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Variant-specific priors clarify colocalisation analysis
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2025-05-27
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DOI：10.1371/journal.pgen.1011697.png)
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摘要
En 中文
将全基因组关联研究（GWAS）变异与其因果基因和背景联系起来仍然是一个持续的挑战。执行此类分析的一种常用方法是用于统计共定位分析的coloc软件包，可用于关联GWAS和表达数量性状基因座（eQTL）关联。目前，coloc假设区域内的所有变异等可能地是因果变异，尽管精细定位方法在利用附加信息调整其先验概率方面取得了成功。在本研究中，我们提出并实施了一种在coloc方法中指定变异特异性先验概率的方法。我们描述并比较了六种用于指定先验概率的信息来源：非编码约束、增强子-基因链接评分、PolyFun方法的结果以及三种表达数量性状基因座转录起始位点（eQTL-TSS）距离密度估计。通过模拟和对真实pQTL-eQTL共定位的分析，我们表明变异特异性先验（尤其是eQTL-TSS距离密度先验）可以提高共定位性能。此外，在全基因组关联研究-eQTL共定位中，变异特异性先验在多达14.1%的共定位中改变了共定位显著性，在某些基因座上揭示了可能的因果基因。
Keyword:
THOUSANDS
LOCI
期刊
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3.7
论文数:
9.8K
被引数:
4.6W
机构
U
Univ Cambridge
学者数:
3.2K
论文数: 1.8K
被引数: 974
引用论文
Integrating Functional Data to Prioritize Causal Variants in Statistical Fine-Mapping Studies在统计精细映射研究中整合功能数据以优先考虑因果变异.png)
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