Genetic diversity of <i>NRAMP1</i> 3'-UTR microsatellite in cattle breeds reared in Sardinia

The aim of this study was to compare the allele frequency of 3'-UTR NRAMP1 (Natural Resistance-Associated Macrophage Protein) microsatellite between local and specialized dairy cattle breeds reared in Sardinia, Italy. Blood samples were collected and DNA was extracted from 97 Sarda, 55 Italian Brown and 36 Italian Friesian cattle and analysed by means of PCR and PCR-SSCP. On the whole, three alleles were found, GT13, GT14, and GT15. GT13 showed the highest frequency in all the breeds: 0.874 in the Sarda, 0.973 in the Italian Brown and 1 in the Italian Friesian. For the Sarda, both GT14 and GT15 showed a frequency of 0.063, while for the Italian Brown 0.018 and 0.009, respectively. Homozygous GT13/GT13 was the unique genotype for the Italian Friesian and the most representative for the Italian Brown (0.964) and Sarda (0.823). The other genotypes for the Sarda were: GT14/GT14 (0.042), GT13/GT14 (0.010), GT13/GT15 (0.094) and GT14/GT15 (0.031); as regards the Italian Brown, both GT14/GT14 and GT13/GT15 showed a genotypic frequency of 0.018. The observed heterozygosity was lower than the expected value both for the Sarda and the Italian Brown. Sarda showed a higher genetic variability than Italian Brown and Italian Friesian

PCR-SSCP analysis of GH gene in Sarda goats: a high variability and its preliminary effects on dairy performances

The growth hormone (GH) gene can be utilized as a major gene because in various domestic livestock its polymorphisms have been associated to milk traits. The aim of this research was to investigate single-strand conformation polymorphism (SSCP) in the exon 3 of gGH (goat GH) gene and to evaluate the possible association with milk traits in Sarda goat breed. Forty-four primiparous lactating goats were randomly chosen, and the productive parameters (milk yield, fat, protein, and lactose percentage) of three consecutive lactations were monitored. The exon 3 of the gGH gene was PCR amplified and the resulting products were analysed by SSCP. Six conformational patterns were detected. The sequencing of SSCP patterns revealed the occurrence of six nucleotide changes, two of which determined amino acid changes in the deduced protein sequence. A preliminary comparative analysis of the productive traits related to three lactations with the genomic profiles derived from the SSCP analysis was performed with the ANOVA statistical method. SSCP polymorphic patterns in exon 3 were associated (P<0.01) with milk yield, fat and protein percentages, and with lactose content (P<0.05). These findings may be used for marker assisted selection in Sarda goat, in order to improve dairy production, preserving genetic diversity of the population

Milk yield, quality, and coagulation properties of 6 breeds of goats: Environmental and individual variability.

ABSTRACT Goat milk and cheese production is continuously increasing and milk composition and coagulation properties (MCP) are useful tools to predict cheesemaking aptitude. The present study was planned to investigate the extension of lactodynamographic analysis up to 60 min in goat milk, to measure the farm and individual factors, and to investigate differences among 6 goat breeds. Daily milk yield (dMY) was recorded and milk samples collected from 1,272 goats reared in 35 farms. Goats were of 6 different breeds: Saanen and Camosciata delle Alpi for the Alpine type, and Murciano-Granadina, Maltese, Sarda, and Sarda Primitiva for the Mediterranean type. Milk composition (fat, protein, lactose, pH; somatic cell score; logarithmic bacterial count) and MCP [rennet coagulation time (RCT, min), curd-firming time (k20, min), curd firmness at 30, 45, and 60 min after rennet addition (a30, a45, and a60, mm)] were recorded, and daily fat and protein yield (dFPY g/d) was calculated as the sum of fat and protein concentration multiplied by the dMY. Data were analyzed using different statistical models to measure the effects of farm, parity, stage of lactation and breed; lastly, the direct and the indirect effect of breed were quantified by comparing the variance of breed from models with or without the inclusion of linear regression of fat, protein, lactose, pH, bacterial, somatic cell counts, and dMY. Orthogonal contrasts were performed to compare least squares means. Almost all traits exhibited high variability, with coefficients of variation between 32 (for RCT) and 63% (for a30). The proportion of variance regarding dMY, dFPY, and milk composition due to the farm was moderate, whereas for MCP it was low, except for a60, at 69%. Parity affected both yield and quality traits of milk, with least squares means of dMY and dFPY showing an increase and RCT and curd firmness traits a decrease from the first to the last parity class. All milk quality traits, excluding fat, were affected by the stage of lactation; RCT and k20 decreased rapidly and a30 was higher from the first to the last part of lactation. Alpine breeds showed the highest dMY and dFPY but Mediterranean the best percentage of protein, fat, and lactose and a shorter k20 and a greater a30. Among the Mediterranean goats, Murciano-Granadina goats had the highest milk yield, fat, and protein contents, whereas Maltese, Sarda, and Sarda Primitiva were characterized by much more favorable technological properties in terms of k20, a30, and a45. In conclusion, as both the farm and individual factors highly influenced milk composition and MCP traits, improvements of these traits should be based both on modifying management and individual goat factors. As expected, several differences were attributable to the breed effect, with the best milk production for the Alpines and milk quality and coagulation for the Mediterranean goats

Effect of composition on coagulation, curd firming, and syneresis of goat milk.

The present study investigated the effect of different levels of fat, protein, and casein on (1) traditional milk coagulation properties, and (2) curd firming over time parameters of 1,272 goat milk samples. Relationships between fat, protein, and casein and some indicators of udder health status (lactose, pH, somatic cells, bacterial count, and NaCl) were also investigated. Traditional milk coagulation properties and modeled curd-firming parameters were analyzed using a mixed model that considered the effect of days in milk, parity, farm, breed, the pendulum of the instrument, and different levels of fat, protein, and casein. Fat, protein, and casein were also tested with the same model but one at a time. Information provided by this model demonstrated the effect of one component alone, without contemporarily considering that of the others. The results allowed us to clarify the effect of the major milk nutrients on coagulation, curd firming, and syneresis ability of goat milk. In particular, milk rich in fat was associated with better coagulation properties, whereas milk rich in protein was associated with delayed coagulation. The high correlation of fat with protein and casein contents suggests that the effect of fat on the cheese-making process is also attributable to the effects of protein and casein. When only protein or only casein was included in the statistical model, the pattern of coagulation, curd firming, and syneresis was almost indistinguishable. The contemporary inclusion of protein and casein in the statistical model did not generate computing problems and allowed us to better characterize the role of protein and casein. Consequently, given their strong association, we also tested the effect of casein-to-protein ratio (i.e., casein number). Higher values of casein number led to a general improvement in the coagulation ability of milk, suggesting that casein-to-protein ratio, not just protein or casein, should be considered when milk is destined for cheese making. These results are especially useful for dairy farmers who want to increase their profits by improving the technological quality of the milk produced

Assessing the Diversity and Population Substructure of Sarda Breed Bucks by Using Mtdna and Y-Chromosome Markers

A sample of 146 Sarda bucks from eight subregions of Sardinia, Italy (Nuorese, Barbagia, Baronia, Ogliastra, Sarrabus, Guspinese, Iglesiente, Sulcis) were characterized for Y-chromosome and mtDNA markers to assess the levels of population substructure. Five polymorphic loci (SRY, AMELY, ZFY, and DDX3Y) on the Y-chromosome were genotyped. The control region of mtDNA was sequenced as a source of complementary information. Analysis of Y-chromosome data revealed the segregation of 5 haplotypes: Y1A (66.43%), Y2 (28.57%), Y1C (3.57%), Y1B1 (0.71%), and Y1B2 (0.71%). High levels of Y-chromosome diversity were observed in populations from Southwest Sardinia. The FST values based on Y-chromosome and mtDNA data were low, although a paternal genetic differentiation was observed when comparing the Nuorese and Barbagia populations (Central Sardinia) with the Sulcis, Iglesiente, and Sarrabus populations (Southern Sardinia). AMOVA analysis supported the lack of population substructure. These results suggest the occurrence of a historical and extensive gene flow between Sarda goat populations from different locations of Sardinia, despite the fact that this island is covered by several large mountain ranges. Introgression with foreign caprine breeds in order to improve milk production might have also contributed to avoiding the genetic differentiation amongst Sarda populations

Polymorphism of Caprine SLC11A1 Gene and Relationships with Hygienic Characteristics of Milk

The solute carrier family 11 member A1 (SLC11A1) gene is associated with resistance to infectious diseases. Genetic variability at the 3’ untranslated region (3’-UTR) of this gene is due to the presence of a polymorphic microsatellites that contain a (GT) n dinucleotide repeat. The microsatellite variability and relationships with milk yield and composition, somatic cell count (SCC) and total microbic count (TMC) were investigated in 260 goats of Sarda breed. Genotyping of the upstream guanine-thymine repeat (GT)n revealed twenty different genotypes and eight alleles (GT11, GT12, GT14, GT15, GT16, GT17, GT18 and GT19). The present study confirmed the high genetic variability of the Sarda goat and that the genotype of the microsatellite at 3’-UTR SLC11A1 affected many chemical and hygienic characteristics of milk as fat, protein and SCC

Polymorphism of Caprine SLC11A1 Gene and Relationships with Hygienic Characteristics of Milk

The solute carrier family 11 member A1 (SLC11A1) gene is associated with resistance to infectious diseases. Genetic variability at the 3’ untranslated region (3’-UTR) of this gene is due to the presence of a polymorphic microsatellites that contain a (GT) n dinucleotide repeat. The microsatellite variability and relationships with milk yield and composition, somatic cell count (SCC) and total microbic count (TMC) were investigated in 260 goats of Sarda breed. Genotyping of the upstream guanine-thymine repeat (GT)n revealed twenty different genotypes and eight alleles (GT11, GT12, GT14, GT15, GT16, GT17, GT18 and GT19). The present study confirmed the high genetic variability of the Sarda goat and that the genotype of the microsatellite at 3’-UTR SLC11A1 affected many chemical and hygienic characteristics of milk as fat, protein and SCC

Effects of indirect indicators of udder health on nutrient recovery and cheese yield traits in goat milk

In dairy goats, very little is known about the effect of the 2 most important indirect indicators of udder health [somatic cell count (SCC) and total bacterial count (TBC)] on milk composition and cheese yield, and no information is available regarding the effects of lactose levels, pH, and NaCl content on the recovery of nutrients in the curd, cheese yield traits, and daily cheese yields. Because large differences exist among dairy species, conclusions from the most studied species (i.e., bovine) cannot be drawn for all types of dairy-producing animals. The aims of this study were to quantify, using milk samples from 560 dairy goats, the contemporary effects of a pool of udder health indirect indicators (lactose level, pH, SCC, TBC, and NaCl content) on the recovery of nutrients in the curd (%REC), cheese yield (%CY), and daily cheese yields (dCY). Cheese-making traits were analyzed using a mixed model, with parity, days in milk (DIM), lactose level, pH, SCC, TBC, and NaCl content as fixed effects, and farm, breed, glass tube, and animal as random effects. Results indicated that high levels of milk lactose were associated with reduced total solids recovery in the curd and lower cheese yields, because of the lower milk fat and protein contents in samples rich in lactose. Higher pH correlated with higher recovery of nutrients in the curd and higher cheese yield traits. These results may be explained by the positive correlation between pH and milk fat, protein, and casein in goat milk. High SCC were associated with higher recovery of solids and energy in the curd but lower recovery of protein. The higher cheese yield obtained from milk with high SCC was due to both increased recovery of lactose in the curd and water retention. Bacterial count proved to be the least important factor affecting cheese-making traits, but it decreased daily cheese yields, suggesting that, even if below the legal limits, TBC should be considered in order to monitor flock management and avoid economic losses. The effect of NaCl content on milk composition was linked with lower recovery of all nutrients in the curd during cheese-making. In addition, high milk NaCl content led to reductions in fresh cheese yield and cheese solids. The indirect indicators of the present study significantly affected the cheese-making process. Such information should be considered, to adjust the milk-to-cheese economic value and the milk payment system

Prediction accuracies of cheese-making traits using Fourier-transform infrared spectra in goat milk

The objectives of this study were to explore the use of Fourier-transform infrared (FITR) spectroscopy on 458 goat milk samples for predicting cheese-making traits, and to test the effect of the farm variability on their prediction accuracy. Calibration equations were developed using a Bayesian approach with three different scenarios: i) a random cross-validation (CV) [80% calibration (CAL); 20% validation (VAL) set], ii) a stratified CV [(SCV), 13 farms used as CAL, and the remaining one as VAL set], and iii) a SCV where 20% of the goats randomly selected from the VAL farm were included in the CAL set (SCV80). The best prediction performance was obtained for cheese yield solids, justifying for its practical application at population level. Overall results were similar to or outperformed those reported for bovine milk. Our results suggest considering specific procedures for calibration development to propose reliable tools applicable along the dairy goat chain

Predictive formulas for different measures of cheese yield using milk composition from individual goat samples

The objective of this study was to develop formulas based on milk composition of individual goat samples for predicting cheese yield (%CY) traits (fresh curd, milk solids, and water retained in the curd). The specific aims were to assess and quantify (1) the contribution of major milk components (fat, protein, and casein) and udder health indicators (lactose, somatic cell count, pH, and bacterial count) on %CY traits (fresh curd, milk solids, and water retained in the curd); (2) the cheese-making method; and (3) goat breed effects on prediction accuracy of the %CY formulas. The %CY traits were analyzed in duplicate from 600 goats, using an individual laboratory cheese-making procedure (9-MilCA method; 9 mL of milk per observation) for a total of 1,200 observations. Goats were reared in 36 herds and belonged to 6 breeds (Saanen, Murciano-Granadina, Camosciata delle Alpi, Maltese, Sarda, and Sarda Primitiva). Fresh %CY (%CYCURD), total solids (%CYSOLIDS), and water retained (%CYWATER) in the curd were used as response variables. Single and multiple linear regression models were tested via different combinations of standard milk components (fat, protein, casein) and indirect udder health indicators (UHI; lactose, somatic cell count, pH, and bacterial count). The 2 %CY observations within animal were averaged, and a cross-validation (CrV) scheme was adopted, in which 80% of observations were randomly assigned to the calibration (CAL) set and 20% to the validation (VAL) set. The procedure was repeated 10 times to account for sampling variability. Further, the model presenting the best prediction accuracy in CrV (i.e., comprehensive formula) was used in a secondary analysis to assess the accuracy of the %CY predictive formulas as part of the laboratory cheese-making procedure (within-animal validation, WAV), in which the first %CY observation within animal was assigned to CAL, and the second to the VAL set. Finally, a stratified CrV (SCrV) was adopted to assess the %CY traits prediction accuracy across goat breeds, again using the best model, in which 5 breeds were included in CAL and the remaining one in the VAL set. Fitting statistics of the formulas were assessed by coefficient of determination of validation (R2VAL) and the root mean square error of validation (RMSEVAL). In CrV, the formula with the best prediction accuracy for all %CY traits included fat, casein, and UHI (R2VAL = 0.65, 0.96, and 0.23 for %CYCURD, %CYSOLIDS, and %CYWATER, respectively). The WAV procedure showed R2VAL higher than those obtained in CrV, evidencing a low effect of the 9-MilCA method and, indirectly, its high repeatability. In the SCrV, large differences for %CYCURD and %CYWATER among breeds evidenced that the breed is a fundamental factor to consider in %CY predictive formulas. These results may be useful to monitor milk composition and quantify the influence of milk traits in the composite selection indices of specific breeds, and for the direct genetic improvement of cheese production