Work

Variable prioritization in nonlinear black box methods: A genetic association case study

Year: 2019

Type: article

Abstract: 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.

Source: The Annals of Applied Statistics

DOI: 10.1214/18-aoas1222

Authors: Lorin Crawford, Seth R. Flaxman, Daniel E. Runcie, Mike West

Institutions: Brown University, Imperial College London, University of California, Davis, Duke University

Language: English

Topic: Gaussian Processes and Bayesian Inference

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

Open Access status: green

Funders: Brown University, Wellcome Trust, National Institute of General Medical Sciences

Awards: WTCCC2 core activities, WTCCC3 core activities., The genetics of weight, BMI, adiposity and obesity, COBRE: Center for Computational Biology of Human Disease