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Populationsgenetische Methoden zum Nachweis evolutionärer Anpassung in Populationen mit komplexer demographischer Struktur

用于检测具有复杂人口结构的群体的进化适应的群体遗传学方法

基本信息

批准号：
86873616
负责人：
Professor Dr. Dirk Metzler
金额：
--
依托单位：
Institut Populationsgenetik
依托单位国家：
德国
项目类别：
Research Units
财政年份：
2008
资助国家：
德国
起止时间：
2007-12-31 至 2015-12-31
项目状态：
已结题

来源：
https://gepris.dfg.de/gepris/projekt/86873616?language=en
关键词：
Populationsgenetische Methoden zum Nachweis evolution

项目摘要

During the first funding period, we developed Jaatha, a statistical method to reconstruct the population history of two closely related species or populations based on population genetic data. During the development of Jaatha, we were guided by a dataset of two closely related species of wild tomatoes, Solanum peruvianum and S. chilense, for which seven genes were available. Since Jaatha is a composite-likelihood based method, it is computationally faster than other methods (including ABC) and at least as accurate as computationally intense methods if recombination rates are high. However, our simulation studies show that precise estimates of demographic parameters are only possible with much larger datasets. Novel sequencing technologies allow the collection of such datasets, but also carry new analytical challenges. Because of its integrated scaling properties, Jaatha offers an excellent starting point for the development of analytical methods to keep pace with technological advances. In the upcoming funding period, we propose to use next-generation sequencing technologies to generate a very large dataset for this pair of wild tomato species. In combination with the development of improved statistical methods, we will investigate the form and strength of selection in natural populations on a genome-wide scale.

在第一个资助期内，我们开发了Jaatha，这是一种统计方法，可以根据种群遗传数据重建两个密切相关的物种或种群的种群历史。在Jaatha的开发过程中，我们以两个密切相关的野生番茄物种Solanum peruvianum和S. chilense，其中7个基因可用。由于Jaatha是一种基于复合似然的方法，因此它在计算上比其他方法（包括ABC）更快，并且如果重组率很高，则至少与计算密集型方法一样准确。然而，我们的模拟研究表明，人口统计参数的精确估计只有在更大的数据集上才有可能。新的测序技术允许收集这样的数据集，但也带来了新的分析挑战。由于其集成的缩放特性，Jaatha为分析方法的开发提供了一个很好的起点，以跟上技术进步的步伐。在即将到来的资助期内，我们建议使用下一代测序技术为这对野生番茄物种生成一个非常大的数据集。结合改进的统计方法的发展，我们将在全基因组范围内研究自然群体中选择的形式和强度。