Abstract:
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We present a novel statistical procedure to detect the nonlinear gene-environment (GXE) interaction with continuous traits in sequencing association studies. Commonly-used approaches for GXE interaction usually assume linear relationship between genetic and environmental factor, thus they suffer power loss when the underlying relationship is nonlinear. A varying-coefficient model (Ma et al., 2011) is proposed to relax the linear assumption, however, it's unable to adjust for population stratification, a major source of confounding in genome-wide association studies. To overcome these limitations, we develop the Varying-Coefficient embedded Linear Mixed Model (VC-LMM) for assessing the nonlinear GXE interaction and accounting for population stratification. The proposed VC-LMM well controls type I error rates when the population stratification is present, and it's powerful for both common and rare variants. We apply computationally efficient algorithms for generating null distributions and estimating parameters in the linear mixed model, thus the computational burden is greatly reduced. Using simulation studies, we demonstrate the performance of VC-LMM.
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