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

项目摘要/摘要 测序生物的数量继续呈指数级增长，为进化生物学家提供了 以前所未有的分辨率绘制基因组中的自然选择图。比较基因组学方法鉴定 通过搜索受自然选择限制的基因组元件来发挥作用。现代比较学 数据集饱和了数百万年进化过程中积累的替换，使得 在几个碱基对的分辨率下要确定的功能元件。由于比较方法依赖于 序列守恒作为自然选择的证据，替换引起的强度波动 选择或罕见的适应性事件可能被误认为缺乏功能。种群基因组数据对 这些问题原则上是衡量约束的最佳方式，但每个站点的密度较低， 限制了可以研究函数的分辨率。在这里，我们提出了一种比较种群基因组学 通过组合多个物种的多态数据来解决这些限制的方法。 具体地说，我们将创建一个史无前例的基因组组装和种群多态的数据集 来自果蝇(科)模式系统的100种每种最多100只个体的数据 果蝇科)。在目标1中，我们将在低于3个碱基对的分辨率下映射选择性约束并使用 这些贴图用于测试约束如何在一个分支上演化。在目标2中，我们将开发一种新的适应性测试 联合利用来自多个物种的替代和多态数据的进化，并测试 同样的基因也被用于果蝇的适应。该项目的成功完成将有助于 通过提供重要的公共信息，对新兴的比较种群基因组学领域具有重大意义 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 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为科学和专业人员做好准备 基金会将成为比较种群基因组学新领域的领先者 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 基因组学和种群遗传学工具，沟通微观和宏观进化过程。作为博士后 斯坦福大学彼得罗夫实验室的同事，我将接受新的湿式实验室技能的科学培训，设计序列 实验和比较基因组学工具，通过生成和分析基因组数据集 史无前例的。我将接受专业领导力和网络建设方面的实质性培训，通过领导 努力为科学界建立这个庞大的基因组资源。

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.