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eQTL 目录 2023:新数据集、X 染色体 QTL 以及转录水平 QTL 的检测和可视化能力提升。

eQTL Catalogue 2023: New datasets, X chromosome QTLs, and improved detection and visualisation of transcript-level QTLs.

机构信息

Institute of Computer Science, University of Tartu, Tartu, Estonia.

Open Targets, South Building, Wellcome Genome Campus, Hinxton, Cambridge, United Kingdom.

出版信息

PLoS Genet. 2023 Sep 18;19(9):e1010932. doi: 10.1371/journal.pgen.1010932. eCollection 2023 Sep.

Abstract

The eQTL Catalogue is an open database of uniformly processed human molecular quantitative trait loci (QTLs). We are continuously updating the resource to further increase its utility for interpreting genetic associations with complex traits. Over the past two years, we have increased the number of uniformly processed studies from 21 to 31 and added X chromosome QTLs for 19 compatible studies. We have also implemented Leafcutter to directly identify splice-junction usage QTLs in all RNA sequencing datasets. Finally, to improve the interpretability of transcript-level QTLs, we have developed static QTL coverage plots that visualise the association between the genotype and average RNA sequencing read coverage in the region for all 1.7 million fine mapped associations. To illustrate the utility of these updates to the eQTL Catalogue, we performed colocalisation analysis between vitamin D levels in the UK Biobank and all molecular QTLs in the eQTL Catalogue. Although most GWAS loci colocalised both with eQTLs and transcript-level QTLs, we found that visual inspection could sometimes be used to distinguish primary splicing QTLs from those that appear to be secondary consequences of large-effect gene expression QTLs. While these visually confirmed primary splicing QTLs explain just 6/53 of the colocalising signals, they are significantly less pleiotropic than eQTLs and identify a prioritised causal gene in 4/6 cases.

摘要

eQTL 目录是一个统一处理的人类分子数量性状基因座 (QTL) 的开放数据库。我们正在不断更新资源,以进一步提高其用于解释与复杂性状相关的遗传关联的能力。在过去的两年中,我们将统一处理的研究数量从 21 项增加到 31 项,并为 19 项相容研究添加了 X 染色体 QTL。我们还实施了 Leafcutter,以便在所有 RNA 测序数据集中直接识别剪接连接使用 QTL。最后,为了提高转录水平 QTL 的可解释性,我们开发了静态 QTL 覆盖图,以可视化所有 170 万个精细映射关联中基因型与该区域中平均 RNA 测序读取覆盖率之间的关联。为了说明 eQTL 目录的这些更新的实用性,我们在英国生物库中的维生素 D 水平和 eQTL 目录中的所有分子 QTL 之间进行了 colocalisation 分析。尽管大多数 GWAS 基因座与 eQTL 和转录水平 QTL 都发生了 colocalisation,但我们发现,有时可以通过目视检查来区分主要的剪接 QTL 和那些似乎是大效应基因表达 QTL 的次要后果的 QTL。虽然这些通过目视检查确认的主要剪接 QTL 仅解释了 colocalising 信号中的 6/53,但它们的多效性明显低于 eQTL,并在 4/6 个案例中确定了一个优先的因果基因。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b21b/10538656/8dd5b291dab7/pgen.1010932.g001.jpg

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