Unbalanced metabolic status in the weeks after calving predisposes dairy cows to metabolic and infectious diseases. Blood glucose, IGF-I, non-esterified fatty acids (NEFA) and β-hydroxybutyrate (BHB) are used as indicators of the metabolic status of cows. This work aims to (1) evaluate the potential of milk mid-IR spectra to predict these blood components individually and (2) to evaluate the possibility of predicting the metabolic status of cows based on the clustering of these blood components. Blood samples were collected from 241 Holstein cows on six experimental farms, at days 14 and 35 after calving. Blood samples were analyzed by reference analysis and metabolic status was defined by k-means clustering (k=3) based on the four blood components. Milk mid-IR analyses were undertaken on different instruments and the spectra were harmonized into a common standardized format. Quantitative models predicting blood components were developed using partial least squares regression and discriminant models aiming to differentiate the metabolic status were developed with partial least squares discriminant analysis. Cross-validations were performed for both quantitative and discriminant models using four subsets randomly constituted. Blood glucose, IGF-I, NEFA and BHB were predicted with respective R 2 of calibration of 0.55, 0.69, 0.49 and 0.77, and R 2 of cross-validation of 0.44, 0.61, 0.39 and 0.70. Although these models were not able to provide precise quantitative values, they allow for screening of individual milk samples for high or low values. The clustering methodology led to the sharing out of the data set into three groups of cows representing healthy, moderately impacted and imbalanced metabolic status. The discriminant models allow to fairly classify the three groups, with a global percentage of correct classification up to 74%. When discriminating the cows with imbalanced metabolic status from cows with healthy and moderately impacted metabolic status, the models were able to distinguish imbalanced group with a global percentage of correct classification up to 92%. The performances were satisfactory considering the variables are not present in milk, and consequently predicted indirectly. This work showed the potential of milk mid-IR analysis to provide new metabolic status indicators based on individual blood components or a combination of these variables into a global status. Models have been developed within a standardized spectral format, and although robustness should preferably be improved with additional data integrating different geographic regions, diets and breeds, they constitute rapid, cost-effective and large-scale tools for management and breeding of dairy cows.

Copy number variations (CNVs) represent a significant source of genomic structural variation. Their length ranges from approximately one hundred to millions of base pair. Genome-wide screenings have clarified that CNVs are a ubiquitous phenomenon affecting essentially the whole genome. Although Bos taurus is one of the most important domestic animal species worldwide and one of the most studied ruminant models for metabolism, reproduction, and disease, relatively few studies have investigated CNVs in cattle and little is known about how CNVs contribute to normal phenotypic variation and to disease susceptibility in this species, compared to humans and other model organisms. Here we characterize and compare CNV profiles in 2654 animals from five dairy and beef Bos taurus breeds, using the Illumina BovineSNP50 genotyping array (54001 SNP probes). In this study we applied the two most commonly used algorithms for CNV discovery (QuantiSNP and PennCNV) and identified 4830 unique candidate CNVs belonging to 326 regions. These regions overlap with 5789 known genes, 76.7% of which are significantly co-localized with segmental duplications (SD). This large scale screening significantly contributes to the enrichment of the Bos taurus CNV map, demonstrates the ubiquity, great diversity and complexity of this type of genomic variation and sets the basis for testing the influence of CNVs on Bos taurus complex functional and production traits.

In this work, the genetic variation of milk FA was investigated in three different bovine breeds, the Jersey, the Piedmontese and the Valdostana, and at different lactation stages. All animals were genotyped for 21 Single Nucleotide Polymorphisms located within nine candidate genes involved in lipid synthesis: diacylglycerol acyltransferase 1 and 2 (DGAT1, 2); stearoyl-CoA desaturase (SCD); growth hormone receptor (GHR); fatty acid synthase (FASN); acyl-CoA dehydrogenase (ACAD); fatty acid binding protein (FABP4); lipoprotein lipase (LPL); and leptin gene (LEP). The highest milk-fat Jersey breed also showed the highest content of saturated FA. Throughout lactation, the breeds showed a similar variation in the FA, with a decrease in the short-chain, this was accompanied by a general increase in the long chain FA at the end of lactation. The increase in long chain saturated FA was particularly evident in the case of the Jersey. The effect ofSCDgene on the C14 desaturation index was confirmed; theDGAT1gene was polymorphic only in the Jersey breed, but its effect was confirmed only on milk fat content; three further potential candidate genes were identified: first, theFABP4gene, which was found to influence medium and long chain FA in all the breeds, but not the desaturation indices; second, theFASNgene, which was found to influence the amount of PUFA in the Piedmontese and the Valdostana, and third, theLPLgene, which was found to affect fat content in the Piedmontese.

The aim of the present study was to investigate the genetic control of the fatty acid (FA) composition in milk from 3 breeds of sheep: Altamurana, Gentile di Puglia, and Sarda. Single nucleotide polymorphisms within genes, encoding enzymes putatively involved in the synthesis and metabolism of milk fat, were selected for analysis, and the allele substitution effects were determined for 16 genes, which were polymorphic in the 3 sheep breeds, upon the milk fat composition. Four genes (alpha-1-antichymotrypsin-2; diacylglycerol O-acyltransferase homolog-2; propionyl Coenzyme A carboxylase, beta polypeptide; and insulin-like growth factor-I) play a role in the desaturation of stearic FA into polyunsaturated fatty acids. Furthermore, 2 genes (growth hormone receptor and zona pellucida glycoprotein-2) affect the variability of the total fat content in addition to the butyric and stearic FA profile, and the fatty acid synthetase gene has an influence on the medium-chain FA. Milk FA profiles play an important role in dairy sheep farming because they have a large effect on cheese characteristics and also because sheep milk may be marketed as a source of nutraceuticals because it contains higher levels of conjugated linoleic acid than milk from other ruminants. The current study evaluated the global effects of a large number of single nucleotide polymorphisms and haplotypes on traits that are not commonly investigated in sheep but that are potentially very useful for improving milk quality.