Association between milk protein gene variants and protein composition traits in dairy cattle

The objective of this study was to identify DNA markers in the 4 casein genes (CSN1S1, CSN1S2, CSN2, and CSN3) and the 2 major whey protein genes (LALBA and LGB) that show associations with milk protein profile measured by reverse-phase HPLC. Fifty-three single nucleotide polymorphisms (SNP) were genotyped for cows in a unique resource population consisting of purebred Holstein and (Holstein × Jersey) × Holstein crossbred animals. Seven traits were analyzed, including concentrations of α(S)-casein (CN), β-CN, κ-CN, α-lactalbumin, β-lactoglobulin, and 2 additional secondary traits, the total concentration of the above 5 milk proteins and the α(S)-CN to β-CN ratio. A substantial fraction of phenotypic variation could be explained by the additive genetic component for the 7 milk protein composition traits studied. Moreover, several SNP were significantly associated with all examined traits at an experiment-wise error rate of 0.05, except for α-lactalbumin. Importantly, the significant SNP explained a large proportion of the phenotypic variation of milk protein composition. Our findings could be used for selecting animals that produce milk with desired composition or desired processing and manufacturing properties.     

Genetic parameters for buffalo milk yield and milk quality traits using Bayesian inference

The availability of accurate genetic parameters for important economic traits in milking buffaloes is critical for implementation of a genetic evaluation program. In the present study, heritabilities and genetic correlations for fat (FY305), protein (PY305), and milk (MY305) yields, milk fat (%F) and protein (%P) percentages, and SCS were estimated using Bayesian methodology. A total of 4,907 lactations from 1,985 cows were used. The (co)variance components were estimated using multiple-trait analysis by Bayesian inference method, applying an animal model, through Gibbs sampling. The model included the fixed effects of contemporary groups (herd-year and calving season), number of milking (2 levels), and age of cow at calving as (co)variable (quadratic and linear effect). The additive genetic, permanent environmental, and residual effects were included as random effects in the model. The posterior means of heritability distributions for MY305, FY305, PY305, %F, P%, and SCS were 0.22, 0.21, 0.23, 0.33, 0.39, and 0.26, respectively. The genetic correlation estimates ranged from −0.13 (between %P and SCS) to 0.94 (between MY305 and PY305). The permanent environmental correlation estimates ranged from −0.38 (between MY305 and %P) to 0.97 (between MY305 and PY305). Residual and phenotypic correlation estimates ranged from −0.26 (between PY305 and SCS) to 0.97 (between MY305 and PY305) and from −0.26 (between MY305 and SCS) to 0.97 (between MY305 and PY305), respectively. Milk yield, milk components, and milk somatic cells counts have enough genetic variation for selection purposes. The genetic correlation estimates suggest that milk components and milk somatic cell counts would be only slightly affected if increasing milk yield were the selection goal. Selecting to increase FY305 or PY305 will also increase MY305, %P, and %F.     

水牛乳蛋白质的组成

分析了摩拉水牛（M）、尼里-拉菲水牛（N）、一代杂交水牛（F1）、二代杂交水牛（F2）和高代杂交水牛（Fh）5个品代水牛的乳蛋白主要组分的相对百分比含量．同时分析了总氨基酸组成及钙、磷含量。结果表明，水牛乳蛋白的主要组分有：α-乳清蛋白（α-LA）、β-乳球蛋白（β-LG）、免疫球蛋白轻链（IgG—L）和重链（IgG—H）、αs1-酪蛋白（αs1-CN）、αs2-酪蛋白（αs2-CN），β-酪蛋白（β-CN）、κ-酪蛋白（κ—CN）、血清白蛋白（SA）和乳铁蛋白（LF）等；CN在水牛乳蛋白中占优势，与荷斯坦牛乳相比，水牛乳中CN的质量分数稍低，而且各品代水牛乳中的CN有显著性差异（P〈0．05）；乳清蛋白中β-LG含量最高；杂交水牛乳蛋白高于纯种摩拉水牛和尼里一拉菲水牛，差异显著（P〈0．05）：各品代水牛乳的氨基酸比例比较接近；不同品代水牛乳中钙、磷含量没有显著性差异。     

Genetic analysis of skinfold thickness and its association with body condition score and milk production traits in Chinese Holstein population

The skin has many important roles in dairy cattle, and skinfold thickness could be used as an indicator of body fat deposition. The objectives of this study were to estimate genetic parameters of skinfold thickness and to explore its association with body condition score (BCS) and milk production traits in a Chinese Holstein population. Skinfold thicknesses over the neck (STN) and the last rib (STR), BCS, and test-day records of milk production traits were available for 6,416 lactating Holstein cows in the summers of 2015 and 2016 in Beijing, China. Multi-trait animal models were used to estimate variance and covariance components using the DMU software. The average STN was 7.15 ± 1.28 mm, and the average STR was 11.76 ± 1.95 mm (mean ± standard deviation). Estimated heritability was 0.13 ± 0.03 for STN and 0.26 ± 0.04 for STR. We detected a high genetic correlation (0.79 ± 0.08; heritability ± standard error) between STN and STR. Genetic correlations between skinfold thickness and BCS were low to moderate: 0.18 between STR and BCS, and 0.33 between STN and BCS. Genetic correlations between skinfold thickness and milk yield, milk fat percentage, and milk protein percentage were negligible, ranging from ?0.02 to 0.15. Collectively, skinfold thickness is characterized as a trait with moderate heritability. Skinfold thickness is sensitive to changes in body condition or fat deposition across parities and lactation stages in milking cows, and we confirmed the complementary nature of skinfold thickness and BCS genetically as well as phenotypically by comparing their changing trends throughout lactation and across lactations. The use of skinfold thickness, together with BCS, can assist in the monitoring of changes in body fat deposition to achieve higher management precision.     

Genome-wide association studies to identify quantitative trait loci affecting milk production traits in water buffalo

Water buffalo is the second largest resource of milk supply around the world, and it is well known for its distinctive milk quality in terms of fat, protein, lactose, vitamin, and mineral contents. Understanding the genetic architecture of milk production traits is important for future improvement by the buffalo breeding industry. The advance of genome-wide association studies (GWAS) provides an opportunity to identify potential genetic variants affecting important economical traits. In the present study, GWAS was performed for 489 buffaloes with 1,424 lactation records using the 90K Affymetrix Buffalo SNP Array (Affymetrix/Thermo Fisher Scientific, Santa Clara, CA). Collectively, 4 candidate single nucleotide polymorphisms (SNP) in 2 genomic regions were found to associate with buffalo milk production traits. One region affecting milk fat and protein percentage was located on the equivalent of Bos taurus autosome (BTA)3, spanning 43.3 to 43.8 Mb, which harbored the most likely candidate genes MFSD14A, SLC35A3, and PALMD. The other region on the equivalent of BTA14 at 66.5 to 67.0 Mb contained candidate genes RGS22 and VPS13B and influenced buffalo total milk yield, fat yield, and protein yield. Interestingly, both of the regions were reported to have quantitative trait loci affecting milk performance in dairy cattle. Furthermore, we suggest that buffaloes with the C allele at AX-85148558 and AX-85073877 loci and the G allele at AX-85106096 locus can be selected to improve milk fat yield in this buffalo-breeding program. Meanwhile, the G allele at AX-85063131 locus can be used as the favorable allele for improving milk protein percentage. Genomic prediction showed that the reliability of genomic estimated breeding values (GEBV) of 6 milk production traits ranged from 0.06 to 0.22, and the correlation between estimated breeding values and GEBV ranged from 0.23 to 0.35. These findings provide useful information to understand the genetic basis of buffalo milk properties and may play a role in accelerating buffalo breeding programs using genomic approaches.     

Milk composition traits in Jamunapari goats: Genetic parameters and effect of CSN1S1 gene variants

Milk protein polymorphism was analysed to improve the protein content in milk. The present study characterises the CSN1S1 gene and the effect of allelic combinations on milk composition traits in Jamunapari goats. The allelic variants obtained from sodium dodecyl sulfate polyacrylamide gel electrophoresis and polymerase chain reaction–restriction fragment length polymorphism were confirmed by cloning and sequencing. Genetic parameters were obtained from 518 records from 48 sires and 131 dams. The A, B and F alleles were observed in the population and the protein percentage in milk was significantly (P < 0.01) affected by allelic variants. The frequencies of A, B and F alleles were 0.456, 0.503 and 0.041, respectively. The protein content in milk was highest in the goats with AB genotype followed by AA > BB > BF > AF > FF. The goats with AB genotype had a significantly (P < 0.01) higher protein percentage in milk than goats with BF (t = 5.311, df = 113), AF (t = 8.13, df = 123) and FF (t = 9.55, df = 115) genotypes. The direct heritability for protein percentage was 0.441. Parity and season of birth had significant effects (P < 0.05) on the solids‐not‐fat percentage and lactose concentrations. The CSN1S1 AA, AB and BB genotypes should be selected for higher protein content and to improve milk quality and processing traits in Indian goats.     

Relationships between milk protein composition, milk protein variants, and cow fertility traits in Dutch Holstein-Friesian cattle

Selective breeding can change milk protein composition to improve the manufacturing properties of milk. However, the effects of such breeding strategies on other economically important traits should be investigated before implementation. The objectives of this study were to examine the association between cow fertility traits and (1) milk protein composition and (2) milk protein variants (β-lactoglobulin, β-casein, κ-casein, and β-κ-casein) in commercial Dutch Holstein-Friesian cattle. Data on 1,644 first-lactation cows were analyzed by fitting linear mixed models. Greater relative concentration of α_S1-casein within total milk protein had a positive phenotypic relationship with nonreturn rates and calving rate after first insemination. Furthermore, results showed virtually no significant relationship between cow fertility and concentration of other milk proteins or milk protein variants. Results of this study can be used to assess the correlated effects of breeding for improved milk protein composition on reproduction, thereby allowing for better evaluation of breeding programs before implementation. Our findings suggest that selecting cows based on milk protein composition or milk protein variants for improved manufacturing properties would have no negative influence on reproductive performance.     

Effect of beta-lactoglobulin polymorphism and seasonality on bovine milk composition

The objective was to evaluate the effect of beta-lactoglobulin (beta-lg) polymorphism and seasonality on milk composition (fat, lactose, total solids, milk urea nitrogen, total protein, true protein, casein and somatic cell counts) of Holstein and Girolando cows. Milk and blood samples from 278 Holsteins cows and 156 Girolando cows were taken during two dry seasons and two rainy seasons, for milk composition analysis and to determine beta-lg genotypes, respectively. BB genotype was the most frequent for both breeds, followed by AA genotype for Holstein (BB>AA>AB) and by AB for Girolando cows (BB>AB>AA). No differences were found in milk compositional characteristics among genetic variants of beta-lg (AA, AB and BB) either between Holstein or Girolando cows. No association between milk composition and beta-lg genetic polymorphism was observed. During the dry season, independently of the breed considered, higher contents of lactose, true protein, casein and casein:true protein ratio were found.     

Effects of the osteopontin gene variants on milk production traits in dairy cattle

Osteopontin (OPN) is a highly phosphorylated glycoprotein whose gene has been cloned and sequenced in different species. Several whole genome scans have identified quantitative trait loci (QTL) affecting milk production traits on bovine chromosome 6 close to the osteopontin gene (OPN) location. The presence of OPN in milk and its elevated expression in mammary gland epithelial cells together with previous QTL studies have prompted us to investigate the effects of OPN variants on milk production traits in the Holstein dairy cattle population. A single nucleotide polymorphism in intron 4 (C/T) was detected and primers were designed to amplify genomic DNA from 1362 bulls obtained from Cooperative Dairy DNA Repository and from 214 cows from the University of Wisconsin herd. For the Repository population, the C allele was associated with an increase in milk protein percentage and milk fat percentage. Correlation between milk protein percentage and milk fat percentage was about 0.57. For the University of Wisconsin herd, the estimates of the effects of allele C were in the same direction as for the Repository population, although these estimates did not reach statistical significance. Our results are consistent with other studies that showed a significant association of the microsatellite markers in the region of OPN with milk protein percentage and other correlated traits.     

Genetic parameters for lactose and its correlation with other milk production traits and fitness traits in pasture-based production systems

Lactose is a major component of milk (typically around 5% of composition) that is not usually directly considered in national genetic improvement programs of dairy cattle. Daily test-day lactose yields and percentage data from pasture-based seasonal calving herds in Australia were analyzed to assess if lactose content can be used for predicting fitness traits and if an additional benefit is achieved by including lactose yield in selecting for milk yield traits. Data on lactose percentage collected from 2007 to 2014, from about 600 herds, were used to estimated genetic parameters for lactose percentage and lactose yield and correlations with other milk yield traits, somatic cell count (SCC), calving interval (CIV), and survival. Daily test-day data were analyzed using bivariate random regression models. In addition, multi-trait models were also performed mainly to assess the value of lactose to predict fitness traits. The heritability of lactose percentage (0.25 to 0.37) was higher than lactose yield (0.11 to 0.20) in the first parity. Genetically, the correlation of lactose percentage with protein percentage varied from 0.3 at the beginning of lactation to ?0.24 at the end of the lactation in the first parity. Similar patterns in genetic correlations were also observed in the second and third parity. At all levels (i.e., genetic, permanent environmental, and residual), the correlation between milk yield and lactose yield was close to 1. The genetic and permanent environmental correlations between lactose percentage and SCC were stronger in the second and third parity and toward the end of the lactation (?0.35 to ?0.50) when SCC levels are at their maximum. The genetic correlation between lactose percentage in the first 120 d and CIV (?0.23) was similar to correlation of CIV with protein percentage (?0.28), another component trait with the potential to predict fertility. Furthermore, the correlations of estimated breeding values of lactose percentage and estimated breeding values of traits such as survival, fertility, SCC, and angularity suggest that the value of lactose percentage as a predictor of fitness traits is weak. The results also suggest that including lactose yield as a trait into the breeding objective is of limited value due to the high positive genetic correlation between lactose yield and protein yield, the trait highly emphasized in Australia. However, recording lactose percentage as part of the routine milk recording system will enable the Australian dairy industry to respond quickly to any future changes and market signals.     

Genetic correlations of mid-infrared-predicted milk fatty acid groups with milk production traits

The objective of this research was to estimate the genetic correlations between milk mid-infrared-predicted fatty acid groups and production traits in first-parity Canadian Holsteins. Contents of short-chain, medium-chain, long-chain, saturated, and unsaturated fatty acid groupings in milk samples can be predicted using mid-infrared spectral data for cows enrolled in milk recording programs. Predicted fatty acid group contents were obtained for 49,127 test-day milk samples from 10,029 first-parity Holstein cows in 810 herds. Milk yield, fat and protein yield, fat and protein percentage, fat-to-protein ratio, and somatic cell score were also available for these test days. Genetic parameters were estimated for the fatty acid groups and production traits using multiple-trait random regression test day models by Bayesian methods via Gibbs sampling. Three separate 8- or 9-trait analyses were performed, including the 5 fatty acid groups with different combinations of the production traits. Posterior standard deviations ranged from <0.001 to 0.01. Average daily genetic correlations were negative and similar to each other for the fatty acid groups with milk yield (?0.62 to ?0.59) and with protein yield (?0.32 to ?0.25). Weak and positive average daily genetic correlations were found between somatic cell score and the fatty acid groups (from 0.25 to 0.36). Stronger genetic correlations with fat yield, fat and protein percentage, and fat-to-protein ratio were found with medium-chain and saturated fatty acid groups compared with those with long-chain and unsaturated fatty acid groups. Genetic correlations were very strong between the fatty acid groups and fat percentage, ranging between 0.88 for unsaturated and 0.99 for saturated fatty acids. Daily genetic correlations from 5 to 305 d in milk with milk, protein yield and percentage, and somatic cell score traits showed similar patterns for all fatty acid groups. The daily genetic correlations with fat yield at the beginning of lactation were decreasing for long-chain and unsaturated fatty acid groups and increasing for short-chain fatty acids. Genetic correlations between fat percentage and fatty acids were increasing at the beginning of lactation for short- and medium-chain and saturated fatty acids, but slightly decreasing for long-chain and unsaturated fatty acid groups. These results can be used in defining fatty acid traits and breeding objectives.     

Effects of milk protein genetic variants on milk yield and composition

Effects of genetic variants of the milk proteins, alpha S1-casein, beta-casein, kappa-casein and beta-lactoglobulin (beta-lg), on milk yield and composition, particularly the protein composition, were investigated in milk samples from 289 Jersey and 249 Friesian cows in eight commercial herds. Milk protein genotypes had no significant effect on yields over a complete lactation of milk and fat, but significant differences in fat content were detected for beta-casein (B, A1B, A2 greater than A1A2) and beta-lg (B, AB greater than A) variants. Significant differences between beta-lg variants were also found with total solids (B, AB greater than A), casein (B, AB greater than A), whey protein (A greater than AB greater than B) and beta-lg (A greater than AB, AC greater than B greater than BC) concentrations. Casein genotypes were not significantly different in total protein and casein concentrations but many differences were found in casein composition. alpha S1-Casein variants significantly affected alpha S1-casein (BC greater than B) and kappa-casein (B greater than BC) concentrations. beta-Casein variants affected concentration and proportion of beta-casein (A1B, A2B greater than A1, A1A2, A2, B), alpha S1-casein (A1, A2 greater than B) and kappa-casein (B greater than A2) and concentration of whey protein (A1 greater than most other beta-casein variants). kappa-Casein variants affected concentration and proportion of kappa-casein (B greater than AB greater than A), proportion of alpha S1-casein (A greater than AB greater than B) and concentrations of beta-lg (A greater than AB, B) and alpha-lactalbumin (A, AB greater than B). Differences in milk composition were found between breeds, herds and ages, and with stage of lactation. The potential use of milk protein genotypes as an aid in dairy cattle breeding is discussed.     

广西水牛奶和荷斯坦牛奶中蛋白质和氨基酸的含量及组成分析

目的研究广西区内生水牛奶和荷斯坦生牛奶中蛋白质、氨基酸含量及其组成特性,分析水牛奶和荷斯坦牛奶的差异。方法采用盐酸水解法处理牛奶样品,通过氨基酸自动分析仪测定氨基酸含量,分析水牛奶和荷斯坦牛奶氨基酸的含量和组成;通过凯氏定氮法测定牛奶中蛋白质含量。结果 18份牛奶样品中蛋白质、氨基酸的测定结果表明,生水牛奶的蛋白质和氨基酸含量均高于荷斯坦生牛奶,而水牛奶和荷斯坦牛奶中各氨基酸含量占氨基酸总量的比值相近。结论该方法为广西生水牛奶和荷斯坦生牛奶的鉴别提供参考。     

Genetic components of milk Fourier-transform infrared spectra used to predict breeding values for milk composition and quality traits in dairy goats

The usual practice today is that milk component phenotypes are predicted using Fourier-transform infrared (FTIR) spectra and they are then, together with pedigree information, used in BLUP for calculation of individual estimated breeding values. Here, this is referred to as the indirect prediction (IP) approach. An alternative approach—a direct prediction (DP) method—is proposed, where genetic analyses are directly conducted on the milk FTIR spectral variables. Breeding values of all derived milk traits (protein, fat, fatty acid composition, and coagulation properties, among others) can then be predicted as traits correlated only to the genetic information of the spectra. For the DP, no need exists to predict the phenotypes before calculating breeding values for each of the traits—the genetic analysis is done once for the spectra, and is applicable to all traits derived from the spectra. The aim of the study was to compare the effects of DP and IP of milk composition and quality traits on prediction error variance (PEV) and genetic gain. A data set containing 27,927 milk FTIR spectral observations and milk composition phenotypes (fat, lactose, and protein) belonging to 14,869 goats of 271 herds was used for training and evaluating models. Partial least squares regression was used for calibrating prediction models for fat, protein, and lactose percentages. Restricted maximum likelihood was used to estimate variance components of the spectral variables after principal components analysis was applied to reduce the spectral dimension. Estimated breeding values were predicted for fat, lactose, and protein percentages using DP and IP methods. The DP approach reduced the mean PEV by 3.73, 4.07, and 7.04% for fat, lactose, and protein percentages, respectively, compared with the IP method. Given the reduction in PEV, relative genetic gains were 2.99, 2.78, and 4.85% for fat, lactose, and protein percentages, respectively. We concluded that more accurate estimated breeding values could be found using genetic components of milk FTIR spectra compared with single-trait animal model analyses on phenotypes predicted from the spectra separately. The potential and application is not only limited to milk FTIR spectra, but could also be extended to any spectroscopy techniques implemented in other species and for other traits.     

Short communication: Prediction of milk coagulation and acidity traits in Mediterranean buffalo milk using Fourier-transform mid-infrared spectroscopy

Milk coagulation and acidity traits are important factors to inform the cheesemaking process. Those traits have been deeply studied in bovine milk, whereas scarce information is available for buffalo milk. However, the dairy industry is interested in a method to determine milk coagulation and acidity features quickly and in a cost-effective manner, which could be provided by Fourier-transform mid-infrared (FT-MIR) spectroscopy. The aim of this study was to evaluate the potential of FT-MIR to predict coagulation and acidity traits of Mediterranean buffalo milk. A total of 654 records from 36 herds located in central Italy with information on milk yield, somatic cell score, milk chemical composition, milk acidity [pH, titratable acidity (TA)], and milk coagulation properties (rennet coagulation time, curd firming time, and curd firmness) were available for statistical analysis. Reference measures of milk acidity and coagulation properties were matched with milk spectral information, and FT-MIR prediction models were built using partial least squares regression. The data set was divided into a calibration set (75%) and a validation set (25%). The capacity of FT-MIR spectroscopy to correctly classify milk samples based on their renneting ability was evaluated by a canonical discriminant analysis. Average values for milk coagulation traits were 13.32 min, 3.24 min, and 39.27 mm for rennet coagulation time, curd firming time, and curd firmness, respectively. Milk acidity traits averaged 6.66 (pH) and 7.22 Soxhlet-Henkel degrees/100 mL (TA). All milk coagulation and acidity traits, except for pH, had high variability (17 to 46%). Prediction models of coagulation traits were moderately to scarcely accurate, whereas the coefficients of determination of external validation were 0.76 and 0.66 for pH and TA, respectively. Canonical discriminant analysis indicated that information on milk coagulating ability is present in the MIR spectra, and the model correctly classified as noncoagulating the 91.57 and 67.86% of milk samples in the calibration and validation sets, respectively. In conclusion, our results can be relevant to the dairy industry to classify buffalo milk samples before processing.     

Genetic analysis of milk urea concentration and its genetic relationship with selected traits of interest in dairy cows

The aim of this study was to estimate genetic parameters of milk urea concentration (MU) and its genetic correlations with milk production traits, longevity, and functional traits in the first 3 parities in dairy cows. The edited data set consisted in 9,107,349 MU test-day records from the first 3 parities of 560,739 cows in 2,356 herds collected during the years 1994 to 2020. To estimate the genetic parameters of MU, data of 109 randomly selected herds, with a total of 770,016 MU test-day records, were used. Genetic parameters and estimated breeding values were estimated using a multiple-trait (parity) random regression model. Herd-test-day, age-year-season of calving, and days in milk classes (every 5 d as a class) were used as fixed effects, whereas effects of herd-year of calving, permanent environment, and animal were modeled using random regressions and Legendre polynomials of order 2. The average daily heritability and repeatability of MU during days in milk 5 to 365 in the first 3 parities were 0.19, 0.22, 0.20, and 0.48, 0.48, 0.47, respectively. The mean genetic correlation estimated among MU in the first 3 parities ranged from 0.96 to 0.97. The average daily estimated breeding values for MU of the selected bulls (n = 1,900) ranged from −9.09 to 7.37 mg/dL. In the last 10 yr, the genetic trend of MU has gradually increased. The genetic correlation between MU and 11 traits of interest ranged from –0.28 (milk yield) to 0.28 (somatic cell score). The findings of this study can be used as the first step for development of a routine genetic evaluation for MU and its inclusion into the genetic selection program in the Walloon Region of Belgium.     

Polymorphism of the growth hormone gene and its association with growth traits in Boer goat bucks

In the present study, the polymorphism of growth hormone (GH) gene was analyzed as a genetic marker candidate for growth traits in Boer goat bucks. Two single nucleotide polymorphisms (SNPs) - A781G (Ser/Gly35) and A1575G (Leu147), were identified by GH gene sequencing and PCR-RFLP (polymerase chain reaction-restriction fragment length polymorphism) analysis. AA genotype resulted in a significant decrease in birth chest girth (P=0.03) and weaning weight (P=0.014) comparing to AB genotype, while CC genotype contributed to weaning height (P=0.04) greater than CD genotype. When in combination, AACD genotype was undesired for lower scores in a series of growth traits including body weight, length, height, and chest girth at birth and weaning, as well as the pre-weaning daily gain and body weight at age of 11 months. These results indicate that new molecular markers associated with caprine growth traits can be used in MAS (marker-assisted selection) in Boer goat bucks.     

Genetic associations of lactose and its ratios to other milk solids with health traits in Austrian Fleckvieh cows

The genetic correlations (r_a) of milk lactose percentage (LP), lactose yield (LY), and ratios of LP to other milk solids with udder, metabolic, and fertility disorders have not been assessed in dairy cattle so far. To evaluate the potential role of milk lactose as indicator of cow health, 142,285 lactation records of 84,289 Austrian Fleckvieh cows were analyzed with univariate and bivariate animal models. Milk traits were on a 150-d basis and health traits were coded as binary (0/1). Other than LP and LY, 3 new phenotypes were defined and included in the present study, namely the lactose-to-fat, lactose-to-protein, and lactose-to-solids ratios. The most heritable trait was LP (0.566 ± 0.008) and heritability of LY was much lower (0.145 ± 0.005). Heritability estimates close to 0.50 were assessed for the ratios. The frequency of health disorders was higher in multiparous cows yielding milk with low LP (≤4.553%) compared with cows yielding milk with high LP (≥5.045%). Heritabilities of health traits were in the expected ranges, with the highest estimate for ovarian cysts (CYS; 0.037 ± 0.004) and the lowest for retained placenta (0.005 ± 0.001). Mastitis (MAS) genetically correlated with LY (0.518 ± 0.057); considering that the amount of synthesized lactose is the key regulator of milk volume, this result confirmed that high-producing cows are more genetically susceptible to MAS than low-producing animals. Similar to MAS, ketosis (KET) was also positively genetically associated with LY (0.420 ± 0.077) and a weak and unfavorable r_a between KET and lactose-to-protein ratio was estimated (0.159 ± 0.077). The r_a of LY with milk fever (MFV) and CYS were approximately 0.20. The r_a of LP with MAS, KET, and MFV were negative (?0.142 on average), supporting the idea that LP is a potential health indicator. Genetic correlations between health traits ranged from zero (retained placenta with MAS and CYS) to 0.463 ± 0.090 (MAS and MFV). Results of the present study suggest that LP has potentiality to be used as indicator trait to improve udder health in Austrian Fleckvieh population.     

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, β-CN, γ-CN, κκ-CN, glycosylated κ-CN (glyco-κ-CN), α-lactalbumin, and β-lactoglobulin was measured by reversed-phase HPLC. Relative contents of α(S1)-CN%, α(S2)-CN%, β-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 β-CN% at the expense of all of the other major casein fractions, especially of κ-CN%. Values of α(S2)-CN%, β-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 β-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 β-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 β-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.