A direct approach to estimating false discovery rates conditional on covariates

PeerJ
Simina M Boca, Jeffrey T Leek

Abstract

Modern scientific studies from many diverse areas of research abound with multiple hypothesis testing concerns. The false discovery rate (FDR) is one of the most commonly used approaches for measuring and controlling error rates when performing multiple tests. Adaptive FDRs rely on an estimate of the proportion of null hypotheses among all the hypotheses being tested. This proportion is typically estimated once for each collection of hypotheses. Here, we propose a regression framework to estimate the proportion of null hypotheses conditional on observed covariates. This may then be used as a multiplication factor with the Benjamini-Hochberg adjusted p-values, leading to a plug-in FDR estimator. We apply our method to a genome-wise association meta-analysis for body mass index. In our framework, we are able to use the sample sizes for the individual genomic loci and the minor allele frequencies as covariates. We further evaluate our approach via a number of simulation scenarios. We provide an implementation of this novel method for estimating the proportion of null hypotheses in a regression framework as part of the Bioconductor package swfdr.

References

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Citations

Jul 4, 2019·FASEB Journal : Official Publication of the Federation of American Societies for Experimental Biology·Fabio SallustioLoreto Gesualdo
Jun 12, 2020·Proceedings of the National Academy of Sciences of the United States of America·Ronald YurkoBernie Devlin
May 3, 2019·JCI Insight·Swetha GarimallaF Eun-Hyung Lee
Jan 12, 2021·Metabolomics : Official Journal of the Metabolomic Society·Yuko YamaguchiRichard D Semba
May 27, 2021·The American Journal of Clinical Nutrition·Pi-I D LinEmily Oken
Oct 13, 2021·Genome Biology·Xinzhou GeJingyi Jessica Li
Oct 14, 2021·American Journal of Human Genetics·Hamzeh M TanhaDale R Nyholt

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Software Mentioned

FDRreg
qvalue
swfdr
Bioconductor
GIANT

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