Effects of the detailed protein composition of milk on curd yield and composition measured by model micro-cheese curd making of individual milk samples.

The effect of the contents of casein (CN) and whey protein fractions on curd yield (CY) and composition was estimated using 964 individual milk samples. Contents of αS1-CN, αS2-CN, ß-CN, γ-CN, glycosylated κ-CN (Gκ-CN), unglycosylated κ-CN, ß-LG, and α-LA of individual milk samples were measured using reversed-phase HPLC. Curd yield and curd composition were measured by model micro-cheese curd making using 25 mL of milk. Dry matter CY (DMCY) was positively associated with all casein fractions but especially with αS1-CN and ß-CN. Curd moisture decreased at increasing ß-CN content and increased at increasing γ-CN and Gκ-CN content. Due to their associations with moisture, Gκ-CN and ß-CN were the fractions with the greatest effect on raw CY, which decreased by 0.66% per 1-standard deviation (SD) increase in the content of ß-CN and increased by 0.62% per 1-SD increase in the content of Gκ-CN. The effects due to variation in percentages of the casein fractions in total casein were less marked than those exerted by contents. A 1-SD increase in ß-CN percentage in casein (+3.8% in casein) exerted a slightly negative effect on DMCY (ß = -0.05%). Conversely, increasing amounts of αS1-CN percentage were associated with a small increase in DMCY. Hence, results suggest that, at constant casein and whey protein contents in milk, the DMCY depends to a limited extent on the variation in the αS1-CN:ß-CN ratio. κ-Casein percentage did not affect DMCY, indicating that the positive relationship detected between the content of κ-CN and DMCY can be attributed to the increase in total casein resulting from the increased amount of κ-CN and not to variation in κ-CN relative content. However, milk with increased Gκ-CN percentage in κ-CN also shows increased raw CY and produces curds with increased moisture content. Curd yield increased at increasing content and relative proportion of ß-LG in whey protein, but this is attributable to an improved capacity of the curd to retain water. Results obtained in this study support the hypothesis that, besides variation in total casein and whey protein contents, variation in protein composition might affect the cheese-making ability of milk, but this requires further studies.

Genetic parameters of measures and population-wide infrared predictions of 92 traits describing the fine composition and technological properties of milk in Italian Simmental cattle.

The objectives of this study were to estimate, for the Italian Simmental cattle population, genetic parameters for 92 traits and their infrared predictions (IP) and to investigate the genetic relationship between measured traits (MT) and IP. Data for milk fat fatty acid composition (n = 1,040), detailed protein composition (n = 3,337), lactoferrin (n = 558), pH (n = 3,438), coagulation properties (n = 3,266), curd yield and composition obtained by a micro-cheese making procedure (n = 1,177), and content of Ca, P, Mg, and K (n = 689) were obtained using reference laboratory analysis. Infrared prediction for all the investigated traits was performed using 143,198 spectra records belonging to 17,619 Italian Simmental cows. (Co)variance components for MT and their IP were estimated in a set of bivariate animal model REML analyses and genetic correlations between MT and IP were estimated using all IP obtained at the population level. A significant positive relationship was observed between the coefficient of determination of the infrared prediction models and the phenotypic and genetic variation of the IP. The decrease in the estimated genetic variance of IP compared with MT was on average 64%. For traits exhibiting calibration models with coefficients of determination in cross-validation (R2CV) greater than 0.9, the decrease in the genetic variance ranged from approximately 20 to 50%. Most traits (88 out of 92) exhibited lower heritability estimates for IP than for the corresponding MT. The estimated genetic correlations between IP and MT (ra) were in general very high. A positive relationship (r = 0.57) between R2CV of calibration models and the estimated ra has been detected. For calibration models exhibiting R2CV higher than 0.75, ra were greater than 0.9. The variability in the estimated correlations increased when R2CV decreased, and for calibration models of moderate predictive ability, estimates of ra ranged from 0.2 to 1. Genetic parameter estimates suggested that IP can be used as indicator traits in breeding programs for the enhancement of fine composition and technological properties of milk. The genetic gain achievable selecting for IP is expected to be high for fatty acid composition, minerals, and for technological properties of milk, whereas it will be low for casein and whey protein composition and for the content of lactoferrin.

Comparison of Bayesian regression models and partial least squares regression for the development of infrared prediction equations.

The objective of this study was to compare the prediction accuracy of 92 infrared prediction equations obtained by different statistical approaches. The predicted traits included fatty acid composition (n = 1,040); detailed protein composition (n = 1,137); lactoferrin (n = 558); pH and coagulation properties (n = 1,296); curd yield and composition obtained by a micro-cheese making procedure (n = 1,177); and Ca, P, Mg, and K contents (n = 689). The statistical methods used to develop the prediction equations were partial least squares regression (PLSR), Bayesian ridge regression, Bayes A, Bayes B, Bayes C, and Bayesian least absolute shrinkage and selection operator. Model performances were assessed, for each trait and model, in training and validation sets over 10 replicates. In validation sets, Bayesian regression models performed significantly better than PLSR for the prediction of 33 out of 92 traits, especially fatty acids, whereas they yielded a significantly lower prediction accuracy than PLSR in the prediction of 8 traits: the percentage of C18:1n-7 trans-9 in fat; the content of unglycosylated κ-casein and its percentage in protein; the content of α-lactalbumin; the percentage of αS2-casein in protein; and the contents of Ca, P, and Mg. Even though Bayesian methods produced a significant enhancement of model accuracy in many traits compared with PLSR, most variations in the coefficient of determination in validation sets were smaller than 1 percentage point. Over traits, the highest predictive ability was obtained by Bayes C even though most of the significant differences in accuracy between Bayesian regression models were negligible.

Comparison between direct and indirect methods for exploiting Fourier transform spectral information in estimation of breeding values for fine composition and technological properties of milk.

The aim of this study was to compare the common method of exploiting infrared spectral data in animal breeding; that is, estimating the breeding values for the traits predicted by infrared spectroscopy, and an alternative approach based on the direct use of spectral information (direct prediction, DP) to predict the estimated breeding values (EBV). Traits were pH, milk coagulation properties, contents of the main casein and whey protein fractions, cheese yield measured by micro-cheese making, lactoferrin, Ca, and fat composition. For the DP method, the number of spectral variables was reduced by principal components analysis to 8 latent traits that explained 99% of the original spectral variation. Restricted maximum likelihood was used to estimate variance components of the latent traits. (Co)variance components of the original spectral traits were obtained by back-transformation and EBV of all derived milk traits were then predicted as traits correlated with the genetic information of the spectra. The rank correlation between the EBV obtained for the infrared-predicted traits and those obtained from the DP method was variable across traits. Rank correlations ranged from 0.07 (for the content of saturated fatty acids expressed as g/100 g of fat) to 0.96 (for dry matter cheese yield, %) and, for most traits, was <0.5. This result can be explained by the nature of the principal components analysis: it does not take into account the covariance between the spectral variables and the reference traits but produces latent traits that maximize the spectral variance explained. Thus, the direct approach is more likely to be effective for traits more related to the main sources of spectral variation (i.e., protein and fat). More research is required to study spectral genetic variation and to determine the best way to choose spectral regions and the type and number of considered latent traits for potential applications.

Short communication: Mid-infrared spectroscopy prediction of fine milk composition and technological properties in Italian Simmental.

The objective of this study was to evaluate the ability of mid-infrared predictions of fine milk composition and technological traits to serve as a tool for large-scale phenotyping of the Italian Simmental population. Calibration equations accurately predicted the fatty acid profile of the milk, but we obtained moderate or poor accuracy for detailed protein composition, coagulation properties, curd yield and composition, lactoferrin, and concentration of major minerals. To evaluate the role of infrared predictions as indicator traits of fine milk composition in indirect selective breeding programs, the genetic parameters of the traits predicted using mid-infrared spectra need to be estimated.

Short communication: predictive ability of Fourier-transform mid-infrared spectroscopy to assess CSN genotypes and detailed protein composition of buffalo milk.

The aim of this work was to test the applicability of Fourier-transform mid-infrared spectroscopy (FT-MIR) for the prediction of the contents of casein (CN) and whey protein fractions in buffalo milk. Buffalo milk samples spectra were collected using a MilkoScan FT2 (Foss, Hillerød, Denmark) over the spectral range from 5,000 to 900 wavenumber × cm(-1). Contents of protein fractions, as well as CSN1S1 and CSN3 genotypes, were assessed by reversed phase HPLC. The highest coefficients of determination in cross-validation (1 - VR) were obtained for the contents (g/L of milk) of total protein and CN (1 - VR=0.92), followed by the content of ß-CN, total whey protein, and αS2-CN (1 - VR of 0.87, 0.77, and 0.63, respectively). Conversely, contents of αS1-CN, γ-CN, glycosylated-κ-CN, total κ-CN, and whey protein fractions were predicted with poor accuracy (1 - VR <0.51). When protein fractions were expressed as percentages to total protein, 1 - VR values were never greater than 0.61 (ß-CN). Only 56 and 70% of the observations were correctly classified by discriminant analysis in each of 2 groups of CSN1S1 and CSN3 genotypes, respectively. Results showed that FT-MIR spectroscopy is not applicable when prediction of detailed milk protein composition with high accuracy is required. Predictions may play a role as indicator traits in selective breeding, if the genetic correlation between FT-MIR predictions and measures of milk protein composition are high enough and predictions of protein fraction contents are sufficiently independent from the predicted total protein content.

Glycosylation of κ-casein: genetic and nongenetic variation and effects on rennet coagulation properties of milk.

The aims of this study were to investigate genetic and nongenetic variation in the degree of glycosylation of κ-casein (κ-CN) and to estimate the effects of glycosylated (G-κCN) and unglycosylated (U-κCN) κ-CN contents on milk coagulation properties of Simmental cows. Measures of contents of the main casein fractions, G-κCN, and U-κCN, and assessment of genotypes at CSN2, CSN3, and BLG were obtained by reversed-phase HPLC analysis of 2,015 individual milk samples. Content of total κ-CN (κ-CNtot, g/L) was the sum of G-κCN and U-κCN, and the glycosylation degree of κ-CN (GD) was measured as the ratio of G-κCN to κ-CNtot. Rennet coagulation time (RCT) and curd firmness were measured by using a computerized renneting meter. Measures of curd firmness were adjusted for RCT before statistical analysis. Variance components of κ-CNtot, G-κCN, U-κCN, and GD were estimated by Bayesian procedures and univariate linear models that included the class effects of the herd-test-day, parity, days in milk, genotypes at milk protein genes, and animal. These class effects, those of G-κCN, U-κCN, and content of other caseins, and the linear effect of milk pH were accounted for by models investigating the influence of κ-CN glycosylation on coagulation properties. The GD ranged from 22 to 76%, indicating that variation in G-κCN depends on the variation both in κ-CNtot and in the efficiency of κ-CN glycosylation. Genotype CSN3 BB exhibited high G-κCN and U-κCN relative to that of CSN3 AA. Heritability of G-κCN, U-κCN, and GD was high and ranged from 0.46 to 0.56. A large proportion of the additive genetic variation in G-κCN and U-κCN was attributable to influence of CSN and BLG, but these genes did not affect variation in GD, and across-genotypes differences in the trait were small or trivial. Average RCT of the milk class having the highest G-κCN was, on average, 2min (standard deviation 0.5) shorter than that of the lowest class. Conversely, U-κCN and content of other caseins were not associated with any effect on RCT, except for a slight delay in coagulation when U-κCN was very high. Curd firmness increased when the contents of both κ-CN fractions and other caseins increased. This study provides evidence that the positive association between RCT and κ-CN content is exclusively attributable to the glycosylated fraction of the protein. Because exploitable additive genetic variation in G-κCN exists, improvement of κ-CN composition through selective breeding might be an effective way to enhance milk coagulation properties.

Protein composition affects variation in coagulation properties of buffalo milk.

The aim of this study was to investigate the effects exerted by the content of casein and whey protein fractions on variation of pH, rennet-coagulation time (RCT), curd-firming time (K20), and curd firmness of Mediterranean buffalo individual milk. Measures of milk protein composition and assessment of genotypes at CSN1S1 and CSN3 were obtained by reversed-phase HPLC analysis of 621 individual milk samples. Increased content of αS1-casein (CN) was associated with delayed coagulation onset and increased K20, whereas average pH, RCT, and K20 decreased when ß-CN content increased. Milk with low κ-CN content exhibited low pH and RCT relative to milk with high content of κ-CN. Increased content of glycosylated κ-CN was associated with unfavorable effects on RCT. Effects of milk protein composition on curd firmness were less important than those on pH, RCT, and K20. Likely, this occurred as a consequence of the very short RCT of buffalo milk, which guaranteed a complete strengthening of the curd even in the restricted 31 min time of analysis of coagulation properties and for samples initially showing soft curds. Effects of CSN1S1-CSN3 genotypes on coagulation properties were not to be entirely ascribed to existing variation in milk protein composition associated with polymorphisms at CSN1S1 and CSN3 genes. Although the role of detailed milk protein composition in variation of cheese yield needs to be further investigated, findings of this study suggest that modification of the relative content of specific CN fractions can relevantly influence the behavior of buffalo milk during processing.

Effect of parity, days in milk, and milk yield on detailed milk protein composition in Mediterranean water buffalo.

The effects of some nongenetic factors on milk protein fraction contents and relative proportions were estimated in 606 individual milk samples of Mediterranean water buffalo. Content of α(S1)-casein (CN), α(S2)-CN, ß-CN, γ-CN, κκ-CN, glycosylated κ-CN (glyco-κ-CN), α-lactalbumin, and ß-lactoglobulin was measured by reversed-phase HPLC. Relative contents of α(S1)-CN%, α(S2)-CN%, ß-CN%, and κ-CN% were, respectively, 32.1, 17.1, 34.5, and 15.7%, whereas γ-CN% accounted for 0.6% of total casein content. Increasing total casein content in milk would result in a greater proportion of ß-CN% at the expense of all of the other major casein fractions, especially of κ-CN%. Values of α(S2)-CN%, ß-CN%, and γ-CN% tended to decrease with parity, although their variations were not significant, whereas α(S1)-CN% and glyco-κ-CN% showed the opposite trend. Contents of most protein fractions showed the typical trends observed for milk components as lactation progressed, with high contents in early lactation, a minimum in midlactation, followed by a gradual increase toward the latter part of lactation. Values of α(S1)-CN% increased during lactation, whereas α(S2)-CN% decreased. The proportion of ß-CN% had its maximum value between 60 and 160 d of lactation, followed by a decrease, whereas κ-CN% had its minimum value in early lactation (<60 d) and remained relatively constant in the period of mid and late lactation. Glyco-κ-CN% and ß-lactoglobulin% decreased in the first part of lactation, to reach their minimum values in midlactation, followed by an increase. Milk of top-producing buffaloes, compared with that of low-producing ones, had a significantly greater value of ß-CN% and glyco-κ-CN%, and lower proportion of α(S1)-CN%. The possible effect exerted by protein genetic variants in affecting variation of milk protein fraction contents and relative proportions should be further considered to better get insight into buffalo milk protein composition.

Short communication: CSN1S1-CSN3 (α(S1)-κ-casein) composite genotypes affect detailed milk protein composition of Mediterranean water buffalo.

The aim of the study was to investigate the effect of composite CSN1S1-CSN3 [α(S1)-κ-casein (CN)] genotype on milk protein composition in Mediterranean water buffalo. Content of α(S1)-CN, α(S2)-CN, ß-CN, γ-CN, κ-CN, glycosylated and unglycosylated κ-CN, α-lactalbumin, and ß-lactoglobulin was measured by reversed-phase HPLC using 621 individual milk samples. Genotypes at CSN1S1 and CSN3 were also obtained by reversed-phase HPLC. Two alleles were detected at CSN1S1 (corresponding to the A and B variants, O62823: p.Leu193Ser,) and at CSN3 (corresponding to the X1 and X2 variants, CAP12622.1: p.Ile156Thr). Increased proportions of α(S1)-CN in total casein (TCN) were associated with genotypes carrying CSN1S1 A. Genotypes associated with a marked decrease of the proportion of α(S1)-CN in TCN (composite genotypes AB-X1X1 and BB-X1X2) were associated with marked increases in the proportion of α(S2)-CN. In addition, composite genotypes carrying the X1 allele at CSN3 were associated with a greater proportion of α(S2)-CN in TCN relative to those carrying CSN3 X2. Composite genotypes greatly affected also the variability of ratios of κ-CN to TCN, with genotypes carrying the X1 allele at CSN3 being associated with decreased ratios. The decreased content of glycosylated κ-CN associated with CSN3 X1 was responsible for the overall lower content of total κ-CN in milk of X1-carrying animals. Increasing the frequency of specific genotypes might be an effective way to alter milk protein composition, namely the proportion of α(S1)-CN, α(S2)-CN, and κ-CN in TCN, and the degree of glycosylation of κ-CN.

Effect of CSN1S1-CSN3 (α(S1)-κ-casein) composite genotype on milk production traits and milk coagulation properties in Mediterranean water buffalo.

The aim of this study was to estimate effects of CSN1S1-CSN3 (α(S1)-κ-casein) composite genotypes on milk production traits and milk coagulation properties (MCP) in Mediterranean water buffalo. Genotypes at CSN1S1 and CSN3 and coagulation properties [rennet clotting time (RCT), curd firming time (K20), and curd firmness (A30)] were assessed by reversed-phase HPLC and computerized renneting meter analysis, respectively, using single test-day milk samples of 536 animals. Alternative protein variants of α(S1)-CN and κ-CN were detected by HPLC, and identification of the corresponding genetic variants was carried out by DNA analysis. Two genetic variants were detected at CSN1S1 (A and B variants) and 2 at CSN3 (X1 and X2 variants). Statistical inference was based on a linear model including the CSN1S1-CSN3 composite genotype effect (7 genotypes), the effects of herd-test-day (8 levels), and a combined days in milk (DIM)-parity class. Composite genotype AB-X2X2 was associated with decreased test-day milk yield [-0.21 standard deviation (SD) units of the trait] relative to genotype BB-X2X2. Genotypes did not affect milk protein content, but genotype AB-X1X1 was associated with increased fat content compared with genotype BB-X2X2 (+0.28 SD units of the trait) and AB-X1X1 (+0.43 SD units of the trait). For RCT, the largest difference (+1.91 min; i.e., 0.61 SD units of the trait) was observed between genotype AA-X1X2 and AB-X1X1. Direction of genotype effects on K(20) was consistent with that for RCT. The maximum variation in K20 due to genotype effects (between AA-X1X2 and AB-X1X1 genotypes) was almost 0.9 SD units of the trait. Magnitude of genotype effects was smaller for A30 than for RCT and K20, with a maximum difference of 0.5 SD units of the trait between genotype AA-X1X2 and AA-X1X1. The B allele at CSN1S1 was associated with increased RCT and K20 and with weaker curds compared with allele A. Allele X2 at CSN3 exerted opposite effects on MCP relative to CSN1S1 B. Because of linkage disequilibrium, allele B at CSN1S1 and allele X2 at CSN3 tend to be associated and this likely makes their effects cancel each other. This study indicates a role for casein genes in variation of MCP of buffalo milk. Further studies are necessary to estimate the effects of casein genetic variants on variation of cheese yield.

Short communication: statistical models for the analysis of coagulation traits using coagulating and noncoagulating milk information.

Aims of this study were to propose statistical models for the analysis of rennet coagulation time (RCT) suitable for making use of coagulating and noncoagulating (NC) milk information, to estimate heritabilities and to obtain rank correlations for sire merit. A total of 1,025 Holstein cows (progeny of 54 sires) reared in 34 herds were milk-sampled once. Data were analyzed using 4 alternative models: a standard linear (SLM), a right-censored linear Gaussian (CLM), a survival (SUM), and a threshold (THM) model. Model SLM analyzed coagulated milk records only, whereas analysis with CLM or SUM considered information of NC samples as censored records. Model THM analyzed occurrence of milk coagulation as a dichotomous trait. An artificial censoring scenario with an endpoint at 18 min (SET18) was considered after the rearrangement of the timeframe originally used for the observation of RCT (SET31). Heritabilities ranged from 0.12 to 0.25. Correlations of sire rankings ranged from 0.23 to 0.92. Differences in sire rankings between SLM and CLM or SUM increased when the proportion of NC records increased. Correlations between sire rankings obtained for SET31 and SET18 were high for CLM and SUM, indicating that rankings provided by these models tended to be stable even when a large fraction of samples with observed RCT was re-classified as NC milk. Results indicate that CLM and SUM are more suitable than SLM and THM for the analysis of coagulation ability when data contain NC milk information.

Effectiveness of mid-infrared spectroscopy for the prediction of detailed protein composition and contents of protein genetic variants of individual milk of Simmental cows.

Mid-infrared (MIR) spectroscopy was used to predict the detailed protein composition of 1,517 milk samples of Simmental cows. Contents of milk protein fractions and genetic variants were quantified by reversed-phase HPLC. The most accurate predictions were those obtained for total protein, casein (CN), α(S1)-CN, ß-lactoglobulin (LG), glycosylated κ-CN, and whey protein content, which exhibited coefficients of determination between predicted and measured values in cross-validation (1-VR) ranging from 0.61 to 0.78. Less favorable were results for ß-CN (1-VR=0.53), α(S2)-CN, and κ-CN (1-VR=0.49). Neither the content of α-LA nor that of γ-CN was accurately predicted by MIR. Predicting the content of the most common milk protein genetic variants (κ-CN A and B; ß-CN A¹, A², and B; and ß-LG A and B) was unfeasible (1-VR <0.15 for the content of κ-CN genetic variants and 1-VR <0.01 for the content of ß-CN variants). The best predictions were obtained for ß-LG A and ß-LG B contents (1-VR of 0.60 and 0.44, respectively). Results indicated that MIR is not applicable for predicting individual milk protein composition with high accuracy. However, MIR spectroscopy predictions may play a role as indicator traits in selective breeding to enhance milk protein composition. The genetic correlation between MIR spectroscopy predictions and measures of milk protein composition needs to be investigated, as it affects the suitability of MIR spectroscopy predictions as indicator traits in selective breeding.

Genetic analysis of detailed milk protein composition and coagulation properties in Simmental cattle.

The objective of this study was to estimate genetic parameters for milk protein fraction contents, milk protein composition, and milk coagulation properties (MCP). Contents of α(S1)-, α(S2)-, ß-, γ-, and κ-casein (CN), ß-lactoglobulin (ß-LG), and α-lactalbumin (α-LA) were measured by reversed-phase HPLC in individual milk samples of 2,167 Simmental cows. Milk protein composition was measured as percentage of each CN fraction in CN (α(S1)-CN%, α(S2)-CN%, ß-CN%, γ-CN%, and κ-CN%) and as percentage of ß-LG in whey protein (ß-LG%). Rennet clotting time (RCT) and curd firmness (a(30)) were measured by a computerized renneting meter. Heritabilities for contents of milk proteins ranged from 0.11 (α-LA) to 0.52 (κ-CN). Heritabilities for α(S1)-CN%, κ-CN%, and ß-CN% were similar and ranged from 0.63 to 0.69, whereas heritability of α(S2)-CN%, γ-CN%, and ß-LG% were 0.28, 0.18, and 0.34, respectively. Effects of CSN2-CSN3 haplotype and BLG genotype accounted for more than 80% of the genetic variance of α(S1)-CN%, ß-CN%, and κ-CN% and 50% of the genetic variance of ß-LG%. The genetic correlations among the contents of CN fractions and between CN and whey protein fractions contents were generally low. When the data were adjusted for milk protein gene effects, the magnitude of the genetic correlations among the contents of milk protein fractions markedly increased, indicating that they undergo a common regulation. The proportion of ß-CN in CN correlated negatively with κ-CN% (r=-0.44). The genetic relationships between CN and whey protein composition were trivial. Low milk pH correlated with favorable MCP. Genetically, contents and proportions of α(S1)- and α(S2)-CN in CN were positively correlated with RCT. The relative proportion of ß-CN in CN exhibited a genetic correlation with RCT of -0.26. Both the content and the relative proportion of κ-CN in CN did not correlate with RCT. Weak curds were genetically associated with increased proportions in CN of α(S1)- and α(S2)-CN, decreased contents of ß-CN and κ-CN, and decreased proportion of κ-CN in CN. Negligible effects on the estimated correlations between a(30) and κ-CN contents or proportion in CN were observed when the model accounted for milk protein gene effects. Increasing ß-CN and κ-CN contents and relative proportions in CN and decreasing the content and proportions of α(S1)-CN and α(S2)-CN and milk pH through selective breeding exert favorable effects on MCP.

Genetic parameters of coagulation properties, milk yield, quality, and acidity estimated using coagulating and noncoagulating milk information in Brown Swiss and Holstein-Friesian cows.

The aim of this study was to estimate heritabilities of rennet coagulation time (RCT) and curd firmness (a(30)) and their genetic correlations with test-day milk yield, composition (fat, protein, and casein content), somatic cell score, and acidity (pH and titratable acidity) using coagulating and noncoagulating (NC) milk information. Data were from 1,025 Holstein-Friesian (HF) and 1,234 Brown Swiss (BS) cows, which were progeny of 54 HF and 58 BS artificial insemination sires, respectively. Milk coagulation properties (MCP) of each cow were measured once using a computerized renneting meter and samples not exhibiting coagulation within 31 min after rennet addition were classified as NC milk. For NC samples, RCT was unobserved. Multivariate analyses, using Bayesian methodology, were performed to estimate the genetic relationships of RCT or a(30) with the other traits and statistical inference was based on the marginal posterior distributions of parameters of concern. For analyses involving RCT, a right-censored Gaussian linear model was used and records of NC milk samples, being censored records, were included as unknown parameters in the model implementing a data augmentation procedure. Rennet coagulation time was more heritable [heritability (h(2))=0.240 and h(2)=0.210 for HF and BS, respectively] than a(30) (h(2)=0.148 and h(2)=0.168 for HF and BS, respectively). Milk coagulation properties were more heritable than a single test-day milk yield (h(2)=0.103 and h(2)=0.097 for HF and BS, respectively) and less heritable than milk composition traits whose heritability ranged from 0.275 to 0.275, with the only exception of fat content of BS milk (h(2)=0.108). A negative genetic correlation, lower than -0.85, was estimated between RCT and a(30) for both breeds. Genetic relationships of MCP with yield and composition were low or moderate and favorable. The genetic correlation of somatic cell score with RCT in BS cows was large and positive and even more positive were those of RCT with pH and titratable acidity in both breeds, ranging from 0.80 to 0.94. Including NC milk information in the data affected the estimated correlations and decreased the uncertainty associated with the estimation process. On the basis of the estimated heritabilities and genetic correlations, enhancement of MCP through selective breeding with no detrimental effects on yield and composition seems feasible in both breeds. Milk acidity may play a role as an indicator trait for indirect enhancement of MCP.

Effect of κ-casein B relative content in bulk milk κ-casein on Montasio, Asiago, and Caciotta cheese yield using milk of similar protein composition.

The aim of this study was to investigate the effect exerted by the relative content of κ-casein (κ-CN) B in bulk milk κ-CN on coagulation properties and cheese yield of 3 Italian cheese varieties (Montasio, Asiago, and Caciotta). Twenty-four cheese-making experiments were carried out in 2 industrial and 1 small-scale dairy plant. Detailed protein composition of bulk milk of 380 herds providing milk to these dairies was analyzed by reversed-phase HPLC. To obtain 2 experimental milks differing in the relative content of κ-CN B in κ-CN, herds were selected on the basis of bulk milk protein composition and relative content of κ-CN genetic variants. Milk was collected and processed separately for the 2 groups of selected herds. A difference of 20% in the relative content of κ-CN B in κ-CN was obtained for the 2 experimental milks for Montasio and a difference of 15% for Asiago and Caciotta. The 2 experimental milks were of similar protein and CN content, casein number, pH, CN composition, and ß-CN genetic composition. For each cheese-making trial, amounts of milk, ranging from 2,000 to 6,000kg, were manufactured. Each vat contained milk collected at least from 4 dairy herds. Cheese yield after brining and at the end of the aging was recorded. Milk with a greater proportion of κ-CN B in κ-CN (HIGHB) exhibited similar coagulation properties and greater cheese yield compared with milk with a lower proportion of κ-CN B in κ-CN (LOWB). The increased cheese yield observed for HIGHB when manufacturing Montasio cheese was ascribed to a greater fat content compared with LOWB. The probability of HIGHB giving a cheese yield 5% greater than that of LOWB ranged from 51 to 67% for Montasio cheese, but was less than 21% for Asiago and Caciotta cheeses. Variation in relative content of κ-CN B in κ-CN content did not relevantly affect industrial cheese yield when milks of similar CN composition were processed. An indirect effect due to the increased κ-CN content of κ-CN B milk is thought to explain the favorable effects of κ-CN B on cheese yield reported in the literature.

Nerve growth factor concentrations in the synovial fluid from healthy dogs and dogs with secondary osteoarthritis.

OBJECTIVE: To measure the concentrations of nerve growth factor (NGF) in the synovial fluid from normal dogs and dogs with osteoarthritis (OA) secondary to common joint disorders. METHODS: Nerve growth factor synovial concentrations were measured by ELISA assay in 50 dogs divided into three groups: 12 healthy, 16 affected by acute lameness within seven days before enrolment, and 22 with chronic lameness persisting by more than one month before enrolment and accompanied by radiological signs of OA. Both acute and chronic lameness were secondary to orthopaedic diseases involving the shoulder, elbow and stifle joints. Nerve growth factor synovial concentrations were compared between means for healthy and acute groups and between the three groups using an F-test. Significance level was set at p <0.05. RESULTS: Nerve growth factor was detected in all canine synovial fluid samples. However, the mean synovial NGF concentration of healthy dogs (3.65 ± 2.18 pg/ml) was not significantly different from the mean value in dogs with acute lameness (6.45 ± 2.45 pg/ml) (p = 0.79). Conversely, the mean synovial NGF concentration in dogs with chronic lameness (20.19 ± 17.51 pg/ml) was found to be significantly higher than that found in healthy dogs (p <0.01). CLINICAL SIGNIFICANCE: This study demonstrates for the first time the presence of NGF in canine synovial fluid and its increased concentrations in dogs with chronic lameness compared to healthy dogs and dogs with acute lameness. The association between chronic lameness and raised synovial concentrations may suggest an involvement of NGF in OA inflammation and chronic pain.

On-site visible-near IR prediction of iodine number and fatty acid composition of subcutaneous fat of raw hams as phenotypes for a heavy pig breeding program.

The quality of subcutaneous fat of raw hams is a trait of interest in selective breeding programs for pig lines used in dry-cured ham production, and rapid, non-invasive methods for its assessment are available. However, the efficacy of such methods to provide indicator traits for breeding programs needs to be proven. The study investigated the accuracy of on-site visible-near IR spectroscopy predictions of iodine number and fatty acid (FA) composition of raw ham subcutaneous fat, and it evaluated their effectiveness as indicator traits of ham fat quality in a pig breeding program. Prediction equations were developed using visible-near IR spectra acquired at the slaughterhouse from five sites in subcutaneous fat of raw hams of 1025 crossbred pigs. Pigs were raised, under standardized rearing and feeding conditions, in the sib-testing program of the Goland C21 boar line and slaughtered at nine months of age and average body weight of 166 ±â€¯15 kg. Accuracy was generally relatively poor, but R2 in external validation was >0.7 for iodine number and concentration of C18:2n-6, polyunsaturated FAs and omega-6 FAs. To assess the effectiveness of the on-site predictions as indicator traits in a breeding program, (co)variance components of the measured traits (OBS) and of their predictions using in-lab (in-lab-PR) or on-site (on-site-PR) spectrometers were estimated. Available records for OBS were 6814 and 2048, for iodine number and FA composition, respectively. Predictions using in-lab were available for pigs slaughtered between 2006 and 2014, for a total of 10 153 records. Predictions using on-site were obtained from spectra collected since 2011, for a total of 10 296 records. The estimated heritabilities for the investigated traits ranged from 0.34 to 0.50 and were greater for on-site-PR than for OBS. Genetic correlations between OBS and in-lab-PR were very close to 1.00 for all the investigated traits, whereas those between OBS and on-site-PRED ranged from 0.86 to 0.94. On-site visible-IR predictions are accurate enough to support the use of this technique for large-scale phenotyping of raw ham fat quality, even when dealing with animals of a single genetic line raised in standardized conditions, and may be implemented as indicator traits in breeding programs.

Estimates of non-genetic effects and genetic parameters for semen traits in Piemontese bulls.

Male reproductive performances are often ignored in cattle breeding programmes, although semen traits might be used to improve bull breeding soundness. Effects of genetic and environmental factors on semen production and quality traits were estimated in 693 Piemontese bulls with the aim of providing the first estimates of genetic parameters for semen traits for this breed. Volume and concentrations of individual ejaculates (up to three per each test-day), and volume, concentration, total number of spermatozoa and post-thawing progressive motility of within test-day pooled semen were available for 19 060 ejaculates. Bulls reached the maximum amount of daily semen production after their third year of age, with concentration rapidly increasing until 23 months of age, and then slowly decreasing. Semen volume was at its highest when collection days were at least 15 days apart, whereas the maximum concentration was reached when the interval was 6 days. Heritability estimates were generally moderate (0.14-0.26), and low for progressive motility (0.08). Estimates of genetic correlation among the volumes of the individual ejaculates were high and positive (≥0.79), as were the genetic correlations among their concentrations (≥0.46). Genetic correlations among volume and concentration traits varied from -0.47 (with a 95% high posterior density interval ranging from -0.65 to -0.23) to -0.32 (with a 95% high posterior density interval ranging from -0.55 to -0.09). Progressive motility was unrelated with the other traits, but moderately positively correlated with volumes of the second and third ejaculates. The magnitude of heritabilities showed that selection for semen traits is possible. However, the unfavourable relationship between volume and concentration must be taken into account if a future selection programme is to be established.

Effects of beta-kappa-casein (CSN2-CSN3) haplotypes and beta-lactoglobulin (BLG) genotypes on milk production traits and detailed protein composition of individual milk of Simmental cows.

The aim of this study was to investigate the effects of CSN2-CSN3 (beta-kappa-casein) haplotypes and BLG (beta-lactoglobulin) genotypes on milk production traits, content of protein fractions, and detailed protein composition of individual milk of Simmental cows. Content of the major protein fractions was measured by reversed-phase HPLC in individual milk samples of 2,167 cows. Protein composition was measured as percentage of each casein (CN) fraction to total CN and as percentage of beta-lactoglobulin (beta-LG) to total whey protein. Genotypes at CSN2, CSN3, and BLG were ascertained by reversed-phase HPLC, and CSN2-CSN3 haplotype probabilities were estimated for each cow. Traits were analyzed by using a linear model including the fixed effects of herd-test-day, parity, days in milk, and somatic cell score class, linear regressions on haplotype probabilities, class of BLG genotype, and the random effect of the sire of the cow. Effects of haplotypes and BLG genotypes on yields were weak or trivial. Genotype BB at BLG and haplotypes carrying CSN2 B and CSN3 B were associated with increased CN content and CN number. Haplotypes including CSN3 B were associated with increased kappa-CN content and percentage of kappa-CN to total CN and with decreased percentages of alpha(S1)- and gamma-CN to total CN. Allele CSN2 B had the effect of increasing beta-CN content and decreasing content of alpha(S1)-CN. Haplotypes including allele CSN2 A(1) exhibited decreased beta-, alpha(S2)-, and gamma-CN concentrations and increased alpha(S1)- and kappa-CN contents, whereas CSN2 I had positive effects on beta-CN concentration and trivial effects on content of other protein fractions. Effects of haplotypes on CN composition were similar to those exerted on content of CN fractions. Allele BLG A was associated with increased beta-LG concentration and percentage of beta-LG to total whey protein and with decreased content of other milk proteins, namely beta-CN and alpha(S1)-CN. Estimated additive genetic variance for investigated traits ranged from 14 to 39% of total variance. Increasing the frequency of specific genotypes or haplotypes by selective breeding might be an effective way to change milk protein composition.