An evolutionary framework to elucidate and interpret the genetic architecture of complex traits in diverse populations - diversity supplement

项目摘要/摘要(出自上级赠款) 环境因素和遗传因素都是造成人群之间疾病风险差异的原因。基因 种群间差异的原因与这些种群的进化史密切相关。 因此，更好地融入进化思维将有助于解释不同种群之间的差异 今天，改善临床实践和个性化护理。为此，江实验室将继续发展 人类进化群体遗传学和遗传流行病学相结合的综合框架， 利用经验数据分析和定量方法发展，更好地探讨遗传 种群内和种群间复杂性状的结构。这一综合框架由三个主要部分组成 焦点：(1)人类复杂性状的遗传结构，(2)人口历史，(3)适应性 人类人口的历史。第一个主题中的研究揭示了基因对人类表型的影响 今天，虽然对后两者的研究解释了变异在体内产生的进化机制 以及人类之间的关系。更重要的是，蒋介石实验室的研究不仅仅关注这些 主题，但也利用一个主题的信息通知另一个主题。在这种模式下，江实验室将把重点放在 关于未来五年的以下三个目标。首先，我们将进行全面的基因研究 旨在解决夏威夷原住民健康差距的计划。具体地说，我们将产生基因组 加速这一群体的基因研究所需的资源。然后，我们将描述 夏威夷原住民的人口学历史以说明在夏威夷进行基因组研究的好处 研究不足的人口，在波利尼西亚人口中进行大规模荟萃分析，以确定人口- 与夏威夷原住民流行的疾病相关的特定等位基因，并与夏威夷原住民接触 未来伙伴关系和合作的社区。第二，我们将研究人类进化病因学 在当今人口中风险增加。以拉美裔人口为例，我们将检查提升的 这一人群中儿童白血病的风险是由于在欧洲接触期间引入的选择性压力造成的。 在16世纪。第三，我们将通过引入一种新的基因相关概念来彻底改变目前的遗传相关概念 个体之间的遗传相似性矩阵，它结合了来自 人口。该矩阵将改进一些统计遗传应用程序的性能，例如 遗传力估计和表型归因。虽然我们以夏威夷原住民和拉丁裔为例 在这一建议中，遗传流行病学和进化的综合框架也将受益 未来对其他未被研究的少数民族的研究。我们处于独特的地位，能够实现这些目标 由于我们在将群体遗传学原理与医学遗传分析和统计学相结合方面的专业知识 基因发育。

Project Summary / Abstract (from Parent Grant) Both environmental and genetic factors contribute to disparity in disease risks between populations. The genetic causes of differences between populations are intimately tied to the evolutionary histories of these populations. Therefore, a better incorporation of evolutionary thinking will help explain the disparity among diverse populations today and improve clinical practices and personalized care. To this end, the Chiang Lab will continue to develop an integrative framework combining evolutionary population genetics with genetic epidemiology in humans, utilizing both empirical data analysis and quantitative methods development to better probe into the genetic architecture of complex traits within and between populations. This integrative framework consists of three main foci: (1) the genetic architecture of human complex traits, (2) the demographic history, and (3) the adaptive history of human populations. Research in the first topic informs the genetic consequences on our phenome today, while research in the latter two explains the evolutionary mechanisms through which variation arise within and between human populations. More importantly, research from the Chiang Lab focuses not solely on these topics, but also leverages information on one to inform the other. Within this paradigm, the Chiang Lab will focus on the following three goals over the next five years. First, we will execute a comprehensive genetic research program to address the health disparities in Native Hawaiians. Specifically, we will generate the genomic resources necessary to accelerate genetic research in this population. We will then characterize the demographic history of the Native Hawaiians to illustrate the benefit of conducting genomic studies in understudied populations, perform large-scale meta-analysis in Polynesian populations to identify population- specific alleles associated with diseases prevalent in Native Hawaiians, and engage the Native Hawaiian community for future partnership and collaborations. Second, we will investigate the evolutionary etiology for elevated risk in present-day populations. Using Latino populations as an example, we will examine if the elevated risk in childhood leukemia in this population is due to the selective pressure introduced during European contact in the 16th century. Third, we will revolutionize the current concept of genetic relatedness by introducing a new genetic similarity matrix among individuals that incorporates information from the genealogical tree of the population. This matrix will improve the performance of a number of statistical genetic applications, such as heritability estimation and phenotype imputation. While we used Native Hawaiians and Latinos as example populations in this proposal, this integrated framework of genetic epidemiology and evolution will also benefit future research in other understudied ethnic minorities. We are uniquely positioned to achieve these goals because of our expertise in combining population genetic principles with medical genetic analysis and statistical genetic development.