Geographic models of selective sweeps

选择性扫描的地理模型

• 批准号: 8198779
• 负责人: PETER Lochhead RALPH
• 金额: $ 5.13万
• 依托单位: UNIVERSITY OF CALIFORNIA AT DAVIS
• 依托单位国家: 美国
• 财政年份: 2011
• 资助国家: 美国
• 起止时间: 2011-07-18 至 2013-07-17
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8198779
• 关键词: Affect; Africa South of the Sahara; Alleles; Biological; Data; Data Analyses; Data Set; Databases; Disease; Drosophila melanogaster; Ecology; Environment; European; Evolution; Frequencies; Genetic; Genetic Variation; Genome; Genomics; Genotype; Geography; Goals; Human; Individual; Insecticide Resistance; Intuition; KITLG gene; Lactase; Lead; Learning; Link; Location; Modeling; Mutation; Natural Selections; Pattern; Phenotype; Physics; Physiological Adaptation; Play; Population; Population Genetics; Probability; Process; Publishing; Recording of previous events; Reporting; Research Personnel; Role; Sampling; Scanning; Site; Spatial Distribution; Structure; Testing; Variant; Work; design; experience; mathematical model; pressure; resistance allele; response; simulation; theories; tool; trait

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. PUBLIC HEALTH RELEVANCE: I will investigate the role that geography plays in the genetic patterns formed by natural selection. This will not only allow us to better reconstruct the evolutionary history of widely distributed species such as our own, but will also help us in identification of genomic patterns of selection, which will include responses to disease and physiological adaptation to local conditions. Such geographic understanding is vital as we begin to obtain large amounts of genetic data from across populations.

描述（由申请人提供）：进化从根本上说是一个空间过程，因为种群在当地杂交并适应环境。这对我们如何理解种群内的遗传多样性模式具有深远的影响，特别是当我们开始从种群中获得大量遗传数据时。对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潜在基因座，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，包括平行的选择扫描（“软”扫描），中性变异的“搭便车”沿着与选定的等位基因，以及等位基因“冲浪”的新描述的现象;并将在人口基因组学数据集中分析其预期的签名。这将开始填补现有群体遗传学理论中的一个主要空白，该理论相对较少明确的空间模型，而包含选择的模型更少。 这些工具不仅能让我们更好地重建地理分布的物种（如我们自己的物种）的适应历史，还能帮助我们更精确地识别对选择压力做出反应的基因组位点，从而找到与疾病反应和对当地条件的生理适应有关的位点。我将通过建立在现有的理论，从人口遗传学，概率，生态学和统计物理学，在密切交谈的目标和实证研究人员的问题。 公共卫生相关性：我将研究地理在自然选择形成的遗传模式中所起的作用。这不仅使我们能够更好地重建像我们这样广泛分布的物种的进化历史，而且还将帮助我们识别选择的基因组模式，其中包括对疾病的反应和对当地条件的生理适应。当我们开始从不同人群中获得大量遗传数据时，这种地理上的理解是至关重要的。