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Open AccessArticle

In Search of Covariates of HIV-1 Subtype B Spread in the United States—A Cautionary Tale of Large-Scale Bayesian Phylogeography

1
Department of Microbiology, Immunology and Transplantation, Rega Institute for Medical Research, KU Leuven, 3000 Leuven, Belgium
2
Spatial Epidemiology Lab (SpELL), Université Libre de Bruxelles, 1050 Brussels, Belgium
3
Department of Biomathematics, David Geffen School of Medicine at UCLA, University of California, Los Angeles, CA 90095, USA
4
Department of Human Genetics, David Geffen School of Medicine at UCLA, University of California, Los Angeles, CA 90095, USA
5
Department of Biostatistics, Fielding School of Public Health, University of California, Los Angeles, CA 90095, USA
6
ARUP Institute for Clinical and Experimental Pathology, Salt Lake City, UT 84108, USA
7
Department of Pathology, University of Utah, Salt Lake City, UT 84112, USA
*
Author to whom correspondence should be addressed.
Viruses 2020, 12(2), 182; https://doi.org/10.3390/v12020182
Received: 20 December 2019 / Revised: 24 January 2020 / Accepted: 28 January 2020 / Published: 5 February 2020
(This article belongs to the Special Issue HIV Molecular Epidemiology for Prevention)
Infections with HIV-1 group M subtype B viruses account for the majority of the HIV epidemic in the Western world. Phylogeographic studies have placed the introduction of subtype B in the United States in New York around 1970, where it grew into a major source of spread. Currently, it is estimated that over one million people are living with HIV in the US and that most are infected with subtype B variants. Here, we aim to identify the drivers of HIV-1 subtype B dispersal in the United States by analyzing a collection of 23,588 pol sequences, collected for drug resistance testing from 45 states during 2004–2011. To this end, we introduce a workflow to reduce this large collection of data to more computationally-manageable sample sizes and apply the BEAST framework to test which covariates associate with the spread of HIV-1 across state borders. Our results show that we are able to consistently identify certain predictors of spread under reasonable run times across datasets of up to 10,000 sequences. However, the general lack of phylogenetic structure and the high uncertainty associated with HIV trees make it difficult to interpret the epidemiological relevance of the drivers of spread we are able to identify. While the workflow we present here could be applied to other virus datasets of a similar scale, the characteristic star-like shape of HIV-1 phylogenies poses a serious obstacle to reconstructing a detailed evolutionary and spatial history for HIV-1 subtype B in the US.
Keywords: HIV; BEAST; BEAGLE; phylogeography; phylodynamics; spread; covariates; predictors HIV; BEAST; BEAGLE; phylogeography; phylodynamics; spread; covariates; predictors
MDPI and ACS Style

Hong, S.L.; Dellicour, S.; Vrancken, B.; Suchard, M.A.; Pyne, M.T.; Hillyard, D.R.; Lemey, P.; Baele, G. In Search of Covariates of HIV-1 Subtype B Spread in the United States—A Cautionary Tale of Large-Scale Bayesian Phylogeography. Viruses 2020, 12, 182.

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