Gene hunting with hidden Markov model knockoffs.

ABSTRACT: 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 appl

SUBMITTER: Sesia M 

PROVIDER: S-EPMC6373422 | biostudies-literature | 2019 Mar

REPOSITORIES: biostudies-literature