Understanding the molecular mechanisms of complex traits is essential for developing targeted interventions. We analyzed liver expression quantitative-trait locus (eQTL) meta-analysis data on 1,183 participants to identify conditionally distinct signals. We found 9,013 eQTL signals for 6,564 genes; 23% of eGenes had two signals, and 6% had three or more signals. We then integrated the eQTL results with data from 29 cardiometabolic genome-wide association study (GWAS) traits and identified 1,582 GWAS-eQTL colocalizations for 747 eGenes. Non-primary eQTL signals accounted for 17% of all colocalizations. Isolating signals by conditional analysis prior to coloc resulted in 37% more colocalizations than using marginal eQTL and GWAS data, highlighting the importance of signal isolation. Isolating signals also led to stronger evidence of colocalization: among 343 eQTL-GWAS signal pairs in multi-signal regions, analyses that isolated the signals of interest resulted in higher posterior probability of colocalization for 41% of tests. Leveraging allelic heterogeneity, we predicted causal effects of gene expression on liver traits for four genes. To predict functional variants and regulatory elements, we colocalized eQTL with liver chromatin accessibility QTL (caQTL) and found 391 colocalizations, including 73 with non-primary eQTL signals and 60 eQTL signals that colocalized with both a caQTL and a GWAS signal. Finally, we used publicly available massively parallel reporter assays in HepG2 to highlight 14 eQTL signals that include at least one expression-modulating variant. This multi-faceted approach to unraveling the genetic underpinnings of liver-related traits could lead to therapeutic development.

中文翻译：

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肝脏 eQTL 荟萃分析阐明了心脏代谢性状的潜在分子机制


了解复杂性状的分子机制对于开发有针对性的干预措施至关重要。我们分析了 1,183 名参与者的肝脏表达定量性状基因座 （eQTL） 荟萃分析数据，以确定条件性不同的信号。我们发现了 6,564 个基因的 9,013 个 eQTL 信号;23% 的 eGenes 有 2 个信号，6% 有 3 个或更多信号。然后，我们将 eQTL 结果与来自 29 个心脏代谢全基因组关联研究 （GWAS） 性状的数据相结合，并确定了 747 个 eGenes 的 1,582 个 GWAS-eQTL 共定位。非原代 eQTL 信号占所有共定位的 17%。与使用边缘 eQTL 和 GWAS 数据相比，在 coloc 之前通过条件分析分离信号导致共定位多 37%，这凸显了信号隔离的重要性。分离信号还导致了更强的共定位证据：在多信号区域的 343 个 eQTL-GWAS 信号对中，分离感兴趣信号的分析导致 41% 的测试共定位的后验概率更高。利用等位基因异质性，我们预测了基因表达对四个基因肝脏性状的因果影响。为了预测功能变异和调节元件，我们将 eQTL 与肝脏染色质可及性 QTL （caQTL） 共定位，发现了 391 个共定位，包括 73 个具有非原发性 eQTL 信号和 60 个与 caQTL 和 GWAS 信号共定位的 eQTL 信号。最后，我们在 HepG2 中使用公开可用的大规模平行报告基因检测来突出 14 个 eQTL 信号，这些信号包括至少一个表达调节变体。这种解开肝脏相关性状遗传基础的多方面方法可能会导致治疗发展。