Animal Molecular Breeding, 2025, Vol.15, No.2, 91-101 http://animalscipublisher.com/index.php/amb 95 4 Genomic and Transcriptomic Tools for Trait Mapping inChanna 4.1 Genome-wide studies of trait variation inChanna With the help of advanced sequencing technologies such as 2b-RAD, researchers have constructed high-density genetic linkage maps for the genus Channa. These maps contain thousands of SNP markers, making genome-wide studies of trait variation possible and helping to identify quantitative trait loci (QTLs) associated with economic traits (such as growth and sex determination). For instance, Liu et al. (2020) used 2b-RAD technology to construct a linkage map containing 3 151 SNP markers, covering 24 linkage groups with a total length of 2 728.9 cM and an average spacing of 0.87 cM. A total of 14 QTLs related to body weight and body length were detected, and the phenotypic variation explained by a single QTL was 9.6%-12.8%. The sex determination locus is concentrated in LG5, which can explain 97.4%-100% of the phenotypic differences and confirm the XX/XY sex determination system. Similarly, the high-density map constructed based on 2b-RAD contains 6 352 SNP markers distributed in 21 linkage groups, with a total map length of 2 143.7 cM and an average marker spacing of 0.34 cM. Nine QTLs related to body weight were found, which can explain 9.8%-11.9% of the phenotypic variation. The sex-related major effect QTL was located in LG2, explaining 98.8%-100% of the phenotypic differences, supporting the XX/XY system (Liu et al., 2021). The study also found that candidate genes such as SATB2 and BMP6 were located in the QTL interval, becoming important genetic markers for marker-assisted selection and breeding improvement. 4.2 Transcriptome analysis inChanna under selective pressure Gene-based association methods, such as PrediXcan, use reference transcriptome data to link genetically regulated gene expression to phenotypic traits. These methods can identify key genes that lead to differences in growth rate, providing new insights into the molecular mechanisms between fast and slow growth phenotypes (Gamazon et al., 2015; Nagpal et al., 2019). Technologies such as transcriptome-wide association studies (TWAS) and single-cell RNA sequencing (scRNA-seq) can analyze changes in gene expression in Channa when faced with environmental stresses (like high temperature or immune challenges) (Nagpal et al., 2019; Cuomo et al., 2021). These tools not only help map expression quantitative trait loci (eQTLs), but also reveal regulatory networks involved in stress and immune responses, providing support for breeding more stress-resistant Channastrains. 5 Breeding Strategies for Genetic Improvement of Channa 5.1 Marker-assisted selection (MAS) inChanna Through genome-wide association studies (GWAS) and SNP chip technology, researchers have identified quantitative trait loci (QTLs) associated with growth traits in Channa, enabling the screening of individuals with better growth performance. For example, in Channa maculata, SNPs screened using GWAS improved the prediction accuracy of body weight and total length, indicating that MAS has great potential in accelerating the genetic progress of growth traits (Cui et al., 2024). Although the disease resistance QTLs of C. maculata have not been reported in detail, the application of MAS to integrated breeding for dual traits of growth and health is considered a worthy direction for development. The practical application of MAS in hatchery farms is increasing, and sex-related and growth-related genetic markers are widely used for screening parents and offspring. With the development of next-generation sequencing (NGS) technology, researchers have developed sex-specific molecular markers that can effectively identify sex genotypes, thereby achieving the production of all-male fish stocks, which usually have faster growth rates and higher economic value (Ou et al., 2017; Sun et al., 2023). 5.2 Genomic selection (GS) for Channa productivity Genomic selection (GS) uses genome-wide SNP data to estimate genomic breeding values (GEBVs) of traits, such as weight and length of fish. A GS simulation study on Epinephelus coioides evaluated the predictive ability of
RkJQdWJsaXNoZXIy MjQ4ODYzNA==