Earlier GxE research focused on candidate gene or twin and sibling studies, but recent statistical advances have extended GxE studies to the genome-wide level. Specifically, the GWGEI models described by Mukherjee et al. (2012), Cornelis et al. (2012), and Thomas et al. (2012) pave the way for social demographers to ask whether specific genetic polymorphisms (across the entire human genome) moderate environmental influences on health outcomes. Despite the publicized need for social science research in this area of gene-environment interplay (Bookman et al. 2011), few efforts have been made to clarify the theoretical contours of the GxE framework and the statistical methods necessary to examine these associations on a genome-wide scale. All GWAS work is plagued by multiple testing problems (e.g., hundreds of thousands or millions of regressions). This is compounded in GWGEI analyses because one will inevitably find some evidence for each of the three GxE conceptual models in Fig. 1. As we show, differentiating this evidence from chance is challenging.
