Genome-wide approaches to polygenic adaptation

描述（由申请人提供）： 进化生物学和人类遗传学的一个关键目标是了解自然选择如何塑造种群内和种群间的遗传和表型变异。 未来十年产生的大量人口基因组数据和基因型-表型作图数据将为解决这些问题带来前所未有的力量。为了最大限度地发挥这些数据的巨大潜力，我们需要开发新的种群基因组模型和工具，以解决多基因适应的日益增加的证据。特别是，虽然很多注意力都集中在理解一个简单的模型的适应效果，全扫描模型，全基因组范围内的软和部分扫描的影响几乎没有得到理论上的关注，也没有方法的发展。此外，虽然我们对许多人类表型变异的遗传基础的理解通过全基因组关联研究得到了极大的改善，但我们对选择如何塑造这种高度多基因变异的理解却显着滞后。 我们提出了一些研究路线，以解决这些重大的缺点。 具体来说，为了使多基因选择和适应在群体基因组数据中的作用的研究，我们将：目的1）开发一个扩展的模型的群体基因组效应的连锁选择。我们将构建新的模型，不同模式的连锁选择的影响-包括背景选择，经常性的部分扫描，和软扫描-遗传多样性的水平，使用尖端的合并方法。在目标2中，我们将开发推理机来推断这个扩展模型的基因组参数的连锁选择。这将使我们能够研究背景选择，硬扫描和软扫描对遗传多样性基因组模式的相对贡献。最后，在目标3中，我们将使用全基因组关联研究提供的数据，创建检测多基因性状局部适应的工具。 这种方法将测试特定表型的遗传基础上的局部适应的协调信号，同时考虑到漂移和共享的群体历史的混杂效应。

DESCRIPTION (provided by applicant): A key goal of evolutionary biology and human genetics is to understand how natural selection has shaped genetic and phenotypic variation within and among populations. The vast amount of population genomic data and genotype-phenotype mapping data generated over the coming decade will bring an unprecedented power to address these questions. To maximize the great potential these data we need the development of novel population genomic models and tools that address the increasing evidence for polygenic adaptation. In particular, while much attention has focused on understanding a simple model of the effect of adaptation, the full sweep model, the genome-wide effects of soft and partial sweeps has received almost no theoretical attention nor methods development. In addition while our understanding of the genetic basis of the variation in many human phenotypes has vastly improved through genome-wide association studies our understanding of how selection has shaped this highly polygenic variation across populations has lagged significantly. We propose a number of lines of research to address these significant shortcomings. Specifically to empower the study of the role of polygenic selection and adaptation in population genomic data we will: Aim 1) Develop an extended model of the population genomic effects of linked selection. We will construct new models of the effect of different modes of linked selection - including background selection, recurrent partial sweeps, and soft sweeps - on levels of genetic diversity, using cutting-edge coalescent methods. In Aim 2 we will develop the inference machinery to infer the genomic parameters of this extended model of linked selection. This will allow us to investigate the relative contribution of background selection, hard, and soft sweeps to genomic patterns of genetic diversity. Finally in aim 3 we will create tools to detect local adaptation on polygenic traits using data provided by genome-wide association studies. This method will test for the concerted signal of local adaptation on the genetic basis of particular phenotypes, while accounting for the confounding effects of drift and shared population history.