International Journal of Marine Science, 2025, Vol.15, No.3, 130-143 http://www.aquapublisher.com/index.php/ijms 140 receptors, targeted editing can be attempted. However, sufficient basic research is needed before this to clarify the functions of these gene sites and whether they will have negative effects (Machado et al., 2022). At the same time, factors such as supervision and public acceptance need to be considered. At present, the application of gene editing in food fish is still in the experimental stage, but its potential cannot be ignored. When the technology and regulations mature in the future, gene editing is expected to quickly give new species such as Spanish mackerel excellent traits such as disease resistance or high tolerance. 6.3 Molecular tools to improve breeding efficiency The advent of the whole genome era provides a powerful molecular toolbox for aquatic breeding. These tools can greatly improve breeding efficiency and reduce the cost of breeding trial and error. For marine fish such as Spanish mackerel, we can make full use of the following new technologies: Genomic selection: Traditional breeding selects parents through phenotypic determination, which has a long cycle and is affected by environmental interference. Whole genome selection uses individual whole genome marker information to predict its breeding value, and can select potential excellent individuals in the juvenile stage. It is particularly effective for high heritability traits such as growth. With the reduction of sequencing costs, low-coverage whole genome sequencing plus genotype filling has become a more economical solution than SNP chips (Yáñez et al., 2023). A review shows that the application of GS in multiple farmed species such as tilapia and Atlantic salmon has significantly improved selection accuracy and genetic progress. Therefore, in the future, it is entirely possible to implement GS breeding plans for target traits of Spanish mackerel (such as growth rate and feed conversion rate). Molecular assisted selection (MAS) and marker development: For some important traits, if clear major effect genes or linked markers are known, MAS can be implemented to accelerate the fixation of favorable alleles. Phylogenetic studies can help us discover potential candidate genes, such as comparing the genomes of individuals at different lineages or phenotype extremes to find sites with extremely high allele frequencies in excellent populations. Subsequently, specific PCR markers or microarray chips can be developed for genotype screening in conventional breeding. For example, if a single nucleotide polymorphism (SNP) is found to be highly correlated with muscle fat content in Spanish mackerel, a molecular test can be designed for the SNP to select individuals with ideal genotypes in young fish and retain them (Yáñez et al., 2023). Gene editing and synthetic biology: Gene editing technologies such as CRISPR-Cas9 allow us to modify target sites at the genome level, achieving targeted improvements that are difficult to achieve with traditional breeding. For example, if you want to breed a non-spawning triploid Spanish mackerel population to improve growth, you can edit genes related to germ cell formation to achieve sterility (similar to what was done with salmon in the past). Another example is that muscle growth can be promoted by knocking out the myostatin gene (MSTN) - this method has been used to obtain "bodybuilding fish" strains in carp and crucian carp. Gene editing can also be used to verify the function of candidate genes, thereby better guiding MAS and GS. For example, by knocking out or overexpressing a candidate gene, observing the growth or disease resistance changes of Spanish mackerel to confirm whether the gene is worthy of consideration in breeding (Halasan et al., 2021). Pan-genome and big data AI applications: As the genome data of different Spanish mackerel species and individuals accumulate, we can construct a pan-genome of the Spanish mackerel genus, covering all genetic variations in all strains. This will help us understand genetic diversity more comprehensively and use information such as structural variation to improve breeding. For example, studies have found that the loss of structural variation (such as large fragment deletions) during breeding may affect traits. Through pan-genome analysis and machine learning algorithms, we may be able to discover the regulatory network of complex traits, thereby improving the accuracy of phenotype prediction. AI technology has been used to analyze massive genomic and phenotypic data to find the optimal combination.
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