Disentangling the causal relationship between rabbit growth and cecal microbiota through structural equation models

Applying recursive modelling to assess the role of the host genome and the gut microbiome on feed efficiency in pigs

The gut microbiome plays an important role in the performance and health of swine by providing essential nutrients and supporting the immune system. Recent studies have demonstrated that the gut microbiome can explain part of the variation observed in growth, health, and meat quality. Feed efficiency is crucial in swine production, as feed cost account for more than 60% of total production costs. This study aimed to assess the relationships between the host genome, gut microbiome, and feed efficiency in Iberian pigs raised under intensive conditions. The specific objectives were to assess the mediating effects of the gut microbiome on feed efficiency and to estimate the direct and total heritability of feed efficiency. The data set included the feed conversion ratio (FCR) and residual feed intake (RFI) from 587 Iberian pigs, as well as the 16S rRNA gut microbial abundance from 151 of those pigs raised in a nucleus of selection. We reparametrised variance components from standard bivariate mixed models into recursive models to disentangle the microbiome's mediating effect on feed efficiency. In our models, the host genome has direct effects on both the phenotype (G→P) and the gut microbiome (G→M). Additionally, there is an indirect effect of the host genome on the phenotype mediated by the microbiome (G→M→P). We identified a total of 14 taxa with relevant effects on FCR and 16 taxa with relevant effects on RFI. We categorised the gut microbiome into groups for potential practical application in pig farming. The gut microbes with relevant causal effects and low heritability can be manipulated through management interventions, while those microbes with relevant causal effects and moderate heritability can be targeted through selective breeding. Our findings indicate that incorporating microbiome data leads to a reduction in total heritability for both FCR and RFI. This study provides new insights into the link between the gut microbiome and feed efficiency, presenting practical methods to target microbes that can be influenced through selective breeding or management interventions.

Investigating relationships between the host genome, rumen microbiome, and dairy cow feed efficiency using mediation analysis with structural equation modeling

The rumen microbiome is crucial for converting feed into absorbable nutrients used for milk synthesis, and the efficiency of this process directly impacts the profitability and sustainability of the dairy industry. Recent studies have found that the rumen microbial composition explains part of the variation in feed efficiency traits, including dry matter intake, milk energy, and residual feed intake. The main goal of this study was to reveal relationships between the host genome, rumen microbiome, and dairy cow feed efficiency using structural equation models. Our specific objectives were to (i) infer the mediation effects of the rumen microbiome on feed efficiency traits, (ii) estimate the direct and total heritability of feed efficiency traits, and (iii) calculate the direct and total breeding values of feed efficiency traits. Data consisted of dry matter intake, milk energy, and residual feed intake records, SNP genotype data, and 16S rRNA rumen microbial abundances from 448 mid-lactation Holstein cows from 2 research farms. We implemented structural equation models such that the host genome directly affects the phenotype (GP → P) and the rumen microbiome (GM → P), while the microbiome affects the phenotype (M → P), partially mediating the effect of the host genome on the phenotype (G → M → P). We found that 7 to 30% of microbes within the rumen microbial community had structural coefficients different from zero. We classified these microbes into 3 groups that could have different uses in dairy farming. Microbes with heritability <0.10 but significant causal effects on feed efficiency are attractive for external interventions. On the other hand, 2 groups of microbes with heritability ≥0.10, significant causal effects, and genetic covariances and causal effects with the same or opposite sign to feed efficiency are attractive for selective breeding, improving or decreasing the trait heritability and response to selection, respectively. In general, the inclusion of the different microbes in genomic models tends to decrease the trait heritability rather than increase it, ranging from -15% to +5%, depending on the microbial group and phenotypic trait. Our findings provide more understanding to target rumen microbes that can be manipulated, either through selection or management interventions, to improve feed efficiency traits.

Genome-wide association studies for diarrhoea outcomes identified genomic regions affecting resistance to a severe enteropathy in suckling rabbits

Selection and breeding strategies to improve resistance to enteropathies are essential to reaching the sustainability of the rabbit production systems. However, disease heterogeneity (having only as major visible symptom diarrhoea) and low disease heritability are two barriers for the implementation of these strategies. Diarrhoea condition can affect rabbits at different life stages, starting from the suckling period, with large negative economic impacts. In this study, from a commercial population of suckling rabbits (derived from 133 litters) that experienced an outbreak of enteropathy, we first selected a few animals that died with severe symptoms of diarrhoea and characterized their microbiota, using 16S rRNA gene sequencing data. Clostridium genus was consistently present in all affected specimens. In addition, with the aim to identify genetic markers in the rabbit genome that could be used as selection tools, we performed genome-wide association studies for symptoms of diarrhoea in the same commercial rabbit population. These studies were also complemented with FST analyses between the same groups of rabbits. A total of 332 suckling rabbits (151 with severe symptoms of diarrhoea, 42 with mild symptoms and 129 without any symptoms till the weaning period), derived from 45 different litters (a subset of the 133 litters) were genotyped with the Affymetrix Axiom OrcunSNP Array. In both genomic approaches, rabbits within litters were paired to constitute two groups (susceptible and resistant, including the mildly affected in one or the other group) and run case and control genome-wide association analyses. Genomic heritability estimated in the designed experimental structure integrated in a commercial breeding scheme was 0.19-0.21 (s.e. 0.09-0.10). A total of eight genomic regions on rabbit chromosome 2 (OCU2), OCU3, OCU7, OCU12, OCU13, OCU16 and in an unassembled scaffold had significant single nucleotide polymorphisms (SNPs) and/or markers that trespassed the FST percentile distribution. Among these regions, three main peaks of SNPs were identified on OCU12, OCU13 and OCU16. The QTL region on OCU13 encompasses several genes that encode members of a family of immunoglobulin Fc receptors (FCER1G, FCRLA, FCRLB and FCGR2A) involved in the immune innate system, which might be important candidate genes for this pathogenic condition. The results obtained in this study demonstrated that resistance to an enteropathy occurring in suckling rabbits is in part genetically determined and can be dissected at the genomic level, providing DNA markers that could be used in breeding programmes to increase resistance to enteropathies in meat rabbits.

Structural equation models to infer relationships between energy-related blood metabolites and milk daily energy output in Holstein cows

During lactation, high-yielding cows experience metabolic disturbances due to milk production. Metabolic monitoring offers valuable insights into how cows manage these challenges throughout the lactation period, making it a topic of considerable interest to breeders. In this study, we used Bayesian networks to uncover potential dependencies among various energy-related blood metabolites, i.e., glucose, urea, beta-hydroxybutyrate (BHB), non-esterified fatty acids (NEFA), cholesterol (CHOL), and daily milk energy output (dMEO) in 1,254 Holstein cows. The inferred causal structure was then incorporated into structural equation models (SEM) to estimate heritabilities and additive genetic correlations among these phenotypes using both pedigree and genotypes from a 100k chip. Dependencies among traits were determined using the Hill-Climbing algorithm, implemented with the posterior distribution of the residuals obtained from the standard multiple-trait model. These identified relationships were then used to construct the SEM, considering both direct and indirect relationships. The relevant dependencies and path coefficients obtained, expressed in units of measurement variation of 1σ, were as follows: dMEO → CHOL (0.181), dMEO → BHB (-0.149), dMEO → urea (0.038), glucose → BHB (-0.55), glucose → urea (-0.194), CHOL → urea (0.175), BHB → urea (-0.049), and NEFA → urea (-0.097). Heritabilities for traits of concern obtained with SEM ranged from 0.09 to 0.2. Genetic correlations with a minimum 95% probability (P) of the posterior mean being >0 for positive means or <0 for negative means include those between dMEO and glucose (-0.583, P = 100), dMEO and BHB (0.349, P = 99), glucose and CHOL (0.325, P = 100), glucose and NEFA (-0.388, P = 100), and NEFA and BHB (0.759, P = 100). The results of this analysis revealed the existence of recursive relationships among the energy-related blood metabolites and dMEO. Understanding these connections is paramount for establishing effective genetic selection strategies, enhancing production and animal welfare.