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).

项目总结