Bayesian inference of reassortment networks reveals fitness benefits of reassortment in human influenza viruses
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Date
2020-07-21
Publication Type
Journal Article
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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.
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published
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Volume
117 (29)
Pages / Article No.
17104 - 17111
Publisher
National Academy of Sciences
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Subject
Phylogenetics; Phylodynamics; Infectious disease; BEAST; MCMC
Organisational unit
09490 - Stadler, Tanja / Stadler, Tanja
Notes
Funding
166258 - System analysis of seasonal Influenza - virus transmission and evolution in the City of Basel (SNF)