Replicability analysis for genome-wide association studies

Research output: Contribution to journalArticlepeer-review

29 Scopus citations

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.

Original languageEnglish
Pages (from-to)481-498
Number of pages18
JournalAnnals of Applied Statistics
Volume8
Issue number1
DOIs
StatePublished - Mar 2014

Keywords

  • Combined analysis
  • Empirical bayes
  • False discovery rate
  • Meta-analysis
  • Replication
  • Reproducibility
  • Type 2 diabetes

Fingerprint

Dive into the research topics of 'Replicability analysis for genome-wide association studies'. Together they form a unique fingerprint.

Cite this