Estimating selection parameters and identifying loci under recent selection based on genome-wide sequence data

根据全基因组序列数据估计选择参数并识别最近选择下的基因座

• 批准号: 190399061
• 负责人: Dr. Bettina Harr
• 金额: --
• 依托单位: Forschungsgruppe Meiotische Rekombination und Genominstabilität
• 依托单位国家: 德国
• 项目类别: Research Grants
• 财政年份: 2011
• 资助国家: 德国
• 起止时间: 2010-12-31 至 2014-12-31
• 项目状态: 已结题
• 来源: https://gepris.dfg.de/gepris/projekt/190399061?language=en
• 关键词: Estimating; selection; parameters; identifying; loci

Next Generation Sequencing technologies provide the opportunity to detect traces of selection, on a genome-wide scale. We wish to use this technology to obtain a basic understanding on nature and frequency of selection in the genome, using the major mammalian model organism, the house mouse. Almost every aspect of the phenotype is better studied in the mouse than any other mammal, including humans. By contrast very little is known about basic population genetic parameters, including strength and frequency of positive and negative selection across the genome. However, these quantities are of major evolutionary importance, as they, together with genetic drift and recombination, determine patterns of genetic variation that influence quantitative traits, including, for example, common genetic diseases in humans. House mouse populations, in contrast to human populations, have the advantage of large effective population sizes that allow the detection of selection even if its magnitude is relatively small. We propose to sequence whole genomes from 24 house mouse individuals originating from three natural populations using Solid4 technology. These data will allow us to estimate evolutionary parameters for coding as well as non-coding DNA.

下一代测序技术提供了在全基因组范围内检测选择痕迹的机会。我们希望利用这项技术，利用主要的哺乳动物模式生物家鼠，对基因组中选择的性质和频率有一个基本的了解。几乎每一个方面的表型是更好地研究小鼠比任何其他哺乳动物，包括人类。相比之下，人们对基本的群体遗传参数知之甚少，包括整个基因组中阳性和阴性选择的强度和频率。然而，这些数量具有重要的进化意义，因为它们与遗传漂变和重组一起决定了影响数量性状的遗传变异模式，包括人类常见的遗传疾病。与人类种群相比，家鼠种群具有有效种群规模大的优势，即使选择的幅度相对较小，也可以检测到选择。我们建议使用Solid4技术对来自三个自然种群的24只家鼠个体的全基因组进行测序。这些数据将使我们能够估计编码和非编码DNA的进化参数。