Animal Molecular Breeding, 2025, Vol.15, No.1, 9-18 http://animalscipublisher.com/index.php/amb 12 use the covariance matrix to quantify the time-cumulative effect of genetic effects and precisely describe the genetic regulatory characteristics of the growth process. Such methods significantly enhance the credibility of the integrated analysis results of aquatic genetics (He et al., 2017; He et al., 2018; Orzuna-Orzuna and Granados-Rivera, 2024). 3.3 Inter-study difference regulation and bias assessment Regulating the heterogeneity among studies is the core link to ensure the reliability of the conclusion. By using subgroup analysis, integrated regression model and sensitivity test method, the root cause of data variation can be effectively traced. This strategy has received empirical support in the study of the association between nutritional intervention and growth (Orzuna-Orzuna and Granos-Rivera, 2024). These methods can clearly distinguish the interference of biological essential differences and experimental operation errors on research conclusions. Publication bias assessment is of decisive significance for the objectivity of research, as positive results tend to be published first. Through the combination of funnel plot - Iger test techniques, such biases can be systematically identified and corrected to ensure that the conclusions cover all valid data (Orzuna-Orzuna and Granados-Rivera, 2024). By synergistically regulating data heterogeneity and reporting bias, the study can provide multi-dimensional theoretical support for the genetic improvement strategy of tilapia (Zhao and Jin, 2024). 4 Main Research Conclusions 4.1 Analysis of the heritability level of growth traits The main growth traits of tilapia (including body size specifications, body length indicators and growth rate) generally show moderate to high genetic characteristics in different aquaculture systems. Studies on freshwater and brackish water aquaculture systems have shown that the heritability estimates of harvest weight, standard body length and average daily growth rate are concentrated in the range of 0.35~0.50, confirming significant genetic regulatory effects and selective breeding responses (Setyawan et al., 2022). The evaluation data of different breeding models (recirculating water, ecological ponds, cages) indicate that the genetic capacity of body weight at 168 days of age can reach 0.62~0.84, further verifying the genetic gain potential of targeted breeding (Turra et al., 2016). This pattern is universal across different strains. Even taking into account the interaction between genes and environment, the weight heritability is steadily distributed within the range of 0.32~0.62 (Thỏa et al., 2016). These high heritability traits suggest that precision breeding can significantly increase the rate of genetic progression, making these traits core targets for optimizing breeding efficiency (Trọng et al., 2013; Thỏa et al., 2016). 4.2 Genetic correlation characteristics among traits The strong genetic correlation among growth traits indicates that the genetic improvement of a single trait may produce a synergistic effect. For instance, harvest weight showed a high genetic correlation of 0.89~0.98 with body length and height, suggesting that larger-sized breeding could simultaneously improve overall body structure (Trọng et al., 2013). The genetic correlation between body weight and trunk length (>0.85) provides theoretical support for the combined breeding of multiple traits (Mourão et al., 2023). However, some traits show a negative association. Studies have shown that under specific breeding conditions, growth indicators such as body weight have a negative genetic correlation with head size, suggesting that rapid growth may inhibit head development (Mourão et al., 2023). Although growth rate is positively correlated with body mass score, the pursuit of weight gain alone may not improve overall health, highlighting the necessity of multitrait balanced breeding (Trọng et al., 2013; LaFrentz et al., 2020). 4.3 The regulatory role of the environment on genetic expression The aquaculture environment significantly regulates the expression intensity of genes on growth traits and affects the evaluation results of genetic parameters. Analysis based on response norms indicates that the heritability of growth traits in different environmental systems can vary by up to 300%, and some genetic correlations even drop
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