Omics in mini-livestock: a genomic perspective on the future of sustainable food systems

Abstract

Mini-livestock refers to small vertebrates and invertebrates used as human food, animal feed, or for other beneficial purposes. They represent sustainable alternatives to conventional livestock, whose potential is now being revealed through advances in omics technologies. Omics approaches such as genomics, transcriptomics, proteomics, metabolomics, and epigenomics provide comprehensive insights into growth, reproduction, adaptation, and disease resistance of these species, enabling the identification of genetic markers to enhance breeding efficiency and to improve productivity. However, the application of omics technologies in mini-livestock remains limited due to challenges such as high costs, lack of reference genomes, and limited bioinformatics resources. Overcoming these barriers will be crucial for fully harnessing the potential of mini-livestock in improving global food security and environmental sustainability.

1 Introduction

“Mini-livestock,” from edible insects to small vertebrates, appear as a sustainable alternative to traditional livestock with hidden potential ready to be unleashed by the latest omics technologies. The term mini-livestock includes all vertebrates and invertebrates, which are used as human food, animal feed, a source of income, or for other applications for human benefit, and are at the same time extremely small forms of conventional farm animals or naturally small-scale mammals, birds, reptiles, molluscs or insects (Vietmeyer, 1984; National Research Council, 1991; Hardouin, 1995; Hardouin et al., 2003; Titilola et al., 2015; Ingweye and Kalio, 2020). Mini-livestock have a versatile use and were proposed to be highly beneficial in terms of raising them in small areas and clusters, especially in rural, peri-urban and urban regions to provide human food, capital or manure (Ogunjimi et al., 2012; Klapwijk et al., 2020). It is anticipated that, in the future, these animals will play an increasing role in food security and economically responsible food production (Branckaert, 1995; Paoletti and Dreon, 2005). Among other livestock, mini-livestock have been co-evolving over decades in stressful environments and adapted to harsh conditions with limited feed resources and water availability (Assan, 2013). As an example, edible mealworm species have an unpretentious nature, low water footprint and offer at the same time a protein-rich food resource (Miglietta et al., 2015; van Broekhoven et al., 2015; Sellem et al., 2024). In order to gain detailed insights into the biology of mini-livestock and to comprehensively understand the genetic basis of traits of interest for future breeding strategies, omics technologies such as genomics, epigenomics, transcriptomics, proteomics or metabolomics have been increasingly recognized as key tools for translating the genome into the phenome (Riggs et al., 2017; Rexroad et al., 2019; Yang X. et al., 2020; Chakraborty et al., 2022; Verardo et al., 2023). Particularly, DNA and RNA sequencing analyses have been frequently applied for conventional and mini-livestock, investigating specific characteristics such as body development and condition, feed conversion, meat quality, or growth (Hu et al., 2007; Lu et al., 2021; Posbergh and Huson, 2021; Manzanilla-Pech et al., 2022; Silva-Vignato et al., 2022; Zhang C. et al., 2022).

Therefore, the aim of this review is to give a comprehensive understanding of mini-livestock and their potential roles across various domains. It explores the application of different omics approaches in advancing mini-livestock research, particularly with regard to improving productivity, product quality, addressing biomedical questions and reducing greenhouse gas emissions and highlights existing limitations and challenges.

2 The concept of mini-livestock

The definition of mini-livestock has evolved over time, making the list of animals classified under this term broad, dynamic, and subject to change (Ingweye and Kalio, 2020). Prior to the current definition of mini-livestock, various earlier terms were employed to describe smaller farm animals (e.g., poultry, rabbits or guinea pigs), as well as breeds that are half of their original size, including micro-pigs or micro-cattle (National Research Council, 1991; Ingweye and Kalio, 2020). The term mini-livestock is not limited to endothermic animals, but also includes ectothermic species like snakes, snails, lizards, frogs, silkworms, honey bees and crickets (Cicogna, 1992; Defoliart, 1995; Hardouin, 1995; Imoru and Babadipe, 2019). Likewise, edible insects like the yellow mealworm, the common house fly or the black soldier fly were considered as mini-livestock and have the highest potential of being animal feed (Voulgari-Kokota et al., 2023).

In the mid-1980s in Latin America, researchers began to categorize livestock based on their different sizes, distinguishing between “main-frames” like cattle and “mini-frames” like sheep. Additionally, the term micro-livestock was introduced to describe small animals suited for household husbandry, especially in resource-poor urban or rural environments in the Global South (Vietmeyer, 1984; Titilola et al., 2015). Micro-livestock typically includes species that adapt to harsh environments, efficiently recycle nutrients, or utilize unconventional resources like rabbits, guinea pigs, or bees (Peters, 1987). However, the term “micro” has also been used more broadly to describe microorganisms, including yeast, fungi or bacteria that serve as a protein source (Hardouin, 1995).

Another early root of the term mini-livestock was the label “unconventional livestock”, referring to species that are not used in conventional agriculture or traditionally domesticated (Peters, 1987). To further differentiate such species, some authors proposed using a relative weight production index, which accounts for the live weight at purchase and meat yield of offspring in proportion to the annual availability of reproductive females (Cicogna, 2000). Based on this index, guinea pigs produced meat equivalent to 6–10 times their own live body weight per year, whereas cattle yielded only about 0.4 times their body weight annually (Cicogna, 2000).

Despite these different backgrounds, the use of the terms “unconventional livestock”, “micro-livestock” or “mini-livestock” remained inconsistent across scientific publications and other sources, which can lead to confusion regarding the species included by these concepts (Hardouin, 1995; Ingweye and Kalio, 2020). Mini-livestock were reported to reproduce quickly, in high numbers, and are economically efficient, resulting in a higher input-output ratio (Hardouin et al., 2003; Titilola et al., 2015). More recently, environmental sustainability has become a key criterion: mini-livestock often generate lower greenhouse gas emissions (CO2-equivalents) per unit of protein produced than conventional livestock (Schanes et al., 2016; Imoru and Babadipe, 2019; Ghosh et al., 2021; Bai et al., 2023). As livestock production is considered to be a large contributor to climate change, accounting for up to 14.5% of all anthropogenic greenhouse gas emissions, more and more efforts are made to study mini-livestock as a potential alternative protein resource (Gerber et al., 2013; Alexander et al., 2017). For example, a study on Global Warming Potential suggested that insect-based resources had a significant potential to reduce the carbon footprints of European consumers, especially when insects are directly consumed as food or used for feeding in broiler production systems (Van Alfen, 2014; Vauterin et al., 2021).

The emission intensity of greenhouse gas has been shown to vary widely across different countries, livestock species, breeds and production systems (Hyslop, 2008; Herrero et al., 2013). Among different strategies to mitigate emissions, breeding schemes that promote the selection of traits enhancing production efficiency, such as residual feed intake or longevity, were suggested to reduce overall emissions (Wall et al., 2010). Ruminants, in particular, served as a model species in this context, as they have been extensively studied for their potential to reduce enteric methane emissions through selective breeding (de Haas et al., 2021). Overall, these studies suggest that genetic selection, alongside improved management practices, offers substantial potential for reducing livestock emissions on a global scale. However, the growing impact of climate change highlights the limitations of mitigation-focused breeding approaches and the pressing need for breeding strategies towards climate-resilience to maintain system integrity (Mutale et al., 2025). Omics approaches present a critical resolution for the identification and selection of traits providing resilience to high temperatures, drought, and climate-driven diseases (Liu et al., 2015; Vitorino Carvalho et al., 2021; Reed et al., 2023; Shi et al., 2023; Feng X. et al., 2024; Karami et al., 2025). In addition to pure physiological tolerance, a variety of adaptive mechanisms have emerged from multi-omics analysis, such as microbially driven plasticity, nutritional stress-driven metabolic flexibility, and epigenetic control in response to varying environments (Wallberg et al., 2017; Guilliet et al., 2022; Sukmak et al., 2024). Furthermore, genomic understanding of photoperiodic and light responses opens a way to consolidate reproductive rhythms or ontogenetic trajectories affected by different climate regions (Morris et al., 2020; Yuyan et al., 2025; Zhao X. et al., 2025).

Building on this, the integration of multi-omics data into breeding programs is essential to unravel the complex biological mechanisms underlying trait variation, enabling more precise and effective selection strategies, not only in conventional livestock but also in emerging mini-livestock species, which have so far received limited attention in this regard (Berry et al., 2011; Yang et al., 2017; Fonseca et al., 2018; Ahmad et al., 2022; Liu and Penagaricano, 2025). While the primary focus of mini-livestock research is on species that contribute to sustainable food and feed production, it is important to recognize that breeding strategies for certain miniature vertebrates, such as laboratory-bred miniature pigs, have followed a different trajectory. These animals are not typically part of food-oriented mini-livestock systems but have made significant contributions as biomedical models, addressing important clinical and translational research questions (Arora et al., 2022; Chakraborty et al., 2022; Jia Y. et al., 2024; Miao et al., 2024b; Zhang J. et al., 2025).

3 A multi-omics perspective on mini-livestock

To fully exploit the genetic potential of mini-livestock in sustainable production systems (Figure 1), comprehensive multi-omics approaches, encompassing genomics, transcriptomics, proteomics, epigenomics, and/or metabolomics, are needed to decode the molecular basis of key traits (Riggs et al., 2017; Yang et al., 2017; Poma et al., 2022). The advent of omics technologies has transformed biological research by shifting the focus from isolated molecular components to the comprehensive analysis of complex biological systems under varying conditions (Hasin et al., 2017; Karczewski and Snyder, 2018). These technologies offer powerful tools for advancing mini-livestock research, enabling deeper insights into the molecular mechanisms underlying key traits (Poma et al., 2022; Xiong et al., 2023; Jia X. et al., 2024). Traditional breeding approaches, initially based on phenotypic selection and later supported by marker-assisted selection (MAS) based on Quantitative Trait Loci (QTL), have contributed significantly to genetic improvement (Meuwissen et al., 2001; Ikeobi et al., 2002; de Koning, 2016). However, these approaches faced limitations, particularly in identifying the genetic basis of complex traits due to high costs and limited resolution (Lande and Thompson, 1990; Grisel, 2000). The integration of genome-wide approaches, such as genome-wide association studies (GWAS), has further advanced our understanding of trait architecture and genetic variation across species, though such studies may still fail to capture systematically gene-gene and gene-environment interactions (Andersson, 2009; Ober et al., 2012; Fang et al., 2019). Subsequently, whole genome sequencing (WGS) has significantly improved the prediction accuracy compared to standard SNP arrays and allowed a more comprehensive assessment of genetic diversity across populations (Moghaddar et al., 2019; Xia et al., 2021). Further insights into the gene expression regulation have been offered by transcriptomics, highlighting the underlying mechanisms and gene-regulatory pathways for traits of interest (Xue Q. et al., 2017; Li J. et al., 2022). Beyond these studies solely focusing on the genetic code itself, epigenomics has emerged as a more recent field, which targets DNA modifications that affect gene expression without altering the DNA sequence itself (Wang and Ibeagha-Awemu, 2020; Zhou et al., 2020). Epigenetic processes, including DNA methylation, chromatin remodelling, histone modifications and non-coding RNA-activity, can lead to heritable changes in gene expression and have been shown to play key roles in development, adaptation, and phenotype variability (Lyko et al., 2010; Felsenfeld, 2014; Herman et al., 2014; Sarg et al., 2015). To fully understand how these regulatory mechanisms shape biological functions, proteomics adds another layer of information targeting the actual protein products and their roles in cellular processes (Haider and Pal, 2013; Aslam et al., 2017). Proteomic analyses enable the investigation of changes in protein levels, post-transcriptional modifications, and interactions in response to various stimuli (Franco et al., 2015; Sierra et al., 2021). Furthermore, metabolomics, as the downstream layer of omics, focuses on the comprehensive profiling of small-molecule metabolites. By examining the metabolome, researchers can gain insights into metabolic pathways and how they impact cellular processes and phenotypic variations (Poma et al., 2022; Du et al., 2025). Complementing these host-centric approaches, metagenomics analyses the collective genomes of microbiota, revealing how microbial communities influence digestion, immune competence, and environmental resilience as factors especially important to the productivity and sustainability of mini-livestock species (Yang et al., 2022; Hou et al., 2024).

FIGURE 1

To fully exploit the complementary nature of these omics’ layers, integrative analytical frameworks are increasingly required. Multi-omics integration enables the joint analysis of diverse molecular layers to understand biological mechanisms that are not detectable when studied in isolation (Hasin et al., 2017; Baiao et al., 2025). Classical approaches rely on correlation-based techniques such as Canonical Correlation Analysis (CCA), which identifies shared patterns across datasets and has been widely applied to multi-omics applications (Jiang et al., 2023). Network-based strategies, including Weighted Gene Co-expression Network Analysis (WGCNA), enable the construction of cross-layer regulatory modules and facilitate the identification of key molecular drivers underlying complex traits (Langfelder and Horvath, 2008). Complementary knowledge-driven approaches, such as pathway and functional enrichment analysis, integrate multi-omics signatures into curated biological pathways through resources such as Kyoto Encyclopedia of Genes and Genomes (KEGG) and Reactome, providing mechanistic interpretability (Kanehisa, 2002; Jassal et al., 2020). More recent advances make use of machine learning, including random forests and deep learning, which can model non-linear relationships and high-dimensional feature interactions characteristic of multi-omics datasets (Libbrecht and Noble, 2015; Abbasi et al., 2024). Together, this integration of omics layers provides a resource for dissecting complex traits and guiding precision breeding and management strategies (Yang et al., 2017).

The technological advances and integrative analytical capabilities have also contributed to the growing adoption of omics-based research in mini-livestock species, with publication numbers increasing steadily since 2010 (Figures 2A,B). In invertebrates, epigenomics was the first omics discipline to be reported, with studies dating back to 1964, whereas transcriptomics was introduced much later, with the first publications appearing in 2007. In vertebrates, the first record in the field was in epigenomics in 1974, and transcriptomics was introduced as the latest field in 2002. Beyond these initial milestones, the composition of the field has shifted considerably. The relative contribution of functional omics, most notably transcriptomics and proteomics, has increased markedly in recent years, as illustrated in Figures 2C,D. However, studies focused on mini-livestock still represent only a small fraction of the approximately 2.5 million omics-related publications currently indexed in PubMed (https://pubmed.ncbi.nlm.nih.gov/), with genomic studies making up the majority of this research. With this growing body of research on omics technologies and their potential applications, we can now examine how these approaches are being applied to different mini-livestock groups. Among them, insects have emerged as a particularly well-studied category, with numerous omics investigations providing deep insights into their physiology, genetics, and potential for sustainable production.

FIGURE 2

4 Omics in invertebrates

Invertebrates, particularly those considered as mini-livestock, have become a major focus of recent omics research due to their potential for sustainable protein production. Among them, edible insects have been studied extensively, revealing key adaptations and breeding opportunities (Table 1). In this review, species are presented according to the extent of available scientific literature and ongoing research activities, which is influenced by industrial relevance, and does not reflect any intended prioritization by the authors. For example, the black soldier fly (BSF, Hermetia illucens), has emerged as a model species due to its nutritional profile, rearing efficiency, and strong potential as a sustainable protein source (Rumpold and Schlüter, 2013; Huang et al., 2019; Zhan et al., 2020). Subsequently, a novel chromosome-level genome assembly was generated in BSF using long reads, linked short reads and chromatin conformation data (Hi-C) (Generalovic et al., 2021). In addition, another research group constructed a high-quality genome from BSF based on short-read sequencing data and highlighted its findings of 50 antimicrobial peptides, the largest antimicrobial peptide family identified in insects to date, as well as a set of core microbiota suggesting a targeted adaptation of BSF to a pathogen-rich environment and digestion of organic waste (Zhan et al., 2020). Using these reference genomes, multiple studies performed WGS to monitor inbreeding of populations and applied genomic information for commercial breeding programs (Cai et al., 2024). To construct mitochondrial assemblies, whole genome shotgun sequencing was performed, enabling the reconstruction of phylogenetic relationships among major lineages and revealing their long-term evolution with low genomic diversity (Guilliet et al., 2022). Furthermore, WGS-based annotations of gene and protein functions of different BSF strains highlighted major metabolic gene functions and pathways involved in nutrient and energy metabolism (Sukmak et al., 2024). Similarly, initial insights into the genomic potential of the house cricket (Acheta domesticus) as a food source and for broader applications were provided by sequencing and assembling a reference genome using both long-read sequencing, Chicago libraries, and Hi-C data (Dossey et al., 2023). Furthermore, sequencing, assembly and annotation of the Mediterranean field cricket (Gryllus bimaculatus) genome, and its comparison to the Hawaiian cricket (Laupala kohalensis) and other insects, revealed that hemimetabolous (incomplete metamorphosis) genomes have expanded largely through transposable element activity (Ylla et al., 2021). In the yellow mealworm (Tenebrio molitor), long and short reads and long-range data obtained from a male pupa, transcripts from 12 different life stages/sexes, as well as an adult individual’s head, highlighted the challenge posed by a comparatively large genome with mostly homogeneous satellite DNA sequences of high copy numbers, and provided a framework for future genomics studies (Eriksson et al., 2020; Eleftheriou et al., 2021; Oppert et al., 2023). A similar approach was used to build a reference genome for the superworm (Zophobas morio = Zophobas atratus) and a further independent reference for the yellow mealworm (Kaur et al., 2023). Subsequent comparison of both genomes revealed extensive macrosynteny across the family Tenebrionidae, as well as numerous within-chromosome rearrangements (Kaur et al., 2023). An even higher level of reference genome completeness was reached in the silkworm (Bombyx mori), for which researchers produced a telomere-to-telomere assembly using long-read sequencing technologies as well as a reference transcriptome (Kawamoto et al., 2019; Yokoi et al., 2021; Zhang T. et al., 2022). In the earthworm (Eisenia andrei), researchers produced a chromosome-level reference genome, a large-scale transcriptome and single-cell RNA-sequencing data to investigate the cause for its strong regenerative ability (Shao et al., 2020). Furthermore, de novo assembly methods were applied in the honey bee (Apis mellifera/dorsata/cerana) to produce draft genomes, which were used to support research in the field of social communication, defensive aggression and scouting behaviour (Elsik et al., 2014; Park et al., 2015; Southey et al., 2016; Oppenheim et al., 2020). Functional genomic analysis revealed 60 genomic variants associated with scout and recruit behavioural castes, within 39 genes corresponding with neuronal function, exoskeleton, immune response, salivary gland development and enzymatic food processing (Southey et al., 2016). In addition, genomic sequences from modern and historic honey bee populations were studied for potential genetic bottlenecks, selection signatures and diversity (Wragg et al., 2016; Parejo et al., 2020; Minozzi et al., 2021; Chen C. et al., 2022).

As a result, genomic studies have provided a comprehensive understanding of the genetic code, whereas epigenetic research has elucidated the complex interplay between environmental factors and gene expression in insect mini-livestock. The utilization of insects as model organisms in epigenetic studies has yielded significant insights into the epigenetic mechanisms underlying disease susceptibility and transgenerational inheritance patterns (Mukherjee et al., 2015). For example, studies on silkworm revealed the role of DNA methylation in rapid phenotypic adaptation mediated by DNA methyltransferase 1 (DNMT1), which might have contributed to the domestication of this species (Xiang et al., 2013; Wang X. et al., 2021). However, on the level of gene expression, transcriptomics studies showed that modern breeding potentially had a stronger selection effect on silk yield traits and pathogen tolerance in the silkworm than its domestication (Fang et al., 2015; Li et al., 2016; Luan et al., 2018). Furthermore, RNA-seq data from larvae provided an insight into the resistance and susceptibility of different strains against fungal, virus or bacterial infection, thermo-tolerance, diapause preparation, detoxification mechanisms and antioxidant defence, highlighting the complexity of the silkworm transcriptome (Li et al., 2012; Chen et al., 2017; Xiao et al., 2017; Xing et al., 2017; Guo et al., 2018; Jiang et al., 2020; Sun et al., 2020; Zhang R. et al., 2021; Ye et al., 2024; Yi and Wu, 2024). In contrast to protein-coding mRNAs, long non-coding RNAs were postulated to be even more specific for different silkworm tissues and were found to be involved as regulators of the biosynthesis, translocation, and secretion of silk proteins (Wu et al., 2016). At even higher resolution, single cell sequencing of silkworm haemocytes revealed a high level of specialization of these cells and showed a significant effect of RNA interference (RNAi) suppression induced by a baculovirus infection (Feng M. et al., 2021; Feng et al., 2022). A similar strategy was used to build long-read-based transcriptome and single-cell transcriptome atlases of the silk gland, which offered a comprehensive and detailed understanding of its function and regulation (Chen et al., 2020; Ma et al., 2022). This was even enhanced by a spatiotemporal transcriptomic atlas of the silk glands, and multi-omics approaches, providing a valuable reference for elucidating the mechanism of efficient silk protein synthesis (Xu H. et al., 2022; Ma et al., 2024). On the protein level, silk gland development and silk protein protection and compositions further completed the picture (Zhang et al., 2006; Dong et al., 2013; Li et al., 2015).

Similar to the silkworm, functional omics studies in BSF were aimed at the non-food potential of this species. De novo transcriptome sequencing has identified genes involved in fat metabolism, thereby contributing to more economical BSF-based biodiesel production (Zhu et al., 2019). In contrast, transcriptome, metabolome and proteome analyses of BSF larvae have elucidated bioconversion performance under different temperature conditions and dependent on the type of organic waste, as well as highlighted an association of UV light treatment of larvae with the generation of functional proteins and bioactive compounds (Lu J. et al., 2022; Zhang S. et al., 2022; Feng X. et al., 2024). It was found that the active intestinal microbes and their functional genes in the BSF gut microbiome delineated the genetic variability in wild-collected and domesticated BSF populations from different continents and showed a response to high concentrations of antibiotics (Khamis et al., 2020; Pei et al., 2023).

In the yellow mealworm, gene expression profiling was particularly carried out to learn more about tissue- or developmental stage-specific genes and their potential function, identifying enzymes involved in chitin metabolism, parasitoid-induced immune-related genes, and factors underlying long-lasting immune response to bacterial challenge (Johnston et al., 2013; Zhu et al., 2013; Li L. et al., 2022). Similarly, a de novo transcriptome assembly and functional annotation in the superworm was used to predict antimicrobial peptides and haemolytic activity (Lee et al., 2021). Both yellow mealworm and superworm also underwent transcriptomic and mass spectrometry analyses of the central nervous system to identify neuropeptides and neuropeptide-like and protein hormones (Marciniak et al., 2022). In general, the brain has been a target of various insect species studies, due to the interest in socially regulated behaviour such as division of labour among honey bees. For example, mRNA or microRNA expression levels in the honey bee brain have been analyzed for their role in behavioural specialization of adult workers or queen (Greenberg et al., 2012; Manfredini et al., 2015). Single-nucleus RNA sequencing and spatial transcriptomics of the honey bees brain revealed expression patterns of brain cells associated with the behavioural maturation from nursing to foraging (Mu et al., 2025). In addition, honey bee caste differentiation was investigated using high-throughput RNA-Seq of larvae (Chen et al., 2012; Mao et al., 2015; He X. J. et al., 2019). More recently, single cell RNA sequencing (scRNA-seq) has been applied to identify caste differentiation-related factors in the queen and to map cell types across developmental stages (prepupa at day 11 and pupa at day 15) of worker honey bees (Zhang W. et al., 2022; Patir et al., 2023). Furthermore, the impact of numerous biotic stressors on honey bees has been extensively studied using functional omics methods; Researchers employed RNA-seq to characterize immune responses to parasite infection or insecticide exposure, and examined methylation patterns in the fat body linked to virus infection (Galbraith et al., 2015; Badaoui et al., 2017; Shi et al., 2017; Fent et al., 2020). Similarly, immune responses were studied in the red palm weevil (Rhynchophorus ferrugineus), with a primary focus on effective pest-management through potential gene knockdowns, although its possible application as a food source was also considered (Yang H. et al., 2020; Fernando et al., 2023).

Although most of these above-mentioned studies focused on individual research questions and species, efforts have also been made to analyse omics data across species; For example, High-Performance Liquid Chromatography–Tandem Mass Spectrometry (HPLC-MS/MS) technology was applied to construct a novel integrated metabolic database for nine insect species across three metamorphosis types identifying 1,442 metabolites (Li et al., 2023). The study by Li et al. revealed significantly enriched pathways, including ABC transporters and tyrosine metabolism, thereby creating a valuable reference that enhances our understanding of insect metabolic evolution and adaptation (Li et al., 2023). Genome assemblies of insects and further omics data have been collected in the InsectBase (http://www.insect-genome.com), which has made significant progress by storing more than 16 million sequences from 815 species to date (Yin et al., 2016; Mei et al., 2022). However, this represents only a small fraction of the 2,205 insects classified as edible (Omuse et al., 2024), highlighting the need for greater efforts to gather more comprehensive data on these species. Modern urban insect farming projects, such as small-scale cricket rearing initiatives in Kenya, BSF bioconversion facilities in Singapore, and a yellow mealworm pilot production in the Netherlands, demonstrated how insect breeding could be integrated into future city environments to supply sustainable protein and manage organic waste streams (Ayieko et al., 2016; Dalton and Al-Zubiedi, 2019; Ramzy et al., 2025).

Beyond insects, other invertebrate groups such as molluscs (Table 2) are also gaining attention, particularly because of their central role in aquaculture and their unique physiological adaptations revealed by omics studies (Klein et al., 2019). As with insects, several mollusc species were already characterized through de novo genome sequencing and chromosome-level assemblies (Liu C. et al., 2018; Guo et al., 2019; Chueca et al., 2021; Ma et al., 2023; Ishii et al., 2025). The combination of short reads and long reads with Hi-C sequences and a transcriptome for annotation substantially improved assembly quality, surpassing that of most other Panpulmonata proteomes (Guo et al., 2019; Ma et al., 2023; De Jode et al., 2024; Ishii et al., 2025). Thus, in the rough periwinkle (Littorina saxatilis/arcana), WGS and mapping to the annotated genome allowed to study inversion polymorphisms often widespread across the species and associated with rapid parallel adaptation to heterogeneous environments (Morales et al., 2019; Reeve et al., 2024). Similarly, the rapid adaptive capacity of the invasive apple snail (Pomacea canaliculate) to diverse environments was allocated to its high genetic diversity studied by different omics techniques (Lu et al., 2024). Furthermore, sequencing of the transcriptome and small RNA sequencing (sRNA-seq) in the Asian tramp snail (Bradybaena similaris) highlighted genes and regulatory elements involved in xenobiotic metabolism (Yang Q. et al., 2020). This complex system for metabolizing xenobiotics was also found to be affected by arsenic pollution in the apple snail, displaying a dose-dependent effect on growth (Bi et al., 2024). To learn more about the genetics of the family of apple snails, the Ampullariidae, a transcriptome database was generated for seven subspecies using WGS and previous RNA-seq data (Ip et al., 2018). Subsequently, taking full advantage of this database, a multi-omics approach was applied to study the perivitelline fluid proteome from apple snail eggs, highlighting the adaptive capacities of different subspecies (Ip et al., 2019).

5 Omics in vertebrates

Omics technologies have significantly advanced research not only in invertebrates but also in vertebrate species classified as mini-livestock, though with distinct applications and challenges. In particular, mammalian research (Table 3) has benefited substantially from progress in the genomic field (Georges et al., 2019). As noted above for the invertebrates, the emphasis on certain species reflects the current availability of scientific literature and research activity, rather than a prioritization by the authors.

In livestock species, such as the domestic pig, reference genome sequences have provided a foundation for genetics and genomics research, enabling the use of genetic variants to study breed-specific traits and signatures of selection (Warr et al., 2020; Berghofer et al., 2022). These findings in porcine genetics were not limited to large high-production pigs but could be readily applied to minipigs, offering new opportunities for basic research, medical applications and breeding programs (Wang et al., 2020; Arora et al., 2022; Jia Y. et al., 2024). Research in miniature pigs has led to the creation of further high-quality chromosome level genome assemblies for various breeds (Bama, Banna, Goettingen, Korean, Ossabaw, Wuzhishan, Wisconsin) that have provided a critical framework for genetic studies (Fang et al., 2012; Heckel et al., 2015; Zhang et al., 2019; Zhang Y. et al., 2021; Chen et al., 2024; Wy et al., 2024; Veith et al., 2025). Re-sequencing and mapping to these genomes have enabled the discovery of selection signatures, facilitating the detection of body-size associated genes such as PLAG1, CHM, and ESR1 and further breed- or minipig-specific traits (Miura et al., 2014; Heckel et al., 2015; Lu et al., 2016; Reimer et al., 2018; Kwon et al., 2019; Son et al., 2020; Wu F. et al., 2020; Berghofer et al., 2022; Kwon et al., 2024). The availability of these genomic studies has also enhanced the value of the minipigs as donors for xenotransplantation (Wang Y. et al., 2024; Peng et al., 2025), for toxicological testing (Liu et al., 2008; Bode et al., 2010; Flisikowska et al., 2022), and use as disease models (Curtasu et al., 2019; Curtasu et al., 2020; Li P. et al., 2020; Vaure et al., 2021; Niu et al., 2023; Jia Y. et al., 2024). For example, in the Göttingen minipig, characteristic diet-epigenome interactions were studied for future treatment of obesity, whereas findings in the back muscle of Diannan small-ear pig highlighted the expression of key genes involved in lipid metabolic and fatty acid biosynthetic process, as well as miRNAs regulating lipid deposition and muscle growth (Wang et al., 2015; Feng Y. et al., 2021). Similarly, metabolomic profiling revealed rapid shifts in plasma metabolites after feeding and obesity-related biomarkers after long-term intake of fructose and resistant starch (Polakof et al., 2015; Curtasu et al., 2020). In Bama pigs, promoter-enhancer interactions were shown to be highly dynamic in adipose depots, whereas large-scale compartments of the chromatin and topologically associated domains (TADs) were mostly conserved (Zhang J. et al., 2022).

Building on these insights into tissue-specific growth processes, transcriptome profiling was applied in the pituitary gland to explore the dynamic gene expression patterns during postnatal development in Bama pigs, as well as to characterize mesenchymal stem cell populations in prepubertal Mini-LEWE, providing a broader perspective on growth regulation in minipigs (Shan et al., 2014; Khaveh et al., 2024). Moreover, the establishment of a single-cell transcriptomic profile of mini-pigs has provided a valuable resource for dissecting cell- or nuclei-specific gene expression patterns with applications ranging from postnatal testicular development to metabolic diseases and immune cell maturation (Miao et al., 2024b; Wang X. et al., 2024; Chen et al., 2025; Zhang J. et al., 2025). Subsequently, numerous studies have particularly focused on minipigs for various reasons, including their small size or biomedical relevance.

Nevertheless, there are other vertebrates traditionally raised as mini-livestock, such as members of the family of rodents, e.g., guinea pigs (Cavia porcellus), capybaras (Cavia aperea), African mole-rat (Cryptomys hottentotus) or cane rat (Thryonomys swinderianus), which also underwent omics studies targeting growth, adaptive evolution or their potential as unconventional meat species (Guo et al., 2012; Weyrich et al., 2014; Sahm et al., 2018; Dalle Zotte and Cullere, 2019; Babarinde and Saitou, 2020; Herrera-Alvarez et al., 2021; Schyman et al., 2021; da Silva et al., 2024). Comparative epigenomic profiling suggested that evolutionary changes in regulatory elements underlie key metabolic and physiological adaptations in naked mole-rat and other African mole-rat species, providing insights into their unique traits and offering a new phylogenetic framework for studying regulatory evolution across species (Parey et al., 2023). Omics studies in rabbits (Oryctolagus cuniculus) investigated evolutionary dynamics, highlighting gene alleles associated with brain development and aggression related to the domestication process (Albert et al., 2012; Carneiro et al., 2014a; Carneiro et al., 2014b). Alongside tameness, growth and meat quality were highly important traits under selection since domestication of rabbits, which were considered to be an ideal food source with high protein, low fat, low cholesterol and low sodium contents (Yang X. et al., 2020). Thus, marker genes were studied for these desired traits using Specific-Locus Amplified Fragment sequencing (SLAF-seq) and subsequent genome-wide association analysis (Yang X. et al., 2020). Furthermore, various research studies explored the metagenome and metabolome of the rabbit gut, caecum and colon across different conditions, attributed to the impact on host health and adaptation (Chen Y. et al., 2022; Wang J. et al., 2022; Hou et al., 2024; Paes et al., 2024; Zhao et al., 2024).

In addition to mammals, poultry (Table 4) has also become a major focus of omics research aiming at an increased production efficiency, disease resistance and understanding phenomena like epigenetic inheritance (Urgessa and Woldesemayat, 2023; Wadood et al., 2025). Population genomics studies characterized genetic diversity across chicken breeds, detecting selection signatures and identifying candidate genes for economically important traits (Fan et al., 2013; Li D. et al., 2019; Qanbari et al., 2019; Shi et al., 2023; Wang H. et al., 2023; Rabbani et al., 2024). By integrating genomics data with transcriptome information or by detecting differential gene expression levels in chicken, researchers could highlight functional genetic effects affecting egg production performance, feeding efficiency and meat quality (Mutryn et al., 2015; Zhou et al., 2015; Zhuo et al., 2015; Piorkowska et al., 2016; Ye et al., 2020; Huang et al., 2022; Cai et al., 2023; Cui et al., 2024; Liu Y. et al., 2024). Additionally, breast muscle proteomes from chicken kept in antibiotic-free or organic farming systems revealed differentially abundant proteins as putative biomarkers for meat or farming system authenticity (Alessandroni et al., 2024). For future enhanced integration studies of multi-omics chicken data, an omics data repository (GalBase) was constructed to facilitate the identification of genetic variants and functional genes associated with common traits of interest (Fu et al., 2022). Similarly, researchers in the field of duck genomics initiated the Duck 1,000 genomes project, which integrates multi-omics data for studies on economically important traits in ducks (Fan et al., 2024). Particularly, growth, feed conversion efficiency, meat quality and meat yield were investigated intensively using genomics, transcriptomics or mass spectrometry (Liu Y. et al., 2018; He J. et al., 2019; Hu et al., 2021a; Hu et al., 2021b; Cai et al., 2023; Cao et al., 2023; Hu and Liu, 2023; Mohammadi, 2024; Yang Y. et al., 2024) (In contrast, in Muscovy ducks, the strong female behaviour of incubating eggs instead of laying was of major interest and therefore explored with different omics methods (Wu et al., 2019; Lin et al., 2021).

Similarly to the duck’s reference genome, research benefited significantly from the availability of a chromosome-level assembly from Tianfu goose (Anser cygnoides) or a hybrid de novo assembly from Chinese indigenous goose, as well as the recent introduction of a “Goose Multi-omics Database” (Li Y. et al., 2020; Ouyang et al., 2022; Huang et al., 2025). Subsequent re-sequencing allowed studies on the evolutionary history of geese and gene flow in domestic populations (Ottenburghs et al., 2016; Wen et al., 2023). Furthermore, transcriptomic approaches using RNA-sequencing elucidated molecular mechanisms underlying economically important traits, including the identification of key genes for differential fat deposition, photoperiodic reproductive control and fertility (Liu Q. et al., 2024; Yuyan et al., 2025; Zhao X. et al., 2025). In a multi-omics joint analysis, the integration of genomic variants and single-cell transcriptomic information allowed the detection of two genes, LHX9 and ARID5B, potentially associated with the rate of degeneration in the right ovary of avian species (Ouyang et al., 2025).

Significant progress has also been made in the turkey (Meleagris gallopavo), where an initial draft genome was improved to a high quality, chromosome-level assembly (Dalloul et al., 2010; Barros et al., 2022). Subsequent population analyses identified large genomic regions under intense selection in commercial lines enriched for economically relevant genes (Aslam et al., 2012; Aslam et al., 2014). Furthermore, transcriptome sequencing studies provided insights into sperm motility, meat quality parameters and adaptation to high dietary selenium (Malila et al., 2014; Sunde and Taylor, 2019; Jastrzebski et al., 2022).

Comparable advances have also been made in the Japanese quail (Coturnix japonica), for which recent genome, transcriptome and mass spectrometry data provided valuable insights into egg white protein functions, lipid metabolism, social behaviour, stress responses, effects of photoperiod and temperature on eggs and birds, muscle development and plumage colour (Hu et al., 2016; Marasco et al., 2016; Wu et al., 2018; Khatri et al., 2019; Morris et al., 2020; Legrand et al., 2021; Vitorino Carvalho et al., 2021; Zhang W. et al., 2024). Similar traits were also of interest in studies on helmeted guinea fowl (Numida meleagris), in which signatures of selection in the genome were investigated for behaviour and locomotion changes or plumage colouration (Vignal et al., 2019; Shen et al., 2021).

Another group of vertebrates, namely amphibians and reptiles (Table 5), are also utilized as edible animals, providing alternative sources of high-quality protein in various regions of the world, and has increasingly been studied using omics approaches. For example, frogs are a popular source of meat in aquaculture worldwide and, like conventional livestock, have implications for both human nutrition and health (Boss et al., 2023; Zhang L. et al., 2025). Genomic studies in different frog species highlighted multi-genome synteny blocks, mechanisms driving or constraining genome size and positively selected genes for adaptation to high altitude (Sun et al., 2015; Lamichhaney et al., 2021; Chen et al., 2023). Transcriptome analyses in the American bullfrog (Lithobates catesbeianus) and the Heilongjiang brown frog (Rana amurensis) revealed target pathways for metabolism and immune response, as well as antimicrobial peptides in the skin (Li W. et al., 2022; Yang P. et al., 2024). In addition, chromosome-level assemblies and de novo transcriptome datasets of reptiles, such as edible lizards and salamanders, have been generated, providing valuable resources for studying their physiology and adaptation (Geng et al., 2017; Yurchenko et al., 2020; Westfall et al., 2021; Wang Q. et al., 2023). In summary, the application of omics technologies in a wide range of vertebrate mini-livestock has provided comprehensive insights into their genetic architecture, physiological regulation, and adaptive diversity.

6 Translating omics insights into mini-livestock improvement: challenges and future directions

The rapid advancement of omics technologies has generated extensive datasets describing the genetic, transcriptomic, proteomic, and metabolic architecture of mini-livestock. Harnessing these insights into practical breeding applications enables more accurate selection for desirable traits, such as growth, meat quality, fat metabolism, feed intake, tameness, and other key productive traits (Albert et al., 2012; Jegou et al., 2016; Southey et al., 2016; Zhong et al., 2022; Zhao Z. et al., 2025).

The global livestock sector faces the dual challenge of ensuring food security for a growing population while controlling its environmental footprint (Niu et al., 2024). In livestock science, omics technologies have become powerful tools for unravelling the molecular mechanisms underlying complex traits, thereby supporting more sustainable and efficient animal production (Chakraborty et al., 2022). Genomics accelerate the early selection of traits of interest, such as milk yield for dairy cows or the quantity of meat produced by beef cattle (Mesbah-Uddin et al., 2022; Nanaie et al., 2025). While these approaches have already advanced genetic improvement in major livestock species, their application in mini-livestock remains comparatively limited. However, the availability of high-quality reference genomes and transcriptomic datasets has recently enabled the first insights into the molecular backgrounds of important production and adaptation traits in these species (Chen et al., 2012; Generalovic et al., 2021; Ouyang et al., 2022; Wy et al., 2024; Chen et al., 2025). Sequencing or re-sequencing of mini-livestock species such as chicken, rabbit or honey bee resulted in millions of SNPs to identify genotype to phenotype correlations for complex traits (Carneiro et al., 2014b; Wallberg et al., 2017; Moreira et al., 2018).

While these omics applications yielded valuable insights into mini-livestock biology and production, several challenges and limitations remain to be addressed before their full potential can be realized in mini-livestock breeding and management. Omics technologies represent a paradigm shift in the optimization of mini-livestock systems by enhancing productivity, sustainability and biomedical relevance as already demonstrated in agricultural and conventional livestock species (Van Emon, 2016; Chakraborty et al., 2022; Rotimi, 2025). A primary limitation is the requirement for highly equipped laboratories and expensive instrumentation for omics data generation (Van Emon, 2016; Chakraborty et al., 2022). Protocols for sample collection and processing have to be optimized for each species and tissue type, as for example in insects, in which the pupal stage was suggested as the most suitable developmental stage for high-quality genomic DNA extraction (Oppert et al., 2019). Downstream of sequencing, the assembly of high-quality reference genomes for many species, particularly insects, remains challenging due to high polymorphism, limited DNA yield from small-bodied individuals, and difficulties in achieving homozygosity (Richards and Murali, 2015; Li F. et al., 2019). Data analysis on omics research requires substantial expertise in statistics, bioinformatics, and computational tools to ensure accurate interpretation (Yamada et al., 2021). Analytical frameworks such as statistical models and network-based approaches evolve rapidly, demanding continual updates to maintain performance (Jiang et al., 2019). Beyond the challenges of handling large and complex single-omics datasets, data integration remains a major obstacle for effective multi-omics analyses (Misra et al., 2019). Furthermore, the implementation of omics technologies in livestock faces practical challenges, as farmers’ acceptance largely depends on the extent to which these tools align with their expertise and operational needs (Hostiou et al., 2017; Vazquez-Diosdado et al., 2019). Thus, handling omics data requires domain-specific knowledge and proper study design to harness the full potential, particularly considering the growing scale of datasets and the economic importance of traditional livestock species (Thornton, 2010).

These limitations discussed above underline the need for future strategies that scale from individual studies to coordinated, community-driven efforts. Experiences from large international consortia demonstrate that shared standards, common data models, and openly accessible workflows are crucial for reproducibility and for lowering the entry barrier into omics research: The initiative of the Functional Annotation of Animal Genomes (FAANG) project, whose aim is to produce comprehensive maps of functional elements in the genomes of domesticated animal species, has already shown significant progress in filling the genotype-to-phenotype gap and the establishment of data analysis standards for various species, including mini-livestock (Andersson et al., 2015; Tuggle et al., 2016). Similarly, the ENCODE Consortium provides clear examples of how community-curated benchmark datasets, guides and uniform data-processing methods can provide comprehensive views of the organization and variability of genes and regulatory information across species and individuals (ENCODE Project Consortium, 2012). Furthermore, the 5,000 arthropod genomes initiative (i5K) highlighted that such coordination was also feasible for less conventional livestock and agricultural species (i5K Consortium, 2013). The European Reference Genome Atlas (ERGA) model, which distributed sequencing across 26 facilities and established coordinated library preparation hubs, offers valuable lessons for scaling mini-livestock genomics programs while ensuring broader participation from researchers in resource-limited settings (Mc Cartney et al., 2024).

A further major development in this context is the implementation of pangenome frameworks representing the global genomic diversity of a species rather than a singular reference (Wang T. et al., 2022). Such approaches are now beginning to appear also in non-traditional model organisms, for example, in the Asian honeybee (Apis cerana), in which the first pangenome recently demonstrated its feasibility in the analysis of genomic variations (Li Y. et al., 2024). This highlights the potential of transferring existing pangenome-related strategies from traditional livestock to mini-livestock to identify variants involved in phenotypic and genotypic diversity, environmental adaptation mechanisms and other desired phenotypes (Zhou et al., 2022; Miao et al., 2024a; Azam et al., 2025; Dai et al., 2025). Beyond improving variant discovery, these resources also provide a foundation for developing cost-effective genotyping approaches. Established procedures in traditional livestock, such as low-density SNP panels combined with imputation (van Binsbergen et al., 2015), genotyping-by-sequencing (Elshire et al., 2011), and low-coverage whole-genome imputation (Zhang et al., 2023), demonstrated that accurate genomic information could be obtained to perform large-scale genome-wide association studies and genomic selection. As more comprehensive variant catalogues and pangenomes become available, these methods could also be tailored to species and breeds where no commercial genotyping tools currently exist (Crysnanto et al., 2019; Bian et al., 2024).

Addressing the remaining gaps in omics applications will additionally require integrating phenotype-level information and advancing multi-layer analytical strategies (Brito et al., 2020). Artificial intelligence-driven phenotyping tools offer a promising route toward generating standardized, high-throughput behavioural and performance data, which are essential for linking molecular variation to observable traits (Distante et al., 2025). Developing applications in insect farming already demonstrated that artificial intelligence can automate identification, counting, behaviour detection, and health monitoring, providing scalable solutions for mini-livestock systems where manual data collection is difficult (Bjerge et al., 2022; Roy et al., 2024). When combined with genomic and management strategies, these technologies have the potential to improve production efficiency and reduce costs (Neculai-Valeanu et al., 2025; Papadopoulos et al., 2025), which are the key factors for ensuring the long-term sustainability and competitiveness of mini-livestock production systems in the future.

7 Conclusion

Omics technologies have demonstrated transformative potential across diverse mini-livestock species, as outlined in this review. Integrating multi-omics approaches has greatly enhanced our understanding of genetic architecture, physiological regulation, and adaptive diversity. Future advances will rely on expanding reference genomes, standardizing phenotyping, and applying robust integration frameworks, such as CCA, WGCNA, pathway-based enrichment tools, and machine-learning approaches. Together, these strategies can accelerate trait mapping and support breeding programs targeting growth efficiency, food and feed utilization, product quality, and resilience to environmental stressors. From a policy perspective, embedding multi-omics integration into agricultural strategies can facilitate evidence-based breeding, emission-reduction goals, and internationally harmonized data-sharing standards.

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