New Routes to Phylogeography: A Bayesian Structured Coalescent Approximation.
Phylogeographic methods aim to infer migration trends and the history of sampled lineages from genetic data. Applications of phylogeography are broad, and in the context of pathogens include the reconstruction of transmission histories and the origin and emergence of outbreaks. Phylogeographic inference based on bottom-up population genetics models is computationally expensive, and as a result faster alternatives based on the evolution of discrete traits have become popular. In this paper, we show that inference of migration rates and root locations based on discrete trait models is extremely unreliable and sensitive to biased sampling. To address this problem, we introduce BASTA (BAyesian STructured coalescent Approximation), a new approach implemented in BEAST2 that combines the accuracy of methods based on the structured coalescent with the computational efficiency required to handle more than just few populations. We illustrate the potentially severe implications of poor model choice for phylogeographic analyses by investigating the zoonotic transmission of Ebola virus. Whereas the structured coalescent analysis correctly infers that successive human Ebola outbreaks have been seeded by a large unsampled non-human reservoir population, the discrete trait analysis implausibly concludes that undetected human-to-human transmission has allowed the virus to persist over the past four decades. As genomics takes on an increasingly prominent role informing the control and prevention of infectious diseases, it will be vital that phylogeographic inference provides robust insights into transmission history.
Institute for Emerging Infections, Oxford Martin School, Oxford, United Kingdom; Nuffield Department of Medicine, University of Oxford, Oxford, United Kingdom.
Nicola
N
De Maio
Nuffield Department of Medicine, University of Oxford, Oxford, United Kingdom.
Chieh-Hsi
CH
Wu
MRC Centre for Outbreak Analysis and Modelling, School of Public Health, Faculty of Medicine, Imperial College London, London, United Kingdom.
Kathleen M
KM
O'Reilly
Institute for Emerging Infections, Oxford Martin School, Oxford, United Kingdom; Nuffield Department of Medicine, University of Oxford, Oxford, United Kingdom; Wellcome Trust Centre for Human Genetics, University of Oxford, Oxford, United Kingdom.
Daniel
D
Wilson
D000465:Algorithms; D025041:Animal Migration; D000818:Animals; D001499:Bayes Theorem; D001717:Birds; D004196:Disease Outbreaks; D029043:Ebolavirus / Q000235:genetics; D019143:Evolution, Molecular; D014644:Genetic Variation; D019142:Hemorrhagic Fever, Ebola / Q000453:epidemiology / Q000635:transmission / Q000821:virology; D006801:Humans; D009980:Influenza A virus / Q000235:genetics; D005585:Influenza in Birds / Q000453:epidemiology / Q000821:virology; D008957:Models, Genetic; D010802:Phylogeny; D058974:Phylogeography / Q000379:methods*; D010935:Plant Diseases; D010942:Plant Viruses / Q000235:genetics; D015047:Zoonoses