Meta-Regression of Gene-Environment Interaction in Genome-Wide Association Studies

Genome-wide association studies (GWAS) have created heightened interest in understanding the effects of gene-environment interaction on complex human diseases or traits. Applying methods for analyzing such interaction can help uncover novel genes and identify environmental hazards that influence only certain genetically susceptible groups. However, the number of interaction analysis methods is still limited, so there is a need to develop more efficient and powerful methods. In this paper, we propose two novel meta-analysis methods of studying gene-environment interaction, based on meta-regression of estimated genetic effects on the environmental factor. The two methods can perform joint analysis of a single nucleotide polymorphism's (SNP) main and interaction effects, or analyze only the effect of the interaction. They can readily estimate any linear or non-linear interactions by simply modifying the gene-environment regression function. Thus, they are efficient methods to be applied to different scenarios. We use numerical examples to demonstrate the performance of our methods. We also compare them with two other methods commonly used in current GWAS, i.e., meta-analysis of SNP main effects (MAIN) and joint meta-analysis of SNP main and interaction effects (JMA). The results show that our methods are more powerful than MAIN when the interaction effect exists, and are comparable to JMA in the linear or quadratic interaction cases. In the numerical examples, we also investigate how the number of the divided groups and the sample size of the studies affect the performance of our methods.