The Association to Function Knowledge Portal: a genomic data resource for translating GWAS associations to biological effects

功能关联知识门户：用于将 GWAS 关联转化为生物效应的基因组数据资源

基本信息

批准号：
10673866
负责人：
Noel P Burtt
金额：
$ 68.46万
依托单位：
BROAD INSTITUTE, INC.
依托单位国家：
美国
项目类别：
财政年份：
2021
资助国家：
美国
起止时间：
2021-09-16 至 2026-04-30
项目状态：
未结题

来源：
https://reporter.nih.gov/project-details/10673866
关键词：
Acceleration Address Area Bioinformatics Biological Biological Assay Calibration Catalogs Cells Clinical Collaborations Communities Complex Computer software Data Data Aggregation Data Commons Data Set Dedications Disease Elements Funding Genes Genetic Genomics Goals Health Human Knowledge Knowledge Portal Manuals Maps Methods Molecular National Human Genome Research Institute National Institute of Diabetes and Digestive and Kidney Diseases Non-Insulin-Dependent Diabetes Mellitus Output Pathway interactions Phenotype Research Research Design Research Personnel Resources Source Specific qualifier value Tissues Translating Trust Variant Visual Work causal variant data access data integration data resource data sharing data visualization design disorder risk epigenomics functional genomics genetic association genetic variant genome resource genome wide association study genomic data graph database human disease improved innovation insight knowledge graph new therapeutic target precision medicine statistics trait transcriptomics web based interface web portal

项目摘要

Abstract Genome wide association studies (GWAS) have produced associations between many thousands of genetic variants and many hundreds of traits. The “functional effects” of most associations, however, have not yet been elucidated – that is, the causal variants and effector genes responsible for them, and the tissues and pathways through which they act, remain largely unknown. Over the past few years, three classes of genomic data have arisen for inferring the functional effects of GWAS associations: summary association statistics (effect sizes and p-values for associations between SNPs and traits), genomic annotations (assays of regulatory activity and genomic functional elements), and bioinformatic methods (computationally predicted functional effects). We argue that two gaps exist in the current resources that aggregate these data: first, no current resource aims to comprehensively curate and catalog all that is known, and all data or methods that could help predict, the functional effects of GWAS associations; second, existing resources are developed with (at best) limited involvement from experts who either originally generated the genomic data and/or understand how to best use them. We propose to address these gaps by building a new genomic community resource – the Association to Function Knowledge Portal (A2FKP) – using a general software platform we initially developed for type 2 diabetes. Our approach makes use of a key innovation to build a resource that is both high quality and comprehensive: we collaborate with disease expert communities to build dedicated knowledge portals for them, motivating them to contribute their data and expertise, and we then integrate these data alongside those of other communities, providing users with access a comprehensive resource. Specific aim 1 addresses gaps in the comprehensiveness and quality of the data aggregated by current resources regarding the functional effects of GWAS associations. It will establish and manage collaborations with a wide range of disease, data, and method experts, and then work with these communities to identify, aggregate, and curate data for 11 classes of disease. Specific aim 2 addresses gaps in current schemas and software platforms for the myriad types of data used for predicting the functional effects of GWAS associations. It will build pipelines for processing genetic and genomic datasets through bioinformatic methods for predicting the functional effects of GWAS associations, apply these pipelines to data aggregated in Aim 1, and transform their outputs to relationships among entities in a knowledge graph. The goal of specific aim 3 is to provide users with direct and visual access to the resources aggregated or computed in Aims 1 and 2. It will develop REST APIs and web portals for querying and visualizing data within the A2FKP. Significance: The project would produce a high quality and comprehensive genomic resource of data and methods for predicting the functional effects of GWAS associations. Easy access to such a resource will accelerate the pace by which GWAS associations can be translated to insights into complex disease.

抽象的全基因组关联研究 (GWAS) 已在数千个基因之间建立了关联然而，大多数关联的“功能效应”并没有发生。尚未阐明——即导致它们的因果变异和效应基因，以及组织和在过去的几年里，三类基因组的作用途径仍然很大程度上未知。用于推断 GWAS 关联功能效果的数据已经出现：关联统计汇总（SNP 和性状之间关联的效应大小和 p 值）、基因组注释（调节活性和基因组功能元件）和生物信息学方法（计算预测我们认为，当前汇总这些数据的资源存在两个差距：第一，没有。当前资源旨在全面整理和编目所有已知信息以及所有数据或方法可以帮助预测 GWAS 协会的功能效果；其次，现有资源的开发（最多）最初生成基因组数据和/或理解的专家的参与有限我们建议通过建立新的基因组社区资源来解决这些差距 - 功能知识门户协会 (A2FKP) – 使用我们最初使用的通用软件平台我们的方法利用一项关键创新来构建一种兼具两者的资源。高质量和全面：我们与疾病专家社区合作建立专门的知识为他们提供门户，激励他们贡献自己的数据和专业知识，然后我们整合这些数据与其他社区的社区一起，为用户提供全面的资源。具体目标 1 解决由以下机构汇总的数据在全面性和质量方面的差距它将建立和管理有关 GWAS 协会功能影响的现有资源。与广泛的疾病、数据和方法专家合作，然后与这些社区合作具体目标 2 旨在识别、汇总和整理 11 类疾病的数据，以解决当前的差距。用于预测功能效果的各种数据的模式和软件平台 GWAS 协会将建立通过生物信息学处理遗传和基因组数据集的管道。预测 GWAS 关联功能影响的方法，将这些管道应用于聚合数据目标 1 中，将其输出转换为知识图中实体之间的关系。具体目标 3 是为用户提供对聚合或计算的资源的直接和可视化访问目标 1 和 2。它将开发 REST API 和门户网站，用于查询和可视化 A2FKP 内的数据。意义：该项目将产生高质量和全面的基因组数据资源以及预测 GWAS 关联的功能效果的方法将轻松访问此类资源。加快 GWAS 关联转化为对复杂疾病的洞察的步伐。