Incorporating geography into statistical methods for analysis of population genomic DNA

将地理学纳入群体基因组 DNA 分析的统计方法

• 批准号: 10200099
• 负责人: Gideon Bradburd
• 金额: $ 37.63万
• 依托单位: MICHIGAN STATE UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-07-01 至 2025-04-30
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10200099
• 关键词: Address; Admixture; Computer software; DNA analysis; Data Set; Demography; Detection; Environment; Etiology; Gene Frequency; Genealogy; Genetic Processes; Genetic Variation; Genome; Genomic DNA; Genomic Segment; Genomics; Genotype; Geography; Goals; Height; Heterogeneity; Human; Human Genetics; Individual; Joints; Learning; Length; Light; Link; Methods; Modeling; Modernization; Movement; Natural Selections; Pattern; Phenotype; Polygenic Traits; Population; Population Analysis; Population Density; Population Genetics; Population Replacements; Population Study; Prevalence; Process; Recording of previous events; Research; Research Personnel; Sampling; Slide; Statistical Methods; Testing; Time; Work; disorder risk; expectation; genetic variant; genomic data; human disease; human genomics; insight; novel; open source; population genetic structure; simulation; spatiotemporal; study population; trait

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.

项目摘要 在人类中，遗传变异在地理上分布，反映了人类跨越 大陆。理解这些空间模式对于人类群体基因组学的许多领域都至关重要， 包括人类进化史的研究和基因型与表型的联系。从历史上看，limi- 经验数据集的大小和范围的变化使研究人员能够采用忽略 地理，但现代基因组数据集需要人口遗传学方法， 空间拟议的研究将产生新的统计方法，将地理纳入 研究群体遗传结构、混合、人口统计学和自然选择。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仿真验证， 的基因组数据，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试验，明确说明由于隔离而导致的地理模式 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， 识别混合样本，并且还将应用于沿着基因组的滑动窗口中，以突出显示基因组 可能已经通过适应性渐渗在种群之间转移的区域。我们还将开发 一种可以联合分析古今样本的时空种群聚类方法。中性 遗传过程预计会在空间分离的样本之间产生种群分化， 时间，因此当确定两个样本是否共享祖先时，此聚类方法将考虑两者。 相同的离散人口。这种方法将被扩展到检测多基因性状的选择， 相对于中性预期，与特定性状相关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的时间选择。最后我们将 对个体之间共享的基因组片段的长度进行建模，这些片段提供了有关家谱的信息。 在过去的不同时间点重叠，以了解人口密度和扩散模式如何 随着时间的推移而改变。 拟议的工作代表了统计群体遗传学领域的一些进展，包括 种群混合检测、适应性渐渗、种群替换和 来自古代和现代样本的DNA，检测多基因性状的选择，并建立异质性模型 在人口统计过程中的作用总之，这项工作将为实证研究人员提供一个有价值的 用于分析现代基因组数据集的工具包，这需要空间明确的方法，并将揭示 人类进化史和人类适应环境的机制 穿越时空