Gene-environment (G×E) interactions may contribute to complex diseases, but their detection has proven challenging; hence, a variety of approaches have been developed to enhance power. Most G×E analyses focus on loci that are strong biological candidates  or those with highly significant marginal effects . The latter approach is attractive because these loci are available in many large cohorts, and can be conveniently followed-up with interaction analyses if environmental data are accessible. Moreover, selecting SNPs with strong and reproducible marginal effect signals is a pragmatic data-reduction step that may improve power , although this approach risks omitting other promising candidates .