Open Targets Genetics: systematic identification of trait-associated genes using large-scale genetics and functional genomics.
Open Targets Genetics: systematic identification of trait-associated genes using large-scale genetics and functional genomics.
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DOI：
10.1093/nar/gkaa840
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发表时间：
2021-01-08
影响因子：
14.9
通讯作者：
Dunham I
中科院分区：
文献类型：
--
作者：
Ghoussaini M;Mountjoy E;Carmona M;Peat G;Schmidt EM;Hercules A;Fumis L;Miranda A;Carvalho-Silva D;Buniello A;Burdett T;Hayhurst J;Baker J;Ferrer J;Gonzalez-Uriarte A;Jupp S;Karim MA;Koscielny G;Machlitt-Northen S;Malangone C;Pendlington ZM;Roncaglia P;Suveges D;Wright D;Vrousgou O;Papa E;Parkinson H;MacArthur JAL;Todd JA;Barrett JC;Schwartzentruber J;Hulcoop DG;Ochoa D;McDonagh EM;Dunham I
开放目标遗传学(https://genetics.opentargets.org))是一个开放获取的综合性资源，它汇集了人类GWAs和功能基因组学数据，包括来自广泛细胞类型和组织的基因表达、蛋白质丰度、染色质相互作用和构象数据，以在GWAs相关基因、变异体和可能的原因基因之间建立牢固的联系。这使得对所有已发表的与性状相关的基因座上可能的因果变异和基因进行系统识别和优先排序。在本文中，我们描述了我们聚合的公共资源、我们使用的技术和分析，以及门户提供的功能。开放靶点遗传学可以按变异、基因或研究/表型进行搜索。它提供了一些工具，使用户能够优先考虑疾病相关基因座上的因果变异和基因，并访问系统的跨疾病和疾病分子特征共定位分析，涉及92种细胞类型和组织，包括eQTL目录。数据可视化，如曼哈顿式的地块、区域地块、研究之间可信的集合重叠和Phewas地块，使用户能够深入探索GWAS信号。综合数据可通过门户网站、批量下载和GraphQL API获得，而且该软件是开源的。这些综合数据的应用包括识别药物发现和药物再利用的新目标。
Open Targets Genetics (https://genetics.opentargets.org) is an open-access integrative resource that aggregates human GWAS and functional genomics data including gene expression, protein abundance, chromatin interaction and conformation data from a wide range of cell types and tissues to make robust connections between GWAS-associated loci, variants and likely causal genes. This enables systematic identification and prioritisation of likely causal variants and genes across all published trait-associated loci. In this paper, we describe the public resources we aggregate, the technology and analyses we use, and the functionality that the portal offers. Open Targets Genetics can be searched by variant, gene or study/phenotype. It offers tools that enable users to prioritise causal variants and genes at disease-associated loci and access systematic cross-disease and disease-molecular trait colocalization analysis across 92 cell types and tissues including the eQTL Catalogue. Data visualizations such as Manhattan-like plots, regional plots, credible sets overlap between studies and PheWAS plots enable users to explore GWAS signals in depth. The integrated data is made available through the web portal, for bulk download and via a GraphQL API, and the software is open source. Applications of this integrated data include identification of novel targets for drug discovery and drug repurposing.
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影响因子：
30.8
作者：
Yang, Jian;Ferreira, Teresa;Morris, Andrew P.;Medland, Sarah E.;Madden, Pamela A. F.;Heath, Andrew C.;Martin, Nicholas G.;Montgomery, Grant W.;Weedon, Michael N.;Loos, Ruth J.;Frayling, Timothy M.;McCarthy, Mark I.;Hirschhorn, Joel N.;Goddard, Michael E.;Visscher, Peter M.
通讯作者：
Visscher, Peter M.
影响因子：
12.3
作者：
McLaren W;Gil L;Hunt SE;Riat HS;Ritchie GR;Thormann A;Flicek P;Cunningham F
通讯作者：
Cunningham F
影响因子：
14.9
作者：
Buniello, Annalisa;MacArthur, Jacqueline A. L.;Parkinson, Helen
通讯作者：
Parkinson, Helen
影响因子：
2.1
作者：
Wakefield, Jon
通讯作者：
Wakefield, Jon
影响因子：
64.8
作者：
Karczewski, Konrad J;Francioli, Laurent C;MacArthur, Daniel G
通讯作者：
MacArthur, Daniel G