Animal Molecular Breeding, 2025, Vol.15, No.2, 82-90 http://animalscipublisher.com/index.php/amb 84 2.3 Analysis of heritability and selection response potential of phenotypic traits Heritability for traits such as growth rate and yield in tilapia is medium to high and is in favor of the success of selective breeding programs (Herkenhoff et al., 2020; Barría et al., 2023). Genomics have identified many regions and candidate genes, including those associated with the growth hormone/insulin-like growth factor axis, that are associated with enhanced growth performance (Herkenhoff et al., 2020; Etherington et al., 2022). The application of molecular markers and genomic tools increases the selection response by more accurate monitoring of desirable traits and greater genetic improvement (Avallone et al., 2020; Herkenhoff et al., 2020; Etherington et al., 2022; Barría et al., 2023). But full expression of selection response is cut short by genetic bottlenecks and absence of diversity in cultured stocks (Geletu and Zhao, 2022; Tibihika et al., 2024). 3 Development of Molecular Markers and Association Analysis Strategies The further development of molecular marker technologies and association analysis strategies transformed genetic improvement programs for fast-growth traits and high-yield tilapia cultures. The MOD method permits the genotyping of massive SNP across the genome and allowed carrying out comprehensive genome-wide association studies for a robust foundation in marker-assisted selection, identifying genetic loci that may become associated with economically important traits that can help build superior tilapia breeds (Han, 2024).. 3.1 Common types of molecular markers and their applicability Due to their frequent occurrence, stability, and amenability to platforms appropriate for high-throughput genotyping, they are the most frequent molecular markers used in current genetic studies. It has been possible to successfully capture most of the common SNPs either directly or indirectly by using linkage disequilibrium, and hence they are highly informative for applications like GWAS and genetic mapping. Analytical strategies involving single-marker and haplotype-based tests have been found to detect a higher proportion of genetic variation, with haplotype-based strategies often detecting a higher proportion of phenotypic variance compared to single-marker tests. The two-pronged strategy improves the ability to find associations and accuracy in marker-assisted selection (Zhang et al., 2021; Du et al., 2024). 3.2 Population construction and accurate phenotypic trait measurement methods Successful association analysis depends on populations and phenotypic data being well structured and exact. Populations are generally built by subdividing genetic resources into groups according to genotype information that serves to control for population structure and prevent spurious associations. Phenotypic trait measurement is exact through standardized protocol, replicated trials, and multi-environment testing, which provides linkage of genotype and phenotype that is trustworthy. Marker selection methods based on linkage disequilibrium and haplotype information allow for the optimization of marker sets, reducing genotyping effort while retaining most of the genetic information. Sample sizes of 50~100 individuals are often sufficient for initial marker selection and population structure analysis, providing consistent and reliable results (Abdoli-Nasab and Rahimi, 2020). 3.3 Phenotype-genotype association analysis and candidate gene discovery Association analysis combines the phenotypic and genotypic data to detect markers associated with the traits of interest. Mixed Linear Models (MLM) are highly adopted while ignoring population structure and kinship for the purpose of reducing false positives and enhancing the accuracy of marker-trait associations. The haplotype-based methods could identify more loci and explain a greater proportion of phenotypic variance than the single-marker tests to aid in discovering candidate genes. Marker selection analysis based on linkage disequilibrium and haplotype data also increases the effectiveness and power of association analysis to aid identification of genetically important regions for marker-assisted selection (Abdoli-Nasab and Rahimi, 2020). 3.4 Statistical methods for qtl mapping and functional region identification Several statistical methods are employed for the purpose of QTL mapping and identification of functional genomic regions. Single marker and multi-marker approaches are utilized; however, the multi-marker and haplotype-based techniques such as principal components along with variance components models have shown a higher detection power for association, especially in cases where complex traits are influenced by a locus.
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