Variable Prioritization in “Black Box” Statistical Methods
University of Michigan School of Public Health
3755 SPH I, 1415 Washington Heights Ann Arbor, MI 48109-2029
A consistent theme of the work done in the Crawford Lab is to take modern computational approaches and develop theory that enable their interpretations to be related back to classical genomic principles. The central aim of this talk is to address variable selection questions in nonlinear and nonparametric regression. Motivated by statistical genetics, where nonlinear interactions are of particular interest, we introduce a novel, interpretable, and computationally efficient way to summarize the relative importance of predictor variables. Methodologically, we develop the “RelATive cEntrality” (RATE) measure to prioritize candidate genetic variants that are not just marginally important, but whose associations also stem from significant covarying relationships with other variants in the data. We will illustrate RATE through Bayesian Gaussian process regression; although, the proposed innovations apply to other nonlinear methods (e.g. deep neural networks). It is known that nonlinear models often exhibit greater predictive accuracy than linear models, particularly for phenotypes generated by complex genetic architectures. With detailed simulations and applications to real genome-wide association mapping studies, we show that applying RATE enables an explanation for this improved performance. Light refreshments for seminar guests will be served at 3:10 p.m. in 3755 Department of Biostatistics

Variable Prioritization in “Black Box” Statistical Methods

Lorin Crawford, Ph.D., Assistant Professor of Biostatistics - Brown University School of Public Health

icon to add this event to your google calendarNovember 8, 2018
3:30 pm - 5:00 pm
3755 SPH I
1415 Washington Heights
Ann Arbor, MI 48109-2029
Sponsored by: Department of Biostatistics
Contact Information: Zhenke Wu (zhenkewu@umich.edu), Han Peisong (peisong@umich.edu)

A consistent theme of the work done in the Crawford Lab is to take modern computational approaches and develop theory that enable their interpretations to be related back to classical genomic principles. The central aim of this talk is to address variable selection questions in nonlinear and nonparametric regression. Motivated by statistical genetics, where nonlinear interactions are of particular interest, we introduce a novel, interpretable, and computationally efficient way to summarize the relative importance of predictor variables. Methodologically, we develop the “RelATive cEntrality” (RATE) measure to prioritize candidate genetic variants that are not just marginally important, but whose associations also stem from significant covarying relationships with other variants in the data. We will illustrate RATE through Bayesian Gaussian process regression; although, the proposed innovations apply to other nonlinear methods (e.g. deep neural networks). It is known that nonlinear models often exhibit greater predictive accuracy than linear models, particularly for phenotypes generated by complex genetic architectures. With detailed simulations and applications to real genome-wide association mapping studies, we show that applying RATE enables an explanation for this improved performance. Light refreshments for seminar guests will be served at 3:10 p.m. in 3755