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Measuring Asymmetry in Time-Stamped Phylogenies

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  • Bethany L Dearlove
  • Simon D W Frost

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.Author Summary: Phylogenetic trees of viruses sampled from different individuals provide clues to the dynamics of transmission. The extent to which the tree is asymmetric may be influenced by biological factors such as differences in infectiousness or contact rates between individuals, but also by nuisance factors such as the pattern of sampling. 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 also detect whether specific clades in the phylogeny drive patterns of asymmetry. We apply our approach to data on HIV, influenza A virus H5N1, and ebola virus.

Suggested Citation

  • Bethany L Dearlove & Simon D W Frost, 2015. "Measuring Asymmetry in Time-Stamped Phylogenies," PLOS Computational Biology, Public Library of Science, vol. 11(7), pages 1-16, July.
    • Handle: RePEc:plo:pcbi00:1004312
    DOI: 10.1371/journal.pcbi.1004312
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    References listed on IDEAS

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    1. Alexei J Drummond & Simon Y W Ho & Matthew J Phillips & Andrew Rambaut, 2006. "Relaxed Phylogenetics and Dating with Confidence," PLOS Biology, Public Library of Science, vol. 4(5), pages 1-1, March.
    2. John D. Storey, 2002. "A direct approach to false discovery rates," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 64(3), pages 479-498, August.
    3. Neil M. Ferguson & Alison P. Galvani & Robin M. Bush, 2003. "Ecological and immunological determinants of influenza evolution," Nature, Nature, vol. 422(6930), pages 428-433, March.
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