Episodic Sexual Transmission of HIV Revealed by Molecular Phylodynamics

Lewis, Fraser; Hughes, G.; Rambaut, Andrew; Pozniak, Anton; Brown, Andrew J. Leigh

doi:10.1371/journal.pmed.0050050

Cited by 330 publications

(382 citation statements)

References 40 publications

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“…These qualitatively antipodal distributions are generated by the same underlying population dynamics, with only the sample fraction being varied. This observation is of practical as well as theoretical interest, since many serological surveys for HIV may reach .20% of infected individuals within a given locality (Lewis et al 2008).…”

Section: Resultsmentioning

confidence: 82%

“…Clusters of related virus are often interpreted as epidemiologically linked. For example, clusters of acute HIV infections may represent short transmission chains between high-risk individuals (Yerly et al 2001;Hue et al 2005;Pao et al 2005;Goodreau 2006;Brenner et al 2007;Drumright and Frost 2008;Lewis et al 2008). Because our model reproduces the moments of the cluster size distribution, it can be used to predict the level of clustering as a function of epidemiological conditions.…”

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confidence: 99%

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Phylodynamics of Infectious Disease Epidemics

et al. 2009

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We present a formalism for unifying the inference of population size from genetic sequences and mathematical models of infectious disease in populations. Virus phylogenies have been used in many recent studies to infer properties of epidemics. These approaches rely on coalescent models that may not be appropriate for infectious diseases. We account for phylogenetic patterns of viruses in susceptibleinfected (SI), susceptible-infected-susceptible (SIS), and susceptible-infected-recovered (SIR) models of infectious disease, and our approach may be a viable alternative to demographic models used to reconstruct epidemic dynamics. The method allows epidemiological parameters, such as the reproductive number, to be estimated directly from viral sequence data. We also describe patterns of phylogenetic clustering that are often construed as arising from a short chain of transmissions. Our model reproduces the moments of the distribution of phylogenetic cluster sizes and may therefore serve as a null hypothesis for cluster sizes under simple epidemiological models. We examine a small cross-sectional sample of human immunodeficiency (HIV)-1 sequences collected in the United States and compare our results to standard estimates of effective population size. Estimated prevalence is consistent with estimates of effective population size and the known history of the HIV epidemic. While our model accurately estimates prevalence during exponential growth, we find that periods of decline are harder to identify.

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Section: Resultsmentioning

confidence: 82%

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confidence: 99%

Phylodynamics of Infectious Disease Epidemics

et al. 2009

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“…3 However, genetic distance and branch support cutoffs markedly vary between studies and the rationale for a given cluster definition is rarely specified. [10][11][12][13][14][15] Many factors can influence the choice of genetic distance and branch support cutoff values used to define clusters, such as the spatial and temporal scale of analysis, HIV subtype, the underlying mode of transmission (e.g., heterosexual vs. injection drug use), and the viral genomic region(s) being analyzed. Branch support values may also be affected by the statistical model used to reconstruct the phylogenies (e.g., maximum likelihood, Bayesian) and the total amount of viral genetic diversity in the dataset.…”

Section: Introductionmentioning

confidence: 99%

Identifying Transmission Clusters with Cluster Picker and HIV-TRACE

Rose¹,

Lamers²,

Dollar³

et al. 2017

AIDS Research and Human Retroviruses

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We compared the behavior of two approaches (Cluster Picker and HIV-TRACE) at varying genetic distances to identify transmission clusters. We used three HIV gp41 sequence datasets originating from the Rakai Community Cohort Study: (1) next-generation sequence (NGS) data from nine linked couples; (2) NGS data from longitudinal sampling of 14 individuals; and (3) Sanger consensus sequences from a cross-sectional dataset (n = 1,022) containing 91 epidemiologically linked heterosexual couples. We calculated the optimal genetic distance threshold to separate linked versus unlinked NGS datasets using a receiver operating curve analysis. We evaluated the number, size, and composition of clusters detected by Cluster Picker and HIV-TRACE at six genetic distance thresholds (1%-5.3%) on all three datasets. We further tested the effect of using all NGS, versus only a single variant for each patient/time point, for datasets (1) and (2). The optimal gp41 genetic distance threshold to distinguish linked and unlinked couples and individuals was 5.3% and 4%, respectively. HIV-TRACE tended to detect larger and fewer clusters, whereas Cluster Picker detected more clusters containing only two sequences. For NGS datasets (1) and (2), HIV-TRACE and Cluster Picker detected all linked pairs at 3% and 4% genetic distances, respectively. However, at 5.3% genetic distance, 20% of couples in dataset (3) did not cluster using either program, and for >1/3 of couples cluster assignment were discordant. We suggest caution in choosing thresholds for clustering analyses in a generalized epidemic.

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“…Achievement of this goal could be facilitated by a better understanding of the structure and dynamics of HIV transmission networks and comprehensive HIV cluster analysis. [1][2][3][4][5][6][7][8][9][10][11][12][13][14][15][16][17][18][19][20] The extent of viral clustering is one of the key factors in making inferences about epidemiologic processes inferred from viral phylogenies. However, it remains to be established how the selection of region across the HIV-1 genome and its length affects the extent of HIV clustering.…”

Section: Introductionmentioning

confidence: 99%

Importance of Viral Sequence Length and Number of Variable and Informative Sites in Analysis of HIV Clustering

Novitsky

Moyo

Lei

et al. 2015

AIDS Research and Human Retroviruses

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To improve the methodology of HIV cluster analysis, we addressed how analysis of HIV clustering is associated with parameters that can affect the outcome of viral clustering. The extent of HIV clustering and tree certainty was compared between 401 HIV-1C near full-length genome sequences and subgenomic regions retrieved from the LANL HIV Database. Sliding window analysis was based on 99 windows of 1,000 bp and 45 windows of 2,000 bp. Potential associations between the extent of HIV clustering and sequence length and the number of variable and informative sites were evaluated. The near full-length genome HIV sequences showed the highest extent of HIV clustering and the highest tree certainty. At the bootstrap threshold of 0.80 in maximum likelihood (ML) analysis, 58.9% of near full-length HIV-1C sequences but only 15.5% of partial pol sequences (ViroSeq) were found in clusters. Among HIV-1 structural genes, pol showed the highest extent of clustering (38.9% at a bootstrap threshold of 0.80), although it was significantly lower than in the near full-length genome sequences. The extent of HIV clustering was significantly higher for sliding windows of 2,000 bp than 1,000 bp. We found a strong association between the sequence length and proportion of HIV sequences in clusters, and a moderate association between the number of variable and informative sites and the proportion of HIV sequences in clusters. In HIV cluster analysis, the extent of detectable HIV clustering is directly associated with the length of viral sequences used, as well as the number of variable and informative sites. Near full-length genome sequences could provide the most informative HIV cluster analysis. Selected subgenomic regions with a high extent of HIV clustering and high tree certainty could also be considered as a second choice.

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Episodic Sexual Transmission of HIV Revealed by Molecular Phylodynamics

Cited by 330 publications

References 40 publications

Phylodynamics of Infectious Disease Epidemics

Phylodynamics of Infectious Disease Epidemics

Identifying Transmission Clusters with Cluster Picker and HIV-TRACE

Importance of Viral Sequence Length and Number of Variable and Informative Sites in Analysis of HIV Clustering

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