Advancing Multi-Omics and Electronic Health Records Computational Methodologies

项目摘要 与DNA相关的大规模电子健康记录（EHR）的表型学进展 生物库开创了一种有效的基因发现方法， 人类遗传学研究，具有巨大的潜力，提供有关生物学的限制， 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，并阻碍了我们的研究。 为复杂疾病开发新的治疗策略的能力。鉴于其巨大的 人类基因组学和精准医学的发现潜力，各种基因分析 人口提供了前所未有的机会，以确定因果遗传机制的基础 人类性状变异 这项研究提案旨在解决这些趋同的发展和关键 并对扩大我们对疾病的理解的努力施加强大的影响 机制和治疗的可能性。在这里，我们假设一个全面的多- 组学、表型学和跨种族计算方法将提供一个强大而严格的 框架.因此，这项建议的目的如下： 目标1：开发基于正则化回归的方法和深度学习框架， 改进基因表达的遗传结构的表征， 预测模型，扩展了全转录组关联研究（TWAS）方法 （称为PrediXcan）。 目的2：通过一个统计学模型， 融合TWAS和孟德尔随机化方法，并将其应用于数千人 性状与可用的GWAS和EHR数据。 目的3：开发分析方法和软件工具，以进一步分析混合 和多民族人口，并为跨民族的多民族经济奠定基础， 方法，使用EHR数据（例如，BioVU，UK Biobank，All of Us）。

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).