Download “Statistical Dissection of Gene-environment Interactions: A Semi-Parametric Perspective” Yuehua Cui

“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.