Strengthening community knowledge bases for genetic association studies and polygenic scores, the GWAS and PGS Catalogs

项目总结 基因组广泛联合研究(GWAS)目录的任务是提供全面和 提供完整的地理信息系统知识资源，并将目录与适当的资源相结合，包括 那些将全球气候变化联盟的知识转化为改善人类健康和增进我们对人类的理解的人 在复杂疾病和相关特征的背景下的变异。在未来五年，我们将继续 为国际用户提供最完整、最精准、最标准化和最公平的全球气候变化数据资源 来自学术和制药公司的生物医学研究人员社区。我们将延长我们的 资源活动，将全球气候变化系统目录与一个主要同源应用--多基因应用--紧密联系起来 得分(PGS)和多基因得分目录。我们将继续与期刊、财团、慈善机构合作 和其他资助者，以确保数据是可访问的，在数据无法共享的地方联合数据元素 由于道德上的限制。我们将改进数据接收、管理、可视化和API组件，以 确保我们根据不断增长的数据量和用户量进行扩展。管理、用户笔录和文献的自动化 提取将实现自动化和增强，从而实现质量控制、协调和公平的知识 对用户而言。通过将数据流与PGS和孟德尔随机化(MR)资源相集成，我们将 数据和必要的元数据易于访问，以供更广泛的用户群进行分析，并减少 跨资源的数据流和获取冗余，巩固我们的资源作为世界主要资源 GWAS知识库。在目标1中，我们将提供新的流程并支持作者沉积的质量控制 重大的SNP-特征关联，支持扩展和利用现有的作者关系。我们的工作是 将继续获取社区宝贵的GWAS汇总统计数据，目标是所有研究的75% 与汇总统计数据相联系，强调非欧洲血统和代表性不足的疾病地区。 AIM 2通过将数据流与PGS集成，为社区使用摘要统计数据提供了改进 和孟德尔随机化(MR)资源。Aim 3解决了对 基础设施，确保它是可移植的和模块化的，并允许共享QC和协调流程。 AIM 4改进了我们的图形用户界面、可视化和数据探索工具以及API，确保 它们可针对前所未有的数据量进行扩展，并适合不断变化的用户需求。把这些放在一起 AIMS将服务于我们不断增长的用户社区，以实现并增强对病因学的理解， 预防和治疗心血管疾病、糖尿病、癌症、精神疾病和其他 复杂的疾病。

PROJECT SUMMARY The Genome Wide Association Studies (GWAS) Catalog’s mission is to provide a comprehensive and complete resource of GWAS knowledge and to integrate the Catalog with appropriate resources, including those that translate GWAS knowledge to improve human health and improve our understanding of human variation in the context of complex disease and related traits. Over the next five years we will continue to provide the most complete, curated, standardised and FAIR resource of GWAS data for an international user community of biomedical researchers from academic and pharmaceutical companies. We will extend our resource activities to closely link the GWAS Catalog with a major cognate application, that of Polygenic Scores (PGS) and the Polygenic Score Catalog. We will continue to work with journals, consortia, charities and other funders to ensure that data is accessible, federating elements of the data where it cannot be shared due to ethical constraints. We will improve the data ingest, curation, visualisation and API components to ensure we scale to increasing data and user volumes. Automation of curation, user deposition and literature extraction will be automated and enhanced resulting in quality controlled, harmonised and FAIR knowledge for users. By integrating data flows with PGS and Mendelian Randomisation (MR) resources, we will make the data and necessary meta data readily accessible for analysis for a wider group of users and reduce redundancy in data flow and acquisition across resources, consolidating our resource as the world’s primary GWAS knowledge base. In Aim 1, we will deliver novel processes and support QC for author deposition of significant SNP-Trait associations enabling scaling and leveraging existing author relationships. Our work to acquire the community’s invaluable GWAS summary statistics will continue, with a target of 75% of all studies linked to summary statistics, emphasising non-European ancestries and under-represented disease areas. Aim 2 provides improvements for community uses of summary statistics by integrating data flows with PGS and Mendelian Randomisation (MR) resources. Aim 3 addresses performance improvements for the infrastructure ensuring it is portable and modular and enabling sharing of QC and harmonisation processes. Aim 4 improves our graphical user interfaces, visualisation and data exploration tools and APIs, ensuring they scale for unprecedented data volumes and are appropriate for evolving user needs. Together these aims will serve our growing user community to both enable and enhance the aetiological understanding, prevention and treatment of cardiovascular disease, diabetes, cancers, psychiatric disorders and other complex diseases.