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

摘要 检测群体基因组数据中的适应性遗传变异对于理解遗传 复杂遗传病的基础架构。人类和其他自然人口一直在进化 在复杂的人口历史下，包括祖先人口的分化，有组织的移民 人口数量，以及过去人口规模的变化。适应性遗传变异和复杂环境下的变异 人口学历史可以导致相似的可观察到的基因组模式，并区分进化 在自然种群中观察到的潜在遗传变异的力量仍然具有挑战性。因此，它具有重要意义。 为了揭开自然人口背后复杂的人口历史，并开发出检测 适应性遗传变异，同时恰当地解释了这些历史。除了当代基因组学 近年来，研究人员一直在从古人类遗骸中收集基因数据。包括 这样的分析数据集有可能极大地提高我们检测人口结构和 适应选择压力的遗传变异。因此，我们将开发几个工具来分析 当代和古代基因组数据集，以揭开人口迁徙的历史 在解释这些历史的同时，检测人类的扩张性和适应性遗传变异。为此， 我们将开发一种新的联合隐马尔可夫模型方法来描述复杂的迁移历史。 我们的新方法将使用比以前的方法更有效的本地家谱表示， 这提高了推理的准确性，并且对数据中的噪声具有更强的鲁棒性。此外，这一点 框架将使我们能够分析来自大型公共数据库的种群基因组数据，以确定自适应 遗传变异。在具有适应性遗传变异的地区，当地家谱将高度倾斜，如 与在中性条件下进化的基因组区域相比。这种新的框架可以用来计算 在基因组的不同位置识别区域的家系概要的后验分布 歪曲的家谱。此外，我们将实施检测适应性遗传变异的方法，基于 有益的遗传变异和相互关联的中性区域的动态的时间正向解决方案。基于一个 以前发展的数值方法，我们将发展合成似然框架的观测 该模型下的基因组序列变异，以检测适应性遗传变异，同时解释 背后复杂的人口学历史。此外，我们还将开发一种旨在检测多基因的方法 从古代DNA改编而来。这种方法将基于潜在等位基因的显式似然模型。 频率动态，并使我们能够检测和量化定向，并与以前的方法不同，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 对古代DNA数据集的适当工具，以解开中世纪欧洲人的遗传反应 黑死病大流行。

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.