Statistical Methods for Enabling Medical and Population Genomics of Admixed Human Populations

用于实现混合人群的医学和群体基因组学的统计方法

• 批准号: 1201234
• 负责人: Carlos Bustamante
• 金额: $ 159.08万
• 依托单位: Stanford University
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2012
• 资助国家: 美国
• 起止时间: 2012-08-01 至 2017-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1201234&HistoricalAwards=false
• 关键词: Statistical; Methods; Enabling; Medical; Population

The intellectual merit of this project lies in the development of population genetic theory and statistical genomic approaches for modeling the demographic history of admixed human populations, including minority populations in the U.S. The project has three major aims: AIM 1: To develop improved population genetic models of admixed human populations. Genomes of admixed individuals can be modeled as mosaics of chromosomal tracts that originate from a finite number of ancestors and populations. Current models of admixture are limited by simplifying assumptions regarding the amount, duration, or direction of the admixture process and fail to accurately predict patterns of genetic variation observed in real data. To redress this issue, the investigators propose a family of higher order Markovian models and coalescent approaches that provide much greater flexibility. AIM 2: To develop statistical inference tools for estimating historical admixture. This project will improve upon existing methods for robustly inferring the population of origin of chromosomal segments in admixed genomes. Given the distribution of ''admixture tracts'', one then wishes to model the recent demographic history of the population. The investigators will use the results of Aims 1 and 2 along with a growing database in the investigator's lab on genomic variation in the Americas to provide detailed models of admixture that can empower medical and association studies in the Americas. AIM 3: To develop a Linkage/Admixture Map of Hispanic/Latino populations. Recently, it was shown that motifs associated with population specific alleles at the PRDM9 locus account for a significant fraction of recombination rate variation among populations. The investigators propose analyzing data from more than 20,000 genetically and ethnically diverse U.S. participants across at least 2.5 million markers to identify population-specific recombination hotspots and their relationship with ancestry. Specifically, the investigators aim to identify Native-American specific recombination hotspots and produce the highest-resolution recombination map of the human genome to date.Genome-Wide Association Studies (GWAS) have dramatically increased the scientific community's understanding of the genetic basis of complex disease by identifying thousands of genetic variants associated with chronic diseases including Type 1 and Type 2 diabetes, heart disease, hypertension, and many cancers. A key limitation of existing studies is that they have focused largely on participants of European descent and some of these genotype/phenotype associations do not readily translate from one ethnic group to another. Furthermore, since the next generation of studies will focus largely on querying rare genetic variants (i.e., 5% frequency) for association, this ''transferability problem'' is likely to get worse and risks perpetuating or even widening existing health disparity among ethnic groups in the U.S. Broadening representation in medical genomics studies is a key mechanism for redressing these biases. Critical to enabling trans- and multi-ethnic medical genetic studies is a rigorous understanding of how 500 years of admixture among diverse European, African, and indigenous American source populations have shaped the genomes of African-American, Hispanic/Latinos, and Native Americans today. A key outcome of this research will be user-friendly software that implements a comprehensive, probabilistic, and flexible family of methods for inferring genetic ancestry. The investigators will train undergraduate, high school, community college, graduate, and post-doctoral students including many from underrepresented minority groups. Project results will be disseminated to the public through the San Jose Tech Museum and participation in conferences aimed at minority groups including SACNAS and the National Urban League. Partnerships with key industry leaders will allow us to reach potentially millions of participants.

该项目的智力价值在于人口遗传学理论和统计基因组方法的发展，用于模拟混合人群的人口统计学历史，包括美国的少数民族人口，该项目有三个主要目标：目的1：开发混合人群的改进人口遗传模型。 混合个体的基因组可以被建模为起源于有限数量的祖先和种群的染色体束的马赛克。 当前的混合模型受到有关混合过程的量、持续时间或方向的简化假设的限制，并且无法准确预测在真实的数据中观察到的遗传变异模式。 为了解决这个问题，研究人员提出了一系列高阶马尔可夫模型和合并方法，提供了更大的灵活性。 目的2：开发用于估计历史混合物的统计推断工具。 该项目将改进现有的方法，用于强有力地推断混合基因组中染色体片段的起源群体。鉴于“混合带”的分布，人们希望对人口的近期人口统计历史进行建模。 研究人员将使用目标1和目标2的结果沿着，以及研究人员实验室中关于美洲基因组变异的不断增长的数据库，以提供详细的混合模型，从而能够在美洲进行医学和相关研究。 目的3：开发西班牙裔/拉丁裔人群的连锁/混合图谱。 最近，研究表明，与PRDM 9基因座上的群体特异性等位基因相关的基序占群体间重组率变异的一个重要部分。 研究人员建议分析来自20，000多名遗传和种族多样化的美国参与者的数据，这些参与者至少有250万个标记，以确定群体特异性重组热点及其与祖先的关系。 具体来说，研究人员的目标是确定美洲原住民特定的重组热点，并产生迄今为止最高分辨率的人类基因组重组图。全基因组关联研究（GWAS）通过确定与1型和2型糖尿病，心脏病，高血压，和许多癌症。 现有研究的一个关键限制是，他们主要集中在欧洲血统的参与者和一些这些基因型/表型协会不容易从一个种族群体转化到另一个。 此外，由于下一代研究将主要集中在查询罕见的遗传变异（即，5%的频率），这种“可转移性问题”可能会变得更糟，并有可能使美国种族群体之间现有的健康差距永久化甚至扩大。 扩大医学基因组学研究的代表性是纠正这些偏见的关键机制。 跨种族和多种族医学遗传学研究的关键是严格理解500年来欧洲、非洲和美洲土著人口的混合如何塑造了今天非洲裔美国人、西班牙裔/拉丁裔和美洲土著人的基因组。 这项研究的一个关键成果将是用户友好的软件，它实现了一个全面的，概率性的和灵活的家庭的方法来推断遗传祖先。调查人员将培训本科生、高中生、社区大学生、研究生和博士后，其中包括许多来自代表性不足的少数群体的学生。 项目成果将通过圣何塞科技博物馆和参加针对少数群体的会议（包括SACNAS和全国城市联盟）向公众传播。 与主要行业领导者的合作将使我们能够接触到潜在的数百万参与者。