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“Statistical Dissection of Gene-environment Interactions: A Semi-Parametric Perspective” Yuehua Cui Department of Statistics and Probability, Michigan State University Abstract The last few years have witnessed a significant development in statistical methods for genetic data analysis, owing to the massive amount of data generated with high-throughput technologies. Identifying gene-environment (G×E) interactions has been one of the central foci along the line, given the importance of G×E interactions in determining the variation of many complex disease traits such as Parkinson disease, type 2 diabetes and cardiovascular diseases. However, the underlying machinery of G×E is still poorly understood due to the lack of powerful statistical methods. Motivated by epidemiological evidences that a disease risk can be modified by simultaneous exposure to multiple environmental agents larger than simple addition of individual factor acting along, we proposed a semi-parametric model to assess how multiple environmental factors acting jointly to interact with genetic variants to affect a disease risk. We derived a profile method to estimate parametric parameters and a B-spline backfitted method to estimate nonlinear functions. The model allows us to study nonlinear interactions between genes and environments. Hypothesis testing for the parametric coefficients and nonparametric functions are conducted. The utility of the method is demonstrated through extensive simulations and a case study.