Animal Molecular Breeding, 2025, Vol.15, No.2, 82-90 http://animalscipublisher.com/index.php/amb 85 Tournament-based marker selection strategies along with Bayesian approaches have helped in the reduction of problems such as high dimensionality and multicollinearity, thus increasing the precision of genomic prediction and decreasing spurious associations. Specific applications where significant power is available at the gene or regional level for detecting associations are for gene or region-based tests, which support the identification of functionally relevant regions responsible for target traits (Filho et al., 2019). 4 QTL and Candidate Gene Studies Related to Rapid Growth and High Yield Traits 4.1 QTL identification and stability evaluation for growth-related traits New technology in QTL mapping has made it possible to map many loci that correspond to growth traits across several species. Field testing on a large scale and genetic mapping through high-density genotyping have made it possible to identify stable QTLs for plant height, biomass, and plant yield. For instance, doubled haploid population and near-isogenic line research found confirmatory evidence of high-effect QTLs on many single chromosome arms, effects of individual shoots on growth characteristics and yield (Li et al., 2018; Liu et al., 2020; Kumar et al., 2022). The QTLs also locate on already characterized loci, reflecting stability and versatility in alternative genetic backgrounds and across varying environments. Meta-QTL analysis is also utilized to narrow these intervals, reducing confidence intervals and precision of QTL detection for marker-assisted selection (Li et al., 2018; Zhang et al., 2021). 4.2 QTL mapping for high-yield traits QTL mapping for high-yielding characteristics has revealed clusters of loci that affect various components of yield. Grain number, kernel weight, and protein content yield characteristics have been recognized for QTL cluster strategies. Multi-environment and population data have been combined; this revealed the presence of QTL clusters with positive correlations among the various yield characteristics, which have been strongly advocated for use in breeding programs. Meta-analyses have merged these hundreds of primary QTLs into fewer, more stable meta-QTLs, which are in many cases validated by genome-wide association studies. The meta-QTLs frequently harbor candidate genes with established functions in yield determination, offering attractive targets for genetic improvement (Zhang et al., 2021; Du et al., 2024). 4.3 Functional annotation and expression validation of candidate genes Prioritization of candidate genes in QTL regions is increasingly best done through integrated multi-omics approaches, integrating sequence variation, gene expression, gene ontology, and protein-protein interaction data. This provides a drastic reduction of possible candidates, often by more than twenty fold. Functional annotation frequently highlights transcription factors and regulatory proteins, such as MADS-box, WRKY, and cytochrome P450 families, as key players in growth and yield traits (Kumar et al., 2023; Keerthi et al., 2024). Further validation of expression is done with contrasting genotypes and RNA-seq data, and several of the studies have been able to show differential expression of candidate genes in high- versus low-yielding lines (Su et al., 2020; Zhang et al., 2021; Kumar et al., 2022). 4.4 Analysis of QTL expression consistency and marker universality across different families Stability of QTL expression and universality of related markers in different genetic backgrounds are essential requirements for their application in breeding. Empirical studies have documented that several QTLs and related markers are stable in different populations and environments and justify application of such markers for marker-assisted selection. Meta-QTL and QTL breeding research have mapped loci that are expressed similarly in different distinct genetic backgrounds (Kumar et al., 2023; Du et al., 2024). Further, confirmation of candidate genes and QTLs in various families and environments ensures their universal use and consistency for improving accelerated growth and high-yielding characters (Li et al., 2018; Liu et al., 2020; Zhang et al., 2021). 5 Practical Application of Marker-Assisted Selection in Tilapia Breeding 5.1 Design of MAS breeding workflow and construction of selection systems Marker-Assisted Selection (MAS) pipeline development for tilapia breeding starts with marker identification and validation of molecular markers associated with key traits like disease resistance, sex determination, and growth.
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