The default causal single-nucleotide polymorphism (SNP) effect size prior in Bayesian fine-mapping studies is usually the Normal distribution. This choice is often based on computational convenience, rather than evidence that it is the most suitable prior distribution. The choice of prior is important because previous studies have shown considerable sensitivity of causal SNP Bayes factors to the form of the prior. In some well-studied diseases there are now considerable numbers of genome-wide association study (GWAS) top hits along with estimates of the number of yet-to-be-discovered causal SNPs. We show how the effect sizes of the top hits and estimates of the number of yet-to-be-discovered causal SNPs can be used to choose between the Laplace and Normal priors, to estimate the prior parameters and to quantify the uncertainty in this estimation. The methodology can readily be applied to other priors. We show that the top hits available from breast cancer GWAS provide overwhelming support for the Laplace over the Normal prior, which has important consequences for variant prioritisation. This work in this paper enables practitioners to derive more objective priors than are currently being used and could lead to prioritisation of different variants.

中文翻译：

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使用GWAS热门歌曲为贝叶斯精细映射关联研究提供先验信息。

在贝叶斯精细映射研究中，默认的因果单核苷酸多态性（SNP）效应大小通常为正态分布。该选择通常基于计算便利性，而不是证明它是最合适的先验分布。先验的选择很重要，因为先前的研究表明因果SNP贝叶斯因子对先验形式的敏感性很高。在一些经过充分研究的疾病中，现在有相当数量的全基因组关联研究（GWAS）热门文章以及对尚未发现的因果SNP数量的估计。我们展示了热门歌曲的效应大小以及尚未发现的因果SNP数量的估计如何用于在拉普拉斯和正常先验之间进行选择，估计先验参数并量化此估计中的不确定性。该方法可以容易地应用于其他先验。我们显示，乳腺癌GWAS可获得的热门命中数为拉普拉斯提供了超过正常先验的压倒性支持，这对变异优先排序具有重要意义。本文中的这项工作使从业人员可以得出比目前使用的更为客观的先验先验，并可能导致对不同变体进行优先排序。