title: A novel method for identifying nonlinear gene-environment interactions in case-control association studies.
abstract: The genetic influences on complex disease traits generally depend on the joint effects of multiple genetic variants, environmental factors, as well as their interplays. Gene × environment (G × E) interactions play vital roles in determining an individual's disease risk, but the underlying genetic machinery is poorly understood. Traditional analysis assuming linear relationship between genetic and environmental factors, along with their interactions, is commonly pursued under the regression-based framework to examine G × E interactions. This assumption, however, could be violated due to nonlinear responses of genetic variants to environmental stimuli. As an extension to our previous work on continuous traits, we proposed a flexible varying-coefficient model for the detection of nonlinear G × E interaction with binary disease traits. Varying coefficients were approximated by a non-parametric regression function through which one can assess the nonlinear response of genetic factors to environmental changes. A group of statistical tests were proposed to elucidate various mechanisms of G × E interaction. The utility of the proposed method was illustrated via simulation and real data analysis with application to type 2 diabetes.
authors: Department of Statistics and Probability, Michigan State University, East Lansing, MI, 48824, USA.
authors: Cen
authors: C
authors: Wu
authors: Yuehua
authors: Y
authors: Cui
mesh_terms: D016022:Case-Control Studies; D015331:Cohort Studies; D003198:Computer Simulation; D003924:Diabetes Mellitus, Type 2 / Q000453:epidemiology / Q000208:ethnology / Q000235:genetics; D005189:False Positive Reactions; D005260:Female; D059647:Gene-Environment Interaction; D020022:Genetic Predisposition to Disease / Q000453:epidemiology*; D055106:Genome-Wide Association Study / Q000706:statistics & numerical data*; D006801:Humans; D008297:Male; D017711:Nonlinear Dynamics; D044465:White People / Q000235:genetics / Q000706:statistics & numerical data
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