International Journal of Marine Science, 2025, Vol.15, No.5, 268-276 http://www.aquapublisher.com/index.php/ijms 273 time-consuming, and not necessarily reliable, especially for thermal tolerance or growth rate. Low sample sizes and compromised data quality can reduce the predictive power and precision of GS models, and precise selection for complicated traits is difficult (Liu et al., 2023). 6.2 Cost and coverage issues of genotyping data The increased availability of high-throughput genotyping technologies, such as SNP arrays and whole-genome resequencing, is still a considerable cost for large-scale breeding programs. Despite improving efficiency, new SNP arrays (e.g., Baoxin-I) and genotyping-by-sequencing technologies, data quality and marker density are still a balance concern, especially for non-model species like abalone (Liu et al., 2022; Li et al., 2024). 6.3 Prediction accuracy of models and cross-population application GS model accuracy depends on the size and diversity of the training population, marker density, and the genetic architecture of the trait. Prediction accuracy varies between models such as GBLUP and Bayesian models, whose performance tends to decline when applied across populations or strains due to genetic background and allele frequency differences. This lowers GS model transferability among populations (Liu et al., 2023). 6.4 Genetic diversity conservation and inbreeding control Intensive selection, like GS, can reduce genetic variation and increase inbreeding at the cost of long-term performance and adaptability. Maintenance of genetic variation and inbreeding control are essential to obtain sustainable breeding outcomes (Sandoval‐Castillo et al., 2018; Dale-Kuys et al., 2020; Wooldridge et al., 2024). 6.5 Necessity of data sharing and international cooperation Because of worldwide distribution and strong connectivity among abalone populations, international collaboration and exchange of data are necessary. Coordinated research among countries will improve reference panels, make predictions more accurate, and promote sustainable management and conservation across nations (Griffiths et al., 2025; Mares-Mayagoitia et al., 2025). 7 Current Status and Comprehensive Analysis of Genomic Selection Strategies for Abalone 7.1 Implementation status of genomic selection in abalone growth rate improvement Genomic selection (GS) has also emerged as a popular method for abalone breeding, particularly in traits for growth rate and heat tolerance. There are new studies that show GS with high-density SNP genotyping and strong statistical models like BayesB and GBLUP have moderate to high accuracy for the prediction of complex traits. GS has been a successful strategy for improving heat tolerance in Pacific abalone, and BayesB has achieved up to 0.55 accuracy for breeding value prediction, which is higher than the traditional methods (Liu et al., 2022). Improved predictive accuracy is also obtained with the application of GWAS-identified SNPs, allowing the implementation of GS in practical abalone breeding schemes (Liu et al., 2022). 7.2 Integration of omics data and environmental factors in breeding program optimization Multi-omics integration—genomics, transcriptomics, proteomics, and metabolomics—is improving in abalone breeding. These approaches allow for gene, pathway, and biomarker discovery regarding growth and environmental adaptation. Environmental data integration allows further insights into trait architecture and the ease of developing strong, environment-driven breeding programs. Such integration will also enhance multifactor trait selection and abalone yield under suboptimal environments (Nguyen et al., 2022). 7.3 Efficiency improvements in genomic selection supported by high-throughput genotyping technologies High-throughput genotyping systems, including the Baoxin-I SNP array, greatly enhanced the cost savings and efficiency of GS in abalone. It is now possible to perform speedy, massive-scale genotyping with high accuracy and replicability by utilizing these technologies. The Baoxin-I array, for instance, exhibited greater call rates and abalone population polymorphism and application in GS had prediction accuracies for growth traits similar to whole-genome resequencing, confirming its suitability in selective breeding (Liu et al., 2022; Wang and Wang, 2024).
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