PT  - JOURNAL ARTICLE
AU  - Nicola F. Müller
AU  - Ugnė Stolz
AU  - Gytis Dudas
AU  - Tanja Stadler
AU  - Timothy G. Vaughan
TI  - Bayesian inference of reassortment networks reveals fitness benefits of reassortment in human influenza viruses
AID  - 10.1101/726042
DP  - 2019 Jan 01
TA  - bioRxiv
PG  - 726042
4099  - http://biorxiv.org/content/early/2019/08/06/726042.short
4100  - http://biorxiv.org/content/early/2019/08/06/726042.full
AB  - 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.