Application of genomic selection in Korean abalone by evaluating growth traits using PBLUP, GBLUP, and ssGBLUP models

Abalone (Haliotis discus hannai) is an economically important marine species in Korea, where it is cultivated for seafood production. This study evaluated the effectiveness of three genetic evaluation methods for estimating breeding values of growth traits in H. discus hannai: pedigree-based best linear unbiased prediction (PBLUP), genomic best linear unbiased prediction (GBLUP), and single-step genomic best linear unbiased prediction (ssGBLUP). Using data from 8450 individuals across six generations and single-nucleotide polymorphism (SNP) genotypes from 1892 individuals, we compared predictive accuracy and heritability estimates of each method for shell length, shell width, and total weight at 18 and 30 months of age. The ssGBLUP method consistently yielded the highest prediction accuracy and heritability estimates, outperforming traditional pedigree-based methods by integrating both genomic and pedigree information. The highest accuracy (0.498) was achieved for shell width at 30 months using ssGBLUP. Heritability estimates from ssGBLUP reached 0.44, indicating moderate genetic control. The impact of SNP marker density was also assessed, and the results showed that 5000–10,000 markers were sufficient for stable prediction performance. These findings highlight the robustness of ssGBLUP in mixed-genotype populations and confirm the potential of genomic selection to enhance genetic improvement in abalone breeding programs, offering a scalable strategy for sustainable aquaculture.