Open Access
June 2019 Variable prioritization in nonlinear black box methods: A genetic association case study
Lorin Crawford, Seth R. Flaxman, Daniel E. Runcie, Mike West
Ann. Appl. Stat. 13(2): 958-989 (June 2019). DOI: 10.1214/18-AOAS1222


The central aim in this paper 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 and interpretable 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 illustrate RATE through Bayesian Gaussian process regression, but the methodological innovations apply to other “black box” methods. 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 two real data association mapping studies, we show that applying RATE enables an explanation for this improved performance.


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Lorin Crawford. Seth R. Flaxman. Daniel E. Runcie. Mike West. "Variable prioritization in nonlinear black box methods: A genetic association case study." Ann. Appl. Stat. 13 (2) 958 - 989, June 2019.


Received: 1 March 2018; Revised: 1 August 2018; Published: June 2019
First available in Project Euclid: 17 June 2019

zbMATH: 1423.62062
MathSciNet: MR3963559
Digital Object Identifier: 10.1214/18-AOAS1222

Keywords: centrality measures , Gaussian processes , Genome-wide association studies , Nonlinear regression , statistical genetics , variable prioritization

Rights: Copyright © 2019 Institute of Mathematical Statistics

Vol.13 • No. 2 • June 2019
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