Several statistical methods have been proposed for testing gene(G)-environment(E) interactions under additive risk models using genome-wide association study data. However, these approaches have strong assumptions on underlying genetic models such as dominant or recessive effects that are known to be less robust when the true genetic model is unknown. We aim to develop a robust trend test employing a likelihood ratio test for detecting G-E interaction under an additive risk model, while incorporating the G-E independence assumption to increase power. We used a constrained likelihood to impose two sets of constraints for (i) the linear trend effect of genotype and (ii) the additive joint effects of G and E. To incorporate the G-E independence assumption, a retrospective likelihood was used versus a standard prospective likelihood. Numerical investigation suggests that the proposed tests are more powerful than tests assuming dominant, recessive, or general models under various parameter settings and under both likelihoods. Incorporation of the independence assumption enhances efficiency by 2.5- fold. We applied the proposed methods to examine gene-smoking interaction for lung cancer and gene-APOE*4 interaction for Alzheimer's disease, which identified two interactions between APOE*4 and loci MS4A and BIN1 at genome-wide significance that were replicated using independent data.
View details for DOI 10.1093/aje/kwaa132
View details for PubMedID 32870973