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

基本信息

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
项目类别：
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

项目摘要

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.

项目概要全基因组关联研究 (GWAS) 将数以万计的常见变异与人类联系起来疾病和特征。全基因组测序 (WGS) 研究和生物样本库的快速扩展提供了了解常见和罕见疾病的生理和病理生理关联的巨大潜力变种。 IGVF联盟旨在系统地研究基因组的功能和表型效应变化;然而，通过实验来表征大量候选变体是不可行的。兴趣。可以准确预测变体的特定上下文影响的计算模型是对于设计有针对性的研究至关重要。我们提出了一种基于框架的方法高可信度的调控元件（RE），我们将从中开发学习 RE 基因链接的方法，执行罕见变异关联测试，并精细映射常见和罕见变异的因果关系。我们的目标是让我们所有的通过公共门户以及 NHGRI 和 NHLBI 数据向社区提供结果、方法和工具公共资源。我们的建议有四个目标：（1）从开放染色质区域开发 RE 的核心框架锚定我们的模型，通过产生更高分辨率的预测来改进过去的方法功能碱基对，使用来自的功能表征数据集生成新颖的 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特定刺激元素。 (2) 使用该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确定 RE 与其靶基因之间的上下文特定联系由 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-gene 链接和调控网络的目录。 (3) 开发统计方法以在 WGS 中执行细胞类型特异性罕见变异关联测试 (cellSTAAR) 研究和潜在变量模型，用于优先考虑性状和疾病的候选功能变异，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、免疫介导和 NHLBI 跨组学精准医学计划的多种族 WGS 数据中的精神疾病 (TOPMed) 和 NHGRI 基因组测序计划 (GSP) 来确定候选因果关系疾病相关变异。 (4) 通过大幅扩大FAVOR，公开所有结果门户包括所有 30 亿个基因组位置的全基因组变异功能注释以及细胞类型特异性注释。我们将在 Data Commons AnVIL 中实现 FAVOR 和 cellSTAAR (NHGRI) 和 BioData Catalyst (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密切合作 IGVF 协调中心 (DACC) 负责联合分析和构建 IGVF 变体目录。