Currently, the only effect size prior that is routinely implemented in a Bayesian fine-mapping multi-single-nucleotide polymorphism (SNP) analysis is the Gaussian prior. Here, we show how the Laplace prior can be deployed in Bayesian multi-SNP fine mapping studies. We compare the ranking performance of the posterior inclusion probability (PIP) using a Laplace prior with the ranking performance of the corresponding Gaussian prior and FINEMAP. Our results indicate that, for the simulation scenarios we consider here, the Laplace prior can lead to higher PIPs than either the Gaussian prior or FINEMAP, particularly for moderately sized fine-mapping studies. The Laplace prior also appears to have better worst-case scenario properties. We reanalyse the iCOGS case–control data from the CASP8 region on Chromosome 2. Even though this study has a total sample size of nearly 90,000 individuals, there are still some differences in the top few ranked SNPs if the Laplace prior is used rather than the Gaussian prior. R code to implement the Laplace (and Gaussian) prior is available at https://github.com/Kevin-walters/lapmapr.

中文翻译：

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使用拉普拉斯先验的贝叶斯多元精细映射

目前，在贝叶斯精细映射多单核苷酸多态性 (SNP) 分析中常规实施的唯一效果大小先验是高斯先验。在这里，我们展示了如何在贝叶斯多 SNP 精细映射研究中部署拉普拉斯先验。我们将使用拉普拉斯先验的后验包含概率 (PIP) 的排名性能与相应的高斯先验和 FINEMAP 的排名性能进行比较。我们的结果表明，对于我们在此考虑的模拟场景，拉普拉斯先验可以导致比高斯先验或 FINEMAP 更高的 PIP，特别是对于中等规模的精细映射研究。拉普拉斯先验似乎也具有更好的最坏情况场景属性。我们重新分析了来自 2 号染色体 CASP8 区域的 iCOGS 病例对照数据。尽管这项研究的总样本量接近 90,000 人，但如果使用拉普拉斯先验而不是高斯先验，排名前几位的 SNP 仍然存在一些差异。用于实现拉普拉斯（和高斯）先验的 R 代码可在 https://github.com/Kevin-walters/lapmapr 获得。