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Epistatic Networks Jointly Influence Phenotypes Related to Metabolic Disease and Gene Expression in Diversity Outbred Mice

Genetic studies of multidimensional phenotypes can potentially link genetic variation, gene expression, and physiological data to create multi-scale models of complex traits. The challenge of reducing these data to specific hypotheses has become increasingly acute with the advent of genome-scale dat...

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Autores principales: Tyler, Anna L., Ji, Bo, Gatti, Daniel M., Munger, Steven C., Churchill, Gary A., Svenson, Karen L., Carter, Gregory W.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Genetics Society of America 2017
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5499176/
https://www.ncbi.nlm.nih.gov/pubmed/28592500
http://dx.doi.org/10.1534/genetics.116.198051
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author Tyler, Anna L.
Ji, Bo
Gatti, Daniel M.
Munger, Steven C.
Churchill, Gary A.
Svenson, Karen L.
Carter, Gregory W.
author_facet Tyler, Anna L.
Ji, Bo
Gatti, Daniel M.
Munger, Steven C.
Churchill, Gary A.
Svenson, Karen L.
Carter, Gregory W.
author_sort Tyler, Anna L.
collection PubMed
description Genetic studies of multidimensional phenotypes can potentially link genetic variation, gene expression, and physiological data to create multi-scale models of complex traits. The challenge of reducing these data to specific hypotheses has become increasingly acute with the advent of genome-scale data resources. Multi-parent populations derived from model organisms provide a resource for developing methods to understand this complexity. In this study, we simultaneously modeled body composition, serum biomarkers, and liver transcript abundances from 474 Diversity Outbred mice. This population contained both sexes and two dietary cohorts. Transcript data were reduced to functional gene modules with weighted gene coexpression network analysis (WGCNA), which were used as summary phenotypes representing enriched biological processes. These module phenotypes were jointly analyzed with body composition and serum biomarkers in a combined analysis of pleiotropy and epistasis (CAPE), which inferred networks of epistatic interactions between quantitative trait loci that affect one or more traits. This network frequently mapped interactions between alleles of different ancestries, providing evidence of both genetic synergy and redundancy between haplotypes. Furthermore, a number of loci interacted with sex and diet to yield sex-specific genetic effects and alleles that potentially protect individuals from the effects of a high-fat diet. Although the epistatic interactions explained small amounts of trait variance, the combination of directional interactions, allelic specificity, and high genomic resolution provided context to generate hypotheses for the roles of specific genes in complex traits. Our approach moves beyond the cataloging of single loci to infer genetic networks that map genetic etiology by simultaneously modeling all phenotypes.
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spelling pubmed-54991762017-07-10 Epistatic Networks Jointly Influence Phenotypes Related to Metabolic Disease and Gene Expression in Diversity Outbred Mice Tyler, Anna L. Ji, Bo Gatti, Daniel M. Munger, Steven C. Churchill, Gary A. Svenson, Karen L. Carter, Gregory W. Genetics Multiparental Populations Genetic studies of multidimensional phenotypes can potentially link genetic variation, gene expression, and physiological data to create multi-scale models of complex traits. The challenge of reducing these data to specific hypotheses has become increasingly acute with the advent of genome-scale data resources. Multi-parent populations derived from model organisms provide a resource for developing methods to understand this complexity. In this study, we simultaneously modeled body composition, serum biomarkers, and liver transcript abundances from 474 Diversity Outbred mice. This population contained both sexes and two dietary cohorts. Transcript data were reduced to functional gene modules with weighted gene coexpression network analysis (WGCNA), which were used as summary phenotypes representing enriched biological processes. These module phenotypes were jointly analyzed with body composition and serum biomarkers in a combined analysis of pleiotropy and epistasis (CAPE), which inferred networks of epistatic interactions between quantitative trait loci that affect one or more traits. This network frequently mapped interactions between alleles of different ancestries, providing evidence of both genetic synergy and redundancy between haplotypes. Furthermore, a number of loci interacted with sex and diet to yield sex-specific genetic effects and alleles that potentially protect individuals from the effects of a high-fat diet. Although the epistatic interactions explained small amounts of trait variance, the combination of directional interactions, allelic specificity, and high genomic resolution provided context to generate hypotheses for the roles of specific genes in complex traits. Our approach moves beyond the cataloging of single loci to infer genetic networks that map genetic etiology by simultaneously modeling all phenotypes. Genetics Society of America 2017-06 2017-06-06 /pmc/articles/PMC5499176/ /pubmed/28592500 http://dx.doi.org/10.1534/genetics.116.198051 Text en Copyright © 2017 by the Genetics Society of America Available freely online through the author-supported open access option.
spellingShingle Multiparental Populations
Tyler, Anna L.
Ji, Bo
Gatti, Daniel M.
Munger, Steven C.
Churchill, Gary A.
Svenson, Karen L.
Carter, Gregory W.
Epistatic Networks Jointly Influence Phenotypes Related to Metabolic Disease and Gene Expression in Diversity Outbred Mice
title Epistatic Networks Jointly Influence Phenotypes Related to Metabolic Disease and Gene Expression in Diversity Outbred Mice
title_full Epistatic Networks Jointly Influence Phenotypes Related to Metabolic Disease and Gene Expression in Diversity Outbred Mice
title_fullStr Epistatic Networks Jointly Influence Phenotypes Related to Metabolic Disease and Gene Expression in Diversity Outbred Mice
title_full_unstemmed Epistatic Networks Jointly Influence Phenotypes Related to Metabolic Disease and Gene Expression in Diversity Outbred Mice
title_short Epistatic Networks Jointly Influence Phenotypes Related to Metabolic Disease and Gene Expression in Diversity Outbred Mice
title_sort epistatic networks jointly influence phenotypes related to metabolic disease and gene expression in diversity outbred mice
topic Multiparental Populations
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5499176/
https://www.ncbi.nlm.nih.gov/pubmed/28592500
http://dx.doi.org/10.1534/genetics.116.198051
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