Exploiting Gene-Environment Interaction to Detect Genetic Associations

TLDR

Complex disease results from the interplay of genetic and environmental factors, yet the optimal use of gene‑environment interaction to locate susceptibility loci in large marker scans remains unclear. The study proposes a joint test of marginal association and gene‑environment interaction for case‑control data. The authors compare the joint test’s power and sample‑size requirements to marginal, logistic‑regression interaction, and case‑only interaction tests. The joint test is nearly optimal across penetrance models, providing better power than marginal tests when effects are exposure‑specific and superior to interaction tests when effects are not exposure‑restricted, making it attractive for large‑scale scans with unknown interaction models.

Abstract

Complex disease by definition results from the interplay of genetic and environmental factors. However, it is currently unclear how gene-environment interaction can best be used to locate complex disease susceptibility loci, particularly in the context of studies where between 1,000 and 1,000,000 markers are scanned for association with disease. We present a joint test of marginal association and gene-environment interaction for case-control data. We compare the power and sample size requirements of this joint test to other analyses: the marginal test of genetic association, the standard test for gene-environment interaction based on logistic regression, and the case-only test for interaction that exploits gene-environment independence. Although for many penetrance models the joint test of genetic marginal effect and interaction is not the most powerful, it is nearly optimal across all penetrance models we considered. In particular, it generally has better power than the marginal test when the genetic effect is restricted to exposed subjects and much better power than the tests of gene-environment interaction when the genetic effect is not restricted to a particular exposure level. This makes the joint test an attractive tool for large-scale association scans where the true gene-environment interaction model is unknown.