克服遗传学研究中赢家诅咒的贝叶斯方法
Bayesian methods to overcome the winner's curse in genetic studies
摘要：
在最初发现样本中报告的相关遗传变异的参数估计值与在后续复制样本中观察到的值相比通常严重夸大。这种类型的偏差是顺序程序的结果，其中相关遗传标记的估计效应必须首先通过严格的显著性阈值。我们提出了一个分层贝叶斯方法，其中使用的尖峰和板坯前占的可能性，显着的测试结果可能是由于机会。我们研究的鲁棒性的方法，使用不同的先验对应于不同程度的置信度的测试结果，并提出了一个贝叶斯模型的平均过程，结合不同的模型产生的估计。贝叶斯估计量产生较小的方差相比，条件似然估计量，并优于后者在低功率的研究。我们调查的性能的方法与模拟和应用程序的四个真实数据的例子。
英文摘要：
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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