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

阐明和解释不同人群复杂性状遗传结构的进化框架

基本信息

批准号：
10727037
负责人：
Charleston Chiang
金额：
$ 3.2万
依托单位：
UNIVERSITY OF SOUTHERN CALIFORNIA
依托单位国家：
美国
项目类别：
财政年份：
2021
资助国家：
美国
起止时间：
2021-08-01 至 2023-11-30
项目状态：
已结题

项目摘要

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.

项目概要/摘要环境和遗传因素都会导致人群之间疾病风险的差异。遗传性种群之间差异的原因与这些种群的进化历史密切相关。因此，更好地结合进化思想将有助于解释不同人群之间的差异今天，改善临床实践和个性化护理。为此，Chiang Lab将不断研发将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定量方法开发来更好地探讨群体内部和群体之间复杂性状的遗传结构。该综合框架包括三个主要焦点：（1）人类复杂性状的遗传结构，（2）人口历史，以及（3）人类的适应历史。第一个主题的研究揭示了遗传对我们的影响今天的现象组，而后两者的研究解释了变异的进化机制出现在人群内部和人群之间。更重要的是，蒋实验室的研究不仅仅关注讨论这些主题，同时也利用其中一个主题的信息来告知另一个主题。在这个范式中，蒋实验室未来五年将重点实现以下三个目标。首先，我们将进行全面的基因检测解决夏威夷原住民健康差异的研究计划。具体来说，我们将生成加速该人群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历史，以说明在夏威夷开展基因组研究的好处对波利尼西亚人群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，并吸引夏威夷原住民未来伙伴关系和合作的社区。其次，我们将研究进化病因。当今人群的风险升高。以拉丁裔人口为例，我们将检查是否该人群儿童白血病风险升高是由于欧洲时期引入的选择压力 16世纪就有接触。第三，我们将彻底改变当前的遗传相关性概念在个体之间引入一个新的遗传相似性矩阵，其中包含来自家谱的信息人口树。该矩阵将提高许多统计遗传应用的性能，例如遗传力估计和表型插补。虽然我们使用夏威夷原住民和拉丁裔作为该提案中的示例人群，遗传流行病学和进化的综合框架也将有利于其他受研究的少数民族的未来研究。我们拥有独特的优势来实现这些目标目标是因为我们在将群体遗传原理与医学遗传分析相结合方面具有专业知识，统计遗传发展。

项目成果

期刊论文数量（6）

专著数量（0）

科研奖励数量（0）

会议论文数量（0）

专利数量（0）

Genome-wide assessment of genetic risk loci for childhood acute lymphoblastic leukemia in Japanese patients.

日本患者儿童急性淋巴细胞白血病遗传风险位点的全基因组评估。

DOI：
发表时间：
2024-04-01
期刊：
影响因子：
10.1
作者：
Hangai, Mayumi;Kawaguchi, Takahisa;Takagi, Masatoshi;Matsuo, Keitaro;Jeon, Soyoung;Chiang, Charleston W K;Dewan, Andrew T;De Smith, Adam J;Imamura, Toshihiko;Okamoto, Yasuhiro;Saito, Akiko M;Deguchi, Takao;Kubo, Michiaki;Tanaka, Yoichi;Ayuka
通讯作者：
Ayuka

Deciphering signatures of natural selection via deep learning.

通过深度学习破译自然选择的特征。