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: 10472610
• 负责人: Manuel Garber
• 金额: $ 75.24万
• 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/10472610
• 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 Diseases; 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; catalyst; causal variant; cell type; cloud based; design; feature selection; genetic variant; genome sequencing; genome wide association study; genomic variation; human disease; improved; innovation; interest; machine learning method; machine learning model; member; molecular phenotype; multi-ethnic; novel; predictive modeling; programs; rare variant; response; statistical and machine learning; tool; trait; transcriptome; 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）已将数万种常见变异与人类遗传易感性相关联。 疾病和特征。全基因组测序（WGS）研究和生物库的快速发展， 了解常见和罕见的生理和病理生理学关联的巨大潜力 变体。IGVF联盟旨在系统地研究基因组的功能和表型效应， 变异;然而，通过实验表征大量的候选变异是不可行的。 兴趣计算模型，可以准确地预测特定的背景下的影响，变异是 在设计有针对性的研究中至关重要。我们提出了一种基于以下框架的方法： 高置信度调控元件（RE），我们将从中开发方法来了解RE基因的联系， 执行罕见变异关联测试，并对常见和罕见变异进行精细绘图。我们的目标是使我们所有的 通过公共门户网站和NHGRI和NHLBI数据向社区提供结果、方法和工具 共享资源我们的建议有四个目标：（1）从开放的染色质区域开发RE的核心框架， 它可以锚我们的模型，通过产生更高分辨率的预测来改进过去的方法， 功能性碱基对，使用功能性表征数据集产生新的RE子分类， IGVF和其他来源，并利用单细胞数据集来描绘谱系和刺激特异性 元素(2)使用这个框架来预测变异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。我们将建立统计和机器学习方法来预测 使用三维构象数据， 由IGVF联盟和外部来源制作。我们将在许多细胞类型中应用这种方法， 执行特征选择以构建高置信度RE基因链接和调控网络的目录。（三） 开发统计方法，在WGS中进行细胞类型特异性罕见变异相关性检验（cellSTAAR） 研究，以及潜在变量模型，以优先考虑性状和疾病的候选功能变体，使用 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， NHLBI跨器官精准医学项目多种族WGS数据中的精神疾病 （TOPMed）和NHGRI基因组测序计划（GSP），以确定候选的因果关系 疾病相关的变异。(4)通过大幅度扩大FAVOR，使所有结果公开 门户网站包括所有30亿个基因组位置的全基因组变体功能注释， 单元格类型特定的注释。我们将在数据共享AnVIL中实现FAVOR和cellSTAAR （NHGRI）和生物数据催化剂（NHLBI），因此研究人员可以使用它们来分析新的数据集， 可扩展的云计算环境。我们将与其他中心和数据分析中心密切合作， 协调中心（DACC）的IGVF的联合分析和建立IGVF变量目录。