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

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 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 three individuals, showing a complex monophyletic-paraphyletic-polyphyletic phylogenetic topology. We detected that seven 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 superinfection 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 themore » 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 nonintuitive and sensitive to details about sampling dates relative to possible infection dates. Here, 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.« less

Authors:

Romero-Severson, Ethan O.  ^[1];

• Search DOE PAGES for author "Romero-Severson, Ethan O."
• Search DOE PAGES for ORCID "0000-0002-8082-1225"

Bulla, Ingo ^[2]; Hengartner, Nick W.  ^[1];

• Search DOE PAGES for author "Hengartner, Nick W."
• Search DOE PAGES for ORCID "0000-0002-4157-134X"

Bartolo, Ines ^[3]; Abecasis, Ana ^[4]; Azevedo-Pereira, Jose M. ^[3]; Taveira, Nuno ^[5]; Leitner, Thomas Kenneth  ^[1]

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• Search DOE PAGES for ORCID "0000-0001-8160-2588"

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+ Show Author Affiliations

1. Los Alamos National Lab. (LANL), Los Alamos, NM (United States)
2. Los Alamos National Lab. (LANL), Los Alamos, NM (United States); Univ. Greifswald (Germany)
3. Univ. de Lisboa (Portugal)
4. Univ. Nova de Lisboa (UNL), Lisboa (Portugal)
5. Univ. de Lisboa (Portugal); Instituto Superior Ciencias da Saude Egas Moniz, Monte de Caparica (Portugal)

Publication Date:

Wed Sep 13 00:00:00 EDT 2017

Research Org.:

Los Alamos National Laboratory (LANL), Los Alamos, NM (United States)

Sponsoring Org.:

National Institutes of Health (NIH); USDOE

OSTI Identifier:

1459629

Report Number(s):

LA-UR-16-27499
Journal ID: ISSN 0016-6731

Grant/Contract Number:  

AC52-06NA25396

Resource Type:

Accepted Manuscript

Journal Name:

Genetics

Additional Journal Information:

Journal Volume: 207; Journal Issue: 3; Journal ID: ISSN 0016-6731

Publisher:

Genetics Society of America

Country of Publication:

United States

Language:

English

Subject:

59 BASIC BIOLOGICAL SCIENCES; Biological Science; coalescent; phylogeny; approximate Bayesian computation; co-infection; superinfection; ancestral node state