Replicability analysis for genome-wide association studies

Abstract

The paramount importance of replicating associations is well recognized in the genome-wide associaton (GWA) research community, yet methods for assessing replicability of associations are scarce. Published GWA studies often combine separately the results of primary studies and of the follow-up studies. Informally, reporting the two separate meta-analyses, that of the primary studies and follow-up studies, gives a sense of the replicability of the results. We suggest a formal empirical Bayes approach for discovering whether results have been replicated across studies, in which we estimate the optimal rejection region for discovering replicated results. We demonstrate, using realistic simulations, that the average false discovery proportion of our method remains small. We apply our method to six type two diabetes (T2D) GWA studies. Out of 803 SNPs discovered to be associated with T2D using a typical meta-analysis, we discovered 219 SNPs with replicated associations with T2D. We recommend complementing a meta-analysis with a replicability analysis for GWA studies.

Published in at http://dx.doi.org/10.1214/13-AOAS697 the Annals of Applied Statistics (http://www.imstat.org/aoas/) by the Institute of Mathematical Statistics (http://www.imstat.org)

References in corpus (6)

• Replication in Genome-Wide Association Studies
• Microarrays, Empirical Bayes and the Two-Groups Model
• Replicability analysis for genome-wide association studies
• An empirical Bayes mixture method for effect size and false discovery rate estimation
• Discovering findings that replicate from a primary study of high dimension to a follow-up study
• Statistical tests for the intersection of independent lists of genes: Sensitivity, FDR, and type I error control

Cited by in corpus (11)

• Replicability analysis for genome-wide association studies
• Deciding whether follow-up studies have replicated findings in a preliminary large-scale "omics' study"
• Assessing replicability of findings across two studies of multiple features
• Extracting replicable associations across multiple studies: algorithms for controlling the false discovery rate
• Searching for consistent associations with a multi-environment knockoff filter
• Randomized p-values for multiple testing and their application in replicability analysis
• Post hoc false discovery proportion inference under a Hidden Markov Model
• Semi-supervised multiple testing
• Optimal detection of weak positive latent dependence between two sequences of multiple tests
• Admissibility in Partial Conjunction Testing
• Optimal exact tests for composite alternative hypotheses on cross tabulated data