From genomes to hologenomes: integrating host and microbiome data for complex trait prediction in livestock and aquaculture

Over recent decades, global livestock and aquaculture systems have significantly increased protein production, largely driven by advancements in nutrition, health management, and selective breeding programs. The integration of genomic data, particularly dense SNP panels, into animal breeding has revolutionized trait prediction, enabling more accurate estimation of breeding values for complex traits such as growth, carcass yield, and disease resistance in animal farming. Currently, animal production faces new challenges, including production efficiency, environmental impact, and emerging and re-emerging diseases. There is broad evidence that variation in host-associated microbiomes is associated with host phenotypic diversity, allowing to predict complex traits in livestock and aquaculture. Additionally, the integration of host genomic and microbial metagenomic data has demonstrated potential to improve prediction accuracy for complex traits, accelerating the rate of genetic gain. These findings have led to the development of new concepts, including microbiability (the proportion of phenotypic variance explained by the microbiome) and holobiability (the joint contribution of host and microbial variance). This review discusses recent advances in incorporating microbiome information as an additional variation source into genomic selection methods, with applications for complex trait prediction in livestock and aquaculture, providing upcoming challenges and opportunities. We highlight the challenges of modeling host–microbiome interactions, the potential of intermediate and functional traits, and considerations when designing holobiont-driven breeding schemes. Integrating these dimensions into breeding programs requires methodological innovations in data collection, modeling, and computation. Advances in high-throughput sequencing, artificial intelligence, and multi-omics facilitate the analysis of both genomic and metagenomic datasets, and support targeted microbiome interventions, including microbiome engineering, diet modulation via prebiotics or probiotics, and microbiome breeding to select holobionts with improved performance for complex traits. Thus, transitioning from genomes to hologenomes and incorporating microbiome data into breeding programs represents a key step toward more precise, efficient, and sustainable animal breeding.

1 Introduction

Global protein production has made considerable progress over the last two decades, reaching 361 and 185 million tons produced in 2022, considering livestock and aquaculture, respectively (FAO, 2024). Economically relevant traits, such as growth rate, carcass weight, milk yield, and egg production have increased by 20-30% in the last 60 years due to advances in nutrition, disease control and genetics (Georges et al., 2019).

The success of the animal production industry has been due in part to genetic and genomic improvements to farm species, such as cattle, pigs, poultry and fish (Yáñez et al., 2022). Animal production faces new demands, which are mainly related to production efficiency, environmental impact, animal welfare and emerging (and re-emerging) diseases (Georges et al., 2019; Ibarra et al., 2018; Molnár, 2022). The animal production industry must address these challenges to provide food and materials for an ever-increasing human population. Some of the current challenges in animal production might be successfully addressed by the inclusion of microbiome information (i.e., via metataxonomy or metagenomics; see Glossary) to drive further innovation in animal selective breeding (Houston et al., 2020).

Animal breeding has typically used phenotypic and genealogical information to improve livestock and aquaculture species, by using the best linear unbiased predictor (BLUP) (Fernando and Grossman, 1989). Currently, dense genomic information, generated from single nucleotide polymorphisms (SNP), can also be incorporated into genetic evaluation methods to estimate individual breeding values and increase the accuracy of selection (Meuwissen et al., 2001). Dense SNP panels are developed and used to understand the genetic basis of complex quantitative traits in livestock (Pena et al., 2016; Palombo et al., 2018; Liu et al., 2021); and aquaculture (Yáñez et al., 2016; Griot et al., 2021; Zhou et al., 2021) species.

New indicators in genomic selection approaches could include host-derived functional information, intermediate correlated traits, and notably microbiome profiles in selection schemes (Legarra and Christensen, 2023). The combination of genome and microbiome data has been shown to increase prediction accuracy (Pérez-Enciso et al., 2021), which in turn helps accelerate the response to selection for traits of interest (Houston et al., 2020). We suggest that including components of an animal’s microbiome, i.e., the characteristic microbial community (and their genes) in and on an animal host (Berg et al., 2020), could improve the estimation of phenotypic variation and accelerate the rate of genetic progress for desirable complex traits.

There is a growing body of research exploring the effect of microbiome variation on economically relevant traits in animal breeding (e.g., Ross et al., 2013; Camarinha-Silva et al., 2017; Difford et al., 2018), as well as the effect of host genotype on the variation of animal microbiomes (e.g., Roehe et al., 2016; Difford et al., 2018). The relationship between the host genome and microbiome can be combined to estimate the joint contribution of both host genomic and microbial metagenomic variation (Theis et al., 2016) to the phenotypic variance. Thus, the use of microbiomes as an additional source of information could increase the accuracy of genomic selection methods, as it has been proposed for aquaculture (Limborg et al., 2018) and livestock breeding (Tous et al., 2022).

The incorporation of microbial metagenomic information into animal breeding has required broadening our conception of the sources of trait variation, which has required a new vocabulary. In parallel with the concept of heritability (see Glossary), the concept of microbiability has been developed, to describe the proportion of the phenotypic variance of a trait which is explained by microbiome variance (Difford et al., 2018). Similarly, the concept of holobiability was proposed to describe the proportion of phenotypic variance explained by the joint contribution of host genetic and microbiome variance (Saborío-Montero et al., 2021). Including these sources of variation into the genetic evaluation models has the potential to accelerate genetic gains for desired traits in animal breeding.

In this review, we explore the current state alongside the opportunities and challenges related to the incorporation of microbiome information into current animal breeding approaches, considering the effect of host-microbiome interactions into genetic evaluation models, and the application of artificial intelligence approaches to integrate host and microbiome information (Hernández Medina et al., 2022). Desirable microbial traits could selected via microbiome breeding (Mueller and Linksvayer, 2022) or manipulated with microbiome engineering (Jin Song et al., 2019), implying significant regulatory, ethical, and animal-welfare considerations, to speed up the rate of genetic progress in animal breeding schemes.

2 Microbiome influence on host phenotypes

Most animals have associated microorganisms, including bacteria, archaea, protozoa, fungi, as well as viruses. There is often a dynamic relationship between animal hosts and their microbiomes, due to the ever-changing interactions between microbial gene products such as peptides/proteins, lipids, polysaccharides, nucleic acids, and metabolites, and host cells (Berg et al., 2020). These microbial interactions contribute to a diversity of phenotypic traits, including aspects of host physiology (Crespo-Piazuelo et al., 2019), metabolism (Li et al., 2019; Dvergedal et al., 2020), homeostatic immunity (Ramayo-Caldas et al., 2021), and nutrition (Li and Guan, 2017), with the ultimate consequence of contributing to host fitness (Gould et al., 2018).

There is broad evidence indicating that the variation in microbiome composition is associated with host phenotypic variation (e.g. Ross et al., 2013; Li and Guan, 2017; Difford et al., 2018), suggesting that changes in microbial components, such as diversity, microbiome functionality, and/or specific microbial taxa can be associated with variation in complex phenotypic traits in animals, including cattle (Martínez-Álvaro et al., 2020; Zang et al., 2022) and fish (Dvergedal et al., 2020) (Figure 1). For instance, microbiota variation influences important traits in dairy cows, contributing with a 15% for the variation of ketosis-related metabolites (Gebreyesus et al., 2020), 17.8% for milk protein yield (Xue et al., 2020), and between 16 and 33% for the production of different volatile fatty acids (Zang et al., 2022) (Table 1).

Figure 1

Figure 1. Conceptual framework illustrating the interplay between host genomics and microbiomes in shaping holobiont phenotypes. The framework integrates host-based approaches, including genomic selection and gene editing, with microbiome-based strategies such as microbiome engineering and microbiome breeding. Hologenomic prediction models incorporate host genetic effects (heritability), microbiome effects (microbiability), and their combined contribution (holobiability), using variance partitioning approaches. Host–microbiome associations are characterized through genome-wide association studies (GWAS) and microbiome-wide association studies (MWAS). Environmental factors are considered as additional modulators of host–microbiome interactions and holobiont phenotypic variation. Created with BioRender.com.

Table 1

Table 1. Estimated heritability (h²) and microbiability (b²) estimates (mean ± SE if reported) for economically important dairy cattle, pigs, and poultry traits.

Microbiome profiles have been used to predict complex host phenotypes, such as methane production traits in cattle (Ross et al., 2013) and sheep (Ross et al., 2020). From there, several associations have been found between components of the bovine rumen microbiota and traits of interest, such as feed efficiency (Li and Guan, 2017), methane emission (Guyader et al., 2014; Difford et al., 2018; Ramayo-Caldas et al., 2020b; Wallace et al., 2019) and fatty acid concentration in bovine milk (Buitenhuis et al., 2019).

For instance, bacterial genera such as Butyrivibrio, Prevotella, and Ruminococcus have been described as indicators of feed efficiency in cattle (Guan et al., 2008; Myer et al., 2015). Importantly, species within these genera form part of the rumen core microbiota and contribute essential metabolites and enzymes that maintain ruminant host metabolism (Tovar-Herrera et al., 2025). Similarly, Nuñez et al. (2025) identified a set of bacterial taxa with relevant effects to feed efficiency in pigs, highlighting Lactobacillus genus with relevant effect on feed conversion rate and residual feed intake. Notably, the effects of Lactobacillus strains isolated from sows have increased the feed conversion ratio of weaned piglets (Li et al., 2025), with the consequence of maintaining intestinal health and host performance.

In fish, several studies have shown the relationship between microbiota composition and host metabolism and performance. Dvergedal et al. (2020) described the association between three operational taxonomic units (OTUs), and carbon metabolism and feed efficiency of Atlantic salmon (Salmo salar). Additionally, differential abundance of Bacillus and Lactococcus genera have been correlated with high and low growth in fish (Zhang et al., 2021), while Rasmussen et al. (2021) investigated the dominance and functionality of Mycoplasma genus in Atlantic salmon, suggesting the positive role in host metabolism. Overall, functional evidence indicates that microbial activity contributes to metabolism-related pathways and supports host adaptation to environmental conditions (Lyons et al., 2017; Rasmussen et al., 2021).

Taken together, these studies suggest that microbiome variation is associated with variation in complex phenotypic traits in livestock and fish, which would allow the use of microbiome components for predicting traits of interest in farming animals. Importantly, these traits are likely influenced by host gut microbiome, and the prediction accuracy vary across breeds, diets, and environments (Le Graverand et al., 2025). Yet, efforts in the development of accurate prediction models have been made, considering machine learning approaches (Prifti et al., 2020), to make more robust and generalizable microbiome-based predictions.

3 Influence of host genetic variation on microbiome

There is growing evidence that host genetic variation can be associated with variation in microbiome composition in livestock species such as cattle (Roehe et al., 2016; Difford et al., 2018; Abbas et al., 2020; Zhang et al., 2020), pigs (Crespo-Piazuelo et al., 2019; Bergamaschi et al., 2020; Ramayo-Caldas et al., 2020a; Yang et al., 2022), rabbits (Velasco-Galilea et al., 2022), and fish (Boutin et al., 2014; Dvergedal et al., 2020; Small et al., 2023). There are also reports that indicate that host genetic variation can influence variation in microbiome functions which are relevant for animal production, such as methane production in cattle (Roehe et al., 2016), growth rate in pigs (Bergamaschi et al., 2020) and metabolism in fish (Dvergedal et al., 2020).

The attributes of microbial communities, such as alpha- and beta- diversity metrics, traditionally obtained from 16S rRNA gene sequencing approaches, can be modeled alongside host genetic information (e.g., pedigree or genotype data) to assess the genetic contribution of the host to these communities (Goodrich et al., 2017; Dvergedal et al., 2020; Li et al., 2019). The above could be achieved by correlating microbial and host genetic distance matrices to evaluate the extend of similarities between microbiome and host genetics, which might be influenced by the genetic make-up of the host (Griffiths et al., 2019; Wallace et al., 2019). In practice, microbial features are adjusted by several host and environmental factors such as breed, sex, diet, and age in farm or aquaculture systems, thus reducing confounding effects from the environment (Wallace et al., 2019).

Another approach to measure the influence of host genetics on microbiome composition is to assess the long-term, cross-generational effects by estimating the heritability and identifying host genetic variants associated with microbiome attributes (Goodrich et al., 2017; Li et al., 2019). Some features of the microbiome may be considered as quantitative traits or phenotypes, including the presence or absence of particular OTUs or amplicon sequencing variants (ASVs), the diversity of the microbiome in a host individual (i.e. Shannon or Simpson’s index), and the presence or absence of particular functional groups of microbes (Legarra and Christensen, 2023). These traits can then be evaluated using mixed linear models to estimate the proportion of microbial variance explained by all SNPs (Yang et al., 2011).

There are studies that have determined the heritability of microbiome features (reviewed in Morris and Bohannan, 2024), in species including cattle (Li et al., 2019; Wallace et al., 2019; Abbas et al., 2020; Zhang et al., 2020), pigs (Camarinha-Silva et al., 2017; Chen et al., 2018; Bergamaschi et al., 2020; Reverter et al., 2021; Aliakbari et al., 2021; Larzul et al., 2024), rabbits (Velasco-Galilea et al., 2022), sheep (Martinez Boggio et al., 2022), and fish (Dvergedal et al., 2020).

Research in livestock have found that host genetics explains up to 41% of the variation in bacterial community composition of the rumen in dairy cattle (Zhang et al., 2020), including heritability estimations between 15 a 19% (Abbas et al., 2020). Consistently, Li et al. (2019) estimated a moderate heritability (h² ≥ 0.15) for relative microbial abundance and total copy number of ruminal bacteria sequences associated with metabolism, a pattern also reported in the rumen microbiome of Lacaune dairy ewes (Martinez Boggio et al., 2022). These persistent and heritable microbes represent core-heritable OTUs or ASVs in the host rumen, affecting phenotypes related to methane emissions, milk production (Wallace et al., 2019), thus sustaining gut and host metabolism (Tovar-Herrera et al., 2025).

There is consistent evidence that host genetics influences the swine gut microbiome, although the extent of this effect varies according to the microbial attributes assessed, as well as the methodology used. Camarinha-Silva et al. (2017) reported moderate to high heritability estimates (0.33 to 0.57) for eight bacteria genera, whereas subsequent studies found heritable taxa with low to moderate heritabilities (Chen et al., 2018; Bergamaschi et al., 2020). Ramayo-Caldas et al. (2020a) also found moderate heritability estimates (0.15 to 0.28) for fungi and protists, as well as for alpha diversity metrics such as Shannon and Simpson indices (0.12 to 0.19) (Aliakbari et al., 2021). These differences could reflect the influence of other sources (e.g., environment) in these host-microbiome interactions.

In addition, associations between host genetics and microbiome features have been estimated via genome-wide association study (GWAS), revealing genes involved in host physiological and immune response (Crespo-Piazuelo et al., 2019; Chen et al., 2018; Ramayo-Caldas et al., 2020a), with implications in pig performance. For instance, Reverter et al. (2021) identified butyrate-producer bacteria under host genome influence (Reverter et al., 2021), while Ramayo-Caldas et al., 2023 reported a positive association between variation in the number of copies of the ABCC2-DNMBP loci with diversity of the pig fecal microbiota, suggesting that other sources of genetic variation, such as structural variants, could immunomodulate gut microbiomes and control the abundance of opportunistic pathogens in livestock.

In fish, there are contrasting results regarding whether genomic regions or candidate genes are associated with variation in microbiome features. For example, Boutin et al. (2014) detected quantitative trait loci (QTLs) associated with two exclusive taxa belonging to Flavobacterium and Methylobacterium in brook charr (Salvelinus fontinalis), while no significant QTL were identified for intestinal microbial abundance for metabolism traits in Atlantic salmon (Salmo salar) (Dvergedal et al., 2020). Small et al. (2023) identified five and nine genomic regions positively associated with gut microbiome dissimilarity in three spine stickleback fish (Gasterosteus aculeatus), quantified by Weighted UniFrac and Bray-Curtis distance metrics, respectively. Further research is needed to elucidate the host genetic contribution to the fish microbiome, with practical consequences for breeding programs in aquaculture.

4 The holobiont paradigm

The holobiont concept (see Glossary) emphasize the interplay between organisms and their associated microorganisms (Bordenstein and Theis, 2015; Theis et al., 2016), underlining the benefits from the joint contribution of host and microbiome to a particular or multiple traits. To date, most of the studies relating host and microbiome have relied in correlated traits between metataxonomy and host traits, without necessary considering the interactions of these components to animal’s fitness (Callaway et al., 2025).

The genetic information from both host and symbiont microbial communities, i.e., the hologenome, could provide a novel source of phenotypic variation in farming animals (Saborío-Montero et al., 2021; Calle-García et al., 2023; Martinez Boggio et al., 2024). Changes in the hologenome are dynamic and can produce variations in the holobiont that could potentially be selected for or avoided (Rosenberg and Zilber-Rosenberg, 2018). For instance, experimental research has demonstrated that selection in blue tilapia (Oreochromis aureus) for cold tolerance resulted in changes in both microbiome composition and host transcriptomic response to temperature (Kokou et al., 2018).

Given the role of microorganisms as indicators of the health status of farmed animals (Ross et al., 2013; Bozzi et al., 2021), the hologenomic framework can help address complex traits such as health, disease resilience and feed efficiency, that depend not only on the host’s genetic architecture but also on shifts in microbial composition (Limborg et al., 2018), offering valuable insights into the interaction between both components. Additionally, the focus shifts from identifying the genetic or microbial factors that contribute to the desired phenotype to understanding how these factors contribute together to it (Tous et al., 2022).

This hologenomic approach has been already tested on fish, by applying multi-omic techniques (metagenomic, genomic, and transcriptomics) to explore the gut-microbiota response to cestode infestations in farmed Atlantic salmon (Brealey et al., 2024), as well as the dynamics of intestinal microbiome of gilthead seabream (Sparus aurata) facing a myxozoa parasite infection by Enteromyxum leei (Toxqui-Rodríguez et al., 2025). The integration of omics technologies has also been applied to swine industry, highlighting the impacts of weaning on both gut microbiota and host metabolomic response in piglets (Saladrigas-García et al., 2021) and Bama miniature pigs (Ma et al., 2024b), including functional adaptations from host-microbe-metabolite interactions to face inflammation during weaning.

Research on dairy cows feed traits such as feed efficiency has been developed from genome-microbiome networks based on genomic and microbiome data, proposing the inclusion of specific bacteria in current selection indexes from breeding programs (Martinez Boggio et al., 2024). Importantly, modelling both genotype and microbiome data has been shown to improve the accuracy when predicting complex traits (Calle-García et al., 2023), although limitations in the stability and standardization of microbiome data, as well as the reduced genetic gain compared with simulated data (Pérez-Enciso et al., 2021) still restrict its practical applicability in breeding programs.

Experimental research in pigs indicates that host phenotypes and microbial enterotypes interact bidirectionally to model productive traits, suggesting the holobiont as a potential unit of selection in breeding programs (Larzul et al., 2024). We hypothesize that genome-microbiome interactions are context dependent in the holobiont: host genetics may exert greater influence in farmed animals that have been selected for many generations, as observed in tilapia (Kokou et al., 2018) and chickens (Zhou et al., 2022b). Conversely, the microbiome may play major role when targeting microbial-dependent traits, such as methane emission (Martínez-Álvaro et al., 2022) or feed conversion (Déru et al., 2022).

Together, the hologenomic information has the potential to be included in animal breeding programs when assessing the phenotypic variation of economically important traits in aquaculture and livestock.

5 Variance decomposition including microbiome information

The use of genetic effects derived from microbial composition has been proposed as novel components in current genomic selection models for explaining the total phenotypic variance in economically important traits of livestock (Pérez-Enciso et al., 2021; see Table 2). This new outlook involves the concepts of microbiability (Difford et al., 2018) and holobiability (Saborío-Montero et al., 2021), both of which are explained below.

Table 2

Table 2. Specifications of the genomic prediction models for livestock and aquaculture considering different causative scenarios proposed by Pérez-Enciso et al. (2021).

5.1 Explaining phenotypic variation due to the microbiome: microbiability

There is growing interest in determining the variance contribution of microbial features on economically important traits in domestic animals, involving the concept of microbiability (Difford et al., 2018). According to Difford et al. (2018) the term microbiability (b²) is defined as the proportion of the phenotypic variance of a trait explained by the microbiome. This term has been studied in several species, including pigs (Camarinha-Silva et al., 2017; Weishaar et al., 2020; Khanal et al., 2021; Sanglard et al., 2020; Tang et al., 2020; Verschuren et al., 2020; Ramayo-Caldas et al., 2021), cattle (Difford et al., 2018; Gebreyesus et al., 2020; Ramayo-Caldas et al., 2020b), rabbits (Velasco-Galilea et al., 2021), sheep (Martinez Boggio et al., 2022; Bilton et al., 2025), and poultry (Vollmar et al., 2020; Borda-Molina et al., 2021). A detailed list of microbiability values for different production traits in livestock species is presented in Table 1.

Furthermore, evidence supports a greater contribution of the microbiota than host genetics in the phenotypic variation of some traits of interest (Gebreyesus et al., 2020; Khanal et al., 2021; Sanglard et al., 2020), specially for traits involved in microbial metabolic activity. In pigs, digestive efficiency traits like the digestibility coefficient showed higher microbiability estimations (from 0.44 ± 0.06 to 0.60 ± 0.05) than their heritabilities (0.25 ± 0.05 to 0.26 ± 0.07), and even more when transitioned from low to high fiber diet (Déru et al., 2022). In cattle, Gebreyesus et al. (2020) determined that rumen microbial composition explains a greater proportion of variance for ketone bodies such as acetone and β-hydroxybutyric acid concentration in milk (b² = 0.15 ± 0.09 and 0.15 ± 0.07) than the contribution of animal genotype (h² = 0.1 ± 0.14 and 0.03 ± 0.13, respectively), as well as two bovine milk fatty acids: pentadecylic acid (b² = 0.38 vs h² = 0.22) and α-linolenic acid (b² = 0.29 vs h² = 0), compared to the model including only host genetic information.

In contrast, microbial contribution is lower when targeted traits encompass host metabolic or developmental processes like growth performance. For instance, heritability estimation for weight traits were higher than their microbiabilities, considering the birth (h² = 0.60 ± 0.13 vs b² = 0.05 ± 0.06) and body weight (h² = 0.36 ± 0.09 to 0.4 ± 0.1 vs b² = 0.17 ± 0.06 to 0.24 ± 0.06) in Hu sheep lambs (Wang et al., 2023), and the same pattern occurred in pigs where daily gain was explained more by host genetics (h² = 0.42 ± 0.14) than the bacterial component (b² = 0.28 ± 0.13) (Camarinha-Silva et al., 2017), although Khanal et al. (2021) reported greater microbiability values than host genetic variance for belly weight (b² = 0.29 ± 0.05 vs h² = 0.18 ± 0.04), as well as ham weight (b² = 0.15 ± 0.05 vs. h² = 0.13 ± 0.04), and carcass average daily gain (b² = 0.22 ± 0.05 vs. h² = 0.18 ± 0.04).

Interestingly, there are some traits with intermediate contribution of host genome and microbiome, linked to immune response, suggesting an interaction effect. In pigs, the analysis of 21 immunity traits suggested similar contribution of the host-genome and the fecal microbiota, with similar heritability and microbiability values for most of the immune traits, except for levels of IgM and IgG in plasma that were dominated by host genetics, and haptoglobin in serum which was the trait with larger microbiability (b² = 0.275) compared to its heritability (h² = 0.138) (Ramayo-Caldas et al., 2021).

However, microbiability estimations can be influenced by multiple environmental and host-related factors, including the developmental stage (Khanal et al., 2021; Wang et al., 2023), diet composition (Déru et al., 2022), as well as the use of different OTU/ASV abundance tables (Ramayo-Caldas et al., 2020b; Wang et al., 2023) and sampling time points (Ross et al., 2020). These additional sources of variation highlight the need for caution when comparing microbiability estimations across studies (Ross and Hayes, 2022).

To date, no microbiability estimates have been reported for aquaculture traits. In livestock species, most microbiome-based predictions could rely on fecal samples from animals (Maltecca et al., 2019b; Khanal et al., 2020). In contrast, obtaining comparable samples from fish is considerably more challenging, as fecal collection in aquaculture systems provides a tank-level representation of fish microbiota, compared to the intestinal content by squeezing techniques (Thormar et al., 2024a). Moreover, livestock systems allow relatively easy access to animals for repeated sampling across multiple time points, whereas aquaculture studies typically require additional logistical effort and specialized handling to operate, thus affecting its overall sustainability (Sidiq et al., 2025).

With these elements, the idea of considering the microbiome information as a relevant variance component for partitioning of phenotypic variation of important traits in livestock and aquaculture is increasingly being addressed (Mueller and Linksvayer, 2022).

5.2 Holobiability

Holobiability (abbreviated as ho²) is defined as the proportion of phenotypic variance explained by both host genetic and microbiome components (Theis et al., 2016). Few studies have assessed this value, with holobiability estimations of 0.58 (± 0.07), 0.62 (± 0.08), and 0.43 (± 0.08) for dry matter intake (DMI), milk energy (NESec), and residual feed intake (RFI) in Holstein cows, respectively (Martinez Boggio et al., 2024), and accounting up to 59% for methane production variance in dairy cattle (Saborío-Montero et al., 2021). However, particular attention should be paid when using the holobiability concept, especially in defining when models account for only the additive effects of host genome and microbiome (i.e. a joint model) or genome-by-microbiome interaction effects (i.e. a recursive model) (Pérez-Enciso et al., 2021). Also, the genotype-environment interactions should be present in the partitioning variance models for both microbiome and host genome (Figure 1, Estellé, 2019) as the environment could determine microbial adaptations in the host depending on environmental changes like ambient temperature (Baldassarre et al., 2023).

6 A quantitative hologenomic approach for economically important traits

Since the first metagenomic predictions made by Ross et al. (2013), many studies have reported the contribution of microbiome components to host phenotype variation in quantitative terms, i.e., microbiability values, and its inclusion into current genomic models for the prediction of complex traits in livestock. Despite this history, methods for modelling microbiome contributions to host phenotype are still under development and they need to take into account several considerations.

A first consideration relates to genomic model construction. For instance, two approaches have been proposed for incorporating both host-genome and microbiome information: i) partial models: including genomic or microbiome information to explain phenotypic variation, or ii) complete models: considering the additive or interaction effects of genome and microbiome when partitioning the phenotypic variance (Pérez-Enciso et al., 2021; Khanal et al., 2020; Saborío-Montero et al., 2021; Déru et al., 2022).

In general, complete models have produced better estimations and more realistic scenarios for the traits evaluated. Calle-García et al. (2023) demonstrated that complete models performed better than partial models that used host-genomic or microbial information alone. However, the explained variance is still lower when considering the additive contribution of host genome and microbiome (joint models) than models including an interaction effect between them (i.e. recursive models) (Saborío-Montero et al., 2018; Martinez Boggio et al., 2024). For instance, including interactions between host-genome and microbiome has improved the estimation of variance components for methane emission in dairy cows, with heritability values ranging from 0.15 to 0.17, microbiability values ranging from 0.15 to 0.2, and holobiability values as large as 0.42 (Saborío-Montero et al., 2021).

A second consideration regarding the methods used to build the microbial relationship matrix, analogous to the genomic relationship matrix (i.e. G matrix) used in genomic predictions. For example, microbial relationship matrices from Ross et al. (2013) were built by using a log-transformed and standardized OTU count table. However, there are many possible ways to construct such matrices. Saborío-Montero et al. (2018) proposed seven different ordination methods for building the microbial relationship matrix, and He et al. (2022) compared eight different approaches. Different approaches have considered kernel functions such as Linear Kernel (LK), polynomial Kernel (PK), Gaussian Kernel (GK), Arc-cosine Kernel with one hidden layer (AK1); dissimilarity metrics such as Bray-Curtis (BC) and Jaccard (JA) distances; and ordination methods such as multidimensional scaling (MDS) and detrended correspondence analysis (DCA). However, none of the approaches has yet emerged as the best method for constructing microbial relationship matrices.

A particular interest of livestock breeders is to accurately predict phenotypes and breeding values in animals. Different mixed models that include microbiome information have been used for this purpose, including semiparametric (De Los Campos et al., 2010) and Bayesian approaches (Habier et al., 2011). Semiparametric models include the reproducing kernel Hilbert space model or RKHS (a GBLUP-like approach) (Saborío-Montero et al., 2021; Déru et al., 2022), while methods within the bayesian framework include the Bayesian ridge regression (BRR) (Ramayo-Caldas et al., 2021), Bayes C (Ramayo-Caldas et al., 2021), and Bayesian Lasso (Maltecca et al., 2019b).

Some studies have performed evaluations between these methods to compare the accuracy between semiparametric and Bayesian models for genomic prediction (Maltecca et al., 2019b; Pérez-Enciso et al., 2021; Calle-García et al., 2023). RKHS models have performed better in terms of their accuracy (Maltecca et al., 2019b), and Calle-García et al. (2023) illustrated the usefulness of including both host-genomic and microbiome information to predict immunocompetence in pigs. However, they also reported that no single predictive method consistently outperformed others across evaluated traits, and that predictive accuracies varied widely, particularly when the microbial contribution to the phenotype (i.e., microbiability) was high.

An interesting alternative to these methods is to incorporate a hologenomic selection index using a two-step model to estimate breeding values for traits of interest (Weishaar et al., 2020), by first calculating the effect of microbiome components (such as the relative abundance of different microbial taxa) on the quantitative trait, and then combining them with the genomic covariance matrix to calculate the selection index. In addition, the influence of host genetics on microbiome components adds a potential indirect effect of the host in studies that consider microbiome and host-genome jointly (Ramayo-Caldas et al., 2020a; Martínez-Álvaro et al, 2022). This can be conceptualized as the community heritability (Hc²), where the host is part of an ecosystem and its genetic variation has predictive effects on the composition of the microbial communities that interact with it (van Opstal and Bordenstein, 2015). Thus, the integration of different sources of information is promising for the development of accurate selection schemes based on hologenomic information.

In studies such as those performed by Camarinha-Silva et al. (2017); Difford et al. (2018) and Calle-García et al. (2023), the inclusion of both host-genomic and microbiome information has resulted in more accurate predictions of production and health traits than just including host-genomic information in livestock. This suggests that the accuracy of prediction of phenotypic variables can be improved with the incorporation of joint information from host-genome and microbiome, compared to only considering host genetic information (Buitenhuis et al., 2019; Verschuren et al., 2020). Nevertheless, we agreed that there is still much room for methodological improvement in holobiont-driven prediction.

7 Microbiome-based phenotypes for animal breeding

Previous sections have underlined the microbial contribution to different host phenotypes of importance for animal breeding, supporting the inclusion of microbiome information in aquaculture and livestock genetic evaluations to accelerate the rate of genetic progress. The concept of “microbiome breeding” has been reviewed by Mueller and Linksvayer (2022) for generating customized microbiomes, through artificial selection, for desired microbially-mediated phenotypes.

Microbiome breeding could be incorporated in the current selection approaches, adopting a hologenomic scheme for selecting host features of productive importance in aquaculture and livestock species (Limborg et al., 2018; Houston et al., 2020). Microbiome breeding schemes should be especially powerful for host selection when microbiomes contribute substantially to the overall phenotypic variation (i.e., microbial metabolic activity), when hosts significantly influence microbiome assembly and stability, and the relevant microbiome features are heritable (Henry et al., 2021). In this regard, collaborative New Zealand national research developed indirect and reliable measures of methane emissions via proxy traits in sheep, highlighting the value of rumen metagenome community (RMC) profiles for predicting methane output across farms and environments (Bilton et al., 2025).

In addition to the microbiome breeding concept, a “microbiome engineering” approach (i.e. the direct manipulation of microbiomes) could also be used to improve traits important to animal farming (Table 3). For example, certain bacteria or consortium of microorganisms could be targeted using several microbiome transplanting schemes or through artificially created microbiomes (Jin Song et al., 2019). The design and construction of synthetic microbiomes is a novel methodology that manipulates microbiomes to perform desired functions, based on the implementation of gene editing technologies (Lawson et al., 2019) and it has been applied in both plants and animals (Lawson et al., 2019; Quides and Atamian, 2021; Hu et al., 2023).

Table 3

Table 3. Comparative table between selective breeding of microbiomes (microbiome breeding) and microbiome engineering.

Manipulating certain bacteria to influence a specific phenotype could be an interesting approach in animals (Lawson et al., 2019; Jin Song et al., 2019). In this regard, Jin Song et al. (2019) have proposed gene editing via CRISPR-Cas9 to manipulate microbial communities and generate synthetic microbiomes with desirable functions in the host. These functions include introducing beneficial microorganisms to the microbiome, removing harmful or pathogenic bacteria present in a host individual, or changing the microbial composition to improve host performance (Jin Song et al., 2019).

Microbiome transplants (i.e. the transfer of microbiomes from a healthy donor to a sick individual) have been performed in livestock species, for example to treat rumen disorders caused by changes in microbiome composition (Mueller and Linksvayer, 2022). Such transplants can be performed between individuals from the same (Weimer et al., 2017) or different species (Santos et al., 2021), providing insights into the relationship between microbiome structure and animal health. In aquatic species, gnotobiotic models (i.e., animals maintained under axenic conditions or with defined microbiomes) such as the zebrafish have been utilized for understanding host-microbe interactions (Pham et al., 2008) and bacterial adaptations to external changes to the host (Robinson et al., 2018).

Gene editing technologies could target specific bacteria present in the gut microbiome, improving host resistance to bacterial or viral infections, potentially reducing the need for antimicrobials, antifungals or antiparasitic drugs in animal production (Jin Song et al., 2019). In addition, engineered probiotics could be created by gene editing of bacterial plasmids and administered to organisms in a manner like vaccination (Yadav and Shukla, 2019), enhancing host metabolic functions linked to epithelial absorption, nutrient utilization, and consequently feed efficiency in animal production (Hu et al., 2023).

7.1 Aquaculture

The aquaculture industry has several challenges regarding efficiency and sustainability. To overcome the main obstacles, a hologenomic perspective has been proposed as an innovative tool that can be applied to understand the underlying mechanisms that affect phenotypes of productive interest from both the host genome and microbiome (Limborg et al., 2018). For instance, Thormar et al. (2024b) proposed a hologenomic approach to elucidate the influence of host genes on intestinal microbiota by using CRISPR/CAS system, in addition to 16S metabarcoding and metabolomics data, while Ma et al. (2024a) developed the holo-2bRAD approach, leveraging the restriction site-associated DNA (RAD), to disentangle the dynamic changes of the scallop hologenome associated with larval viability.

One major concern in aquaculture involves estimating the effect of different diet compositions on host performance, with consequences in feed efficiency, disease resistance, and host metabolism, among others. For instance, replacing fishmeal and fish oils with inputs from alternative protein sources has an impact on the gut microbiota, host transcriptomics, and production performance of aquaculture species (Pulgar et al., 2021; Zheng et al., 2023). These host-microbiome interactions from microbial and host genome profiles could then be combined in holo-omic frameworks, associating changes in metabolic functions, genotypes, and microbiome changes with host performance (Limborg et al., 2018).

In practice, studies considering both host genotypes and microbial metataxonomics could allow to quantify the phenotypic variance of host (heritability) and microbiome (microbiability) components when transitioning from fishmeal to vegetable sources, expecting a higher contribution of the microbiome to a high fiber diet as shown in pigs (Déru et al., 2022). In addition, network-based studies and Bayesian inference regarding causality can advance in the recognizing of these key taxa associated with dietary changes (Domingo-Bretón et al., 2025), that could be used in microbiome-based interventions such as microbiome breeding.

Prebiotics and probiotics are regularly used for controlling and treating diseases in aquaculture (reviewed by Newaj-Fyzul et al., 2014). In Atlantic salmon, the use of prebiotics favored the presence of beneficial bacteria Bacillus and Mycoplasma spp. in the fish gut (Baumgärtner et al., 2022). Microbiome-editing technologies could be used to design specific gut microbiomes containing beneficial bacteria to increase fish performance and select for these groups in selective breeding programs focused on growth performance.

Current genomic selection models could benefit from including microorganisms as predictors of fish performance (Houston et al., 2020). For instance, metabolism traits have been associated with bacterial taxa such as Pseudoalteromonas genus in Atlantic salmon (Dvergedal et al., 2020). Pseudoalteromonas strains have been previously used as a probiotic in fish because of its antimicrobial properties and beneficial active compounds (Wesseling et al., 2016). Another example is the high prevalence of Mycoplasma-related bacteria in the gut of rainbow trout (Lyons et al., 2017; Zhou et al., 2022a) and Atlantic salmon (Bozzi et al., 2021; Rasmussen et al., 2023) in healthy conditions, providing potential biomarkers in future hologenomic studies for monitoring health status in aquaculture species.

7.2 Livestock

Multi-omics studies have been proposed as important approaches for understanding economically relevant traits in livestock (Alberdi et al., 2022; Tous et al., 2022). These include complex traits such as methane emissions in sheep (Shi et al., 2014; Bilton et al., 2025), chicken performance and welfare (Tous et al., 2022), feed efficiency, and immunocompetence in swine (Maltecca et al., 2019a; Ramayo-Caldas et al., 2021; Calle-García et al., 2023), where recurrent and heritable microbial taxa across studies could be identified and proposed as reliable microbial markers for livestock breeding.

With respect to methane emissions, the archaeal Methanobrevibacter genus has been found to be heritable in the rumen of dairy cows (h² = 0.22 ± 0.09) (Difford et al., 2018), recurrent across species (Tovar-Herrera et al., 2025), and positively associated with rumen methanogenesis (Bharanidharan et al., 2021; Saborío-Montero et al., 2021), and organic compounds secretion (Tovar-Herrera et al., 2025), although other studies reported weaker correlations with methane yield regarding dietary conditions (Martínez-Álvaro et al., 2022; Miller et al., 2023) and stability over time (Lima et al., 2024). This reinforces the complex relationship between this genus and methane emission, in order to include core rumen microbes into selection programs for reducing methane production (Wallace et al., 2019; Ramayo-Caldas et al., 2020b).

Microbial selection targets should be selected based in their temporal stability to be associated with animal performance traits. For instance, Lima et al. (2024) identified a temporally stable core microbiome across six timepoints, explaining between a 45 and 83% of the phenotypic variation of relevant productive traits (e.g., average daily gain, daily feed intake, and methane emissions) in beef cattle, highlighting Methanobacterium genus and a set of high-level microbial genes to predict host performance traits across the production cycle. Thus, heritable and stable microorganisms could represent reliable targets for microbial-based breeding in dairy cows (Wallace et al., 2019).

Dietary interventions could be also regarded from an hologenomic perspective. Verschuren et al. (2020) and Déru et al. (2022) reported a higher contribution of microbial communities than host genetics to explain digestive efficiency traits in pigs. Similarly, it has been shown that incorporating macroalgae into the rumen microbiome from cattle can result in a reduction in methane emissions under both in vitro (Machado et al., 2014) and in vivo (Roque et al., 2019) conditions. Further studies could target specific rumen or intestinal microbial components with a high cellulose digestion capacity for macroalgae metabolism and improved feed efficiency in livestock species.

A microbiome-wide association analysis performed on the Japanese quail (C. japonica) gut microbiota demonstrated that feed-related traits were associated with multiple members of the microbiome (Vollmar et al., 2020), which is important for modeling an hologenomic schema in poultry. In pigs, reports of high microbiability values for fecal nutrient digestibility factors and associations between microbiome composition and innate immunity traits (Calle-García et al., 2023) supports the use of hologenomic approaches in swine breeding (Verschuren et al., 2020), potentially reducing feed costs, environmental impact and increasing immunocompetence.

In sheep, Bilton et al. (2025) showed that rumen metagenome community (RMC) profiles not only predict methane measurements but are also genetically correlated with them. Consequently, RMC profiles represent a promising proxy trait for assessing the genetic merit of an animal’s methane emissions, suggesting the integration of RMC information into current breeding programs to support the selective breeding of low methane-emitting sheep.

8 Opportunities

Holobiont-driven prediction opens a wide set of innovations related to characterizing and assessing the interplay between host genome and its associated microorganisms in animal breeding programs. Progress has been made not only in the characterization of microbiome taxonomic composition (e.g. based on 16S SSU rRNA gene sequencing), but also in the characterization of gene content (estimated from shotgun metagenomic data), and genome structure (i.e. from Metagenome-Assembled Genomes or MAGs). These advances allow for the integration of taxonomic information with the gene repertoire and the functional potential of the microbiome.

At the same time, a reduction in the cost of microbiome information has been possible by using alternative approaches to taxonomic identification, i.e., by using cost-effective approaches such as the 2bRAD (restriction-site associated DNA marker generation with type IIB restriction endonucleases) sequencing for microbiomes (Hess et al., 2020; Sun et al., 2022). For example, enzyme-reduced representation sequencing has been applied in ruminants to generate RMC profiles (Hess et al., 2023; Bilton et al., 2025), with similar costs as taking methane emission measurements, but with higher scalability and throughput (Bilton et al., 2025).

Additional techniques like, dual-RNA seq could be used to disentangle the holobiont contribution of fish and its skin microbiome to bacterial infections (Le Luyer et al., 2021). In this context, fish skin microbiome has emerged as a valuable, non-invasive matrix for assessing complex traits related to fish health and detecting environmental changes associated with marine pollution and anthropogenic pressures (Fernández-Alacid et al., 2019; Montenegro et al., 2020; Liu et al., 2024). Beyond its growing use in environmental monitoring, skin microbiome has also been proposed as a practical biomarker for health surveillance in aquaculture, offering clear advantages over conventional and invasive genomic sampling methods such as fin clipping (Bozzi et al., 2021; Tilley et al., 2024).

With respect to representative microbiome features, Frioux et al. (2023) recently suggested the usefulness of non-negative matrix factorization (NMF) to decompose the human gut microbiota into microbial guilds, referred to as Enterosignatures. NMF is a multivariate approach which considers the intrinsic ecosystem information by providing a proportional representation of bacterial assemblages. The enterosignature approach has proven effective in dissecting the gut microbiome and elucidating its impact on host physiology in both humans (Frioux et al., 2023), pigs (Vourlaki et al., 2025a), and ruminants (Vourlaki et al., 2025b).

Particularly in pigs, enterosignatures have been associated with growth and feed efficiency and appears to be valuable for monitoring microbial perturbations and animal welfare. Moreover, these enterosignatures are partially shaped by host genetics, with heritability estimates ranging from 0.24 to 0.36 (Vourlaki et al., 2025a). Recently, the concept of ruminosignatures, referring to the existence of ruminal microbial guilds, has been proposed in cattle (Vourlaki et al., 2025b). Similarly, the composition of ruminosignatures appears to be under host genetic control, with heritability estimates ranging from 0.12 to 0.44, and has been associated with methane and CO₂ emissions. Therefore, these microbial consortia achive key criteria for integration into breeding programs, offering potential improvements in predicting breeding values and host performance, while also advancing microbiome-informed precision farming.

9 Pitfalls and challenges

A key objective in microbiome research is the development of targeted interventions aiming to manipulate the microbiome to enhance host health and performance, ideally supported by mechanistic evidence. However, determining the contribution of host genetic factors on microbiome composition remains a complex challenge, especially because of the influence of non-host-genetic factors such as environmental variation (Goodrich et al., 2017; Rothschild et al., 2018).

Environmental factors such as the year or pen location could have a strong effect on microbiota composition, as described in the ruminal tract of lambs (Le Graverand et al., 2023). Thus, modelling microbiome traits should also consider environmental factors or covariates for the desired phenotype (Maltecca et al., 2019), for instance by modelling enviromics matrices accounting for genotype-by-environment (GXE) and microbiome-by-environment (MXE) interactions (Achcar Trevisan et al., 2025). Yet, it is not clear which approach could be optimal to model host-microbiome-environmental interactions (Estellé, 2019).

There is evidence reporting limited or even counteracting effects of the host genome on microbiota composition. For instance, Wen et al. (2019) found a weak influence of host genetics on the gut microbiota in yellow broiler chickens. Similarly, Nuñez et al. (2025) reported reduced heritability estimates when microbiome data was included into the mixed models for both FCR and RFI in Iberian pigs, as well as Martinez Boggio et al. (2023) and Mora et al. (2022) stated for feed efficiency and growth traits in Holstein cows and rabbits, respectively. Together, these results reinforce the idea of indirect effects of the host genome on microbiome-mediated phenotypes, requiring careful interpretation (Mueller and Linksvayer, 2022).

Numerous studies that have reported estimates of microbiability for economically important traits (Table 1), but the reliability of these estimates remains uncertain. As reviewed by Pérez-Enciso et al. (2021), estimates may be influenced by the estimation procedure itself, highlighting the need for further research including alternatives models to improve their accuracy. Despite the improvement in predictive ability when including microbiome is incorporated into genetic evaluations (Calle-García et al., 2023), practical and computational issues remain in animal breeding organizations. These include the construction of the microbial relationship matrix (MRM) for large-scale implementation, the definition and evaluation of appropriate reference and validation populations, and the integration of microbiome-derived breeding values into current genetic evaluation systems (Bilton et al., 2025).

Additionally, the usefulness of microbiome data for predicting complex traits depends on its stability over time and across farms and environments in order to be included into breeding schemas (Le Graverand et al., 2025). Since microbial communities can vary, particularly early in life, data collected after weaning, when the microbiome becomes more stable, seems to be more reliable for prediction (Pérez-Enciso et al., 2021). Also, cohort standardization, i.e., similar farm, sex, birth year, and feeding, is necessary to build MRMs when predicting the genetic merit of animals to ensure robustness and better accuracy of predictions (Bilton et al., 2025).

There are also limitations in estimating the host genetic effect on microbiomes. Genome-wide association studies aiming to identify SNPs associated with microbial traits in livestock often involve relatively small sample sizes. Potential biases when choosing microbial features such ASVs, OTUs, or a subset of candidate taxa have been also documented (Maltecca et al., 2019a; Morris and Bohannan, 2024; Calle-García et al., 2023). To reduce these limitations, some authors have recommended working with the entire microbial community, as well as working with the microbiome and the host genome jointly when making genome-microbiome associations (Weissbrod et al., 2018; Saborío-Montero et al., 2021; Calle-García et al., 2023).

Additional effort is needed to distinguish functional bacterial activity, measured through metagenomics or metatranscriptomics, from functional microbial profiles inferred from 16S rRNA gene sequencing (Weinroth et al., 2022). Although functional prediction of gut microbiome has been evaluated by amplicon sequencing and phylogenetic-based inferences tools (Fan et al., 2020), these methods lack the sensitivity required to detect meaningful phenotypic changes from the microbiome function (MatChado et al., 2024). Holo-omic approaches that captures functional activity and microbial processes, such as horizontal gene transfer, offer greater resolution and could enhance the effectiveness of microbiome-informed decisions for improved host performance (Hu et al., 2024).

10 Regulatory, ethical, and animal-welfare implications

Microbiome-driven interventions could represent an effective alternative to antibiotics by improving animal health, enhancing digestibility and reducing the use of antimicrobials, taking into account regulatory and ethical issues. For instance, microbial-derived products like probiotics require safety and efficacy validation to be considered “Generally recognized as Safe” (GRAS) (Callaway et al., 2021) and be used in microbiome transplants or engineered microbiomes. The latter requires a consensus in the efficacity of probiotics in preventing microbiome disruption or enhancing microbial restauration (Szajewska et al., 2025), especially because of the risk of antimicrobial resistance or virulence factors that could be carried by these probiotic strains (Wannaprasat et al., 2009; Rokon-Uz-Zaman et al., 2023).

Disparities emerge from gene editing animals across countries and nations. While the European Union remains conservative, countries like Argentina, Australia, Brazil, Colombia, and Japan have implemented regulatory policies recognizing mutations from gene editing in animals as equivalent to natural genetic variation, thus animals produced by these technologies are regulated as those selected by breeding programs instead as genetic modified organisms (GMOs) (Ledesma and Van Eenennamm, 2024). In that sense, a fundamental step must consider the classification of microbiome-edited organisms in one of these categories, creating a general framework for CRISPR applications.

Additional ethical and regulatory responsibilities should be implemented to reduce the risks of misuse when working with microbiome-edited technologies (Temitayo et al., 2025), that could contribute to antimicrobial resistance spread (Chen and Li, 2025). Integrating these approaches will help with the scaling-up and commercialization of gene editing products or services from livestock and aquaculture programs, balance the socioeconomic issues of technology adoption in least developed countries, and the adoption of FAO/WHO safety frameworks to democratize access (Tayyab et al., 2025).

Like in animals, social and consumer acceptance to microbial-engineered animals should also be with addressed by developing social trust and transparent labelling systems (Chen and Li, 2025), such the case of the CD163-edited pigs that are resistant to the Porcine Reproductive and Respiratory syndrome (PRRS) (Chen et al., 2019). Finally, adoptions with the Sustainable Development Goals should also be considered for a sustainable animal production (Tayyab et al., 2025).

11 Concluding remarks and future perspectives

The inclusion of microbiome information has the potential to revolutionize genetic improvement programs in livestock and aquaculture species. Phenotypic variation can be explained by variance components associated with both host genetics and microbiome in several economically important traits in domestic animals. Estimation of breeding values and phenotype predictions could be enhanced by hologenomics models that consider the joint contribution of the host and its associated microbiome. In certain scenarios, the microbial component accounts for a greater proportion of phenotypic variance than the host genome. In such cases, it might be useful to engineer microbiomes, via microbial therapeutics including approaches such as gene editing technologies, the use of biotics (prebiotics, probiotics, postbiotics, symbiotics) or animal microbiome transplants to create tailor-made microbiomes to improve performance in relevant traits. These technologies might be incorporated in ongoing selective breeding programs in both aquaculture and livestock systems to boost genetic gains of economically key traits for animal farming, such as feed efficiency, disease resistance and host metabolism, among others.

Author contributions

LV: Conceptualization, Visualization, Writing – original draft, Writing – review & editing. BB: Writing – original draft, Writing – review & editing. ND: Writing – original draft, Writing – review & editing. YR: Writing – original draft, Writing – review & editing. JY: Conceptualization, Funding acquisition, Methodology, Writing – original draft, Writing – review & editing.

Funding

The author(s) declared that financial support was received for this work and/or its publication. LV is funded by doctoral scholarship 21202088 from the National Research Agency of Chile (ANID). YRC is recipient of a Ramón y Cajal post-doctoral fellowship (RYC2019-027244-I) funded by the Spanish Ministry of Science and Innovation. BJMB is grateful for the support of the Gordon and Betty Moore Foundation (https://doi.org/10.37807/GBMF10001), the National Institutes of Health (award P01GM125576), the Mercator Foundation, and the International Partnership for Advancing Microbiome-informed Aquaculture (IPAMA).

Conflict of interest

The authors declared that this work was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

The authors YR-C and JY declared that they were an editorial board member of Frontiers, at the time of submission. This had no impact on the peer review process and the final decision.

Generative AI statement

The author(s) declared that generative AI was used in the creation of this manuscript. AI-assisted tools were used to improve grammar and syntax during the manuscript writing process.

Any alternative text (alt text) provided alongside figures in this article has been generated by Frontiers with the support of artificial intelligence and reasonable efforts have been made to ensure accuracy, including review by the authors wherever possible. If you identify any issues, please contact us.

Publisher’s note

All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.

Glossary

CRISPR/Cas9: gene editing tool that uses a guide RNA to cut a specific DNA sequence with the Cas9 enzyme, after which it harnesses natural DNA repair processes to modify the gene in the desired manner

Genome-wide association study (GWAS): examination of a genome-wide set of genomic regions associated with a trait of interest

Heritability: the proportion of the phenotypic variance associated with additive genetic variance

Holobiability: the proportion of phenotypic variance associated with the combination of host genetic variance and microbiome variance

Holobiont: entity composed by the host, the symbiont microorganisms, including viruses, bacteria, and cellular microorganisms, as well as their interactions with the individual

Hologenome: the simultaneous contribution of genetic information from the host and its microbiota

Microbiability: proportion of the phenotypic variance associated with microbiome variance

Microbiome: characteristic microbial community, and its genes, occupying a well-defined habitat which has distinct physio-chemical properties (such as an animal host)

Metagenome: gene repertoires of the microbiota

Metataxonomy: taxonomic profiling using amplicon gene sequencing targeting taxonomic markers (e.g., 16S, 18S SSU rRNA genes, COI, ITS, etc.)

Microbiome breeding: artificial selection approaches that seek to modify the genetic composition of microbiomes to benefit plant or animal hosts

Microbiome engineering: targeted manipulation of the microbiome to alter the composition or activity of complex microbial populations

Multi-omics: crossover application of multiple high‐throughput technologies represented by genomics, transcriptomics, single‐cell transcriptomics, proteomics and metabolomics, spatial transcriptomics, epigenomics, among others.

References

Abbas W., Howard J. T., Paz H. A., Hales K. E., Wells J. E., Kuehn L. A., et al. (2020). Influence of host genetics in shaping the rumen bacterial community in beef cattle. Sci. Rep. 10, 15101. doi: 10.1038/s41598-020-72011-9

PubMed Abstract | Crossref Full Text | Google Scholar

Achcar Trevisan B., Silva Junqueira V., de Mello Florêncio B., Siqueira Guedes Coelho A., Eduardo Marcatti G., and Tassinari Resende R. (2025). A framework for building enviromics matrices in mixed models. Braz. J. Biometr. 43, e–43865. doi: 10.28951/bjb.v43i4.865

Crossref Full Text | Google Scholar

Alberdi A., Andersen S. B., Limborg M. T., Dunn R. R., and Gilbert M. T. P. (2022). Disentangling host–microbiota complexity through hologenomics. Nat. Rev. Genet. 23, 281–297. doi: 10.1038/s41576-021-00421-0

PubMed Abstract | Crossref Full Text | Google Scholar

Aliakbari A., Zemb O., Billon Y., Barilly C., Ahn I., Riquet J., et al. (2021). Genetic relationships between feed efficiency and gut microbiome in pig lines selected for residual feed intake. J. Anim. Breed. Genet. 138 (4), 491–507. doi: 10.1111/jbg.12539

PubMed Abstract | Crossref Full Text | Google Scholar

Baldassarre L., Reitzel A. M., and Fraune S. (2023). Genotype-environment interactions determine microbiota plasticity in the sea anemone Nematostella vectensis. PloS Biol. 21, e3001726. doi: 10.1371/journal.pbio.3001726

PubMed Abstract | Crossref Full Text | Google Scholar

Baumgärtner S., James J., and Ellison A. (2022). The supplementation of a prebiotic improves the microbial community in the gut and the skin of Atlantic salmon (Salmo salar). Aquac. Rep. 25, 101204. doi: 10.1016/j.aqrep.2022.101204

PubMed Abstract | Crossref Full Text | Google Scholar

Berg G., Rybakova D., Fischer D., Cernava T., Vergès M. C. C., Charles T., et al. (2020). Microbiome definition re-visited: old concepts and new challenges. Microbiome 8, 40168. doi: 10.1186/s40168-020-00875-0

PubMed Abstract | Crossref Full Text | Google Scholar

Bergamaschi M., Maltecca C., Schillebeeckx C., McNulty N. P., Schwab C., Shull C., et al. (2020). Heritability and genome-wide association of swine gut microbiome features with growth and fatness parameters. Sci. Rep. 10, 1–12. doi: 10.1038/s41598-020-66791-3

PubMed Abstract | Crossref Full Text | Google Scholar

Bharanidharan R., Lee C. H., Thirugnanasambantham K., Ibidhi R., Woo Y. W., Lee H. G., et al. (2021). Feeding systems and host breeds influence ruminal fermentation, methane production, microbial diversity and metagenomic gene abundance. Front. Microbiol. 12. doi: 10.3389/FMICB.2021.701081

PubMed Abstract | Crossref Full Text | Google Scholar

Bilton T. P., Alemu S. W., Dodds K. G., Henry H., Hess M. K., Jordan R., et al. (2025). Rumen metagenome profiles are heritable and rank the New Zealand national sheep flock for enteric methane emissions. Genet. Sel. Evol. 57, 25. doi: 10.1186/s12711-025-00973-3

PubMed Abstract | Crossref Full Text | Google Scholar

Borda-Molina D., Iffland H., Schmid M., Müller R., SChad S., Seifert J., et al. (2021). Gut microbial composition and predicted functions are not associated with feather pecking and antagonistic behavior in laying hens. Life 11, 235. doi: 10.3390/life11030235

PubMed Abstract | Crossref Full Text | Google Scholar

Bordenstein S. R. and Theis K. R. (2015). Host biology in light of the microbiome: Ten principles of holobionts and hologenomes. PloS Biol. 13 (8), e1002226. doi: 10.1371/journal.pbio.1002226

PubMed Abstract | Crossref Full Text | Google Scholar

Boutin S., Sauvage C., Bernatchez L., Audet C., and Derome N. (2014). Inter individual variations of the fish skin microbiota: Host genetics basis of mutualism? PloS One 9 (7), e102649. doi: 10.1371/journal.pone.0102649

PubMed Abstract | Crossref Full Text | Google Scholar

Bozzi D., Rasmussen J. A., Carøe C., Sveier H., Nordøy K., Gilbert M. T. P., et al. (2021). Salmon gut microbiota correlates with disease infection status: potential for monitoring health in farmed animals. Anim. Microbiome. 3, 30. doi: 10.1186/s42523-021-00096-2

PubMed Abstract | Crossref Full Text | Google Scholar

Brealey J. C., Kodama M., Rasmussen J. A., Hansen S. B., Santos-Bay L., Lecaudey L. A., et al. (2024). Host-gut microbiota interactions shape parasite infections in farmed Atlantic salmon. mSystems 9, e0104323. doi: 10.1128/msystems.01043-23

PubMed Abstract | Crossref Full Text | Google Scholar

Buitenhuis B., Lassen J., Noel S. J., Plichta D. R., Sørensen P., Difford G. F., et al. (2019). Impact of the rumen microbiome on milk fatty acid composition of Holstein cattle. Genet. Sel. Evol. 51, 1–8. doi: 10.1186/s12711-019-0464-8

PubMed Abstract | Crossref Full Text | Google Scholar

Callaway T. R., Lillehoj H., Chuanchuen R., and Gay C. G. (2021). Alternatives to antibiotics: A symposium on the challenges and solutions for animal health and production. Antibiotics 10, 471. doi: 10.3390/antibiotics10050471

PubMed Abstract | Crossref Full Text | Google Scholar

Callaway T., Perez H. G., Corcionivoschi N., Bu D., and Fluharty F. L. (2025). International Symposium on Ruminant Physiology: The holobiont concept in ruminant physiology—More of the same, or something new and meaningful to food quality, food security, and animal health? J. Dairy. Sci. 108, 7567–7575. doi: 10.3168/JDS.2024-25847

PubMed Abstract | Crossref Full Text | Google Scholar

Calle-García J., Ramayo-Caldas Y., Zingaretti L. M., Quintanilla R., Ballester M., and Pérez-Enciso M. (2023). On the holobiont ‘predictome’ of immunocompetence in pigs. Genet. Sel. Evol. 55, 29. doi: 10.1186/s12711-023-00803-4

PubMed Abstract | Crossref Full Text | Google Scholar

Camarinha-Silva A., Maushammer M., Wellmann R., Vital M., Preuss S., and Bennewitz J. (2017). Host genome influence on gut microbial composition and microbial prediction of complex traits in pigs. Genetics 206, 1637–1644. doi: 10.1534/genetics.117.200782

PubMed Abstract | Crossref Full Text | Google Scholar

Chen C., Huang X., Fang S., Yang H., He M., Zhao Y., et al. (2018). Contribution of host genetics to the variation of microbial composition of cecum lumen and feces in pigs. Front. Microbiol. 9. doi: 10.3389/fmicb.2018.02626

PubMed Abstract | Crossref Full Text | Google Scholar

Chen X. and Li X. (2025). Gene editing in livestock: bridging gut microbiome modulation and antibiotic resistance mitigation. Theor. Natural Sci. 104, 8–15. doi: 10.54254/2753-8818/2025.22538

Crossref Full Text | Google Scholar

Chen J., Wang H., Bai J., Liu W., Liu X., Yu D., et al. (2019). Generation of pigs resistant to highly pathogenic-porcine reproductive and respiratory syndrome virus through gene editing of CD163. Int. J. Biol. Sci. 15, 481–492. doi: 10.7150/ijbs.25862

PubMed Abstract | Crossref Full Text | Google Scholar

Crespo-Piazuelo D., Migura-Garcia L., Estellé J., Criado-Mesas L., Revilla M., Castelló A., et al. (2019). Association between the pig genome and its gut microbiota composition. Sci. Rep. 9, 1–11. doi: 10.1038/s41598-019-45066-6

PubMed Abstract | Crossref Full Text | Google Scholar

De Los Campos G., Gianola D., Rosa G. J. M., Weigel K. A., and Crossa J. (2010). Semi-parametric genomic-enabled prediction of genetic values using reproducing kernel Hilbert spaces methods. Genet. Res. 92, 295–308. doi: 10.1017/S0016672310000285

PubMed Abstract | Crossref Full Text | Google Scholar

Déru V., Tiezzi F., Carillier-Jacquin C., Blanchet B., Cauquil L., Zemb O., et al. (2022). Gut microbiota and host genetics contribute to the phenotypic variation of digestive and feed efficiency traits in growing pigs fed a conventional and a high fiber diet. Genet. Sel. Evol. 54, 55. doi: 10.1186/s12711-022-00742-6

PubMed Abstract | Crossref Full Text | Google Scholar

Difford G. F., Plichta D. R., Løvendahl P., Lassen J., Noel S. J., Højberg O., et al. (2018). Host genetics and the rumen microbiome jointly associate with methane emissions in dairy cows. PloS Genet. 14, 1–22. doi: 10.1371/journal.pgen.1007580

PubMed Abstract | Crossref Full Text | Google Scholar

Domingo-Bretón R., Cools S., Moroni F., Belenguer A., Calduch-Giner J. A., Croes E., et al. (2025). Intestinal microbiota shifts by dietary intervention during extreme heat summer episodes in farmed gilthead sea bream (Sparus aurata). Aquacult. Rep. 40, 102566. doi: 10.1016/J.AQREP.2024.102566

Crossref Full Text | Google Scholar

Dvergedal H., Sandve S. R., Angell I. L., Klemetsdal G., and Rudi K. (2020). Association of gut microbiota with metabolism in juvenile Atlantic salmon. Microbiome 8 (1), 160. doi: 10.1186/s40168-020-00938-2

PubMed Abstract | Crossref Full Text | Google Scholar

Estellé J. (2019). Benefits from the joint analysis of host genomes and metagenomes: Select the holobiont. J. Anim. Breed. Genet. 136, 75–76. doi: 10.1111/jbg.12383

PubMed Abstract | Crossref Full Text | Google Scholar

Fan P., Bian B., Teng L., Nelson C. D., Driver J., Elzo M. A., et al. (2020). Host genetic effects upon the early gut microbiota in a bovine model with graduated spectrum of genetic variation. ISME. J. 14, 302–317. doi: 10.1038/S41396-019-0529-2

PubMed Abstract | Crossref Full Text | Google Scholar

FAO (2024). World Food and Agriculture – Statistical Yearbook 2024 (Rome).

Google Scholar

Fernández-Alacid L., Sanahuja I., Ordóñez-Grande B., Sánchez-Nuño S., Herrera M., and Ibarz A. (2019). Skin mucus metabolites and cortisol in meagre fed acute stress-attenuating diets: Correlations between plasma and mucus. Aquaculture 499, 185–194. doi: 10.1016/j.aquaculture.2018.09.039

Crossref Full Text | Google Scholar

Fernando R. and Grossman M. (1989). Marker assisted selection using best linear unbiased prediction. Genet. Sel. Evol. 21, 467. doi: 10.1186/1297-9686-21-4-467

Crossref Full Text | Google Scholar

Frioux C., Ansorge R., Özkurt E., Ghassemi Nedjad C., Fritscher J., Quince C., et al. (2023). Enterosignatures define common bacterial guilds in the human gut microbiome. Cell Host Microbe 31, 1111–1125.e6. doi: 10.1016/j.chom.2023.05.024

PubMed Abstract | Crossref Full Text | Google Scholar

Gebreyesus G., Difford G. F., Buitenhuis B., Lassen J., Noel S. J., Højberg O., et al. (2020). Predictive ability of host genetics and rumen microbiome for subclinical ketosis. J. Dairy. Sci. 103, 4557–4569. doi: 10.3168/jds.2019-17824

PubMed Abstract | Crossref Full Text | Google Scholar

Georges M., Charlier C., and Hayes B. (2019). Harnessing genomic information for livestock improvement. Nat. Rev. Genet. 20, 135–156. doi: 10.1038/s41576-018-0082-2

PubMed Abstract | Crossref Full Text | Google Scholar

Goodrich J. K., Davenport E. R., Clark A. G., and Ley R. E. (2017). The relationship between the human genome and microbiome comes into view. Annu. Rev. Genet. 51, 413–433. doi: 10.1146/annurev-genet-110711

PubMed Abstract | Crossref Full Text | Google Scholar

Gould A. L., Zhang V., Lamberti L., Jones E. W., Obadia B., Korasidis N., et al. (2018). Microbiome interactions shape host fitness. PNAS 115, E11951–E11960. doi: 10.1073/pnas.1809349115

PubMed Abstract | Crossref Full Text | Google Scholar

Griffiths S. M., Antwis R. E., Lenzi L., Lucaci A., Behringer D. C., Butler M. J., et al. (2019). Host genetics and geography influence microbiome composition in the sponge Ircinia campana. J. Anim. Ecol. 88, 1684–1695. doi: 10.1111/1365-2656.13065

PubMed Abstract | Crossref Full Text | Google Scholar

Griot R., Allal F., Phocas F., Brard-Fudulea S., Morvezen R., Bestin A., et al. (2021). Genome-wide association studies for resistance to viral nervous necrosis in three populations of European sea bass (Dicentrarchus labrax) using a novel 57k SNP array DlabChip. Aquaculture 530, 735930. doi: 10.1016/J.AQUACULTURE.2020.735930

Crossref Full Text | Google Scholar

Guan L. L., Nkrumah J. D., Basarab J. A., and Moore S. S. (2008). Linkage of microbial ecology to phenotype: Correlation of rumen microbial ecology to cattle’s feed efficiency. FEMS Microbiol. Lett. 288, 85–91. doi: 10.1111/j.1574-6968.2008.01343.x

PubMed Abstract | Crossref Full Text | Google Scholar

Guyader J., Eugène M., Nozière P., Morgavi D. P., Doreau M., and Martin C. (2014). Influence of rumen protozoa on methane emission in ruminants: A meta-analysis approach. Animal 8, 1816–1825. doi: 10.1017/S1751731114001852

PubMed Abstract | Crossref Full Text | Google Scholar

Habier D., Fernando R. L., Kizilkaya K., and Garrick D. J. (2011). Extension of the bayesian alphabet for genomic selection. BMC Bioinf. 12, 186. doi: 10.1186/1471-2105-12-186

PubMed Abstract | Crossref Full Text | Google Scholar

He Y., Tiezzi F., Jiang J., Howard J., Huang Y., Gray K., et al. (2022). Exploring methods to summarize gut microbiota composition for microbiability estimation and phenotypic prediction in swine. J. Anim. Sci. 100, skac231. doi: 10.1093/JAS/SKAC231

PubMed Abstract | Crossref Full Text | Google Scholar

Henry L. P., Bruijning M., Forsberg S. K. G., and Ayroles J. F. (2021). The microbiome extends host evolutionary potential. Nat. Commun. 12, 5141. doi: 10.1038/s41467-021-25315-x

PubMed Abstract | Crossref Full Text | Google Scholar

Hernández Medina R., Kutuzova S., Nielsen K. N., Johansen J., Hansen L. H., Nielsen M., et al. (2022). Machine learning and deep learning applications in microbiome research. ISME. Commun. 2, 98. doi: 10.1038/s43705-022-00182-9

PubMed Abstract | Crossref Full Text | Google Scholar

Hess M. K., Hodgkinson H. E., Hess A. S., Zetouni L., Budel J. C. C., Henry H., et al. (2023). Large-scale analysis of sheep rumen metagenome profiles captured by reduced representation sequencing reveals individual profiles are influenced by the environment and genetics of the host. BMC Genomics 24, 551. doi: 10.1186/s12864-023-09660-3

PubMed Abstract | Crossref Full Text | Google Scholar

Hess M. K., Rowe S. J., van Stijn T. C., Henry H. M., Hickey S. M., Brauning R., et al. (2020). A restriction enzyme reduced representation sequencing approach for low-cost, high-throughput metagenome profiling. PloS One 15, e0219882. doi: 10.1371/JOURNAL.PONE.0219882

PubMed Abstract | Crossref Full Text | Google Scholar

Houston R. D., Bean T. P., Macqueen D. J., Gundappa M. K., Jin Y. H., Jenkins T. L., et al. (2020). Harnessing genomics to fast-track genetic improvement in aquaculture. Nat. Rev. Genet. 21, 389–409. doi: 10.1038/s41576-020-0227-y

PubMed Abstract | Crossref Full Text | Google Scholar

Hu J., Chen J., Ma L., Hou Q., Zhang Y., Kong X., et al. (2024). Characterizing core microbiota and regulatory functions of the pig gut microbiome. ISME. J. 18(1), wrad037. doi: 10.1093/ISMEJO/WRAD037

PubMed Abstract | Crossref Full Text | Google Scholar

Hu Y., Liu D., Jin X., Feng Y., and Guo Y. (2023). Synthetic microbiome for a sustainable poultry industry. Innovat. 4, 100357. doi: 10.1016/j.xinn.2022.100357

PubMed Abstract | Crossref Full Text | Google Scholar

Ibarra R., Rich K. M., Adasme M., Kamp A., Singer R. S., Atlagich M., et al. (2018). Animal production, animal health and food safety: Gaps and challenges in the Chilean industry. Food Microbiol. 75, 114–118. doi: 10.1016/J.FM.2017.10.004

PubMed Abstract | Crossref Full Text | Google Scholar

Jin Song S., Woodhams D. C., Martino C., Allaband C., Mu A., Javorschi-Miller-Montgomery S., et al. (2019). Engineering the microbiome for animal health and conservation. Exp. Biol. Med. 244, 494–504. doi: 10.1177/1535370219830075

PubMed Abstract | Crossref Full Text | Google Scholar

Khanal P., Maltecca C., Schwab C., Fix J., Bergamaschi M., and Tiezzi F. (2020). Modeling host-microbiome interactions for the prediction of meat quality and carcass composition traits in swine. Genet. Sel. Evol. 52, 41. doi: 10.1186/s12711-020-00561-7

PubMed Abstract | Crossref Full Text | Google Scholar

Khanal P., Maltecca C., Schwab C., Fix J., and Tiezzi F. (2021). Microbiability of meat quality and carcass composition traits in swine. J. Anim. Breed. Genet. 138, 223–236. doi: 10.1111/jbg.12504

PubMed Abstract | Crossref Full Text | Google Scholar

Kokou F., Sasson G., Nitzan T., Doron-Faigenboim A., Harpaz S., Cnaani A., et al. (2018). Host genetic selection for cold tolerance shapes microbiome composition and modulates its response to temperature. eLife 7, e36398. doi: 10.7554/eLife.36398.001

PubMed Abstract | Crossref Full Text | Google Scholar

Larzul C., Estellé J., Borey M., Blanc F., Lemonnier G., Billon Y., et al. (2024). Driving gut microbiota enterotypes through host genetics. Microbiome 12, 116. doi: 10.1186/s40168-024-01827-8

PubMed Abstract | Crossref Full Text | Google Scholar

Lawson C. E., Harcombe W. R., Hatzenpichler R., Lindemann S. R., Löffler F. E., O’Malley M. A., et al. (2019). Common principles and best practices for engineering microbiomes. Nat. Rev. Microbiol. 17, 725–741. doi: 10.1038/s41579-019-0255-9

PubMed Abstract | Crossref Full Text | Google Scholar

Ledesma A. V. and van Eenennaam A. L. (2024). Global status of gene edited animals for agricultural applications. Vet J. 305, 106142. doi: 10.1016/J.TVJL.2024.106142

PubMed Abstract | Crossref Full Text | Google Scholar

Legarra A. and Christensen O. F. (2023). Genomic evaluation methods to include intermediate correlated features such as high-throughput or omics phenotypes. JDS. Commun. 4 (1), 55–60. doi: 10.3168/jdsc.2022-0276

PubMed Abstract | Crossref Full Text | Google Scholar

Le Graverand Q., Lambe N., McGovern F., Navajas E. A., Johnson P., McHugh N., et al. (2025). Comparison of country-specific predictions of feed intake and methane emissions in sheep using different proxies. Livestock. Sci. 296, 105716. doi: 10.1016/J.LIVSCI.2025.105716

Crossref Full Text | Google Scholar

Le Graverand Q., Marie-Etancelin C., Meynadier A., Weisbecker J. L., Marcon D., and Tortereau F. (2023). Predicting feed efficiency traits in growing lambs from their ruminal microbiota. Animal 17, 100824. doi: 10.1016/J.ANIMAL.2023.100824

PubMed Abstract | Crossref Full Text | Google Scholar

Le Luyer J., Schull Q., Auffret P., Lopez P., Crusot M., Belliard C., et al. (2021). Dual RNAseq highlights the kinetics of skin microbiome and fish host responsiveness to bacterial infection. Anim. Microbiome. 3, 35. doi: 10.1186/s42523-021-00097-1

PubMed Abstract | Crossref Full Text | Google Scholar

Li F. and Guan L. L. (2017). Metatranscriptomic profiling reveals linkages between the active rumen microbiome and feed efficiency in beef cattle. Appl. Environ. Microbiol. 83, e00061–e00017. doi: 10.1128/AEM.00061-17

PubMed Abstract | Crossref Full Text | Google Scholar

Li F., Li C., Chen Y., Liu J., Zhang C., Irving B., et al. (2019). Host genetics influence the rumen microbiota and heritable rumen microbial features associate with feed efficiency in cattle. Microbiome 7 (1), 92. doi: 10.1186/s40168-019-0699-1

PubMed Abstract | Crossref Full Text | Google Scholar

Li S., Zheng J., Zhao Y., He J., Liu H., Zhong W., et al. (2025). Lactobacillus strains from sows improve swine appetite by reducing the pro-inflammatory cytokine concentrations. Anim. Nutr. 23, 112–137. doi: 10.1016/J.ANINU.2025.03.021

PubMed Abstract | Crossref Full Text | Google Scholar

Lima J., Martínez-Álvaro M., Mattock J., Auffret M. D., Duthie C. A., Cleveland M. A., et al. (2024). Temporal stability of the rumen microbiome and its longitudinal associations with performance traits in beef cattle. Sci. Rep. 14, 20772. doi: 10.1038/s41598-024-70770-3

PubMed Abstract | Crossref Full Text | Google Scholar

Limborg M. T., Alberdi A., Kodama M., Roggenbuck M., Kristiansen K., and Gilbert M. T. P. (2018). Applied hologenomics: feasibility and potential in aquaculture. Trends Biotechnol. 36, 252–264. doi: 10.1016/j.tibtech.2017.12.006

PubMed Abstract | Crossref Full Text | Google Scholar

Liu J., Pan Y., Jin S., Zheng Y., Xu J., Fan H., et al. (2024). Effects of Citrobacter freundii on sturgeon: Insights from skin mucosal immunology and microbiota. Fish Shellfish Immunol. 149, 109527. doi: 10.1016/j.fsi.2024.109527

PubMed Abstract | Crossref Full Text | Google Scholar

Liu Z., Sun C., Yan Y., Li G., Li X. C., Wu G., et al. (2021). Design and evaluation of a custom 50K Infinium SNP array for egg-type chickens. Poultry. Sci. 100, 101044. doi: 10.1016/J.PSJ.2021.101044

PubMed Abstract | Crossref Full Text | Google Scholar

Lyons P. P., Turnbull J. F., Dawson K. A., and Crumlish M. (2017). Phylogenetic and functional characterization of the distal intestinal microbiome of rainbow trout Oncorhynchus mykiss from both farm and aquarium settings. J. Appl. Microbiol. 122, 347–363. doi: 10.1111/jam.13347

PubMed Abstract | Crossref Full Text | Google Scholar

Ma C., Xu C., Zhang T., Mu Q., Lv J., Xing Q., et al. (2024). Tracking the hologenome dynamics in aquatic invertebrates by the holo-2bRAD approach. Commun. Biol. 7, 827. doi: 10.1038/s42003-024-06509-7

PubMed Abstract | Crossref Full Text | Google Scholar

Ma W., Yin L., Hu Y., Liu X., Guo Z., Zhong B., et al. (2024). Multi-omics analysis reveals interactions between host and microbes in Bama miniature pigs during weaning. Front. Microbiol. 15. doi: 10.3389/fmicb.2024.1482925

PubMed Abstract | Crossref Full Text | Google Scholar

Machado L., Magnusson M., Paul N. A., de Nys R., and Tomkins N. (2014). Effects of marine and freshwater macroalgae on in vitro total gas and methane production. PloS One 9 (1), e85289. doi: 10.1371/journal.pone.0085289

PubMed Abstract | Crossref Full Text | Google Scholar

Maltecca C., Bergamaschi M., and Tiezzi F. (2019a). The interaction between microbiome and pig efficiency: a review. J. Anim. Breed. Genet. 137, 4–13. doi: 10.1111/jbg.12443

PubMed Abstract | Crossref Full Text | Google Scholar

Maltecca C., Lu D., Schillebeeckx C., McNulty N. P., Schwab C., Shull C., et al. (2019b). Predicting growth and carcass traits in swine using microbiome data and machine learning algorithms. Sci. Rep. 9 (1), 6574. doi: 10.1038/s41598-019-43031-x

PubMed Abstract | Crossref Full Text | Google Scholar

Martínez-Álvaro M., Auffret M. D., Duthie C. A., Dewhurst R. J., Cleveland M. A., Watson M., et al. (2022). Bovine host genome acts on rumen microbiome function linked to methane emissions. Commun. Biol. 5, 350. doi: 10.1038/s42003-022-03293-0

PubMed Abstract | Crossref Full Text | Google Scholar

Martínez-Álvaro M., Auffret M. D., Stewart R. D., Dewhurst R. J., Duthie C. A., Rooke J. A., et al. (2020). Identification of complex rumen microbiome interaction within diverse functional niches as mechanisms affecting the variation of methane emissions in bovine. Front. Microbiol. 11. doi: 10.3389/fmicb.2020.00659

PubMed Abstract | Crossref Full Text | Google Scholar

Martinez Boggio G., Christensen O. F., Legarra A., Meynadier A., and Marie-Etancelin C. (2023). Microbiability of milk composition and genetic control of microbiota effects in sheep. J. Dairy. Sci. 106, 6288–6298. doi: 10.3168/JDS.2022-22948

PubMed Abstract | Crossref Full Text | Google Scholar

Martinez Boggio G., Meynadier A., Buitenhuis A. J., and Marie-Etancelin C. (2022). Host genetic control on rumen microbiota and its impact on dairy traits in sheep. Genet. Sel. Evol. 54, 77. doi: 10.1186/s12711-022-00769-9

PubMed Abstract | Crossref Full Text | Google Scholar

Martinez Boggio G., Monteiro H. F., Lima F. S., Figueiredo C. C., Bisinotto R. S., Santos J. E. P., et al. (2024). Host and rumen microbiome contributions to feed efficiency traits in Holstein cows. J. Dairy. Sci. 107, 3090–3103. doi: 10.3168/jds.2023-23869

PubMed Abstract | Crossref Full Text | Google Scholar

MatChado M. S., Rühlemann M., Reitmeier S., Kacprowski T., Frost F., Haller D., et al. (2024). On the limits of 16S rRNA gene-based metagenome prediction and functional profiling. Microb. Genom. 10, 1203. doi: 10.1099/mgen.0.001203

PubMed Abstract | Crossref Full Text | Google Scholar

Meuwissen T. H. E., Hayes B. J., and Goddard M. E. (2001). Prediction of total genetic value using genome-wide dense marker maps. Genetics 157, 1819–1829. doi: 10.1093/genetics/157.4.1819

PubMed Abstract | Crossref Full Text | Google Scholar

Miller G. A., Auffret M. D., Roehe R., Nisbet H., and Martínez-Álvaro M. (2023). Different microbial genera drive methane emissions in beef cattle fed with two extreme diets. Front. Microbiol. 14. doi: 10.3389/FMICB.2023.1102400

PubMed Abstract | Crossref Full Text | Google Scholar

Molnár M. (2022). Transforming intensive animal production: challenges and opportunities for farm animal welfare in the european union. Animals 12, 2086. doi: 10.3390/ani12162086

PubMed Abstract | Crossref Full Text | Google Scholar

Montenegro D., Astudillo-García C., Hickey T., and Lear G. (2020). A non-invasive method to monitor marine pollution from bacterial DNA present in fish skin mucus. Environ. pollut. 263 (Pt A), 114438. doi: 10.1016/j.envpol.2020.114438

PubMed Abstract | Crossref Full Text | Google Scholar

Mora M., Velasco-Galilea M., Sánchez J., Ramayo-Caldas Y., and Piles M.. (2022). Disentangling the causal relationship between rabbit growth and cecal microbiota through structural equation models. Genet. Sel. Evol. 54, 81. doi: 10.1186/s12711-022-00770-2

PubMed Abstract | Crossref Full Text | Google Scholar

Morris A. H. and Bohannan B. J. M. (2024). Estimates of microbiome heritability across hosts. Nat. Microbiol. 9, 3110–3119. doi: 10.1038/s41564-024-01865-w

PubMed Abstract | Crossref Full Text | Google Scholar

Mueller U. G. and Linksvayer T. A. (2022). Microbiome breeding: conceptual and practical issues. Trends Microbiol. 30, 997–1011. doi: 10.1016/j.tim.2022.04.003

PubMed Abstract | Crossref Full Text | Google Scholar

Myer P. R., Smith T. P. L., Wells J. E., Kuehn L. A., and Freetly H. C. (2015). Rumen microbiome from steers differing in feed efficiency. PloS One 10 (6), e0129174. doi: 10.1371/journal.pone.0129174

PubMed Abstract | Crossref Full Text | Google Scholar

Newaj-Fyzul A., Al-Harbi A. H., and Austin B. (2014). Review: Developments in the use of probiotics for disease control in aquaculture. Aquaculture 431, 1–11. doi: 10.1016/j.aquaculture.2013.08.026

Crossref Full Text | Google Scholar

Nuñez P., Martinez-Boggio G., Casellas J., Varona L., Peñagaricano F., and Ibáñez-Escriche N. (2025). Applying recursive modelling to assess the role of the host genome and the gut microbiome on feed efficiency in pigs. Animal 19, 101453. doi: 10.1016/J.ANIMAL.2025.101453

PubMed Abstract | Crossref Full Text | Google Scholar

Palombo V., Milanesi M., Sgorlon S., Capomaccio S., Mele M., Nicolazzi E., et al. (2018). Genome-wide association study of milk fatty acid composition in Italian Simmental and Italian Holstein cows using single nucleotide polymorphism arrays. J. Dairy. Sci. 101, 11004–11019. doi: 10.3168/jds.2018-14413

PubMed Abstract | Crossref Full Text | Google Scholar

Pena R. N., Ros-Freixedes R., Tor M., and Estany J. (2016). Genetic marker discovery in complex traits: A field example on fat content and composition in pigs. Int. J. Mol. Sci. 17, 2100. doi: 10.3390/IJMS17122100

PubMed Abstract | Crossref Full Text | Google Scholar

Pérez-Enciso M., Zingaretti L. M., Ramayo-Caldas Y., and De los Campos G. (2021). Opportunities and limits of combining microbiome and genome data for complex trait prediction. Genet. Sel. Evol. 53, 56. doi: 10.1186/s12711-021-00658-7

PubMed Abstract | Crossref Full Text | Google Scholar

Pham L. N., Kanther M., Semova I., and Rawls J. F. (2008). Methods for generating and colonizing gnotobiotic zebrafish. Nat. Protoc. 3, 1862–1875. doi: 10.1038/nprot.2008.186

PubMed Abstract | Crossref Full Text | Google Scholar

Prifti E., Chevaleyre Y., Hanczar B., Belda E., Danchin A., Clément K., et al. (2020). Interpretable and accurate prediction models for metagenomics data. GigaScience 9, 1–11. doi: 10.1093/GIGASCIENCE/GIAA010

PubMed Abstract | Crossref Full Text | Google Scholar

Pulgar R., Mandakovic D., Salgado P., Venegas L., Ortiz D., Peña-Neira Á., et al. (2021). Micro-encapsulated grape pomace extract (MGPE) as a feed additive improves growth performance, antioxidant capacity, and shifts the gut microbiome of rainbow trout. Aquaculture 544, 737129. doi: 10.1016/j.aquaculture.2021.737129

Crossref Full Text | Google Scholar

Quides K. W. and Atamian H. S. (2021). A microbiome engineering framework to evaluate rhizobial symbionts of legumes. Plant Soil. 463, 631–642. doi: 10.1007/s11104-021-04892-2

Crossref Full Text | Google Scholar

Ramayo-Caldas Y., Crespo-Piazuelo D., Morata J., González-Rodríguez O., Sebastià C., Castello A., et al. (2023). Copy number variation on ABCC2-DNMBP loci affects the diversity and composition of the fecal microbiota in pigs. Microbiol. Spectr. 11, e05271–e05222. doi: 10.1128/spectrum.05271-22

PubMed Abstract | Crossref Full Text | Google Scholar

Ramayo-Caldas Y., Prenafeta-Boldú F., Zingaretti L. M., Gonzalez-Rodriguez O., Dalmau A., Quintanilla R., et al. (2020a). Gut eukaryotic communities in pigs: diversity, composition and host genetics contribution. Anim. Microbiome. 2, 18. doi: 10.1186/s42523-020-00038-4

PubMed Abstract | Crossref Full Text | Google Scholar

Ramayo-Caldas Y., Zingaretti L. M., Pérez-Pascual D., Alexandre P. A., Reverter A., Dalmau A., et al. (2021). Leveraging host-genetics and gut microbiota to determine immunocompetence in pigs. Anim. Microbiome. 3, 74. doi: 10.1186/S42523-021-00138-9

PubMed Abstract | Crossref Full Text | Google Scholar

Ramayo-Caldas Y., Zingaretti L., Popova M., Estellé J., Bernard A., Pons N., et al. (2020b). Identification of rumen microbial biomarkers linked to methane emission in Holstein dairy cows. J. Anim. Breed. Genet. 137, 49–59. doi: 10.1111/jbg.12427

PubMed Abstract | Crossref Full Text | Google Scholar

Rasmussen J. A., Kiilerich P., Madhun A. S., Waagbø R., Lock E. J. R., Madsen L., et al. (2023). Co-diversification of an intestinal Mycoplasma and its salmonid host. ISME. J. 17, 682–692. doi: 10.1038/s41396-023-01379-z

PubMed Abstract | Crossref Full Text | Google Scholar

Rasmussen J. A., Villumsen K. R., Duchêne D. A., Puetz L. C., Delmont T. O., Sveier H., et al. (2021). Genome-resolved metagenomics suggests a mutualistic relationship between Mycoplasma and salmonid hosts. Commun. Biol. 4, 579. doi: 10.1038/s42003-021-02105-1

PubMed Abstract | Crossref Full Text | Google Scholar

Reverter A., Ballester M., Alexandre P. A., Mármol-Sánchez E., Dalmau A., Quintanilla R., et al. (2021). A gene co-association network regulating gut microbial communities in a Duroc pig population. Microbiome 9, 52. doi: 10.1186/s40168-020-00994-8

PubMed Abstract | Crossref Full Text | Google Scholar

Robinson C. D., Klein H. S., Murphy K. D., Parthasarathy R., Guillemin K., and Bohannan B. J. M. (2018). Experimental bacterial adaptation to the zebrafish gut reveals a primary role for immigration. PloS Biol. 16, e2006893. doi: 10.1371/journal.pbio.2006893

PubMed Abstract | Crossref Full Text | Google Scholar

Roehe R., Dewhurst R. J., Duthie C. A., Rooke J. A., McKain N., Ross D. W., et al. (2016). Bovine host genetic variation influences rumen microbial methane production with best selection criterion for low methane emitting and efficiently feed converting hosts based on metagenomic gene abundance. PloS Genet. 12, e1005846. doi: 10.1371/journal.pgen.1005846

PubMed Abstract | Crossref Full Text | Google Scholar

Rokon-Uz-Zaman M., Bushra A., Pospo T. A., Runa M. A., Tasnuva S., Parvin M. S., et al. (2023). Detection of antimicrobial resistance genes in Lactobacillus spp. from poultry probiotic products and their horizontal transfer among Escherichia coli. Vet. Anim. Sci. 20, 100292. doi: 10.1016/J.VAS.2023.100292

PubMed Abstract | Crossref Full Text | Google Scholar

Roque B. M., Salwen J. K., Kinley R., and Kebreab E. (2019). Inclusion of Asparagopsis armata in lactating dairy cows’ diet reduces enteric methane emission by over 50 percent. J. Clean. Prod. 234, 132–138. doi: 10.1016/j.jclepro.2019.06.193

Crossref Full Text | Google Scholar

Rosenberg E. and Zilber-Rosenberg I. (2018). The hologenome concept of evolution after 10 years. Microbiome 6, 78. doi: 10.1186/s40168-018-0457-9

PubMed Abstract | Crossref Full Text | Google Scholar

Ross E. M. and Hayes B. J. (2022). Metagenomic Predictions: A Review 10 years on. Front. Genet. 13. doi: 10.3389/FGENE.2022.865765

PubMed Abstract | Crossref Full Text | Google Scholar

Ross E. M., Hayes B. J., Tucker D., Bond J., Denman S. E., and Oddy V. H. (2020). Genomic predictions for enteric methane production are improved by metabolome and microbiome data in sheep (Ovis aries). J. Anim. Sci. 98, skaa262. doi: 10.1093/JAS/SKAA262

PubMed Abstract | Crossref Full Text | Google Scholar

Ross E. M., Moate P. J., Marett L. C., Cocks B. G., and Hayes B. J. (2013). Metagenomic predictions: from microbiome to complex health and environmental phenotypes in humans and cattle. PloS One 8 (9), e73056. doi: 10.1371/journal.pone.0073056

PubMed Abstract | Crossref Full Text | Google Scholar

Rothschild D., Weissbrod O., Barkan E., Kurilshikov A., Korem T., Zeevi D., et al. (2018). Environment dominates over host genetics in shaping human gut microbiota. Nature 555, 210–215. doi: 10.1038/nature25973

PubMed Abstract | Crossref Full Text | Google Scholar

Saborío-Montero A., Bach A., and González-Recio O. (2018). “Predicting complex traits using microbiome information: a comparison of metagenome distance matrices,” in Conference: XIX Reunión Nacional de Mejora Genética Animal. (León, Spain: Comunicaciones, ACTEON), Jun 14-15.

Google Scholar

Saborío-Montero A., Gutiérrez-Rivas M., López-García A., García-Rodríguez A., Atxaerandio R., Goiri I., et al. (2021). Holobiont effect accounts for more methane emission variance than the additive and microbiome effects on dairy cattle. Livest. Sci. 250, 104538. doi: 10.1016/j.livsci.2021.104538

Crossref Full Text | Google Scholar

Saladrigas-García M., D’Angelo M., Ko H. L., Nolis P., Ramayo-Caldas Y., Folch J. M., et al. (2021). Understanding host-microbiota interactions in the commercial piglet around weaning. Sci. Rep. 11, 23488. doi: 10.1038/s41598-021-02754-6

PubMed Abstract | Crossref Full Text | Google Scholar

Sanglard L. P., Schmitz-Esser S., Gray K. A., Linhares D. C. L., Yeoman C. J., Dekkers J. C. M., et al. (2020). Investigating the relationship between vaginal microbiota and host genetics and their impact on immune response and farrowing traits in commercial gilts. J. Anim. Breed. Genet. 137, 84–102. doi: 10.1111/jbg.12456

PubMed Abstract | Crossref Full Text | Google Scholar

Santos B. P., Bessegatto J. A., Alfieri A. A., Lisbôa J. A. N., Weese J. S., and Costa M. C. (2021). Transfaunation of the ruminal fluid from cows alters ruminal microbiota structure but not dominant protozoa in healthy sheep. Small. Ruminant. Res. 194, 106283. doi: 10.1016/j.smallrumres.2020.106283

Crossref Full Text | Google Scholar

Shi W., Moon C. D., Leahy S. C., Kang D., Froula J., Kittelmann S., et al. (2014). Methane yield phenotypes linked to differential gene expression in the sheep rumen microbiome. Genome Res. 24, 1517–1525. doi: 10.1101/gr.168245.113

PubMed Abstract | Crossref Full Text | Google Scholar

Sidiq M. J., Ganie P. A., Posti R., Magloo A. H., and Bhat R. A. H. (2025). “Current challenges in fisheries and aquaculture sector: A technological perspective,” in Information Technology in Fisheries and Aquaculture. Eds. Ganie P. A., Posti R., and Pandey P. K. (Springer, Singapore). doi: 10.1007/978-981-97-8553-7_3

Crossref Full Text | Google Scholar

Small C. M., Beck E. A., Currey M. C., Tavalire H. F., Bassham S., and Cresko W. A. (2023). Host genomic variation shapes gut microbiome diversity in threespine stickleback fish. MBio 14, e0021923. doi: 10.1128/MBIO.00219-23

PubMed Abstract | Crossref Full Text | Google Scholar

Sun Z., Huang S., Zhu P., Tzehau L., Zhao H., Lv J., et al. (2022). Species-resolved sequencing of low-biomass or degraded microbiomes using 2bRAD-M. Genome Biol. 23, 36. doi: 10.1186/s13059-021-02576-9

PubMed Abstract | Crossref Full Text | Google Scholar

Szajewska H., Scott K. P., de Meij T., Forslund-Startceva S. K., Knight R., Koren O., et al. (2025). Antibiotic-perturbed microbiota and the role of probiotics. Nat. Rev. Gastroenterol. Hepatol. 22, 155–172. doi: 10.1038/S41575-024-01023-X

PubMed Abstract | Crossref Full Text | Google Scholar

Tang S., Xin Y., Ma Y., Xu X., Zhao S., and Cao J. (2020). Screening of microbes associated with swine growth and fat deposition traits across the intestinal tract. Front. Microbiol. 11. doi: 10.3389/fmicb.2020.586776

PubMed Abstract | Crossref Full Text | Google Scholar

Tayyab M., Zhao Y., and Zhang Y. (2025). Microbiome engineering to enhance disease resistance in aquaculture: current strategies and future directions. Front. Microbiol. 16. doi: 10.3389/FMICB.2025.1625265/FULL

PubMed Abstract | Crossref Full Text | Google Scholar

Temitayo I. A., Francis A. C., Kehinde A. B., Ali V. E., Aderogba R. U., Umar M. M., et al. (2025). Microbiome editing in infectious disease prevention and therapy: CRISPR Applications in Host-Microbe Interactions. Int. J. Biol. Pharm. Sci. Arch. 9, 085–114. doi: 10.53771/IJBPSA.2025.9.2.0045

Crossref Full Text | Google Scholar

Theis K. R., Dheilly N. M., Klassen J. L., Brucker R. M., Baines J. F., Bosch T. C. G., et al. (2016). Getting the hologenome concept right: an eco-evolutionary framework for hosts and their microbiomes. MSystems 1, 1–6. doi: 10.1128/msystems.00028-16

PubMed Abstract | Crossref Full Text | Google Scholar

Thormar E. A., Hansen S. B., Jørgensen L., von G., and Limborg M. T. (2024a). Sampling fish gut microbiota - A genome-resolved metagenomic approach. Ecol. Evol. 14, e70302. doi: 10.1002/ECE3.70302;PAGE:STRING:ARTICLE/CHAPTER

PubMed Abstract | Crossref Full Text | Google Scholar

Thormar E. A., Rasmussen J. A., Mathiessen H., Marana M. H., Clausen C. G., Hansen M., et al. (2024b). A zebrafish model to elucidate the impact of host genes on the microbiota. Environ. DNA. 6, e513. doi: 10.1002/edn3.513

Crossref Full Text | Google Scholar

Tilley C., Barber I., and Norton W. (2024). Skin swabbing protocol to collect DNA samples from small-bodied fish species. F1000Res 10, 1064. doi: 10.12688/f1000research.73115.2

PubMed Abstract | Crossref Full Text | Google Scholar

Tous N., Marcos S., Goodarzi Boroojeni F., Pérez de Rozas A., Zentek J., Estonba A., et al. (2022). Novel strategies to improve chicken performance and welfare by unveiling host-microbiota interactions through hologenomics. Front. Physiol. 13. doi: 10.3389/fphys.2022.884925

PubMed Abstract | Crossref Full Text | Google Scholar

Tovar-Herrera O. E., Grinshpan I., Sorek G., Lybovits I., Levin L., Moraïs S., et al. (2025). Core rumen microbes are functional generalists that sustain host metabolism and gut ecosystem function. Nat. Ecol. Evol. 10, 44–l58. doi: 10.1038/s41559-025-02904-3

PubMed Abstract | Crossref Full Text | Google Scholar

Toxqui-Rodríguez S., Estensoro I., Domingo-Bretón R., Del Pozo R., Pérez-Sánchez J., Sipkema D., et al. (2025). Interactions between gilthead seabream intestinal transcriptome and microbiota upon Enteromyxum leei infection: a multi–omic approach. Anim. Microbiome. 7, 22. doi: 10.1186/s42523-025-00388-x

PubMed Abstract | Crossref Full Text | Google Scholar

van Opstal E. J. and Bordenstein S. R. (2015). Rethinking heritability of the microbiome. Science 349, 1172–1173. doi: 10.1126/science.aab3958

PubMed Abstract | Crossref Full Text | Google Scholar

Velasco-Galilea M., Piles M., Ramayo-Caldas Y., and Sánchez J. P. (2021). The value of gut microbiota to predict feed efficiency and growth of rabbits under different feeding regimes. Sci. Rep. 11, 19495. doi: 10.1038/s41598-021-99028-y

PubMed Abstract | Crossref Full Text | Google Scholar

Velasco-Galilea M., Piles M., Ramayo-Caldas Y., Varona L., and Sánchez J. P. (2022). Use of Bayes factors to evaluate the effects of host genetics, litter and cage on the rabbit cecal microbiota. Genet. Sel. Evol. 54, 46. doi: 10.1186/s12711-022-00738-2

PubMed Abstract | Crossref Full Text | Google Scholar

Verschuren L. M. G., Schokker D., Bergsma R., Jansman A. J. M., Molist F., and Calus M. P. L. (2020). Prediction of nutrient digestibility in grower-finisher pigs based on fecal microbiota composition. J. Anim. Breed. Genet. 137, 23–35. doi: 10.1111/jbg.12433

PubMed Abstract | Crossref Full Text | Google Scholar

Vollmar S., Wellmann R., Borda-Molina D., Rodehutscord M., Camarinha-Silva A., and Bennewitz J. (2020). The gut microbial architecture of efficiency traits in the domestic poultry model species Japanese quail (Coturnix japonica) assessed by mixed linear models. G3-Genes. Genomes Genet. 10, 2553–2562. doi: 10.1534/g3.120.401424

PubMed Abstract | Crossref Full Text | Google Scholar

Vourlaki I. T., Furman O., Tapio I., Mizrahi I., Fischer D., Creevey C., et al. (2025a). “Co-occurring bacterial guilds in dairy cows: diversity, composition and influence on host performance,” in Proceedings of the 76th EAAP Annual Meeting. (Innsbruck, Austria: EAAP), p.25–p.29.

Google Scholar

Vourlaki I. T., Rio-Lopez R., Clavell-Sansalvador A., Ramírez-Ayala L. C., Ballester M., Sanchez J. P., et al. (2025b). Co-occurring microbial guilds in pig fecal microbiota: key drivers and effects on host performance. Genet. Sel. Evol. 57, 27. doi: 10.1186/s12711-025-00979-x

PubMed Abstract | Crossref Full Text | Google Scholar

Wallace R. J., Sasson G., Garnsworthy P. C., Tapio I., Gregson E., Bani P., et al. (2019). A heritable subset of the core rumen microbiome dictates dairy cow productivity and emissions. Sci. Adv. 5, eaav8391. doi: 10.1126/sciadv.aav8391

PubMed Abstract | Crossref Full Text | Google Scholar

Wang W., Zhang Y., Zhang X., Li C., Yuan L., Zhang D., et al. (2023). Heritability and recursive influence of host genetics on the rumen microbiota drive body weight variance in male Hu sheep lambs. Microbiome 11, 197. doi: 10.1186/s40168-023-01642-7

PubMed Abstract | Crossref Full Text | Google Scholar

Wannaprasat W., Koowatananukul C., Ekkapobyotin C., and Chuanchuen R. (2009). Quality analysis of commercial probiotic products for food animals. Southeast. Asian J. Trop. Med. Public Health 40, 1103–1112.

PubMed Abstract | Google Scholar

Weimer P. J., Cox M. S., Vieira de Paula T., Lin M., Hall M. B., and Suen G. (2017). Transient changes in milk production efficiency and bacterial community composition resulting from near-total exchange of ruminal contents between high- and low-efficiency Holstein cows. J. Dairy. Sci. 100, 7165–7182. doi: 10.3168/jds.2017-12746

PubMed Abstract | Crossref Full Text | Google Scholar

Weinroth M. D., Belk A. D., Dean C., Noyes N., Dittoe D. K., Rothrock M. J., et al. (2022). Considerations and best practices in animal science 16S ribosomal RNA gene sequencing microbiome studies. J. Anim. Sci. 100, 1–18. doi: 10.1093/JAS/SKAB346

PubMed Abstract | Crossref Full Text | Google Scholar

Weishaar R., Wellmann R., Camarinha-Silva A., Rodehutscord M., and Bennewitz J. (2020). Selecting the hologenome to breed for an improved feed efficiency in pigs—A novel selection index. J. Anim. Breed. Genet. 137, 14–22. doi: 10.1111/jbg.12447

PubMed Abstract | Crossref Full Text | Google Scholar

Weissbrod O., Rothschild D., Barkan E., and Segal E. (2018). Host genetics and microbiome associations through the lens of genome wide association studies. Curr. Opin. Microbiol. 44, 9–19. doi: 10.1016/j.mib.2018.05.003

PubMed Abstract | Crossref Full Text | Google Scholar

Wen C., Yan W., Sun C., Ji C., Zhou Q., Zhang D., et al. (2019). The gut microbiota is largely independent of host genetics in regulating fat deposition in chickens. ISME. J. 13, 1422–1436. doi: 10.1038/s41396-019-0367-2

PubMed Abstract | Crossref Full Text | Google Scholar

Wesseling W., Lohmeyer M., Wittka S., Bartels J., Kroll S., Soltmann C., et al. (2016). Adverse effects of immobilized Pseudoalteromonas on the fish pathogenic Vibrio Anguillarum: an in vitro study. J. Mar. Sci. 2016, 3683809. doi: 10.1155/2016/3683809

Crossref Full Text | Google Scholar

Xue M. Y., Sun H. Z., Wu X. H., Liu J. X., and Guan L. L. (2020). Multi-omics reveals that the rumen microbiome and its metabolome together with the host metabolome contribute to individualized dairy cow performance. Microbiome 8, 64. doi: 10.1186/s40168-020-00819-8

PubMed Abstract | Crossref Full Text | Google Scholar

Yadav M. and Shukla P. (2019). Recent systems biology approaches for probiotics use in health aspects: a review. 3 Biotech. 9, 448. doi: 10.1007/s13205-019-1980-5

PubMed Abstract | Crossref Full Text | Google Scholar

Yáñez J. M., Naswa S., López M. E., Bassini L., Correa K., Gilbey J., et al. (2016). Genomewide single nucleotide polymorphism discovery in Atlantic salmon (Salmo salar): validation in wild and farmed American and European populations. Mol. Ecol. Resour. 16, 1002–1011. doi: 10.1111/1755-0998.12503

PubMed Abstract | Crossref Full Text | Google Scholar

Yáñez J. M., Xu P., Carvalheiro R., and Hayes B. (2022). Genomics applied to livestock and aquaculture breeding. Evol. Appl. 15, 517–522. doi: 10.1111/eva.13378

PubMed Abstract | Crossref Full Text | Google Scholar

Yang H., Wu J., Huang X., Zhou Y., Zhang Y., Liu M., et al. (2022). ABO genotype alters the gut microbiota by regulating GalNAc levels in pigs. Nature 606, 358–367. doi: 10.1038/s41586-022-04769-z

PubMed Abstract | Crossref Full Text | Google Scholar

Yang J., Manolio T. A., Pasquale L. R., Boerwinkle E., Caporaso N., Cunningham J. M., et al. (2011). Genome partitioning of genetic variation for complex traits using common SNPs. Nat Genet. 43 (6), 519–525. doi: 10.1038/ng.823

PubMed Abstract | Crossref Full Text | Google Scholar

Zang X.-W., Sun H.-Z., Xue M.-Y., Zhang Z., Plastow G., Yang T., et al. (2022). Heritable and nonheritable rumen bacteria are associated with different characters of lactation performance of dairy cows. MSystems 7 (5), e0042222. doi: 10.1128/MSYSTEMS.00422-22

PubMed Abstract | Crossref Full Text | Google Scholar

Zhang Q., Difford G., Sahana G., Løvendahl P., Lassen J., Lund M. S., et al. (2020). Bayesian modeling reveals host genetics associated with rumen microbiota jointly influence methane emission in dairy cows. ISME. J. 14, 2019–2033. doi: 10.1038/s41396-020-0663-x

PubMed Abstract | Crossref Full Text | Google Scholar

Zhang Y., Wen B., David M. A., Gao J. Z., and Chen Z. Z. (2021). Comparative analysis of intestinal microbiota of discus fish (Symphysodon haraldi) with different growth rates. Aquaculture 540, 736740. doi: 10.1016/j.aquaculture.2021.736740

Crossref Full Text | Google Scholar

Zheng Y., Hou C., Chen J., Wang H., Yuan H., Hu N., et al. (2023). Integrating microbiome and transcriptome analyses to understand the effect of replacing fishmeal with Tenebrio molitor meal in Pacific white shrimp (Litopenaeus vannamei) diets. Aquaculture 575, 739818. doi: 10.1016/J.AQUACULTURE.2023.739818

Crossref Full Text | Google Scholar

Zhou Q., Chen Y.d., Lu S., Liu Y., Xu W.t., Li Y.z., et al. (2021). Development of a 50K SNP array for Japanese flounder and its application in genomic selection for disease resistance. Engineering 7, 406–411. doi: 10.1016/J.ENG.2020.06.017

Crossref Full Text | Google Scholar

Zhou H., Yang L., Ding J., Dai R., He C., Xu K., et al. (2022b). Intestinal microbiota and host cooperate for adaptation as a hologenome. mSystems 7, e0126121. doi: 10.1128/msystems.01261-21

PubMed Abstract | Crossref Full Text | Google Scholar

Zhou C., Yang S., Ka W., Gao P., Li Y., Long R., et al. (2022a). Association of gut microbiota with metabolism in rainbow trout under acute heat stress. Front. Microbiol. 13. doi: 10.3389/fmicb.2022.846336

PubMed Abstract | Crossref Full Text | Google Scholar