%0 Journal Article %A Simina M. Boca %A Jeffrey T. Leek %T A regression framework for the proportion of true null hypotheses %D 2015 %R 10.1101/035675 %J bioRxiv %P 035675 %X 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. We provide both finite sample and asymptotic conditions under which this covariate-adjusted estimate is conservative - leading to appropriately conservative false discovery rate estimates. We demonstrate the viability of our approach through simulation and an application to estimating different prior probabilities for a range of sample sizes and allele frequencies in a GWAS meta-analysis. %U https://www.biorxiv.org/content/biorxiv/early/2015/12/30/035675.full.pdf