Short communication: Confirmation of candidate causative variants on milk composition and cheesemaking properties in Montbéliarde cows.

In a previous study, we identified candidate causative variants located in 24 functional candidate genes for milk protein and fatty acid composition in Montbéliarde, Normande, and Holstein cows. We designed these variants on the custom part of the EuroG10K BeadChip (Illumina Inc., San Diego, CA), which is routinely used for genomic selection analyses in French dairy cattle. To validate the effects of these candidate variants on milk composition and to estimate their effects on cheesemaking properties, a genome-wide association study was performed on milk protein, fatty acid and mineral composition, as well as on 9 cheesemaking traits (3 laboratory cheese yields, 5 coagulation traits, and milk pH). All the traits were predicted from midinfrared spectra in the Montbéliarde cow population of the Franche-Comté region. A total of 194 candidate variants located in 24 genes and 17 genomic regions were imputed on 19,862 cows with phenotypes and genotyped with either the BovineSNP50 (Illumina Inc.) or the EuroG10K BeadChip. We then tested the effect of each SNP in a mixed linear model including random polygenic effects estimated with a genomic relationship matrix. We confirm here the effects of candidate causative variants located in 17 functional candidate genes on both cheesemaking properties and milk composition traits. In each candidate gene, we identified the most plausible causative variant: 4 are missense in the ALPL, SLC26A4, CSN3, and SCD genes, 7 are located in 5'UTR (AGPAT6), 3' untranslated region (GPT), or upstream (CSN1S1, CSN1S2, PAEP, DGAT1, and PICALM) regions, and 6 are located in introns of the SLC37A1, MGST1, CSN2, BRI3BP, FASN, and ANKH genes.

Development and validation of a model for early prediction of residual feed intake in beef cattle using plasma biomarkers.

Identification of plasma biomarkers for feed efficiency in growing beef cattle offers a promising opportunity for developing prediction models to improve precision feeding strategies. However, these models must accurately predict feed efficiency at early stages of fattening. Our study aimed to evaluate the reliability of candidate biomarkers previously identified in late-fattening cattle when analysed during early fattening stages and to develop diet-specific prediction equations for residual feed intake (RFI). From a total of 364 Charolais bulls across seven cohorts, we selected 64 animals with extreme RFI values. The animals were fed either a corn­ or grass-silage diets. These animals were chosen from four out of the available seven cohorts. Animals from three cohorts (24 high-RFI and 24 low-RFI, having a mean RFI difference of 1.48 kg/d) were used for biomarker confirmation and prediction model training. Animals from a fourth cohort (8 high-RFI and 8 low-RFI, having a mean RFI difference of 0.98 kg/d) were used for model external validation. Blood samples were collected at the beginning of the feed efficiency test (333 ± 20 days), and plasma underwent targeted metabolomic for 630 metabolites, natural abundance of 15N (δ15N), insulin, and IGF-1 analysis. Seven previously identified plasma biomarkers for RFI in late-fattening beef cattle still kept their capability for discriminating low and high RFI animals when analysed during early fattening stages (P < 0.05). Among these confirmed biomarkers, five were common for both grass- and corn-fed animals (creatinine, ß-alanine, triglyceride TG18:0_34:2, symmetric dimethyl-arginine and phosphatidylcholine PC aa C30:2) while two were diet-specific (IGF-1 for grass silage-based diet, and isoleucine for corn silage-based diet. No new plasma biomarkers of RFI were identified at early-fattening stages (false discovery rate  > 0.05). Prediction models were developed based on seven confirmed RFI biomarkers analysed during early-fattening. Two logistic regression models incorporating creatinine and either IGF-1 (for grass silage-based diet) or PC aa C30:2 (for corn silage-based diet) effectively distinguished between high- and low-RFI animals with high sensitivity and specificity (area under the curve > 0.80). The biomarkers used in the models showed moderate to high repeatability between early and late fattening stages (0.45 < r < 0.65). The models were successfully externally validated, with more than 85% of animals from the fourth cohort correctly classified. Once validated in larger cohorts and utilising cost-effective and rapid analytical methods, these models could support precision feeding and breeding programmes, aiming to reduce the cost of raising beef cattle.

Plasma proteins δ15N vs plasma urea as candidate biomarkers of between-animal variations of feed efficiency in beef cattle: Phenotypic and genetic evaluation.

Identifying animals that are superior in terms of feed efficiency may improve the profitability and sustainability of the beef cattle sector. However, measuring feed efficiency is costly and time-consuming. Biomarkers should thus be explored and validated to predict between-animal variation of feed efficiency for both genetic selection and precision feeding. In this work, we aimed to assess and validate two previously identified biomarkers of nitrogen (N) use efficiency in ruminants, plasma urea concentrations and the 15N natural abundance in plasma proteins (plasma δ15N), to predict the between-animal variation in feed efficiency when animals were fed two contrasted diets (high-starch vs high-fibre diets). We used an experimental network design with a total of 588 young bulls tested for feed efficiency through two different traits (feed conversion efficiency [FCE] and residual feed intake [RFI]) during at least 6 months in 12 cohorts (farm × period combination). Animals reared in the same cohort, receiving the same diet and housed in the same pen, were considered as a contemporary group (CG). To analyse between-animal variations and explore relationships between biomarkers and feed efficiency, two statistical approaches, based either on mixed-effect models or regressions from residuals, were conducted to remove the between-CG variability. Between-animal variation of plasma δ15N was significantly correlated with feed efficiency measured through the two criteria traits and regardless of the statistical approach. Conversely, plasma urea was not correlated to FCE and showed only a weak, although significant, correlation with RFI. The response of plasma δ15N to FCE variations was higher when animals were fed a high-starch compared to a high-fibre diet. In addition, we identified two dietary factors, the metabolisable protein to net energy ratio and the rumen protein balance that influenced the relation between plasma δ15N and FCE variations. Concerning the genetic evaluation, and despite the moderate heritability of the two biomarkers (0.28), the size of our experimental setup was insufficient to detect significant genetic correlations between feed efficiency and the biomarkers. However, we validated the potential of plasma δ15N to phenotypically discriminate two animals reared in identical conditions in terms of feed efficiency as long as they differ by at least 0.049 g/g for FCE and 1.67 kg/d for RFI. Altogether, the study showed phenotypic, but non-genetic, relationships between plasma proteins δ15N and feed efficiency that varied according to the efficiency index and the diet utilised.