Optimal multiple testing under a Gaussian prior on the effect sizes

• 成果类型: Article
• 署名作者: Dobriban, Edgar; Fortney, Kristen; Kim, Stuart K.; Owen, Art B.
• 署名单位: Stanford University; Stanford University
• 刊物名称: BIOMETRIKA
• ISSN/ISSBN: 0006-3444
• DOI: 10.1093/biomet/asv050
• 发表日期: 2015
• 页码: 753766
• 关键词: prior information; association; disease; POWER; loci; pleiotropy; longevity; traits; error; rates

摘要

We develop a new method for large-scale frequentist multiple testing with Bayesian prior information. We find optimal p-value weights that maximize the average power of the weighted Bonferroni method. Due to the nonconvexity of the optimization problem, previous methods that account for uncertain prior information are suitable for only a small number of tests. For a Gaussian prior on the effect sizes, we give an efficient algorithm that is guaranteed to find the optimal weights nearly exactly. Our method can discover new loci in genome-wide association studies and compares favourably to competitors. An open-source implementation is available.