Advanced tools for using ancient DNA to study biology and history

项目总结/摘要 人口历史塑造了人类生物学、遗传变异和疾病风险。尽管它的重要性，分析 在全基因组范围内研究历史的方法在分辨率和定性能力方面都很有限， 重建过去的某些方面。这使得开发能够模拟 由于基因组和古代DNA数据，已记录的主要人口混合事件 过去十年的革命。目前，世界上的古代DNA数据受到主要技术偏见的影响， 没有得到适当的控制，研究现代和古代人口混合的分析工具包 DNA是不够的，我们才刚刚开始开发方法，实现古代DNA的潜力， 揭示了生物学和历史学一样多的东西。该提案旨在通过扩大 资助R 01 GM 100233，从2012-2020年支持PI的中央研究计划， 开发研究人类人口历史的方法，并利用这些信息来了解 生物学，导致70个出版物与赠款有关。我们提出三个新目标： (1)创建一个处理古代DNA的无偏见的管道，并使用它重新分析世界上的数据; (2)扩展混合图用于种群历史推断的能力; (3)介绍分析种群历史和生物学的新工具。 这笔赠款将在四个方面发挥价值。(a)它将支持开发方法和用户友好的 以及对进化和医学遗传学很重要的文档化软件。(b)它将支持 这项工作将导致与在最近或最近感染的人群中发现疾病基因相关的见解， 古代混合。(c)它将带来关于人类历史的新发现。(d)它将产生一个公开的 世界古代DNA数据的再处理版本，将对社区有广泛的用途。 与医学遗传学的联系很重要。在过去，我们已经成功地绘制了一个直接的 我们实验室在检测和表征人口混合方面的工作与人类之间的联系 生物学，包括遗传易感性疾病。我们利用了非裔美国人的混合史 发现新的疾病基因（例如，前列腺癌的风险因素）， 在疾病风险的人群，并记录在生物学的重组之间的差异 非洲裔美国人和没有西非血统的人，预计会导致不同的 与重组错误相关的疾病风险水平。我们的重点是建立 人口历史和疾病风险也突出了发现隐性疾病基因的机会 在南亚数以千计的创始人群体中。我们预计，我们开发的方法和资源 这项拨款将继续与有关人类变异和疾病风险的最新研究产生协同作用。

PROJECT SUMMARY / ABSTRACT Population history shapes human biology, genetic variation, and disease risk. Despite its importance, analytical methods to study history on a genome-wide scale are limited in both their resolution and qualitative ability to reconstruct aspects of the past. This makes it a priority to develop new methods that are able to model the major population mixture events that have been documented as a result of the genomic and ancient DNA data revolutions in the last decade. At present, the world's ancient DNA data suffer from major technical biases that are not properly controlled for, the analytical toolkit for studying population mixture with modern and ancient DNA is inadequate, and we are only beginning to develop methods that realize the potential of ancient DNA to reveal as much about biology as about history. This proposal aims to address these needs by extending funding for grant R01 GM100233, which from 2012-2020 supported the PIs' central research program on developing methods for studying human population history and leveraging this information to learn about biology, resulting in 70 publications linked to the grant. We propose three new Aims: (1) To create an unbiased pipeline for processing ancient DNA, and to use it reanalyze the world's data; (2) To extend the capabilities of admixture graphs for population history inference; (3) To introduce new tools for analyzing population history and biology. This grant will be of value in four ways. (a) It will support the development of methods and user-friendly and well-documented software that will be important for evolutionary and medical genetics. (b) It will support work that will result in insights relevant to finding disease genes in human populations that are recently or anciently admixed. (c) It will lead to new discoveries about human history. (d) It will produce a publicly available reprocessed version of the world's ancient DNA data that will be of broad use to the community. The link to medical genetics is important. In the past, we have been successful at drawing a direct connection between our laboratory's work on detecting and characterizing population mixture, and human biology including genetic susceptibility to disease. We leveraged the history of admixture in African Americans to make new disease gene discoveries (for example, risk factors for prostate cancer), to understand variation in disease risk across populations, and to document differences in the biology of recombination between African Americans and people who do not have West African ancestry, which are predicted to lead to different levels of risk for diseases associated with errors in recombination. Our focus on drawing connections between population history and disease risk also highlighted the opportunities for discovery of recessive disease genes in thousands of founder groups in South Asia. We anticipate that the methods and resources we develop with the support of this grant will continue to synergize with the latest research on human variation and disease risk.