Genome-wide association studies (GWAS) provide an important approach for identifying common genetic variants that predispose to human disease. doi: 10.1371/journal.pgen.1006916. Often, the effects identified in an initial GWAS suffer from winner's curse, where the detected effect is likely stronger in the GWAS sample than in the general population . As an example, for GWAS of type 2 diabetes, winner’s curse correction improved prediction R 2 from 2.29% based on the standard PRS to 3.10% (P = 0.0017) and incorporating functional annotation data further improved R 2 to 3.53% (P = 2×10 −5). However, odds ratio (OR) estimates for the reported findings from GWAS discovery data are typically affected by a bias away from the null sometimes referred to the "winner's curse". Genome-wide association studies (GWAS) have identified thousands of genetic variants associated with complex human traits, but only a fraction of variants identified in discovery studies achieve significance in replication studies. This means that replication samples should ideally be larger to account for the over-estimation of effect size. As an example, for GWAS of type 2 dia-betes, winner’s curse correction improved prediction R2 from 2.29% based on the standard PRS to 3.10% (P = 0.0017) and incorporating functional annotation data further improved R2 to 3.53% (P = 2×10−5). Although the detrimental effect of the winner’s curse in underestimation of the necessary sample size for a suc-cessful replication study is known, statistical methods and computational tools suitable for large-scale genome-wide Winner’s Curse in GWAS • Similarly when running a GWAS and discovering a SNP association, you will OVERESTIMATE the strength of the association • Power calculations use an effect size to know how many samples you need to detect this effect • If the effect size is actually SMALLER than you Secondly, to maximize polygenic prediction accuracy, the GWAS summary 2019). Genome‐wide association studies (GWAS) provide an important approach for identifying common genetic variants that predispose to human disease. Our simulation studies … Genome-wide association studies (GWAS) provide an important approach for identifying common genetic variants that predispose to human disease. However, odds ratio (OR) estimates for the reported findings from GWAS discovery data are typically affected by a bias away from the null sometimes referred to the “winner's curse”. This framework proposes two random effects models. However, odds ratio (OR) estimates for the reported findings from GWAS discovery data are typically affected by a bias away from the null sometimes referred to the “winner's curse”. Firstly, GWAS effect-sizes are inflated through Winners curse, and unbiased estimates can only be obtained through an independent training sample, with these effect-size estimates then used to calculate polygenic scores in a further independent sample 6. To determine whether these factors could explain the lower than expected rate of replication, we applied a statistical framework that jointly models Winner’s Curse and study-specific heterogeneity in two GWAS studies of the same phenotype (Zou et al. The effect sizes for all SNPs were slightly lower than that in the discovery GWAS, as expected due to ‘winners curse’, but the chromosome 19 and 20 hits in particular have substantial effects on age at menopause with a reduction in the menopausal age of 3 months (0.257 years) and an increase of 11 months (0.924 years) per allele, respectively. Statistical correction of the Winner's Curse explains replication variability in quantitative trait genome-wide association studies PLoS Genet . 2017 Jul 17;13(7):e1006916. In the context of high‐dimensional multiple testing, adjustment of association estimates for winner's curse inflation is critical for interpretation and replication of findings, including large‐scale candidate gene studies, genome‐wide association studies (GWAS), and next‐generation sequencing whole‐genome analyses. winner’s curse adjustment in meta-GWAS analysis and in specification of risk prediction models.

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