Covariate-adjusted multiple testing in genome-wide association studies via factorial hidden Markov models

Tingting Cui (), Pengfei Wang () and Wensheng Zhu ()
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Tingting Cui: Northeast Normal University
Pengfei Wang: Northeast Normal University
Wensheng Zhu: Northeast Normal University

TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, 2021, vol. 30, issue 3, No 10, 737-757

Abstract: Abstract It is more and more important to consider the dependence structure among multiple testings, especially for the genome-wide association studies (GWAS). The existing procedures, such as local index of significance (LIS) and pooled local index of significance (PLIS), were proposed to test hidden Markov model (HMM)-dependent hypotheses under the framework of compound decision theory, which was successfully applied to GWAS. However, the etiology of complex diseases is not only with respect to the genetic effects, but also the environmental factors. Failure to account for the covariates in multiple testing can produce misleading bias of the association of interest, or suffer from loss of testing efficiency. In this paper, we develop a covariate-adjusted multiple testing procedure, called covariate-adjusted local index of significance (CALIS), to account for the effects of environmental factors via a factorial hidden Markov model. The theoretical results show that our procedure can control the false discovery rate (FDR) at the nominal level and has the smallest false non-discovery rate (FNR) among all valid FDR procedures. We further demonstrate the advantage of our novel procedure over the existing procedures by simulation studies and a real data analysis.

Keywords: Factorial hidden Markov model; Covariate adjustment; Multiple hypotheses testing; False discovery rate; GWAS; 62M02; 62P10; 62E20 (search for similar items in EconPapers)
Date: 2021
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Citations: View citations in EconPapers (1)

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DOI: 10.1007/s11749-020-00746-8

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