International Journal of Marine Science, 2025, Vol.15, No.5, 277-286 http://www.aquapublisher.com/index.php/ijms 284 for high heritability traits such as growth. At present, with the significant reduction in sequencing costs, low-coverage whole genome sequencing combined with genotype filling has become a more economical solution than SNP chips. Review studies show that GS significantly improves the selection accuracy and genetic progress of disease resistance and growth traits in a variety of farmed fish such as Atlantic salmon and tilapia. Therefore, in the future, GS breeding programs can be fully implemented for the important traits of mackerel. At present, scientific researchers have successfully constructed the chromosomal genome of spotted mackerel (Indo-Pacific sharp-tooth mackerel). On this basis, by genotyping a large number of individuals in the breeding population, establishing a phenotype-genotype database, and using statistical models to predict the breeding value of individuals, we can accurately select excellent seedlings from the offspring. The introduction of genome selection will greatly improve breeding efficiency, and it is expected to shorten the generation interval of mackerels and obtain new varieties with excellent performance as soon as possible. When there are major genes or known molecular markers in certain important traits, marker-assisted selection can be used to accelerate breeding. If key genes or QTL sites related to mackerel growth rate, muscle quality, etc. can be found, corresponding DNA markers can be developed to assist seed selection. For example, if a single nucleotide polymorphic locus (SNP) is found to be highly correlated with the muscle fat content of mackerel fish, we can screen young fish carrying favorable alleles through molecular testing and retain them as parents. Label-assisted selection has been successful in some aquatic animals, such as Pacific oysters that improve antivibriopathy through molecular marker selection, and Atlantic salmon that significantly reduces ISA virus infection rates through family marker selection. For mackerels, genetic markers related to disease resistance and stress resistance can be identified through genome-wide association analysis (GWAS), so as to carry out targeted MAS breeding. The emergence of gene editing technologies such as CRISPR-Cas9 provides the possibility to accurately improve the traits of aquaculture varieties. Through gene editing, target sites can be modified at the genome level and targeted improvements that are difficult to achieve in traditional breeding. For example, knocking out myostatin (MSTN), a negatively regulated muscle growth, can promote significant hyperplasia of fish muscles and accelerate individual growth. This technology has been used to cultivate "bodybuilding fish" crucian carp and carp strains, confirming its potential to improve growth. For mackerels, if a key gene that limits growth can be identified, it can also be tried to silence or knock out through gene editing to directly increase growth rate. In addition, gene editing can also be used to verify the function of candidate genes and better guide the MAS and GS selection directions. For example, by knocking out or overexpressing a gene that is suspected to affect the growth or disease resistance of mackerels, observing its trait changes, to determine whether the gene is worthy of focus in breeding. As genomic data accumulates for more mackerel species and populations, we can construct a pan-genomic map of mackerel fish to cover genetic variation information for all lines. This helps us to have a more comprehensive understanding of the genetic diversity of mackerels and to improve breeding strategies using information such as structural variation. Some studies have found that the loss of structural variations in certain large fragments during domestic domestication may affect the performance of traits, which can be monitored and taken into consideration through pan-genomic analysis. At the same time, using AI technologies such as machine learning, we can mine massive genomic and phenotypic data to find the genetic laws of complex traits, thereby improving the accuracy of trait prediction. Acknowledgments The authors would like to thank all teachers and colleagues who provided guidance and assistance during this research, and for the peer review's revision suggestions. Conflict of Interest Disclosure The authors confirm that the study was conducted without any commercial or financial relationships and could be interpreted as a potential conflict of interest.
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