European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, Hinxton, Cambridge CB10 1SD, UK.
Open Targets, Wellcome Genome Campus, Hinxton, Cambridge CB10 1SD, UK.
Nucleic Acids Res. 2023 Jan 6;51(D1):D977-D985. doi: 10.1093/nar/gkac1010.
The NHGRI-EBI GWAS Catalog (www.ebi.ac.uk/gwas) is a FAIR knowledgebase providing detailed, structured, standardised and interoperable genome-wide association study (GWAS) data to >200 000 users per year from academic research, healthcare and industry. The Catalog contains variant-trait associations and supporting metadata for >45 000 published GWAS across >5000 human traits, and >40 000 full P-value summary statistics datasets. Content is curated from publications or acquired via author submission of prepublication summary statistics through a new submission portal and validation tool. GWAS data volume has vastly increased in recent years. We have updated our software to meet this scaling challenge and to enable rapid release of submitted summary statistics. The scope of the repository has expanded to include additional data types of high interest to the community, including sequencing-based GWAS, gene-based analyses and copy number variation analyses. Community outreach has increased the number of shared datasets from under-represented traits, e.g. cancer, and we continue to contribute to awareness of the lack of population diversity in GWAS. Interoperability of the Catalog has been enhanced through links to other resources including the Polygenic Score Catalog and the International Mouse Phenotyping Consortium, refinements to GWAS trait annotation, and the development of a standard format for GWAS data.
NHGRI-EBI GWAS 目录(www.ebi.ac.uk/gwas)是一个 FAIR 知识库,每年为来自学术研究、医疗保健和行业的超过 20 万用户提供详细、结构化、标准化和可互操作的全基因组关联研究 (GWAS) 数据。该目录包含超过 45000 个已发表的 GWAS 中与变体特征相关的关联和支持元数据,涵盖了超过 5000 个人类特征,以及超过 40000 个完整的 P 值汇总统计数据集。内容是通过出版物进行策展,或者通过新的提交门户和验证工具从作者提交的预发表汇总统计数据中获取。近年来,GWAS 数据量大大增加。我们已经更新了我们的软件,以应对这一扩展挑战,并能够快速发布提交的汇总统计数据。该存储库的范围已经扩大,包括了社区感兴趣的其他数据类型,包括基于测序的 GWAS、基于基因的分析和拷贝数变异分析。通过与其他资源(包括多基因评分目录和国际小鼠表型联盟)的链接、GWAS 特征注释的改进以及 GWAS 数据的标准格式的开发,提高了目录的互操作性。