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2dFDR: a new approach to confounder adjustment substantially increases detection power in omics association studies

Year: 2021
Type: article
Abstract: One challenge facing omics association studies is the loss of statistical power when adjusting for confounders and multiple testing. The traditional statistical procedure involves fitting a confounder-adjusted regression model for each omics feature, followed by multiple testing correction. Here we show that the traditional procedure is not optimal and present a new approach, 2dFDR, a two-dimensional false discovery rate control procedure, for powerful confounder adjustment in multiple testing. Through extensive evaluation, we demonstrate that 2dFDR is more powerful than the traditional procedure, and in the presence of strong confounding and weak signals, the power improvement could be more than 100%.
DOI: 10.1186/s13059-021-02418-8
Authors: Sangyoon Yi, Xianyang Zhang, Lu Yang, Jinyan Huang, Yuanhang Liu, Chen Wang, Daniel J. Schaid, Jun Chen
Institutions: Texas A&M University, Mayo Clinic, Shanghai Jiao Tong University, Ruijin Hospital, Shanghai Institute of Hematology, State Key Laboratory of Medical Genomics
Language: English
Open Access status: gold
Estimated APC paid: $4,890
Awards: G4208551716, ATD: Collaborative Research: Predicting the Threat of Vector-Borne Illnesses Using Spatiotemporal Weather Patterns, G5704055085, Leveraging Covariate and Structural Information for Efficient Large-Scale and High-Dimensional Inference, G8000334768, G9000211890
OpenAlex ID: W3180066447
FWCI: 0.134
Cites: 47
Cited by: 3
Related to: 10