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Inference of direction, diversity, and frequency of HIV-1 transmission using approximate Bayesian computation

Ethan O. Romero-Severson, Ingo Bulla, Nick Hengartner, Inês Bártolo, Ana Abecasis, José M. Azevedo-Pereira, Nuno Taveira, Thomas Leitner
doi: https://doi.org/10.1101/071050
Ethan O. Romero-Severson
1Theoretical Biology and Biophysics Group, Los Alamos National Laboratory, Los Alamos, NM 87545, USA
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Ingo Bulla
1Theoretical Biology and Biophysics Group, Los Alamos National Laboratory, Los Alamos, NM 87545, USA
2Institut für Mathematik und Informatik, Universität Greifswald, Walther-Rathenau-Straße 47, 17487 Greifswald, Germany
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Nick Hengartner
1Theoretical Biology and Biophysics Group, Los Alamos National Laboratory, Los Alamos, NM 87545, USA
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Inês Bártolo
3HIV Evolution, Epidemiology and Prevention, Research Institute for Medicines /Instituto de Investigação do Medicamento (iMed.ULisboa), Faculdade de Farmácia, Universidade de Lisboa, Portugal
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Ana Abecasis
4Global Health and Tropical Medicine, GHTM, Instituto de Higiene e Medicina Tropical, IHMT, Universidade Nova de Lisboa, UNL, Rua da Junqueira 100, 1349-008 Lisboa, Portugal
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José M. Azevedo-Pereira
5Host-Pathogen Interaction Unit, Research Institute for Medicines /Instituto de Investigação do Medicamento (iMed.ULisboa), Faculdade de Farmácia, Universidade de Lisboa, Portugal
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Nuno Taveira
3HIV Evolution, Epidemiology and Prevention, Research Institute for Medicines /Instituto de Investigação do Medicamento (iMed.ULisboa), Faculdade de Farmácia, Universidade de Lisboa, Portugal
6Centro de Investigação Interdisciplinar Egas Moniz (CiiEM), Instituto Superior Ciências da Saúde Egas Moniz, Monte de Caparica, Portugal
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Thomas Leitner
1Theoretical Biology and Biophysics Group, Los Alamos National Laboratory, Los Alamos, NM 87545, USA
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ABSTRACT

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 measure the diversity of the founding population 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 heterosexual transmission pair showing a complex paraphyletic-polyphyletic donor-recipient phylogenetic topology. We found evidence identifying the donor that was consistent with the known facts of the case (Bayes factor >20). We also found that while the evidence for ongoing transmission between the pair was as good or better than the singular transmission event model, it was only viable when the rate of ongoing transmission was implausibly high (~1/day). We concluded that the singular transmission model, which was able to estimate the diversity of the founding population (mean 7% substitutions/site), was more biologically plausible. Our study provides a formal inference framework to investigate HIV-1 direction, diversity, and frequency of transmission. The ability to measure the diversity of founding populations in both simple and complex transmission situations is essential to understanding the relationship between the phylogeny and epidemiology of HIV-1 as well as in efforts developing new prevention technologies.

Footnotes

  • GenBank Accession numbers: KT123041-KT123171

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The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY-NC-ND 4.0 International license.
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Posted August 24, 2016.
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Inference of direction, diversity, and frequency of HIV-1 transmission using approximate Bayesian computation
Ethan O. Romero-Severson, Ingo Bulla, Nick Hengartner, Inês Bártolo, Ana Abecasis, José M. Azevedo-Pereira, Nuno Taveira, Thomas Leitner
bioRxiv 071050; doi: https://doi.org/10.1101/071050
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Inference of direction, diversity, and frequency of HIV-1 transmission using approximate Bayesian computation
Ethan O. Romero-Severson, Ingo Bulla, Nick Hengartner, Inês Bártolo, Ana Abecasis, José M. Azevedo-Pereira, Nuno Taveira, Thomas Leitner
bioRxiv 071050; doi: https://doi.org/10.1101/071050

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