Predictive Modeling of the Functional and Phenotypic Impacts of Genetic Variants

遗传变异的功能和表型影响的预测模型

• 批准号: 10626068
• 负责人: Manuel Garber
• 金额: $ 79.19万
• 依托单位: UNIV OF MASSACHUSETTS MED SCH WORCESTER
• 依托单位国家: 美国
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-08-20 至 2026-05-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10626068
• 关键词: 3-Dimensional; Algorithms; Base Pairing; Biological Assay; Catalogs; Cells; Chromatin; Classification; Cloud Computing; Collaborations; Collection; Communities; Computer Models; Computing Methodologies; Data; Data Analyses; Data Commons; Data Set; Databases; Disease; Elements; Environment; Gene Expression; Genes; Genomics; Human; Human Genome; Immune; Joints; Knowledge; Learning; Link; Maps; Mediating; Mental disorders; Metabolic; Methods; Modeling; Molecular Conformation; National Heart, Lung, and Blood Institute; National Human Genome Research Institute; Nuclear Structure; Phenotype; Physiological; Population; Population Heterogeneity; Positioning Attribute; Preventive Medicine; RNA; Records; Regulator Genes; Regulatory Element; Research Personnel; Resolution; Role; Science; Scientist; Source; Statistical Methods; Stimulus; Targeted Research; Testing; Tissues; Trans-Omics for Precision Medicine; Untranslated RNA; Variant; Veterans; Visualization software; Work; advanced analytics; analytical method; biobank; candidate identification; catalyst; causal variant; cell growth; cell type; cloud based; design; feature selection; genetic variant; genome sequencing; genome wide association study; genomic variation; human disease; improved; innovation; machine learning method; machine learning model; member; molecular phenotype; multi-ethnic; novel; predictive modeling; programs; rare variant; response; statistical learning; tool; trait; transcriptome; variant of interest; web portal; whole genome

PROJECT SUMMARY Genome-wide association studies (GWAS) have associated tens of thousands of common variants with human diseases and traits. The rapid expansion of Whole-Genome Sequencing (WGS) studies and biobanks offer great potential to understand the physiologic and pathophysiologic associations of both common and rare variants. The IGVF Consortium aims to systematically study the functional and phenotypic effects of genomic variation; it is not, however, feasible to experimentally characterize the vast number of candidate variants of interest. Computational models which can accurately predict the context-specific effects of variants are essential in designing targeted research. We propose an approach anchored on a framework of high-confidence regulatory elements (REs), from which we will develop methods to learn RE-gene links, perform rare variant association tests, and finemap causal common and rare variants. We aim to make all our results, methods, and tools available to the community through a public portal and the NHGRI and NHLBI Data Commons. Our proposal has four aims: (1) Develop a core framework of REs from open chromatin regions on which to anchor our models, improving on past approaches by producing higher-resolution predictions of functional base-pairs, producing novel RE subclassifications using functional characterization datasets from IGVF and other sources, and harnessing single-cell datasets to delineate lineage- and stimulus-specific elements. (2) Use this framework to predict the roles of variants in molecular phenotypes, specifically gene expression and cellular response to stimuli. We will build statistical and machine-learning methods to predict context-specific links between REs and their target genes, using three-dimensional conformation data produced by the IGVF Consortium and external sources. We will apply this method across many cell types and perform feature selection to build a catalog of high-confidence RE-gene links and regulatory networks. (3) Develop statistical methods to perform cell type-specific rare variant association tests (cellSTAAR) in WGS studies, and a latent variable model to prioritize candidate functional variants for traits and diseases, using results from Aims 1 and 2. We will apply these methods to analyze various metabolic, immune-mediated, and psychiatric disorders in the multi-ethnic WGS data of the NHLBI Trans-Omic Precision Medicine Program (TOPMed) and the NHGRI Genome Sequencing Program (GSP) to identify candidate causal disease-associated variants. (4) Make all the results publicly available by substantially expanding the FAVOR Portal to include whole genome variant functional annotations of all three billion genomic positions as well as cell type-specific annotations. We will implement both FAVOR and cellSTAAR in the Data Commons AnVIL (NHGRI) and BioData Catalyst (NHLBI) so researchers may use them for analysis of new datasets in a scalable cloud computing environment. We will work closely with other centers and the Data Analysis Coordinating Center (DACC) of the IGVF on joint analyses and building the IGVF Variant Catalog.

项目摘要 全基因组关联研究（GWAS）与人类具有数万个共同变异 疾病和特征。全基因组测序（WGS）研究和生物库的快速扩张提供 了解常见和罕见的生理和病理生理关联的巨大潜力 变体。 IGVF财团旨在系统地研究基因组的功能和表型效应 变化;但是，实验表征大量的候选变体是不可行的 兴趣。可以准确预测变体的上下文特定效果的计算模型是 设计有针对性的研究至关重要。我们提出了一种固定在一个框架上的方法 高信心监管元素（RES），我们将从中开发出学习重新链接的方法， 执行稀有变体关联测试，以及Finemap因果共同和稀有变体。我们的目标是使我们的一切 通过公共门户以及NHGRI和NHLBI数据可为社区提供的结果，方法和工具 下议院。我们的建议有四个目标：（1）从开放染色质区域开发RES的核心框架 为了锚定我们的模型，通过产生更高分辨率的预测来改善过去的方法 功能碱基对，使用功能表征数据集生成新的重新分类。 IGVF和其他来源，并利用单细胞数据集来描述谱系和刺激特定的谱系 元素。 （2）使用此框架预测变体在分子表型中的作用，特别是基因 表达和细胞对刺激的反应。我们将构建统计和机器学习方法以预测 使用三维构象数据，RES与其目标基因之间的上下文特定联系 由IGVF财团和外部来源生产。我们将在许多单元类型中应用此方法， 执行功能选择以构建高信任性重新链接和监管网络的目录。 （3） 开发统计方法以在WGS中执行细胞类型特异性稀有变体关联测试（Cellstaar） 研究以及使用特征和疾病的候选功能变体优先级的潜在变量模型 目标1和2的结果。我们将应用这些方法来分析各种代谢，免疫介导的和 NHLBI Trans-Omic Precision Medicine计划的多种族WGS数据中的精神疾病 （顶部）和NHGRI基因组测序程序（GSP），以识别候选因果关系 与疾病相关的变体。 （4）通过大大扩展青睐，使所有结果公开获得 门户包括所有三十亿个基因组位置的全基因组变体功能注释以及 特定于细胞类型的注释。我们将在数据共享Anvil中实施FAY和Cellstaar （NHGRI）和生物催化剂（NHLBI），因此研究人员可以使用它们来分析新数据集 可扩展的云计算环境。我们将与其他中心紧密合作和数据分析 IGVF的协调中心（DACC）在关节分析和构建IGVF变体目录中。