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Genome-wide meta-analysis of joint tests for genetic and gene-environment interaction effects.

ABSTRACT: BACKGROUND:There is growing interest in the study of gene-environment interactions in the context of genome-wide association studies (GWASs). These studies will likely require meta-analytic approaches to have sufficient power. METHODS:We describe an approach for meta-analysis of a joint test for genetic main effects and gene-environment interaction effects. Using simulation studies based on a meta-analysis of five studies (total n = 10,161), we compare the power of this test to the meta-analysis of marginal test of genetic association and the meta-analysis of standard 1 d.f. interaction tests across a broad range of genetic main effects and gene-environment interaction effects. RESULTS:We show that the joint meta-analysis is valid and can be more powerful than classical meta-analytic approaches, with a potential gain of power over 50% compared to the marginal test. The standard interaction test had less than 1% power in almost all the situations we considered. We also show that regardless of the test used, sample sizes far exceeding those of a typical individual GWAS will be needed to reliably detect genes with subtle gene-environment interaction patterns. CONCLUSION:The joint meta-analysis is an attractive approach to discover markers which may have been missed by initial GWASs focusing on marginal marker-trait associations.

SUBMITTER: Aschard H

PROVIDER: S-EPMC3085519 | biostudies-literature | 2010

REPOSITORIES: biostudies-literature

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Genome-wide meta-analysis of joint tests for genetic and gene-environment interaction effects.

Aschard Hugues H Hancock Dana B DB London Stephanie J SJ Kraft Peter P

Human heredity 20100101 4

<h4>Background</h4>There is growing interest in the study of gene-environment interactions in the context of genome-wide association studies (GWASs). These studies will likely require meta-analytic approaches to have sufficient power.<h4>Methods</h4>We describe an approach for meta-analysis of a joint test for genetic main effects and gene-environment interaction effects. Using simulation studies based on a meta-analysis of five studies (total n = 10,161), we compare the power of this test to th ...[more]

PMID: 21293137

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Project description:A complex disease has, by definition, multiple genetic causes. In theory, these causes could be identified individually, but their identification will likely benefit from informed use of anticipated interactions between causes. In addition, characterizing and understanding interactions must be considered key to revealing the etiology of any complex disease. Large-scale collaborative efforts are now paving the way for comprehensive studies of interaction. As a consequence, there is a need for methods with a computational efficiency sufficient for modern data sets as well as for improvements of statistical accuracy and power. Another issue is that, currently, the relation between different methods for interaction inference is in many cases not transparent, complicating the comparison and interpretation of results between different interaction studies. In this paper we present computationally efficient tests of interaction for the complete family of generalized linear models (GLMs). The tests can be applied for inference of single or multiple interaction parameters, but we show, by simulation, that jointly testing the full set of interaction parameters yields superior power and control of false positive rate. Based on these tests we also describe how to combine results from multiple independent studies of interaction in a meta-analysis. We investigate the impact of several assumptions commonly made when modeling interactions. We also show that, across the important class of models with a full set of interaction parameters, jointly testing the interaction parameters yields identical results. Further, we apply our method to genetic data for cardiovascular disease. This allowed us to identify a putative interaction involved in Lp(a) plasma levels between two 'tag' variants in the LPA locus (p = 2.42 ⋅ 10-09) as well as replicate the interaction (p = 6.97 ⋅ 10-07). Finally, our meta-analysis method is used in a small (N = 16,181) study of interactions in myocardial infarction.

Project description:Background & aimsIn contrast to most other common diseases, few genetic variants have been identified that impact risk of cirrhosis. We aimed to identify new genetic variants that predispose to cirrhosis, to test whether such variants, aggregated into a polygenic score, enable genomic risk stratification, and to test whether alcohol intake or body mass index interacts with polygenic predisposition.MethodsWe conducted a multi-trait genome-wide association study combining cirrhosis and alanine aminotransferase levels performed in 5 discovery studies (UK Biobank, Vanderbilt BioVU, Atherosclerosis Risk in Communities study, and 2 case-control studies including 4829 individuals with cirrhosis and 72,705 controls and 362,539 individuals with alanine aminotransferase levels). Identified variants were replicated in 3 studies (Partners HealthCare Biobank, FinnGen, and Biobank Japan including 3554 individuals with cirrhosis and 343,826 controls). A polygenic score was tested in Partners HealthCare Biobank.ResultsFive previously reported and 7 newly identified genetic variants were associated with cirrhosis in both the discovery studies multi-trait genome-wide association study (P < 5 × 10-8) and the replication studies (P < .05), including a missense variant in the APOE gene and a noncoding variant near EFN1A. These 12 variants were used to generate a polygenic score. Among Partners HealthCare Biobank individuals, high polygenic score-defined as the top quintile of the distribution-was associated with significantly increased risk of cirrhosis (odds ratio, 2.26; P < .001) and related comorbidities compared with the lowest quintile. Risk was even more pronounced among those with extreme polygenic risk (top 1% of the distribution, odds ratio, 3.16; P < .001). The impact of extreme polygenic risk was substantially more pronounced in those with elevated alcohol consumption or body mass index. Modeled as risk by age 75 years, probability of cirrhosis with extreme polygenic risk was 13.7%, 20.1%, and 48.2% among individuals with no or modest, moderate, and increased alcohol consumption, respectively (Pinteraction < .001). Similarly, probability among those with extreme polygenic risk was 6.5%, 10.3%, and 19.5% among individuals with normal weight, overweight, and obesity, respectively (Pinteraction < .001).ConclusionsTwelve independent genetic variants, 7 of which are newly identified in this study, conferred risk for cirrhosis. Aggregated into a polygenic score, these variants identified a subset of the population at substantially increased risk who are most susceptible to the hepatotoxic effects of excess alcohol consumption or obesity.

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Genome-wide meta-analysis of joint tests for genetic and gene-environment interaction effects.

Publications

Genome-wide meta-analysis of joint tests for genetic and gene-environment interaction effects.

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