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

项目总结 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 至少有两个缺陷阻碍了目前的研究在阐明基因方面发挥其潜力 对所有人类来说都是复杂特征的建筑。首先，目前的遗传学研究基本上忽略了基因 研究中人与人之间的关系。这些关系中的许多可能是遥远的，但仍然可以是 通过合并过程连接在基因组每个位置的系谱树上。收藏品 由祖先重组图(ARG)编码这种(未观察到的)树。第二，基因研究 通常偏向于相对同质的大陆人口，如欧洲或东方 亚裔人口，部分原因是缺乏针对混杂人口的方法。在这份提案中，我们 目的开发新的方法来解决这两个缺点。我们的框架利用了最近的突破 这第一次实现了对ARG的准确和可扩展的估计。在目标1中，我们将利用一个新的 基于ARG的关联度估计器，能从不完全关联中保留更多的关联度信息 将遗传数据(例如，阵列基因数据)与当前的相关性标准估计器进行比较。我们会 用这个估计器来估计复杂性状的性状遗传力和跨群体遗传相关性 人类的疾病，以及纠正由于全基因组中的种群结构造成的混淆 协会研究。在目标2中，我们将开发一个关联测试框架，它使用ARG来识别 与性状相关的基因组区域，并优先确定与性状相关的单倍型。这种有原则的方法可以 自然地解释了等位基因的异质性，并有可能提高关联研究的能力 通过降低多重测试负担，这对未被研究的人群尤其重要，因为 招募参与者更具挑战性。最后，在目标3中，我们将开发一个种群遗传 使用ARGS对人口的混合历史进行建模的框架。使用这个模型，我们将开发 检测对最近的选择有反应的基因并识别具有 在不同的表型选择下进化而来。重要的是，我们的框架将解决这些问题 混合种群中每个祖先组成部分的进化问题。在每一个目标中，我们都会 通过广泛的模拟对我们的方法进行基准测试。我们还将使用大量的数据来评估我们的方法 扩展真实世界的人类基因数据。最后，我们将把我们的方法应用于基因分型和测序数据 从混合种群中发现与人类疾病和/或经历的自然疾病相关的新基因座 过去的选择。总而言之，我们将从ARG中挖掘丰富的信息并解决 基本的种群和人类遗传学问题，特别是在研究不足和混合的种群中。

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.