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: 10456685
• 负责人: Syed Arslan Abbas Zaidi
• 金额: $ 3.78万
• 依托单位: UNIVERSITY OF PENNSYLVANIA
• 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 2022-12-28
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10456685
• 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; Demography; Developing Countries; Diagnosis; Disease; Electronic Health Record; Ethnic group; Ethnography; Genes; Genetic; Genetic Epistasis; Genetic Research; Genetic Risk; Genetic Structures; Genetic study; Genome; Goals; Haplogroup; Heart Diseases; Human; Individual; Lead; 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.

利用人类进化史来提高我们的基本认识 复杂的疾病结构 翻译后摘要：这项研究的首要目标是提高遗传风险的适用性 预测内和跨人群利用我们的最新进展， 了解人类进化史。在目标1中，我将执行 模拟研究如何在大型全基因组关联研究中精细尺度子结构 (GWAS;例如英国生物银行）使我们对复杂性状结构和多基因风险的推断产生偏差 得分预测我将利用这些发现来开发统计和计算工具， 纠正这种偏见。在目标2中，我将研究人类最近的混合物是否会产生 非裔美国人线粒体和核DNA之间的不相容性。为了验证这一点，我将 分析来自不同种族的宾夕法尼亚医学的遗传和电子健康记录数据， 生物样本库，以测试线粒体-核不一致-祖先差异程度是否 线粒体和核基因组-与常见疾病的风险有关， 非裔美国人此外，我将测试选择对线粒体核不相容性， 最近的混合人口。在目标3中，我将研究如何实行内婚制， 巴基斯坦人之间的血缘关系塑造了他们的疾病风险结构。我将进一步评估 目前使用的GWAS方法的能力和局限性，这些方法通常是在远系繁殖后建模的 鉴于巴基斯坦人复杂的人口结构，我 我将对这些方法进行改进，从而使GWAS更广泛地适用于不同的环境 的人。我的三个目标中的每一个都是独立的，但它们将一起导致改善。 诊断、治疗和预防人类疾病----这是NIGMS的首要使命。 我将在我的顾问委员会的帮助下学习实现这些目标所需的技能， 包括伊恩·马西森博士、莎拉·蒂什科夫博士、道格·华莱士博士和玛丽莲·里奇博士， 遗传学研究领域的世界级领导者。根据我概述的培训计划， 在宾夕法尼亚大学的资源，我相信K99奖将帮助我 实现我的目标，成为统计遗传学领域的独立科学家。