This review outlines foundational applications of omics in aquaculture and highlights the characteristics and current applications of distinct omics approaches, and summarizes three core application domains of omics in aquaculture.
Abstract
In recent years, aquaculture, a critical pillar of global food security, has faced challenges including germplasm resource degradation, frequent disease outbreaks, and insufficient environmental adaptability. To address these issues, omics technologies—centered on genomics, transcriptomics, and related fields—are driving the transformation of aquaculture toward precision and intelligence by establishing a three-dimensional “genotype–phenotype–environment” knowledge network. In this review, we outline foundational applications of omics in aquaculture and highlight the characteristics and current applications of distinct omics approaches. Furthermore, we summarize three core application domains of omics in aquaculture: (1) genetic breeding via genomic selection (GS), gene editing, and multi-omics molecular dissection of economic traits; (2) disease prevention through host–pathogen interaction studies and microbial engineering; and (3) environmental assessment through multi-omics characterization of biological and ecological responses. Additionally, we identify challenges in applying omics technologies to aquatic breeding and database development. Overall, this review aims to provide aquaculture practitioners with actionable insights by enhancing understanding of omics-driven innovations in sustainable aquaculture advancement.
Despite the challenges in standardization, cost, and large-scale validation, advances in artificial intelligence, portable analytics, and open access omics data bases are accelerating the translation of these technologies into practical feed innovation.
Mir Ishfaq Nazir, Pankaj Gargotra, O. Asimi et al.· Frontiers in Fish Science· 0 citations
Background: Livestock production is indispensable for global food security, yet it faces constant pressure to maintain a sustainable balance between environmental, economic, and ethical demands. Objective: This study explores the past, present, and future of the livestock industry through genomic technologies. Methods/Approach: Tools such as Next-Generation Sequencing (NGS), Whole-Genome Sequencing (WGS), Genome-Wide Association Studies (GWAS), and Genomic Selection (GS) have accelerated precise genetic improvement of traits like feed efficiency, productivity, and disease resistance. Key Tools: CRISPR-Cas9 gene-editing further fosters livestock with improved genetic potential. Impact: Genomic-based breeding strategies reduce environmental impacts through lower methane-emitting ruminants and rumen microbiome utilization, while enhancing animal welfare via selection for stress resilience and innate disease resistance, reducing veterinary intervention. Conclusion: Overall, genomic approaches provide a powerful pathway for healthier, more productive, and sustainable livestock, supporting global food security amid climate and resource challenges.
Muhammad Younus· Zeugma Biological Science· 0 citations
Agricultural systems are increasingly challenged by climate change, resource scarcity, environmental degradation, and the need to ensure food security for a growing global population. Addressing these multifaceted challenges requires innovative, science-driven approaches that enhance productivity while promoting ecological sustainability. This editorial examines the evolving role of plant biotechnology as a key driver of modern agricultural advancement, highlighting recent developments in genomics, multi-omics technologies, molecular breeding, genome editing, plant tissue culture, and digital agriculture. It discusses how these technologies contribute to the development of resilient crop varieties with improved tolerance to biotic and abiotic stresses, enhanced nutrient-use efficiency, and superior agronomic performance. The editorial further emphasizes the integration of artificial intelligence, high-throughput phenotyping, bioinformatics, and systems biology into precision crop improvement, enabling more efficient translation of molecular discoveries into field applications. The importance of interdisciplinary collaboration, responsible innovation, biosafety, and science-based regulatory frameworks is also considered in supporting the sustainable deployment of emerging biotechnologies. Looking ahead, the convergence of advanced molecular tools with computational and ecological sciences is expected to accelerate the development of resilient agricultural systems capable of addressing future environmental and food production challenges. Continued investment in research, technological innovation, and international collaboration will be essential to maximize the societal and environmental benefits of next-generation crop improvement strategies.
Md. Mosharraf Hossen· Journal of Agriculture and F...· 0 citations
This review synthesises the current state of genomic and transcriptomic resources available for spiny and slipper lobsters (Achelata), contextualising these within the broader decapod framework and proposing future directions integrating genomics, transcriptomics, and functional tools to accelerate sustainable domestication and genetic improvement of lobsters.
Courtney Lewis, Ahmad Farhadi, Susan Glendinning et al.· Reviews in Aquaculture· 0 citations
This work provides a critical evaluation of the functional gaps between genomic potential and in situ microbial activity and offers a novel synthesis of how multiomics integration and predictive modeling can move beyond species cataloging toward a more robust, evidence‐based framework for environmental sustainability.
Poultry meat and egg quality result from complex interactions among host genetics, metabolism, nutrition, microbiome ecology, physiology, management practices, and environmental conditions. These multidimensional interactions limit the predictive capacity of conventional phenotype-based approaches and increasingly necessitate systems-level frameworks capable of capturing biological complexity. Recent advances in multi-omics technologies have transformed poultry quality research by enabling integrated analyses of genomic, transcriptomic, proteomic, metabolomic, lipidomic, epigenomic, and microbiome datasets. These approaches have substantially enhanced understanding of the molecular, cellular, physiological, and ecological networks associated with product quality, production efficiency, physiological resilience, and environmental adaptation. Integrated multi-omics analyses, particularly when combined with artificial intelligence and machine-learning approaches, have the potential to identify biologically interpretable biomarkers, candidate mechanistic pathways, and predictive signatures associated with meat and egg quality traits; however, most proposed signatures remain at early stages of validation and require rigorous external testing before commercial deployment. This review synthesizes current advances in omics-driven poultry research and critically evaluates emerging applications in precision nutrition, breeding, health monitoring, environmental adaptation, and sustainable production systems. To provide a unifying biological framework, we propose the Adaptive Systems Theory of Poultry Quality (ASTPQ), which conceptualizes poultry quality as an emergent adaptive phenotype arising from coordinated interactions among mitochondrial function, redox homeostasis, immune competence, metabolic flexibility, physiological resilience, endocrine–immune regulation, and host–microbiome dynamics. Within this conceptual framework, adaptive-system capacity is proposed as the principal integrative mechanism linking molecular regulation with phenotypic quality outcomes across diverse production environments. Despite substantial advances, commercial implementation remains constrained by biological heterogeneity, methodological variability, limited external validation, computational complexity, challenges in data integration, infrastructure requirements, and economic barriers. Current evidence suggests that predictive performance depends less on increasing molecular dimensionality than on developing biologically interpretable, externally validated, economically feasible, and operationally scalable systems. Future progress will likely require integrated precision-production frameworks that combine molecular biomarkers, physiological monitoring, environmental sensing, microbiome-informed interventions, explainable artificial intelligence, and rigorous large-scale field validation to support sustainable, resilient, and commercially applicable poultry production systems.
A new method for surgically removing training examples from a model reveals that as datasets grow, the link between what a model learns and what it produces dissolves.
