Population genetic methods to detect population structure and adaptation using modern and ancient genomic datasets

使用现代和古代基因组数据集检测种群结构和适应的种群遗传学方法

• 批准号: 10605315
• 负责人: Matthias Steinruecken
• 金额: $ 33.3万
• 依托单位: UNIVERSITY OF CHICAGO
• 依托单位国家: 美国
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-04-15 至 2027-02-28
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10605315
• 关键词: Accounting; Address; Affect; Altitude; Bubonic Plague; Characteristics; Collaborations; Complement; Complex; Computer software; DNA; Data; Data Set; Disease; Environment; Europe; European; Exhibits; Exposure to; Gene Frequency; Genealogy; Genetic; Genetic Diseases; Genetic Drift; Genetic Materials; Genetic Models; Genetic Predisposition to Disease; Genetic Processes; Genetic Variation; Genetic study; Genome; Genomic Segment; Genomics; Genotype; Historical Demography; Human; Hypoxia; Immune response; Individual; Lactase; Link; Location; Medical; Methods; Modeling; Modernization; Motivation; Noise; Organism; Pattern; Phenotype; Population; Population Genetics; Population Group; Population Sizes; Positioning Attribute; Prevalence; Process; Recording of previous events; Research Personnel; Sampling; Series; Shapes; Signal Transduction; Structure; Time; Variant; detection method; disorder risk; expectation; flexibility; genetic approach; genetic architecture; genomic data; genomic signature; genomic variation; human data; improved; individual patient; large datasets; markov model; migration; novel; novel strategies; pandemic disease; pathogen; pressure; public database; response; tool; trait

ABSTRACT Detecting adaptive genetic variation in population genomic datasets is important for understanding the genetic architecture underlying complex genetic diseases. Humans and other natural populations have been evolving under complex demographic histories, including divergence of ancestral populations, migration in structured populations, and past population size changes. Adaptive genetic variation and variation subject to complex demographic histories can result in similar observable genomic patterns, and distinguishing the evolutionary forces underlying genetic variation observed in natural population remains challenging. It is thus of importance to unravel the complex demographic histories underlying natural populations, and develop methods that detect adaptive genetic variation while properly accounting for these histories. In addition to contemporary genomic data, researchers have been gathering genetic data from ancient human remains in recent years. Including such datasets into the analyses has the potential to vastly improve our ability to detect population structure and genetic variation adapting to selective pressure. Thus, we will develop several tools for the analysis of contemporary and ancient genomic datasets to unravel the migration histories underlying the population expansion of humans and to detect adaptive genetic variation while accounting for these histories. To this end, we will develop a novel Coalescent Hidden Markov Model method to characterize complex migration histories. Our novel approach will use more efficient representations of local genealogies then previous approaches, which increases the accuracy of the inference and is more robust to noise in the data. Moreover, this framework will allow us to analyze population genomic data from large public databases to identify adaptive genetic variation. The local genealogies will be highly skewed in regions with adaptive genetic variation, as compared to genomic regions evolving under neutrality. The novel framework can be used to compute the posterior distribution of genealogical summaries at different locations in the genome to identify regions with skewed genealogies. In addition, we will implement approaches to detect adaptive genetic variation based on forward-in-time solutions of the dynamics of beneficial genetic variation and linked neutral regions. Based on a previously developed numerical approach, we will develop composite likelihood frameworks of observed genomic sequence variation under this model to detect adaptive genetic variation, while accounting for the underlying complex demographic history. Moreover, we will develop a method that aims at detecting polygenic adaptation from ancient DNA. This approach will be based on explicit likelihood models of the underlying allele frequency dynamics and allow us to detect and quantify directional and, unlike previous approaches, stabilizing selection on complex traits. Lastly, we will collaborate with colleagues to apply these methods and other appropriate tools to ancient DNA datasets to unravel the genetic response of medieval European populations to the Black Death pandemic.

抽象的 检测群体基因组数据集中的适应性遗传变异对于理解遗传变异非常重要 复杂遗传疾病的结构。人类和其他自然群体一直在进化 在复杂的人口历史下，包括祖先人口的分化，结构性的移民 人口以及过去人口规模的变化。适应性遗传变异和复杂变异 人口统计历史可以导致类似的可观察到的基因组模式，并区分进化 在自然群体中观察到的遗传变异背后的力量仍然具有挑战性。因而具有重要意义 揭示自然种群背后复杂的人口历史，并开发检测方法 适应性遗传变异，同时正确解释这些历史。除了当代基因组学 近年来，研究人员一直在从古代人类遗骸中收集遗传数据。包括 将此类数据集纳入分析有可能极大提高我们检测人口结构和 适应选择压力的遗传变异。因此，我们将开发几种工具来分析 当代和古代基因组数据集，以揭示人口的迁徙历史 人类的扩张并在解释这些历史的同时检测适应性遗传变异。为此， 我们将开发一种新颖的合并隐马尔可夫模型方法来表征复杂的迁移历史。 我们的新方法将使用比以前的方法更有效的当地谱系表示， 这提高了推理的准确性，并且对数据中的噪声更加鲁棒。而且，这 框架将使我们能够分析来自大型公共数据库的群体基因组数据，以确定适应性 遗传变异。在具有适应性遗传变异的地区，当地的家谱将会高度扭曲，因为 与中性条件下进化的基因组区域相比。该新颖的框架可用于计算 基因组中不同位置的谱系摘要的后验分布，以识别具有 扭曲的家谱。此外，我们将实施基于检测适应性遗传变异的方法 有益遗传变异和相关中性区域动态的实时解决方案。基于一个 先前开发的数值方法，我们将开发观察到的复合可能性框架 该模型下的基因组序列变异可检测适应性遗传变异，同时考虑 潜在的复杂的人口历史。此外，我们将开发一种旨在检测多基因的方法 来自古代 DNA 的适应。该方法将基于基础等位基因的显式似然模型 频率动态，使我们能够检测和量化方向，并且与以前的方法不同，稳定 对复杂性状的选择。最后，我们将与同事合作应用这些方法和其他方法 古代 DNA 数据集的适当工具可解开中世纪欧洲人群的遗传反应 到黑死​​病大流行。