Characterizing modes of natural selection via diverse ancient and modern samples

通过不同的古代和现代样本表征自然选择模式

• 批准号: 9788501
• 负责人: Emilia Huerta-Sanchez
• 金额: $ 37.91万
• 依托单位: BROWN UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2018
• 资助国家: 美国
• 起止时间: 2018-09-19 至 2023-08-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9788501
• 关键词: Address; Admixture; Americas; Biological; Biology; Collection; DNA; DNA Sequence; Data; Data Set; Disease; Environment; European; Event; Evolution; Genes; Genetic Variation; Genomics; Goals; Human; Human Genome; Individual; Knowledge; Libraries; Medical; Methods; Modernization; Mutation; Natural Selections; Pattern; Phenotype; Play; Population; Preparation; Process; Publishing; Recording of previous events; Research; Research Project Grants; Role; Sampling; Statistical Methods; Technology; Work; base; comparative; genetic makeup; genome-wide analysis; human disease; improved; method development; novel sequencing technology; personalized medicine; risk variant; tool

Project Summary   With the advent of new sequencing technologies, we now have generated more data than ever before. These data have greatly improved our knowledge of human history, human adaptation to different environments and human disease. Genome-wide studies have highlighted many genes or genomic loci that may play a role in adaptive or disease related phenotypes of biological importance. Now that we have access to thousands of human genomes from a diverse set of populations around the globe, we can zoom in at the local scale (e.g. within a gene) and leverage information from multiple populations to understand the observed patterns of genetic variation, without the ascertainment bias associated with the older array-based technologies. In addition, thanks to advances in DNA extraction and library preparation, we now are beginning to have access to DNA sequence data from ancient human samples. We propose to leverage these collections of modern and ancient sequence data to address some key questions in the field, and we have identified opportunities for methods development and biological discovery. The common theme in the proposal is to understand the different modes of natural selection in human populations. We plan to develop methods to detect shared selective events across populations by means of extending current statistical summaries, and methods for detecting admixture-facilitated adaptation which we believe is likely a common mode of natural selection based on our earlier published work. We will apply these tools to new datasets to characterize the interplay of natural selection, archaic and modern admixture in populations in the Americas and make a comparative analysis of modern and ancient European samples to understand the changing profile of medically important risk alleles for disease. As a result our work will reveal evolutionary processes that have played an important role in human evolution and disease.

项目摘要   随着新的测序技术的出现，我们现在已经产生了更多的数据， 比以往任何时候都多这些数据极大地增进了我们对人类历史的了解， 人类对不同环境的适应和人类疾病。全基因组研究 已经强调了许多基因或基因组位点，可能在适应性或 具有生物学重要性的疾病相关表型。现在我们有了 来自地球仪不同人群的数千个人类基因组， 可以在局部尺度上放大（例如，在基因内），并利用来自 多个群体，以了解观察到的遗传变异模式， 与旧的基于阵列的技术相关的确定偏差。此外，本发明还提供了一种方法， 由于DNA提取和文库制备的进步，我们现在开始 能接触到古人类样本的DNA序列数据 我们建议利用这些现代和古代序列数据的集合， 解决该领域的一些关键问题，我们已经确定了 方法开发和生物发现。提案的共同主题是 来理解人类种群中自然选择的不同模式。我们计划 制定方法，通过以下方式检测人群中共有的选择性事件： 扩展当前的统计摘要，以及用于检测混合促进的方法， 我们相信适应可能是一种常见的自然选择模式， 早期出版的作品。我们将把这些工具应用到新的数据集上， 自然选择的相互作用，古代和现代的混合物在人口中， 并对现代和古代欧洲的样本进行比较分析 了解疾病的重要医学风险等位基因的变化概况。作为 结果，我们的工作将揭示进化过程中发挥了重要作用， 人类进化和疾病