Another motivation for characterization of joint effects and GxE is for potential translational applications of epidemiologic research. Studies of GxE may help public health researchers develop strategies for targeted intervention for risk-factor modification based on individuals’ genetic profile [Hunter 2005]. Some cancers have strong environmental risk factors, and in considering the practicality of intervention, the environment is often more easily modified than genetic factors. If an intervention can be applied only to a subset of the population due to ethical issues, risk of side effects, cost, or other practical considerations, then targeting the intervention to high-risk subjects could be more beneficial in terms of number of diseases prevented. In this context, when a GxE interaction is found, the joint effects can be modeled to identify subgroups for whom interventions may best be targeted. Further, although tests for statistical significance could be performed based on multiplicative models of relative risks, the magnitude of benefit from targeted intervention cannot be assessed without reference to absolute risks [Garcia-Closas, et al. 2013].
