Several methods have been proposed to allow functional genomic information to inform prior distributions in Bayesian fine-mapping case-control association studies. None of these methods allow the inclusion of partially observed functional genomic information. We use functional significance (FS) scores that combine information across multiple bioinformatics sources to inform our effect size prior distributions. These scores are not available for all single-nucleotide polymorphisms (SNPs) but by partitioning SNPs into naturally occurring FS score groups, we show how missing FS scores can easily be accommodated via finite mixtures of elicited priors. Most current approaches adopt a formal Bayesian variable selection approach and either limit the number of causal SNPs allowed or use approximations to avoid the need to explore the vast parameter space. We focus instead on achieving differential shrinkage of the effect sizes through prior scale mixtures of normals and use marginal posterior probability intervals to select candidate causal SNPs. We show via a simulation study how this approach can improve localisation of the causal SNPs compared to existing mutli-SNP fine-mapping methods. We also apply our approach to fine-mapping a region around the CASP8 gene using the iCOGS consortium breast cancer SNP data.

中文翻译：

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在精细映射关联研究中使用部分观察到的分类先验信息进行贝叶斯变量选择。

已经提出了几种方法来允许功能基因组信息告知贝叶斯精细映射病例对照研究中的先前分布。这些方法均不允许包含部分观察到的功能基因组信息。我们使用功能重要性（FS）评分，该评分结合了多种生物信息学来源的信息，以告知我们的效应大小先前的分布。这些分数并非适用于所有单核苷酸多态性（SNP），但是通过将SNP划分为自然出现的FS分数组，我们显示了如何通过引出先验的有限混合轻松地弥补缺少的FS分数。当前大多数方法采用正式的贝叶斯变量选择方法，或者限制允许的因果SNP数量，或者使用近似值来避免探索巨大的参数空间的需要。相反，我们专注于通过法线的先验尺度混合来实现效应量的差异缩小，并使用边际后验概率区间选择候选因果SNP。通过模拟研究，我们将展示与现有的多SNP精细映射方法相比，该方法如何改善因果SNP的定位。我们还使用iCOGS联合会乳腺癌SNP数据，将我们的方法用于精细映射CASP8基因周围的区域。