Characterizing Human-Pathogen Interactions and Natural Selection with Ancient DNA

项目总结 从考古样本中恢复古代DNA(ADNA)分子的可能性已经产生 为研究人类进化和历史提供了巨大的机遇。与传统数据集相比 由目前收集的单个时间点组成的aDNA允许检测丢失的基因 对不同时间段的等位基因频率进行直接评估。 从考古材料中获取基因组数据的技术推动了这一发展 在古基因组学领域，但利用时间序列基因信息的统计方法 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 开发和应用利用aDNA研究人类宿主-病原体协同进化和适应的方法。 我们试图通过生成包含大量 每个考古遗址的样本大小，并开发新的方法和途径 来自时间序列基因数据的信息。我们将利用这些新资源来研究寄主病原体 欧亚大陆鼠疫暴发(例如黑死病)期间的相互作用 南美洲土著人民与欧洲殖民者之间的接触。对比模型 分析和计算的种群进化，观察到的等位基因频率和其他 汇总统计在不同的时间段，我们将识别寄主-病原体的遗传特征 相互作用，表征了人类对病原体的反应的进化过程，以及 推断选择性扫荡的强度和时机。此外，我们还将描述 不同时间段人类基因组新突变的适合度效应(DFE) 根据疾病暴发和其他事件造成的环境变化分析其演变。 这项研究代表了古基因组学中最先进的知识的进步，因为它专注于 尚未以DNA为特征的进化过程(特别是宿主-病原体共同进化)， 使用时间序列数据表征DFE，并开发新资源(数据集 和方法)。阿莫林实验室在实现这些目标方面处于独特的地位，因为我们 基于模型的方法与古今基因组数据分析相结合的经验 DNA，以及我们以前研究中世纪欧洲人和美洲土著人的经验 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.