• 文献检索
  • 文档翻译
  • 深度研究
  • 学术资讯
  • Suppr Zotero 插件Zotero 插件
  • 邀请有礼
  • 套餐&价格
  • 历史记录
应用&插件
Suppr Zotero 插件Zotero 插件浏览器插件Mac 客户端Windows 客户端微信小程序
定价
高级版会员购买积分包购买API积分包
服务
文献检索文档翻译深度研究API 文档MCP 服务
关于我们
关于 Suppr公司介绍联系我们用户协议隐私条款
关注我们

Suppr 超能文献

核心技术专利:CN118964589B侵权必究
粤ICP备2023148730 号-1Suppr @ 2026

文献检索

告别复杂PubMed语法,用中文像聊天一样搜索,搜遍4000万医学文献。AI智能推荐,让科研检索更轻松。

立即免费搜索

文件翻译

保留排版,准确专业,支持PDF/Word/PPT等文件格式,支持 12+语言互译。

免费翻译文档

深度研究

AI帮你快速写综述,25分钟生成高质量综述,智能提取关键信息,辅助科研写作。

立即免费体验

TSEA-DB:人类复杂特征和疾病的特征组织关联图谱。

TSEA-DB: a trait-tissue association map for human complex traits and diseases.

机构信息

Center for Precision Health, School of Biomedical Informatics, The University of Texas Health Science Center at Houston, Houston, TX 77030, USA.

Human Genetics Center, School of Public Health, The University of Texas Health Science Center at Houston, Houston, TX 77030, USA.

出版信息

Nucleic Acids Res. 2020 Jan 8;48(D1):D1022-D1030. doi: 10.1093/nar/gkz957.

DOI:10.1093/nar/gkz957
PMID:31680168
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC7145616/
Abstract

Assessing the causal tissues of human traits and diseases is important for better interpreting trait-associated genetic variants, understanding disease etiology, and improving treatment strategies. Here, we present a reference database for trait-associated tissue specificity based on genome-wide association study (GWAS) results, named Tissue-Specific Enrichment Analysis DataBase (TSEA-DB, available at https://bioinfo.uth.edu/TSEADB/). We collected GWAS summary statistics data for a wide range of human traits and diseases followed by rigorous quality control. The current version of TSEA-DB includes 4423 data sets from the UK Biobank (UKBB) and 596 from other resources (GWAS Catalog and literature mining), totaling 5019 unique GWAS data sets and 15 770 trait-associated gene sets. TSEA-DB aims to provide reference tissue(s) enriched with the genes from GWAS. To this end, we systematically performed a tissue-specific enrichment analysis using our recently developed tool deTS and gene expression profiles from two reference tissue panels: the GTEx panel (47 tissues) and the ENCODE panel (44 tissues). The comprehensive trait-tissue association results can be easily accessed, searched, visualized, analyzed, and compared across the studies and traits through our web site. TSEA-DB represents one of the many timely and comprehensive approaches in exploring human trait-tissue association.

摘要

评估人类特征和疾病的因果组织对于更好地解释与特征相关的遗传变异、了解疾病病因和改进治疗策略非常重要。在这里,我们展示了一个基于全基因组关联研究 (GWAS) 结果的与特征相关的组织特异性参考数据库,命名为组织特异性富集分析数据库 (TSEA-DB,可在 https://bioinfo.uth.edu/TSEADB/ 获得)。我们收集了广泛的人类特征和疾病的 GWAS 汇总统计数据,并进行了严格的质量控制。目前的 TSEA-DB 版本包括来自英国生物银行 (UKBB) 的 4423 个数据集和来自其他资源 (GWAS 目录和文献挖掘) 的 596 个数据集,总计 5019 个独特的 GWAS 数据集和 15770 个与特征相关的基因集。TSEA-DB 旨在提供富含 GWAS 基因的参考组织。为此,我们使用我们最近开发的工具 deTS 系统地进行了组织特异性富集分析,并使用两个参考组织面板的基因表达谱:GTEx 面板 (47 个组织) 和 ENCODE 面板 (44 个组织)。通过我们的网站,可以轻松访问、搜索、可视化、分析和比较跨研究和特征的综合特征-组织关联结果。TSEA-DB 代表了探索人类特征-组织关联的许多及时和全面的方法之一。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/340f/7145616/24eb83cd11f9/gkz957fig4.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/340f/7145616/127e2a0f5836/gkz957fig1.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/340f/7145616/76831da8c9f1/gkz957fig2.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/340f/7145616/46f092855b9c/gkz957fig3.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/340f/7145616/24eb83cd11f9/gkz957fig4.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/340f/7145616/127e2a0f5836/gkz957fig1.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/340f/7145616/76831da8c9f1/gkz957fig2.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/340f/7145616/46f092855b9c/gkz957fig3.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/340f/7145616/24eb83cd11f9/gkz957fig4.jpg

相似文献

1
TSEA-DB: a trait-tissue association map for human complex traits and diseases.TSEA-DB:人类复杂特征和疾病的特征组织关联图谱。
Nucleic Acids Res. 2020 Jan 8;48(D1):D1022-D1030. doi: 10.1093/nar/gkz957.
2
CSEA-DB: an omnibus for human complex trait and cell type associations.CSEA-DB:人类复杂性状和细胞类型关联的综合数据库。
Nucleic Acids Res. 2021 Jan 8;49(D1):D862-D870. doi: 10.1093/nar/gkaa1064.
3
Pinpointing miRNA and genes enrichment over trait-relevant tissue network in Genome-Wide Association Studies.在全基因组关联研究中,针对与性状相关组织网络的 miRNA 和基因富集进行精确定位。
BMC Med Genomics. 2020 Dec 28;13(Suppl 11):191. doi: 10.1186/s12920-020-00830-w.
4
GWAS Atlas: a curated resource of genome-wide variant-trait associations in plants and animals.GWAS 图谱:一个经过策展的植物和动物全基因组变异-性状关联资源。
Nucleic Acids Res. 2020 Jan 8;48(D1):D927-D932. doi: 10.1093/nar/gkz828.
5
Estimating colocalization probability from limited summary statistics.从有限的汇总统计数据中估计共定位概率。
BMC Bioinformatics. 2021 May 17;22(1):254. doi: 10.1186/s12859-021-04170-z.
6
Investigation of multi-trait associations using pathway-based analysis of GWAS summary statistics.基于 GWAS 汇总统计数据的通路分析探究多性状关联。
BMC Genomics. 2019 Feb 4;20(Suppl 1):79. doi: 10.1186/s12864-018-5373-7.
7
Influence of tissue context on gene prioritization for predicted transcriptome-wide association studies.组织背景对预测的全转录组关联研究中基因优先级排序的影响。
Pac Symp Biocomput. 2019;24:296-307.
8
ncRNA-eQTL: a database to systematically evaluate the effects of SNPs on non-coding RNA expression across cancer types.ncRNA-eQTL:一个系统评估 SNPs 对多种癌症中非编码 RNA 表达影响的数据库。
Nucleic Acids Res. 2020 Jan 8;48(D1):D956-D963. doi: 10.1093/nar/gkz711.
9
An integrative functional genomics framework for effective identification of novel regulatory variants in genome-phenome studies.一种整合功能基因组学框架,用于在基因组-表型研究中有效识别新型调控变体。
Genome Med. 2018 Jan 29;10(1):7. doi: 10.1186/s13073-018-0513-x.
10
Estimating the causal tissues for complex traits and diseases.估计复杂性状和疾病的因果组织。
Nat Genet. 2017 Dec;49(12):1676-1683. doi: 10.1038/ng.3981. Epub 2017 Oct 23.

引用本文的文献

1
Integrating Proteomics and GWAS to Identify Key Tissues and Genes Underlying Human Complex Diseases.整合蛋白质组学与全基因组关联研究以鉴定人类复杂疾病背后的关键组织和基因。
Biology (Basel). 2025 May 16;14(5):554. doi: 10.3390/biology14050554.
2
Disease-specific prioritization of non-coding GWAS variants based on chromatin accessibility.基于染色质可及性的疾病特异性非编码 GWAS 变体优先级排序。
HGG Adv. 2024 Jul 18;5(3):100310. doi: 10.1016/j.xhgg.2024.100310. Epub 2024 May 21.
3
Crop-GPA: an integrated platform of crop gene-phenotype associations.

本文引用的文献

1
Tissue-specific genes as an underutilized resource in drug discovery.组织特异性基因是药物发现中未充分利用的资源。
Sci Rep. 2019 May 10;9(1):7233. doi: 10.1038/s41598-019-43829-9.
2
Abundant associations with gene expression complicate GWAS follow-up.与基因表达的大量关联使全基因组关联研究的后续工作变得复杂。
Nat Genet. 2019 May;51(5):768-769. doi: 10.1038/s41588-019-0404-0.
3
A Convergent Study of Genetic Variants Associated With Crohn's Disease: Evidence From GWAS, Gene Expression, Methylation, eQTL and TWAS.一项与克罗恩病相关的基因变异的汇聚性研究:来自全基因组关联研究、基因表达、甲基化、表达数量性状基因座及全转录组关联研究的证据
作物 GPA:作物基因-表型关联的综合平台。
NPJ Syst Biol Appl. 2024 Feb 12;10(1):15. doi: 10.1038/s41540-024-00343-7.
4
MicroRNA expression in extracellular vesicles as a novel blood-based biomarker for Alzheimer's disease.细胞外囊泡中的 microRNA 表达作为阿尔茨海默病新型的基于血液的生物标志物。
Alzheimers Dement. 2023 Nov;19(11):4952-4966. doi: 10.1002/alz.13055. Epub 2023 Apr 18.
5
A gene regulatory network approach harmonizes genetic and epigenetic signals and reveals repurposable drug candidates for multiple sclerosis.一种基因调控网络方法协调了遗传和表观遗传信号,并揭示了多发性硬化症的可重新利用药物候选物。
Hum Mol Genet. 2023 Mar 6;32(6):998-1009. doi: 10.1093/hmg/ddac265.
6
scGWAS: landscape of trait-cell type associations by integrating single-cell transcriptomics-wide and genome-wide association studies.scGWAS:通过整合单细胞转录组学全基因组关联研究和全基因组关联研究来描绘性状-细胞类型关联景观。
Genome Biol. 2022 Oct 17;23(1):220. doi: 10.1186/s13059-022-02785-w.
7
Drug-Target Network Study Reveals the Core Target-Protein Interactions of Various COVID-19 Treatments.药物-靶点网络研究揭示了各种 COVID-19 治疗方法的核心靶蛋白相互作用。
Genes (Basel). 2022 Jul 6;13(7):1210. doi: 10.3390/genes13071210.
8
Spatiotemporal MicroRNA-Gene Expression Network Related to Orofacial Clefts.与口腔面裂相关的时空 microRNA-基因表达网络。
J Dent Res. 2022 Oct;101(11):1398-1407. doi: 10.1177/00220345221105816. Epub 2022 Jun 30.
9
WebCSEA: web-based cell-type-specific enrichment analysis of genes.WebCSEA:基于网络的基因细胞类型特异性富集分析。
Nucleic Acids Res. 2022 Jul 5;50(W1):W782-W790. doi: 10.1093/nar/gkac392.
10
Prioritization of risk genes in multiple sclerosis by a refined Bayesian framework followed by tissue-specificity and cell type feature assessment.通过改良的贝叶斯框架对多发性硬化症中的风险基因进行优先级排序,然后进行组织特异性和细胞类型特征评估。
BMC Genomics. 2022 May 11;23(Suppl 4):362. doi: 10.1186/s12864-022-08580-y.
Front Genet. 2019 Apr 9;10:318. doi: 10.3389/fgene.2019.00318. eCollection 2019.
4
Tissue-specificity in cancer: The rule, not the exception.癌症中的组织特异性:是规律,而非例外。
Science. 2019 Mar 15;363(6432):1150-1151. doi: 10.1126/science.aaw3472.
5
deTS: tissue-specific enrichment analysis to decode tissue specificity.组织特异性富集分析,用于解码组织特异性。
Bioinformatics. 2019 Oct 1;35(19):3842-3845. doi: 10.1093/bioinformatics/btz138.
6
Investigation of multi-trait associations using pathway-based analysis of GWAS summary statistics.基于 GWAS 汇总统计数据的通路分析探究多性状关联。
BMC Genomics. 2019 Feb 4;20(Suppl 1):79. doi: 10.1186/s12864-018-5373-7.
7
The NHGRI-EBI GWAS Catalog of published genome-wide association studies, targeted arrays and summary statistics 2019.NHGRI-EBI GWAS Catalog 于 2019 年发布的已发表全基因组关联研究、靶向基因芯片和汇总统计数据
Nucleic Acids Res. 2019 Jan 8;47(D1):D1005-D1012. doi: 10.1093/nar/gky1120.
8
An atlas of genetic associations in UK Biobank.英国生物银行中的遗传关联图谱
Nat Genet. 2018 Nov;50(11):1593-1599. doi: 10.1038/s41588-018-0248-z. Epub 2018 Oct 22.
9
Phenotype-Specific Enrichment of Mendelian Disorder Genes near GWAS Regions across 62 Complex Traits.GWAS 区域附近 62 种复杂性状中孟德尔疾病基因的表型特异性富集。
Am J Hum Genet. 2018 Oct 4;103(4):535-552. doi: 10.1016/j.ajhg.2018.08.017.
10
Using an atlas of gene regulation across 44 human tissues to inform complex disease- and trait-associated variation.利用 44 个人类组织的基因调控图谱来研究复杂疾病和特征相关的变异。
Nat Genet. 2018 Jul;50(7):956-967. doi: 10.1038/s41588-018-0154-4. Epub 2018 Jun 28.