RHC genotyping in Chinese Han population

The Rh blood group system is characterized by its complexity and polymorphism, encompassing 56 different antigens. Accurately predicting the presence of the C antigen using genotyping methods has been challenging. The objective of this study was to evaluate the accuracy of various genotyping methods for predicting the Rh C and to identify a suitable method for the Chinese Han population.

In total, 317 donors, consisting 223 D+ (including 20 with the Del phenotype) and 94 D- were randomly selected. For RHC genotyping, 48C and 109bp insertion were detected on the Real-time PCR platform and -292 substitution was analyzed via restriction fragment length polymorphism (RFLP). Moreover, the promoter region of the RHCE gene was sequenced to search for other nucleotide substitutions between RHC and RHc. Agreement between prediction methods was evaluated using the Kappa statistic, and comparisons between methods were conducted via the χ² test.

The analysis revealed that the 48C allele, 109bp insertion, a specific pattern observed in RFLP results, and wild-type alleles of seven single nucleotide polymorphisms (SNPs) were in strong agreement with the Rh C, with Kappa coefficients exceeding 0.8. However, there were instances of false positives or false negatives (0.6% false negative rate for 109bp insertion and 5.4-8.2% false positive rates for other methods). The 109bp insertion method exhibited the highest accuracy in predicting the Rh C, at 99.4%, compared to other methods (P values≤0.001). Although no statistical differences were found among other methods for predicting Rh C (P values>0.05), the accuracies in descending order were 48C (94.6%) > rs586178 (92.7%) > rs4649082, rs2375313, rs2281179, rs2072933, rs2072932, and RFLP (92.4%) > rs2072931 (91.8%).

None of the methods examined can independently and accurately predict the Rh C. However, the 109bp insertion test demonstrated the highest accuracy for predicting the Rh C in the Chinese Han population. Utilizing the 109bp insertion test in combination with other methods may enhance the accuracy of Rh C prediction.