Tests for Gene-Environment Interactions and Joint Effects With Exposure Misclassification.

Philip S Boonstra,Stephanie L Schmit,Stephen B Gruber,Jaeil Ahn,Nilanjan Chatterjee,Bhramar Mukherjee

doi:10.1093/aje/kwv198

Abstract

The number of methods for genome-wide testing of gene-environment (G-E) interactions continues to increase, with the aim of discovering new genetic risk factors and obtaining insight into the disease-gene-environment relationship. The relative performance of these methods, assessed on the basis of family-wise type I error rate and power, depends on underlying disease-gene-environment associations, estimates of which may be biased in the presence of exposure misclassification. This simulation study expands on a previously published simulation study of methods for detecting G-E interactions by evaluating the impact of exposure misclassification. We consider 7 single-step and modular screening methods for identifying G-E interaction at a genome-wide level and 7 joint tests for genetic association and G-E interaction, for which the goal is to discover new genetic susceptibility loci by leveraging G-E interaction when present. In terms of statistical power, modular methods that screen on the basis of the marginal disease-gene relationship are more robust to exposure misclassification. Joint tests that include main/marginal effects of a gene display a similar robustness, which confirms results from earlier studies. Our results offer an increased understanding of the strengths and limitations of methods for genome-wide searches for G-E interaction and joint tests in the presence of exposure misclassification.

Highlights

Many complex diseases (D) have a multifactorial etiology resulting from the interplay of genetic factors (G) and environmental exposures (E)
Relative to testing all markers, modular procedures that leverage empirical G-E and/or D-G associations to first screen or prioritize markers may have more power to detect gene-environment interactions (GEI). In the first such two-stage procedure, which uses only G-E association [4], the power gain depends on choosing the optimal value of screening significance level, which in turn depends on the case-control ratio, number of markers, and disease prevalence [11, 18]
Later two-step procedures that account for D-G association (H2, joint marginal/association screening (EDGxE), CT) do not exhibit this undesirable property

Summary

Introduction

Many complex diseases (D) have a multifactorial etiology resulting from the interplay of genetic factors (G) and environmental exposures (E). Numerous statistical and epidemiological papers have considered the discovery and characterization of gene-environment interaction (GEI) [1]–[16] including discussions regarding efficiently testing GEIs [17] and conducting Gene Environment Wide Interaction Studies (GEWIS) [18, 19]. These have examined the effect of violations to geneenvironment (G-E) independence in great detail. We evaluate fourteen GEI and gene discovery methods

Methods

Results

Conclusion