A comparative population genomic approach for high-resolution inference of natural selection in fruit flies

用于果蝇自然选择高分辨率推断的比较群体基因组方法

• 批准号: 10066688
• 负责人: Bernard Youngsoo Kim
• 金额: $ 6.49万
• 依托单位: STANFORD UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-07-20 至 2022-07-19
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10066688
• 关键词: Address; Bar Codes; Base Pairing; Biological; Biological Models; Biological Process; Biology; Communities; Complement; DNA Resequencing; Data; Data Set; Demography; Drosophila genus; Drosophilidae; Elements; Event; Evolution; Family; Fellowship; Foundations; Future; Genes; Genetic Polymorphism; Genetic Variation; Genome; Genomic Segment; Genomic approach; Genomics; Goals; Hybrids; Individual; Lead; Leadership; Logistics; Maps; Measures; Methods; Modeling; Modernization; National Research Service Awards; Natural Selections; Organism; Pattern; Phenotype; Population; Population Genetics; Positioning Attribute; Postdoctoral Fellow; Process; Radiation; Research; Research Personnel; Research Proposals; Resolution; Resources; Sampling; Secure; Signal Transduction; Site; Subgroup; Testing; Time; Training; Variant; Work; base; comparative; comparative genomics; cost; density; design; empowered; experience; experimental study; fly; genomic data; genomic tools; improved; novel strategies; pesticide resistance; response; simulation; skills; tool

Project Summary/Abstract The number of sequenced organisms continues to grow exponentially, providing evolutionary biologists with an unprecedented resolution for mapping natural selection in the genome. Comparative genomic methods identify function by searching for genomic elements that are constrained by natural selection. Modern comparative datasets are saturated with substitutions accumulated over many millions of years of evolution, allowing functional elements to be identified at the resolution of a few base pairs. Since comparative methods rely on sequence conservation as evidence of natural selection, substitutions caused by fluctuations in the strength of selection or rare adaptive events can be mistaken for a lack of function. Population genomic data are robust to these issues and are the best way to measure constraint in principle, but suffer from low per-site densities, limiting the resolution at which function can be studied. Here, we propose a comparative population genomics approach for addressing these limitations by combining polymorphism data across multiple species. Specifically, we will create an unprecedented dataset of genome assemblies of and population polymorphism data of up to 100 individuals from each of 100 species from the model system of fruit flies (family Drosophilidae). In Aim 1, we will map selective constraint at the resolution of less than 3 base pairs and use these maps to test how constraint evolves across a clade. In Aim 2, we will develop new a test for adaptive evolution that jointly utilizes substitution and polymorphism data from multiple species and test whether the same genes are utilized by adaptation in drosophilids. Successful completion of the project will contribute significantly to the emerging field of comparative population genomics by providing an important publicly available genomic dataset for the scientific community, new ways to design and analyze large sequencing experiments, and test fundamental assumptions about the relationship between evolution in populations and evolution over macro-evolutionary time scales. The primary goal of this NRSA F32 fellowship is to prepare me with the scientific and professional foundation to become a leader in the new field of comparative population genomics as an independent researcher. My long-term scientific goal is to lead an independent research group that utilizes comparative genomics and population genetics tools to bridge micro and macroevolutionary processes. As a postdoctoral fellow in the Petrov Lab at Stanford, I will receive new scientific training in wet lab skills, designing sequencing experiments, and comparative genomics tools by generating and analyzing a genomic dataset that will be the first of its kind. I will receive substantial training in professional leadership and network building by leading the effort to build this large genomic resource for the scientific community.

项目概要/摘要 已测序的生物体数量继续呈指数增长，为进化生物学家提供了 绘制基因组自然选择图谱的前所未有的分辨率。比较基因组方法鉴定 通过搜索受自然选择限制的基因组元件来发挥功能。现代比较 数据集充满了数百万年进化中积累的替换，使得 以几个碱基对的分辨率来识别功能元件。由于比较方法依赖于 序列保守作为自然选择的证据，由强度波动引起的替换 选择或罕见的适应性事件可能会被误认为是功能缺乏。群体基因组数据稳健 这些问题原则上是衡量约束的最佳方法，但受到每个站点密度较低的影响， 限制了可以研究函数的分辨率。在这里，我们提出了比较群体基因组学 通过结合多个物种的多态性数据来解决这些限制的方法。 具体来说，我们将创建一个前所未有的基因组组装和群体多态性数据集 果蝇模型系统（科）中 100 个物种中每个物种最多 100 个个体的数据 果蝇科）。在目标 1 中，我们将以小于 3 个碱基对的分辨率映射选择性约束并使用 这些地图来测试约束如何在进化枝中演变。在目标 2 中，我们将开发新的自适应测试 联合利用来自多个物种的替代和多态性数据的进化，并测试是否 果蝇的适应过程中也利用了相同的基因。项目的顺利完成将作出贡献 通过提供重要的公开信息，对比较群体基因组学的新兴领域产生了重大影响 科学界可用的基因组数据集，设计和分析大型测序的新方法 实验，并测试关于种群进化和进化之间关系的基本假设 宏观进化时间尺度上的进化。 NRSA F32 奖学金的主要目标是让我做好科学和专业的准备 基金会作为独立的机构成为比较群体基因组学新领域的领导者 研究员。我的长期科学目标是领导一个独立的研究小组，利用比较 连接微观和宏观进化过程的基因组学和群体遗传学工具。作为一名博士后 作为斯坦福大学彼得罗夫实验室的研究员，我将接受湿实验室技能、设计测序方面的新科学培训 实验和比较基因组学工具，通过生成和分析基因组数据集来 首创。我将通过领导 努力为科学界建立这一庞大的基因组资源。