Incorporating geography into statistical methods for analysis of population genomic DNA

项目概要 在人类中，遗传变异是按地理分布的，反映了人类跨区域移动的历史。 各大洲。了解这些空间模式对于人类群体基因组学的许多领域至关重要， 包括人类进化史的研究以及基因型和表型的联系。从历史上看，限制 经验数据集的大小和范围使研究人员能够采用忽略 地理，但现代基因组数据集需要结合地理的群体遗传方法 空间。拟议的研究将产生新颖的统计方法，将地理纳入 研究种群遗传结构、混合、人口学和自然选择。这些方法将 作为开源软件开发和实施，使用最先进的前向时间模拟进行验证 系统蒸发散，并应用于公开可用的人类基因组数据集。 我们将开发人口混合测试，明确考虑由于隔离而产生的地理模式 通过距离。这些测试将用于分析密集采样的欧亚人类基因组数据集，以 识别混合样本，也将应用于沿着基因组的滑动窗口以突出基因组 可能通过适应性渗入在群体之间转移的区域。我们还将开发 一种可以联合分析古代和现代样本的时空群体聚类方法。中性 遗传过程预计会在空间或空间中分离的样本之间产生群体分化 时间，因此在确定两个样本是否共享祖先时，该聚类方法将考虑两者 相同的离散群体。该方法将扩展到通过测试来检测多基因性状的选择 相对于中性期望，涉及特定性状的等位基因频率的总体增加。 我们将应用这种方法来测试欧亚大陆人类身高随时间的选择。最后，我们将 对个体之间共享基因组片段的长度进行建模，这些片段提供有关谱系的信息 过去不同时间点的重叠，以了解人口密度和分散模式如何变化 随着时间的推移，地理空间发生了变化。 拟议的工作代表了统计群体遗传学许多领域的进步，包括 群体混合检测、适应性渗入、群体替换以及联合分析 来自古代和现代样本的 DNA，检测多基因性状的选择并建模异质性 随着时间的推移人口统计过程。总而言之，这项工作将为实证研究人员提供有价值的 用于分析现代基因组数据集的工具包，这需要空间显式方法，并将提供启示 关于人类进化史和人类适应环境的机制 跨越空间和时间。

Project Summary In humans, genetic variation is distributed geographically, reflecting the history of human movements across the continents. Understanding these spatial patterns is crucial for many fields in human population genomics, including the study of human evolutionary history and linking genotypes and phenotypes. Historically, limi- tations in the size and scope of empirical datasets have allowed researchers to employ models that ignore geography, but modern genomic datasets demand population genetic methods that incorporate geographic space. The proposed research will generate novel statistical methods that incorporate geography into the study of population genetic structure, admixture, demography, and natural selection. These methods will be developed and implemented as open-source software, validated using state-of-the-art forward-time simula- tions, and applied to publicly available human genomic datasets. We will develop tests for population admixture that explicitly account for geographic patterns due to isolation by distance. These tests will be used to analyze densely sampled Eurasian human genomic datasets to identify admixed samples, and will also be applied in sliding windows along the genome to highlight genomic regions that may have been transferred between populations via adaptive introgression. We will also develop a spatiotemporal population clustering method that can jointly analyze ancient and modern samples. Neutral genetic processes are expected to generate population differentiation between samples separated in space or time, so this clustering method will account for both when determining whether two samples share ancestry in the same discrete population. This method will be extended to detect selection on polygenic traits by testing for an aggregate increase in the frequency of alleles involved in a particular trait relative to the neutral expectation. We will apply this method to test for selection through time on human height across Eurasia. Finally, we will model the lengths of shared genomic segments between individuals, which are informative about genealogical overlap at different points in the past, to learn about how population density and dispersal patterns have changed across geographic space through time. The proposed work represents advances in a number of fields in statistical population genetics, including the detection of population admixture, adaptive introgression, population replacement and the joint analysis of DNA from ancient and modern samples, detecting selection on polygenic traits, and modeling heterogeneity in demographic processes through time. Taken together, this work will offer empirical researchers a valuable toolkit for the analysis of modern genomic datasets, which require spatially explicit methods, and will shed light on both human evolutionary history and the mechanisms by which humans have adapted to their environment across space and time.