1 Introduction

Oreochromis niloticus, as a major species in global aquaculture, has achieved large-scale farming in more than 130 countries and regions and occupies an important position in freshwater fishery (Herkenhoff et al., 2020; Yanez et al., 2020). This species has become an important biological resource for ensuring the supply of aquatic products due to its strong adaptability, short growth cycle and high market acceptance.

In aquaculture practice, key traits such as body type parameters, body weight indicators and muscle content directly affect the aquaculture benefits and industrial economic returns (Thodesen et al., 2013; He et al., 2017; Yoshida et al., 2019). Shortening the breeding cycle can not only increase the annual output, but also significantly increase the economic income of practitioners (Thodesen et al., 2013; Yoshida et al., 2019; Robisalmi et al., 2023). Therefore, enhancing these traits through genetic improvement remains the core objective of current tilapia breeding (Thodesen et al., 2013; Yoshida et al., 2019; Yanez et al., 2020).

This study focuses on the genetic improvement technology system of tilapia. Although traditional breeding and new biotechnologies have been implemented for many years, the molecular regulatory network of its growth and development has not been fully elucidated. Although conventional breeding has achieved genetic gain, new technologies such as genomic selection and gene marker-assisted breeding are revolutionizing the traditional model and are expected to accelerate the process of genetic improvement. At present, it is urgently necessary to systematically sort out the relevant research progress, integrate effective technical paths, and provide theoretical support for cultivating excellent strains that take into account both ecological benefits and high-yield characteristics. Through systematic analysis of existing genetic research, it is helpful to summarize successful experiences, optimize breeding plans, and ultimately achieve efficient genetic improvement of tilapia growth traits.

2 The Growth Characteristics of Nile Tilapia

2.1 Key growth parameters: body weight, body length and specific growth rate

Body weight, body length and specific growth rate (SGR) are key indicators for evaluating the growth performance of Nile tilapia. These parameters are listed as routine observation indicators in scientific research and aquaculture management, and are used to test the effectiveness of nutrition programs, genetic improvement and breeding strategies. Kamble et al. (2024) found that supplementing guava leaves and currant extracts in the feed could increase the body length growth rate of fish by 23% and the daily weight gain rate by 18%, verifying the effectiveness of the nutritional regulation strategy. Integrated analysis indicates that plant essential oil additives can increase the terminal body length by 15%~20% and significantly enhance the SGR value, highlighting their crucial role in growth assessment (Orzuna-Orzuna and Granados-Rivera, 2024).

Gene editing technology has a significant regulatory effect on the above indicators. The group modified by myostatin gene had a 49.45% increase in body weight compared with the control group, and the body length increased by 12%~15% simultaneously with SGR, confirming the targeted improvement ability of genetic engineering on growth traits (Figure 1) (Wu et al., 2022). Statistical analysis showed a strong correlation between body length and body weight (r=0.89), emphasizing the necessity of multi-trait collaborative breeding (Kamble et al., 2024).

2.2 Feed conversion efficiency and sexual maturity regulation

Feed conversion rate (FCR) is a core efficiency parameter for measuring the conversion of feed nutrients into biomass in fish. A lower FCR value indicates a better nutrient conversion efficiency. Studies have shown that nano-phosphorus complexes and plant essential oil additives can reduce FCR by 0.3~0.5, simultaneously shorten 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. 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 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 to zero or negative values, suggesting that dominant genotypes may have environmental specificity (Mourão et al., 2023). This requires that breeding strategies must take environmental adaptability into account.

Nevertheless, under the same breeding conditions, the genetic consistency between body weight and growth rate is relatively high (0.65~0.99), enabling the breeding results to be effectively transformed among similar systems (Trọng et al., 2013; Turra et al., 2016; Thỏa et al., 2016; Setyawan et al., 2022). However, the genetic expression of body type characteristics is more environmentally sensitive, and differentiated breeding schemes need to be developed to adapt to different breeding scenarios (Trọng et al., 2013; Nguyen et al., 2017).

5 Genetic Regulatory Mechanism

5.1 The core regulatory role of QTL and functional genes

Quantitative trait loci (QTL), insulin-like growth factor 1 (IGF1), growth hormone (GH) and other key genes have core functions in the growth regulation of tilapia. Genome-wide association analysis has identified multiple QTL regions significantly associated with body size parameters, among which some loci can explain more than 70% of the weight variations (Liu et al., 2014).

Important genes in these regions, such as growth hormone receptor 2 (GHR2), significantly affect the development process by regulating the activity of the IGF-1 signaling pathway, and different GHR2 genotypes correspond to differentiated growth manifestations (Liu et al., 2014).

Molecular biological studies have confirmed that the GH/IGF signaling axis and myostatin (MSTN) constitute the main growth regulatory network. High growth performance strains generally show the characteristics of high expression of GH/IGF pathway genes and low expression of MSTN, making them important targets for genetic improvement (Herkenhoff et al., 2020; Wu et al., 2022).

5.2 Breeding applications of molecular marker technology

Molecular marker techniques based on single nucleotide polymorphisms (SNPS) have been widely applied in the dynamic tracking of growth traits. By constructing high-density genetic maps, researchers have identified SNP loci closely related to body type, gender and morphological characteristics, providing technical support for marker-assisted breeding (Liu et al., 2014; Wang et al., 2024). For example, the specific SNP markers of the IGF1 gene can be used as reliable genetic markers for the breeding of growth traits across populations (Table 1) (Ukenye et al., 2020).

The discovery of QTL linkage sites enables the screening of fast-growing individuals at the juvenile fish stage, significantly improving the breeding efficiency of important economic traits (Liu et al., 2014; Wang et al., 2024).

5.3 Current development status of genome selection technology

Although genomic selection technology (using whole-genome information to predict breeding values) has application prospects, it is still in the stage of technological improvement at present. The latest research, through genome-wide association analysis (GWAS) and transcriptome techniques, has resolved genetic pathway networks such as MAPK and VEGF that are closely related to growth adaptation (Powell et al., 2021; Wang et al., 2024).

Despite the technological breakthroughs, the practical application of this method still faces challenges such as the analysis of gene-environment interaction effects and the modeling of the genetic architecture of complex traits. With the continuous expansion of the genomic database, this technology is expected to become a new accelerator for aquatic genetic improvement (Powell et al., 2021; Wang et al., 2024).

6 Genetic Improvement Technology Pathways

6.1 Family breeding and hybridization technology application

Family screening and interspecific hybridization constitute the basic techniques for genetic improvement of tilapia. Family selection and breeding screen dominant families for subsequent reproduction by evaluating core indicators such as the growth performance of the parent population and its offspring (Samara et al., 2020). This strategy can gradually accumulate favorable alleles while maintaining the genetic diversity of the population and promote the continuous improvement of traits such as growth rate (Yoshida et al., 2021).

Interspecific hybridization technology achieves trait complementarity by integrating the advantageous characteristics of different varieties. For instance, the all-male offspring produced by the hybridization of Nile tilapia with closely related species not only have a growth rate increased by more than 20%, but also exhibit enhanced salinity tolerance and survival ability (Herkenhoff et al., 2020; Mtaki et al., 2021). Such technologies can not only optimize the output efficiency of aquaculture, but also effectively prevent and control industrial pain points such as disorderly breeding and environmental adaptation (Mtaki et al., 2021).

6.2 Molecular markers and genomic breeding techniques

Molecular marker-assisted breeding (MAS) and whole-genome selection (GS) significantly improve the efficiency and accuracy of breeding. MAS achieves early screening of phenotypic undetectable traits such as meat quality by locating DNA markers associated with target traits (such as SNPS) (Herkenhoff et al., 2020; Abwao et al., 2021) 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 genetic database and integrating the ternary data stream of genotype-phenotype-environment can achieve cross-regional sharing of breeding resources and transformation of achievements (Yanez et al., 2020).

Such platforms help formulate adaptive breeding strategies by tracking global breeding progress in real time. With the expansion of the industrial scale, a sound data collaboration system will become the cornerstone for maintaining the sustainability of genetic gain (Ruan, 2016; Yanez et al., 2020).

8 Concluding Remarks

Systematic analysis confirmed that the growth traits such as body size and muscle production of Nile tilapia have moderate to high genetic improvement potential, and significant genetic gain can be achieved through targeted breeding strategies. Typical examples show that six consecutive generations of selection and breeding have cumulatively increased the increment and growth rate of muscle tissue by the order of 1.87 standard deviations, and the single-generation genetic gain has been stably maintained within the range of 10%~15%. This improvement process was achieved on the premise of maintaining a stable survival rate, confirming the biological feasibility of the genetic improvement strategy.

Modern technologies such as molecular marker-assisted breeding and genomic prediction systems are driving industrial upgrading by enhancing the efficiency of genetic assessment. Compared with traditional family analysis methods, genomic selection technology has increased the accuracy of breeding value prediction by 37%, while the precise localization of growth-related gene clusters provides molecular targets for trait improvement. The breakthroughs in the research and development of gene function analysis technology and SNP high-throughput screening platform have laid a technical foundation for building excellent strains with high growth performance and low feed consumption.

Maximizing genetic potential requires the establishment of an international collaborative mechanism and an open data platform. International genetic improvement programs (such as the GIFT project) have verified the feasibility of cross-border integration of genetic resources and joint breeding models. Building a multi-dimensional database integrating genotypes, phenotypes and environmental factors can accelerate the transformation of technological achievements, achieve cross-regional genetic data linkage analysis, and promote the development of low-carbon tilapia farming models.

Acknowledgments

We would like to thank my colleague continuous support throughout the development of this study.

Conflict of Interest Disclosure

The authors affirm that this research was conducted without any commercial or financial relationships that could be construed as a potential conflict of interest.

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