Probabilistic fine-mapping of transcriptome-wide association studies
Probabilistic fine-mapping of transcriptome-wide association studies
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DOI：
10.1038/s41588-019-0367-1
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发表时间：
2019-04-01
期刊：
影响因子：
30.8
通讯作者：
Pasaniuc, Bogdan
中科院分区：
文献类型：
--
作者：
Mancuso, Nicholas;Freund, Malika K.;Pasaniuc, Bogdan
使用预测表达的全转录组关联研究已经确定了数千个基因，其局部调控表达与复杂性状和疾病相关。在这项工作中，我们表明连锁不平衡诱导非因果基因的显著基因-性状关联，作为用于表达预测的表达数量性状位点权重的函数。我们引入了一个概率框架，对全转录组关联研究信号之间的相关性进行建模，为风险区域中的每个基因分配一个概率，以解释观察到的关联信号。重要的是，当因果基因的表达数据无法通过利用其他组织的表达预测在因果组织中获得时，我们的方法仍然是准确的。我们的方法产生了在名义置信度（例如，90%）上包含因果基因的可靠基因集，可用于对功能分析的基因进行优先排序。我们通过使用脂质性状的综合分析来说明我们的方法，其中我们的方法优先考虑具有强烈因果关系证据的基因。
Transcriptome-wide association studies using predicted expression have identified thousands of genes whose locally regulated expression is associated with complex traits and diseases. In this work, we show that linkage disequilibrium induces significant gene-trait associations at non-causal genes as a function of the expression quantitative trait loci weights used in expression prediction. We introduce a probabilistic framework that models correlation among transcriptome-wide association study signals to assign a probability for every gene in the risk region to explain the observed association signal. Importantly, our approach remains accurate when expression data for causal genes are not available in the causal tissue by leveraging expression prediction from other tissues. Our approach yields credible sets of genes containing the causal gene at a nominal confidence level (for example, 90%) that can be used to prioritize genes for functional assays. We illustrate our approach by using an integrative analysis of lipid traits, where our approach prioritizes genes with strong evidence for causality.