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.Author Summary: When studying infectious diseases it is often important to understand how germs spread from location-to-location, person-to-person, or even one part of the body to another. Using phylogeographic methods, it is possible to recover the history of spread of pathogens (or other organisms) by studying their genetic material. Here we reveal that some popular, fast phylogeographic methods are inaccurate, and we introduce a new more reliable method to address the problem. By comparing different phylogeographic methods based on principled population models and fast alternatives, we found that different approaches can give diametrically opposed results, and we offer concrete examples in the context of the ongoing Ebola outbreak in West Africa and the world-wide outbreaks of Avian Influenza Virus and Tomato Yellow Leaf Curl Virus. We found that the most popular phylogeographic method often produces completely inaccurate conclusions. One of the reasons for its popularity has been its computational speed, which has allowed users to analyse large genetic datasets with complex models. More accurate approaches have until now been considerably slower, and therefore we propose a new method called BASTA that achieves good accuracy in a reasonable time. We are relying more and more on genetic sequencing to learn about the origin and spread of infections, and as this role continues to grow, it will be essential to use accurate phylogeographic methods when designing policies to prevent or curb the spread of disease.