Literature DB >> 19140131

Quantifying and correcting for the winner's curse in genetic association studies.

Rui Xiao1, Michael Boehnke.   

Abstract

Genetic association studies are a powerful tool to detect genetic variants that predispose to human disease. Once an associated variant is identified, investigators are also interested in estimating the effect of the identified variant on disease risk. Estimates of the genetic effect based on new association findings tend to be upwardly biased due to a phenomenon known as the "winner's curse." Overestimation of genetic effect size in initial studies may cause follow-up studies to be underpowered and so to fail. In this paper, we quantify the impact of the winner's curse on the allele frequency difference and odds ratio estimators for one- and two-stage case-control association studies. We then propose an ascertainment-corrected maximum likelihood method to reduce the bias of these estimators. We show that overestimation of the genetic effect by the uncorrected estimator decreases as the power of the association study increases and that the ascertainment-corrected method reduces absolute bias and mean square error unless power to detect association is high. 2009 Wiley-Liss, Inc.

Entities:  

Mesh:

Year:  2009        PMID: 19140131      PMCID: PMC2706290          DOI: 10.1002/gepi.20398

Source DB:  PubMed          Journal:  Genet Epidemiol        ISSN: 0741-0395            Impact factor:   2.135


  15 in total

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Journal:  Am J Hum Genet       Date:  2002-02-08       Impact factor: 11.025

2.  Replication validity of genetic association studies.

Authors:  J P Ioannidis; E E Ntzani; T A Trikalinos; D G Contopoulos-Ioannidis
Journal:  Nat Genet       Date:  2001-11       Impact factor: 38.330

3.  Upward bias in estimation of genetic effects.

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Journal:  Am J Hum Genet       Date:  2002-10-17       Impact factor: 11.025

4.  Meta-analysis of genetic association studies supports a contribution of common variants to susceptibility to common disease.

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Journal:  Nat Genet       Date:  2003-01-13       Impact factor: 38.330

5.  Reduction of selection bias in genomewide studies by resampling.

Authors:  Lei Sun; Shelley B Bull
Journal:  Genet Epidemiol       Date:  2005-05       Impact factor: 2.135

6.  Joint analysis is more efficient than replication-based analysis for two-stage genome-wide association studies.

Authors:  Andrew D Skol; Laura J Scott; Gonçalo R Abecasis; Michael Boehnke
Journal:  Nat Genet       Date:  2006-01-15       Impact factor: 38.330

7.  Locus-specific heritability estimation via the bootstrap in linkage scans for quantitative trait loci.

Authors:  Long Yang Wu; Lei Sun; Shelley B Bull
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8.  Overcoming the winner's curse: estimating penetrance parameters from case-control data.

Authors:  Sebastian Zollner; Jonathan K Pritchard
Journal:  Am J Hum Genet       Date:  2007-02-16       Impact factor: 11.025

9.  Bias-reduced estimators and confidence intervals for odds ratios in genome-wide association studies.

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Journal:  Biostatistics       Date:  2008-02-28       Impact factor: 5.899

10.  Two-stage designs for gene-disease association studies.

Authors:  Jaya M Satagopan; David A Verbel; E S Venkatraman; Kenneth E Offit; Colin B Begg
Journal:  Biometrics       Date:  2002-03       Impact factor: 2.571

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  84 in total

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Review 2.  Overview of techniques to account for confounding due to population stratification and cryptic relatedness in genomic data association analyses.

Authors:  M J Sillanpää
Journal:  Heredity (Edinb)       Date:  2010-07-14       Impact factor: 3.821

3.  Quantifying and correcting for the winner's curse in quantitative-trait association studies.

Authors:  Rui Xiao; Michael Boehnke
Journal:  Genet Epidemiol       Date:  2011-01-31       Impact factor: 2.135

4.  Fine-mapping of genome-wide association study-identified risk loci for colorectal cancer in African Americans.

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Journal:  Heart Rhythm       Date:  2017-06-10       Impact factor: 6.343

Review 6.  The Psychiatric Genomics Consortium Posttraumatic Stress Disorder Workgroup: Posttraumatic Stress Disorder Enters the Age of Large-Scale Genomic Collaboration.

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Journal:  Neuropsychopharmacology       Date:  2015-04-23       Impact factor: 7.853

7.  Empirical Bayes and semi-Bayes adjustments for a vast number of estimations.

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Journal:  Eur J Epidemiol       Date:  2009-10-08       Impact factor: 8.082

8.  Gene expression elucidates functional impact of polygenic risk for schizophrenia.

Authors:  Menachem Fromer; Panos Roussos; Solveig K Sieberts; Jessica S Johnson; David H Kavanagh; Thanneer M Perumal; Douglas M Ruderfer; Edwin C Oh; Aaron Topol; Hardik R Shah; Lambertus L Klei; Robin Kramer; Dalila Pinto; Zeynep H Gümüş; A Ercument Cicek; Kristen K Dang; Andrew Browne; Cong Lu; Lu Xie; Ben Readhead; Eli A Stahl; Jianqiu Xiao; Mahsa Parvizi; Tymor Hamamsy; John F Fullard; Ying-Chih Wang; Milind C Mahajan; Jonathan M J Derry; Joel T Dudley; Scott E Hemby; Benjamin A Logsdon; Konrad Talbot; Towfique Raj; David A Bennett; Philip L De Jager; Jun Zhu; Bin Zhang; Patrick F Sullivan; Andrew Chess; Shaun M Purcell; Leslie A Shinobu; Lara M Mangravite; Hiroyoshi Toyoshiba; Raquel E Gur; Chang-Gyu Hahn; David A Lewis; Vahram Haroutunian; Mette A Peters; Barbara K Lipska; Joseph D Buxbaum; Eric E Schadt; Keisuke Hirai; Kathryn Roeder; Kristen J Brennand; Nicholas Katsanis; Enrico Domenici; Bernie Devlin; Pamela Sklar
Journal:  Nat Neurosci       Date:  2016-09-26       Impact factor: 24.884

9.  Genome-wide association study in Han Chinese identifies three novel loci for human height.

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10.  Generalization of variants identified by genome-wide association studies for electrocardiographic traits in African Americans.

Authors:  Janina M Jeff; Marylyn D Ritchie; Joshua C Denny; Abel N Kho; Andrea H Ramirez; David Crosslin; Loren Armstrong; Melissa A Basford; Wendy A Wolf; Jennifer A Pacheco; Rex L Chisholm; Dan M Roden; M Geoffrey Hayes; Dana C Crawford
Journal:  Ann Hum Genet       Date:  2013-03-28       Impact factor: 1.670

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