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March 2011 Bayesian methods to overcome the winner’s curse in genetic studies
Lizhen Xu, Radu V. Craiu, Lei Sun
Ann. Appl. Stat. 5(1): 201-231 (March 2011). DOI: 10.1214/10-AOAS373


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.


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Lizhen Xu. Radu V. Craiu. Lei Sun. "Bayesian methods to overcome the winner’s curse in genetic studies." Ann. Appl. Stat. 5 (1) 201 - 231, March 2011.


Published: March 2011
First available in Project Euclid: 21 March 2011

zbMATH: 1220.62027
MathSciNet: MR2810395
Digital Object Identifier: 10.1214/10-AOAS373

Keywords: association study , Bayesian model averaging , hierarchical Bayes model , spike-and-slab prior , winner’s curse

Rights: Copyright © 2011 Institute of Mathematical Statistics


Vol.5 • No. 1 • March 2011
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