Inference of gene-environment interaction from heterogeneous case-parent trios

Abstract

In genetic epidemiology, log-linear models of population risk may be used to study the effect of genotypes and exposures on the relative risk of a disease. Such models may also include gene environment interaction terms that allow the genotypes to modify the effect of the exposure, or equivalently, the exposure to modify the effect of genotypes on the relative risk. When a measured test locus is in linkage disequilibrium with a causal locus, exposure-related genetic population structure can lead to spurious gene-environment interaction; that is, to apparent gene-environment interaction at the test locus in the absence of true gene-environment interaction at the causal locus. Exposure-related genetic population structure occurs when the distributions of exposures and of haplotypes at the test and causal locus both differ across population strata (i.e., population substructure). In a case-parent trio design researchers collect genotypes and exposures on affected children and genotypes on their parents. The design permits inference of genetic main effects while avoiding confounding bias due to population stratification. Unfortunately, when there is exposure-related genetic population structure, the protection against confounding bias for the main effect does not extend to the interaction term. We show that current methods to reduce the bias in estimated gene-environment interactions can only account for simple population structure involving two strata. To fill this gap, we propose to directly accommodate multiple population strata by adjusting for genetic principal components. We evaluate our approach through simulation and illustrate it on data from a study of genetic modifiers of cleft palate.