International Journal of Marine Science, 2025, Vol.15, No.5, 268-276 http://www.aquapublisher.com/index.php/ijms 274 7.4 Practical effects of machine learning and AI-assisted breeding decision-making Machine learning (ML) and artificial intelligence (AI) are being increasingly embraced in GS pipelines to enhance the accuracy of predictions and decision-making for breeding. ML models can identify sophisticated, non-linear genotype-phenotype relationships and are particularly well-suited when traits have multiple minor-effect loci or interactions with the environment. The utilization of these sophisticated computational techniques is poised to make abalone breeding programs even more precise and efficient (Alemu et al., 2024). 8 Concluding Remarks Research on the genetic variability of growth rate in abalone has also made significant progress in the last several years, providing valuable information on heritability estimates, genetic parameters, and the genetic architecture underlying the traits. The establishment of molecular markers, QTL mapping, and GWAS has allowed a deeper understanding of the intricate traits defining growth performance in different abalone species and populations. All this information has established a solid foundation for introducing advanced breeding technologies. Genomic selection (GS) is a new approach to abalone breeding that can greatly enhance selection accuracy and genetic gain. By utilizing genome-wide marker information and phenotype data, GS enables the estimation of breeding values more accurately than with traditional methods. Early applications and case studies demonstrate its potential for enhancing growth characteristics and overall production efficiency and economic viability in abalone aquaculture. In the future, development of an effective and sustainable modern breeding program for abalone will need to create genomic selection in addition to traditional breeding, aided by multi-omics information and sophisticated computational methods such as machine learning. Genetic diversity, as well as pragmatic issues such as the accuracy and cost of phenotyping, will also need to be addressed. These integrated breeding methods will be key in addressing the demand for high-quality abalone commodities, enhancing business sustainability, and stimulating global aquaculture growth. Acknowledgments The authors extend sincere gratitude to Professor Zhou for his invaluable support and patient assistance throughout the fish research process, particularly in literature collection and organization. The authors also wholeheartedly thank the two anonymous peer reviewers for their constructive feedback on this manuscript, which played a crucial role in enhancing both the quality and completeness of the paper. Conflict of Interest Disclosure The authors affirm that this research was conducted without any commercial or financial relationships that could be construed as a potential conflict of interest. References Abdollahi-Arpanahi R., Gianola D., and Peñagaricano F., 2020, Deep learning versus parametric and ensemble methods for genomic prediction of complex phenotypes, Genetics Selection Evolution, 52(1): 12. https://doi.org/10.1186/s12711-020-00531-z Alemu A., Åstrand J., Montesinos-López O., Sánchez J.I., Fernández-González J., Tadesse W., Vetukuri R., Carlsson A., Ceplitis A., Crossa J., Ortiz R., and Chawade A., 2024, Genomic selection in plant breeding: key factors shaping two decades of progress, Molecular Plant, 17(4): 552-578. https://doi.org/10.1016/j.molp.2024.03.007 Ćeran M., Đorđević V., Miladinović J., Vasiljević M., Đukić V., Ranđelović P., and Jaćimović S., 2024, Selective genotyping and phenotyping for optimization of genomic prediction models for populations with different diversity, Plants, 13(7): 975. https://doi.org/10.3390/plants13070975 Cui Y., Li R., Li G., Zhang F., Zhu T., Zhang Q., Ali J., Li Z., and Xu S., 2019, Hybrid breeding of rice via genomic selection, Plant Biotechnology Journal, 18: 57-67. https://doi.org/10.1111/pbi.13170 Dale-Kuys R.C., Roodt‐Wilding R., and Rhode C., 2020, Genome-wide linkage disequilibrium in South African abalone Haliotis midae and implications for understanding complex traits, Aquaculture, 523: 735002. https://doi.org/10.1016/j.aquaculture.2020.735002
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