Coevolution in action in host and parasite genomes

当宿主和病原体施加强大的相互选择压力时，支持相互作用的基因在短时间尺度上共同进化，而种群则经历了急剧的年度规模波动。然而，令人惊讶的是，从未开发出任何方法来使用随时间推移对当地宿主和病原体种群进行连续采样来追踪经历共同进化动态的基因。我们已经开发了第一个近似贝叶斯计算（ABC）方法，它允许揭示共同进化动力学的遗传基础，共同使用主机和寄生虫的全基因组多态性数据。重要基因的相互作用的共同进化动力学可以区分从宿主和病原体种群的人口动态的评估和建模随着时间的推移和整个基因组的遗传多样性。因此，该方法适用于一个给定的共同进化的主机和病原体population.The目标是扩展这种方法来研究更复杂的，但也是现实的设置的共同进化：1）主机和寄生虫的空间结构，并没有异质性的选择，和2）违反经典的Wright-Fisher模型假设的寄生虫物种。首先，我们研究协同进化的空间使用主机和寄生虫基因组多态性数据从几个种群。我们将建立一个空间同质和异质的宿主和寄生虫种群模型，在该模型中，可以使用全基因组数据来推断种群规模的迁移和局部变化。ABC方法将被用来搜索基因下的协同进化和推断参数的地理镶嵌的协同进化。其次，寄生虫由于其生命周期可能会在两个方面违反我们模型的假设：1）寄生虫的种群大小随时间变化而产生周期性瓶颈，以及2）寄生虫物种在后代分布中可能表现出很大的方差。我们的推理方法在处理病毒或细菌等寄生虫时可能会有偏差。因此，我们将这两个关键方面的寄生虫的生命周期到我们的模型和我们的ABC推理方法，并研究它们如何影响协同进化的动力学和多态性的协同进化位点和全基因组。因此，我们的目标是提供一种新的推理方法，适用于大多数宿主-寄生虫系统的全基因组数据可用于几个人口和几个时间点。

When hosts and pathogens exert strong reciprocal selection pressure, the genes underpinning the interaction coevolve over short time scales, while populations undergo steep year-to-year size fluctuations. Surprisingly, however, no method was ever developed to track genes undergoing coevolutionary dynamics using serial sampling of local host and pathogen populations over time. We have developed a first Approximate Bayesian Computation (ABC) method which allows to reveal the genetic underpinnings of coevolutionary dynamics using jointly host and parasite whole-genome polymorphism data. The coevolutionary dynamics of important genes for the interaction can be distinguished from demographic dynamics of host and pathogen populations by assessing and modelling genetic diversity over time and over the whole genome. The method is thus applicable to serial samplings of one given coevolving host and pathogen population.The goal of this project is to extend this method to study more complex but also realistic set ups of coevolution: 1) host and parasite spatial structure with and without heterogeneity in selection, and 2) violation of the classic Wright-Fisher model assumption in parasite species. First, we study coevolution in space by using host and parasite genome polymorphism data from several populations. We will build a spatial homogeneous and heterogeneous host and parasite population model, in which migration and local changes in population size can be inferred using full genome data. The ABC method will then be used to search for genes under coevolution and infer the parameters of the geographic mosaic of coevolution. It is of special interest to assess if sampling of several populations in space is equivalent to time sampling used in our current method.Second, parasite due to their life cycles may violate the assumptions of our models in two ways: 1) parasites show large variation in their population size over time generating recurrent bottlenecks, and 2) parasite species can exhibit large variance in offspring distribution. Our inference method may be biased when dealing with parasites such as viruses or bacteria. We will therefore integrate these two key aspects of parasite life-cycles into our model and our ABC inference method, and study how they affect the coevolutionary dynamics and polymorphism at the coevolving loci and genome-wide. We therefore aim to provide a novel inference method applicable to most host-parasite systems for which full genome data are available for several populations and several time points.