A two-stage testing strategy for detecting genes×environment interactions in association studies.

Identifying gene×environment (G×E) interactions, especially when rare variants are included in genome-wide association studies, is a major challenge in statistical genetics. However, the detection of G×E interactions is very important for understanding the etiology of complex diseases. Although currently some statistical methods have been developed to detect the interactions between genes and environment, the detection of the interactions for the case of rare variants is still limited. Therefore, it is particularly important to develop a new method to detect the interactions between genes and environment for rare variants. In this study, we extend an existing method of adaptive combination of P-values (ADA) and design a novel strategy (called iSADA) for testing the effects of G×E interactions for rare variants. We propose a new two-stage test to detect the interactions between genes and environment in a certain region of a chromosome or even for the whole genome. First, the score statistic is used to test the associations between trait value and the interaction terms of genes and environment and obtain the original P-values. Then, based on the idea of the ADA method, we further construct a full test statistic via the P-values of the preliminary tests in the first stage, so that we can comprehensively test the interactions between genes and environment in the considered genome region. Simulation studies are conducted to compare our proposed method with other existing methods. The results show that the iSADA has higher power than other methods in each case. A GAW17 data set is also applied to illustrate the applicability of the new method.