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

在第一个资助期间，我们开发了Jaatha，这是一种基于种群遗传数据重建两个密切相关物种或种群的种群历史的统计方法。在Jaatha的发育过程中，我们得到了两个密切相关的野生番茄物种的数据集的指导，这两个物种是Solanum peruvianum和S.Chilense，其中有七个基因是可用的。由于Jaatha是一种基于复合似然的方法，它在计算上比其他方法(包括ABC)更快，如果重组率很高，它至少与计算密集的方法一样准确。然而，我们的模拟研究表明，只有在更大的数据集上才有可能精确估计人口统计参数。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为开发与技术进步保持同步的分析方法提供了一个极好的起点。在即将到来的资助期间，我们建议使用下一代测序技术来为这两个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调查自然种群中选择的形式和强度。

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.