Empirical comparison of analytical approaches for identifying molecular HIV-1 clusters

Abstract

Public health interventions guided by clustering of HIV-1 molecular sequences may be impacted by choices of analytical approaches. We identified commonly-used clustering analytical approaches, applied them to 1886 HIV-1 Rhode Island sequences from 2004–2018, and compared concordance in identifying molecular HIV-1 clusters within and between approaches. We used strict (topological support ≥ 0.95; distance 0.015 substitutions/site) and relaxed (topological support 0.80–0.95; distance 0.030–0.045 substitutions/site) thresholds to reflect different epidemiological scenarios. We found that clustering differed by method and threshold and depended more on distance than topological support thresholds. Clustering concordance analyses demonstrated some differences across analytical approaches, with RAxML having the highest (91%) mean summary percent concordance when strict thresholds were applied, and three (RAxML-, FastTree regular bootstrap- and IQ-Tree regular bootstrap-based) analytical approaches having the highest (86%) mean summary percent concordance when relaxed thresholds were applied. We conclude that different analytical approaches can yield diverse HIV-1 clustering outcomes and may need to be differentially used in diverse public health scenarios. Recognizing the variability and limitations of commonly-used methods in cluster identification is important for guiding clustering-triggered interventions to disrupt new transmissions and end the HIV epidemic.

Molecular Epidemiological Analysis of the Origin and Transmission Dynamics of the HIV-1 CRF01_AE Sub-Epidemic in Bulgaria

“…This distance threshold was later reduced to 0.5% to identify even more recent and rapidly growing clusters of concern [33,38,39]. Our cluster analysis results also suggest that non-B subtypes may require different genetic distant cutoffs to better identify transmission clusters, which is in line with other reports [40].…”

The Role of Phylogenetics in Unravelling Patterns of HIV Transmission towards Epidemic Control: The Quebec Experience (2002–2020)

“…There have been debates as to which methodological approaches should be used to assign viral linkage, i.e., clustering, and reconstruct transmission networks [ 26 , 33 , 34 , 35 , 36 ]. We applied MEGA7/10 phylogenetic analyses across the viral reverse transcriptase (RT) and protease regions based on strict criteria of high bootstrap support and short genetic distance [ 34 , 35 , 36 ].…”

“…This shorter genetic distance is estimated to evolve over approximately 2–3 years [ 44 ]. Our findings found that this lower cut-off may not always assist in forecasting cluster growth [ 26 , 27 , 33 ]. Analyses at genetic distances of 1.5% and 2.5% thresholds assisted in distinguishing patterns of spread of large cluster networks implicated in the Quebec epidemic.…”

“…The genetic distance threshold of 1.5% is commonly used in research studies [ 25 , 27 ]. The longer 2.5% genetic distance threshold was included to describe the growth trajectories of several long-lived large cluster variants [ 25 , 26 ].…”

“…Molecular transmission networks of cluster members established based on NJ-MEGA10 phylogenetic tree analyses were reconstructed and visualized using HIV-TRACE (https:// hivtrace.datamonkey.org/hivtrace, accessed on 16 April 2021) and MicrobeTrace (http://github.com/cdcgov/microbetrace, accessed on 16 April 2021). Clustering patterns were assessed at genetic distance thresholds of 1.5% and 2.5%, using an ambiguity of 0.15 to resolve ambiguous nucleotides [19,20,25,26]. The genetic distance threshold of 1.5% is commonly used in research studies [25,27].…”