Background: HBV infection is a public health problem affecting approximately 2 billion people and leading to >350 million chronic carriers of the virus worldwide. Phylogenetic analysis can give valuable insight to help in clarifying the history of viral infections around the world and in elucidating routes of transmission of the different viral strains present in the infected host population. These analyses rely on an accurate estimate of the rate of mutations.
Methods: In this study, we investigated the robustness of rate estimations based on Bayesian analysis obtained so far and examined, in particular, the choice of prior for the substitution rate.
Results: Most previous studies have concentrated on estimating the parameters of simple demographic models for HBV, such as exponential growth and constant population size. Here, we introduce a method that automatically partitions the genome in components that show a different rate of mutation and fit different substitution models.
Conclusions: In conclusion, we find that, due to inaccuracy in the sampling dates from the samples where viral sequences were obtained, lack of a sufficiently large geographical and time spread of available and trustworthy sample dates, sensitivity to priors and model misspecification and rate estimation based on molecular methods, are not reliable. We suggest that rate estimates taking into account calibration points based on relevant historical events are more robust due to the lack of trustworthy sampling dates. For example, the known history of colonization of the Americas should be used to accurately study the current diversity of genotype F, which is the most frequent genotype in almost all Spanish speaking countries in South America.