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

在第一个资助期内，我们开发了Jaatha，这是一种基于种群遗传数据重建两个近缘物种或种群历史的统计方法。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的数据集为指导，其中有7个基因可用。由于Jaatha是一种基于复合似然的方法，因此它的计算速度比其他方法（包括ABC）快，并且在重组率很高的情况下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能实现。新的测序技术允许收集这些数据集，但也带来了新的分析挑战。由于其集成的缩放特性，Jaatha为分析方法的发展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。结合改进统计方法的发展，我们将在全基因组范围内研究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.