Overcoming the winner's curse: estimating penetrance parameters from case-control data.

By S. Zollner, J. K. Pritchard. 2007

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In Genetics

Citations in the corpus

Opportunities and obstacles for deep learning in biology and medicine (2017, background): “First, research samples are frequently non-representative of the general population of interest; they tend to be disproportionately sick [124], male [125], and European in ancestry [126].”
The MR-Base platform supports systematic causal inference across the human phenome (2018, positive): “Ideally, one would only use replicated GWAS effect sizes for instrumental variables because this reduces the impact of winner’s curse ( Zollner and Pritchard, 2007 ; Bowden and Dudbridge, 2009 ).”
Why Most Discovered True Associations Are Inflated (2008, background): “Although the replication record is better with genome-wide association studies, theoretical considerations again show the early discovered effects are inflated.(23,24) Furthermore, if the observed effects are used as estimates in designing replication studies, these subsequent studies will be underp”
Chapter 11: Genome-Wide Association Studies (2012, background): “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 [44].”
Prediction of individual genetic risk to disease from genome-wide association studies. (2007, methodology): “More complex methods could be used to select SNPs and to ensure unbiased estimates of effect size of variants contributing to the genomic profile (e.g., Meuwissen et al. 2001; Tibshirani et al. 2002; Storey and Tibshirani 2003; Zollner and Pritchard 2007), and such methods could be adapted to accoun”
Estimating Effects and Making Predictions from Genome-Wide Marker Data (2010, background): “Methods such as [14, 54, 55] attempt to correct for this bias so that the average of b̂ over the significant replicates is b.”
The genomics of local adaptation in trees: are we out of the woods yet? (2017, methodology): “…that break down into smaller effect loci (Remington 2015), the Beavis effect (Beavis 1994; Xu 2003), and the Winner’s Curse (Görning et al. 2001; Zöllner & Pritchard 2007) where locus effects are inflated by using the same data for both gene identification and phenotypic prediction (see Box 1…”
Replication in genome-wide association studies (2009, background): “Replication should take this into account during the sample size calculations for the replication efforts; the effect estimate from the initial study may be inflated, leading to an under-estimate of the number of subjects needed to reliably detect it [69, 70, 75, 76].”
Genome-wide association studies in diverse populations (2010, background): “Although efforts have been devoted to statistical issues of replication in relation to sample size and measured effect-siz”