Abstract
Previous work has shown that asymmetry in viral phylogenies may be indicative of heterogeneity in transmission, for example due to acute HIV infection or the presence of ‘core groups’ with higher contact rates. Hence, evidence of asymmetry may provide clues to underlying population structure, even when direct information on, for example, stage of infection or contact rates, are missing. However, current tests of phylogenetic asymmetry (a) suffer from false positives when the tips of the phylogeny are sampled at different times and (b) only test for global asymmetry, and hence suffer from false negatives when asymmetry is localised to part of a phylogeny. We present a simple permutation-based approach for testing for asymmetry in a phylogeny, where we compare the observed phylogeny with random phylogenies with the same sampling and coalescence times, to reduce the false positive rate. We also demonstrate how profiles of measures of asymmetry calculated over a range of evolutionary times in the phylogeny can be used to identify local asymmetry. In combination with different metrics of asymmetry, this combined approach offers detailed insights of how phylogenies reconstructed from real viral datasets may deviate from the simplistic assumptions of commonly used coalescent and birth-death process models.
Highlights
Genetic approaches to investigating infectious diseases are well-established, exploiting the naturally high genetic diversity in pathogen populations such as HIV and influenza to reconstruct both their evolutionary and epidemiological dynamics [1]
Phylogenetic trees of viruses sampled from different individuals provide clues to the dynamics of transmission
We have devised a simple statistical test for asymmetry, which controls for sampling patterns and potentially complex temporal dynamics by conditioning on the sampling and coalescence times in a phylogeny, and can detect whether specific clades in the phylogeny drive patterns of asymmetry
Summary
Genetic approaches to investigating infectious diseases are well-established, exploiting the naturally high genetic diversity in pathogen populations such as HIV and influenza to reconstruct both their evolutionary and epidemiological dynamics [1]. There can be confounding factors when trying to convert evolutionary dynamics into epidemiological quantities such as transmission rates, and ideally we want to be able to explicitly model viral transmission in an evolutionary framework, taking into account features such as the host population structure (for example, differences in contact rates between groups of individuals) and the natural course of infection (for example, differences in infectiousness during the acute and chronic phases of HIV infection) [5]. One way to investigate the extra biological complexity of such patterns is to consider the shape or branching structure of the phylogeny, a feature that is arguably underused despite being relatively straightforward to infer. Since many tree models assume homogeneity in the population, it is important to be able to identify which parts of the tree might be driving asymmetry, and whether or not this is problematic under the modelling assumptions—preferably before running computationally expensive analyses
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
