Incorporating geography into statistical methods for analysis of population genomic DNA

项目摘要 在人类中，基因变异在地理上是分布的，反映了人类迁徙的历史 各大洲。了解这些空间模式对人类种群基因组学的许多领域都至关重要， 包括研究人类进化史以及将基因类型和表型联系起来。从历史上看，利米- 经验数据集的大小和范围的调整使研究人员能够使用忽略 地理学，但现代基因组数据集需要结合地理信息的群体遗传学方法 太空。拟议的研究将产生新的统计方法，将地理因素纳入到 研究种群遗传结构、混合、人口学和自然选择。这些方法将是 作为开源软件开发和实施，使用最先进的向前时间模拟进行验证- 并应用于公开可用的人类基因组数据集。 我们将开发种群混合测试，明确说明由于隔离而产生的地理模式 按距离计算。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，并将在基因组滑动窗口中应用，以突出基因组 可能已经通过适应性渐渗在种群之间转移的区域。我们还将发展 一种可以联合分析古代和现代样本的时空种群聚类法。中性 遗传过程有望在空间或空间上分离的样本之间产生种群分化 时间，因此在确定两个样本是否具有相同的祖先时，此聚类方法将考虑两者 同样的离散种群。该方法将扩展到检测多基因性状的选择，通过测试 相对于中性期望而言，某一特定性状所涉及的等位基因频率的总体增加。 我们将应用这种方法来测试整个欧亚大陆人类身高的时间选择。最后，我们会 对个体之间共享的基因组片段的长度进行建模，这是关于系谱的信息 在过去的不同点上重叠，以了解人口密度和扩散模式如何 随着时间的推移在地理空间中发生变化。 这项拟议的工作代表了统计人口遗传学在许多领域的进展，包括 种群混合检测、自适应递推、种群置换及联合分析 从古代和现代样本中提取DNA，检测多基因性状的选择，并模拟异质性 随着时间的推移，在人口统计过程中。综上所述，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，这需要空间上明确的方法，并将揭示 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.