One goal for GxE studies is to characterize risks associated with joint effects of putative or known genetic and environmental factors. In this setting the goal is often estimation rather than statistical testing. In many GxE interaction studies the common practice to simply model interaction terms, scan p-values and report “significant” interaction terms. This is often done without considering the context of the direction and interpretation of the full joint effects. However, such practice is not ideal from a biological or public health point of view [Knol, et al. 2009; Knol and VanderWeele 2012]. Instead, understanding joint risks may be important both for obtaining etiologic insights, and for translation to public health applications such as risk-based screening and intervention. In these studies, the joint effects would ideally be estimated empirically using data within each particular GxE strata. Obtaining adequate sample sizes for each unique combination of risk factors is often not feasible, so we often rely on models for parsimonious description of joint effects.
