Journal
ANNALS OF APPLIED STATISTICS
Volume 5, Issue 1, Pages 201-231Publisher
INST MATHEMATICAL STATISTICS
DOI: 10.1214/10-AOAS373
Keywords
Association study; Bayesian model averaging; hierarchical Bayes model; spike-and-slab prior; winner's curse
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Funding
- Canadian Institute of Health Research (CIHR)
- Natural Sciences and Engineering Research Council of Canada (NSERC)
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Parameter estimates for associated genetic variants, reported 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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