The functional false discovery rate with applications to genomics. Author Xiongzhi Chen, David Robinson, John Storey Publication Year 2021 Type Journal Article Abstract The false discovery rate (FDR) measures the proportion of false discoveries among a set of hypothesis tests called significant. This quantity is typically estimated based on p-values or test statistics. In some scenarios, there is additional information available that may be used to more accurately estimate the FDR. We develop a new framework for formulating and estimating FDRs and q-values when an additional piece of information, which we call an "informative variable", is available. For a given test, the informative variable provides information about the prior probability a null hypothesis is true or the power of that particular test. The FDR is then treated as a function of this informative variable. We consider two applications in genomics. Our first application is a genetics of gene expression (eQTL) experiment in yeast where every genetic marker and gene expression trait pair are tested for associations. The informative variable in this case is the distance between each genetic marker and gene. Our second application is to detect differentially expressed genes in an RNA-seq study carried out in mice. The informative variable in this study is the per-gene read depth. The framework we develop is quite general, and it should be useful in a broad range of scientific applications. Keywords Animals, Mice, Gene Expression Profiling, Genomics, Genetic Markers, Probability, Saccharomyces cerevisiae Journal Biostatistics Volume 22 Issue 1 Pages 68-81 Date Published 2021 Jan 28 ISSN Number 1468-4357 DOI 10.1093/biostatistics/kxz010 Alternate Journal Biostatistics PMCID PMC7846131 PMID 31135886 PubMedPubMed CentralGoogle ScholarBibTeXEndNote X3 XML