Extracting replicable associations across multiple studies: Empirical Bayes algorithms for controlling the false discovery rate.

ABSTRACT: In almost every field in genomics, large-scale biomedical datasets are used to report associations. Extracting associations that recur across multiple studies while controlling the false discovery rate is a fundamental challenge. Here, we propose a new method to allow joint analysis of multiple studies. Given a set of p-values obtained from each study, the goal is to identify associations that recur in at least k > 1 studies while controlling the false discovery rate. We propose several new algorithms that differ in how the study dependencies are modeled, and compare them and extant methods under various simulated scenarios. The top algorithm, SCREEN (Scalable Cluster-based REplicability ENhancement), is our new algorithm that works in three stages: (1) clustering an estimated correlation

SUBMITTER: Amar D 

PROVIDER: S-EPMC5576761 | biostudies-literature | 2017 Aug

REPOSITORIES: biostudies-literature