upper cascade of a",
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              "text": "genes rapidly that may be in the same genetic network as the gene you are interested in. Then you need to validate the role of that gene and to identify its function in that network. The point is this is a powerful methodology that can provide data in half an hour that allows you to form hypotheses that you can then spend years investigating.\nReference\nLee PD, Ge B, Greenwood CM et al 2006 Mapping cis-acting regulatory variation in recombi-\nnant congenic strains. Physiol Genomics 25:294302",
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              "text": "Confronted with this daunting complexity, the field often \nprogresses in small steps. A study may identify one or two relevant genes and assess their interactions with other factors. Gradually, genetic knowledge from many studies then can be assembled into a larger system of interactants that enables us to understand a set of related behaviors. We term this perspective behavioral genomics ( Fig. 2b ).2005 Nature Publishing Group  http://www.nature.com/natureneuroscience",
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              "content": "GeneNetwork.org can contribute to advancements in genetic engineering in several ways:\n\n1. **Predictive Medicine and Systems Genetics**: GeneNetwork.org is an exciting resource for predictive medicine and systems genetics, providing a platform for multi-omics analysis and data from multiple species [1]. This can help in understanding complex genetic interactions and predicting phenotypic outcomes, which are crucial for genetic engineering.\n\n2. **Identification of Master Regulatory Genes**: The platform allows users to identify novel potential master regulatory genes for further investigation [4]. This capability is essential for genetic engineering as it can help in pinpointing key genes that control important traits or pathways.\n\n3. **Increased Mapping Resolution**: By increasing the number of strains studied and the genetic diversity of the founders of a recombinant inbred (RI) set, GeneNetwork.org enhances the mapping resolution [5]. This improvement can lead to more precise genetic modifications and better understanding of genetic variations.\n\n4. **Construction of Biological Networks**: GeneNetwork.org facilitates the construction of biological networks by integrating data on DNA variation, gene expression, protein interactions, and DNA-protein binding [6]. These networks are predictive of molecular interactions, which can guide genetic engineering efforts.\n\n5. **Virtual Laboratory for Hypothesis Testing**: GeneNetwork.org, along with its companion tools, can be used as a virtual and free laboratory to test specific biological hypotheses or generate new ideas [7]. This feature supports experimental design and innovation in genetic engineering.\n\nOverall, GeneNetwork.org provides a comprehensive analytical environment and a wealth of data that can significantly aid in the advancement of genetic engineering by enabling detailed genetic analysis, hypothesis testing, and the discovery of key genetic regulators.",
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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",
              "title": "2020 - Gene network a completely updated tool for systems genetics analyses.pdf",
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              "text": "GeneNetwork.org is also a valuable\nteaching tool. While mainly designed for\nresearchers interested in testing gene-to-\nphenotype relationships, GeneNetwork.\norghas been adapted for dry-lab teaching\nin neuroscience and genetics ( Grisham et\nal., 2017 ). A useful approach is to assign\nsets of vetted questions, such as the exam-\nples discussed above, and to help students\nwork toward answers, solutions, or novelquestions. Several examples relating to the",
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              "text": "GeneNetwork.org is also a valuable\nteaching tool. While mainly designed for\nresearchers interested in testing gene-to-\nphenotype relationships, GeneNetwork.\norghas been adapted for dry-lab teaching\nin neuroscience and genetics ( Grisham et\nal., 2017 ). A useful approach is to assign\nsets of vetted questions, such as the exam-\nples discussed above, and to help students\nwork toward answers, solutions, or novelquestions. Several examples relating to the",
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              "text": "Its use is centred upon user-specied genes and can identify novel\npotential master regulatory genes for further investigation. We are\nworking to increase the functionality and power of the GeneNet-\nwork and systems genetics further in a number of areas. In partic-\nular, increasing the number of strains studied can increase the\nmapping resolution. By increasing the genetic diversity of the\nfounders of an RI set, the potential for observing regulatory poly-",
              "title": "2008 - Systems_genetics_can_provide_new_insight.pdf",
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              "text": "and can identify novel potential master regulatory genes for further investigation. We are working to increase the functionality and power of GeneNetwork and systems genetics in a number of areas. In particular, the mapping resolution can be increased by increasing the number of strains studied. By increasing the genetic diversity of the founders of an RI set, the potential for observing regulatory poly-morphisms increases dramatically. In this context, the availability of 1000 RI strains from The Gene",
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              "text": "GeneNetwork http://www.genenetwork.org is anexample of a bioinformatics tool that can be used to\nexplore systems genetics data.\nThe importance of defining biological networks and\npredicting molecular interactions has been emphasized\nby several reports [1,2]. Such studies emphasize that\nwhen knowledge about DNA variation within popula-\ntions is interfaced with data on gene expression, protein\ninteractions and DNA-protein binding, biological\nnetworks can be constructed that are predictive of the",
              "title": "2010 - Systems genetics analyses predict a transcription role for P2P-R Molecular confirmation that P2P-R is a transcriptional co-repressor.pdf",
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              "text": "files on GeneNetwork) will also reduce the energy barrier of adopting powerful systems \ngenetics and systems behavioral approaches. Web services such as GeneNetwork and its \ncompanionsGeneWeaver ( Baker et al., 2012 ), WebGestalt ( Zhang et al., 2005 ), DAVID \n(Huang et al., 2009a ; Huang et al., 2009b ), and the Allen Brain Atlas ( Lein et al., 2007 )\ncan now be used as virtual and free laboratories to test specific biological hypothesis, or they \ncan be used to generate new ideas ab initio .",
              "title": "2012 - Genetic and Molecular Network Analysis of Behavior.pdf",
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              "text": "within GeneNetwork's analytical en vironment we provide simple we b access to the data for the\nresearch community. In this envi ronment, a combination of correl ation analysis and linkage mapping\nprovides the potential to identify and substant iate gene targets for saturation mapping and\npositional cloning. By integratin g datasets from an unsequenced cr op plant (barley) in a database\nthat has been designed for an  animal model species (mouse) with a well established genome",
              "title": "2008 - Towards systems genetic analyses in barley Integration of phenotypic, expression and genotype data into GeneNetwork.pdf",
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              "text": "need to read the help files, FAQs, or one of the references(Chesler et al., 2003; Grisham et al., 2010, www.lifescied.org/content/9/2/98.full.pdf). GeneNetwork is one ofan interlinked trio of sites built up by NIAAA (GeneWeaverand WebGestalt are the other two) to house extensivedata for human, monkey, rat, mouse, and fruit fly. Itincludes hundreds of data sets on responsesto alcohol,particularly in a family of mice called the BXDs. Dataare linked with powerful gene analysis and mappingtools. Think of it as",
              "title": "2012 - Genetic and Genomic Web Resources for Research on Alcohol Use and Abuse.pdf",
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              "text": "will be convincing both the community and its leaders that it is \ndoable today.\nacKnoWledgmenTs\nThanks to Glenn D. Rosen and Kathryn Graehl for comments and editorial improvements. Thanks also to the NIH for support of many \ndata sets for GRPs now being incorporated into GeneNetwork, a \npilot project in systems genetics.\nreferences\nCarneiro, A. M., Airey, D. C., Thompson,  B., \nZhu, C. B., Lu, L., Chesler, E. J., \nErikson,  K. M., and Blakely, R. D. \n(2009). Functional coding variation",
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