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Large-Scale Multi-Omics Genome-Wide Association Studies (Mo-GWAS): Guidelines for Sample Preparation and Normalization
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Quantifying and correcting for the winner's curse in quantitative-trait association studies.

Rui Xiao1, Michael Boehnke

  • 1Department of Biostatistics and Epidemiology, University of Pennsylvania School of Medicine, Philadelphia, Pennsylvania 19104-6021, USA. rxiao@mail.med.upenn.edu

Genetic Epidemiology
|February 2, 2011
PubMed
Summary

The winner's curse can overestimate genetic effects in quantitative trait (QT) association studies. New methods reduce this bias, especially when statistical power is low, with a one-parameter model offering smaller variance.

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Area of Science:

  • Human genetics
  • Statistical genetics
  • Genetic epidemiology

Background:

  • Quantitative traits (QT) are crucial in human genetic studies for understanding traits and disease risk.
  • Large-scale QT association studies, including genome-wide association studies (GWAS), often face the 'winner's curse,' leading to overestimated genetic effect sizes.

Purpose of the Study:

  • To analyze the impact of the winner's curse on quantitative trait (QT) association studies.
  • To develop and evaluate methods for reducing ascertainment bias caused by the winner's curse in genetic effect size estimation.

Main Methods:

  • Analytical calculations to assess the relationship between statistical power and slope overestimation.
  • Development of a three-parameter maximum likelihood method to reduce bias.
  • Simplification to a one-parameter method by removing nuisance parameters.

Main Results:

  • Overestimation of the regression slope estimate decreases as statistical power increases.
  • Both the three-parameter and one-parameter methods effectively reduce bias, particularly at low to moderate statistical power.
  • The one-parameter model demonstrates a generally smaller variance in the estimated genetic effect size.

Conclusions:

  • The winner's curse significantly impacts genetic effect size estimation in QT association studies.
  • Proposed maximum likelihood methods provide effective bias reduction, especially in scenarios with limited statistical power.
  • The simplified one-parameter model offers a practical approach with improved precision for genetic effect size estimation.