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Home / Publications / Donor-Recipient Identification in Para- and Poly-phyletic Trees Under Alternative HIV-1 Transmission Hypotheses Using Approximate Bayesian Computation

Donor-Recipient Identification in Para- and Poly-phyletic Trees Under Alternative HIV-1 Transmission Hypotheses Using Approximate Bayesian Computation

  • Authors: Abecasis A, Azevedo-Pereira JM, Bártolo I, Bulla I, Hengartner N, Leitner T, Romero-Severson EO, Taveira N
  • Publication Year: 2017
  • Journal: Genetics

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. These variants can influence the efficacy of putative prevention methods, and the reconstruction of epidemiologic histories.

The authors created an approximate Bayesian computation (ABC) method based on a set of statistics measuring phylogenetic topology, branch lengths, and genetic diversity. They made it to infer who-infected-whom. Also to compute the probability of alternative transmission scenarios while explicitly taking phylogenetic uncertainty into account.

In addition, they applied the method to a suspected heterosexual transmission case involving three individuals, showing a complex monophyletic-paraphyletic-polyphyletic phylogenetic topology. Furthermore, they detected that seven phylogenetic lineages had been transmitted between two of the individuals based on the available samples. This could imply that many more unsampled lineages had also been transmitted.

The authors tested whether the lineages had been transmitted at one time or over some length of time suggested that an ongoing superinfection process over several years was most likely. They also found, surprisingly, one individual unlinked to the other two. This happened when evaluating two competing epidemiological priors, the donor of the two that did infect each other was not identified by the host root-label. Thus, was also not the primary suspect in that transmission.

Moreover, this highlights the importance to take epidemiological information into account when analyzing support for one transmission hypothesis over another. The results may be nonintuitive and sensitive to details about sampling dates relative to possible infection dates.

In conclusion, the study provides a formal inference framework to include information on infection and sampling times. It also investigated the ancestral node-label states, transmission direction, transmitted genetic diversity, and frequency of transmission.

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About GHTM

GHTM is a R&D Unit that brings together researchers with a track record in Tropical Medicine and International & Global Health. It aims at strengthening Portugal's role as a leading partner in the development and implementation of a global health research agenda. Our evidence-based interventions contribute to the promotion of equity in health and to improve the health of populations.

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