Modeling Epistasis with Approximate Kernel Regression Methods

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Wednesday, October 25, 2017 4:00pm - 5:00pm
Watson CIT - SWIG Boardroom (CIT241)

Lorin Crawford
Assistant Professor of Biostatistics
Brown University

Modeling Epistasis with Approximate Kernel Regression Methods

 Epistasis, commonly defined as the interaction between multiple genes, is an important genetic component underlying phenotypic variation. In light of this, nonlinear kernel regression models are often used for phenotypic prediction because they are more accurate than linear models. Association mapping within kernel regression models is a challenge partly because, unlike in the linear regression setting, there is no clear concept of an effect size for regression coefficients. In this talk, we present a novel framework that provides an effect size analog for each explanatory variable in Bayesian kernel regression models when the nonlinear kernel function is shift-invariant. We will also propose the idea of a post-hoc unified approach for association mapping and epistasis detection via distributional “centrality measures” using Küllback-Leibler (KL) divergence