A genome-wide genealogical framework for statistical and population genetic analysis

用于统计和群体遗传分析的全基因组谱系框架

• 批准号: 10658562
• 负责人: Charleston Chiang
• 金额: $ 56.21万
• 依托单位: UNIVERSITY OF SOUTHERN CALIFORNIA
• 依托单位国家: 美国
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-09-01 至 2027-06-30
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10658562
• 关键词: Address; Admixture; African American population; African ancestry; Alleles; Asian population; Benchmarking; Collection; Complex; DNA; Data; Data Set; Development; Discipline; Disease; Distant; East Asian; Epidemiologist; Etiology; European; Event; Evolution; Genealogical Tree; Genealogy; Genes; Genetic; Genetic Recombination; Genetic Research; Genetic Variation; Genetic study; Genome; Genomic Segment; Genomic medicine; Genomics; Genotype; Geography; Goals; Graph; Haplotypes; Heritability; Heterogeneity; Hispanic Populations; Historical Demography; Human; Human Genetics; Indigenous American; Individual; Inherited; Latino; Methods; Minority Groups; Modeling; Mutation; Native Hawaiian; Natural Selections; Patient Recruitments; Persons; Phenotype; Polynesian; Population; Population Genetics; Population Heterogeneity; Population Sizes; Positioning Attribute; Process; Recording of previous events; Research; Sampling; Structure; Testing; Time; Trees; United States; analytical method; causal variant; design; ethnic minority population; experience; genetic analysis; genetic architecture; genetic pedigree; genetic risk factor; genome wide association study; genome-wide; human disease; improved; innovation; interest; method development; novel; novel strategies; open source; population based; prevent; simulation; trait

PROJECT SUMMARY Genetic studies have improved our understanding of disease etiology and treatment. However, there are at least two shortcomings preventing current studies from reaching their potential in elucidating the genetic architecture of complex traits for all humans. First, current genetic studies largely ignore the genetic relationships among individuals in a study. Many of these relationships may be distant, but nonetheless can be connected on genealogical trees at every position of the genome through a coalescent process. The collection of such (unobserved) trees is encoded by the ancestral recombination graph (ARG). Second, genetic studies are generally biased towards relatively homogeneous, continental, populations such as European or East Asian populations, in part due to a lack of methods tailored towards admixed populations. In this proposal we aim to develop new methods to address both shortcomings. Our framework leverages recent breakthroughs that allow, for the first time, accurate and scalable estimation of ARGs. In Aim 1 we will leverage a new estimator of relatedness based on the ARG that can retain more information of relatedness from incomplete genetic data (e.g. array genotype data) compared to the current standard estimator for relatedness. We will use this estimator to estimate trait heritability and cross-population genetic correlation of complex traits and diseases in humans, as well as to correct for confounding due to population structure in genome-wide association studies. In Aim 2, we will develop an association-testing framework that uses the ARG to identify trait-associated genomic regions and prioritize trait-associated haplotypes. This principled approach can naturally account for allelic heterogeneity and has the potential to improve the power of association studies through lowered multiple testing burden, which is particularly important for understudied populations where recruitment of participants is more challenging. Finally, in Aim 3 we will develop a population genetic framework that uses ARGs to model the admixture history of a population. Using this model, we will develop new ways to detect genes that have responded to recent selection and identify complex traits that have evolved under different kinds of phenotypic selection. Importantly, our framework will address these evolutionary questions in each ancestral component of the admixed population. Throughout each Aim we will benchmark our methods with extensive simulations. We will also evaluate our methods empirically using large- scale real-world human genetic data. Finally, we will apply our methods to genotyping and sequencing data from admixed populations to discover new loci associated with human diseases and/or experienced natural selection in the past. In summary, we will mine the wealth of information from the ARG and address fundamental population- and human-genetic questions, particularly in understudied and admixed populations.

项目概要 遗传学研究提高了我们对疾病病因和治疗的理解。然而，有在 至少有两个缺点阻碍了当前的研究发挥其阐明遗传的潜力 所有人类的复杂特征的架构。首先，目前的遗传学研究很大程度上忽视了遗传因素。 研究中个体之间的关系。其中许多关系可能很遥远，但仍然可以 通过合并过程连接在基因组每个位置的谱系树上。收藏品 这种（未观察到的）树的数量由祖先重组图（ARG）编码。二、遗传学研究 通常偏向于相对同质的大陆人口，例如欧洲或东方 亚洲人口，部分原因是缺乏针对混合人口的方法。在这个提案中我们 旨在开发新方法来解决这两个缺点。我们的框架利用了最近的突破 首次实现了 ARG 的准确且可扩展的估计。在目标 1 中，我们将利用一个新的 基于ARG的相关性估计器，可以从不完整的信息中保留更多的相关性信息 遗传数据（例如阵列基因型数据）与当前标准相关性估计量相比。我们将 使用此估计量来估计复杂性状的性状遗传力和跨群体遗传相关性 人类疾病，以及纠正由于全基因组人口结构造成的混淆 关联研究。在目标 2 中，我们将开发一个关联测试框架，使用 ARG 来识别 性状相关的基因组区域并优先考虑性状相关的单倍型。这种原则性的方法可以 自然地解释等位基因异质性，并有可能提高关联研究的能力 通过降低多重测试负担，这对于未充分研究的人群尤其重要 招募参与者更具挑战性。最后，在目标 3 中，我们将开发群体遗传 使用 ARG 来模拟种群混合历史的框架。使用这个模型，我们将开发 检测对最近选择做出反应的基因并识别对最近的选择做出反应的复杂性状的新方法 在不同类型的表型选择下进化。重要的是，我们的框架将解决这些问题 混合种群的每个祖先组成部分的进化问题。在每个目标中，我们将 通过广泛的模拟对我们的方法进行基准测试。我们还将使用大量经验评估我们的方法 扩展真实世界的人类遗传数据。最后，我们将应用我们的方法进行基因分型和测序数据 从混合人群中发现与人类疾病和/或经历过的自然灾害相关的新位点 过去的选择。总之，我们将从 ARG 中挖掘大量信息并解决 基本的人口和人类遗传问题，特别是在研究不足和混合人口中。