Abstract
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.
Highlights
Reassortment is an important source of genetic diversity in segmented viruses and is the main source of novel pathogenic influenza viruses
While many approaches to inferring ancestral recombination graphs (ARGs) exist, some are restricted to tree-based networks [12, 13], meaning that the networks consist of a base tree where recombination edges always attach to edges on the base tree
This is true when we only look at reassortment events between pairs of segments and drops when we look at three or four segments
Summary
Reassortment is an important source of genetic diversity in segmented viruses and is the main source of novel pathogenic influenza viruses. 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. In order to perform inference under such a model, the reassortment network and the embedding of each segment tree within that network must be jointly inferred This is similar to the well-known and challenging problem of inferring ancestral recombination graphs (ARGs). With the exception of accidental release of antigenically lagged human influenza viruses [3], reassortment remains the sole documented mechanism for generating pandemic influenza strains (e.g., refs. 4–6)
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