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March 2014 Replicability analysis for genome-wide association studies
Ruth Heller, Daniel Yekutieli
Ann. Appl. Stat. 8(1): 481-498 (March 2014). DOI: 10.1214/13-AOAS697


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.


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Ruth Heller. Daniel Yekutieli. "Replicability analysis for genome-wide association studies." Ann. Appl. Stat. 8 (1) 481 - 498, March 2014.


Published: March 2014
First available in Project Euclid: 8 April 2014

zbMATH: 06302244
MathSciNet: MR3191999
Digital Object Identifier: 10.1214/13-AOAS697

Keywords: Combined analysis , Empirical Bayes , False discovery rate , Meta-analysis , replication , reproducibility , type 2 diabetes

Rights: Copyright © 2014 Institute of Mathematical Statistics


Vol.8 • No. 1 • March 2014
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