Composite kernel machine regression based on likelihood ratio test for joint testing of genetic and gene–environment interaction effect

Abstract

In this article, we develop a kernel machine regression framework to model the overall genetic effect of a SNP-set, considering the possible Gene-Environment (GE) interaction. We propose a likelihood ratio test (LRT) and a restricted likelihood ratio test (RLRT) for statistical significance. We derive a Monte Carlo approach for the finite sample distributions of LRT and RLRT statistics.