Invited commentary: GE-Whiz! Ratcheting gene-environment studies up to the whole genome and the whole exposome

Abstract

One goal in the post-genome-wide association study era is characterizing gene-environment interactions, including scanning for interactions with all available polymorphisms, not just those showing significant main effects. In recent years, several approaches to such "gene-environment-wide interaction studies" have been proposed. Two contributions in this issue of the American Journal of Epidemiology provide systematic comparisons of the performance of these various approaches, one based on simulation and one based on application to 2 real genome-wide association study scans for type 2 diabetes. The authors discuss some of the broader issues raised by these contributions, including the plausibility of the gene-environment independence assumption that some of these approaches rely upon, the need for replication, and various generalizations of these approaches.

Figure 1.

Empirical power to detect a single causal single nucleotide polymorphism (SNP) for the case-control and 2-step (TS) analyses (for fixed α₁ = 0.05, 0.0005, and optimal α₁) as a function of the ratio of number of controls to number of cases (n₀/n₁) (A) and optimal α₁ as a function of n₀/n₁ (B). Assumed parameter values are those used by Mukherjee et al. (11), specifically: M = 100,000 SNPs; n₁ = 2,000 cases; R_g = R_e = 1.0, R_ge = 1.8; and Pr(E) = 0.5, minor allele frequency of dominantly coded casual SNP = 0.2.