International Journal of Molecular Zoology, 2025, Vol.15, No.1, 29-37 http://animalscipublisher.com/index.php/ijmz 33 5 Prospects of Genomic Selection inChannaBreeding 5.1 Principles and workflow of genomic selection (GS) Genomic selection (GS) uses genome-wide genetic markers such as SNPs to predict breeding values of individuals for quantitative traits. The typical protocol is to genotype a reference population, fit marker effects with statistical models, and derive the genetic merit of selection candidates from their marker profiles. The process enables more accurate and quicker selection compared to traditional methods, especially for traits with moderate heritability (Hu et al., 2018; Cui et al., 2024). 5.2 Identification of quantitative trait loci (QTL) and genome-wide association studies (GWAS) for target traits Genome-wide association studies (GWAS) are required to map quantitative trait loci (QTL) of economically important traits by genome scanning for marker-trait associations. GWAS have been applied in growth traits in Channa maculata and identified SNPs highly associated with weight and total length. These SNPs selected by GWAS also enhance the accuracy of GS models by selecting those with the best predictive power towards target traits (Fernández-González et al., 2024). 5.3 Case studies on the application of gs in growth, disease resistance, and sex control One such recent investigation of the blotched snakehead (Channa maculata) for GS growth potential had earlier reported GS potential. Using a 50K SNP array, researchers were able to detect around 46 000 good-quality SNPs and reported that GS models, particularly when augmented with GWAS-selected SNPs, considerably improved predictions for weight and total length. The research found that even low-density panels of SNPs are of high predictive accuracy and therefore can be an effective tool for selective breeding in Channa. While growth was an issue of concern, the approach and outcome provide a foundation for the application of GS for other characteristics such as disease resistance and sex control in subsequent research (Zhang et al., 2017). 5.4 Integration potential of GS with MAS and conventional breeding strategies The integration of GS with MAS and conventional breeding is employed to enhance the genetic improvement. The GWAS-selected SNPs can be integrated into traditional markers in MAS, and GS can offer multi-trait selection in parallel with hastening the cycle of breeding (Wang and Chen, 2024). Together, the approaches can enhance Channa breeding programme productivity and efficiency to enable development of high-quality strains for aquaculture (Fernández-González et al., 2024). 6 Current Challenges and Technical Bottlenecks 6.1 Precision and standardization issues in phenotypic data collection Reliable and uniform collection of phenotypic data is an ongoing problem in Channa breeding. Difference in measurement procedures, environmental conditions, and qualitative assessment techniques can lead to variable results, reducing the validity of the trait assessment and hindering the effectiveness of genomic selection and association studies. Lack of standard phenotyping among different research groups as well as breeding programs also complicates integration and data comparison, with ultimate effects on the accuracy of selection decisions (Nord and Li, 2018; Pollio et al., 2019). 6.2 Limitations in the quality and representativeness of reference genomes Although highly quality reference genomes have been constructed for several Channa species, there is still room for improvement in completeness, accuracy of the annotation, and coverage of the genetic diversity. Some assemblies may be lacking repetitive or difficult-to-assemble genomic components, and reference genomes consist of one individual or population, and these may not represent the entire scope of genetic variability of the species. These limitations can decrease the detection of useful functional genes and decrease the efficiency of subsequent activities such as GWAS and marker development (Juels et al., 2016; Zhou, 2024).
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