Estimates of heritabilities and genetic correlations for calving ease over parities were obtained for the Italian Piedmontese population using animal models. Field data were calving records of 50,721 first- and 44,148 second-parity females and 142,869 records of 38,213 cows of second or later parity. Calving ability was scored in five categories and analyzed using either a univariate or a bivariate linear model, treating performance over parities as different traits. The bivariate model was used to investigate the genetic relationship between first- and second- or between first- and third-parity calving ability. All models included direct and maternal genetic effects, which were assumed to be mutually correlated. (Co)variance components were estimated using restricted maximum likelihood procedures. In the univariate analyses, the heritability for direct effects was .19 +/- .01, .10 +/- .01, and .08 +/- .004 for first, second, and second and later parities, respectively. The heritability for maternal effects was .09 +/- .01, .11 +/- .01, and .05 +/- .01, respectively. All genetic correlations between direct and maternal effects were negative, ranging from -.55 to -.43. Approximated standard errors of genetic correlations between direct and maternal effects ranged from .041 to .062. For multiparous cows, the fraction of total variance due to the permanent environment was greater than the maternal heritability. With bivariate models, direct heritability for first parity was smaller than the corresponding univariate estimate, ranging from .18 to .14. Maternal heritabilities were slightly higher than the corresponding univariate estimates. Genetic correlation between first and second parity was .998 +/- .00 for direct effects and .913 +/- .01 for maternal effects. When the bivariate model analyzed first- and third-parity calving ability, genetic correlation was .907 +/- .02 for direct effects and .979 +/- .01 for maternal effects. Residual correlations were low in all bivariate analyses, ranging from .13 for analysis of first and second parity to .07 for analysis of first and third parity. In conclusion, estimates of genetic correlations for calving ease in different parities obtained in this study were very high, but variance components and heritabilities were clearly heterogeneous over parities.

Growth hormone (GH) exerts its effects on growth and metabolism by interacting with a specific receptor (GHR) on the surface of the target cells. Therefore, GHR has been suggested as candidate gene for traits related to meat production in cattle. The aim of the study was to analyse the polymorphism at position 257 in exon 10 of the GHR gene and investigate relationships with 14 in vivo traits and four meat characteristics in Piemontese animals. The biallelic polymorphism already described was detected using a new PCR procedure. The statistical analysis did not show significant gene substitution effects on growth, size and meat conformation traits. As for meat characteristics, a significant gene substitution of GHR(A) over GHR(G) was observed for drip losses at day 3, with the allele GHR(A) associated with higher values. A significant dominance effect was also observed for this trait. Further investigations in other breeds will be useful for better understanding information on the effect of this GHR polymorphism.

Cheese yield is the most important technological parameter in the dairy industry in many countries. The aim of this study was to infer (co)variance components for cheese yields (CY) and nutrient recoveries in curd (REC) predicted using Fourier-transform infrared (FTIR) spectroscopy of samples collected during milk recording on Holstein, Brown Swiss, and Simmental dairy cows. A total of 311,354 FTIR spectra representing the test-day records of 29,208 dairy cows (Holstein, Brown Swiss, and Simmental) from 654 herds, collected over a 3-yr period, were available for the study. The traits of interest for each cow consisted of 3 cheese yield traits (%CY: fresh curd, curd total solids, and curd water as a percent of the weight of the processed milk), 4 curd nutrient recovery traits (REC: fat, protein, total solids, and the energy of the curd as a percent of the same nutrient in the processed milk), and 3 daily cheese production traits (daily fresh curd, total solids, and the water of the curd per cow). Calibration equations (freely available upon request to the corresponding author) were used to predict individual test-day observations for these traits. The (co)variance components were estimated for the CY, REC, milk production, and milk composition traits via a set of 4-trait analyses within each breed. All analyses were performed using REML and linear animal models. The heritabilities of the %CY were always higher for Holstein and Brown Swiss cows (0.22 to 0.33) compared with Simmental cows (0.14 to 0.18). In general, the fresh cheese yield (%CYCURD) showed genetic variation and heritability estimates that were slightly higher than those of its components, %CYSOLIDS and %CYWATER. The parameter RECPROTEIN was the most heritable trait in all the 3 breeds, with values ranging from 0.32 to 0.41. Our estimation of the genetic relationships of the CY and REC with milk production and composition revealed that the current selection strategies used in dairy cattle are expected to exert only limited effects on the REC traits. Instead, breeders may be able to exploit genetic variations in the %CY, particularly RECFAT and RECPROTEIN. This last component is not explained by the milk protein content, suggesting that its direct selection could be beneficial for cheese production aptitude. Collectively, our findings indicate that breeding strategies aimed at enhancing CY and REC could be easily and rapidly implemented for dairy cattle populations in which FTIR spectra are routinely acquired from individual milk samples.

The aims of this study were 1) to investigate the potential application of near-infrared spectroscopy (NIRS) to predict beef quality (BQ) traits, 2) to assess genetic variations of BQ measures and their predictions obtained by NIRS, and 3) to infer the genetic relationship between measures of BQ and their predictions. Young Piedmontese bulls (n = 1,230) were raised and fattened on 124 farms and slaughtered at the same commercial abattoir. The BQ traits evaluated were shear force (SF, kg), cooking loss (CL, %), drip loss (DL, %), lightness (L*), redness (a*), yellowness (b*), saturation index (SI), and hue angle. Near-infrared spectra were collected using a Foss NIRSystems 5000 instrument over a spectral range of 1,100 to 2,498 nm every 2 nm, in reflectance mode. After editing, prediction models were developed on a calibration subset (n = 268) using partial least squares regressions, followed by application of these models to the validation subset (n = 940). Estimations of (co)variance for measures of BQ and NIRS-based predictions were obtained through a set of bivariate Bayesian analyses on the validation subset. Near-infrared predictions were satisfactory for measurements of L* (R(2) = 0.64), a* (R(2) = 0.68), hue angle (R(2) = 0.81), and saturation index (R(2) = 0.59), but not for b*, DL, CL, and SF. The loss of additive genetic variance of predicted vs. measured L*, a*, DL, CL, and SF was generally high and was similar to the loss of residual variance, being a function of the calibration parameter R(2). As a consequence, estimated heritabilities of measures and predictions of BQ were similar for traits with high calibration R(2) values. Genetic correlations between BQ measures and predictions were high for all color traits and DL, and were greater than the corresponding phenotypic correlations, whereas both the phenotypic and genetic correlations for SF and CL were nil. Results suggest that NIRS-based predictions for color features and DL may be used as indicator traits to improve meat quality of the Piedmontese breed.

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.

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The aim of this study was to estimate genetic parameters for type traits of hypertrophic Piemontese cows. Seven traditional type trait evaluations (70 to 100 grid scores), 2 body measurements (cm), and 13 linear description traits (1 to 9 grid scores) recorded on 21,757 Piemontese primiparous cows reared in 990 farms were used. Data were analyzed using a multiple-trait (22 traits) animal model with canonical transformation, accounting for a unique design matrix with the following effects: herd-year-classifier, days in milk, age at calving, and the genetic additive cow effect. Heritability estimates of traditional type evaluation traits were low for thorax, rump, feet and legs, and dairyness (≤0.10), intermediate for fleshiness and overall score evaluations (0.13 to 0.15), and medium to high for body size (0.26). Genetic correlations of dairyness with all the other traditional type traits were low (from -0.14 to 0.16), those of feet and legs were moderate (0.19 to 0.44), and the remaining 5 traits were high (≥0.55), with an exception regarding fleshiness and body size (0.28). Medium-high heritability estimates were obtained for withers height (0.31) and trunk length (0.21), with a very high genetic correlation between these traits (0.97). The genetic correlations of body measurements with body size were also very high (about 0.96), high with thorax, rump, and overall score (0.47 to 0.59), and moderate with the other traditional type traits (0.04 to 0.27). Heritability estimates of all linear traits were moderate (0.09 to 0.15), with the exceptions of top line (0.07) and condition score (0.05). Genetic correlations between linear traits were generally low to moderate (from -0.11 to 0.44) with the only exceptions of the 6 fleshiness traits and body condition, which showed very high correlations (0.60 to 0.96). Moreover, skeletal traits as top line, bone thinness, and head scores presented moderate genetic correlations (0.51 to 0.65). Genetic correlations between linear traits and traditional type traits were consistent with the trend observed between type traits. In conclusion, body measurements seem to describe body size better than traditional evaluation or linear descriptors. The genetic correlations among type evaluation and linear description traits suggest the need for a reduction in the number of traits scored, particularly of those relating to muscular development.

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