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

标题：利用人类进化史提高我们的基本认识 复杂的疾病架构 摘要：本研究的首要目标是提高遗传风险的适用性。 通过利用我们的最新进展在人类群体内部和跨人群进行预测 对人类进化史的理解。在目标1中，我将进行经验指导 模拟研究大型全基因组关联研究中细微尺度的亚结构 (例如，英国生物库)偏向了我们对复杂性状结构和多基因风险的推断 分数预测。我将利用这些发现来开发统计和计算工具来 纠正这种偏见。在目标2中，我将研究人类最近的混合是否会产生 非裔美国人线粒体和核DNA之间的不相容。为了测试这一点，我将 分析来自种族多元化的宾夕法尼亚医学中心的遗传和电子健康记录数据 生物库将测试有丝分裂核不一致性-血统程度之间的差异 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，我还将测试是否存在有丝分裂核不兼容性 最近混杂的种群。在目标3中，我将调查内婚制和 巴基斯坦人之间的血缘关系决定了他们的疾病风险架构。我将进一步评估 当前使用的GWAS方法的能力和局限性，这些方法通常是以远缘繁殖为模型的 考虑到巴基斯坦人复杂的人口结构，推断疾病结构。我 将对这些方法进行改进，从而使GWA更广泛地适用于不同的集合 一群人。我的三个目标中的每一个都是独立的，但它们加在一起将导致 诊断、治疗和预防人类疾病--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， 由Iain Mathieson、Sarah Tishkoff、Doug Wallace和Marilyn Ritchie博士组成，他们是 遗传学研究领域的世界级领导者。根据我所概述的培训计划和 在宾夕法尼亚大学的资源，我相信K99奖将帮助我 实现我成为统计遗传学领域的一名独立科学家的目标。

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.