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 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.
1 BioInfoExperts , Thibodaux, Louisiana.
Rebecca
R
Rose
1 BioInfoExperts , Thibodaux, Louisiana.
Susanna L
SL
Lamers
1 BioInfoExperts , Thibodaux, Louisiana.
James J
JJ
Dollar
2 Department of Epidemiology, Bloomberg School of Public Health, Johns Hopkins University , Baltimore, Maryland.
Mary K
MK
Grabowski
3 Institute for Evolutionary Biology, University of Edinburgh , Edinburgh, United Kingdom .
Emma B
EB
Hodcroft
3 Institute for Evolutionary Biology, University of Edinburgh , Edinburgh, United Kingdom .
Manon
M
Ragonnet-Cronin
4 Department of Medicine, University of California , San Diego, California.
Joel O
JO
Wertheim
5 Laboratory of Immunoregulation, Division of Intramural Research, National Institute of Allergy and Infectious Diseases, National Institutes of Health , Bethesda, Maryland.|6 School of Medicine, Johns Hopkins University , Baltimore, Maryland.
Andrew D
AD
Redd
2 Department of Epidemiology, Bloomberg School of Public Health, Johns Hopkins University , Baltimore, Maryland.
Danielle
D
German
5 Laboratory of Immunoregulation, Division of Intramural Research, National Institute of Allergy and Infectious Diseases, National Institutes of Health , Bethesda, Maryland.|6 School of Medicine, Johns Hopkins University , Baltimore, Maryland.
Oliver
O
Laeyendecker
D000293:Adolescent; D000328:Adult; D016000:Cluster Analysis; D018562:Disease Transmission, Infectious; D005260:Female; D006678:HIV / Q000145:classification / Q000235:genetics / Q000302:isolation & purification; D015658:HIV Infections / Q000635:transmission*; D006801:Humans; D008297:Male; D008875:Middle Aged; D017720:Molecular Epidemiology / Q000379:methods*; D017422:Sequence Analysis, DNA; D055815:Young Adult
HIV; Uganda; viral clustering