Abstract
As human complex diseases are influenced by the interaction between genetics and the environment, identifying gene-environment interactions (G×E) is crucial for understanding disease mechanisms and predicting risk. Developing robust quantitative tools for G×E analysis can enhance the study of complex diseases. However, many existing methods that explore G×E focus on the interplay between an environmental factor and genetic variants, exclusively for common or rare variants. In this study, we developed MAGEIT_RAN and MAGEIT_FIX to identify interactions between an environmental factor and a set of genetic markers, including both rare and common variants, based on the MinQue for Summary statistics. The genetic main effects in MAGEIT_RAN and MAGEIT_FIX are modeled as random and fixed effects, respectively. Simulation studies showed that both tests had type I error under control, with MAGEIT_RAN being the most powerful test. Applying MAGEIT to a genome-wide analysis of gene-alcohol interactions on hypertension and seated systolic blood pressure in the Multiethnic Study of Atherosclerosis revealed genes like EIF2AK2, CCNDBP1, and EPB42 influencing blood pressure through alcohol interaction. Pathway analysis identified 1 apoptosis and survival pathway involving PKR and 2 signal transduction pathways associated with hypertension and alcohol intake, demonstrating MAGEIT_RAN's ability to detect biologically relevant gene-environment interactions.