HIV-1 Full-Genome Phylogenetics of Generalized Epidemics in Sub-Saharan Africa: Impact of Missing Nucleotide Characters in Next-Generation Sequences

Abstract

To characterize HIV-1 transmission dynamics in regions where the burden of HIV-1 is greatest, the ‘Phylogenetics and Networks for Generalised HIV Epidemics in Africa’ consortium (PANGEA-HIV) is sequencing full-genome viral isolates from across sub-Saharan Africa. We report the first 3,985 PANGEA-HIV consensus sequences from four cohort sites (Rakai Community Cohort Study, n=2,833; MRC/UVRI Uganda, n=701; Mochudi Prevention Project, n=359; Africa Health Research Institute Resistance Cohort, n=92). Next-generation sequencing success rates varied: more than 80% of the viral genome from the gag to the nef genes could be determined for all sequences from South Africa, 75% of sequences from Mochudi, 60% of sequences from MRC/UVRI Uganda, and 22% of sequences from Rakai. Partial sequencing failure was primarily associated with low viral load, increased for amplicons closer to the 3’ end of the genome, was not associated with subtype diversity except HIV-1 subtype D, and remained significantly associated with sampling location after controlling for other factors. We assessed the impact of the missing data patterns in PANGEA-HIV sequences on phylogeny reconstruction in simulations. We found a threshold in terms of taxon sampling below which the patchy distribution of missing characters in next-generation sequences has an excess negative impact on the accuracy of HIV-1 phylogeny reconstruction, which is attributable to tree reconstruction artifacts that accumulate when branches in viral trees are long. The large number of PANGEA-HIV sequences provides unprecedented opportunities for evaluating HIV-1 transmission dynamics across sub-Saharan Africa and identifying prevention opportunities. Molecular epidemiological analyses of these data must proceed cautiously because sequence sampling remains below the identified threshold and a considerable negative impact of missing characters on phylogeny reconstruction is expected.

Quantifying HIV transmission flow between high-prevalence hotspots and surrounding communities: a population-based study in Rakai, Uganda

“…Sex specific estimates in panels A and C were obtained with Bayesian logistic regression models using the Stan software, version 2.19. moderate quality. 24 Analysis was restricted to samples from 2652 individuals who satisfied minimum criteria on read length and read depth, 20 which implied a sampling fraction of 68•4% among participants who were infected and self-reported being ART-naive, and an estimated sampling fraction of 45•1% among censuseligible individuals who were infected with unsuppressed virus. 20 Sequence sampling rates varied by sex, age, migration status, and across RCCS communities (appendix p 16).…”

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

“…Second, we utilized partial HIV-1 pol sequences. Longer viral sequences may be more informative for identification of molecular HIV clusters 30 – 33 . Third, although we chose the most commonly-used analytical approaches for identification of clusters, there are other analytical approaches (e.g., Bayesian methods or alternative ways of measuring distance).…”

Easy and accurate reconstruction of whole HIV genomes from short-read sequence data with shiver

“…2017 determined that the fraction of variability in HIV set-point viral load that is explained by viral genetic factors was around one third, using 1,373 European whole genomes. Ratmann et al. 2017 found predictors of HIV sequencing success or failure for 3,985 African whole genomes, and studied the effect of the observed amplification failure patterns on phylogenetic inference.…”