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.
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.
Authors: Luiz Carlos Junior Alcantara; Sharon Cassol; Pieter Libin; Koen Deforche; Oliver G Pybus; Marc Van Ranst; Bernardo Galvão-Castro; Anne-Mieke Vandamme; Tulio de Oliveira Journal: Nucleic Acids Res Date: 2009-05-29 Impact factor: 16.971
Authors: Sergei L Kosakovsky Pond; David Posada; Eric Stawiski; Colombe Chappey; Art F Y Poon; Gareth Hughes; Esther Fearnhill; Mike B Gravenor; Andrew J Leigh Brown; Simon D W Frost Journal: PLoS Comput Biol Date: 2009-11-26 Impact factor: 4.475
Authors: Robert J Gifford; Tulio de Oliveira; Andrew Rambaut; Oliver G Pybus; David Dunn; Anne-Mieke Vandamme; Paul Kellam; Deenan Pillay Journal: J Virol Date: 2007-09-26 Impact factor: 5.103