Characterizing Human-Pathogen Interactions and Natural Selection with Ancient DNA

项目摘要 从考古样本中恢复古代DNA（aDNA）分子的可能性已经产生了 这是研究人类进化和历史的绝佳机会。与传统数据集相比 由目前收集的单个时间点组成，aDNA允许检测丢失的遗传物质。 谱系，并能够直接评估等位基因频率在不同的时间断面。 从考古材料中获取基因组数据的技术推动了 但是利用时间序列遗传学信息的统计方法， 数据集落后于这些技术进步。在接下来的五年里，阿莫林实验室将 开发和应用方法，研究人类宿主-病原体的共同进化和适应使用aDNA。 我们试图通过生成包含大的DNA数据集来推进古基因组学领域。 每个考古遗址的样本量，并开发新的方法和途径， 时间序列基因数据。我们将利用这些新的资源来研究宿主-病原体 欧亚大陆鼠疫爆发期间（如黑死病）和 南美洲原住民与欧洲殖民者之间的接触。对比模型 人口进化，分析和计算，观察等位基因频率和其他 在不同时间断面的汇总统计，我们将确定宿主-病原体的遗传特征 相互作用，表征人类对病原体反应所涉及的进化过程， 推断选择性扫描的强度和时间。此外，我们还将描述 人类基因组中新突变在不同时间断面上的适应性效应（DFE）， 根据疾病爆发和其他事件引起的环境变化分析其演变。 这项研究代表了古基因组学最先进知识的进步，因为它专注于 进化过程尚未以aDNA为特征（特别是宿主-病原体共同进化）， 使用时间序列数据表征DFE，以及开发新的资源（数据集 方法）。阿莫林实验室是唯一的定位，以实现这些目标，因为我们的 将基于模型的方法与古今基因组数据分析相结合的经验 DNA，以及我们以前对中世纪欧洲人和美洲土著人的研究经验， 人口。

PROJECT SUMMARY The possibility of recovering ancient DNA (aDNA) molecules from archeological samples has yielded great opportunities for the study of human evolution and history. Compared to traditional datasets composed of a single time point collected in the present, aDNA allows for the detection of lost genetic lineages and enables the direct assessment of allele frequencies in different time transects. Technologies for obtaining genomic data from archeological material have catapulted the development of the field of paleogenomics, but statistical methods to leverage information from time-series genetic datasets lag behind these technological advances. Over the next five years, the Amorim Lab will develop and apply methods to study human host-pathogen coevolution and adaptation using aDNA. We seek to advance the field of paleogenomics by generating aDNA datasets that comprise large sample sizes per archeological site and developing new methods and approaches to leverage information from time-series genetic data. We will use these novel resources to study host-pathogen interactions during the outbreaks of the plague in Eurasia (e.g. the Black Death) and the period of contact between Indigenous peoples from South America and European colonizers. Contrasting models of population evolution, both analytical and computational, with observed allele frequencies and other summary statistics in different time transects, we will identify the genetic signatures of host-pathogen interactions, characterize the evolutionary processes involved in human response to pathogens, and infer the strength and timing of selective sweeps. In addition, we will characterize the Distribution of Fitness Effects (DFE) of new mutations in the human genome across different time transects and analyze its evolution in light of the environmental shifts caused by disease outbreaks and other events. This study represents an advance over the state-of-the-art knowledge in paleogenomics for its focus on evolutionary process not yet characterized with aDNA (in particular, host-pathogen coevolution), the characterization of the DFE using time-series data, and the development of new resources (datasets and methods). The Amorim Lab is uniquely positioned to accomplish these goals because of our experience in combining model-based approaches with genome data analysis from ancient and modern DNA, as well as our previous experience with the study of medieval Europeans and Native American populations.