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Population genomic methodology for analysis of ancient DNA: assessing population relationships and the temporal dynamics of natural selection

Population genomic methodology for analysis of ancient DNA: assessing population relationships and the temporal dynamics of natural selection
用于分析古代 DNA 的群体基因组方法:评估群体关系和自然选择的时间动态
批准号:
9381393
负责人:
Joshua G Schraiber
金额:
$36.94万
依托单位国家:
美国
项目类别:
财政年份:
2017
资助国家:
美国
项目状态:
已结题
起止时间:
2017-09-01 至 2022-07-31

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中文摘要
翻译
项目摘要 现在,包括人类在内的各种物种都有数以百计的古代基因组。是这样的 数据提供了了解最近过去人口统计学和自然选择历史的直接窗口,并 极大地促进了对人类如何在离开非洲后殖民世界的理解 散开。尽管如此,分析古代DNA的统计方法已经落后于 测序技术的发展。拟议的研究将开发新的方法,基于 种群基因组学理论推动了古代DNA研究领域的发展。这些方法将应用于 来自远古人类的数据,为过去5万年的人类进化提供了新的见解。 此外,通过与国际古代DNA实验室合作,这些方法的应用将 有助于理解动物驯化,这是允许人类形成 复杂的社会。我们将开发统计方法来评估古代样本与 以一种明确的方式解释了这样一个事实,即等位基因作为新的突变出现并正在 因此,在古代样本中出现的频率较低。通过与研究猪驯化的合作者合作,我们将 关于人类运动如何与农业和 家养动物的运输。我们还将开发方法论,以阐明如何现代的细节 人类和远古人类,如尼安德特人进行了杂交。通过利用尼安德特人的变异模式 在欧亚种群内部和之间共享DNA,我们将确定人类和 尼安德特人进行了杂交,并决定了泛大陆尼安德特人的人口结构。 最后,我们将使用古老的DNA来完善我们对驯化马匹过程中自然选择的理解 和人类的分散。与国际合作者合作,我们将使用古代DNA来检测 对马匹驯化至关重要,并确定这些突变在驯化之前是否存在,或者 它们是在驯化后重新出现的。我们还将研究健身效果的分布如何 通过比较古代人类健康效应的分布,发现人类在时间和空间上发生了变化 不同年龄的样本,以便了解人类适应是如何由 过去十万年的人口历史和气候变化。
英文摘要
Project Summary There are now hundreds of ancient genomes available from a wide range of species, including humans. Such data provide a direct window into the history of demography and natural selection in the recent past, and have greatly contributed to the understanding of how humans colonized the world following the out-of-Africa dispersal. Nonetheless, the statistical methods for analyzing ancient DNA have lagged behind the developments in sequencing technology. The proposed research will develop novel methods based on population genomics theory to advance the field of ancient DNA research. These methods will be applied to data from ancient humans to provide new insights into the last 50 thousand years of human evolution. Moreover, via collaboration with international ancient DNA laboratories, applications of these methods will contribute to the understanding of animal domestication, a key innovation that allowed humans to form complex societies. We will develop statistical methods to assess the relationship of ancient samples to present-day individuals in a way that explicitly accounts for the fact that alleles arose as new mutations and are thus at lower frequency in ancient samples. By working with collaborators studying pig domestication, we will test hypotheses about how movement of humans coincided with the advent of agriculture and the transportation of domesticated animals. We will also develop methodology to elucidate details of how modern humans and archaic humans, such as Neandertals interbred. By leveraging patterns of variation in Neandertal DNA sharing within and among Eurasian populations, we will determine the number of times that humans and Neandertals interbred, and determine demographic structure in the pan-continental Neandertal population. Finally, we will use ancient DNA to refine our understanding of natural selection during horse domestication and human dispersal. Working with international collaborators, we will use ancient DNA to detect genes that were critical to horse domestication, and determine whether the mutations existed before domestication or if they arose de novo following domestication. We will also examine how the distribution of fitness effects has changed over time and space in humans by comparing the distribution of fitness effects inferred for ancient samples of varying ages, in order to gain an understanding of how human adaptation has been shaped by demographic history and climate change over the last hundred thousand years.
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