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QTLbase2:一个增强的人类数量性状基因座综合分子表型目录。

QTLbase2: an enhanced catalog of human quantitative trait loci on extensive molecular phenotypes.

机构信息

Wuxi School of Medicine, Jiangnan University, Wuxi, China.

Department of Pharmacology, The Province and Ministry Co-sponsored Collaborative Innovation Center for Medical Epigenetics, Tianjin Key Laboratory of Inflammation Biology, School of Basic Medical Sciences, Tianjin Medical University, Tianjin, China.

出版信息

Nucleic Acids Res. 2023 Jan 6;51(D1):D1122-D1128. doi: 10.1093/nar/gkac1020.

Abstract

Deciphering the fine-scale molecular mechanisms that shape the genetic effects at disease-associated loci from genome-wide association studies (GWAS) remains challenging. The key avenue is to identify the essential molecular phenotypes that mediate the causal variant and disease under particular biological conditions. Therefore, integrating GWAS signals with context-specific quantitative trait loci (QTLs) (such as different tissue/cell types, disease states, and perturbations) from extensive molecular phenotypes would present important strategies for full understanding of disease genetics. Via persistent curation and systematic data processing of large-scale human molecular trait QTLs (xQTLs), we updated our previous QTLbase database (now QTLbase2, http://mulinlab.org/qtlbase) to comprehensively analyze and visualize context-specific QTLs across 22 molecular phenotypes and over 95 tissue/cell types. Overall, the resource features the following major updates and novel functions: (i) 960 more genome-wide QTL summary statistics from 146 independent studies; (ii) new data for 10 previously uncompiled QTL types; (iii) variant query scope expanded to fit 195 QTL datasets based on whole-genome sequencing; (iv) supports filtering and comparison of QTLs for different biological conditions, such as stimulation types and disease states; (v) a new linkage disequilibrium viewer to facilitate variant prioritization across tissue/cell types and QTL types.

摘要

从全基因组关联研究 (GWAS) 中破译影响疾病相关基因座的精细分子机制仍然具有挑战性。关键途径是确定介导因果变异体和特定生物学条件下疾病的基本分子表型。因此,将 GWAS 信号与来自广泛分子表型的特定于上下文的数量性状基因座 (QTL)(如不同组织/细胞类型、疾病状态和扰动)相结合,将为全面了解疾病遗传学提供重要策略。通过对大规模人类分子性状 QTL(xQTL)的持续策展和系统数据处理,我们更新了之前的 QTLbase 数据库(现为 QTLbase2,http://mulinlab.org/qtlbase),以全面分析和可视化 22 种分子表型和 95 种以上组织/细胞类型中的特定于上下文的 QTL。总的来说,该资源具有以下主要更新和新功能:(i) 来自 146 项独立研究的 960 项更多全基因组 QTL 汇总统计数据;(ii) 10 种以前未编译的 QTL 类型的新数据;(iii) 变体查询范围扩大到基于全基因组测序的 195 个 QTL 数据集;(iv) 支持不同生物学条件(如刺激类型和疾病状态)的 QTL 过滤和比较;(v) 新的连锁不平衡查看器,以促进跨组织/细胞类型和 QTL 类型的变体优先级排序。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/06fd/9825467/a1ed570c3d57/gkac1020fig1.jpg

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