Genome-wide approaches to polygenic adaptation

多基因适应的全基因组方法

• 批准号: 8796727
• 负责人: Graham Coop
• 金额: $ 20.86万
• 依托单位: UNIVERSITY OF CALIFORNIA AT DAVIS
• 依托单位国家: 美国
• 财政年份: 2014
• 资助国家: 美国
• 起止时间: 2014-02-01 至 2020-01-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8796727
• 关键词: Accounting; Address; Affect; Alleles; Attention; Biology; Cartoons; Data; Data Set; Development; Frequencies; Generations; Genetic; Genetic Drift; Genetic Variation; Genomics; Genotype; Goals; Grant; Haplotypes; Health; Heritability; Human; Human Genetics; Learning; Link; Maps; Methodology; Methods; Modeling; Mutation; Natural Selections; Pattern; Phenotype; Polygenic Traits; Population; Population Genetics; Population Statistics; Property; Quantitative Genetics; Recording of previous events; Recurrence; Relative (related person); Research; Role; Shapes; Signal Transduction; Statistical Methods; Taxon; Testing; Thinking; Variant; Work; base; design; disorder risk; empowered; genetic approach; genome wide association study; genome-wide; improved; method development; model development; novel; tool; trait; trend

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.

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群体内部和群体之间的遗传和表型变异。 未来十年产生的大量群体基因组数据和基因型-表型图谱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，我们需要开发新的群体基因组模型和工具来解决越来越多的多基因适应证据。特别是，虽然很多注意力都集中在理解适应效应的简单模型（全扫描模型）上，但软扫描和部分扫描的全基因组效应几乎没有受到理论关注或方法开发。此外，虽然通过全基因组关联研究，我们对许多人类表型变异的遗传基础的理解已经大大提高，但我们对选择如何塑造这种跨人群的高度多基因变异的理解却明显滞后。 我们提出了一系列研究来解决这些重大缺陷。 具体来说，为了研究多基因选择和适应在群体基因组数据中的作用，我们将： 目标 1) 开发连锁选择的群体基因组效应的扩展模型。我们将使用尖端的合并方法，构建不同模式的连锁选择（包括背景选择、循环部分扫描和软扫描）对遗传多样性水平的影响的新模型。在目标 2 中，我们将开发推理机制来推断这个连锁选择扩展模型的基因组参数。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的基因组模式的相对贡献。最后，在目标 3 中，我们将创建工具，利用全基因组关联研究提供的数据来检测多基因性状的局部适应性。 该方法将测试特定表型遗传基础上局部适应的一致信号，同时考虑漂移和共同种群历史的混杂影响。