The Genome Aggregation Database (gnomAD)

基因组聚合数据库 (gnomAD)

• 批准号: 10347300
• 负责人: Mark Joseph Daly
• 金额: $ 230.9万
• 依托单位: BROAD INSTITUTE, INC.
• 依托单位国家: 美国
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-02-08 至 2026-01-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10347300
• 关键词: All of Us Research Program; Architecture; Basic Science; Biological; ClinVar; Clinical; Collection; Communities; Computer software; DNA; Data; Data Aggregation; Data Set; Databases; Diagnostic; Disease; Ensure; Frequencies; Funding; Genes; Genetic Variation; Genome; Genomic medicine; Genomics; Genotype; Health; Human; Human Genetics; Individual; Infrastructure; Intuition; Joints; Lead; Link; Maintenance; Maps; Medical Genetics; Methods; Mutation; Natural Selections; Pathogenicity; Phenotype; Population; Production; Publications; Quality Control; Resolution; Resources; Sample Size; Sampling; Scientist; Single Nucleotide Polymorphism; Statistical Data Interpretation; Training; Untranslated RNA; Variant; Work; base; biobank; cloud based; cohort; cost efficient; data access; data sharing; exome; functional genomics; genetic variant; genome sequencing; genomic data; health care settings; improved; insertion/deletion mutation; large datasets; novel; phenotypic data; pressure; protein expression; rare variant; tool; virtual; web site

Project Summary The Genome Aggregation Database (gnomAD) is a ubiquitous resource for basic research and clinical interpretation. The world’s largest genetic variation resource, the gnomAD dataset is used in virtually all clinical genetic diagnostic pipelines worldwide, and the website has over 20 million page views to date. Here we outline a proposal that will expand the gnomAD resource to millions of samples across diverse global populations. Our proposal will scale variant-calling and quality control to match this sample size, integrate statistical tools and other genomic resources critical to clinical interpretation, and ensure that the data we aggregate will continue to be shared freely with the biomedical community. To accomplish this we will apply a highly computationally efficient strategy to call all classes of variation (including SNVs, small indels, and the mutational spectrum of structural variants) across millions of sequenced samples enriched for under- represented ancestry groups. We will deploy a cloud-based framework for the efficient storage and automated quality control of these very large and heterogeneous sequence data sets using the massively parallel Hail architecture. We will leverage the scale of gnomAD to provide increasingly high-resolution maps of the depletion of functional variation across regions of the genome (highlighting genome regions where natural selection constrains DNA change) and provide statistical frameworks for quantitatively assessing whether the population frequency of a variant is consistent with pathogenicity, linking this information with evidence from the ClinVar resource. We will continue to share all of this data as rapidly and openly as possible with the biomedical community, long before publication. We will support and expand functionality in our widely accessed data browser as well as create scalable and publicly accessible datasets that integrate our variation data with clinical and functional genomic annotations, accessible through API frameworks to empower novel applications of the datasets. We will also provide resources and training to improve the use of gnomAD resources by the clinical genetics and wider biomedical communities.

项目概要 基因组聚合数据库 (gnomAD) 是基础研究和临床的普遍资源 gnomAD 数据集是世界上最大的遗传变异资源，几乎用于所有临床。 全球基因诊断管道，迄今为止该网站的页面浏览量已超过 2000 万次。 概述了一项提案，该提案将把 gnomAD 资源扩展到全球不同地区的数百万个样本 我们的建议将扩展变异调用和质量控制以匹配该样本量，并进行整合。 统计工具和其他对临床解释至关重要的基因组资源，并确保我们的数据 为了实现这一目标，我们将继续与生物医学界自由共享。 高计算效率的策略来调用所有类别的变异（包括 SNV、小插入缺失和 结构变体的突变谱）跨越数百万个测序样本，这些样本富集了欠缺的 我们将部署一个基于云的框架来实现高效存储和自动化。 使用大规模并行 Hail 对这些非常大且异构的序列数据集进行质量控制 我们将利用 gnomAD 的规模来提供越来越高分辨率的地图。 基因组各区域功能变异的耗尽（突出显示天然存在的基因组区域） 选择约束 DNA 变化）并提供统计框架来定量评估是否 变异的群体频率与致病性一致，将此信息与证据联系起来 我们将继续尽可能快速、公开地与 ClinVar 资源共享所有这些数据。 生物医学界，早在出版之前我们就会广泛支持和扩展功能。 访问数据浏览器以及创建集成我们的变体的可扩展且可公开访问的数据集 具有临床和功能基因组注释的数据，可通过 API 框架访问，以增强新颖性 我们还将提供资源和培训来改进 gnomAD 的使用。 临床遗传学和更广泛的生物医学界的资源。