International Journal of Molecular Zoology, 2025, Vol.15, No.1, 29-37 http://animalscipublisher.com/index.php/ijmz 34 6.3 Influence of genetic background differences and genotype-by-environment interactions on selection efficiency Genetic heterogeneity within populations and large genotype-by-environment (G×E) interactions will have profound impacts on selection efficiency of Channa breeding. Selection characters achieved in one population or under some conditions may be useless under other conditions, which will reduce genetic gain and make breeding results unpredictable. This calls for multi-environment and multi-population testing, as well as the creation of valid statistical models to estimate G×E effects in genomic selection (Xu et al., 2021; Zhuravkov et al., 2024). 6.4 Technical costs and constraints in commercial application The high cost of large-scale genotyping, sequencing, and analysis remains a barrier to the general application of sophisticated genomic tools in commercial Channa breeding. In addition, the requirement for experts, expertise facilities, and bioinformatics laboratories can limit the utilization of these technologies, especially for small- and medium-scale farmers. These constraints hinder the conversion of research advances to real breeding advances and limit scalability of genomic selection schemes (Pollio et al., 2019; Mahmoodi et al., 2022). 7 Development Directions and Strategic Recommendations for Molecular Breeding inChanna 7.1 Promote multi-omics data integration to improve the resolution of complex trait analysis Integration of multi-omics data including genomics, transcriptomics, and phenomics can give a much higher resolution of dissection of complex traits in Channa. Integration of such levels of information can enable scientists to improve detection of the genetic architecture of economically important traits, improve accuracy of breeding value, and detect major regulatory modules. This approach gives more precise selection and improves genetic progress in breeding schemes (Xu et al., 2017). 7.2 Develop high-throughput marker screening platforms and breeding databases for Channa Creation of high-throughput SNP genotyping platforms such as 50 K SNP arrays facilitates massive and cost-effective Channa genetic marker screening. Coupled with precise breeding databases, they enable genotypic and phenotypic data management, perform population structure analysis, and enable convenient selection of outstanding breeding candidates. Such infrastructures are the pillars of Channa modernization and evidence-based decision-making (Cui et al., 2024). 7.3 Explore integrated breeding systems combining genomic selection, MAS, and phenotypic selection Co-implementation of GS, MAS, and phenotypic selection can realize the highest genetic gain and breeding efficiency (Han, 2024). For example, integration of GWAS-selected SNPs with GS models has been found to significantly improve the precision of predictions of Channa maculata growth traits using low-density SNP panels. The integrated approach allows simultaneous selection of several characters and high-performance strains to be rapidly bred (Xu et al., 2017). 7.4 Facilitate the adoption of cost-effective genomic technologies in grassroots breeding units For broader use, cheap genomic technologies and easy platforms have to be pushed from the farm gate. The application of low-density SNP panels, which are highly efficient in prediction, can maintain genotyping cost low and make advanced breeding tools accessible to small- and medium-scale farmers. Roll-out of technology has to be accompanied by training and capacity development to maximize its effect (Cui et al., 2024). 8 Concluding Remarks Recent years have witnessed unprecedented technology development in Channa breeding, including success in the development of high-quality reference genomes for a number of species, development of high-density SNP arrays, and inclusion of transcriptomic and genomic resources. This has enabled the identification of significant genes and markers for economically important traits, improved the population genetic structure knowledge, and enabled the utilization of genomic tools in selective breeding programs. Genomic selection (GS) has transformed Channa breeding by making it possible to accurately predict breeding values and bulk selection of numerous complex traits. The application of GWAS-selected SNPs and high-density
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