Reassortment is an important source of genetic diversity in segmented viruses and is the main source of novel pathogenic influenza viruses. Despite this, studying the reassortment process has been constrained by the lack of a coherent, model-based inference framework. Here, we introduce a coalescent-based model that allows us to explicitly model the joint coalescent and reassortment process. In order to perform inference under this model, we present an efficient Markov chain Monte Carlo algorithm to sample rooted networks and the embedding of phylogenetic trees within networks. This algorithm provides the means to jointly infer coalescent and reassortment rates with the reassortment network and the embedding of segments in that network from full-genome sequence data. Studying reassortment patterns of different human influenza datasets, we find large differences in reassortment rates across different human influenza viruses. Additionally, we find that reassortment events predominantly occur on selectively fitter parts of reassortment networks showing that on a population level, reassortment positively contributes to the fitness of human influenza viruses.

重新分类是分段病毒中遗传多样性的重要来源，并且是新型致病性流感病毒的主要来源。尽管如此，由于缺乏一致的，基于模型的推理框架，研究重分配过程受到了限制。在这里，我们介绍了一个基于合并的模型，该模型使我们可以显式地对联合合并和重新分配过程进行建模。为了在该模型下进行推理，我们提出了一种有效的马尔可夫链蒙特卡罗算法来对根网络和系统内的系统树进行嵌入进行采样。该算法提供了一种方法，可以与重新分类网络联合推断合并率和重新分类率，并从全基因组序列数据中推断片段在该网络中的嵌入。研究不同人类流感数据集的重排模式，我们发现不同人类流感病毒的重配率差异很大。此外，我们发现重排事件主要发生在重排网络的选择性装配部分，这表明在人群水平上，重排对人类流感病毒的适应性有积极作用。