Phylogenetically resolving epidemiologic linkage

Proc Natl Acad Sci U S A. 2016 Mar 8;113(10):2690-5. doi: 10.1073/pnas.1522930113. Epub 2016 Feb 22.

Abstract

Although the use of phylogenetic trees in epidemiological investigations has become commonplace, their epidemiological interpretation has not been systematically evaluated. Here, we use an HIV-1 within-host coalescent model to probabilistically evaluate transmission histories of two epidemiologically linked hosts. Previous critique of phylogenetic reconstruction has claimed that direction of transmission is difficult to infer, and that the existence of unsampled intermediary links or common sources can never be excluded. The phylogenetic relationship between the HIV populations of epidemiologically linked hosts can be classified into six types of trees, based on cladistic relationships and whether the reconstruction is consistent with the true transmission history or not. We show that the direction of transmission and whether unsampled intermediary links or common sources existed make very different predictions about expected phylogenetic relationships: (i) Direction of transmission can often be established when paraphyly exists, (ii) intermediary links can be excluded when multiple lineages were transmitted, and (iii) when the sampled individuals' HIV populations both are monophyletic a common source was likely the origin. Inconsistent results, suggesting the wrong transmission direction, were generally rare. In addition, the expected tree topology also depends on the number of transmitted lineages, the sample size, the time of the sample relative to transmission, and how fast the diversity increases after infection. Typically, 20 or more sequences per subject give robust results. We confirm our theoretical evaluations with analyses of real transmission histories and discuss how our findings should aid in interpreting phylogenetic results.

Keywords: HIV-1; coalescent; paraphyly; phylogeny; transmission.

Publication types

  • Research Support, N.I.H., Extramural
  • Research Support, Non-U.S. Gov't

MeSH terms

  • Algorithms*
  • Genetic Variation
  • HIV Infections / epidemiology
  • HIV Infections / transmission*
  • HIV Infections / virology
  • HIV-1 / classification
  • HIV-1 / genetics*
  • HIV-1 / physiology
  • Host-Pathogen Interactions
  • Humans
  • Models, Genetic*
  • Phylogeny*
  • Population Density
  • Population Dynamics
  • Time Factors