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

项目摘要/摘要（来自家长资助） 环境和遗传因素都造成了人群之间疾病风险的差异。遗传 种群间差异的原因与这些种群的进化历史密切相关。 因此，更好地纳入进化思想将有助于解释不同人群之间的差异 并改善临床实践和个性化护理。为此，Chiang Lab将继续发展 一个将进化群体遗传学与人类遗传流行病学相结合的综合框架， 利用实证数据分析和定量方法开发，以更好地探讨遗传 群体内和群体间复杂性状的结构。这一综合框架包括三个主要方面： 焦点：（1）人类复杂性状的遗传结构，（2）人口统计学史，（3）适应性 人类的历史。第一个主题的研究告知我们的表型遗传后果 今天，虽然后两者的研究解释了内部变异产生的进化机制， 和人类之间的关系。更重要的是，Chiang Lab的研究不仅关注这些， 主题，而且还利用一个主题的信息来通知另一个主题。在这种模式下，Chiang Lab将专注于 在未来五年实现以下三个目标。首先，我们将进行全面的基因研究， 该计划旨在解决夏威夷原住民的健康差距。具体来说，我们将生成基因组 加快这一人群的遗传研究所需的资源。然后我们将描述 夏威夷原住民的人口历史，以说明在夏威夷进行基因组研究的好处。 研究不足的人群，在波利尼西亚人群中进行大规模荟萃分析，以确定人群- 与夏威夷原住民中流行的疾病相关的特定等位基因，并使夏威夷原住民 未来的合作与合作。第二，我们将研究进化的病因， 在当今的人群中，风险更高。以拉丁裔人口为例，我们将研究是否升高 这一人群中儿童白血病的风险是由于欧洲接触期间引入的选择性压力 在世纪。第三，我们将通过引入一种新的 个体之间的遗传相似性矩阵，其包含来自个体的系谱树的信息。 人口该矩阵将提高许多统计遗传应用程序的性能，例如 遗传力估计和表型插补。虽然我们以夏威夷原住民和拉丁美洲人为例， 在这项建议中，遗传流行病学和进化的综合框架也将受益 未来的研究在其他未充分研究的少数民族。我们处于实现这些目标的独特地位 由于我们在将人口遗传学原理与医学遗传学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.