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Identifying transmission clusters with Cluster Picker and HIV-TRACE

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 data sets originating from the Rakai Community Cohort Study: (1) next-generation sequences (NGS) from nine linked couples; (2) NGS... Full description

Journal Title: AIDS research and human retroviruses 2016, Vol.33 (ja), p.211-218
Main Author: Rose, Rebecca
Other Authors: Lamers, Susanna L. , Dollar, James Jarad , Grabowski, Mary , Hodcroft, Emma B , Ragonnet-Cronin, Manon , Wertheim, Joel O , Redd, Andrew D , German, Danielle , Laeyendecker, Oliver
Format: Electronic Article Electronic Article
Language: English
Subjects:
HIV
Quelle: Alma/SFX Local Collection
Publisher: United States: Mary Ann Liebert, Inc
ID: ISSN: 0889-2229
Link: https://www.ncbi.nlm.nih.gov/pubmed/27824249
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recordid: cdi_pubmedcentral_primary_oai_pubmedcentral_nih_gov_5333565
title: Identifying transmission clusters with Cluster Picker and HIV-TRACE
format: Article
creator:
  • Rose, Rebecca
  • Lamers, Susanna L.
  • Dollar, James Jarad
  • Grabowski, Mary
  • Hodcroft, Emma B
  • Ragonnet-Cronin, Manon
  • Wertheim, Joel O
  • Redd, Andrew D
  • German, Danielle
  • Laeyendecker, Oliver
subjects:
  • Adolescent
  • Adult
  • AIDS/HIV
  • Cluster Analysis
  • Clustering
  • Cross sections
  • Datasets
  • Disease Transmission, Infectious
  • Epidemics
  • Epidemiology
  • Female
  • Gene loci
  • Genetic distance
  • Glycoprotein gp41
  • HIV
  • HIV - classification
  • HIV - genetics
  • HIV - isolation & purification
  • HIV Infections - transmission
  • Humans
  • Lentivirus
  • Male
  • Middle Aged
  • Molecular Epidemiology - methods
  • Retroviridae
  • Sequence Analysis, DNA
  • Thresholds
  • Uganda
  • viral clustering
  • Young Adult
ispartof: AIDS research and human retroviruses, 2016, Vol.33 (ja), p.211-218
description: 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 data sets originating from the Rakai Community Cohort Study: (1) next-generation sequences (NGS) from nine linked couples; (2) NGS from longitudinal sampling of 14 individuals; and (3) Sanger consensus sequences from a cross-sectional dataset (n=1022) 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 (ROC). 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 sequences, versus only a single variant for each patient/time point, for data sets (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 data sets (1) and (2), HIV-TRACE and Cluster Picker detected all linked pairs at 3% and 4% genetic distance, respectively. However at 5.3% genetic distance, 20% of couples in data set (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.
language: eng
source: Alma/SFX Local Collection
identifier: ISSN: 0889-2229
fulltext: fulltext
issn:
  • 0889-2229
  • 1931-8405
url: Link


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descriptionWe 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 data sets originating from the Rakai Community Cohort Study: (1) next-generation sequences (NGS) from nine linked couples; (2) NGS from longitudinal sampling of 14 individuals; and (3) Sanger consensus sequences from a cross-sectional dataset (n=1022) 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 (ROC). 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 sequences, versus only a single variant for each patient/time point, for data sets (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 data sets (1) and (2), HIV-TRACE and Cluster Picker detected all linked pairs at 3% and 4% genetic distance, respectively. However at 5.3% genetic distance, 20% of couples in data set (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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subjectAdolescent ; Adult ; AIDS/HIV ; Cluster Analysis ; Clustering ; Cross sections ; Datasets ; Disease Transmission, Infectious ; Epidemics ; Epidemiology ; Female ; Gene loci ; Genetic distance ; Glycoprotein gp41 ; HIV ; HIV - classification ; HIV - genetics ; HIV - isolation & purification ; HIV Infections - transmission ; Humans ; Lentivirus ; Male ; Middle Aged ; Molecular Epidemiology - methods ; Retroviridae ; Sequence Analysis, DNA ; Thresholds ; Uganda ; viral clustering ; Young Adult
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descriptionWe 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 data sets originating from the Rakai Community Cohort Study: (1) next-generation sequences (NGS) from nine linked couples; (2) NGS from longitudinal sampling of 14 individuals; and (3) Sanger consensus sequences from a cross-sectional dataset (n=1022) 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 (ROC). 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 sequences, versus only a single variant for each patient/time point, for data sets (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 data sets (1) and (2), HIV-TRACE and Cluster Picker detected all linked pairs at 3% and 4% genetic distance, respectively. However at 5.3% genetic distance, 20% of couples in data set (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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abstractWe 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 data sets originating from the Rakai Community Cohort Study: (1) next-generation sequences (NGS) from nine linked couples; (2) NGS from longitudinal sampling of 14 individuals; and (3) Sanger consensus sequences from a cross-sectional dataset (n=1022) 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 (ROC). 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 sequences, versus only a single variant for each patient/time point, for data sets (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 data sets (1) and (2), HIV-TRACE and Cluster Picker detected all linked pairs at 3% and 4% genetic distance, respectively. However at 5.3% genetic distance, 20% of couples in data set (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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