High-resolution melting combines with Bayes discriminant analysis: a novel hepatitis C virus genotyping method

Abstract

Current hepatitis C virus (HCV) genotyping techniques are often highly technical, costly, or need improvements in sensitivity and specificity. These limitations indicate the need of novel methods for HCV genotyping. The present study aimed to develop a novel genotyping method combining high-resolution melting (HRM) analysis with Bayes discriminant analysis (BDA). Target gene fragment including 5′-untranslated and core region was selected. Four or five inner amplicons for every serum were amplified using nested PCR, HRM was used to determine the melting temperature of the amplicons, and HCV genotypes were then analyzed utilizing BDA. In initial genotyping (HCV genotypes were classified into 1b, 2a, 3a, 3b, and 6a), both the overall accuracy rate and the cross-validation accuracy rate were 92.6 %, external validation accuracy rate was 95.0 %. To enhance the accuracy rate of genotyping, HCV genotypes were firstly classified into 1b, 3a, 3b, and 2a–6a, followed by a supplementary genotyping for 2a–6a. Both the overall accuracy rate and the cross-validation accuracy rate reached 97.5 %, and external validation accuracy rate was 100 %. Comparing adjusted HRM genotyping with type-specific probe technique, the difference in accuracy rates was not significant. However, the limit of detection and cost were lower for HRM. Comparing with sequencing, the limit detection of HRM was the same as the former, but the cost of HRM was lower. Hence, HRM combined with BDA was a novel method that equipped with superior accuracy, high sensitivity, and lower cost and therefore could be a better technique for HCV genotyping.