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

加强遗传关联研究和多基因评分、GWAS 和 PGS 目录的社区知识库

• 批准号: 10494308
• 负责人: Fiona Cunningham
• 金额: $ 119.01万
• 依托单位: EUROPEAN MOLECULAR BIOLOGY LABORATORY
• 依托单位国家: 美国
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-09-20 至 2027-06-30
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10494308
• 关键词: Address; Architecture; Area; Atlases; Automation; Biomedical Research; Cardiovascular Diseases; Catalogs; Charities; Collaborations; Communities; Complex; Consent; Data; Data Analyses; Data Element; Data Reporting; Data Set; Deposition; Diabetes Mellitus; Disease; Ensure; Ethics; Evaluation; Feedback; Future; Genes; Genetic Variation; Genomics; Gold; Health; Human; Infrastructure; International; Journals; Knowledge; Legal; Link; Literature; Malignant Neoplasms; Mental disorders; Metadata; Methods; Mission; Modeling; Online Systems; Ontology; Performance; Persons; Pharmacologic Substance; Population; Prevention; Privacy; Process; Quality Control; Randomized; Relative Risks; Reporting; Reproducibility; Research; Research Personnel; Resource Development; Resources; Scoring Method; Source; Standardization; Structure; Surveys; Techniques; Time; Translating; Translations; Update; Variant; Visualization; Work; base; biobank; cloud based; computational platform; data access; data acquisition; data exploration; data ingestion; data quality; database of Genotypes and Phenotypes; design; distributed data; diverse data; exome; experience; feeding; genetic association; genome analysis; genome resource; genome sequencing; genome wide association study; genomic variation; graphical user interface; improved; knowledge base; large datasets; machine learning algorithm; novel; portability; resilience; statistics; text searching; tool; trait; web site; whole genome; working group

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.

项目概要 全基因组关联研究 (GWAS) 目录的使命是提供全面且 完整的 GWAS 知识资源，并将目录与适当的资源相整合，包括 那些将 GWAS 知识转化为改善人类健康并增进我们对人类的理解的人 复杂疾病和相关特征背景下的变异。未来五年我们将继续 为国际用户提供最完整、精选、标准化和公平的 GWAS 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 将 GWAS 目录与主要同源应用（多基因应用）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 和其他资助者确保数据可访问，在无法共享的情况下联合数据元素 由于道德约束。我们将改进数据摄取、管理、可视化和 API 组件，以 确保我们能够扩展以增加数据和用户量。策展、用户沉积和文献的自动化 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 对于用户。通过将数据流与 PGS 和孟德尔随机化 (MR) 资源集成，我们将使得 数据和必要的元数据易于为更广泛的用户群体进行分析，并减少 跨资源的数据流和获取的冗余，巩固我们的资源作为世界主要资源 GWAS 知识库。在目标 1 中，我们将提供新颖的流程并支持作者沉积的质量控制 重要的 SNP-性状关联能够扩展和利用现有的作者关系。我们的工作是为了 将继续获取社区宝贵的 GWAS 摘要统计数据，目标是覆盖所有研究的 75% 与汇总统计数据相关，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 集成来改进社区对汇总统计数据的使用 和孟德尔随机化 (MR) 资源。目标 3 解决了性能改进问题 确保其便携性和模块化的基础设施，并实现质量控制和协调流程的共享。 目标 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 它们可以适应前所未有的数据量，并且适合不断变化的用户需求。一起这些 目标将为我们不断增长的用户社区提供服务，以实现和增强对病因的理解， 预防和治疗心血管疾病、糖尿病、癌症、精神疾病等 复杂的疾病。