This study evaluated the effect of dietary inclusion of grape seed and linseed, alone or in combination, on sheep milk fatty acids (FA) profile using 24 Sarda dairy ewes allocated to 4 isoproductive groups. Groups were randomly assigned to 4 dietary treatments consisting of a control diet (CON), a diet including 300 g/d per animal of grape seed (GS), a diet including 220 g/d per animal of extruded linseed (LIN), and a diet including a mix of 300 g/d per animal of grape seed and 220 g/d per animal of extruded linseed (MIX). The study lasted 10 wk, with a 2-wk adaptation period and an 8-wk experimental period. Milk FA composition was analyzed in milk samples collected in the last 4 wk of the trial. The milk concentration of saturated fatty acids (SFA) decreased and that of unsaturated, monounsaturated, and polyunsaturated fatty acids (UFA, MUFA, and PUFA, respectively) increased in GS, LIN, and MIX groups compared with CON. The MIX group showed the lowest values of SFA and the highest of UFA, MUFA, and PUFA. Milk from ewes fed linseed (LIN and MIX) showed an enrichment of vaccenic acid (VA), oleic acid (OA), α-linolenic acid (LNA), and cis-9,trans-11 conjugated linoleic acid (CLA) compared with milk from the CON group. The GS group showed a greater content of milk oleic acid (OA) and linoleic acid (LA) and tended to show a greater content of VA and cis-9,trans-11 CLA than the CON group. The inclusion of grape seed and linseed, alone and in combination, decreased the milk concentration of de novo synthesized FA C10:0, C12:0, and C14:0, with the MIX group showing the lowest values. In conclusion, grape seed and linseed could be useful to increase the concentration of FA with potential health benefits, especially when these ingredients are included in combination in the diet.

Fatty acid (FA) composition is one of the most important aspects of milk nutritional quality. However, the inclusion of this trait as a breeding goal for dairy species is hampered by the logistics and high costs of phenotype recording. Fourier-transform infrared spectroscopy (FTIR) is a valid and cheap alternative to laboratory gas chromatography (GC) for predicting milk FA composition. Moreover, as for other novel phenotypes, the efficiency of selection for these traits can be enhanced by using genomic data. The objective of this research was to compare traditional versus genomic selection approaches for estimating genetic parameters and breeding values of milk fatty acid composition in dairy sheep using either GC-measured or FTIR-predicted FA as phenotypes. Milk FA profiles were available for a total of 923 Sarda breed ewes. The youngest 100 had their own phenotype masked to mimic selection candidates. Pedigree relationship information and genotypes were available for 923 and 769 ewes, respectively. Three statistical approaches were used: the classical-pedigree-based BLUP, the genomic BLUP that considers the genomic relationship matrix G, and the single-step genomic BLUP (ssGBLUP) where pedigree and genomic relationship matrices are blended into a single H matrix. Heritability estimates using pedigree were lower than ssGBLUP, and very similar between GC and FTIR regarding the statistical approach used. For some FA, mostly associated with animal diet (i.e., C18:2n-6, C18:3n-3), random effect of combination of flock and test date explained a relevant quota of total variance, reducing the heritability estimates accordingly. Genomic approaches (genomic BLUP and ssGBLUP) outperformed the traditional pedigree method both for GC and FTIR FA. Prediction accuracies in the older cohort were larger than the young cohort. Genomic prediction accuracies (obtained using either G or H relationship matrix) in the young cohort of animals, where their own phenotypes were masked, were similar for GC and FTIR. Multiple-trait analysis slightly affected genomic breeding value accuracies. These results suggest that FTIR-predicted milk FA composition could represent a valid option for inclusion in breeding programs.

Domestication and selection are processes that alter the pattern of within- and between-population genetic variability. They can be investigated at the genomic level by tracing the so-called selection signatures. Recently, sequence polymorphisms at the genome-wide level have been investigated in a wide range of animals. A common approach to detect selection signatures is to compare breeds that have been selected for different breeding goals (i.e. dairy and beef cattle). However, genetic variations in different breeds with similar production aptitudes and similar phenotypes can be related to differences in their selection history. In this study, we investigated selection signatures between two Italian beef cattle breeds, Piemontese and Marchigiana, using genotyping data that was obtained with the Illumina BovineSNP50 BeadChip. The comparison was based on the fixation index (Fst), combined with a locally weighted scatterplot smoothing (LOWESS) regression and a control chart approach. In addition, analyses of Fst were carried out to confirm candidate genes. In particular, data were processed using the varLD method, which compares the regional variation of linkage disequilibrium between populations. Genome scans confirmed the presence of selective sweeps in the genomic regions that harbour candidate genes that are known to affect productive traits in cattle such as DGAT1, ABCG2, CAPN3, MSTN and FTO. In addition, several new putative candidate genes (for example ALAS1, ABCB8, ACADS and SOD1) were detected. This study provided evidence on the different selection histories of two cattle breeds and the usefulness of genomic scans to detect selective sweeps even in cattle breeds that are bred for similar production aptitudes.

Milk composition and its technological properties are traits of interest for the dairy sheep industry because almost all milk produced is processed into cheese. However, several variables define milk technological properties and a complex correlation pattern exists among them. In the present work, we measured milk composition, coagulation properties, and individual cheese yields in a sample of 991 Sarda breed ewes in 47 flocks. The work aimed to study the correlation pattern among measured variables and to obtain new synthetic indicators of milk composition and cheese-making properties. Multivariate factor analysis was carried out on individual measures of milk coagulation parameters; cheese yield; fat, protein, and lactose percentages; somatic cell score; casein percentage; NaCl content; pH; and freezing point. Four factors that were able to explain about 76% of the original variance were extracted. They were clearly interpretable: the first was associated with composition and cheese yield, the second with udder health status, the third with coagulation, and the fourth with curd characteristics. Factor scores were then analyzed by using a mixed linear model that included the fixed effect of parity, lambing month, and lactation stage, and the random effect of flock-test date. The patterns of factor scores along lactation stages were coherent with their technical meaning. A relevant effect of flock-test date was detected, especially on the 2 factors related to milk coagulation properties. Results of the present study suggest the existence of a simpler latent structure that regulates relationships between variables defining milk composition and coagulation properties in sheep. Heritability estimates for the 4 extracted factors were from low to moderate, suggesting potential use of these new variables as breeding goals.