Animal Molecular Breeding, 2025, Vol.15, No.1, 9-18 http://animalscipublisher.com/index.php/amb 11 the breeding cycle and improve economic benefits (Elamawy et al., 2023; Orzuna-Orzuna and Granados-Rivera, 2024). Feeding experiments on β -glucan have shown that optimizing FCR can not only increase the weight gain rate, but also enhance the immune function, especially with significant effects in high-density intensive farming (Dawood et al., 2020). The time of sexual maturity has a regulatory effect on aquaculture output. Premature sexual maturity will prompt the distribution of energy to the reproductive system, resulting in a decline in the size of commercial fish. Molecular biological evidence indicates that targeted gene editing (such as myostatin gene knockout) can simultaneously regulate the growth trajectory and sexual maturity nodes, and the improvement effect is particularly prominent in the male population (Wu et al., 2022). Precise regulation of these traits plays a crucial role in achieving a balance between breeding benefits and market specification demands. 2.3 Genetic correlations among growth traits Significant genetic correlations often exist among growth traits in Nile tilapia, meaning that selection for one trait can lead to correlated responses in others. For example, high heritability estimates for body weight and strong genetic correlations between weight measured at different ages and across various farming systems suggest that genetic improvement for one growth trait can simultaneously enhance others (Turra et al., 2016). This is supported by findings that the genetic correlation between body weight at 168 days in different rearing systems is very high, indicating that selection for growth in one environment is likely to be effective across multiple production systems. Molecular and transcriptomic analyses further reveal that key metabolic and hormonal pathways, such as the GH/IGF axis and myostatin regulation, jointly influence multiple growth-related traits (Herkenhoff et al., 2020; Wu et al., 2022). These interconnected pathways suggest that genetic selection targeting one growth trait may have beneficial effects on others, reinforcing the importance of considering genetic correlations in breeding strategies for Nile tilapia. 3 Comprehensive Statistical Analysis Methods 3.1 Establishment of research screening criteria Systematic integrated analysis requires the formulation of clear literature screening criteria to ensure the quality of research and the relevance of the topic. Take the PRISMA guidelines as an example. This framework has been widely used in the research screening process, such as the review study on plant-based feed additives for tilapia. Eventually, 45 literatures that met the requirements were selected for in-depth analysis (Orzuna-Orzuna and Granados-Rivera, 2024; Zhao et al., 2024). The screening dimensions mainly include the research subjects (limited Nile tilapia), target traits (such as growth parameters, feed efficiency), experimental design norms and the completeness of quantitative data to ensure the comparability of data across studies. The standardized data collection process plays an important role in reducing systematic errors. The core recording elements cover the experimental group Settings, aquaculture environment parameters, trait determination values and statistical indicators. By strictly implementing these norms, researchers can construct a high-confidence database and thereby accurately analyze the association rules between genetic factors and phenotypic characteristics (Orzuna-Orzuna and Granados-Rivera, 2024). 3.2 Random effects model for genetic parameter estimation The use of the random effects model can effectively solve the influence of heterogeneity among studies on the results. Typical applications such as the Der-Simonian-Laird algorithm effectively correct the natural variations caused by different biological samples and experimental conditions by calculating the weighted average and the confidence interval of the effect size (Orzuna-Orzuna and Granados-Rivera, 2024). This method provides universal conclusions for revealing the genetic laws and trait association mechanisms of tilapia. In the field of genetic assessment, random regression analysis models (RRM) and multiple mixed effects models (MRRM) are widely used in the dynamic analysis of growth traits (He et al., 2017; He et al., 2018). These models
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