ArticleStatistica Sinica2019
Large-Scale Simultaneous Testing of Cross-Covariance Matrices with Applications to PheWAS.
Article in Statistica Sinica, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
3 citing papers in PubMed.
- Model-Free Statistical Inference on High-Dimensional Data.Journal of the American Statistical Association · 2025Article
- Article
- An integrative functional genomics framework for effective identification of novel regulatory variants in genome-phenome studies.Genome medicine · 2018Article
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Authors and funding
4 authors.
Funding
Abstract
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.
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