Semiparametric Analysis of Complex Polygenic Gene-Environment Interactions in Case-Control Studies

Abstract

Gene-environment interactions can be efficiently estimated in case-control data by existing retrospective methods that assume gene-environment independence in the source population, but such techniques require parametric modeling of the genetic variables. Standard logistic regression analysis of case-control data has low power to detect gene-environment interactions, but it has been the only method capable of analyzing complex polygenic data for which parametric distributional models are not feasible. This dissertation proposes a general, computationally simple, semiparametric method for analysis of case-control studies that allows exploitation of the assumption of gene-environment independence without any further parametric modeling assumptions about the marginal distributions of any of the two sets of factors. The method relies on the key observation that an underlying efficient profile likelihood depends on the distribution of genetic factors only through certain expectation terms that can be evaluated empirically. This method is further improved by treating the genetic and environmental variables symmetrically to generate two sets of parameter estimates that are combined to generate a more efficient estimate. A semiparametric framework is employed to develop the asymptotic theory of the estimators, and their performance is evaluated via simulation studies. The methods are illustrated using data from a case-control study of breast cancer, and free software implementing both methods is demonstrated.