Integration of Two Allele-Specific qPCR SNP Markers for Assigning Red Seabream (Pagrus major) to Korean and Japanese Reference Groups

A previously reported allele-specific qPCR (AS-qPCR) dataset for red seabream (Pagrus major) reference-group assignment was reanalyzed to develop a two-marker statistical classifier. Mean Ct values for two previously selected markers, SNP001 and SNP008, from Korean and Japanese reference samples were integrated using logistic regression, and model performance was evaluated by ROC curve analysis, Youden index-based cutoff optimization, confusion matrix analysis, and repeated stratified 10-fold cross-validation. Single-marker analyses yielded AUCs of 0.869 for SNP001 and 0.891 for SNP008, whereas the two-SNP logistic model showed an AUC of 0.943. At the optimal P(Korea) cutoff of 0.387, the model achieved 87.08% accuracy, 92.22% sensitivity for the Korean reference group, and 81.82% specificity for the Japanese reference group in the model-development dataset. Internal validation by repeated stratified 10-fold cross-validation yielded a mean AUC of 0.9396 ± 0.0580. In a small separately collected market-sourced confirmation set, 18 of 20 samples were evaluable, with an accuracy of 72.22%, sensitivity of 77.78%, and specificity of 66.67%, all calculated relative to the country-of-purchase labels. Addition of Ct standard deviation or coefficient of variation did not improve cross-validated discrimination. These results support a parsimonious two-marker mean Ct-based framework for reference-group assignment while indicating that further validation using provenance-verified independent samples is required before routine application.