Colocalization of GWAS and eQTL Signals Detects Target Genes
Colocalization of GWAS and eQTL Signals Detects Target Genes
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DOI：
10.1016/j.ajhg.2016.10.003
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发表时间：
2016-12-01
影响因子：
9.8
通讯作者：
Eskin, Eleazar
中科院分区：
文献类型：
--
作者：
Hormozdiari, Farhad;van de Bunt, Martijn;Eskin, Eleazar
绝大多数全基因组关联研究 (GWAS) 风险位点位于基因组的非编码区域。一种可能的假设是，这些 GWAS 风险位点通过影响不同组织中的基因表达来改变个体的疾病风险。为了了解 GWAS 风险位点的驱动机制，确定特定组织类型中哪些基因受到影响是有帮助的。例如，如果负责 GWAS 基因座的相同变异也影响基因表达，则相关基因和组织可能在疾病机制中发挥作用。在 GWAS 和表达定量轨迹基因座 (eQTL) 研究中确定同一变异是否具有因果关系具有挑战性，因为连锁不平衡引起的不确定性以及某些基因座包含多个因果变异的事实。然而，当前解决这个问题的方法假设每个基因座包含一个因果变异。在本文中，我们提出了 eCAVIAR，这是一种概率方法，与现有方法相比具有几个关键优势。首先，我们的方法可以解释任何给定基因座中的多个因果变异。其次，它可以利用汇总统计数据，而无需访问单个基因型数据。我们使用模拟和真实数据集来证明我们方法的实用性。使用 45 种不同组织的公开 eQTL 数据，我们证明 eCAVIAR 可以优先考虑一组葡萄糖和胰岛素相关性状基因座的可能相关组织和目标基因。
The vast majority of genome-wide association study (GWAS) risk loci fall in non-coding regions of the genome. One possible hypothesis is that these GWAS risk loci alter the individual's disease risk through their effect on gene expression in different tissues. In order to understand the mechanisms driving a GWAS risk locus, it is helpful to determine which gene is affected in specific tissue types. For example, the relevant gene and tissue could play a role in the disease mechanism if the same variant responsible for a GWAS locus also affects gene expression. Identifying whether or not the same variant is causal in both GWASs and expression quantitative trail locus (eQTL) studies is challenging because of the uncertainty induced by linkage disequilibrium and the fact that some loci harbor multiple causal variants. However, current methods that address this problem assume that each locus contains a single causal variant. In this paper, we present eCAVIAR, a probabilistic method that has several key advantages over existing methods. First, our method can account for more than one causal variant in any given locus. Second, it can leverage summary statistics without accessing the individual genotype data. We use both simulated and real datasets to demonstrate the utility of our method. Using publicly available eQTL data on 45 different tissues, we demonstrate that eCAVIAR can prioritize likely relevant tissues and target genes for a set of glucose-and insulin-related trait loci.