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Large-Scale Simultaneous Testing of Cross-Covariance Matrices with Applications to PheWAS
Statistica Sinica ( IF 1.4 ) Pub Date : 2019-01-01 , DOI: 10.5705/ss.202017.0189
Tianxi Cai 1 , T Tony Cai 2 , Katherine Liao 3 , Weidong Liu 4
Affiliation  

Motivated by applications in phenome-wide association studies (PheWAS), we consider in this paper simultaneous testing of columns of high-dimensional cross-covariance matrices and develop a multiple testing procedure with theoretical guarantees. It is shown that the proposed testing procedure maintains a desired false discovery rate (FDR) and false discovery proportion (FDP) under mild regularity conditions. We also provide results on the magnitudes of the signals that can be detected with high power. Simulation studies demonstrate that the proposed procedure can be substantially more powerful than existing FDR controlling procedures in the presence of correlation of unknown structure. The proposed multiple testing procedure is applied to a PheWAS of two auto-immune genetic markers using a rheumatoid arthritis patient cohort constructed from the electronic medical records of Partners Healthcare System.

中文翻译:

交叉协方差矩阵的大规模同时测试与 PheWAS 的应用

受全现象关联研究 (PheWAS) 应用的启发,我们在本文中考虑同时测试高维交叉协方差矩阵列,并开发具有理论保证的多重测试程序。结果表明,所提出的测试程序在温和的规律性条件下保持了所需的错误发现率(FDR)和错误发现率(FDP)。我们还提供了可以用高功率检测到的信号幅度的结果。模拟研究表明,在存在未知结构的相关性的情况下,所提出的程序可以比现有的 FDR 控制程序更强大。
更新日期:2019-01-01
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