TY - JOUR T1 - A direct approach to estimating false discovery rates conditional on covariates JF - bioRxiv DO - 10.1101/035675 SP - 035675 AU - Simina M. Boca AU - Jeffrey T. Leek Y1 - 2017/01/01 UR - http://biorxiv.org/content/early/2017/07/25/035675.abstract N2 - Modern scientific studies from many diverse areas of research abound with multiple hypothesis testing concerns. The false discovery rate is one of the most commonly used error rates for measuring and controlling rates of false discoveries when performing multiple tests. Adaptive false discovery rates 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 false discovery rate estimator. We provide both finite sample and asymptotic conditions under which this covariate-adjusted estimate is conservative - leading to appropriately conservative false discovery rate estimates. Our case study concerns a genome-wise association meta-analysis which considers associations with 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. ER -