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.

项目摘要 基因组广泛的协会研究（GWAS）目录的使命是提供全面的和 GWAS知识的完整资源，并将目录与适当的资源整合在一起，包括 那些转化GWAS知识以改善人类健康并提高我们对人类的理解的人 复杂疾病和相关性状的差异。在接下来的五年中，我们将继续 为国际用户提供GWAS数据的最完整，精选，标准化和公平资源 来自学术和制药公司的生物医学研究人员社区。我们将扩展我们的 资源活动将GWAS目录与主要同源应用紧密联系起来，多基因 分数（PG）和多基因评分目录。我们将继续与期刊，财团，慈善机构合作 以及其他资金以确保数据可访问，将数据无法共享的数据元素与数据元素联系起来 由于道德限制。我们将把数据摄入，策展，可视化和API组件提高到 确保我们扩展以增加数据和用户量。策展，用户沉积和文献的自动化 提取将自动化并增强，从而获得质量控制，协调和公平的知识 适用于用户。通过将数据流与PGS和Mendelian随机化（MR）资源集成，我们将使 数据和必要的元数据可容易访问，以便为更广泛的用户组进行分析，并减少 跨资源的数据流和获取的冗余，将我们的资源巩固为世界的主要资源 GWAS知识库。在AIM 1中，我们将提供新颖的过程，并支持QC作为作者证词 大量的SNP特征协会实现了缩放和利用现有作者关系。我们的工作 获得社区的宝贵GWAS摘要统计数据将继续，所有研究的目标占75％ 与摘要统计数据有关，强调非欧洲裔祖先和代表性不足的疾病领域。 AIM 2通过将数据流与PG集成在一起，为摘要统计的社区使用提供了改进 和孟德尔随机化（MR）资源。 AIM 3解决了改进的性能 基础架构确保它是便携式和模块化的，并使QC和协调过程共享。 AIM 4改善了我们的图形用户界面，可视化和数据探索工具和API，以确保 它们扩展到前所未有的数据量，适合不断发展的用户需求。在一起 AIMS将为我们不断增长的用户社区提供启用和增强病因的理解， 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。