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

Abstract

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.

Accommodating individual travel history and unsampled diversity in Bayesian phylogeographic inference of SARS-CoV-2

“…While downsampling genomic data from locations in unbalanced data sets has become a common practice 17,20 , we present here an alternative approach that adds unsampled taxa to assess the sensitivity of inferences to sampling bias. We emphasize that even though the inclusion of unsampled taxa is informed by epidemiological data, these unsampled taxa should never be considered as additional observations.…”

“…Formally integrating such travel data in phylogeographic reconstructions may therefore help to address or correct for sampling bias. In general, epidemiological information provides important context to assess genomic sampling biases, and this can be used to subsample genomes by location in situations where large collections are available 20 . The question therefore emerges how such information can be formally embedded in phylodynamic models.…”

Accommodating individual travel history, global mobility, and unsampled diversity in phylogeography: a SARS-CoV-2 case study

“…While downsampling genomic data from locations in unbalanced data sets has become a common practice 15,18 , we present here an alternative approach that adds unsampled taxa to assess the sensitivity of inferences to sampling bias. We emphasize that even though the inclusion of unsampled taxa is informed by epidemiological data, these unsampled taxa should never be considered as additional observations.…”

“…genomes by location in situations where large collections are available18 . The question therefore emerges how such information can be formally embedded in phylodynamic models.…”

Methodological synthesis of Bayesian phylodynamics, HIV-TRACE, and GEE: HIV-1 transmission epidemiology in a racially/ethnically diverse Southern U.S. context

“…In the present study, our primary goal was to identify potential drivers of structuring HIV-1 transmission clustering patterns between different subpopulations who act as sources and recipients of HIV-1 infection in the culturally and racially/ethnically diverse context of Houston/Harris County, TX. Our study took a hybrid heuristic and Bayesian approach 15 and introduced an innovative methodology by synthesizing the HIV-TRAnsmission Cluster Engine (HIV-TRACE), Bayesian phylodynamics, and generalized estimating equations 16 (GEE). Our approach enabled us to reconstruct HIV-1 transmission clusters; infer demographic and risk parameters of HIV-1 transmission dynamics by jointly estimating viral transmission rates across racial/ethnic, age, and transmission risk groups; and assess the effects of network mixing patterns based on racial/ethnic and age characteristics of the index person and partners on the degree of network connectivity.…”