Diversity of the founding population of Human Immunodeficiency Virus Type 1 (HIV-1) transmissions raises many important biological, clinical, and epidemiological issues. In up to 40% of sexual infections there is clear evidence for multiple founding variants, which can influence the efficacy of putative prevention methods and the reconstruction of epidemiologic histories. To infer who-infected-whom and to compute the probability of alternative transmission scenarios, while explicitly taking phylogenetic uncertainty into account, we created an Approximate Bayesian Computation (ABC) method based on a set of statistics measuring phylogenetic topology, branch lengths, and genetic diversity. We applied our method to a suspected heterosexual transmission case involving 3 individuals, showing a complex monophyletic-paraphyletic-polyphyletic phylogenetic topology. We detected that 7 phylogenetic lineages had been transmitted between two of the individuals based on the available samples, implying that many more unsampled lineages had also been transmitted. Testing whether the lineages had been transmitted at one time or over some length of time suggested that an ongoing super-infection process over several years was most likely. While one individual was found unlinked to the other two, surprisingly, when evaluating two competing epidemiological priors, the donor of the two that did infect each other was not identified by the host root-label, and was also not the primary suspect in that transmission. This highlights that it is important to take epidemiological information into account when analyzing support for one transmission hypothesis over another, as results may be non-intuitive and sensitive to details about sampling dates relative to possible infection dates. Our study provides a formal inference framework to include information on infection and sampling times, and to investigate ancestral node-label states, transmission direction, transmitted genetic diversity, and frequency of transmission.
- ancestral node state
- approximate Bayesian computation
UN Sustainable Development Goals (SDGs)
- SDG 3 - Good Health and Well-Being