Short communication: Influence of composite casein genotypes on additive genetic variation of milk production traits and coagulation properties in Holstein-Friesian cows

The aim of the study was to quantify the effects of composite β- and κ-casein (CN) genotypes on genetic variation of milk coagulation properties (MCP); milk yield; fat, protein, and CN contents; somatic cell score; pH; and titratable acidity (TA) in 1,042 Italian Holstein-Friesian cows. Milk coagulation properties were defined as rennet coagulation time (RCT) and curd firmness (a₃₀). Variance components were estimated using 2 animal models: model 1 included herd, days in milk, and parity as fixed effects and animal and residual as random effects, and model 2 was model 1 with the addition of composite β- and κ-CN genotype as a fixed effect. Genetic correlations between RCT and a₃₀ and between these traits and milk production traits were obtained with bivariate analyses, based on the same models. The inclusion of casein genotypes led to a decrease of 47, 68, 18, and 23% in the genetic variance for RCT, a₃₀, pH, and TA, respectively, and less than 6% for other traits. Heritability of RCT and a₃₀ decreased from 0.248 to 0.143 and from 0.123 to 0.043, respectively. A moderate reduction was found for pH and TA, whereas negligible changes were detected for other milk traits. Estimates of genetic correlations were comparable between the 2 models. Results show that composite β- and κ-CN genotypes are important for RCT and a₃₀ but cannot replace the recording of MCP themselves.     

Genetic parameters for milk coagulation properties in Estonian Holstein cows

The objective of this study was to estimate heritabilities and repeatabilities for milk coagulation traits [milk coagulation time (RCT) and curd firmness (E₃₀)] and genetic and phenotypic correlations between milk yield and composition traits (milk fat percentage and protein percentage, urea, somatic cell count, pH) in first-lactation Estonian Holstein dairy cattle. A total of 17,577 test-day records from 4,191 Estonian Holstein cows in 73 herds across the country were collected during routine milk recordings. Measurements of RCT and E₃₀ determined with the Optigraph (Ysebaert, Frepillon, France) are based on an optical signal in the near-infrared region. The cows had at least 3 measurements taken during the period from April 2005 to January 2009. Data were analyzed using a repeatability animal model. There was substantial variation in milk coagulation traits with a coefficient of variation of 27% for E₃₀ and 9% for the log-transformed RCT. The percentage of variation explained by herd was 3% for E₃₀ and 4% for RCT, suggesting that milk coagulation traits are not strongly affected by herd conditions (e.g., feeding). Heritability was 0.28 for RCT and 0.41 for E₃₀, and repeatability estimates were 0.45 and 0.50, respectively. Genetic correlation between both milk coagulation traits was negligible, suggesting that RCT and E₃₀ have genetically different foundations. Milk coagulation time had a moderately high positive genetic (0.69) and phenotypic (0.61) correlation with milk pH indicating that a high pH is related to a less favorable RCT. Curd firmness had a moderate positive genetic (0.48) and phenotypic (0.45) correlation with the protein percentage. Therefore, a high protein percentage is associated with favorable curd firmness. All reported genetic parameters were statistically significantly different from zero. Additional univariate random regression analysis for milk coagulation traits yielded slightly higher average heritabilities of 0.38 and 0.47 for RCT and E₃₀ compared with the heritabilities of the repeatability model.     

Milk coagulation ability of five dairy cattle breeds

Samples of herd milk (506) were analyzed to assess sources of variation for milk coagulation properties (MCP) for 5 different dairy cattle breeds. Data were recorded in 55 single-breed dairy herds in the Trento province, a mountain area in northeast Italy. The 5 cattle breeds were Holstein-Friesian (8 herds), Brown Swiss (16 herds), Simmental (10 herds), Rendena (13 herds), and Alpine Gray (8 herds). Herd milk samples were analyzed for the MCP traits, milk rennet coagulation time (RCT), curd-firming time, and curd firmness (a₃₀), as well as protein and fat percentages, somatic cell count, Soxhlet-Henkel acidity, and bacterial count. An ANOVA was performed to study the effect of breed, herd within breed, DIM, month of lactation, protein and fat percentages, somatic cell score, titratable acidity, and log bacterial count within breed on MCP. Breed was the most important source of variation. In particular, the Rendena breed showed the best MCP traits at 13.5 min and 27.0 mm for RCT and a₃₀, respectively. The Holstein-Friesian breed had the worst coagulation properties at 18.0 min and 17.5 mm for RCT and a₃₀, respectively. The other 3 breeds showed intermediate coagulation properties. The RCT values were better at the beginning of lactation, whereas RCT and a₃₀ values were better in September and October (14.3 min and 25.7 mm, respectively). Among the composition traits, only the titratable acidity affected MCP traits of herd milk positively.     

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 (a₃₀) 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 a₃₀ 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 (h²) = 0.240 and h² = 0.210 for HF and BS, respectively] than a₃₀ (h² = 0.148 and h² = 0.168 for HF and BS, respectively). Milk coagulation properties were more heritable than a single test-day milk yield (h² = 0.103 and h² = 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 (h² = 0.108). A negative genetic correlation, lower than −0.85, was estimated between RCT and a₃₀ 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.     

Genetic parameters of milk coagulation properties and their relationships with milk yield and quality traits in Italian Holstein cows

Milk coagulation properties (MCP) are an important aspect in assessing cheese-making ability. Several studies showed that favorable conditions of milk reactivity with rennet, curd formation rate, and curd strength, as well as curd syneresis, have a positive effect on the entire cheese-making process and subsequently on the ripening of cheese. Moreover, MCP were found to be heritable, but little scientific literature is available about their genetic aspects. The aims of this study were to estimate heritability of MCP and genetic correlations among MCP and milk production and quality traits. A total of 1,071 Italian Holstein cows (progeny of 54 sires) reared in 34 herds in Northern Italy were sampled from January to July 2004. Individual milk samples were collected during the morning milking and analyzed for coagulation time (RCT), curd firmness (a₃₀), pH, titratable acidity, fat, protein, and casein contents, and somatic cell count. About 10% of individual milk samples did not coagulate in 31 min, so they were removed from the analyses. Estimates of heritability for RCT and a₃₀ were 0.25 ± 0.04 and 0.15 ± 0.03, respectively. Estimates of genetic correlations between MCP traits and milk production traits were negligible except for a₃₀ with protein and casein contents (0.44 ± 0.10 and 0.53 ± 0.09, respectively). Estimates of genetic correlations between MCP traits and somatic cell score were strong and favorable, as well as those between MCP and pH and titratable acidity. Selecting for high casein content, milk acidity, and low somatic cell count might be an indirect way to improve MCP without reducing milk yield and quality traits.     

Mid-infrared spectroscopy predictions as indicator traits in breeding programs for enhanced coagulation properties of milk

The aims of this study were to investigate variation of milk coagulation property (MCP) measures and their predictions obtained by mid-infrared spectroscopy (MIR), to investigate the genetic relationship between measures of MCP and MIR predictions, and to estimate the expected response from a breeding program focusing on the enhancement of MCP using MIR predictions as indicator traits. Individual milk samples were collected from 1,200 Brown Swiss cows (progeny of 50 artificial insemination sires) reared in 30 herds located in northern Italy. Rennet coagulation time (RCT, min) and curd firmness (a₃₀, mm) were measured using a computerized renneting meter. The MIR data were recorded over the spectral range of 4,000 to 900 cm⁻¹. Prediction models for RCT and a₃₀ based on MIR spectra were developed using partial least squares regression. A cross-validation procedure was carried out. The procedure involved the partition of available data into 2 subsets: a calibration subset and a test subset. The calibration subset was used to develop a calibration equation able to predict individual MCP phenotypes using MIR spectra. The test subset was used to validate the calibration equation and to estimate heritabilities and genetic correlations for measured MCP and their predictions obtained from MIR spectra and the calibration equation. Point estimates of heritability ranged from 0.30 to 0.34 and from 0.22 to 0.24 for RCT and a₃₀, respectively. Heritability estimates for MCP predictions were larger than those obtained for measured MCP. Estimated genetic correlations between measures and predictions of RCT were very high and ranged from 0.91 to 0.96. Estimates of the genetic correlation between measures and predictions of a₃₀ were large and ranged from 0.71 to 0.87. Predictions of MCP provided by MIR techniques can be proposed as indicator traits for the genetic enhancement of MCP. The expected response of RCT and a₃₀ ensured by the selection using MIR predictions as indicator traits was equal to or slightly less than the response achievable through a single measurement of these traits. Breeding strategies for the enhancement of MCP based on MIR predictions as indicator traits could be easily and immediately implemented for dairy cattle populations where routine acquisition of spectra from individual milk samples is already performed.     

Phenotypic factors affecting coagulation properties of milk from Sarda ewes

In this study, milk-coagulation properties (MCP) were characterized in the Sarda sheep breed. Milk composition and MCP [rennet-coagulation time (RCT), curd-firming time [time to reach a curd firmness of 20 mm (k₂₀)], and curd firmness (a₃₀), (a₄₅), and (a₆₀)] were obtained extending the lactodynamographic analysis from 30 to 60 min from a population of 1,121 ewes from 23 different farms. Managerial characteristics of farms and parity, individual daily milk yields and stage of lactation of ewes were recorded. Data were analyzed using a mixed-model procedure with fixed effects of days in milk, parity, daily milk yield, and flock size and the random effect of the flock/test day nested within flock size. Sampled farms were classified as small (<300 ewes) and medium (300 to 600 ewes), and these were kept by family operations, or as large (>600 ewes), often operated through hired workers. Daily milk yield was, on average, 1.58 ± 0.79 L/d and variability for this trait was very high. The average content of fat, protein, and casein was respectively 6.41, 5.39, and 4.20%. The class of flock size had a significant effect only on curd firmness, whereas days in milk affected RCT and k₂₀. The flock test day, parity, and daily milk yield were important sources of variation for all MCP. The mean value of RCT (8.6 min) and the low occurrence of noncoagulating samples (0.44%) confirmed the excellent coagulation ability of sheep milk compared with cattle milk. A more rapid coagulation was observed in mid-lactating, primiparous, and high-yielding ewes. The k₂₀ was usually reached in less than 2 min after gelation, with the most favorable values at mid lactation. The mean value of curd firmness 30 min after rennet addition (a₃₀) was, on average, 50 mm and decreased to 46 and 42 mm respectively after 45 (a₄₅) and 60 min (a₆₀). The decreasing value of curd-firmness traits was likely to be caused by curd syneresis and whey expulsion. The correlation between RCT and a₃₀ was much lower than in dairy cows and about null for a₄₅ and a₆₀. This means that curd firmness in dairy ewes is almost independent of gelation time and this can provide specific information for this species. In conclusion, this study showed that milk from Sarda sheep is characterized by an earlier gelation, a faster increase in curd firmness with time, and greater curd firmness after 30 min compared with dairy cows. Furthermore, correlations between MCP in sheep are much lower than in bovines and some of the assumptions and interpretations related to cows cannot be applied to sheep.     

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

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.     

Reproducibility and repeatability of measures of milk coagulation properties and predictive ability of mid-infrared reflectance spectroscopy

The objectives of the study were to estimate the reproducibility and repeatability of milk coagulation properties (MCP) measured by a computerized renneting meter (CRM) and to evaluate the predictive ability of mid-infrared spectroscopy (MIRS) as an innovative technology for the assessment of rennet coagulation time (RCT, min) and curd firmness (a₃₀, mm). Four samples without addition of preservative (NP) and 4 samples with Bronopol addition (PS) were collected from each of 83 Holstein-Friesian cows. Six hours after collection, 2 replicated measures of MCP were obtained with CRM using 1 NP and 1 PS sample from each cow. Mid-infrared spectra of the remaining NP and PS samples from each animal were recorded after 6 h, 4 d, and 8 d after sampling. Two groups of calibration equations were developed using MIRS spectra and CRM measures of MCP as reference data obtained from analysis of NP and PS, respectively. Reproducibility and repeatability of CRM measures were obtained from REML estimation of variance components on the basis of a linear model including the fixed effects of herd and days in milk class and the random effects of cows, sample treatment (addition or no addition of preservative), and the interaction between cow and sample treatment. Coefficient of reproducibility is an indicator of the agreement between 2 measurements of MCP for the same milk sample preserved with or without addition of Bronopol. Coefficient of repeatability is an indicator of the agreement between repeated measures of MCP. Pearson correlations between MCP measures for NP and PS were 0.97 and 0.83 for RCT and a₃₀, respectively. Reproducibility of CRM measures under different preserving conditions of milk was 93.5% for RCT and 64.6% for a₃₀. Repeatabilities of RCT and a₃₀ measures were 95.7 and 77.3%, respectively. Based on the estimated cross-validation standard errors and coefficients of determination and ratios of standard errors of cross-validation to standard deviation of reference data, the predictive ability of MIRS calibration equations was moderate for RCT and unsatisfactory for a_30. Predictive ability of equations based on spectra and MCP measures of PS was greater than that of equations based on data of NP. The study did not provide conclusive evidence on the effectiveness of MIRS as a predictive tool for MCP and it requires an enlargement of the variability of milk sampling circumstances. Because the relevance of MIRS predictions in relation to breeding programs for MCP based on indicator traits relies on the genetic variation of MIRS predictions and on phenotypic and genetic correlations between MIRS predictions and MCP measures, additional specific investigations on these topics are needed.     

Selection for milk coagulation properties predicted by Fourier transform infrared spectroscopy in the Italian Holstein-Friesian breed

Milk coagulation is based on a series of physicochemical changes at the casein micelle level, resulting in formation of a gel. Milk coagulation properties (MCP) are relevant for cheese quality and yield, important factors for the dairy industry. They are also evaluated in herd bulk milk to reward or penalize producers of Protected Designation of Origin cheeses. The economic importance of improving MCP justifies the need to account for this trait in the selection process. A pilot study was carried out to determine the feasibility of including MCP in the selection schemes of the Italian Holstein. The MCP were predicted in 1,055 individual milk samples collected in 16 herds (66 ± 24 cows per herd) located in Brescia province (northeastern Italy) by means of Fourier transform infrared (FTIR) spectroscopy. The coefficient of determination of prediction models indicated moderate predictions for milk rennet coagulation time (RCT = 0.65) and curd firmness (a₃₀ = 0.68), and poor predictions for curd-firming time (k₂₀ = 0.49), whereas the range error ratio (8.9, 6.9, and 9.5 for RCT, k₂₀, and a₃₀, respectively) indicated good practical utility of the predictive models for all parameters. Milk proteins were genotyped and casein haplotypes (α_S1-, β-, α_S2-, and κ-casein) were reconstructed. Data from 51 half-sib families (19.9 ± 16.4 daughters per sire) were analyzed by an animal model to estimate (1) the genetic parameters of predicted RCT, k₂₀, and a₃₀; (2) the breeding values for these predicted clotting variables; and (3) the effect of milk protein genotypes and casein haplotypes on predicted MCP (pMCP). This is the first study to estimate both genetic parameters and breeding values of pMCP, together with the effects of milk protein genotypes and casein haplotypes, that also considered k₂₀, probably the most important parameter for the dairy industry (because it indicates the time for the beginning of curd-cutting). Heritability of predicted RCT (0.26) and k₂₀ (0.31) were close to the average heritability described in literature, whereas the heritability of a₃₀ was higher (0.52 vs. 0.27). The effects of milk proteins were statistically significant and similar to those obtained on measured MCP. In particular, haplotypes including uncommon variants showed positive (B-I-A-B) or negative (B-A¹-A-E) effects. Based on these findings, FTIR spectroscopy-pMCP is proposed as a potential selection criterion for the Italian Holstein.     

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-, β-, γ-, and κ-casein (CN), β-lactoglobulin (β-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%, β-CN%, γ-CN%, and κ-CN%) and as percentage of β-LG in whey protein (β-LG%). Rennet clotting time (RCT) and curd firmness (a₃₀) 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 β-CN% were similar and ranged from 0.63 to 0.69, whereas heritability of α_S2-CN%, γ-CN%, and β-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%, β-CN%, and κ-CN% and 50% of the genetic variance of β-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 β-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 β-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 β-CN and κ-CN, and decreased proportion of κ-CN in CN. Negligible effects on the estimated correlations between a₃₀ and κ-CN contents or proportion in CN were observed when the model accounted for milk protein gene effects. Increasing β-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.     

Invited review: Genetics and modeling of milk coagulation properties

Milk coagulation properties (MCP) are conventionally measured using computerized renneting meters, mechanical or optical devices that record curd firmness over time (CF_t). The traditional MCP are rennet coagulation time (RCT, min), curd firmness (a₃₀, mm), and curd-firming time (k₂₀, min). The milk of different ruminant species varies in terms of CF_t pattern. Milk from Holstein-Friesian and some Scandinavian cattle breeds yields higher proportions of noncoagulating samples, samples with longer RCT and lower a₃₀, and samples for which k₂₀ is not estimable, than does milk from Brown Swiss, Simmental, and other local Alpine breeds. The amount, proportion, and genetic variants (especially κ-casein) of milk protein fractions strongly influence MCP and explain variable proportions of the observed differences among breeds and among individuals of the same breed. In addition, other major genes have been shown to affect MCP. Individual repeatability of MCP is high, whereas any herd effect is low; thus, the improvement of MCP should be based principally on selection. Exploitable additive genetic variation in MCP exists and has been assessed using different breeds in various countries. Several models have been formulated that either handle noncoagulating samples or not. The heritability of MCP is similar to that of other milk quality traits and is higher than the heritability of milk yield. Rennet coagulation time and a₃₀ are highly correlated, both phenotypically and genetically. This means that the use of a₃₀ data does not add valuable information to that obtainable from RCT; both traits are genetically correlated mainly with milk acidity. Moreover, a₃₀ is correlated with casein content. The major limitations of traditional MCP can be overcome by prolonging the observation period and by using a novel CF_t modeling, which uses all available information provided by computerized renneting meters and allows the estimation of RCT, the potential asymptotic curd firmness, the curd-firming rate, and the syneresis rate. Direct measurements of RCT obtained from both mechanical and optical devices show similar heritabilities and exhibit high phenotypic and genetic correlations. Moreover, mid-infrared reflectance spectroscopy can predict MCP. The heritabilities of predicted MCP are higher than those of measured MCP, and the 2 sets of values are strongly correlated. Therefore, mid-infrared reflectance spectroscopy is a reliable and cheap method whereby MCP can be improved at the population level; this is because such spectra are already routinely acquired from the milk of cows enrolled in milk recording schemes.     

Understanding cow evaluations in univariate and multivariate animal and random regression models

The relationship between cow evaluations from a 305-d lactation yield animal model [i.e., lactation model (LM)] and a random regression model (RRM) were studied using the first-lactation milk yield of 2,477,807 Holstein heifers. In the LM analysis, 2 values of heritability were used, 0.35 (LM1-H) or 0.57 (LM2-H), the latter being equal to that used in the random regression model for the analysis of the Holstein test-day records (RRM-H). The relative weights on parent average (PA) and yield deviations (YD) were computed and studied to understand factors contributing to reranking of cows’ predicted transmitting abilities (PTA) from the various models. The degree of relatedness and inbreeding were calculated for the top 2,000 cows from the various models. Analyses of Jersey milk yield in the first 3 parities was implemented using 305-d lactation yield multivariate animal (MLM-J) and random regression models (MRRM-J). The ability of both models using only first-parity yield records to predict evaluations in second and third parities when records for these later parities were excluded was studied in a sample of cows. The correlations of cow PTA between LM1-H or LM2-H and RRM-H were 0.91 and 0.92, respectively, in the Holstein data. The data sets used were identical in this case for all models in terms of number of cows and yield records. The correlations were slightly lower at 0.89, 0.87, and 0.88 for parities 1, 2, and 3 in the Jersey analyses, where the data sets were not identical. The relative weights on PA and YD were 0.28 (0.11) and 0.72 (0.89), respectively, from the LM2-H (RRM-H). The RRM-H placed more emphasis on YD and therefore on Mendelian sampling deviations. Thus, the top 2,000 cows from the RRM-H were less related and inbred. The average additive genetic relationship was 22% greater in the LM2-H and average inbreeding coefficients were 0.68 and 0.43% for the LM2-H and RRM-H, respectively. When records were initially available in the first parity, the MRRM-J predicted PTA in parities 2 and 3 with about 2 to 7% greater accuracy compared with the MLM-J.     

Genetic effects of heat stress on milk yield of Thai Holstein crossbreds

The threshold for heat stress on milk yield of Holstein crossbreds under climatic conditions in Thailand was investigated, and genetic effects of heat stress on milk yield were estimated. Data included 400,738 test-day milk yield records for the first 3 parities from 25,609 Thai crossbred Holsteins between 1990 and 2008. Mean test-day milk yield ranged from 12.6 kg for cows with <87.5% Holstein genetics to 14.4 kg for cows with ≥93.7% Holstein genetics. Daily temperature and humidity data from 26 provincial weather stations were used to calculate a temperature-humidity index (THI). Test-day milk yield varied little with THI for first parity except above a THI of 82 for cows with ≥93.7% Holstein genetics. For third parity, test-day milk yield started to decline after a THI of 74 for cows with ≥87.5% Holstein genetics and declined more rapidly after a THI of 82. A repeatability test-day model with parities as correlated traits was used to estimate heat stress parameters; fixed effects included herd-test month-test year and breed groups, days in milk, calving age, and parity; random effects included 2 additive genetic effects, regular and heat stress, and 2 permanent environment, regular and heat stress. The threshold for effect of heat stress on test-day milk yield was set to a THI of 80. All variance component estimates increased with parity; the largest increases were found for effects associated with heat stress. In particular, genetic variance associated with heat stress quadrupled from first to third parity, whereas permanent environmental variance only doubled. However, permanent environmental variance for heat stress was at least 10 times larger than genetic variance. Genetic correlations among parities for additive effects without heat stress considered ranged from 0.88 to 0.96. Genetic correlations among parities for additive effects of heat stress ranged from 0.08 to 0.22, and genetic correlations between effects regular and heat stress effects ranged from −0.21 to −0.33 for individual parities. Effect of heat stress on Thai Holstein crossbreds increased greatly with parity and was especially large after a THI of 80 for cows with a high percentage of Holstein genetics (≥93.7%). Individual sensitivity to heat stress was more environmental than genetic for Thai Holstein crossbreds.     

Short communication: Factors affecting coagulation properties of Mediterranean buffalo milk

The aim of this study was to investigate sources of variation of milk coagulation properties (MCP) of buffalo cows. Individual milk samples were collected from 200 animals in 5 herds located in northern Italy from January to March 2010. Rennet coagulation time (RCT, min) and curd firmness after 30 min from rennet addition (a₃₀, mm) were measured using the Formagraph instrument (Foss Electric, Hillerød, Denmark). In addition to MCP, information on milk yield, fat, protein, and casein contents, pH, and somatic cell count (SCC) was available. Sources of variation of RCT and a₃₀ were investigated using a linear model that included fixed effects of herd, days in milk (DIM), parity, fat content, casein content (only for a₃₀), and pH. The coefficient of determination was 51% for RCT and 48% for a₃₀. The most important sources of variation of MCP were the herd and pH effects, followed by DIM and fat content for RCT, and casein content for a₃₀. The relevance of acidity in explaining the variation of both RCT and a₃₀, and of casein content in explaining that of a₃₀, confirmed previous studies on dairy cows. Although future research is needed to investigate the effect of these sources of variation on cheese yield, findings from the present study suggest that casein content and acidity may be used as indicator traits to improve technological properties of buffalo milk.     

Effects of β-κ-casein (CSN2-CSN3) haplotypes, β-lactoglobulin (BLG) genotypes, and detailed protein composition on coagulation properties of individual milk of Simmental cows

The aim of this study was to investigate the effects of CSN2-CSN3 (β-κ-casein) haplotypes, BLG (β-lactoglobulin) genotypes, content of milk protein fractions, and protein composition on coagulation properties of milk (MCP). Rennet coagulation time (RCT) and curd firmness (a₃₀) were measured using a computerized renneting meter, and the contents of major milk protein fractions were quantified by reversed-phase HPLC in individual milk samples of 2,167 Simmental cows. Cow genotypes at CSN2, CSN3, and BLG were ascertained by reversed-phase HPLC, and CSN2-CSN3 haplotype probabilities were estimated for each cow. Phenotypes for MCP were regressed on CSN2-CSN3 haplotype probabilities using linear models that also included the effects of herd-test-day, parity, days in milk, pH, somatic cell score, renneting meter sensor, sire of the cow, BLG genotype, and content of major protein fractions or, alternatively, protein composition. When the statistical model did not account for protein fraction contents or protein composition, haplotypes carrying CSN3 B were associated with shorter RCT and greater a₃₀ compared with those carrying CSN3 A. Haplotypes carrying CSN2 B had the effect of decreasing RCT and increasing a₃₀ relative to haplotype A²A. When effects of protein fractions content or protein composition were added to the model, no difference across haplotypes due to CSN3 and CSN2 alleles was observed for MCP, with the exception of the effect of CSN2 B on RCT, which remained markedly favorable. Hence, the effect of CSN3 B on MCP is related to a variation in protein composition caused by the allele-specific expression of κ-casein, rather than to a direct role of the protein variant on the coagulation process. In addition, the favorable effect exerted by CSN2 B on a₃₀ was caused by the increased β-casein content in milk. Conversely, CSN2 B is likely to exert a direct genetic effect on RCT, which does not depend upon variation of β-casein content associated with CSN2 B. Increased RCT was observed for milk yielded by BLG BB cows, even when models accounted for protein composition. Rennet clotting time was favorably affected by κ-casein content and percentage of κ-casein to total casein, whereas a₃₀ increased when contents and percentages of β-CN and κ-CN increased. Changes of milk protein composition and allele frequency at casein and whey protein genes affect variation of MCP.     

Short communication: estimates of genetic parameters of body condition score in the first 3 lactations using a random regression animal model

The objective of this research was to estimate the genetic parameters of body condition score (BCS) in the first 3 lactations in Canadian Holstein dairy cattle using a multiple-lactation random regression animal model. Field staff from Valacta milk recording agency (Sainte-Anne-de-Bellevue, QC, Canada) collected BCS from Québec herds several times throughout each lactation. Approximately 32,000, 20,000, and 11,000 first-, second-, and third-parity BCS were analyzed, respectively, from a total of 75 herds. Body condition score was a moderately heritable trait over the lactation for parity 1, 2, and 3, with average daily heritabilities of 0.22, 0.26, and 0.30, respectively. Daily heritability ranged between 0.14 and 0.26, 0.19 and 0.28, and 0.24 and 0.33 for parity 1, 2, and 3, respectively. Genetic variance of BCS increased with days in milk within lactations. The low genetic variance in early lactation suggests that the evolution of the ability to mobilize tissue reserves in early lactation provided cattle with a major advantage, and is, therefore, somewhat conserved. The increasing genetic variance suggests that more genetic differences were related to how well cows recovered from the negative energy balance state. More specifically, increasing genetic variation as lactation progressed could be a reflection of genetic differences in the ability of cows to efficiently control the rate of mobilization of tissue reserves, which would not be crucial in early lactation. The shape of BCS curves was similar across parities. From first to third parity, differences included the progressively deeper nadir and faster rate of recovery of condition. Daily genetic correlations between parities were calculated from 5 to 305 DIM, and were summed and divided by 301 to obtain average daily genetic correlations. The average daily genetic correlations were 0.84 between parity 1 and 2, 0.83 between parity 1 and 3, and 0.86 between parity 2 and 3. Although not 1, these genetic correlations are still strong, so much of the variation observed in BCS was controlled by the same genes for each of the first 3 lactations. If a genetic evaluation for BCS is developed, regular collection of first-lactation BCS records should be sufficient for genetic evaluation.     

Investigating the effect of temporal,geographic, and management factors on US Holstein lactation curve parameters

We fit the Wood's lactation model to an extensive database of test-day milk production records of US Holstein cows to obtain lactation-specific parameter estimates and investigated the effects of temporal, spatial, and management factors on lactation curve parameters and 305-d milk yield. Our approach included 2 steps as follows: (1) individual animal-parity parameter estimation with nonlinear least-squares optimization of the Wood's lactation curve parameters, and (2) mixed-effects model analysis of 8,595,413 sets of parameter estimates from individual lactation curves. Further, we conducted an analysis that included all parities and a separate analysis for first lactation heifers. Results showed that parity had the most significant effect on the scale (parameter a), the rate of decay (parameter c), and the 305-d milk yield. The month of calving had the largest effect on the rate of increase (parameter b) for models fit with data from all lactations. The calving month had the most significant effect on all lactation curve parameters for first lactation models. However, age at first calving, year, and milking frequency accounted for a higher proportion of the variance than month for first lactation 305-d milk yield. All parameter estimates and 305-d milk yield increased as parity increased; parameter a and 305-d milk yield rose, and parameters b and c decreased as year and milking frequency increased. Calving month estimates parameters a, b, c, and 305-d milk yield were the lowest values for September, May, June, and July, respectively. The results also indicated the random effects of herd and cow improved model fit. Lactation curve parameter estimates from the mixed-model analysis of individual lactation curve fits describe well US Holstein lactation curves according to temporal, spatial, and management factors.     

Genome-wide association for milk production traits and somatic cell score in different lactation stages of Ayrshire,Holstein, and Jersey dairy cattle

We performed genome-wide association analyses for milk, fat, and protein yields and somatic cell score based on lactation stages in the first 3 parities of Canadian Ayrshire, Holstein, and Jersey cattle. The genome-wide association analyses were performed considering 3 different lactation stages for each trait and parity: from 5 to 95, from 96 to 215, and from 216 to 305 d in milk. Effects of single nucleotide polymorphisms (SNP) for each lactation stage, trait, parity, and breed were estimated by back-solving the direct breeding values estimated using the genomic best linear unbiased predictor and single-trait random regression test-day models containing only the fixed population average curve and the random genomic curves. To identify important genomic regions related to the analyzed lactation stages, traits, parities and breeds, moving windows (SNP-by-SNP) of 20 adjacent SNP explaining more than 0.30% of total genetic variance were selected for further analyses of candidate genes. A lower number of genomic windows with a relatively higher proportion of the explained genetic variance was found in the Holstein breed compared with the Ayrshire and Jersey breeds. Genomic regions associated with the analyzed traits were located on 12, 8, and 15 chromosomes for the Ayrshire, Holstein, and Jersey breeds, respectively. Especially for the Holstein breed, many of the identified candidate genes supported previous reports in the literature. However, well-known genes with major effects on milk production traits (e.g., diacylglycerol O-acyltransferase 1) showed contrasting results among lactation stages, traits, and parities of different breeds. Therefore, our results suggest evidence of differential sets of candidate genes underlying the phenotypic expression of the analyzed traits across breeds, parities, and lactation stages. Further functional studies are needed to validate our findings in independent populations.     

Genetic correlation patterns between somatic cell score and protein yield in the Italian Holstein-Friesian population

Genetic parameters for somatic cell score (SCS) in the Italian Holstein-Friesian population were estimated addressing the pattern of genetic correlation with protein yield in different parities (first, second, and third) and on different days in milk within each parity. Three approaches for parameter estimation were applied using random samples of herds from the national database of the Italian Holstein Association. Genetic correlations for lactation measures (305-d protein yield and lactation SCS) were positive in the first parity (0.31) and close to zero in the second (0.01) and third (0.09) parities. These results indicated that larger values of SCS were genetically associated with increased production. The second and third sets of estimates were based on random regression test-day models, modeling the shape of lactation curve with the Wilmink function and fourth-order Legendre polynomials, respectively. Genetic correlations from both random regression models showed a specific pattern associated with days in milk within and across parities. Estimates varied from positive to negative in the first and second parity, and from null to negative in the third parity. Patterns were similar for both random regression models. The average overall correlation between SCS and protein yield was zero or slightly positive in the first lactation and ranged from zero to negative in later lactations. Correlation estimates differed by parity and stage of lactation. They also demonstrated the dubiousness of applying a single genetic correlation measure between SCS and protein in setting selection strategies. Differences in magnitude and the sign of genetic correlations between SCS and yields across and within parities should be accounted for in selection schemes.