Increased inbreeding is an inevitable consequence of selection in livestock populations. The analysis of high-density single nucleotide polymorphisms (SNPs) facilitates the identification of long and uninterrupted runs of homozygosity (ROH) that can be used to identify chromosomal regions that are identical by descent. In this work, the distribution of ROH of different lengths in five Italian cattle breeds is described. A total of 4095 bulls from five cattle breeds (2093 Italian Holstein, 749 Italian Brown, 364 Piedmontese, 410 Marchigiana and 479 Italian Simmental) were genotyped at 54K SNP loci. ROH were identified and used to estimate molecular inbreeding coefficients (FROH ), which were compared with inbreeding coefficients estimated from pedigree information (FPED ) and using the genomic relationship matrix (FGRM ). The average number of ROH per animal ranged from 54 ± 7.2 in Piedmontese to 94.6 ± 11.6 in Italian Brown. The highest number of short ROH (related to ancient consanguinity) was found in Piedmontese, followed by Simmental. The Italian Brown and Holstein had a higher proportion of longer ROH distributed across the whole genome, revealing recent inbreeding. The FPED were moderately correlated with FROH > 1 Mb (0.662, 0.700 and 0.669 in Italian Brown, Italian Holstein and Italian Simmental respectively) but poorly correlated with FGRM (0.134, 0.128 and 0.448 for Italian Brown, Italian Holstein and Italian Simmental respectively). The inclusion of ROH > 8 Mb in the inbreeding calculation improved the correlation of FROH with FPED and FGRM . ROH are a direct measure of autozygosity at the DNA level and can overcome approximations and errors resulting from incomplete pedigree data. In populations with high linkage disequilibrium (LD) and recent inbreeding (e.g. Italian Holstein and Italian Brown), a medium-density marker panel, such as the one used here, may provide a good estimate of inbreeding. However, in populations with low LD and ancient inbreeding, marker density would have to be increased to identify short ROH that are identical by descent more precisely.

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.

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.

Selection is the major force affecting local levels of genetic variation in species. The availability of dense marker maps offers new opportunities for a detailed understanding of genetic diversity distribution across the animal genome. Over the last 50 years, cattle breeds have been subjected to intense artificial selection. Consequently, regions controlling traits of economic importance are expected to exhibit selection signatures. The fixation index (Fst ) is an estimate of population differentiation, based on genetic polymorphism data, and it is calculated using the relationship between inbreeding and heterozygosity. In the present study, locally weighted scatterplot smoothing (LOWESS) regression and a control chart approach were used to investigate selection signatures in two cattle breeds with different production aptitudes (dairy and beef). Fst was calculated for 42 514 SNP marker loci distributed across the genome in 749 Italian Brown and 364 Piedmontese bulls. The statistical significance of Fst values was assessed using a control chart. The LOWESS technique was efficient in removing noise from the raw data and was able to highlight selection signatures in chromosomes known to harbour genes affecting dairy and beef traits. Examples include the peaks detected for BTA2 in the region where the myostatin gene is located and for BTA6 in the region harbouring the ABCG2 locus. Moreover, several loci not previously reported in cattle studies were detected.

Genome-wide selection aims to predict genetic merit of individuals by estimating the effect of chromosome segments on phenotypes using dense single nucleotide polymorphism (SNP) marker maps. In the present paper, principal component analysis was used to reduce the number of predictors in the estimation of genomic breeding values for a simulated population. Principal component extraction was carried out either using all markers available or separately for each chromosome. Priors of predictor variance were based on their contribution to the total SNP correlation structure. The principal component approach yielded the same accuracy of predicted genomic breeding values obtained with the regression using SNP genotypes directly, with a reduction in the number of predictors of about 96% and computation time of 99%. Although these accuracies are lower than those currently achieved with Bayesian methods, at least for simulated data, the improved calculation speed together with the possibility of extracting principal components directly on individual chromosomes may represent an interesting option for predicting genomic breeding values in real data with a large number of SNP. The use of phenotypes as dependent variable instead of conventional breeding values resulted in more reliable estimates, thus supporting the current strategies adopted in research programs of genomic selection in livestock.