The Gaussian distribution is usually the default causal single-nucleotide polymorphism (SNP) effect size prior in Bayesian population-based fine-mapping association studies, but a recent study showed that the heavier-tailed Laplace prior distribution provided a better fit to breast cancer top hits identified in genome-wide association studies. We investigate the utility of the Laplace prior as an effect size prior in univariate fine-mapping studies. We consider ranking SNPs using Bayes factors and other summaries of the effect size posterior distribution, the effect of prior choice on credible set size based on the posterior probability of causality, and on the noteworthiness of SNPs in univariate analyses. Across a wide range of fine-mapping scenarios the Laplace prior generally leads to larger 90% credible sets than the Gaussian prior. These larger credible sets for the Laplace prior are due to relatively high prior mass around zero which can yield many noncausal SNPs with relatively large Bayes factors. If using conventional credible sets, the Gaussian prior generally yields a better trade off between including the causal SNP with high probability and keeping the set size reasonable. Interestingly when using the less well utilised measure of noteworthiness, the Laplace prior performs well, leading to causal SNPs being declared noteworthy with high probability, whilst generally declaring fewer than 5% of noncausal SNPs as being noteworthy. In contrast, the Gaussian prior leads to the causal SNP being declared noteworthy with very low probability.

中文翻译：

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贝叶斯精细映射研究中拉普拉斯效应大小先验分布的效用

在基于贝叶斯群体的精细映射关联研究中，高斯分布通常是默认的因果单核苷酸多态性 (SNP) 效应大小先验，但最近的一项研究表明，重尾拉普拉斯先验分布更适合乳腺癌顶部在全基因组关联研究中确定的命中。我们调查了拉普拉斯先验作为单变量精细映射研究中先验效应大小的效用。我们考虑使用贝叶斯因子和其他影响大小后验分布的摘要对 SNP 进行排序，优先选择对基于因果关系后验概率的可信集大小的影响，以及单变量分析中 SNP 的值得注意程度。在广泛的精细映射场景中，拉普拉斯先验通常比高斯先验产生更大的 90% 可信集。拉普拉斯先验的这些更大的可信集是由于在零附近相对较高的先验质量，这可以产生许多具有相对较大贝叶斯因子的非因果 SNP。如果使用传统的可信集，高斯先验通常会在包含高概率的因果 SNP 和保持集大小合理之间进行更好的权衡。有趣的是，当使用不太常用的值得注意的度量时，拉普拉斯先验表现良好，导致因果 SNP 被宣布为值得注意的概率很高，同时通常将不到 5% 的非因果 SNP 宣布为值得注意。相比之下，高斯先验导致因果 SNP 被宣布为值得注意的概率非常低。