The advent of metagenomics in animal breeding poses the challenge of statistically modeling the relationship between the microbiome, the host genetics and relevant complex traits. A set of structural equation models (SEMs) of a recursive type within a Markov chain Monte Carlo (MCMC) framework was proposed here to jointly analyze the host-metagenome-phenotype relationship. A non-recursive bivariate model was set as benchmark to compare the recursive model. The relative abundance of rumen microbes (RA), methane concentration (CH4) and the host genetics was used as a case of study. Data were from 337 Holstein cows from 12 herds in the north and north-west of Spain. Microbial composition from each cow was obtained from whole metagenome sequencing of ruminal content samples using a MinION device from Oxford Nanopore Technologies. Methane concentration was measured with Guardian ® NG infrared gas monitor from Edinburgh Sensors during cow's visits to the milking automated system. A quarterly average from the methane eructation peaks for each cow was computed, and used as phenotype for CH4. Heritability of CH4 was estimated at 0.12 ± 0.01 in both, the recursive and bivariate, models. Likewise, heritability estimates for the relative abundance of the taxa overlapped between models and ranged between 0.08 and 0.48.Genetic correlations between the microbial composition and CH4 ranged from -0.76 to 0.65 in the non-recursive bivariate model and from -0.68 to 0.69 in the recursive model.Regardless of the statistical model used, positive genetic correlations with methane were estimated consistently for the 7 genera pertaining to the Ciliophora phylum, as well as for those genera belonging to the Euryarchaeota (Methanobrevibacter sp.), Chytridiomycota (Neocallimastix sp.) and Fibrobacteres (Fibrobacter sp.) phyla. These results suggest that rumen's whole metagenome recursively regulate methane emissions in dairy cows, and that both CH4 and the microbiota compositions are partially controlled by the host genotype. Recursive models are an interesting approach to disentangle this complex relationship.

The aim of this study was to determine risk factors associated with milk fever (MF) occurrence in Costa Rican grazing dairy cattle. A total of 69,870 cows from 126 dairy herds were included in the study. Data were collected in the Veterinary Automated Management and Production Control Program software by the Population Medicine Research Program of the Veterinary Medicine School, National University of Costa Rica, from 1985 to 2014. To determine the risk factors for MF, 2 logistic regression mixed models were evaluated. The first model used breed, month of calving, ecological life zone, herd nested within ecological life zone, and parity as fixed effects. The second model excluded first-lactation animals and cows without production information, had the same fixed effects of the first model, and added previous MF case, previous lactation length, previous dry period length, previous corrected 305-d milk yield, and calving interval length as fixed effects. Both models used animal and year as random effects. Of the 235,971 recorded lactations, 4,312 (1.83%) reported MF event. The significantly associated risk factors for MF occurrence, ranked by their highest odds ratio (OR), were parity (OR = 52.59), previous dry period length (OR = 4.21), ecological life zone (OR = 3.20), breed (OR = 3.04), previous corrected 305-d milk yield (OR = 2.39), previous MF case (OR = 2.35), and month of calving (OR = 1.36). The findings of this study are the first data reported using an epidemiological approach to study risk factors for MF in Costa Rican dairy cattle. Some of these results might be used to improve preventive management practices at the farms to reduce the incidence of this metabolic disease in grazing dairy herds.

IntroductionThe low pregnancy rate by artificial insemination in sheep represents a fundamental challenge for breeding programs. In this species, oestrus synchronization is carried out by manipulating hormonal regimens through the insertion of progestogen intravaginal devices. This reproductive strategy may alter the vaginal microbiota affecting the artificial insemination outcome.MethodsIn this study, we analyzed the vaginal microbiome of 94 vaginal swabs collected from 47 ewes with alternative treatments applied to the progesterone-releasing intravaginal devices (probiotic, maltodextrin, antibiotic and control), in two sample periods (before placing and after removing the devices). To our knowledge, this is the first study using nanopore-based metagenome sequencing for vaginal microbiome characterization in livestock.ResultsOur results revealed a significant lower abundance of the genera Oenococcus (Firmicutes) and Neisseria (Proteobacteria) in pregnant compared to non-pregnant ewes. We also detected a significant lower abundance of Campylobacter in the group of samples treated with the probiotic.DiscussionAlthough the use of probiotics represents a promising practice to improve insemination results, the election of the suitable species and concentration requires further investigation. In addition, the use of progestogen in the synchronization devices seemed to increase the alpha-diversity and decrease the abundance of harmful microorganisms belonging to Gammaproteobacteria and Fusobacteriia classes, suggesting a beneficial effect of their use.

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