Gene hunting with hidden Markov model knockoffs

M Sesia, C Sabatti and E J Candès

Biometrika, 2019, vol. 106, issue 1, 1-18

Abstract: SUMMARY Modern scientific studies often require the identification of a subset of explanatory variables. Several statistical methods have been developed to automate this task, and the framework of knockoffs has been proposed as a general solution for variable selection under rigorous Type I error control, without relying on strong modelling assumptions. In this paper, we extend the methodology of knockoffs to problems where the distribution of the covariates can be described by a hidden Markov model. We develop an exact and efficient algorithm to sample knockoff variables in this setting and then argue that, combined with the existing selective framework, this provides a natural and powerful tool for inference in genome-wide association studies with guaranteed false discovery rate control. We apply our method to datasets on Crohn’s disease and some continuous phenotypes.

Keywords: False discovery rate; Genome-wide association study; Knockoff; Variable selection (search for similar items in EconPapers)
Date: 2019
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Citations: View citations in EconPapers (6)

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