Title: Assessment of automated genotyping protocols as tools for surveillance of HIV-1 genetic diversity
Authors: Gifford R, de Oliveira T, Rambaut A, Myers RE, Gale CV, Dunn D, Shafer R, Vandamme AM, Kellam P, Pillay D; UK Collaborative Group on HIV Drug Resistance.
Journal: AIDS,20(11):1521-9 (2006)
Journal Impact Factor (I.F.): 5.46
Number of citations (Google Scholar): 33
BACKGROUND: The routine use of drug resistance testing provides an abundant source of HIV-1 sequence data. However, it is not clear how reliable standard genotyping of these sequences is for describing HIV-1 genetic variation and for detecting novel genetic variants and epidemiological trends.
OBJECTIVES: To compare assignment of HIV-1 resistance test sequences to reference strains across commonly used genotyping protocols.
METHODS: Subtype assignments were compared across three standard genotyping protocols for 10 537 resistance test sequences, representing approximately one-fifth of all reported infections in the United Kingdom. Sequences that were inconsistently genotyped across methods, or that were unassigned by at least one method, were examined for evidence of recombination using sliding-window-based approaches.
RESULTS: Although agreement across methods was high for subtypes B, C and H, it was generally much lower (< 50%) for other subtypes. Disagreement between methods typically involved closely related, but epidemiologically distinct, groups or involved a significant proportion ( approximately 12%) of divergent sequences in which analysis revealed widespread evidence of recombination and a remarkable diversity of unusual recombinant forms.
CONCLUSIONS: With frequent long-distance transfer of viral strains and widespread recombination between them, genetic and epidemiological relationships within HIV-1 are becoming increasingly complex. Current methods of subtype assignment vary in their ability to identify novel genetic variants and to distinguish epidemiologically distinct strains. Capturing meaningful epidemiological information from resistance test data will require a critical understanding of the methodologies used in order to appreciate the possible sources of error and misclassification.
Citation: Gifford R, de Oliveira T, Rambaut A, Myers RE, Gale CV, Dunn D, Shafer R, Vandamme AM, Kellam P, Pillay D; UK Collaborative Group on HIV Drug Resistance. Assessment of automated genotyping protocols as tools for surveillance of HIV-1 genetic diversity AIDS,20(11):1521-9 (2006).