Short communication: Milk protein genetic variation and casein haplotype structure in the Original Pinzgauer cattle

Milk protein genetic polymorphisms are often used for characterizing domesticated mammalian species and breeds, and for studying associations with economic traits. The aim of this work was to analyze milk protein genetic variation in the Original Pinzgauer, a dual-purpose (dairy and beef) cattle breed of European origin that was influenced in the past by human movements from different regions as well as by crossbreeding with Red Holstein. A total of 485 milk samples from Original Pinzgauer from Austria (n = 275) and Germany (n = 210) were typed at milk proteins α_S1-casein, β-casein, κ-casein, α-lactalbumin, and β-lactoglobulin by isoelectrofocusing to analyze the genetic variation affecting the protein amino acid charge. The Original Pinzgauer breed is characterized by a rather high genetic variation affecting the amino acid charge of milk proteins, with a total of 15 alleles, 12 of which were found at a frequency >0.05. The most polymorphic protein was β-casein with 4 alleles detected. The prevalent alleles were CSN1S1*B, CSN2*A², CSN1S2*A, CSN3*A, LGB*A, and LAA*B. A relatively high frequency of CSN1S2*B (0.202 in the whole data set) was found, mainly occurring within the C-A²-B-A haplotype (in the order CSN1S1-CSN2-CSN1S2-CSN3), which seems to be peculiar to the Original Pinzgauer, possibly because the survival of an ancestral haplotype or the introgression of Bos indicus.     

The use of milk mid-infrared spectroscopy to improve genomic prediction accuracy of serum biomarkers

Breeding objectives in the dairy industry have shifted from being solely focused on production to including fertility, animal health, and environmental impact. Increased serum concentrations of candidate biomarkers of health and fertility, such as β-hydroxybutyric acid (BHB), fatty acids, and urea are difficult and costly to measure, and thus limit the number of records. Accurate genomic prediction requires a large reference population. The inclusion of milk mid-infrared (MIR) spectroscopic predictions of biomarkers may increase genomic prediction accuracy of these traits. Our objectives were to (1) estimate the heritability of, and genetic correlations between, selected serum biomarkers and their respective MIR predictions, and (2) evaluate genomic prediction accuracies of either only measured serum traits, or serum traits plus MIR-predicted traits. The MIR-predicted traits were either fitted in a single trait model, assuming the measured trait and predicted trait were the same trait, or in a multitrait model, where measured and predicted trait were assumed to be correlated traits. We performed all analyses using relationship matrices constructed from pedigree (A matrix), genotypes (G matrix), or both pedigree and genotypes (H matrix). Our data set comprised up to 2,198 and 9,657 Holstein cows with records for serum biomarkers and MIR-predicted traits, respectively. Heritabilities of measured serum traits ranged from 0.04 to 0.07 for BHB, from 0.13 to 0.21 for fatty acids, and from 0.10 to 0.12 for urea. Heritabilities for MIR-predicted traits were not significantly different from those for the measured traits. Genetic correlations between measured traits and MIR-predicted traits were close to 1 for urea. For BHB and fatty acids, genetic correlations were lower and had large standard errors. The inclusion of MIR predicted urea substantially increased prediction accuracy for urea. For BHB, including MIR-predicted BHB reduced the genomic prediction accuracy, whereas for fatty acids, prediction accuracies were similar with either measured fatty acids, MIR-predicted fatty acids, or both. The high genetic correlation between urea and MIR-predicted urea, in combination with the increased prediction accuracy, demonstrated the potential of using MIR-predicted urea for genomic prediction of urea. For BHB and fatty acids, further studies with larger data sets are required to obtain more accurate estimates of genetic correlations.     

The β-casein (CSN2) A2 allelic variant alters milk protein profile and slightly worsens coagulation properties in Holstein cows

Milk proteins genetic variants have long attracted interest as they are associated with important issues relating to milk composition and technological properties. An important debate has recently opened at an international level on the role of β-casein (β-CN) A1 and A2 polymorphisms, toward human health. For this reason, a lot of efforts has been put into the promotion of A2 milk by companies producing and selling A1-free milk, leading the farmers and breeders to switch toward A2 milk production without paying attention on the potential effect of the processability of milk into cheese. The aim of the present work was to evaluate the effects of β-CN, specifically the A1 and A2 allelic variants, on the detailed milk protein profile and cheese-making traits in individual milk samples of 1,133 Holstein Friesian cows. The protein fractions were measured with reversed-phase (RP)-HPLC (expressed in g/L and % N), and the cheese-making traits, namely milk coagulation properties, cheese yield, and curd nutrient recoveries assessed at the individual level, with a nano-scale cheese-making procedure. The β-CN (CSN2), κ-CN (CSN3), and β-lactoglobulin (LGB) genetic variants were first identified through RP-HPLC and then confirmed through genotyping. Estimates of the effects of protein genotypes were obtained using a mixed inheritance model that considered, besides the standard nuisance variables (i.e., days in milk, parity, and herd-date), the milk protein genes located on chromosome 6 (CSN2, CSN3) and on chromosome 11 (LGB), and the polygenic background of the animals. Milk protein genes (CSN2, CSN3, and LGB) explained an important part of the additive genetic variance in the traits evaluated. The β-CN A1A1 was associated with a significantly lower production of whey proteins, particularly of β-lactoglobulin (?8.2 and ?6.8% for g/L and % N, respectively) and α-lactalbumin (?4.7 and ?4.4% for g/L and % N, respectively), and a higher production of β-CN (6.8 and 6.1% for g/L and % N, respectively) with respect to the A2A2 genotype. Regarding milk cheese-making ability, the A2A2 genotype showed the worst performance compared with the other genotypes, particularly with respect to the BA1, with a higher rennet coagulation time (7.1 and 28.6% compared with A1A1 and BA1, respectively) and a lower curd firmness at 30 min. Changes in milk protein composition through an increase in the frequency of the A2 allele in the production process could lead to a worsening of the coagulation and curd firming traits.     

Short communication: Effect of αS1-casein (CSN1S1) and κ-casein (CSN3) genotypes on milk composition in Murciano-Granadina goats

The effects of the caprine α_S1-casein (CSN1S1) polymorphisms on milk quality have been widely demonstrated. However, much less is known about the consequences of the κ-casein (CSN3) genotype on milk composition in goats. Moreover, the occurrence of interactions between CSN3 and CSN1S1 genotypes has not been investigated. In this study, an association analysis between CSN1S1 and CSN3 genotypes and milk quality traits was performed in 89 Murciano-Granadina goats. Total milk yield as well as total protein, fat, solids-not-fat, lactose, α_S1-casein (CSN1S1), and α_S2-casein (CSN1S2) contents were recorded every other month during a whole lactation (316 observations). Data analysis using a linear mixed model for repeated observations revealed no interaction between the CSN1S1 and CSN3 genotypes. With regard to the effect of the CSN3 locus, AB and BB genotypes were significantly associated with higher levels of total casein and protein content compared with the AA CSN3 genotype. In strong contrast with French breeds, the CSN1S1 genotype did not affect protein, casein, and fat concentrations in Murciano-Granadina goats. These results highlight the importance of taking into consideration the CSN3 genotype when performing selection for milk composition in dairy goats.     

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 K₂₀ 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.     

Genome-wide scan for bovine milk-fat composition. I. Quantitative trait loci for short- and medium-chain fatty acids

A genome-wide scan was performed to identify quantitative trait loci (QTL) for short- and medium-chain fatty acids (expressed in wt/wt %). Milk samples were available from 1,905 cows from 398 commercial herds in the Netherlands, and milk-fat composition was measured by gas chromatography. DNA was available from 7 of the paternal half-sib families: 849 cows and their 7 sires. A genetic map was constructed comprising 1,341 SNP and 2,829 cM, with an average information content of 0.83. Multimarker interval mapping was used in an across-family regression on corrected phenotypes for the 7 half-sib families. Four QTL were found: on Bos taurus autosome (BTA) 6, a QTL was identified for C6:0 and C8:0; on BTA14, a QTL was identified for fat percentage, all odd-chain fatty acids, and C14:0, C16:0, C16:1, and their unsaturation indices; on BTA19, a QTL affected C14:0; and on BTA26, a QTL was identified for the monounsaturated fatty acids and their unsaturation indices. The QTL explained 3 to 19% of phenotypic variance. Furthermore, 49 traits with suggestive evidence for linkage were found on 21 chromosomes. Additional analyses revealed that the QTL on BTA14 was most likely caused by a mutation in DGAT1, whereas the QTL on BTA26 was most likely caused by a mutation in the SCD1 gene. Quantitative trait loci that affect specific fatty acids might increase the understanding of physiological processes regarding fat synthesis and the position of the causal genes.     

The occurrence of noncoagulating milk and the association of bovine milk coagulation properties with genetic variants of the caseins in 3 Scandinavian dairy breeds

Substantial variation in milk coagulation properties has been observed among dairy cows. Consequently, raw milk from individual cows and breeds exhibits distinct coagulation capacities that potentially affect the technological properties and milk processing into cheese. This variation is largely influenced by protein composition, which is in turn affected by underlying genetic polymorphisms in the major milk proteins. In this study, we conducted a large screening on 3 major Scandinavian breeds to resolve the variation in milk coagulation traits and the frequency of milk with impaired coagulation properties (noncoagulation). In total, individual coagulation properties were measured on morning milk collected from 1,299 Danish Holstein (DH), Danish Jersey (DJ), and Swedish Red (SR) cows. The 3 breeds demonstrated notable interbreed differences in coagulation properties, with DJ cows exhibiting superior coagulation compared with the other 2 breeds. In addition, milk samples from 2% of DH and 16% of SR cows were classified as noncoagulating. Furthermore, the cows were genotyped for major genetic variants in the α_S1- (CSN1S1), β- (CSN2), and κ-casein (CSN3) genes, revealing distinct differences in variant frequencies among breeds. Allele I of CSN2, which had not formerly been screened in such a high number of cows in these Scandinavian breeds, showed a frequency around 7% in DH and DJ, but was not detected in SR. Genetic polymorphisms were significantly associated with curd firming rate and rennet coagulation time. Thus, CSN1S1 C, CSN2 B, and CSN3 B positively affected milk coagulation, whereas CSN2 A², in particular, had a negative effect. In addition to the influence of individual casein genes, the effects of CSN1S1-CSN2-CSN3 composite genotypes were also examined, and revealed strong associations in all breeds, which more or less reflected the single gene results. Overall, milk coagulation is under the influence of additive genetic variation. Optimal milk for future cheese production can be ensured by monitoring the frequency of unfavorable variants and thus preventing an increase in the number of cows producing milk with impaired coagulation. Selective breeding for variants associated with superior milk coagulation can potentially increase raw milk quality and cheese yield in all 3 Scandinavian breeds.     

Detection of quantitative trait loci affecting the milk fatty acid profile on sheep chromosome 22: Role of the stearoyl-CoA desaturase gene in Spanish Churra sheep

Sheep milk fat contains several components that may provide human health benefits, such as monounsaturated fatty acids and conjugated linoleic acid (CLA). Most of the CLA in ruminant milk is synthesized in the mammary gland by the action of the enzyme stearoyl-CoA desaturase (SCD) on circulating vaccenic acid (trans-11 C18:2; VA). Previous studies have found significant associations between polymorphisms in the SCD gene and the fatty acid composition of ruminant products, including sheep milk. Based on this, we performed a quantitative trait loci (QTL) analysis of an ovine chromosome (22) that harbors the SCD gene for effects on milk fatty acid composition traits and classical milk production traits. We identified a suggestive QTL influencing the CLA/VA ratio with the maximum statistic at position 26 cM of the studied chromosome, whereas the SCD gene has been mapped to position 41.6 cM. The individual introduction of 4 SCD single nucleotide polymorphisms in the QTL model did not cause a reduction of the variance explained by the QTL, which suggests that the SCD gene is not directly responsible for the detected effect in the Churra population studied herein. This conclusion was supported by the lack of any significant association identified between the 4 SCD single nucleotide polymorphisms and the CLA/VA ratio. This association analysis suggested a possible effect of the SCD gene on milk fat percentage in Churra sheep. An independent confirmation of these primary results will be required before attempting its practical implementation in selection programs.     

Novel genetic variation associated to CSN3 strongly affects rennet-induced milk coagulation

The effect on rennet-induced milk coagulation of three novel genetic haplotypes in close proximity to CSN3 encoding κ-casein was evaluated. Milk samples were collected from 71 Danish Holstein cows homozygous for the three novel haplotypes named according to which genetic variants of CSN3 they were characterised by: AE, A and B, respectively. The results documented that haplotype AE had significantly longer rennet coagulation time and lower curd firming rate compared with haplotypes A and B. Haplotype AE milk was further characterised by larger casein micelles and lower relative content of κ-casein, whereas the total protein contents did not differ among haplotypes. These findings indicate that the genetic κ-casein A variant can be divided into two groups with poor and good milk coagulation properties. Furthermore, three milk samples were identified as non-coagulating. These were all associated with the haplotype AE.     

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.     

Effects of β-κ-casein (CSN2-CSN3) haplotypes and β-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 (β-κ-casein) haplotypes and BLG (β-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 β-lactoglobulin (β-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 κ-CN content and percentage of κ-CN to total CN and with decreased percentages of α_S1- and γ-CN to total CN. Allele CSN2 B had the effect of increasing β-CN content and decreasing content of α_S1-CN. Haplotypes including allele CSN2 A¹ exhibited decreased β-, α_S2-, and γ-CN concentrations and increased α_S1- and κ-CN contents, whereas CSN2 I had positive effects on β-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 β-LG concentration and percentage of β-LG to total whey protein and with decreased content of other milk proteins, namely β-CN and α_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.     

Relation between αS1-casein,genotype, and quality traits of milk from Swedish dairy goats

Locally produced food is becoming popular among Swedish consumers. One product that has increased in popularity is artisan-manufactured goat cheese, and although the dairy goat industry in Sweden is small-scale, production is gradually increasing. In goats, the CSN1S1 gene regulates expression of the protein α_S1-casein (α_S1-CN), which has been found to be important for cheese yield. Over the years, breeding animals have been imported to Sweden from Norway. Historically, a high frequency of the Norwegian goat population carried a polymorphism at the CSN1S1 gene. This polymorphism, called the Norwegian null allele (D), leads to zero or significantly reduced expression of α_S1-CN. Using milk samples from 75 goats, this study investigated associations between expression of α_S1-CN and genotype at the CSN1S1 gene on milk quality traits from Swedish Landrace goats. Milk samples were grouped according to relative level of α_S1-CN (low: 0–6.9% of total protein; medium-high: 7–25% of total protein) and genotype (DD, DG, DA/AG/AA). While the D allele leads to extremely low expression of α_S1-CN, the G allele is low expressing and the A allele is highly expressing for this protein. Principal component analysis was used to explore the total variation in milk quality traits. To evaluate the effect of different allele groups on milk quality attributes, 1-way ANOVA and Tukey pairwise comparison tests were used. The majority (72%) of all goat milk samples investigated showed relative α_S1-CN content of 0% to 6.82% of total protein. The frequency of individuals homozygous for the Norwegian null allele (DD) was 59% in the population of sampled goats, and only 15% carried at least one A allele. A low relative concentration of α_S1-CN was associated with lower total protein, higher pH, and higher relative concentration of β-casein and levels of free fatty acids. Milk from goats homozygous for the null allele (DD) showed a similar pattern as milk with low relative concentration of α_S1-CN, but total protein was only numerically lower, and somatic cell count and α_S2-CN were higher than for the other genotypes. The associations between levels of α_S1-CN and the investigated genotype at the CSN1S1 gene indicate a need for a national breeding program for Swedish dairy goats.     

Effects of composite beta- and kappa-casein genotypes on milk coagulation, quality, and yield traits in Italian Holstein cows

The aim of the study was to estimate the effect of the composite CSN2 and CSN3 genotypes on milk coagulation, quality, and yield traits in Italian Holstein cows. A total of 1,042 multiparous Holstein cows reared on 34 commercial dairy herds were sampled once, concurrently with monthly herd milk recording. The data included the following traits: milk coagulation time; curd firmness; pH and titratable acidity; fat, protein, and casein contents; somatic cell score; and daily milk, fat, and protein yields. A single-trait animal model was assumed with fixed effects of herd, days in milk, parity, composite casein genotype of CSN2 and CSN3 (CSN2-CSN3), and random additive genetic effect of an animal. The composite genotype of CSN2-CSN3 showed a strong effect on both milk coagulation traits and milk and protein yields, but not on fat and protein contents and other milk quality traits. For coagulation time, the best CSN2-CSN3 genotypes were those with at least one B allele in both the CSN2 and CSN3 loci. The CSN3 locus was associated more strongly with milk coagulation traits, whereas the CSN2 locus was associated more with milk and protein yields. However, because of the tight linkage between the 2 loci, the composite genotypes, or haplotypes, are more appropriate than the single-locus genotypes if they were considered for use in selection.     

Characterization of the DGAT1 mutations and the CSN1S1 promoter in the German Angeln dairy cattle population

The identification of quantitative trait loci (QTL) and genes with influence on milk production traits has been the objective of various mapping studies in the last decade. In the centromeric region of Bos taurus autosome (BTA) 14, the acyl-CoA:diacylglycerol acyltransferase1 gene (DGAT1) has been identified as the most likely causative gene underlying a QTL for milk fat yield and content. Recently, a second polymorphism in the promoter of DGAT1 emerged as an additional source of variation. In this study, the frequencies and the effects of alleles at the DGAT1 K232A and at the DGAT1 promoter variable number of tandem repeat (VNTR) locus on BTA14, and of alleles at the CSN1S1 (α_S1-casein-encoding gene) promoter on BTA6 in the German Angeln dairy cattle population were investigated. Analyzed traits were milk, fat, protein, lactose, and milk energy yield, fat, protein, lactose, and milk energy content and somatic cell score. The lysine variant of the DGAT1 K232A mutation showed significant effects for most of the milk production traits. A specific allele of the DGAT1 promoter VNTR showed significant effects on the traits lactose yield and content, milk energy content, and SCS compared with the other alleles. Additionally, a regulation mechanism between the DGAT1 K232A mutation and the DGAT1 promoter VNTR was found for fat yield and content, which could be caused by an upper physiological bound for the effects of the DGAT1 gene. At the CSN1S1 promoter, 2 of 4 alleles showed significant allele substitution effects on the milk yield traits.     

Short communication: Genome-wide scan for bovine milk-fat composition. II. Quantitative trait loci for long-chain fatty acids

We present the results of a genome-wide scan to identify quantitative trait loci (QTL) that contribute to genetic variation in long-chain milk fatty acids. Milk-fat composition phenotypes were available on 1,905 Dutch Holstein-Friesian cows. A total of 849 cows and their 7 sires were genotyped for 1,341 single nucleotide polymorphisms across all Bos taurus autosomes (BTA). We detected significant QTL on BTA14, BTA15, and BTA16: for C18:1 cis-9, C18:1 cis-12, C18:2 cis-9,12, CLA cis-9,trans-11, C18:3 cis-9,12,15, the C18 index, the total index, total saturated fatty acids, total unsaturated fatty acids (UFA), and the ratio of saturated fatty acids:unsaturated fatty acids on BTA14; for C18:1 trans fatty acids on BTA15; and for the C18 and CLA indices on BTA16. The QTL explained 3 to 19% of the phenotypic variance. Suggestive QTL were found on 16 other chromosomes. The diacylglycerol acyltransferase 1 (DGAT1) K232A polymorphism on BTA14, which is known to influence fatty acid composition, most likely explains the QTL that was detected on BTA14.     

Association of candidate gene polymorphisms with milk technological traits,yield, composition,and somatic cell score in Italian Holstein-Friesian sires

Advances in DNA-based marker technology have enabled the identification of genomic regions underlying complex phenotypic traits in livestock species. The incorporation of detected quantitative trait loci into genetic evaluation provides great potential to enhance selection accuracies, hence expediting the genetic improvement of economically important traits. The objective of the present study was to investigate 96 single nucleotide polymorphisms (SNP) located in 53 candidate genes previously reported to have effects on milk production and quality traits in a population of highly selected Holstein-Friesian bulls. A total of 423 semen samples were used to genotype the bulls through a custom oligo pool assay. Forty-five SNP in 32 genes were found to be associated with at least 1 of the tested traits. Most significant and favorable SNP trait associations were observed for polymorphisms located in CCL3 and AGPAT6 genes for fat yield (0.037 and 0.033 kg/d, respectively), DGKG gene for milk yield (0.698 kg/d), PPARGC1A, CSN1S1, and AGPAT6 genes for fat percentage (0.127, 0.113, and 0.093%, respectively), GHR gene for protein (0.064%) and casein percentage (0.053%), and TLR4 gene for fat (0.090%), protein (0.066%), and casein percentage (0.050%). Somatic cell score was favorably affected by GHR (?0.095) and POU1F1 (?0.137), and interesting SNP-trait associations were observed for polymorphisms located in CSN2, POU1F1, and AGPAT6 genes for rennet coagulation time (?0.592, ?0.558, and ?0.462 min, respectively), and GHR and CSN2 genes for curd firmness 30 min after rennet addition (1.264 and 1.183 mm, respectively). In addition to the influence of individual SNP, the effects of composite genotypes constructed by grouping SNP according to their individual effects on traits considered in the analysis were also examined. Favorable and significant effects on milk traits were observed for 2 composite genotypes, one including 10 SNP and the other 4 SNP. The former was associated with an increase of milk (0.075 kg/d), fat (0.097 kg/d), protein (0.083 kg/d), and casein yields (0.065 kg/d), and the latter was associated with an increase of fat (0.244%), protein (0.071%), and casein percentage (0.047%). Although further research is required to validate the identified SNP loci in other populations and breeds, our results can be considered as a preliminary foundation for further replication studies on gene-assisted selection programs.     

Short communication: Composition of milk protein and milk fatty acids is stable for cows differing in genetic merit for milk production

Changing the composition of milk protein and of milk fatty acids alters nutritional and physical properties of dairy products and their consumer appeal. Genetic selection for milk yield decreases concentrations of milk protein and of milk fat. Little is known, however, about how the decrease affects composition of milk protein and milk fatty acids. The objective of this study was to quantify changes in composition of milk protein and of milk fatty acids in cows differing in genetic merit for milk production. Three measures of genetic merit for milk production were used for each cow: genetic line, parent average predicted transmitting ability (PTA) for milk, and cow milk PTA. Composition of milk protein and milk fatty acids were compared in 448 milk samples from 178 cows representing 2 divergent lines of Holsteins that were bred for high or average PTA for milk and combined milk protein and fat yield. High-line cows (n = 97) produced more milk that contained less fat and had higher proportions of α_S1-casein in milk protein than did average-line cows (n = 81). We additionally obtained from 233 cows (178 cows representing the 2 genetic lines and 55 cows with ancestors from both genetic lines) the parent average milk PTA and cow milk PTA and compared composition of milk protein and of milk fatty acids in 592 milk samples. Cows whose parent average milk PTA was above or equal to the median of the 233 cows produced more milk that contained less protein and less fat and that tended to have greater proportions of α_S1-casein in milk protein than cows whose average milk PTA was below the median. Similarly, cows with above or equal median milk PTA of the 233 cows produced more milk that contained less protein and less fat and had greater proportions of α_S1- casein in milk protein than did cows with below-median milk PTA. Milk fatty acid composition was not consistently different between groups. Therefore, selection for milk yield decreased concentrations of milk protein and milk fat but had little effect on composition of milk protein and milk fatty acids.     

An attempt at predicting blood β-hydroxybutyrate from Fourier-transform mid-infrared spectra of milk using multivariate mixed models in Polish dairy cattle

Fourier transform mid-infrared (FT-MIR) spectra of milk are commonly used for phenotyping of traits of interest through links developed between the traits and milk FT-MIR spectra. Predicted traits are then used in genetic analysis for ultimate phenotypic prediction using a single-trait mixed model that account for cows' circumstances at a given test day. Here, this approach is referred to as indirect prediction (IP). Alternatively, FT-MIR spectral variable can be kept multivariate in the form of factor scores in REML and BLUP analyses. These BLUP predictions, including phenotype (predicted factor scores), were converted to single-trait through calibration outputs; this method is referred to as direct prediction (DP). The main aim of this study was to verify whether mixed modeling of milk spectra in the form of factors scores (DP) gives better prediction of blood β-hydroxybutyrate (BHB) than the univariate approach (IP). Models to predict blood BHB from milk spectra were also developed. Two data sets that contained milk FT-MIR spectra and other information on Polish dairy cattle were used in this study. Data set 1 (n = 826) also contained BHB measured in blood samples, whereas data set 2 (n = 158,028) did not contain measured blood values. Part of data set 1 was used to calibrate a prediction model (n = 496) and the remaining part of data set 1 (n = 330) was used to validate the calibration models, as well as to evaluate the DP and IP approaches. Dimensions of FT-MIR spectra in data set 2 were reduced either into 5 or 10 factor scores (DP) or into a single trait (IP) with calibration outputs. The REML estimates for these factor scores were found using WOMBAT. The BLUP values and predicted BHB for observations in the validation set were computed using the REML estimates. Blood BHB predicted from milk FT-MIR spectra by both approaches were regressed on reference blood BHB that had not been used in the model development. Coefficients of determination in cross-validation for untransformed blood BHB were from 0.21 to 0.32, whereas that for the log-transformed BHB were from 0.31 to 0.38. The corresponding estimates in validation were from 0.29 to 0.37 and 0.21 to 0.43, respectively, for untransformed and logarithmic BHB. Contrary to expectation, slightly better predictions of BHB were found when univariate variance structure was used (IP) than when multivariate covariance structures were used (DP). Conclusive remarks on the importance of keeping spectral data in multivariate form for prediction of phenotypes may be found in data sets where the trait of interest has strong relationships with spectral variables.     

Genetic parameters for cheese-making properties and milk composition predicted from mid-infrared spectra in a large data set of Montbéliarde cows

Cheese-making properties of pressed cooked cheeses (PCC) and soft cheeses (SC) were predicted from mid-infrared (MIR) spectra. The traits that were best predicted by MIR spectra (as determined by comparison with reference measurements) were 3 measures of laboratory cheese yield, 5 coagulation traits, and 1 acidification trait for PCC (initial pH; pH_0PPC). Coefficients of determination of these traits ranged between 0.54 and 0.89. These 9 traits as well as milk composition traits (fatty acid, protein, mineral, lactose, and citrate content) were then predicted from 1,100,238 MIR spectra from 126,873 primiparous Montbéliarde cows. Using this data set, we estimated the corresponding genetic parameters of these traits by REML procedures. A univariate or bivariate repeatability animal model was used that included the fixed effects of herd × test day × spectrometer, stage of lactation, and year × month of calving as well as the random additive genetic, permanent environmental, and residual effects. Heritability estimates varied between 0.37 and 0.48 for the 9 cheese-making property traits analyzed. Coagulation traits were the ones with the highest heritability (0.42 to 0.48), whereas cheese yields and pH_{0 PPC} had the lowest heritability (0.37 to 0.39). Strong favorable genetic correlations, with absolute values between 0.64 and 0.97, were found between different measures of cheese yield, between coagulation traits, between cheese yields and coagulation traits, and between coagulation traits measured for PCC and SC. In contrast, the genetic correlations between milk pH_{0 PPC} and CY or coagulation traits were weak (?0.08 to 0.09). The genetic relationships between cheese-making property traits and milk composition were moderate to high. In particular, high levels of proteins, fatty acids, Ca, P, and Mg in milk were associated with better cheese yields and improved coagulation. Proteins in milk were strongly genetically correlated with coagulation traits and, to a lesser extent, with cheese yields, whereas fatty acids in milk were more genetically correlated with cheese yields than with coagulation traits. This study, carried out on a large scale in Montbéliarde cows, shows that MIR predictions of cheese yields and milk coagulation properties are sufficiently accurate to be used for genetic analyses. Cheese-making traits, as predicted from MIR spectra, are moderately heritable and could be integrated into breeding objectives without additional phenotyping cost, thus creating an opportunity for efficient improvement via selection.     

Effects of the CSN1A(G) allele on the clotting time of cow milk and on the rheological properties of rennet-curd

The aim of this research was to study the effects of the CSN1A(G) allele on the main rennet coagulation properties of milk. The study was carried out on individual milk samples with low alphas1-casein obtained from 19 Italian Brown cows heterozygous for the CSN1A(G) allele (seventeen CSN1A BG and two CSN1A CG) from four herds in the province of Parma (Italy). Control cows (sixteen CSN1A BB and three CSN1A BC) giving milk with normal alphas1-casein levels were chosen from within the same herds in order to establish pairs of cows with identical environment and management conditions, and comparable lactation stages and numbers. Individual milk samples from single pairs of cows with somatic cell counts and lactose and chloride levels within the normal ranges were collected and analysed in parallel. Rennet coagulation properties of milk were analysed using Formagraph and Gel Tester. Milk from low alphas1-casein cows was characterized by lower casein content, lower titratable acidity and a higher proportion of kappa-casein in total casein. The clotting time of this milk was approximately 23% lower than that obtained with milk from normal alphas1-casein cows. Rennet curd from low alphas1-casein milk was obtained more rapidly and had a higher final firmness: curd-firming time was approximately 35% lower and curd firmness measured 30 min after rennet addition was approximately 27% higher compared with that for normal alphas1-casein milk. In addition, curd from low alphas1-casein milk had a higher resistance to compression. These results suggest that, although a role for the CSN2 locus cannot be definitely excluded, the CSN1A(G) allele can considerably affect the main rennet coagulation properties of milk.