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


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Article Info

DATE

2021-07-13

AUTHORS

Sangyoon Yi, Xianyang Zhang, Lu Yang, Jinyan Huang, Yuanhang Liu, Chen Wang, Daniel J. Schaid, Jun Chen

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%. More... »

PAGES

208

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    URI

    http://scigraph.springernature.com/pub.10.1186/s13059-021-02418-8

    DOI

    http://dx.doi.org/10.1186/s13059-021-02418-8

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    42 schema:description 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%.
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