Replicability analysis for genome-wide association studies

TL;DR

This paper introduces an empirical Bayes method for assessing the replicability of associations in genome-wide association studies, improving the reliability of findings by formally estimating replicated results.

Contribution

It proposes a novel empirical Bayes approach to formally evaluate replicability in GWA studies, complementing traditional meta-analyses.

Findings

The method maintains a low false discovery proportion in simulations.

Applied to T2D studies, it identified 219 replicated SNPs out of 803.

Recommends combining meta-analysis with replicability analysis for better results.

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.