Advancing Multi-Omics and Electronic Health Records Computational Methodologies

项目总结 与DNA连锁的大规模电子健康记录(EHR)的表现学进展 生物库开创了一种高效的基因发现方法，这种方法已经改变了 人类基因研究，有巨大的潜力限制相关的生物学 在广泛的人类表型上的机制。尽管如此，我们对 遗传关联的下游分子后果仍然有限，并阻碍我们的 能够为复杂疾病开发新的治疗策略。考虑到他们巨大的 人类基因组学和精确医学的发现潜力，遗传分析在不同领域 种群提供了前所未有的机会来确定潜在的因果遗传机制 人类的特征变异。 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了解的努力产生强大的影响 机制和治疗可能性。在这里，我们假设一个全面的多- 组学、表征学和跨种族计算方法将提供稳健和严格的 框架。因此，这项建议有以下目的： 目标1：开发基于正则化回归的方法和深度学习框架 改善基因表达的遗传结构的特征，并建立健壮的 预测模型，扩展了全基因组关联研究(TWAS)方法 (称为PrediXcan)是我们开发的。 目标2：建立与性状相关的遗传变异的统计因果模型 收敛的TWA和孟德尔随机化方法及其在数千人中的应用 与现有的GWAs和EHR数据进行比较。 目标3：开发分析方法和软件工具，以进一步进行混合遗传分析 和多民族人口，并为跨民族多体奠定基础 方法，使用EHR数据(例如，BioVU、UK Biobank、我们所有人)。

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).