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

项目摘要/摘要 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 进化群体遗传学与遗传流行病学相结合的综合框架 人类，利用经验数据分析和量化方法的发展，更好地探讨 种群内和种群间复杂性状的遗传结构。这一综合框架包括 三个主要焦点：(1)人类复杂特征的遗传结构，(2)人口历史，以及(3) 人类种群的适应性历史。第一个主题中的研究告诉我们基因对我们的影响 而对后两者的研究解释了变异通过 出现在人类群体内部和之间。更重要的是，江实验室的研究关注的不仅仅是 关于这些主题，但也利用其中一个的信息来通知另一个。在这种模式下，江实验室 今后五年将重点实现以下三个目标。首先，我们将执行一项全面的遗传 旨在解决夏威夷原住民健康差距的研究计划。具体地说，我们将生成 加速这一群体的遗传研究所需的基因组资源。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 这一人群中儿童白血病风险的增加是由于在欧洲 接触发生在16世纪。第三，我们将通过以下方式彻底改变目前的遗传相关概念 引入了一个新的个体间遗传相似性矩阵，该矩阵结合了来自系谱的信息 种群之树。该矩阵将改进许多统计遗传应用的性能， 如遗传力估计、表型归因等。虽然我们用夏威夷原住民和拉丁人作为 在这项提案中，这一遗传流行病学和进化的综合框架还将 有益于其他未被研究的少数民族的未来研究。我们处于独特的地位，可以实现这些目标 目标，因为我们在将群体遗传学原理与医学遗传分析和 统计学上的遗传发育。

Project Summary / Abstract 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 population 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.