Gene hunting with hidden Markov model knockoffs

• 成果类型: Article
• 署名作者: Sesia, M.; Sabatti, C.; Candes, E. J.
• 署名单位: Stanford University
• 刊物名称: BIOMETRIKA
• ISSN/ISSBN: 0006-3444
• DOI: 10.1093/biomet/asy033
• 发表日期: 2019
• 页码: 118
• 关键词: genome-wide association; False Discovery Rate; genotype data; Linkage Disequilibrium; missing heritability; inference; selection; blocks; imputation; algorithm

摘要

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.