Leveraging human evolutionary history to improve our understanding of complex disease architecture

利用人类进化史来提高我们对复杂疾病结构的理解

• 批准号: 10752744
• 负责人: Syed Arslan Abbas Zaidi
• 金额: $ 24.9万
• 依托单位: UNIVERSITY OF MINNESOTA
• 依托单位国家: 美国
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-08-01 至 2026-01-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10752744
• 关键词: Address; Admixture; Advisory Committees; African American population; Aging; Apoptosis; Architecture; Asian population; Award; Cell Respiration; Communities; Complex; Computer Simulation; Consanguinity; DNA; Data; Demographic Impact; Demography; Developing Countries; Diagnosis; Disease; Electronic Health Record; Ethnic Population; Ethnography; Genes; Genetic; Genetic Epistasis; Genetic Research; Genetic Risk; Genetic Structures; Genetic study; Genome; Goals; Haplogroup; Heart Diseases; Human; Individual; Learning Skill; Medicine; Metabolism; Methods; Mission; Mitochondria; Mitochondrial DNA; Modeling; National Institute of General Medical Sciences; Non-Insulin-Dependent Diabetes Mellitus; Nuclear; Pakistan; Pattern; Pennsylvania; Performance; Persons; Phenotype; Play; Population; Population Heterogeneity; Premature Birth; Prevention; Recording of previous events; Research; Residual state; Resources; Risk; Role; Scientist; Shapes; South Asian; Statistical Bias; Statistical Methods; Structure; Testing; Training; Uncertainty; United States National Institutes of Health; Universities; Variant; Veterans; biobank; causal variant; clinical application; computerized tools; disorder risk; ethnic diversity; genetic architecture; genetic predictors; genetic risk factor; genome wide association study; human disease; improved; mortality; novel strategies; phenome; polygenic risk score; precision medicine; programs; rare variant; risk prediction; simulation; success; trait

Title: Leveraging human evolutionary history to improve our fundamental understanding of complex disease architecture Abstract: The overarching goal of this research is to improve the applicability of genetic risk predictions within and across human populations by leveraging recent advances in our understanding of human evolutionary history. In Aim 1, I will carry out empirically-guided simulations to investigate how fine-scale substructure in large genome-wide association studies (GWAS; e.g. UK Biobank) biases our inference of complex trait architecture and polygenic risk score prediction. I will leverage these findings to develop statistical and computational tools to correct for such biases. In Aim 2, I will investigate whether recent admixture in humans generates incompatibilities between mitochondrial and nuclear DNA in African Americans. To test this, I will analyze genetic and electronic health record data from the ethnically diverse Penn Medicine Biobank to test whether mito-nuclear discordance—degree of ancestry divergence between mitochondrial and nuclear genomes—is associated with the risk of diseases common among African Americans. Additionally, I will test for selection against mito-nuclear incompatibilities in recently admixed populations. In Aim 3, I will investigate how the practice of endogamy and consanguinity among Pakistanis shapes their disease risk architecture. I will further evaluate the ability and limitations of currently used GWAS methods, which are typically modeled after outbred populations, to infer disease architecture given the complex population structure in Pakistanis. I will improve upon these methods, thereby making GWAS more widely applicable to a diverse set of people. Each of my three aims is independent, yet together they will lead to improvements in diagnosis, treatment, and prevention of human diseases—the overarching mission of the NIGMS. I will learn the skills needed to accomplish these aims with the help of my advisory committee, comprising of Drs. Iain Mathieson, Sarah Tishkoff, Doug Wallace, and Marilyn Ritchie, who are world-class leaders in genetics research. With the training plan that I have outlined and the resources at the University of Pennsylvania, I am confident that the K99 award will help me achieve my goal of becoming an independent scientist in the field of statistical genetics.

标题：利用人类进化史来提高我们的基本理解 复杂疾病建筑 摘要：这项研究的总体目标是提高遗传风险的适用性 通过利用我们的最新进展，预测人口内部和整个人口 了解人类进化史。在AIM 1中，我将进行经验指导 模拟研究大型基因组关联研究中的优质子结构如何 （GWAS;例如英国生物库）偏见我们对复杂性状结构和多基因风险的推论 得分预测。我将利用这些发现来开发统计和计算工具 纠正此类偏见。在AIM 2中，我将调查最近在人类中的混合物是否产生 非裔美国人线粒体和核DNA之间的不兼容。为了测试这个，我会 分析来自种族多样化的宾夕法尼亚医学的遗传和电子健康记录数据 生物库来测试Mito-核核的不一致 - 祖先差异之间的差异 线粒体和核基因组与常见的疾病风险有关 非裔美国人。此外，我将测试针对MITO核核不兼容的选择 最近混合了人口。在AIM 3中，我将调查内婚的实践和 巴基斯坦人之间的血缘关系塑造了他们的疾病风险建筑。我将进一步评估 当前使用的GWAS方法的能力和局限 鉴于巴基斯坦人的种群结构复杂的种群结构，种群推断疾病结构。我 将改进这些方法，从而使GWAS更广泛地适用于多样化的集合 人。我的三个目标中的每一个都是独立的，但是它们将共同改善 人类疾病的诊断，治疗和预防 - 诺格姆斯的总体任务。 我将在我的咨询委员会的帮助下学习实现这些目标所需的技能 Iain Mathieson，Sarah Tishkoff，Doug Wallace和Marilyn Ritchie， 遗传学研究的世界一流领导者。通过我概述的培训计划和 宾夕法尼亚大学的资源，我相信K99奖会帮助我 实现我成为统计遗传学领域的独立科学家的目标。