International Journal of Molecular Zoology, 2025, Vol.15, No.1, 38-47 http://animalscipublisher.com/index.php/ijmz 42 individuals (Liu et al., 2017; Hsu et al., 2021), indicating that metabolic and bone-related genes played a core role behind the growth differences. 4.2 Functional validation of candidate genes in groupers In the study of grouper, qRT-PCR is a common method to verify the function of candidate genes. It is mainly used to confirm whether the key differentially expressed genes (DEGs) screened by RNA-seq really have expression differences in tissues. In hybrid grouper, the study selected a total of 15 growth-related DEGs for verification, and the results showed that the expression trends of these genes were basically consistent with the transcriptome analysis. Among them, PTEN (phosphatase and tensin homolog) performed the most prominently and is considered to be a key factor in regulating the growth advantage of hybrid individuals (Cao et al., 2024). In brown-spotted grouper, there are genes such as meox1 and etv4, which not only have differential expressions, but also happen to fall within the QTL interval related to growth, indicating that they may be important regulatory targets affecting growth traits (Yang et al., 2022). SSR and SNP molecular markers derived from expressed sequence tags (ESTs) developed based on transcriptome data have also been used to associate specific genetic variations with growth traits. This not only promotes the implementation of marker-assisted selection (MAS), but also provides strong support for the management of functional gene diversity in grouper breeding (Hsu et al., 2021). 4.3 Integration of transcriptomic data in selection of groupers Integrating transcriptome data into breeding programs can promote the selection of superior growth traits in grouper. Current marker-assisted selection (MAS) has widely used molecular markers (such as SNPs and SSRs) from transcriptomes to identify and screen individuals carrying favorable growth alleles (Hsu et al., 2021). At the same time, the identification of growth-related differentially expressed genes (DEGs) and pathways provides a molecular basis for the selection of breeding parents, while the establishment of high-quality genome assembly and genetic linkage maps further improves the accuracy of selection strategies (Zhou et al., 2019; Yang et al., 2022). 5 Genomic Selection Strategies in Grouper Breeding 5.1 Building genomic prediction models To make genomic selection (GS) work well in grouper, we first need a solid training group with good data on both genes and traits. In earlier studies, researchers did this by collecting lots of SNP data from hundreds of fish. They also measured traits like growth and ammonia tolerance very carefully. This helped the models stay accurate and made the predicted breeding values (GEBVs) more trustworthy (Shan et al., 2021; 2023). Some simulation studies also gave useful tips. They showed that GS becomes more accurate if you use a larger reference group, include more genetic markers, and focus on traits that are easier to pass down from parent to offspring (Ma and You, 2021). In the estimation of GEBV in grouper, commonly used statistical models include BayesA, BayesB, BayesC, rrBLUP and gBLUP. These models are generally comparable in prediction accuracy, among which rrBLUP performs slightly better in low heritability traits, while BayesA and BayesC are more robust under small sample conditions (Ma and You, 2021; Shan et al., 2023). In addition, combining GWAS studies to screen out informative SNP sites can further improve prediction accuracy and reduce genotyping costs (Shan et al., 2021). 5.2 Implementation in breeding programs Genomic selection (GS) makes breeding no longer completely dependent on phenotypic performance. Now, it is possible to directly predict which parents may be more suitable for key traits such as fast growth and strong stress resistance through individual genomic information. For example, methods used to estimate genomic breeding values (GEBVs) have been applied to grouper breeding. Technically, high-throughput SNP typing methods - such as multiplex PCR combined with enrichment capture sequencing-can process multiple grouper populations at the same time, which is not only efficient but also very accurate (Shan et al., 2023).
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