Bayesian methods to overcome the winner's curse in genetic studies

TL;DR

This paper introduces a hierarchical Bayesian approach with spike-and-slab priors to correct for the winner's curse in genetic effect size estimates, improving accuracy especially in low-power studies.

Contribution

It proposes a novel Bayesian method that accounts for chance in significant genetic findings and combines multiple models for more reliable estimates.

Findings

Bayesian estimators have smaller variance than traditional methods.

The method outperforms in low-power genetic studies.

Applications to real data demonstrate improved effect size estimates.

Abstract

Parameter estimates for associated genetic variants, report ed in the initial discovery samples, are often grossly inflated compared to the values observed in the follow-up replication samples. This type of bias is a consequence of the sequential procedure in which the estimated effect of an associated genetic marker must first pass a stringent significance threshold. We propose a hierarchical Bayes method in which a spike-and-slab prior is used to account for the possibility that the significant test result may be due to chance. We examine the robustness of the method using different priors corresponding to different degrees of confidence in the testing results and propose a Bayesian model averaging procedure to combine estimates produced by different models. The Bayesian estimators yield smaller variance compared to the conditional likelihood estimator and outperform the latter in studies with low power. We investigate the performance of the method with simulations and applications to four real data examples.