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Bayesian inference of reassortment networks reveals fitness benefits of reassortment in human influenza viruses

View ORCID ProfileNicola F. Müller, Ugnė Stolz, View ORCID ProfileGytis Dudas, View ORCID ProfileTanja Stadler, View ORCID ProfileTimothy G. Vaughan
doi: https://doi.org/10.1101/726042
Nicola F. Müller
aETH Zurich, Department of Biosystems Science and Engineering, 4058 Basel, Switzerland
bSwiss Institute of Bioinformatics (SIB), Switzerland
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  • ORCID record for Nicola F. Müller
  • For correspondence: nicola.felix.mueller@gmail.com timothy.vaughan@bsse.ethz.ch
Ugnė Stolz
aETH Zurich, Department of Biosystems Science and Engineering, 4058 Basel, Switzerland
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Gytis Dudas
cGothenburg Global Biodiversity Centre, Gothenburg, Sweden
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Tanja Stadler
aETH Zurich, Department of Biosystems Science and Engineering, 4058 Basel, Switzerland
bSwiss Institute of Bioinformatics (SIB), Switzerland
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Timothy G. Vaughan
aETH Zurich, Department of Biosystems Science and Engineering, 4058 Basel, Switzerland
bSwiss Institute of Bioinformatics (SIB), Switzerland
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  • For correspondence: nicola.felix.mueller@gmail.com timothy.vaughan@bsse.ethz.ch
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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. We here introduce a novel 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. Together, these provide 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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  • https://github.com/nicfel/Reassortment-Material

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The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY-NC 4.0 International license.
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Posted August 06, 2019.
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Bayesian inference of reassortment networks reveals fitness benefits of reassortment in human influenza viruses
Nicola F. Müller, Ugnė Stolz, Gytis Dudas, Tanja Stadler, Timothy G. Vaughan
bioRxiv 726042; doi: https://doi.org/10.1101/726042
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Bayesian inference of reassortment networks reveals fitness benefits of reassortment in human influenza viruses
Nicola F. Müller, Ugnė Stolz, Gytis Dudas, Tanja Stadler, Timothy G. Vaughan
bioRxiv 726042; doi: https://doi.org/10.1101/726042

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