Advancing Multi-Omics and Electronic Health Records Computational Methodologies

推进多组学和电子健康记录计算方法

• 批准号: 9979509
• 负责人: Eric R Gamazon
• 金额: $ 33.07万
• 依托单位: VANDERBILT UNIVERSITY MEDICAL CENTER
• 依托单位国家: 美国
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-08-07 至 2025-05-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/9979509
• 关键词: Address; Algorithms; All of Us Research Program; Alleles; Biological; Catalogs; Chromatin; Complex; Computing Methodologies; DNA; Data; Data Set; Development; Discipline; Disease; Electronic Health Record; Estrogen receptor positive; Expression Profiling; Gene Expression; Genetic; Genetic Variation; Genetic study; Genomic medicine; Genomics; Heterogeneity; Human; Human Genetics; Image; Link; Machine Learning; Methodological Studies; Methodology; Methylation; Modeling; Molecular; Molecular Analysis; Nature; Performance; Phenotype; Population; Population Heterogeneity; RNA; Randomized; Regulation; Regulatory Element; Research; Research Proposals; Resources; Robotics; Single Nucleotide Polymorphism; Software Tools; Therapeutic; Tissues; Training; Translational Research; Underrepresented Populations; Variant; base; biobank; causal model; cell type; comorbidity; computerized tools; data warehouse; deep learning; disorder risk; functional genomics; genetic analysis; genetic architecture; genetic association; genetic epidemiology; genetic variant; genome wide association study; genomic data; high dimensionality; histone modification; human genomics; improved; multiple omics; novel therapeutics; phenome; phenomics; pleiotropism; precision medicine; predictive modeling; protein metabolite; public health relevance; recruit; repository; response; trait; transcriptome

PROJECT SUMMARY Phenomic advances from large-scale electronic health records (EHR) linked to DNA biobanks have pioneered an efficient approach to genetic discovery that has transformed human genetic studies, with the enormous potential to provide constraints on relevant biological mechanisms on a wide spectrum of human phenotypes. Nevertheless, our understanding of the downstream molecular consequences of genetic associations remains limited and impedes our ability to develop novel therapeutic strategies for complex diseases. Given their enormous discovery potential for human genomics and precision medicine, genetic analyses in diverse populations offer unprecedented opportunities to identify causal genetic mechanisms underlying human trait variation. This research proposal aims to address these convergent developments and critical gaps and to exert a powerful influence on efforts to expand our understanding of disease mechanisms and therapeutic possibilities. Here we hypothesize that a comprehensive multi- omic, phenomic, and trans-ethnic computational methodology will provide a robust and rigorous framework. This proposal thus has the following aims: AIM 1: Develop a regularized regression based methodology and a deep learning framework to improve characterization of the genetic architecture of gene expression and to build robust prediction models, extending a Transcriptome-Wide Association Study (TWAS) methodology (called PrediXcan) that we developed. AIM 2: Develop statistical causal modeling of trait-associated genetic variation through a convergent TWAS and Mendelian Randomization approach and apply it to thousands of human traits with available GWAS and EHR data. AIM 3: Develop analytic approaches and software tools to further genetic analyses in admixed and multi-ethnic populations and to lay the groundwork for trans-ethnic multi-omic methodologies, using EHR data (e.g., BioVU, UK Biobank, All of Us).

项目摘要 与DNA相关的大规模电子健康记录（EHR）的现象进步 生物库已经开创了一种有效的遗传发现方法 人类遗传研究，具有对相关生物学的限制的巨大潜力 广泛的人类表型的机制。然而，我们对 遗传关联的下游分子后果仍然有限，并阻碍了我们 有能力为复杂疾病开发新颖的治疗策略。鉴于他们的巨大 人类基因组学和精确医学的发现潜力，多种多样的遗传分析 人口提供了前所未有的机会来识别因果关系机制 人类性状变化。 该研究建议旨在解决这些融合的发展和关键 差距并对扩大我们对疾病的理解的努力产生强大的影响 机制和治疗可能性。在这里，我们假设这是一个综合的多种多样的 OMIC，现象和跨种族计算方法学将提供强大而严格的 框架。因此，该提案具有以下目的： 目的1：开发基于回归的方法论和深度学习框架 改善基因表达的遗传结构的表征，并建立鲁棒 预测模型，扩展了整个转录组的关联研究（TWA）方法论 （称为predixcan）我们开发的。 AIM 2：通过A发展与性状相关遗传变异的统计因果模型 收敛twas和孟德尔随机方法，并将其应用于数千人 具有可用GWAS和EHR数据的特征。 目标3：开发分析方法和软件工具以进一步的遗传分析 和多种族人口，为跨种族多族人奠定基础 方法论，使用EHR数据（例如Biovu，UK Biobank，我们所有人）。