Meta-analysis identifies gene-by-environment interactions as demonstrated in a study of 4,965 mice.

PLoS Genet
Authors
Keywords
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.

Year of Publication
2014
Journal
PLoS Genet
Volume
10
Issue
1
Pages
e1004022
Date Published
2014 Jan
ISSN
1553-7404
URL
DOI
10.1371/journal.pgen.1004022
PubMed ID
24415945
PubMed Central ID
PMC3886926
Links
Grant list
U01- DA024417 / DA / NIDA NIH HHS / United States
R01-GM083198 / GM / NIGMS NIH HHS / United States
P01 HL028481 / HL / NHLBI NIH HHS / United States
R01-ES022282 / ES / NIEHS NIH HHS / United States
P01-HL28481 / HL / NHLBI NIH HHS / United States
R01-MH101782 / MH / NIMH NIH HHS / United States
K25-HL080079 / HL / NHLBI NIH HHS / United States
R01 ES022282 / ES / NIEHS NIH HHS / United States
P01-HL30568 / HL / NHLBI NIH HHS / United States