Decoding noises in HIV computational genotyping

MingRui Jia, Timothy Shaw, Xing Zhang, Dong Liu, Ye Shen, Amara E. Ezeamama, Chunfu Yang, Ming Zhang

Lack of a consistent and reliable genotyping system can critically impede HIV genomic research on pathogenesis, fitness, virulence, drug resistance, and genomic-based healthcare and treatment. At present, mis-genotyping, i.e., background noises in molecular genotyping, and its impact on epidemic surveillance is unknown. For the first time, we present a comprehensive assessment of HIV genotyping quality. HIV sequence data were retrieved from worldwide published records, and subjected to a systematic genotyping assessment pipeline. Results showed that mis-genotyped cases occurred at 4.6% globally, with some regional and high-risk population heterogeneities. Results also revealed a consistent mis-genotyping pattern in gp120 in all studied populations except the group of men who have sex with men. Our study also suggests novel virus diversities in the mis-genotyped cases. Finally, this study reemphasizes the importance of implementing a standardized genotyping pipeline to avoid genotyping disparity and to advance our understanding of virus evolution in various epidemiological settings.