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1.
Bioinformatics ; 35(14): i568-i576, 2019 Jul 15.
Artigo em Inglês | MEDLINE | ID: mdl-31510680

RESUMO

MOTIVATION: Late onset Alzheimer's disease is currently a disease with no known effective treatment options. To better understand disease, new multi-omic data-sets have recently been generated with the goal of identifying molecular causes of disease. However, most analytic studies using these datasets focus on uni-modal analysis of the data. Here, we propose a data driven approach to integrate multiple data types and analytic outcomes to aggregate evidences to support the hypothesis that a gene is a genetic driver of the disease. The main algorithmic contributions of our article are: (i) a general machine learning framework to learn the key characteristics of a few known driver genes from multiple feature sets and identifying other potential driver genes which have similar feature representations, and (ii) A flexible ranking scheme with the ability to integrate external validation in the form of Genome Wide Association Study summary statistics. While we currently focus on demonstrating the effectiveness of the approach using different analytic outcomes from RNA-Seq studies, this method is easily generalizable to other data modalities and analysis types. RESULTS: We demonstrate the utility of our machine learning algorithm on two benchmark multiview datasets by significantly outperforming the baseline approaches in predicting missing labels. We then use the algorithm to predict and rank potential drivers of Alzheimer's. We show that our ranked genes show a significant enrichment for single nucleotide polymorphisms associated with Alzheimer's and are enriched in pathways that have been previously associated with the disease. AVAILABILITY AND IMPLEMENTATION: Source code and link to all feature sets is available at https://github.com/Sage-Bionetworks/EvidenceAggregatedDriverRanking.

2.
Sci Data ; 6(1): 180, 2019 09 24.
Artigo em Inglês | MEDLINE | ID: mdl-31551426

RESUMO

Schizophrenia and bipolar disorder are serious mental illnesses that affect more than 2% of adults. While large-scale genetics studies have identified genomic regions associated with disease risk, less is known about the molecular mechanisms by which risk alleles with small effects lead to schizophrenia and bipolar disorder. In order to fill this gap between genetics and disease phenotype, we have undertaken a multi-cohort genomics study of postmortem brains from controls, individuals with schizophrenia and bipolar disorder. Here we present a public resource of functional genomic data from the dorsolateral prefrontal cortex (DLPFC; Brodmann areas 9 and 46) of 986 individuals from 4 separate brain banks, including 353 diagnosed with schizophrenia and 120 with bipolar disorder. The genomic data include RNA-seq and SNP genotypes on 980 individuals, and ATAC-seq on 269 individuals, of which 264 are a subset of individuals with RNA-seq. We have performed extensive preprocessing and quality control on these data so that the research community can take advantage of this public resource available on the Synapse platform at http://CommonMind.org .

4.
Nat Genet ; 51(4): 659-674, 2019 04.
Artigo em Inglês | MEDLINE | ID: mdl-30911161

RESUMO

Transcriptomic imputation approaches combine eQTL reference panels with large-scale genotype data in order to test associations between disease and gene expression. These genic associations could elucidate signals in complex genome-wide association study (GWAS) loci and may disentangle the role of different tissues in disease development. We used the largest eQTL reference panel for the dorso-lateral prefrontal cortex (DLPFC) to create a set of gene expression predictors and demonstrate their utility. We applied DLPFC and 12 GTEx-brain predictors to 40,299 schizophrenia cases and 65,264 matched controls for a large transcriptomic imputation study of schizophrenia. We identified 413 genic associations across 13 brain regions. Stepwise conditioning identified 67 non-MHC genes, of which 14 did not fall within previous GWAS loci. We identified 36 significantly enriched pathways, including hexosaminidase-A deficiency, and multiple porphyric disorder pathways. We investigated developmental expression patterns among the 67 non-MHC genes and identified specific groups of pre- and postnatal expression.


Assuntos
Encéfalo/fisiopatologia , Expressão Gênica/genética , Esquizofrenia/genética , Estudos de Casos e Controles , Predisposição Genética para Doença , Estudo de Associação Genômica Ampla/métodos , Genótipo , Humanos , Polimorfismo de Nucleotídeo Único/genética , Locos de Características Quantitativas/genética , Risco , Transcriptoma/genética
5.
Nat Commun ; 9(1): 4418, 2018 10 24.
Artigo em Inglês | MEDLINE | ID: mdl-30356117

RESUMO

The response to respiratory viruses varies substantially between individuals, and there are currently no known molecular predictors from the early stages of infection. Here we conduct a community-based analysis to determine whether pre- or early post-exposure molecular factors could predict physiologic responses to viral exposure. Using peripheral blood gene expression profiles collected from healthy subjects prior to exposure to one of four respiratory viruses (H1N1, H3N2, Rhinovirus, and RSV), as well as up to 24 h following exposure, we find that it is possible to construct models predictive of symptomatic response using profiles even prior to viral exposure. Analysis of predictive gene features reveal little overlap among models; however, in aggregate, these genes are enriched for common pathways. Heme metabolism, the most significantly enriched pathway, is associated with a higher risk of developing symptoms following viral exposure. This study demonstrates that pre-exposure molecular predictors can be identified and improves our understanding of the mechanisms of response to respiratory viruses.

6.
Nat Neurosci ; 21(8): 1126-1136, 2018 Aug.
Artigo em Inglês | MEDLINE | ID: mdl-30038276

RESUMO

Risk variants for schizophrenia affect more than 100 genomic loci, yet cell- and tissue-specific roles underlying disease liability remain poorly characterized. We have generated for two cortical areas implicated in psychosis, the dorsolateral prefrontal cortex and anterior cingulate cortex, 157 reference maps from neuronal, neuron-depleted and bulk tissue chromatin for two histone marks associated with active promoters and enhancers, H3-trimethyl-Lys4 (H3K4me3) and H3-acetyl-Lys27 (H3K27ac). Differences between neuronal and neuron-depleted chromatin states were the major axis of variation in histone modification profiles, followed by substantial variability across subjects and cortical areas. Thousands of significant histone quantitative trait loci were identified in neuronal and neuron-depleted samples. Risk variants for schizophrenia, depressive symptoms and neuroticism were significantly over-represented in neuronal H3K4me3 and H3K27ac landscapes. Our Resource, sponsored by PsychENCODE and CommonMind, highlights the critical role of cell-type-specific signatures at regulatory and disease-associated noncoding sequences in the human frontal lobe.

7.
Am J Hum Genet ; 102(6): 1169-1184, 2018 06 07.
Artigo em Inglês | MEDLINE | ID: mdl-29805045

RESUMO

Causal genes and variants within genome-wide association study (GWAS) loci can be identified by integrating GWAS statistics with expression quantitative trait loci (eQTL) and determining which variants underlie both GWAS and eQTL signals. Most analyses, however, consider only the marginal eQTL signal, rather than dissect this signal into multiple conditionally independent signals for each gene. Here we show that analyzing conditional eQTL signatures, which could be important under specific cellular or temporal contexts, leads to improved fine mapping of GWAS associations. Using genotypes and gene expression levels from post-mortem human brain samples (n = 467) reported by the CommonMind Consortium (CMC), we find that conditional eQTL are widespread; 63% of genes with primary eQTL also have conditional eQTL. In addition, genomic features associated with conditional eQTL are consistent with context-specific (e.g., tissue-, cell type-, or developmental time point-specific) regulation of gene expression. Integrating the 2014 Psychiatric Genomics Consortium schizophrenia (SCZ) GWAS and CMC primary and conditional eQTL data reveals 40 loci with strong evidence for co-localization (posterior probability > 0.8), including six loci with co-localization of conditional eQTL. Our co-localization analyses support previously reported genes, identify novel genes associated with schizophrenia risk, and provide specific hypotheses for their functional follow-up.

8.
Mol Psychiatry ; 2018 May 08.
Artigo em Inglês | MEDLINE | ID: mdl-29740122

RESUMO

Transcription at enhancers is a widespread phenomenon which produces so-called enhancer RNA (eRNA) and occurs in an activity-dependent manner. However, the role of eRNA and its utility in exploring disease-associated changes in enhancer function, and the downstream coding transcripts that they regulate, is not well established. We used transcriptomic and epigenomic data to interrogate the relationship of eRNA transcription to disease status and how genetic variants alter enhancer transcriptional activity in the human brain. We combined RNA-seq data from 537 postmortem brain samples from the CommonMind Consortium with cap analysis of gene expression and enhancer identification, using the assay for transposase-accessible chromatin followed by sequencing (ATACseq). We find 118 differentially transcribed eRNAs in schizophrenia and identify schizophrenia-associated gene/eRNA co-expression modules. Perturbations of a key module are associated with the polygenic risk scores. Furthermore, we identify genetic variants affecting expression of 927 enhancers, which we refer to as enhancer expression quantitative loci or eeQTLs. Enhancer expression patterns are consistent across studies, including differentially expressed eRNAs and eeQTLs. Combining eeQTLs with a genome-wide association study of schizophrenia identifies a genetic variant that alters enhancer function and expression of its target gene, GOLPH3L. Our novel approach to analyzing enhancer transcription is adaptable to other large-scale, non-poly-A depleted, RNA-seq studies.

9.
Nat Neurosci ; 20(10): 1418-1426, 2017 Oct.
Artigo em Inglês | MEDLINE | ID: mdl-28869584

RESUMO

We report a multi-omic resource generated by applying quantitative trait locus (xQTL) analyses to RNA sequence, DNA methylation and histone acetylation data from the dorsolateral prefrontal cortex of 411 older adults who have all three data types. We identify SNPs significantly associated with gene expression, DNA methylation and histone modification levels. Many of these SNPs influence multiple molecular features, and we demonstrate that SNP effects on RNA expression are fully mediated by epigenetic features in 9% of these loci. Further, we illustrate the utility of our new resource, xQTL Serve, by using it to prioritize the cell type(s) most affected by an xQTL. We also reanalyze published genome wide association studies using an xQTL-weighted analysis approach and identify 18 new schizophrenia and 2 new bipolar susceptibility variants, which is more than double the number of loci that can be discovered with a larger blood-based expression eQTL resource.


Assuntos
Encéfalo/metabolismo , Epigênese Genética/genética , Genoma Humano/genética , Locos de Características Quantitativas/genética , Transcriptoma/genética , Transtorno Bipolar/genética , Epigenômica , Predisposição Genética para Doença , Estudo de Associação Genômica Ampla , Humanos , Polimorfismo de Nucleotídeo Único/genética , Esquizofrenia/genética
11.
Biol Psychiatry ; 81(2): 162-170, 2017 01 15.
Artigo em Inglês | MEDLINE | ID: mdl-27113501

RESUMO

BACKGROUND: The nervous system may include more than 100 residue-specific posttranslational modifications of histones forming the nucleosome core that are often regulated in cell-type-specific manner. On a genome-wide scale, some of the histone posttranslational modification landscapes show significant overlap with the genetic risk architecture for several psychiatric disorders, fueling PsychENCODE and other large-scale efforts to comprehensively map neuronal and nonneuronal epigenomes in hundreds of specimens. However, practical guidelines for efficient generation of histone chromatin immunoprecipitation followed by deep sequencing (ChIP-seq) datasets from postmortem brains are needed. METHODS: Protocols and quality controls are given for the following: 1) extraction, purification, and NeuN neuronal marker immunotagging of nuclei from adult human cerebral cortex; 2) fluorescence-activated nuclei sorting; 3) preparation of chromatin by micrococcal nuclease digest; 4) ChIP for open chromatin-associated histone methylation and acetylation; and 5) generation and sequencing of ChIP-seq libraries. RESULTS: We present a ChIP-seq pipeline for epigenome mapping in the neuronal and nonneuronal nuclei from the postmortem brain. This includes a stepwise system of quality controls and user-friendly data presentation platforms. CONCLUSIONS: Our practical guidelines will be useful for projects aimed at histone posttranslational modification mapping in chromatin extracted from hundreds of postmortem brain samples in cell-type-specific manner.


Assuntos
Córtex Cerebral/metabolismo , Epigênese Genética , Epigenômica/métodos , Sequenciamento de Nucleotídeos em Larga Escala/métodos , Histonas/metabolismo , Nucleossomos/metabolismo , Acetilação , Antígenos Nucleares/metabolismo , Imunoprecipitação da Cromatina , Humanos , Metilação , Proteínas do Tecido Nervoso/metabolismo , Neurônios/metabolismo , Processamento de Proteína Pós-Traducional
13.
Nat Neurosci ; 19(11): 1442-1453, 2016 11.
Artigo em Inglês | MEDLINE | ID: mdl-27668389

RESUMO

Over 100 genetic loci harbor schizophrenia-associated variants, yet how these variants confer liability is uncertain. The CommonMind Consortium sequenced RNA from dorsolateral prefrontal cortex of people with schizophrenia (N = 258) and control subjects (N = 279), creating a resource of gene expression and its genetic regulation. Using this resource, ∼20% of schizophrenia loci have variants that could contribute to altered gene expression and liability. In five loci, only a single gene was involved: FURIN, TSNARE1, CNTN4, CLCN3 or SNAP91. Altering expression of FURIN, TSNARE1 or CNTN4 changed neurodevelopment in zebrafish; knockdown of FURIN in human neural progenitor cells yielded abnormal migration. Of 693 genes showing significant case-versus-control differential expression, their fold changes were ≤ 1.33, and an independent cohort yielded similar results. Gene co-expression implicates a network relevant for schizophrenia. Our findings show that schizophrenia is polygenic and highlight the utility of this resource for mechanistic interpretations of genetic liability for brain diseases.


Assuntos
Regulação da Expressão Gênica/genética , Predisposição Genética para Doença , Herança Multifatorial/genética , Esquizofrenia/genética , Encéfalo/metabolismo , Feminino , Estudo de Associação Genômica Ampla , Humanos , Masculino , Polimorfismo de Nucleotídeo Único , Risco
14.
Nat Commun ; 7: 12460, 2016 08 23.
Artigo em Inglês | MEDLINE | ID: mdl-27549343

RESUMO

Rheumatoid arthritis (RA) affects millions world-wide. While anti-TNF treatment is widely used to reduce disease progression, treatment fails in ∼one-third of patients. No biomarker currently exists that identifies non-responders before treatment. A rigorous community-based assessment of the utility of SNP data for predicting anti-TNF treatment efficacy in RA patients was performed in the context of a DREAM Challenge (http://www.synapse.org/RA_Challenge). An open challenge framework enabled the comparative evaluation of predictions developed by 73 research groups using the most comprehensive available data and covering a wide range of state-of-the-art modelling methodologies. Despite a significant genetic heritability estimate of treatment non-response trait (h(2)=0.18, P value=0.02), no significant genetic contribution to prediction accuracy is observed. Results formally confirm the expectations of the rheumatology community that SNP information does not significantly improve predictive performance relative to standard clinical traits, thereby justifying a refocusing of future efforts on collection of other data.


Assuntos
Anticorpos Monoclonais Humanizados/uso terapêutico , Artrite Reumatoide/tratamento farmacológico , Predisposição Genética para Doença/genética , Polimorfismo de Nucleotídeo Único , Fator de Necrose Tumoral alfa/antagonistas & inibidores , Adulto , Idoso , Anticorpos Monoclonais/uso terapêutico , Antirreumáticos/uso terapêutico , Artrite Reumatoide/genética , Artrite Reumatoide/patologia , Certolizumab Pegol/uso terapêutico , Estudos de Coortes , Crowdsourcing , Feminino , Humanos , Masculino , Pessoa de Meia-Idade , Prognóstico , Resultado do Tratamento , Fator de Necrose Tumoral alfa/imunologia
15.
Am J Epidemiol ; 176(5): 423-30, 2012 Sep 01.
Artigo em Inglês | MEDLINE | ID: mdl-22865700

RESUMO

Large-scale genome-wide association studies (GWAS) have identified over 40 genomic regions significantly associated with type 2 diabetes mellitus. However, GWAS results are not always straightforward to interpret, and linking these loci to meaningful disease etiology is often difficult without extensive follow-up studies. The authors expanded on previously reported type 2 diabetes mellitus GWAS from the nested case-control studies of 2 prospective US cohorts by incorporating expression single nucleotide polymorphism (SNP) information and applying SNP set enrichment analysis to identify sets of SNPs associated with genes that could provide further biologic insight to traditional genome-wide analysis. Using data collected between 1989 and 1994 in these previous studies to form a nested case-control study, the authors found that 3 of the most significantly associated SNPs to type 2 diabetes mellitus in their study are expression SNPs to the lymphocyte antigen 75 gene (LY75), the ubiquitin-specific peptidase 36 gene (USP36), and the phosphatidylinositol transfer protein, cytoplasmic 1 gene (PITPNC1). SNP set enrichment analysis of the GWAS results identified enrichment for expression SNPs to the macrophage-enriched module and the Gene Ontology (GO) biologic process fat cell differentiation human, which includes the transcription factor 7-like 2 gene (TCF7L2), as well as other type 2 diabetes mellitus-associated genes. Integrating genome-wide association, gene expression, and gene set analysis may provide valuable biologic support for potential type 2 diabetes mellitus susceptibility loci and may be useful in identifying new targets or pathways of interest for the treatment and prevention of type 2 diabetes mellitus.


Assuntos
Antígenos CD/genética , Diabetes Mellitus Tipo 2/genética , Lectinas Tipo C/genética , Proteínas de Membrana Transportadoras/genética , Polimorfismo de Nucleotídeo Único , Receptores de Superfície Celular/genética , Ubiquitina Tiolesterase/genética , Proteínas ADAM/genética , Proteína ADAMTS9 , Estudos de Casos e Controles , Estudos de Coortes , Feminino , Regulação da Expressão Gênica , Marcadores Genéticos , Estudo de Associação Genômica Ampla , Genótipo , Humanos , Masculino , Antígenos de Histocompatibilidade Menor , Estudos Prospectivos , Proteína 2 Semelhante ao Fator 7 de Transcrição/genética
17.
Genome Res ; 21(7): 1008-16, 2011 Jul.
Artigo em Inglês | MEDLINE | ID: mdl-21602305

RESUMO

To map the genetics of gene expression in metabolically relevant tissues and investigate the diversity of expression SNPs (eSNPs) in multiple tissues from the same individual, we collected four tissues from approximately 1000 patients undergoing Roux-en-Y gastric bypass (RYGB) and clinical traits associated with their weight loss and co-morbidities. We then performed high-throughput genotyping and gene expression profiling and carried out a genome-wide association analyses for more than 100,000 gene expression traits representing four metabolically relevant tissues: liver, omental adipose, subcutaneous adipose, and stomach. We successfully identified 24,531 eSNPs corresponding to about 10,000 distinct genes. This represents the greatest number of eSNPs identified to our knowledge by any study to date and the first study to identify eSNPs from stomach tissue. We then demonstrate how these eSNPs provide a high-quality disease map for each tissue in morbidly obese patients to not only inform genetic associations identified in this cohort, but in previously published genome-wide association studies as well. These data can aid in elucidating the key networks associated with morbid obesity, response to RYGB, and disease as a whole.


Assuntos
Mucosa Gástrica/metabolismo , Fígado/metabolismo , Obesidade Mórbida/epidemiologia , Obesidade Mórbida/genética , Adiposidade/genética , Adulto , Estudos de Coortes , Comorbidade , Bases de Dados Genéticas , Feminino , Derivação Gástrica , Perfilação da Expressão Gênica , Estudo de Associação Genômica Ampla/métodos , Genótipo , Humanos , Masculino , Pessoa de Meia-Idade , Obesidade Mórbida/cirurgia , Polimorfismo de Nucleotídeo Único , Perda de Peso
18.
Mamm Genome ; 21(3-4): 143-52, 2010 Apr.
Artigo em Inglês | MEDLINE | ID: mdl-20135320

RESUMO

The remarkable success in mapping genes linked to a number of disease traits using genome-wide association studies (GWAS) in human cohorts has renewed interest in applying this same technique in model organisms such as inbred laboratory mice. Unlike humans, however, the limited genetic diversity in the ancestry of laboratory mice combined with selection pressure over the past decades have yielded an intricate population genetic structure that can complicate the results obtained from association studies. This problem is further exacerbated by the small number of strains typically used in such studies where multiple spurious associations arise as a result of random chance. We sought to empirically assess the viability of GWAS in inbred mice using hundreds of expression traits for which the true location of the expression quantitative trait locus was known a priori. We then measured transcript abundance levels for these expression traits in 16 classical and 3 wild-derived inbred strains and carried out a genome-wide association scan, demonstrating the low statistical power of such studies and empirically estimating the large extent to which allelic association of transcripts gives rise to spurious associations. We provide evidence illustrating that in a large fraction of cases, the marker with the most significant p values fails to map to the location of the true eQTL. Finally, we provide experimental support for hundreds of traits, and that combining linkage analysis with association mapping provides significant increases in statistical power over a stand-alone GWAS as well as significantly higher mapping resolution than either study alone.


Assuntos
Estudo de Associação Genômica Ampla/métodos , Camundongos Endogâmicos/genética , Animais , Ligação Genética , Camundongos , Locos de Características Quantitativas/genética
19.
Nature ; 452(7186): 429-35, 2008 Mar 27.
Artigo em Inglês | MEDLINE | ID: mdl-18344982

RESUMO

Identifying variations in DNA that increase susceptibility to disease is one of the primary aims of genetic studies using a forward genetics approach. However, identification of disease-susceptibility genes by means of such studies provides limited functional information on how genes lead to disease. In fact, in most cases there is an absence of functional information altogether, preventing a definitive identification of the susceptibility gene or genes. Here we develop an alternative to the classic forward genetics approach for dissecting complex disease traits where, instead of identifying susceptibility genes directly affected by variations in DNA, we identify gene networks that are perturbed by susceptibility loci and that in turn lead to disease. Application of this method to liver and adipose gene expression data generated from a segregating mouse population results in the identification of a macrophage-enriched network supported as having a causal relationship with disease traits associated with metabolic syndrome. Three genes in this network, lipoprotein lipase (Lpl), lactamase beta (Lactb) and protein phosphatase 1-like (Ppm1l), are validated as previously unknown obesity genes, strengthening the association between this network and metabolic disease traits. Our analysis provides direct experimental support that complex traits such as obesity are emergent properties of molecular networks that are modulated by complex genetic loci and environmental factors.


Assuntos
Redes Reguladoras de Genes/genética , Predisposição Genética para Doença/genética , Variação Genética/genética , Síndrome Metabólica/genética , Obesidade/genética , Tecido Adiposo/metabolismo , Animais , Apolipoproteína A-II/genética , Cromossomos de Mamíferos/genética , Feminino , Desequilíbrio de Ligação , Lipase Lipoproteica/genética , Fígado/metabolismo , Escore Lod , Macrófagos/metabolismo , Masculino , Proteínas de Membrana/genética , Síndrome Metabólica/enzimologia , Síndrome Metabólica/metabolismo , Camundongos , Obesidade/enzimologia , Obesidade/metabolismo , Fenótipo , Fosfoproteínas Fosfatases/deficiência , Fosfoproteínas Fosfatases/genética , Fosfoproteínas Fosfatases/metabolismo , Locos de Características Quantitativas , Reprodutibilidade dos Testes , Proteínas Ribossômicas/genética
20.
Mamm Genome ; 18(6-7): 389-401, 2007 Jul.
Artigo em Inglês | MEDLINE | ID: mdl-17653589

RESUMO

Testing hundreds of thousands of DNA markers in human, mouse, and other species for association to complex traits like disease is now a reality. However, information on how variations in DNA impact complex physiologic processes flows through transcriptional and other molecular networks. In other words, DNA variations impact complex diseases through the perturbations they cause to transcriptional and other biological networks, and these molecular phenotypes are intermediate to clinically defined disease. Because it is also now possible to monitor transcript levels in a comprehensive fashion, integrating DNA variation, transcription, and phenotypic data has the potential to enhance identification of the associations between DNA variation and diseases like obesity and diabetes, as well as characterize those parts of the molecular networks that drive these diseases. Toward that end, we review methods for integrating expression quantitative trait loci (eQTLs), gene expression, and clinical data to infer causal relationships among gene expression traits and between expression and clinical traits. We further describe methods to integrate these data in a more comprehensive manner by constructing coexpression gene networks that leverage pairwise gene interaction data to represent more general relationships. To infer gene networks that capture causal information, we describe a Bayesian algorithm that further integrates eQTLs, expression, and clinical phenotype data to reconstruct whole-gene networks capable of representing causal relationships among genes and traits in the network. These emerging network approaches, aimed at processing high-dimensional biological data by integrating data from multiple sources, represent some of the first steps in statistical genetics to identify multiple genetic perturbations that alter the states of molecular networks and that in turn push systems into disease states. Evolving statistical procedures that operate on networks will be critical to extracting information related to complex phenotypes like disease, as research goes beyond a single-gene focus. The early successes achieved with the methods described herein suggest that these more integrative genomics approaches to dissecting disease traits will significantly enhance the identification of key drivers of disease beyond what could be achieved by genetic association studies alone.


Assuntos
Doenças Genéticas Inatas/genética , Modelos Genéticos , Biologia de Sistemas , Animais , Mapeamento Cromossômico , Redes Reguladoras de Genes , Humanos , Padrões de Herança , Modelos Biológicos , Locos de Características Quantitativas
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