Resampling to Address the Winner's Curse in Genetic Association Analysis of Time to Event. Issue 7 (28th September 2015)

• Record Type: Journal Article
• Authors: Poirier, Julia G.; Faye, Laura L.; Dimitromanolakis, Apostolos; Paterson, Andrew D.; Sun, Lei; Bull, Shelley B.
• Is Part Of: Genetic epidemiology. Volume 39:Issue 7(2015)
• Page Start: 518
• Page End: 528
• Publication Date: 2015-09-28
• Subjects: Genetic epidemiology -- Periodicals; Heredity -- Periodicals; Medical geography -- Periodicals; 614
• DOI: 10.1002/gepi.21920
• Languages: English
• ISSNs: 0741-0395

Abstract

<abstract abstract-type="main"> <title>ABSTRACT</title> <p>The "winner's curse" is a subtle and difficult problem in interpretation of genetic association, in which association estimates from large‐scale gene detection studies are larger in magnitude than those from subsequent replication studies. This is practically important because use of a biased estimate from the original study will yield an underestimate of sample size requirements for replication, leaving the investigators with an underpowered study. Motivated by investigation of the genetics of type 1 diabetes complications in a longitudinal cohort of participants in the Diabetes Control and Complications Trial/Epidemiology of Diabetes Interventions and Complications (DCCT/EDIC) Genetics Study, we apply a bootstrap resampling method in analysis of time to nephropathy under a Cox proportional hazards model, examining 1, 213 single‐nucleotide polymorphisms (SNPs) in 201 candidate genes custom genotyped in 1, 361 white probands. Among 15 top‐ranked SNPs, bias reduction in log hazard ratio estimates ranges from 43.1% to 80.5%. In simulation studies based on the observed DCCT/EDIC genotype data, genome‐wide bootstrap estimates for false‐positive SNPs and for true‐positive SNPs with low‐to‐moderate power are closer to the true values than uncorrected naïve estimates, but tend to overcorrect SNPs with high power. This bias‐reduction technique is generally applicable for complex trait studies including quantitative, binary, and time‐to‐event traits.</p> </abstract>