ArticleGenetic epidemiology2015
Resampling to Address the Winner's Curse in Genetic Association Analysis of Time to Event.
Article in Genetic epidemiology, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers, 1 of them a synthesis that pooled it.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
6 citing papers in PubMed, 1 synthesis or guideline pooled it, 13 citations in OpenAlex.
- The genetics of drug-related movement disorders, an umbrella review of meta-analyses.Molecular psychiatry · 2020Pooled it
- Lessons learned from a candidate gene study investigating aromatase inhibitor treatment outcome in breast cancer.NPJ breast cancer · 2025Article
- Gene-environment interactions in the associations of PFAS exposure with insulin sensitivity and beta-cell function in a Faroese cohort followed from birth to adulthood.Environmental research · 2023Article
- Estimation of genetic variance contributed by a quantitative trait locus: correcting the bias associated with significance tests.Genetics · 2021Article
- Enrichment of B cell receptor signaling and epidermal growth factor receptor pathways in monoclonal gammopathy of undetermined significance: a genome-wide genetic interaction study.Molecular medicine (Cambridge, Mass.) · 2018Article
- Illustrating, Quantifying, and Correcting for Bias in Post-hoc Analysis of Gene-Based Rare Variant Tests of Association.Frontiers in genetics · 2017Article
Corrections and comments
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Authors and funding
6 authors at 3 institutions in 1 country.
Funding
Abstract
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.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.