Gene-Environment Interactions in Genome-Wide Association Studies: A Comparative Study of Tests Applied to Empirical Studies of Type 2 Diabetes

Department of Nutrition, Harvard School of Public Health, Boston, Massachusetts, USA.
American journal of epidemiology (Impact Factor: 5.23). 12/2011; 175(3):191-202. DOI: 10.1093/aje/kwr368
Source: PubMed


The question of which statistical approach is the most effective for investigating gene-environment (G-E) interactions in the context of genome-wide association studies (GWAS) remains unresolved. By using 2 case-control GWAS (the
Nurses’ Health Study, 1976–2006, and the Health Professionals Follow-up Study, 1986–2006) of type 2 diabetes, the authors
compared 5 tests for interactions: standard logistic regression-based case-control; case-only; semiparametric maximum-likelihood
estimation of an empirical-Bayes shrinkage estimator; and 2-stage tests. The authors also compared 2 joint tests of genetic
main effects and G-E interaction. Elevated body mass index was the exposure of interest and was modeled as a binary trait to avoid an inflated
type I error rate that the authors observed when the main effect of continuous body mass index was misspecified. Although
both the case-only and the semiparametric maximum-likelihood estimation approaches assume that the tested markers are independent
of exposure in the general population, the authors did not observe any evidence of inflated type I error for these tests in
their studies with 2,199 cases and 3,044 controls. Both joint tests detected markers with known marginal effects. Loci with
the most significant G-E interactions using the standard, empirical-Bayes, and 2-stage tests were strongly correlated with the exposure among controls.
Study findings suggest that methods exploiting G-E independence can be efficient and valid options for investigating G-E interactions in GWAS.

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