Nicolò Pietro Paolo Macciotta

Effects of lactation stage, parity, β-lactoglobulin genotype and milk SCC on whey protein composition in Sarda dairy ewes

Abstract In 90 Sarda dairy ewes the effects of lactation stage, parity, β-lactoglobulin genotypes, and somatic cell count (SCC) on the milk content of total protein (TP), casein (CN), whey protein (WP) and its fractions α-lactalbumin (ALA), β-lactoglob-ulin (BLG), serum albumin (SA), immunoglobulin (IG) and lactoferrin (LF) were analysed using a linear mixed model. Mean values of variables (g/l) were: TP (54.0), CN (43.0), WP (11.0), BLG (4.78), ALA (1.37), SA (0.61), IG (3.83) and LF (0.28). The lactation stage significantly affected all the variables analysed. TP, CN and WP concentrations tended to increase throughout lactation, with the increase of WP being more pronounced than the corresponding variation in CN. There was no definite trend in BLG content, whereas ALA concentration decreased as lactation progressed. The parity affected almost all variables studied. WP concentration differed significantly only between the second and fourth parity (10.45 vs 11.44 g/l). BLG and SA concentrations were significantly lower in the youngest ewes. The BLG genotype affected milk yield, but no effects were observed on the components of the milk. The SCC influenced almost all variables studied. The TP concentration was significantly higher in milk with SCC >1,000,000 (55.0 g/l) than in milk with lower SCC (53.4 g/l). This was mainly due to the increase of WP (12.52 and 10.24 g/l in milk with SCC above and below 1,000,000/ml respectively), especially in those WP fractions originating from blood.

Analysis of runs of homozygosity and their relationship with inbreeding in five cattle breeds farmed in Italy

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.

Use of multivariate factor analysis to define new indicator variables for milk composition and coagulation properties in Brown Swiss cows

The aim of this study was to elucidate the structure of relationships between milk yield, composition, and coagulation properties of Brown Swiss cattle. Multivariate factor analysis was used to derive new synthetic variables that can be used for selection purposes. For this reason, genetic parameters of these new variables were estimated. Individual records on milk yield, fat and protein percentages, casein content, lactose percentage, somatic cell count, titratable acidity, and pH were taken on 1,200 Italian Brown Swiss cows located in 38 herds. Factor analysis was able to extract 4 latent variables with an associated communality equal to 70% of the total original variance. The 4 latent factors were interpreted as indicators of milk composition, coagulation, acidity, and mammary gland health, respectively. Factor scores calculated for each animal exhibited coherent patterns along the lactation and across different parities. Estimation of genetic parameters of factor scores carried out with a multiple-trait Bayesian hierarchical model showed moderate to low heritabilities (raging from 0.10 to 0.23) and genetic correlations (from -0.15 to 0.46). Results of the present study support the hypothesis of a simpler structure that controls, at least in part, the covariance of milk composition and coagulation properties. Moreover, extracted variables may be useful for both breeding and management purposes, being able to represent, with a single value for each animal, complex traits such as milk coagulation properties or health status of the mammary gland.

Short communication: Effects of β-lactoglobulin, stearoyl-coenzyme A desaturase 1, and sterol regulatory element binding protein gene allelic variants on milk production, composition, acidity, and coagulation properties of Brown Swiss cows

Associations of allelic variants of the β-lactoglobulin (LGB), stearoyl-coenzyme A desaturase 1 (SCD), and sterol regulatory element binding protein (SREBP-1) genes with milk production, composition (fat, protein, and casein content), acidity (pH and titratable acidity), and coagulation properties (rennet coagulation time and curd firmness) were investigated in Brown Swiss cows. In total, 294 animals (progeny of 15 sires) reared in 16 herds were milk-sampled once. The additive effects of LGB (rs109625649:C>T), SCD (ss469414220:C>T), and SREBP-1 (AB355704: g.101_185ins) polymorphisms on the aforementioned traits were analyzed through Bayesian linear models. The LGB genotype affected rennet coagulation time, with TT (or BB) alleles showing longer rennet coagulation time compared with CC (or AA) cows. The SCD gene allelic variants were found to be associated with protein and casein contents and curd firmness: CC animals had the lowest values for the aforementioned traits. An association was found between SREBP-1 alleles and fat content, with the highest values for cows carrying the 84-bp insertion (or L) allele. Results suggest a possible use of these loci in gene-assisted selection programs for the improvement of milk quality traits and coagulation properties in Brown Swiss cattle.

Lactation curves of Sarda breed goats estimated with test day models.

Test day records of milk yield (38,765), fat and protein contents (11,357) of Sarda goats (the most numerous Italian goat breed) were analysed with mixed linear models in order to estimate the effects of test date (month and year of kidding for fat and protein contents) parity, number of kids born, altitude of location of flocks (<200 m asl, 200-500 m asl, >500 m asl), flocks within altitude and lactation stage (eight days-in-milk intervals of 30 d each) on milk production. All factors considered in the models affected milk traits significantly. Milk yield was lower in first parity goats than in higher parities whereas fat and protein contents showed an opposite trend. Goats with two kids at parturition had a higher milk yield than goats with one kid and tended to have lower fat and protein percentages. Repeatability between test days within lactation was 0.34, 0.17 and 0.45 for milk yield, fat content and protein content, respectively. Lactation curves of goats farmed at different altitudes were clearly separated, especially for milk yield. Results of the present study highlight differences in milk production traits among the three subpopulations that have been previously identified within the Sarda breed on the basis of the morphological structure of animals and altitude of location of flocks.

Milk composition and feeding in the Italian dairy sheep

Abstract Milk production represents a relevant quota of the energy consumption of the dairy ewe. Studies on relationships among level of production, milk composition and metabolic aspects are the first fundamental step in the development of a feeding system aimed at satisfying nutritive requirements of the animals. This paper reviews the knowledge about the milk composition of main Italian dairy sheep breeds, the relationship among secretion kinetics of milk and protein and productive level of animals, the algorithms used for estimating fat (6.5%) and protein (5.8%) corrected milk yield, the evolution over time of milk production during lactation and the relationships between feeding and milk composition.

Using eigenvalues as variance priors in the prediction of genomic breeding values by principal component analysis

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.

The mathematical description of lactation curves in dairy cattle

This review gives an overview of the mathematical modelling of lactation curves in dairy cattle. Over the last ninety years, the development of this field of study has followed the main requirements of the dairy cattle industry. Non-linear parametric functions have represented the preferred tools for modelling average curves of homogeneous groups of animals, with the main aim of predicting yields for management purposes. The increased availability of records per individual lactations and the genetic evaluation based on test day records has shifted the interest of modellers towards more flexible and general linear functions, as polynomials or splines. Thus the main interest of modelling is no longer the reconstruction of the general pattern of the phenomenon but the fitting of individual deviations from an average curve. Other specific approaches based on the modelling of the correlation structure of test day records within lactation, such as mixed linear models or principal component analysis, have been used to test the statistical significance of fixed effects in dairy experiments or to create new variables expressing main lactation curve traits. The adequacy of a model is not an absolute requisite, because it has to be assessed according to the specific purpose it is used for. Occurrence of extended lactations and of new productive and functional traits to be described and the increase of records coming from automatic milking systems likely will represent some of the future challenges for the mathematical modelling of the lactation curve in dairy cattle.

Use of the canonical discriminant analysis to select SNP markers for bovine breed assignment and traceability purposes

Several market research studies have shown that consumers are primarily concerned with the provenance of the food they eat. Among the available identification methods, only DNA-based techniques appear able to completely prevent frauds. In this study, a new method to discriminate among different bovine breeds and assign new individuals to groups was developed. Bulls of three cattle breeds farmed in Italy - Holstein, Brown, and Simmental - were genotyped using the 50K SNP Illumina BeadChip. Multivariate canonical discriminant analysis was used to discriminate among breeds, and discriminant analysis (DA) was used to assign new observations. This method was able to completely identify the three groups at chromosome level. Moreover, a genome-wide analysis developed using 340 linearly independent SNPs yielded a significant separation among groups. Using the reduced set of markers, the DA was able to assign 30 independent individuals to the proper breed. Finally, a set of 48 high discriminant SNPs was selected and used to develop a new run of the analysis. Again, the procedure was able to significantly identify the three breeds and to correctly assign new observations. These results suggest that an assay with the selected 48 SNP could be used to routinely track monobreed products.

Predicting haplotype carriers from SNP genotypes in Bos taurus through linear discriminant analysis

BackgroundSNP (single nucleotide polymorphisms) genotype data are increasingly available in cattle populations and, among other things, can be used to predict carriers of specific haplotypes. It is therefore convenient to have a practical statistical method for the accurate classification of individuals into carriers and non-carriers. In this paper, we present a procedure combining variable selection (i.e. the selection of predictive SNPs) and linear discriminant analysis for the identification of carriers of a haplotype on BTA19 (Bos taurus autosome 19) known to be associated with reduced cow fertility. A population of 3645 Brown Swiss cows and bulls genotyped with the 54K SNP-chip was available for the analysis.ResultsThe overall error rate for the prediction of haplotype carriers was on average very low (∼≤1%). The error rate was found to depend on the number of SNPs in the model and their density around the region of the haplotype on BTA19. The minimum set of SNPs to still achieve accurate predictions was 5, with a total test error rate of 1.59.ConclusionsThe paper describes a procedure to accurately identify haplotype carriers from SNP genotypes in cattle populations. Very few misclassifications were observed, which indicates that this is a very reliable approach for potential applications in cattle breeding.