Animal Molecular Breeding, 2025, Vol.15, No.1, 9-18 http://animalscipublisher.com/index.php/amb 15 GS technology integrates whole-genome information to predict individual breeding values, and its genetic assessment has a wider range and stronger reliability. Empirical research shows that the prediction accuracy of GS for growth traits is improved by more than 35% compared with the traditional pedigree method, and the continuous decrease in genotyping cost promotes its large-scale application (Yoshida et al., 2019; Yanez et al., 2020). Although GS is still in the promotion stage in the tilapia field, its synergistic application with conventional breeding will accelerate the process of high-yield strain cultivation (Yanez et al., 2020). 6.3 Prospects and challenges of gene editing technology Gene editing tools such as CRISPR/Cas9 can achieve rapid genetic improvement by targeting and modifying growth regulatory genes (Yanez et al., 2020). For example, knocking out the myostatin gene can increase the weight gain efficiency by 50%, and this technology breaks through the generational interval limit of traditional breeding. However, a strict regulatory system needs to be established for the application of technology. Ecological ethical risks (such as the impact of gene drift on wild populations) and commercial promotion norms urgently need to establish an assessment framework (Yanez et al., 2020). Only through full life cycle monitoring and biosafety assessment can it be ensured that this technology plays a positive role in the sustainable development of aquaculture. 7 Technical Bottlenecks and Development Paths 7.1 Obstacles to data standardization and integrated analysis The main limiting factor currently faced by tilapia genetic research is the significant heterogeneity of the research data. This difference stems from the divergence of breeding goals among studies, the influence of environmental variables, the differences in population genetic background and the different breeding management measures, resulting in the difficulty of integrating cross-study data (Ponzoni et al., 2011; Nguyen, 2016). Typical cases are like a strain showing opposite genetic expression trends in different salinity environments, which poses a challenge to the formulation of universal breeding standards (Bentsen et al., 1998; Ponzoni et al., 2011). The non-uniformity of data collection methods and evaluation indicators further weakens the comparability of the research. Establishing a full-process standardized system covering experimental design, phenotypic determination, and environmental parameter recording will become a key measure to improve the quality of integrated analysis (Ponzoni et al., 2011; Nguyen, 2016). 7.2 Optimization requirements for the multi-trait comprehensive evaluation system The existing breeding systems overly focus on single traits such as growth rate, while neglecting the genetic improvement of compound traits such as stress resistance and environmental adaptability (Ponzoni et al., 2011; Nguyen, 2016). The development of multi-dimensional trait assessment models, combined with advanced algorithms such as Bayesian statistics and mixed linear models, can effectively coordinate the genetic antagonistic effects among traits (Ponzoni et al., 2011). The environmental effect correction technology urgently needs to be upgraded. In view of the current situation of global tilapia farming, it is necessary to construct a cross-environmental genetic effect prediction model. By using QTL localization and gene function verification techniques, the gene-environment interaction mechanism can be analyzed, providing theoretical support for differentiated breeding (Bentsen et al., 1998; Ponzoni et al., 2011; Yanez et al., 2020). 7.3 Construction of a global data collaboration platform Establishing a cross-border data sharing network is the core path to break through the bottleneck of genetic improvement. The current phenomenon of data silos and the lack of collaboration mechanisms seriously restrict the efficiency of large-scale genomic data analysis (Ruan, 2016; Yanez et al., 2020). Constructing a standardized
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