Geographic models of selective sweeps

选择性扫描的地理模型

基本信息

批准号：
8370584
负责人：
PETER Lochhead RALPH
金额：
$ 2.49万
依托单位：
UNIVERSITY OF CALIFORNIA AT DAVIS
依托单位国家：
美国
项目类别：
财政年份：
2011
资助国家：
美国
起止时间：
2011-07-18 至 2013-01-01
项目状态：
已结题

项目摘要

DESCRIPTION (provided by applicant): Evolution is fundamentally a spatial process, as populations interbreed locally and adapt to their environments. This has profound implications for how we understand patterns of genetic diversity within populations, especially as we begin to obtain large amounts of genetic data from populations. Good models and predictions of these processes will be necessary to understand the pat- terns we see, for instance, by allowing us to identify loci underlying adaptive traits, and to distinguish local adaptation from other processes. I will create and study geographically explicit models of selection, incorporating parallel selective sweeps ("soft" sweeps), the "hitchhiking" of neutral variation along with a selected allele, and the newly-described phenomenon of allele "surfing"; and will analyze their expected signatures in population genomics datasets. This will begin to fill a major gap in existing population genetics theory, which has relatively few explicit spatial models, and even fewer that incorporate selection. Such tools will not only allow us to better reconstruct the history of adaptation in geographically distributed species such as our own, but will also help us to more precisely identify genomic loci responding to selection pressures, and so find loci involved in disease response and in physiological adaptation to local conditions. I will do this by building on existing theory from population genetics, probability, ecology, and statistical physics, in close conversation with the goals and problems of empirical researchers.

描述（由申请人提供）：进化从根本上来说是一个空间过程，因为种群在当地杂交并适应其环境。这对于我们如何理解种群内遗传多样性的模式具有深远的影响，特别是当我们开始从种群中获取大量遗传数据时。这些过程的良好模型和预测对于理解我们所看到的模式是必要的，例如，通过允许我们识别适应性特征的基因座，并将局部适应与其他过程区分开来。我将创建和研究地理上明确的选择模型，结合并行选择性扫描（“软”扫描）、中性变异与选定等位基因的“搭便车”以及新描述的等位基因“冲浪”现象；并将分析他们在群体基因组数据集中的预期特征。这将开始填补现有群体遗传学理论的一个主要空白，该理论的明确空间模型相对较少，包含选择的模型则更少。这些工具不仅使我们能够更好地重建地理分布物种（例如我们自己的物种）的适应历史，而且还将帮助我们更精确地识别响应选择压力的基因组位点，从而找到与疾病反应和生理相关的位点。因地制宜。我将通过建立群体遗传学、概率学、生态学和统计物理学的现有理论，并与实证研究人员的目标和问题进行密切对话来实现这一目标。