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Inferring HIV-1 transmission networks and sources of epidemic spread in Africa with deep-sequence phylogenetic analysis

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Title: Inferring HIV-1 transmission networks and sources of epidemic spread in Africa with deep-sequence phylogenetic analysis
Authors: Ratmann, O
Grabowski, MK
Hall, M
Golubchik, T
Wymant, C
Abeler-Dörner, L
Bonsall, D
Hoppe, A
Brown, AL
De Oliveira, T
Gall, A
Kellam, P
Pillay, D
Kagaayi, J
Kigozi, G
Quinn, TC
Wawer, MJ
Laeyendecker, O
Serwadda, D
Gray, RH
Fraser, C
Item Type: Journal Article
Abstract: To prevent new infections with human immunodeficiency virus type 1 (HIV-1) in sub-Saharan Africa, UNAIDS recommends targeting interventions to populations that are at high risk of acquiring and passing on the virus. Yet it is often unclear who and where these ‘source’ populations are. Here we demonstrate how viral deep-sequencing can be used to reconstruct HIV-1 transmission networks and to infer the direction of transmission in these networks. We are able to deep-sequence virus from a large population-based sample of infected individuals in Rakai District, Uganda, reconstruct partial transmission networks, and infer the direction of transmission within them at an estimated error rate of 16.3% [8.8–28.3%]. With this error rate, deep-sequence phylogenetics cannot be used against individuals in legal contexts, but is sufficiently low for population-level inferences into the sources of epidemic spread. The technique presents new opportunities for characterizing source populations and for targeting of HIV-1 prevention interventions in Africa.
Issue Date: 29-Mar-2019
Date of Acceptance: 22-Feb-2019
URI: http://hdl.handle.net/10044/1/68242
DOI: https://dx.doi.org/10.1038/s41467-019-09139-4
ISSN: 2041-1723
Publisher: Nature Research (part of Springer Nature)
Journal / Book Title: Nature Communications
Volume: 10
Issue: 1
Copyright Statement: © 2019 The Author(s). This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/ licenses/by/4.0/.
Sponsor/Funder: Bill & Melinda Gates Foundation
Bill & Melinda Gates Foundation
Funder's Grant Number: GCAEN 511473
1705CR001/LD1
Keywords: PANGEA Consortium and Rakai Health Sciences Program
MD Multidisciplinary
Publication Status: Published
Open Access location: https://www.nature.com/articles/s41467-019-09139-4
Article Number: 1411
Online Publication Date: 2019-03-29
Appears in Collections:Mathematics
Statistics
Faculty of Medicine
Faculty of Natural Sciences
Epidemiology, Public Health and Primary Care



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