Most common diseases are believed to be a result of the combined effect of genes, environmental factors, and their interactions. However, current genome-wide association studies (GWAS) are designed to detect the main effect, that is, the direct association of a single-nucleotide polymorphism (SNP) or cluster of SNPs with disease [Browning and Browning, 2007; Zhao et al., 2006]. Investigators may therefore miss important genetic variants that are specific to subgroups of the population defined by some environmental exposure. In fact, main effect tests will have no power to detect variants that have effects in opposite directions within subgroups (crossing interaction). Despite the potential importance of gene-environment (G×E) interactions in the etiology of common diseases, little work has been done to develop methods for detecting these types of interactions in GWAS data.
