title: Meta-analysis identifies gene-by-environment interactions as demonstrated in a study of 4,965 mice.
abstract: Identifying environmentally-specific genetic effects is a key challenge in understanding the structure of complex traits. Model organisms play a crucial role in the identification of such gene-by-environment interactions, as a result of the unique ability to observe genetically similar individuals across multiple distinct environments. Many model organism studies examine the same traits but under varying environmental conditions. For example, knock-out or diet-controlled studies are often used to examine cholesterol in mice. These studies, when examined in aggregate, provide an opportunity to identify genomic loci exhibiting environmentally-dependent effects. However, the straightforward application of traditional methodologies to aggregate separate studies suffers from several problems. First, environmental conditions are often variable and do not fit the standard univariate model for interactions. Additionally, applying a multivariate model results in increased degrees of freedom and low statistical power. In this paper, we jointly analyze multiple studies with varying environmental conditions using a meta-analytic approach based on a random effects model to identify loci involved in gene-by-environment interactions. Our approach is motivated by the observation that methods for discovering gene-by-environment interactions are closely related to random effects models for meta-analysis. We show that interactions can be interpreted as heterogeneity and can be detected without utilizing the traditional uni- or multi-variate approaches for discovery of gene-by-environment interactions. We apply our new method to combine 17 mouse studies containing in aggregate 4,965 distinct animals. We identify 26 significant loci involved in High-density lipoprotein (HDL) cholesterol, many of which are consistent with previous findings. Several of these loci show significant evidence of involvement in gene-by-environment interactions. An additional advantage of our meta-analysis approach is that our combined study has significantly higher power and improved resolution compared to any single study thus explaining the large number of loci discovered in the combined study.
authors.affiliation: Department of Computer Science, University of California, Los Angeles, Los Angeles, California, United States of America.
authors.forename: Eun Yong
authors.initials: EY
authors.lastname: Kang
authors.affiliation: Division of Genetics, Brigham & Women's Hospital, Harvard Medical School, Boston, Massachusetts, United States of America ; Partners HealthCare Center for Personalized Genetic Medicine, Boston, Massachusetts, United States of America ; Program in Medical and Population Genetics, Broad Institute of Harvard and MIT, Cambridge, Massachusetts, United States of America.
authors.forename: Buhm
authors.initials: B
authors.lastname: Han
authors.affiliation: Department of Computer Science, University of California, Los Angeles, Los Angeles, California, United States of America.
authors.forename: Nicholas
authors.initials: N
authors.lastname: Furlotte
authors.affiliation: Interdepartmental Program in Bioinformatics, University of California, Los Angeles, Los Angeles, California, United States of America.
authors.forename: Jong Wha J
authors.initials: JW
authors.lastname: Joo
authors.affiliation: Department of Medicine, University of California, Los Angeles, Los Angeles, California, United States of America.
authors.forename: Diana
authors.initials: D
authors.lastname: Shih
authors.affiliation: Department of Medicine, University of California, Los Angeles, Los Angeles, California, United States of America.
authors.forename: Richard C
authors.initials: RC
authors.lastname: Davis
authors.affiliation: Department of Medicine, University of California, Los Angeles, Los Angeles, California, United States of America ; Department of Human Genetics, University of California, Los Angeles, Los Angeles, California, United States of America.
authors.forename: Aldons J
authors.initials: AJ
authors.lastname: Lusis
authors.affiliation: Department of Computer Science, University of California, Los Angeles, Los Angeles, California, United States of America ; Department of Human Genetics, University of California, Los Angeles, Los Angeles, California, United States of America.
authors.forename: Eleazar
authors.initials: E
authors.lastname: Eskin
mesh_terms: D000818:Animals; D008076:Cholesterol, HDL / Q000235:genetics*; D004777:Environment; D059647:Gene-Environment Interaction; D016678:Genome; D051379:Mice; D008962:Models, Theoretical; D040641:Quantitative Trait Loci / Q000235:genetics*
chemical_list: D008076:Cholesterol, HDL