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Accurate error control in high‐dimensional association testing using conditional false discovery rates

Published version
Peer-reviewed

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Abstract

Abstract: High‐dimensional hypothesis testing is ubiquitous in the biomedical sciences, and informative covariates may be employed to improve power. The conditional false discovery rate (cFDR) is a widely used approach suited to the setting where the covariate is a set of p‐values for the equivalent hypotheses for a second trait. Although related to the Benjamini–Hochberg procedure, it does not permit any easy control of type‐1 error rate and existing methods are over‐conservative. We propose a new method for type‐1 error rate control based on identifying mappings from the unit square to the unit interval defined by the estimated cFDR and splitting observations so that each map is independent of the observations it is used to test. We also propose an adjustment to the existing cFDR estimator which further improves power. We show by simulation that the new method more than doubles potential improvement in power over unconditional analyses compared to existing methods. We demonstrate our method on transcriptome‐wide association studies and show that the method can be used in an iterative way, enabling the use of multiple covariates successively. Our methods substantially improve the power and applicability of cFDR analysis.

Description

Funder: Johnson and Johnson; Id: http://dx.doi.org/10.13039/100004331

Keywords

RESEARCH PAPER, MULTIPLE INFERENCE, conditional false discovery rate, empirical Bayes, false discovery rate, high‐dimensional association study, transcriptome‐wide association study, unsupervised learning

Journal Title

Biometrical Journal

Conference Name

Journal ISSN

0323-3847
1521-4036

Volume Title

63

Publisher

Sponsorship
Wellcome Trust (WT107881)
Medical Research Council (MC_UU_00002/4)