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

项目摘要 全基因组关联研究（GWAS）目录的使命是提供一个全面的， GWAS知识的完整资源，并将目录与适当的资源集成，包括 那些将GWAS知识转化为改善人类健康和提高我们对人类健康的理解的人， 复杂疾病和相关性状背景下的变异。未来五年，我们将继续 为国际用户提供最完整、最有组织、最标准化和最公平的GWAS数据资源 来自学术界和制药公司的生物医学研究人员社区。我们将扩大我们的 将GWAS目录与主要同源应用程序（多基因）紧密联系起来的资源活动 评分（PGS）和多基因评分目录。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，在无法共享的地方联合数据元素 由于道德约束。我们将改进数据摄取、策展、可视化和API组件， 确保我们能够扩展以增加数据量和用户量。策展、用户沉积和文献的自动化 提取将自动化和增强，从而产生质量受控、协调和公平的知识 为用户通过将数据流与PGS和孟德尔随机化（MR）资源相结合，我们将使 数据和必要的Meta数据易于为更广泛的用户群体进行分析， 数据流和跨资源获取的冗余，巩固我们作为世界主要 GWAS知识库。在目标1中，我们将提供新的流程，并支持QC，以便作者沉积 显著的SNP-Trait关联使得能够扩展和利用现有的作者关系。我们的工作取得 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% 与汇总统计相关联，强调非欧洲血统和代表性不足的疾病领域。 目标2通过将数据流与PGS相结合，改进社区对汇总统计的使用 Mendelian Randomisation（MR）资源目标3针对 基础设施，确保其便携性和模块化，并实现质量控制和协调过程的共享。 Aim 4改进了我们的图形用户界面、可视化和数据探索工具和API， 它们可扩展到前所未有的数据量，并适合不断变化的用户需求。综合这些 aims将服务于我们不断增长的用户社区，以实现和增强对病原学的理解， 预防和治疗心血管疾病、糖尿病、癌症、精神疾病和其他疾病 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.