Meta-Analysis of SNP-Environment Interaction with Heterogeneity.

Abstract

Meta-analyses are widely used in genome-wide association studies to combine the results obtained from multiple studies. Classical random-effects methods treat genetic heterogeneity as a random effect and consider it as a portion of the variance associated with a fixed effect of the variant. Recent work suggests performing hypothesis testing with the null hypothesis under which neither fixed nor random effects exist for a variant. This method has been shown to perform better than classical random-effects methods. In this work, we propose a meta-analysis of testing single nucleotide polymorphism (SNP)-environment interaction in the presence of genetic heterogeneity. We introduced the random effects of the SNP and SNP-environment interaction under test into a meta-regression model to account for heterogeneity. A test for the SNP-environment interaction was formulated to test for fixed and random effects of the interaction simultaneously. Similarly, a test for total genetic effects was formulated to test for fixed effects of the SNP and the SNP-environment interaction together with their random effects. We performed simulations to study the null distribution and statistical power of the proposed tests. We show that the new methods have higher power than classical random-effects and fixed-effects meta-regression methods when heterogeneity effects are large.