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A Bayesian approach to infer recombination patterns in coronaviruses.
Müller, Nicola F; Kistler, Kathryn E; Bedford, Trevor.
  • Müller NF; Vaccine and Infectious Disease Division, Fred Hutchinson Cancer Research Center, Seattle, WA, USA. nicola.felix.mueller@gmail.com.
  • Kistler KE; Vaccine and Infectious Disease Division, Fred Hutchinson Cancer Research Center, Seattle, WA, USA.
  • Bedford T; Molecular and Cellular Biology Program, University of Washington, Seattle, WA, USA.
Nat Commun ; 13(1): 4186, 2022 07 20.
Article in English | MEDLINE | ID: covidwho-1947343
ABSTRACT
As shown during the SARS-CoV-2 pandemic, phylogenetic and phylodynamic methods are essential tools to study the spread and evolution of pathogens. One of the central assumptions of these methods is that the shared history of pathogens isolated from different hosts can be described by a branching phylogenetic tree. Recombination breaks this assumption. This makes it problematic to apply phylogenetic methods to study recombining pathogens, including, for example, coronaviruses. Here, we introduce a Markov chain Monte Carlo approach that allows inference of recombination networks from genetic sequence data under a template switching model of recombination. Using this method, we first show that recombination is extremely common in the evolutionary history of SARS-like coronaviruses. We then show how recombination rates across the genome of the human seasonal coronaviruses 229E, OC43 and NL63 vary with rates of adaptation. This suggests that recombination could be beneficial to fitness of human seasonal coronaviruses. Additionally, this work sets the stage for Bayesian phylogenetic tracking of the spread and evolution of SARS-CoV-2 in the future, even as recombinant viruses become prevalent.
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Full text: Available Collection: International databases Database: MEDLINE Main subject: Coronavirus 229E, Human / COVID-19 Type of study: Prognostic study / Randomized controlled trials Limits: Humans Language: English Journal: Nat Commun Journal subject: Biology / Science Year: 2022 Document Type: Article Affiliation country: S41467-022-31749-8

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Full text: Available Collection: International databases Database: MEDLINE Main subject: Coronavirus 229E, Human / COVID-19 Type of study: Prognostic study / Randomized controlled trials Limits: Humans Language: English Journal: Nat Commun Journal subject: Biology / Science Year: 2022 Document Type: Article Affiliation country: S41467-022-31749-8