A Regression Framework for the Proportion of True Null Hypotheses

• Activity Number: 109 - Learning from External Covariates in High-Dimensional Genomic Data Analysis
• Type: Topic Contributed
• Date/Time: Monday, July 31, 2017 : 8:30 AM to 10:20 AM
• Sponsor: Section on Statistics in Genomics and Genetics
• Author(s): Simina Boca* and Jeffrey Leek
• Companies: Georgetown University Medical Center and Johns Hopkins Bloomberg School of Public Health
• Keywords: genome-wide association study ; false discovery rate ; multiple hypothesis testing

Abstract

Many current studies in genetics and genomics 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. 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.