1 Introduction

The rapid development of grouper aquaculture is due to consumer preference for high-quality, fast-growing fish that can improve production efficiency and economic benefits (Liu et al., 2022; Ai et al., 2023; Wu et al., 2024). Therefore, breeding high-performance grouper varieties that meet market demand is an important driving force to support the development of sustainable aquaculture (Ai et al., 2023; Hsu et al., 2024). Growth rate is one of the most core traits in grouper breeding, which directly affects yield, production cycle and economic returns (Liu et al., 2022; Wu et al., 2023).

In the study of giant grouper (Epinephelus lanceolatus), Wu et al. (2023) constructed a high-density genetic map containing 2 988 SNPs and found 6 growth-related QTLs, which can explain 4.65% to 12.56% of the phenotypic variation. Through RNA-Seq analysis, 27 differentially expressed genes overlapping with these QTLs were screened out, mainly involving key functions such as PPAR signaling pathways, carbon metabolism, and RNA transport. Judging from these results, breeding faster-growing varieties has become the highlight of grouper farming. Genetic improvement projects are also increasingly inclined to be carried out around these growth-related genetic markers, and precision breeding is gradually replacing traditional empirical methods.

But, improving growth traits in grouper is not easy. One problem is that, groupers have long generation times. They also need large numbers of fish for breeding, and it’s hard to measure their traits accurately when they’re still young. These challenges slow down genetic progress and make traditional breeding methods less effective (Wu et al., 2023; Hsu et al., 2024). Also, growth traits in grouper are controlled by many different genes and are influenced by the environment.

So, it is difficult to analyze its genetic mechanism and apply effective selection strategies (Yang et al., 2020; Zhang et al., 2022; Wu et al., 2023). In addition, due to the polygenic nature of growth traits and the need for high-resolution genomic tools, the work of locating quantitative trait loci (QTLs) and candidate genes is also more complicated (Ai et al., 2023; Wu et al., 2023).

This study focuses on the rapid growth trait of grouper, and attempts to find the key influencing factors from the perspective of genetic regulation. High-density genetic maps, GWAS and a new generation of typing technology provide data support and decision-making basis for marker-assisted selection and genomic selection in actual breeding. The study found that compared with traditional methods, this type of strategy emphasizes efficiency and accuracy, and is particularly suitable for solving bottleneck problems such as long breeding cycles and complex target traits. Through these technical means, it is expected to accelerate the pace of breeding of high-quality grouper varieties and lay a more solid foundation for the sustainable development of the aquaculture industry.

2 Overview of Growth Traits in Grouper

2.1 Biological basis of growth in grouper

The growth of grouper cannot be explained by a single gene or pathway. It is affected by multiple factors such as genetic background, hormone regulation and nutritional status. In recent years, QTL positioning and transcriptome studies have found a number of genes closely related to growth, mainly involving metabolic pathways, RNA transport, PPAR signaling and carbon metabolism (Wu et al., 2023; Wang et al., 2023a). These functions are closely related to cell proliferation, signal transduction and bone development. For example, genes such as npy2r (neuropeptide Y receptor Y2) and bmp2k have been found to be related to bone formation, energy metabolism and growth rate, reflecting that endocrine and nutritional factors play an important role in the entire growth process (Yu et al., 2018; Yang et al., 2020).

Environmental factors such as temperature and nutritional conditions also affect the growth rate and developmental pattern of groupers. For instance, growth models fitted at the juvenile and juvenile stages showed that the growth characteristics of groupers can show exponential or decelerating changes depending on different environmental conditions and species or hybrid types (Sun and Wang, 2024). Another study pointed out that 30 °C is the optimal temperature for hybrid groupers (such as E. fuscoguttatus× E. lanceolatus), and its specific growth rate (SGR) is as high as (16.25 ± 2.11)%/day (Das et al., 2022). These research results emphasize that the combined effects of genetic and environmental factors on growth performance must be considered in breeding programs.

2.2 Key phenotypic indicators of fast growth

Phenotypic indicators such as body weight, body length, body height and specific growth rate are usually used to evaluate the growth performance of grouper (Ai et al., 2023; Sun and Wang, 2024). Genome-wide association analysis and quantitative trait loci (QTL) mapping studies have found multiple single nucleotide polymorphism (SNP) sites and QTLs associated with these traits, and these genetic loci can explain a significant proportion of phenotypic variation (Yu et al., 2016; Yang et al., 2020; Wu et al., 2023).

How much muscle a grouper can build and how well it turns feed into growth are also key signs of fast growth. Researchers have found several important genes that play roles in muscle growth, metabolism, and cell division. These genes are seen as likely candidates that affect these traits. Because of this, they can be used as helpful markers when choosing fish for breeding (Wu et al., 2023; Wang et al., 2023a).

2.3 Intraspecific variation and trait heritability

By using high-density SNP typing and building linkage maps, the study showed that there is clear genetic difference both between and within grouper populations (Hsu et al., 2021). In research on red-spot grouper (Epinephelus akaara), Wang et al. (2019) created a detailed genetic map that was 2 300.12 cM long and included 3 435 SNP markers. The average distance between markers was 0.67 cM. They found 17 QTLs related to growth, which explained between 10.7% and 12.9% of the differences in traits.

The heritability of growth traits in grouper is medium to high. Some QTLs and SNPs that have been found can each explain up to 12.56% of the differences seen in a single trait (Yang et al., 2020; Ai et al., 2023; Wu et al., 2023). This shows that growth traits are clearly influenced by genes. These results support the idea that selective breeding and genomic selection can work well to improve grouper growth.

3 Genetic Architecture of Fast Growth in Groupers (Epinephelus)

3.1 Growth-related QTL mapping in Epinephelus

Researchers have constructed high-density genetic linkage maps using thousands of SNP markers, covering hybrids and purebred strains of grouper, thereby achieving precise QTL positioning for growth traits (Liu et al., 2022; Wu et al., 2023). In hybrid strains, like Yunlong grouper (E. moara × E. lanceolatus), linkage maps revealed multiple growth-related QTLs, which provided a basis for marker-assisted selection and hybrid vigor research (Liu et al., 2022). Similarly, purebred strains such as giant grouper (E. lanceolatus) and spotted grouper (E. coioides) have also been mapped with linkage maps, and important genomic regions closely related to growth have been identified (Wu et al., 2019; Ai et al., 2023).

Multiple QTLs associated with growth and body shape traits (including weight, length, height, and thickness) have been identified on multiple linkage groups and chromosomes (Yang et al., 2020; Liu et al., 2022). For instance, in giant grouper, six growth-related QTLs can explain 4.65% to 12.56% of the phenotypic variation; and in brown grouper, important QTLs associated with weight, length, and height were also identified (Yang et al., 2020; Wu et al., 2023).

3.2 Key candidate genes regulating fast growth

Near multiple key QTL regions, the study identified a group of candidate genes closely related to growth regulation, mainly concentrated in the GH-IGF axis and its downstream pathways. Core genes such as ghr (growth hormone receptor) are expressed at significantly higher levels in fast-growing grouper individuals. At the same time, related genes in signaling pathways such as PI3K/AKT/mTOR and MAPK also showed similar expression trends, indicating that they may be directly involved in the molecular process of regulating growth rate (Wang et al., 2023b; Cao et al., 2024). Except that, some genes related to hormone signaling and metabolic regulation also showed traces of positive selection or accelerated evolution, further emphasizing the role of these genetic factors in the formation of growth advantage in grouper (Zhou et al., 2019).

It has been confirmed that some genes related to muscle development, such as mustn1, bmp7, tnni2 and bmp2k, as well as metabolism-related genes prkcd, acyp2 and lacs5, play an important role in the rapid growth of grouper (Yang et al., 2020; Ai et al., 2023; Wang et al., 2023a). These genes are involved in multiple key processes such as the regulation of the actin skeleton, protein folding, and energy metabolism, directly affecting the formation and deposition of muscle tissue and improving overall growth efficiency (Yang et al., 2020; Wang et al., 2023a).

3.3 Molecular mechanisms underlying trait variation

Studies using transcriptomes and QTLs show that both nearby DNA elements (cis) and far-away regulators (trans) play a role in how groupers grow. Some genes that are turned on or off differently are found in QTL regions. This means that local control and long-distance signals work together to affect how growth-related genes behave (Wu et al., 2023; Cao et al., 2024). In groupers that grow faster, the PI3K/AKT/mTOR pathway is more active. This shows that the pathway might help the fish use nutrients better and build more muscle (Wang et al., 2023a; Cao et al., 2024).

Structural genomic variation, including chromosome rearrangement and gene family expansion, has been observed in fast-growing groupers, which may affect the expression of growth-related genes. Zhou et al. (2019) constructed the chromosome-level genome of giant grouper (E. lanceolatus) for the first time, revealing the genetic basis of its rapid growth and innate immune mechanism. The study successfully located 24 chromosomes and annotated 24 718 protein-coding genes. By comparing with 11 other bony fish species, it was found that its immune-related gene family, such as NLRP1, NLRP12, ASC and CARD8, was significantly expanded, enhancing its NOD-like receptor signaling system (Figure 1). In addition, 416 rapidly evolving genes are widely involved in signaling pathways such as insulin, JAK-STAT, and PI3K-Akt, supporting its rapid growth characteristics.

4 Transcriptomic and Functional Genomic Insights in Groupers

4.1 RNA-seq analysis of fast-growing vs slow-growing groupers

When studying the growth differences of groupers, RNA-seq technology provides direct evidence. For example, in hybrid groupers, transcriptome sequencing of brain, liver and muscle tissues revealed a large number of differentially expressed genes (DEGs), 27 of which were closely related to growth traits. It is worth noting that genes involved in the regulation of the actin skeleton and genes related to the GH/IGF system are generally upregulated in fast-growing individuals (Cao et al., 2024). These expression changes suggest that these pathways may be the key to the growth advantage of hybrid individuals.

In the study of giant grouper (E. lanceolatus), Wu et al. (2023) constructed a high-density genetic map containing 2,988 SNP loci and found 6 growth-related QTLs, which can explain 4.65% to 12.56% of the phenotypic differences. Further RNA-seq analysis identified a total of 484 differentially expressed genes (DEGs), which were enriched in pathways such as RNA transport, carbon metabolism, and PPAR signaling. Among them, 27 DEGs overlapped with the QTL interval and involved key processes such as cell growth, sugar metabolism, and bone development. Kalrn, ccnd2, and mybpc2 were significantly upregulated in the fast-growing group. Similar patterns also appear in other grouper species. Transcriptome data revealed that metabolic pathways such as glycolysis and gluconeogenesis, as well as structural genes, were more highly expressed in fast-growing individuals (Liu et al., 2017; Hsu et al., 2021), indicating that metabolic and bone-related genes played a core role behind the growth differences.

4.2 Functional validation of candidate genes in groupers

In the study of grouper, qRT-PCR is a common method to verify the function of candidate genes. It is mainly used to confirm whether the key differentially expressed genes (DEGs) screened by RNA-seq really have expression differences in tissues. In hybrid grouper, the study selected a total of 15 growth-related DEGs for verification, and the results showed that the expression trends of these genes were basically consistent with the transcriptome analysis. Among them, PTEN (phosphatase and tensin homolog) performed the most prominently and is considered to be a key factor in regulating the growth advantage of hybrid individuals (Cao et al., 2024).

In brown-spotted grouper, there are genes such as meox1 and etv4, which not only have differential expressions, but also happen to fall within the QTL interval related to growth, indicating that they may be important regulatory targets affecting growth traits (Yang et al., 2022). SSR and SNP molecular markers derived from expressed sequence tags (ESTs) developed based on transcriptome data have also been used to associate specific genetic variations with growth traits. This not only promotes the implementation of marker-assisted selection (MAS), but also provides strong support for the management of functional gene diversity in grouper breeding (Hsu et al., 2021).

4.3 Integration of transcriptomic data in selection of groupers

Integrating transcriptome data into breeding programs can promote the selection of superior growth traits in grouper. Current marker-assisted selection (MAS) has widely used molecular markers (such as SNPs and SSRs) from transcriptomes to identify and screen individuals carrying favorable growth alleles (Hsu et al., 2021). At the same time, the identification of growth-related differentially expressed genes (DEGs) and pathways provides a molecular basis for the selection of breeding parents, while the establishment of high-quality genome assembly and genetic linkage maps further improves the accuracy of selection strategies (Zhou et al., 2019; Yang et al., 2022).

5 Genomic Selection Strategies in Grouper Breeding

5.1 Building genomic prediction models

To make genomic selection (GS) work well in grouper, we first need a solid training group with good data on both genes and traits. In earlier studies, researchers did this by collecting lots of SNP data from hundreds of fish. They also measured traits like growth and ammonia tolerance very carefully. This helped the models stay accurate and made the predicted breeding values (GEBVs) more trustworthy (Shan et al., 2021; 2023). Some simulation studies also gave useful tips. They showed that GS becomes more accurate if you use a larger reference group, include more genetic markers, and focus on traits that are easier to pass down from parent to offspring (Ma and You, 2021).

In the estimation of GEBV in grouper, commonly used statistical models include BayesA, BayesB, BayesC, rrBLUP and gBLUP. These models are generally comparable in prediction accuracy, among which rrBLUP performs slightly better in low heritability traits, while BayesA and BayesC are more robust under small sample conditions (Ma and You, 2021; Shan et al., 2023). In addition, combining GWAS studies to screen out informative SNP sites can further improve prediction accuracy and reduce genotyping costs (Shan et al., 2021).

5.2 Implementation in breeding programs

Genomic selection (GS) makes breeding no longer completely dependent on phenotypic performance. Now, it is possible to directly predict which parents may be more suitable for key traits such as fast growth and strong stress resistance through individual genomic information. For example, methods used to estimate genomic breeding values (GEBVs) have been applied to grouper breeding. Technically, high-throughput SNP typing methods - such as multiplex PCR combined with enrichment capture sequencing-can process multiple grouper populations at the same time, which is not only efficient but also very accurate (Shan et al., 2023).

By accurately predicting the breeding values of candidate individuals at an early stage, GS technology can shorten the generation interval and increase the speed of genetic improvement, which is more efficient than traditional pedigree-based selection methods (Sonesson and Meuwissen, 2009; Shan et al., 2021).

5.3 Evaluation and improvement of GS efficiency

The genomic selection (GS) model used in grouper has shown very high accuracy. For traits like body weight and ammonia tolerance, the prediction accuracy can reach up to 96% (Shan et al., 2023). But to keep this level of accuracy over many generations, the training group and marker sets must be updated regularly (Sonesson and Meuwissen, 2009). Simulation studies show that if sibling testing is done in every generation, the accuracy stays stable. However, if testing happens less often, the accuracy drops.

Using important SNP markers found through GWAS, along with fast and low-cost genotyping tools, can help lower the cost and time needed for genomic selection (GS). This makes GS more practical for use in commercial fish farms (Shan et al., 2021; 2023). Even though GS still costs more than older methods-because it needs both gene testing and trait measurements-it offers big benefits in the long run. These include faster genetic improvement and a lower risk of inbreeding (Sonesson and Meuwissen, 2009; Shan et al., 2021).

6 Case Studies in Grouper Fast-Growth Breeding

6.1 Identification of the fast-growing lineage of grouper

In the breeding process of tomato grouper (Cephalopholis sonnerati), since the germplasm sources are mostly wild parents or mixed breeding models, farms often lack clear parent pairing information, resulting in bottlenecks in the genetic improvement of growth traits. To address this problem, Hsu et al. (2023) used PCR-based ISSRseq high-throughput genotyping technology to conduct assisted breeding research on fry without parental information. The study screened out 24 fastest and slowest growing individuals from a batch of more than 10 000 fry, and used SNP data to conduct genetic diversity assessment and pedigree structure analysis, confirming that the samples have high genetic differences.

Through population structure division, principal component discriminant analysis (DAPC) and kinship network integration analysis, three genetic lineages were clearly identified, and one of the lineages was found to gather 92.3% of fast-growing individuals (Figure 2) (Hsu et al., 2024). This result shows that ISSRseq can not only make up for the lack of parental information, but also has the ability to divide families with high growth potential. In addition, the study also identified 53 lineage-specific molecular markers, most of which are concentrated in fast (F) and slow (S) lineages, providing a key tool for the subsequent construction of a stable genetic breeding system.

6.2 Hybrid groupers and heterosis research

The natural population of grouper is limited, and hybrid breeding has become an effective means to improve its growth performance. For instance, the hybridization between striped grouper (E. fuscoguttatus) and giant grouper (E. lanceolatus) has produced several hybrid varieties with excellent growth performance. The growth rate of these hybrid fish is significantly higher than that of the parents. Studies have reported that their absolute weight growth rate can reach 1.6 times that of the mother, and their muscle yield is also better than that of the father (Bunlipatanon and Taynapun, 2017; Gong et al., 2025).

Cao et al. (2024) combined full-length transcriptome sequencing with next-generation sequencing technology to deeply explore the molecular mechanism of growth advantage of hybrid grouper (Cromileptes altivelas × Epinephelus lanceolatus). The study pointed out that hybrids have a large number of growth-related differentially expressed genes (DEGs) in brain and muscle tissues, of which 15 core genes (such as PTEN, ACTC, FGFR3, etc.) play a key role in regulating the cytoskeleton and MAPK signaling pathways, especially PTEN is considered to be a possible upstream regulatory factor (Figure 3) (Cao et al., 2024). Besides, hybrids are generally in an intermediate state between the parents in terms of expression levels, suggesting that their growth advantage may come from the "intermediate parent effect" of expression regulation.

7 Challenges in Grouper Genomic Breeding

7.1 Limitations in current GS applications

Although genomic selection (GS) of grouper has made some progress, it is still constrained by multiple factors, the most important of which is the lack of high-quality, comprehensive reference genomes and detailed gene annotation information. At present, studies have constructed chromosome-level genome assemblies for species such as giant grouper and brown grouper, but incomplete annotations and limited identification of functional variant sites still hinder the in-depth application of GS in actual breeding (Zhou et al., 2019; Yang et al., 2022).

In addition, many grouper breeding programs still rely on low-density genotyping platforms, which limits the detection accuracy of key trait loci and the accuracy of genomic prediction. Although a variety of high-throughput SNP typing technologies have been developed, they have not yet been widely promoted in the industry, especially in commercial hatcheries, and cost issues are still one of the obstacles to large-scale application (Shan et al., 2021; 2023).

7.2 Multi-trait selection and trade-off management

Breeding groupers is not as simple as just looking at who grows faster. In reality, if you blindly pursue growth rate, you may ignore equally critical traits such as fertility and disease resistance. The problem is that there is often a kind of "one rises while the other falls" relationship between these traits. If not handled properly, you may lose one while focusing on the other. Studies have shown that the immune-related gene family of some grouper species has expanded, which to some extent shows that while improving growth performance, health traits actually have room for improvement (Zhou et al., 2019; Yang et al., 2022). But, whether both ends can be taken into account ultimately depends on whether the genetic correlation between these traits can be scientifically managed and precisely regulated.

In addition to growth rate and disease resistance, traits directly linked to benefits, such as meat quality and feed conversion efficiency, must also be taken into account during breeding, especially consumer preferences, which are increasingly valued by the industry. The problem is that, the genomic tools for these economic traits are not mature enough at this stage, and many are still in the initial stage. If we want to really advance, we must continue to work hard on the identification of molecular markers and the construction of trait prediction models so that these key traits can also be included in the main channel of improvement (Wu et al., 2024).

8 Concluding Remarks

Current research has identified a number of key genes and molecular markers that are closely related to the rapid growth of grouper. Most of these genes are related to physiological processes such as energy metabolism, cell cycle regulation, and bone development. Give an example, in high-density QTL positioning and RNA-seq analysis, a number of potential candidate genes such as kalrn, ypel1, supt7l, lacs5, ccnd2, mybpc2, and bmp2k were identified. At the same time, a number of SNP sites and QTL regions closely related to growth traits were found. These achievements provide very practical basic resources for the subsequent marker-assisted selection and genetic improvement.

Genomic selection (GS) has shown good results in predicting breeding values and accelerating genetic progress in growth traits, ammonia tolerance, etc. Combining GWAS informative sites with advanced statistical models has not only improved prediction accuracy but also cost-effectiveness, making GS a viable tool in grouper breeding practice. However, most current studies focus on gene associations and expression patterns, and direct functional validation experiments and systematic annotation of reference genomes are still relatively lacking.

The growth traits of grouper are also relatively complex and are affected by multiple genes and environmental factors. The interaction between genotype and environment, as well as the need to weigh other key traits such as disease resistance and reproductive capacity in addition to growth traits, make breeding strategies more challenging, and a more integrated research approach is urgently needed.

In the future, grouper breeding will benefit from the continued construction of high-quality genomic resources, the deep integration of marker-assisted selection and genomic selection, and the introduction of advanced breeding technologies. These efforts will help achieve the breeding goals of rapid growth, strong stress resistance and high quality, and promote the development of aquaculture in a sustainable and efficient direction.

Acknowledgments

The authors sincerely thank Dr. Li for reviewing the manuscript and providing valuable suggestions, which contributed to its improvement. Additionally, heartfelt gratitude is extended to the two anonymous peer reviewers for their comprehensive evaluation of the manuscript.

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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