Many gene expression quantitative trait locus (eQTL) studies have published their summary statistics, which can be used to gain insight into complex human traits by downstream analyses, such as fine mapping and co-localization. However, technical differences between these datasets are a barrier to their widespread use. Consequently, target genes for most genome-wide association study (GWAS) signals have still not been identified. In the present study, we present the eQTL Catalogue (https://www.ebi.ac.uk/eqtl), a resource of quality-controlled, uniformly re-computed gene expression and splicing QTLs from 21 studies. We find that, for matching cell types and tissues, the eQTL effect sizes are highly reproducible between studies. Although most QTLs were shared between most bulk tissues, we identified a greater diversity of cell-type-specific QTLs from purified cell types, a subset of which also manifested as new disease co-localizations. Our summary statistics are freely available to enable the systematic interpretation of human GWAS associations across many cell types and tissues.

许多基因表达数量性状位点 (eQTL) 研究已发布其汇总统计数据，可用于通过精细绘图和共定位等下游分析来深入了解复杂的人类特征。然而，这些数据集之间的技术差异是其广泛使用的障碍。因此，大多数全基因组关联研究（GWAS）信号的靶基因尚未确定。在本研究中，我们提出了 eQTL 目录 (https://www.ebi.ac.uk/eqtl)，这是来自 21 项研究的质量控制、统一重新计算的基因表达和剪接 QTL 的资源。我们发现，对于匹配细胞类型和组织，eQTL 效应大小在研究之间具有高度可重复性。尽管大多数 QTL 在大多数组织之间共享，但我们从纯化的细胞类型中鉴定出更多多样性的细胞类型特异性 QTL，其中一个子集也表现为新的疾病共定位。我们的汇总统计数据可免费获取，以便能够系统地解释人类跨多种细胞类型和组织的 GWAS 关联。