A Review of Genomics Application in Aquaculture: Status, Challenges, and Priorities for Future Direction*

The implementation of genomics tools in aquaculture breeding has marked a revolutionary shift in genetic improvement strategies, offering a powerful complement to traditional selective breeding strategies. Genome-enabled breeding begins with the identification of genetic variants associated with production traits of interest. Currently, whole-genome association analysis (GWAS) have been the most commonly used and reliable approach for screening markers linked to quantitative trait loci (QTL) into aquaculture breeding programs. The resulting GWAS datasets serve as foundational resources for breeding programs, including genomic selection (GS) and marker-assisted selection (MAS), thereby accelerating genetic improvement programs. For MAS, a landmark success story is the discovery of a major QTL controlling 80% of the genetic variance for infectious pancreatic necrosis virus (IPNV) resistance in Atlantic salmon, which significantly reduced disease-related mortality in commercial populations (Gutierrez et al., 2018). Moreover, the identification of sex-linked markers in species like blue tilapia (Oreochromis aureus) has enabled the development of mono-sex breeding lines, which have shown considerable economic benefits (Wu et al., 2021).

Unlike MAS, which often depends on the phenotypic expression of traits, GS leverages dense genome-wide marker data capturing both additive and non-additive genetic effects that enables the identification of genetic markers that can predict genomic breeding values (GEBVs), of individuals even before the traits are expressed. This predictive power greatly accelerates breeding programs, making GS more efficient and precise, particularly effective for complex, polygenic traits like disease resistance, growth, feed conversion efficiency, and environmental adaptability, which are crucial for the sustainability of the aquaculture industry. Despite the predictive accuracy of GS, widespread adoption is compromised by several limitations, including; genotype-environment interactions, limited diversity of the training population, and financial resources for genotyping and infrastructure development remain the most paramount challenges for the widespread adoption of GS. Current research indicates that integration of multi-omics data (transcriptomic, genomic, proteomic and epigenomics) significantly enhances prediction accuracy, enhancing our understanding of complex and polygenic traits by extrapolating genetic markers with phenotypic outcomes mainly through genotype-to-phenotype (G2P) associations. Notably, the convergence of AI/ML with multi-omics frameworks has demonstrated particular promise for complex trait dissection, as evidenced by recent applications in breeding programs of aquatic species including in channel catfish (Ott et al., 2024), striped bass (M. saxatilis) (Rajab et al., 2024), eastern oyster (Crassostrea virginica) (Proestou et al., 2023), rainbow trout (Ali et al., 2021), etc. Moving forward, the continued refinement of long-read sequencing platforms, coupled with computational advancement and artificial intelligence (AI), will further democratize GS in aquaculture, paving the way for next-generation precision breeding strategies.

In the last decade, advances in genome manipulation technologies including genome editing, gene transfer and RNA interference (RNAi) have opened new frontiers in aquaculture breeding through precise modifications in key genes (at both DNA and RNA levels) associated with economically important traits, offering unprecedented opportunities for genetic improvement (Wang et al., 2025). These technologies have been used to generate several improved fish and shell fish lines (Puthumana, et al., 2024), such as disease resistance (Wang and Shen, 2023, 2024 b), improved growth (Coogan et al., 2022; Wang et al., 2024 c), improved feed intake (Kishimoto et al., 2018), and sex control (Xu and Ma, 2022; Booncherd et al., 2024; Yoshikawa et al., 2024) (Table 3). These advancements hold the potential to significantly boost aquaculture breeding programs.

Aquaculture genomics studies aim to uncover the genetic factors influencing performance and production traits, which can be utilized in genetic improvement programs. Multiple published studies have shown promising results regarding genetic improvements in aquatic production, particularly for economic traits such as growth rate and disease resistance.

Growth is the most preferred economic trait of aquaculture species; therefore, it is one of the most studied traits in breeding programs (Ashton et al., 2019). Growth-related traits are quantitative and controlled by numerous genes across the genome (Omeka et al., 2022). Numerous recent investigations conducted in various aquaculture species have provided valuable insights into myostatin (mstn) mutations and their pivotal functions in regulating cell proliferation and muscle growth (Yan et al., 2022; Özcan Gökçek et al., 2023; Wu et al., 2023). In zebrafish, knockout of the Activin A receptor, type II (acvr2), and pro-opiomelanocortin (pomc) induces muscle fiber development and enhances growth performance (Che et al., 2023; Yang et al., 2023 b). In mollusks, Li et al. (2021) reported a noticeable impact on larval muscle myogenesis and sarcomeric arrangement of thin filaments in the MELC-CRISPR/Cas9 mutant group, indicating that MELC is involved in larval muscle contraction and myogenesis in oyster larvae. Recently, there has been growing interest in utilizing epigenetics to enhance the growth traits of aquatic animals, including fish and shellfish (Liu et al., 2022 c; Roy et al., 2022). To explore the mechanism behind the phenomenon of serious growth retardation problem in M. rosenbergii, Jiang et al. (2020) investigated the epigenetic variations that existed between “iron prawn” and normal prawn using the fluorescent-labeled methylation-sensitive amplified polymorphism method (F-MSAP). Recently, Konstantinidis et al. (2023) demonstrated hypomethylation in muscular tissues and tissue-specific differential methylation of genes related to somatic growth, such as growth hormones and their receptors.

Sexual dimorphism, describing systematic differences between males and females of the same species, is crucial in aquaculture research due to its impact on husbandry, management, and productivity (Liao et al., 2023). While the production of monosexual fish exhibiting enhanced growth rates holds significant economic importance, it necessitates the development of sex-specific markers or sex-related genes for efficient sex-reversal technology and effective methods of sex identification and differentiation (Li et al., 2022). Over the last two decades, various conventional techniques such as AFLPs, RAPD, and SSRs have been effectively employed to identify sex-specific sequences. Nevertheless, these approaches have become obsolete because of their low throughput and labor intensity (Liu et al., 2023). Recently, highly efficient cutting-edge genomic approaches have been developed to aid sex-specific marker screening. Examples include next-generation sequencing (NGS)-based and high-resolution melting (HRM) typing systems (Gao et al., 2020; Yang et al., 2020 a: Han et al., 2023), linkage mapping of the sex quantitative trait locus (QTL) or sex determination (SD) locus (Zhang et al., 2019 a; Liu et al., 2020 a), restriction-site associated DNA sequencing (RAD-seq) (Liang et al., 2019; Fang et al., 2020), type IIB restriction endonuclease-produced sequencing (2b-RAD-seq), double digestion restriction site-associated DNA sequencing (ddRAD-seq) (Brown et al., 2016), specific-locus amplified fragment sequencing (SLAF-seq) (Waiho et al., 2019), comparative transcriptome (RNA-seq) (Sun et al., 2018), and PCR amplification refractory mutation system (Xiao et al., 2020). A RAD-Seq computational analytic workflow called RADSex was recently developed by Feron et al. (2021) to identify sex-specific markers precisely and to study the genetic basis of sex determination. This workflow enables researchers to precisely detect sex-specific genomic sequences and XY sex determination systems in various fish species (Feron et al., 2021). Using these genomic tools, a batch of sex-determining master genes, such as Dnmt3aa, dmy, gsdf6a, amhr2, sdy, pfpdz1, and bcar1, were identified in the model and non-model fish species (Gong et al., 2018; Bao et al., 2019; Wang et al., 2021; Wei et al., 2023).

In recent years, the application of genome editing technology for sex control in aquaculture has attracted much attention worldwide. Qin et al. (2022) found that double electroporation of TALEN plasmids targeting the GnRH gene effectively sterilized channel catfish. In Chinese common carp, Neo-male was created by knockout of the cyp17a1 gene using CRISPR/Cas9 technology, and the all-female population was produced by crossing XX and XX female carp (Zhai et al., 2022). Currently, complete functional sex reversal has been achieved in M. rosenbergii. They produced neo-females (Tan et al., 2020) and all males (Xu and Ma, 2022) after silencing insulin-like androgenic gland hormone (IAG). IAG is a male endocrine organ secreted by the androgenic gland (AG) and plays a crucial role in regulating sexual differentiation and maturity in crustaceans (Levy and Sagi, 2020). Functional studies in various crustacean species have confirmed the vital involvement of the IAG in gonadal development and sex-related phenotypic characteristics. A recent RNAi experiment in crayfish Procambarus clarkii (Savaya et al., 2020) has provided insights into the possibility of manipulating IAG to produce a mono-sex population of fish species. Overall, large-scale monosex production has been achieved in various aquaculture species, significantly increasing aquaculture production.

In the past two decades, the aquaculture industry has witnessed a significant leap in integrative research aimed at bridging the mechanistic understanding of cellular and molecular stress responses in both natural ecosystems and aquaculture systems (Connon et al., 2018). Genomic tools have been pivotal enablers of these cross-scale efforts. The integration of genomic methodologies into physiological and ecological research has dramatically increased our insights into the identification of key genes, pathways, and epigenetic modifications associated with stress resilience. Consequently, the field has matured towards a more unified framework for understanding biological responses to various environmental stressors. Beyond fundamental genomics, the application of metagenomics combined with environmental DNA (eDNA) monitoring allows for real-time assessment of phenotypic plasticity and adaptive potential to environmental and anthropogenic drivers of habitat change. Blockchain technology is being explored to track stress-resistant genetic lines across supply chains and ensure transparency in breeding programs. Emerging technologies, such as artificial intelligence (AI) and portable sequencing devices, are poised to transform aquatic stress management further.

Progressive advancements from traditional genetic markers to genomics, where genome-wide sequences can be explored more effectively, have significantly increased the power of conservation genetics with unprecedented spatial resolution. Recent genomic-based studies have provided valuable insights into the molecular basis of long-standing questions, such as the domestication-related genomic signatures of Atlantic salmon (López et al., 2019), demographic history of smallmouth bass populations (Gunn et al., 2022), local adaptation of tiger shrimp (Vu et al., 2021), and detailed inbreeding depression in small yellow croaker (Larimichthys polyactis) (Zheng et al., 2022 a). Yang et al. (2020 d) used GBS technology to evaluate the population genetic structure of Sillago japonica along the coasts of Japan and China. They found significant genetic differentiation among populations, which differed from the mtDNA marker analysis. Recently, Vu et al. (2021) employed genome-wide SNP genotyping to evaluate the spatial population structure, local adaptation, and connectivity of tiger shrimp populations in the Indo-Pacific region. Their study provides novel genome-level insights directly affecting tiger shrimp aquaculture management. Genome-scan investigations have yielded novel insights into the genetic basis of temporal microevolutionary alterations in adaptive diversity resulting from fishery-induced evolution and climate change (Aramburu et al., 2023).

The ongoing development and application of the eDNA approach in conservation genomics has facilitated aquatic biodiversity surveys. It is primarily cost-effective and efficient (McColl-Gausden et al., 2021; Keck et al., 2022). Nelson-Chorney et al. (2019) developed a species-specific qPCR method by amplifying mtDNA from lake sediment eDNA to identify the colonization history of native and introduced trout (O. clarkia bouvieri) populations, thus providing an important tool for invasion assessment and management. Using eDNA metabarcoding, Zhang et al. (2022) examined the impact of urbanization and water quality variables on fish assemblages in lentic and lotic systems across Beijing. The results revealed distinct native and non-native fish diversity patterns, highlighting the ability of eDNA to resolve urban aquatic biodiversity. The utilization of genomic information in sustainable aquaculture resource management is increasingly being recognized, as evidenced by research programs such as FishPopTrace (Martinsohn et al., 2009), LabelFish (González Sotelo et al., 2014), and AquaTrace, which focus on food traceability, industry regulation, and risk assessments. Collectively, genomic tools offer a comprehensive understanding of aquaculture animal population genetics, local adaptation, and connectivity, potentially enhancing aquaculture and fishery practices.

The aquaculture sector has entered a new paradigm for disease management through strategic implementation of genomic technologies. These cutting-edge approaches have revolutionized the prevention, diagnosis, and control of aquatic diseases across production systems.

In recent years, WGS applications in aquaculture have expanded to encompass comprehensive characterization of microbial communities, including detailed analyses of resistomes, virulence, and mobilomes across diverse aquatic species (Tekedar et al., 2019; Qian et al., 2023; Salgueiro et al., 2023). As summarized in a recent review (Sciuto et al., 2022), several studies have utilized WGS and functional annotation to identify the major QTL that control disease resistance or susceptibility in many aquaculture species (see Table 4). Complementary transcriptomic analyses have further enhanced our understanding of host-pathogen dynamics by comparing gene expression profiles between susceptible and resistant phenotypes (Yang et al., 2019; Zhou et al., 2022), whereas single-cell RNA sequencing has provided unprecedented resolution in characterizing cellular responses to infection (Daniels et al., 2023). The integration of bulk segregant analysis with RNA-Seq (BSR-Seq) has proven particularly effective for identifying QTL-associated genes, as demonstrated in the studies of enteric septicemia (ESC) resistance in catfish (Jin et al., 2022). Moreover, metagenomics has emerged as a particularly powerful diagnostic tool, facilitating proactive and rapid pathogen surveillance (Taş et al., 2021) through the simultaneous sequencing of all nucleic acids present in environmental or biological samples. Metagenomics provides information regarding the diversity of microorganisms in specific areas and can reveal details regarding their functions and biological roles, even before they cause outbreaks (Tully et al., 2018). This approach has revealed complex microbial ecosystems in aquaculture environments, including the prevalence of clinically significant antimicrobial resistance genes, such as mcr, blaKPC, and blaCTX-M (Xu et al., 2020 a), as well as identifying emerging pathogenic threats through the detection of dysbiotic states in farmed fish populations (Haro-Moreno et al., 2020). Recently, Rasmussen et al. (2022) highlighted the potential of metagenomics to study the effects of bacterial synbiotics and probiotics on the microbiome of rainbow trout. The capacity of this technology to monitor microbiome alterations in response to therapeutic interventions, such as probiotic administration in rainbow trout (Rasmussen et al., 2022), has opened new avenues for microbial management. These genomic surveillance methodologies do not only provide critical early warning systems for disease outbreaks but also facilitate the tracking of antimicrobial resistance evolution challenges that directly inform the development of therapeutic interventions.

The culmination of these genomic advancements has revolutionized aquaculture vaccinology, transforming the field from its early reliance on inactivated whole-organism vaccines (Ma et al., 2019) to contemporary genetic immunization strategies (Miccoli et al., 2021). While conventional vaccines remain constrained by safety concerns and variable efficacy across host species (Mohd-Aris et al., 2019), third-generation vaccines that leverage genomic insights now employ precisely targeted antigens with enhanced immunogenicity (Miccoli et al., 2021; Priya and Kappalli, 2022). DNA vaccines have demonstrated particular success against viral pathogens, including nodaviruses and rhabdoviruses, as well as against bacterial diseases (Embregts et al., 2019), with innovative delivery systems such as engineered probiotic vectors (Hua et al., 2021) and bacterial ghost platforms (Zheng et al., 2021) showing considerable promise. The regulatory approval of recombinant vaccines like Clynav against salmonid alphavirus-3 (SAV3) (Bedekar and Kole, 2022) marks a significant milestone in this technological transition, with current vaccine development programs encompassing diverse commercially important species ranging from salmonids to marine finfish (Robinson et al., 2023). These advances, grounded in fundamental genomic research and informed by comprehensive pathogen surveillance, are progressively reducing dependence on antimicrobials, while simultaneously improving vaccine stability, cost-effectiveness, and commercial scalability (Collins et al., 2019). As genomic technologies continue to converge from CRISPR-based antigen design to metagenomics-informed vaccine development, the aquaculture industry stands at the threshold of a new era in precision disease prevention.

The primary problem to effective genomic applications in aquaculture is associated with the lack of high quality reference genomes, coupled with insufficiently annotated gene functions and poorly characterized genetic markers. Although recent initiatives, including Fish10K (Fan et al., 2020), FishmiRNA (Desvignes et al., 2022), Aqua-FAANG (Johnston et al., 2024), and NCBI Ref Seq updates (O'Leary et al., 2023), have made progress in improving genome annotations for aquatic species, yet many fish genomes remain incompletely assembled, with fragmented gene models and uneven taxonomic representation. Moreover, majority of sequenced fish genomes are derived from economically significant aquaculture species (e.g., salmonids, shrimp, tilapia, and catfish) or biomedical model organisms (e.g., zebrafish), leaving minor species underrepresented (Robinson et al., 2023). An additional challenge lies in the inadequate annotation of non-coding RNAs (ncRNAs) and regulatory elements, which play pivotal roles in development, environmental adaptation, and disease resistance.

Concerted effort to improve genome quality, annotation accuracy, and taxonomic inclusivity is essential to unlock the full potential of genomics in aquaculture, facilitating sustainable production and genetic resilience in the face of global environmental challenges. Strategic advancements should include: (1) expanding whole-genome assembly technologies, such as long-read sequencing (PacBio, Oxford Nanopore) and Hi-C scaffolding, to resolve complex genomic regions. (2) comparative genome re-sequencing leveraging well-annotated model species (e.g., zebrafish and medaka) to accelerate functional gene discovery, particularly for traits such as growth efficiency, disease resistance, and environmental resilience. (3) expansion of taxonomic representation in sequencing initiatives to prioritize underrepresented species, ensuring equitable coverage across diverse aquatic lineages.

The financial constraints associated with large-scale genotyping pose a substantial obstacle to the implementation of genomic selection (GS) in aquaculture breeding programs, particularly in small enterprises and developing economies. In contrast to terrestrial livestock breeding, aquaculture operations typically require genotyping of thousands of individuals per generation, especially for traits with low heritability, resulting in prohibitive costs that hinder widespread adoption (Tsairidou et al., 2020). This economic challenge is especially pronounced for lower-value species and resource-limited production systems, creating an innovation gap that threatens to exacerbate the disparities in aquaculture productivity. Nonetheless, several promising strategies have emerged for enhancing the accessibility of genomic breeding.

For example, genotype imputation has demonstrated particular promise, wherein high-density genotyping of parental stocks is combined with low-density genotyping of candidate selections followed by computational imputation to achieve high-density coverage (Yoshida et al., 2018; Tsairidou et al., 2020). This approach has been shown to maintain genomic prediction accuracy while reducing costs by 40–60% across multiple aquaculture species (Song and Hu, 2022). Collaborative efforts to develop multispecies genotyping arrays through industrial consortia represent another cost-reduction strategy, where economies of scale from high-volume species such as Atlantic salmon can subsidize array development for emerging species. This approach has been suggested to maintain prediction accuracy while reducing financial barriers in small- and lower-income economies. Genotyping pooled DNA from sib groups is another cost-reducing strategy that has been explored to minimize expenses while maintaining the effectiveness of genetic analysis. Furthermore, next-generation sequencing (NGS) techniques, such as genotyping-by-sequencing (GBS) and restriction site-associated DNA (RAD) sequencing, can create SNP panels for species lacking a reference genome, thereby lowering genotyping expenses. These methods have been successfully applied to various aquaculture species, including Atlantic salmon, large yellow croaker, rainbow trout, European sea bass, and Nile tilapia. By focusing on a limited set of restriction fragments, 3D-GBS allows fourfold more samples to be genotyped at the same minimal coverage depth. This approach allows for ultra-high-throughput and ultra-low-cost genotyping, reducing costs by approximately 86–92% for genomic selection and QTL mapping studies (De Ronne et al., 2023). The strategic integration of these cost-reduction approaches, tailored to specific breeding program requirements and economic contexts, is essential for realizing the full potential of genomic selection in the global aquaculture sector.

Bioinformatics proficiency and computational constraints remain significant obstacles hindering the extensive implementation of genomics in the aquaculture industry. Most aquaculture research institutions and production facilities lack the bioinformatics expertise needed to properly manage, analyze, and interpret sequencing data (Robinson et al., 2023). Advanced genomics, including next-generation sequencing (NGS) data analysis, complex population genetics simulations, and machine learning approaches for genotype-phenotype prediction, demands bioinformatics expertise rarely covered in standard genomics training programs. Even so, aquaculture education programs emphasize husbandry practices while underemphasizing bioinformatics training. Furthermore, the distinctive features of aquatic genomes, often characterized by higher heterozygosity and complex repeat structures, create substantial analytical hurdles that demand specialized bioinformatics approaches. Additionally, inadequate computational infrastructure further exacerbates the existing gap in bioinformatics skills in aquaculture genomics. Many institutions continue to rely on outdated computational systems that are ill equipped to handle contemporary genomic datasets (Rana et al.,, 2020). Although commercial cloud computing services (e.g., AWS, Google Cloud) offer potential solutions, their implementation remains cost-prohibitive for routine aquaculture applications (Krissaane et al., 2020), with many operations lacking funding for essential bioinformatics software licenses and HPC access (Rana et al., 2020).

To address the existing bioinformatics challenges, a multifaceted approach must be implemented, beginning with the integration of bioinformatics training into university curricula and vocational programs to develop workforce capacity, complemented by specialized workshops and online courses tailored to aquaculture applications. The development of customized analytical pipelines for non-model species will enhance variant calling and GWAS efficiency, while the creation of open-source, GUI-based platforms, such as the conceptualized AquaFAST, will improve accessibility for non-specialists. Infrastructure limitations should be addressed through the strategic adoption of cost-effective cloud computing solutions and the implementation of edge computing architectures for decentralized data processing in remote locations. Finally, establishing public-private partnerships to subsidize computational resources and forming international consortia for knowledge sharing will be critical for standardizing practices and ensuring equitable access to genomic resources across the aquaculture sector. Aquaculture may draw inspiration from successful collaboration in human genomics and crop science, as seen in The Cancer Genome Atlas (TCGA) and the International Wheat Genome Sequencing Consortium (IWGSC). This comprehensive strategy addresses both immediate technical barriers and long-term capacity-building needs, creating a sustainable framework for advancing genomic applications in aquaculture research and production systems.

Aquaculture genomics suffers from a scarcity of well-curated genomic resources, creating a significant knowledge gap that hinders genetic improvement efforts. To support the sustainable development of the aquaculture industry, there is a pressing need for a globally accessible database that integrates multi-omics data within a unified framework for both academic and industrial applications. Such a database should consolidate genomic resources with associated metadata, including evolutionary history, phenotypic traits, geographic distribution (GIS-based), and environmental parameters, all of which are accessible through an intuitive web-based interface. Currently, major repositories such as NCBI, Ensembl, and species-specific databases such as FishDB (Yang et al., 2020 c) and MiFish (Sato et al., 2018) serve as primary sources for fish genomic data. However, these platforms are not tailored to the specific demands of aquaculture and fishery research. A potential model for improvement is the AgBioData Consortium (Harper et al., 2018), which standardizes agricultural genomic data and can be adapted for aquaculture applications. Recognizing this gap, the Food and Agriculture Organization (FAO) developed AquaGRIS (https://www.fao.org/fishery/aquagris/home) (FAO, 2022), a prototype global information system for farmed aquatic genetic resources. This initiative aims to enhance data hosting, processing, annotation, and analysis, facilitating the better utilization of genomic resources in aquaculture breeding and conservation programs. Expanding such integrated databases will be crucial for advancing precision aquaculture and ensuring long-term industry sustainability.