Multi-population GWAS and enrichment analyses reveal novel genomic regions and promising candidate genes underlying bovine milk fatty acid composition.

BACKGROUND: The power of genome-wide association studies (GWAS) is often limited by the sample size available for the analysis. Milk fatty acid (FA) traits are scarcely recorded due to expensive and time-consuming analytical techniques. Combining multi-population datasets can enhance the power of GWAS enabling detection of genomic region explaining medium to low proportions of the genetic variation. GWAS often detect broader genomic regions containing several positional candidate genes making it difficult to untangle the causative candidates. Post-GWAS analyses with data on pathways, ontology and tissue-specific gene expression status might allow prioritization among positional candidate genes. RESULTS: Multi-population GWAS for 16 FA traits quantified using gas chromatography (GC) in sample populations of the Chinese, Danish and Dutch Holstein with high-density (HD) genotypes detects 56 genomic regions significantly associated to at least one of the studied FAs; some of which have not been previously reported. Pathways and gene ontology (GO) analyses suggest promising candidate genes on the novel regions including OSBPL6 and AGPS on Bos taurus autosome (BTA) 2, PRLH on BTA 3, SLC51B on BTA 10, ABCG5/8 on BTA 11 and ALG5 on BTA 12. Novel genes in previously known regions, such as FABP4 on BTA 14, APOA1/5/7 on BTA 15 and MGST2 on BTA 17, are also linked to important FA metabolic processes. CONCLUSION: Integration of multi-population GWAS and enrichment analyses enabled detection of several novel genomic regions, explaining relatively smaller fractions of the genetic variation, and revealed highly likely candidate genes underlying the effects. Detection of such regions and candidate genes will be crucial in understanding the complex genetic control of FA metabolism. The findings can also be used to augment genomic prediction models with regions collectively capturing most of the genetic variation in the milk FA traits.

Combining multi-population datasets for joint genome-wide association and meta-analyses: The case of bovine milk fat composition traits.

In genome-wide association studies (GWAS), sample size is the most important factor affecting statistical power that is under control of the investigator, posing a major challenge in understanding the genetics underlying difficult-to-measure traits. Combining data sets available from different populations for joint or meta-analysis is a promising alternative to increasing sample sizes available for GWAS. Simulation studies indicate statistical advantages from combining raw data or GWAS summaries in enhancing quantitative trait loci (QTL) detection power. However, the complexity of genetics underlying most quantitative traits, which itself is not fully understood, is difficult to fully capture in simulated data sets. In this study, population-specific and combined-population GWAS as well as a meta-analysis of the population-specific GWAS summaries were carried out with the objective of assessing the advantages and challenges of different data-combining strategies in enhancing detection power of GWAS using milk fatty acid (FA) traits as examples. Gas chromatography (GC) quantified milk FA samples and high-density (HD) genotypes were available from 1,566 Dutch, 614 Danish, and 700 Chinese Holstein Friesian cows. Using the joint GWAS, 28 additional genomic regions were detected, with significant associations to at least 1 FA, compared with the population-specific analyses. Some of these additional regions were also detected using the implemented meta-analysis. Furthermore, using the frequently reported variants of the diacylglycerol acyltransferase 1 (DGAT1) and stearoyl-CoA desaturase (SCD1) genes, we show that significant associations were established with more FA traits in the joint GWAS than the remaining scenarios. However, there were few regions detected in the population-specific analyses that were not detected using the joint GWAS or the meta-analyses. Our results show that combining multi-population data set can be a powerful tool to enhance detection power in GWAS for seldom-recorded traits. Detection of a higher number of regions using the meta-analysis, compared with any of the population-specific analyses also emphasizes the utility of these methods in the absence of raw multi-population data sets to undertake joint GWAS.

Genome-wide association study for αS1- and αS2-casein phosphorylation in Dutch Holstein Friesian.

Phosphorylation of caseins (CN) is a crucial post-translational modification that allows caseins to form colloid particles known as casein micelles. Both αS1- and αS2-CN show varying degrees of phosphorylation (isoforms) in cow milk and were suggested to be more relevant for stabilizing internal micellar structure than ß- and κ-CN. However, little is known about the genetic background of individual αS2-CN phosphorylation isoforms and the phosphorylation degrees of αS1- and αS2-CN (αS1-CN PD and αS2-CN PD), defined as the proportion of isoforms with higher degrees of phosphorylation in total αS1- and αS2-CN, respectively. We aimed to identify genomic regions associated with these traits using 50K single nucleotide polymorphisms for 1,857 Dutch Holstein Friesian cows. A total of 10 quantitative trait loci (QTL) regions were identified for all studied traits on 10 Bos taurus autosomes (BTA1, 2, 6, 9, 11, 14, 15, 18, 24, and 28). Regions associated with multiple traits were found on BTA1, 6, 11, and 14. We showed 2 QTL regions on BTA1, one affecting αS2-CN production and the other harboring the SLC37A1 gene, which encodes a phosphorus antiporter and affects αS1- and αS2-CN PD. The QTL on BTA6 harbors the casein gene cluster and affects individual αS2-CN phosphorylation isoforms. The QTL on BTA11 harbors the PAEP gene that encodes for ß-lactoglobulin and affects relative concentrations of αS2-CN-10P and αS2-CN-11P as well as αS1-CN PD and αS2-CN PD. The QTL on BTA14 harbors the DGAT1 gene and affects relative concentrations of αS2-CN-10P and αS2-CN-11P as well as αS1-CN PD and αS2-CN PD. Our results suggest that effects of identified genomic regions on phosphorylation of αS1- and αS2-CN are related to changes in milk synthesis and phosphorus secretion in milk. The actual roles of SLC37A1, PAEP, and DGAT1 in αS1- and αS2-CN phosphorylation in Dutch Holstein Friesian require further investigation.

Genetic parameters for αS1-casein and αS2-casein phosphorylation isoforms in Dutch Holstein Friesian.

Relative concentrations of αS1-casein and αS2-casein (αS1-CN and αS2-CN) phosphorylation isoforms vary considerably among milk of individual cows. We estimated heritabilities for αS2-CN phosphorylation isoforms, determined by capillary zone electrophoresis from 1,857 morning milk samples, and genetic correlations among αS2-CN phosphorylation isoforms in Dutch Holstein Friesian. To investigate if phosphorylation of αS1-CN and αS2-CN are due to the same genetic mechanism, we also estimated genetic correlations between αS1-CN and αS2-CN phosphorylation isoforms as well as the genetic correlations between the phosphorylation degrees (PD) of αS1-CN and αS2-CN defined as the proportion of isoforms with higher degrees of phosphorylation in total αS1-CN and αS2-CN, respectively. The intra-herd heritabilities for the relative concentrations of αS2-CN phosphorylation isoforms were high and ranged from 0.54 for αS2-CN-10P to 0.89 for αS2-CN-12P. Furthermore, the high intra-herd heritabilities of αS1-CN PD and αS2-CN PD imply a strong genetic control of the phosphorylation process, which is independent of casein production. The genetic correlations between αS2-CN phosphorylation isoforms are positive and moderate to high (0.33-0.90). Furthermore, the strong positive genetic correlation (0.94) between αS1-CN PD and αS2-CN PD suggests that the phosphorylation processes of αS1-CN and αS2-CN are related. This study shows the possibility of breeding for specific αS1-CN and αS2-CN phosphorylation isoforms, and relations between the phosphorylation degrees of αS1-CN and αS2-CN and technological properties of milk need to be further investigated to identify potential benefits for the dairy industry.

Milk fat triacylglycerols and their relations with milk fatty acid composition, DGAT1 K232A polymorphism, and milk production traits.

Milk fat (MF) triacylglycerols (TAG) determine the physical and functional properties of butter and products rich in MF. To predict these properties, it is necessary to understand the variability of fatty acids, TAG, their associations, and their effect on milk productive traits, days in milk (DIM), and genes related to fat synthesis. Therefore, the aim of this research was to study the variability of TAG using MF from individual cows and to investigate the effect of fatty acid composition, DGAT1 K232A polymorphism, DIM, and milk production traits (fat content and morning milk yield) on MF TAG profile in the Dutch Holstein-Friesian dairy cattle population. Large differences in MF TAG profiles were seen among cows. We showed that the variability of TAG is highest for low and high molecular weight TAG [TAG with carbon numbers (Cn) 26-30 and Cn52-54, respectively] and lowest for TAG with Cn38, which was the most abundant TAG. Saturation index (saturated fatty acid/unsaturated fatty acid) and the ratio C16:0/C18:1 cis-9 showed significant effects on TAG Cn34, Cn36, Cn52, and Cn54: TAG Cn34 and Cn36 increased as the saturation index and ratio C16:0/C18:1 cis-9 increased, whereas the opposite was seen for TAG Cn52 and Cn54. Moreover, the DGAT1 K232A polymorphism significantly affected TAG Cn38. We showed that the relative concentration of TAG with Cn38 was higher in cows with DGAT1 KK genotype. Production traits (fat content and morning milk yield) and DIM had no significant effect on TAG profile. This is a relevant observation because considerable increases of milk yield and fat content have been seen in the Netherlands over the last 60 yr. The large differences shown between individual cows in MF TAG profile imply differences in physical properties of MF.

Effects of the diacylglycerol o-acyltransferase 1 (DGAT1) K232A polymorphism on fatty acid, protein, and mineral composition of dairy cattle milk.

Several studies have described associations between the diacylglycerol o-acyltransferase 1 (DGAT1) K232A polymorphism and routinely collected milk production traits but not much is known about effects of the DGAT1 polymorphism on detailed milk composition. The aim of this study was to estimate effects of the DGAT1 polymorphism on milk fatty acid, protein, and mineral composition. We looked for effects that were significant and consistent in Danish Holstein Friesian (HF), Danish Jersey, and Dutch HF as these are likely to be true effects of the DGAT1 K232A polymorphism rather than being effects of linked loci. For fatty acid composition, significant and consistent effects of the DGAT1 polymorphism were detected on C14:0, C16:0, C15:0, C16:1, C18:1 cis-9, conjugated linoleic acid (CLA) cis-9,trans-11, C18:2 cis-9,cis-12, and C18:3 cis-9,cis-12,cis-15 content (percent by weight, wt/wt %). For C16:0, C16:1, and C18:1 cis-9, the DGAT1 polymorphism explained more than 10% of the phenotypic variation. Significant effects on milk protein composition in Dutch HF could not be confirmed in Danish Jersey or Danish HF. For mineral content, significant and consistent effects of the DGAT1 polymorphism on calcium, phosphorus, and zinc were detected. In the Dutch HF population, the contribution of the DGAT1 K232A polymorphism to phenotypic variance was 12.0% for calcium, 8.3% for phosphorus, and 6.1% for zinc. Different from effects on fatty acid composition, effects of the DGAT1 polymorphism on yields of long-chain fatty acids C18:1 cis-9, CLA cis-9,trans-11, C18:2 cis-9,cis-12, and C18:3 cis-9,cis-12,cis-15 were not significant. This indicates that effects of DGAT1 on these fatty acids are indirect, not direct, effects: DGAT1 affects de novo synthesis of fatty acids and, consequently, the contribution of the long-chain fatty acids to total fat is decreased. In addition, effects of the DGAT1 polymorphism on yields of Ca, P, and Zn were not significant, which indicates that effects on these minerals are the result of indirect rather than direct effects of DGAT1: effects on calcium, phosphorus, and zinc content can be explained by effects of DGAT1 on milk volume. The reported effects of the DGAT1 polymorphism on fatty acid and mineral composition of milk are substantial and therefore relevant for milk quality.

Effects of the DGAT1 polymorphism on test-day milk production traits throughout lactation.

Several studies have shown that the diacylglycerol O-acyltransferase 1 (DGAT1) K232A polymorphism has a major effect on milk production traits. It is less clear how effects of DGAT1 on milk production traits change throughout lactation, if dominance effects of DGAT1 are relevant, and whether DGAT1 also affects lactose content, lactose yield, and total energy output in milk. Results from this study, using test-day records of 3 subsequent parities of around 1,800 cows, confirm previously reported effects of the DGAT1 polymorphism on milk, fat, and protein yield, as well as fat and protein content. In addition, we found significant effects of the DGAT1 polymorphism on lactose content and lactose yield. No significant effects on somatic cell score were detected. The effect of DGAT1 on total energy excreted in milk was only significant in parity 1 and is mainly due to a higher energy output in milk of heterozygous cows. Significant but relatively small dominance effects of DGAT1 on fat content and yield were detected, which are of little practical relevance. Significant DGAT1 by lactation stage interaction was detected for milk yield, lactose yield, fat content, and protein content, indicating that the effect of the DGAT1 polymorphism changes during lactation. In general, the DGAT1 effect shows a large increase during early lactation (from the start of lactation to d 50 to 150) and tends to decrease later in lactation. No DGAT1 by lactation stage interaction for fat yield was observed. Similar to DGAT1, effects of other genes also might vary throughout lactation and, therefore, using longitudinal models is recommended.

Phosphorylation of αS1-casein is regulated by different genes.

Casein phosphorylation is a posttranslational modification catalyzed by kinase enzymes that attach phosphate groups to specific AA in the protein sequence. This modification is one of the key factors responsible for the stabilization of calcium phosphate nanoclusters in casein micelles and for the internal structure of the casein micelles. α(S1)-Casein (α(s1)-CN) is of special interest because it constitutes up to 40% of the total casein fraction in milk, and it has 2 common phosphorylation states, with 8 (α(S1)-CN-8P) and 9 (α(S1)-CN-9P) phosphorylated serine residues. Factors affecting this variation in the degree of phosphorylation are not currently known. The objective of this research was to determine the genetic background of α(S1)-CN-8P and α(S1)-CN-9P. The genetic and phenotypic correlation between α(S1)-CN-8P and α(S1)-CN-9P was low (0.18 and 0.19, respectively). This low genetic correlation suggests a different genetic background. These differences were further investigated by means of a genome-wide association study, which showed that both α(S1)-CN-8P and α(S1)-CN-9P were affected by a region on Bos taurus autosome (BTA) 6, but only α(S1)-CN-8P was affected by a region on BTA11 that contains the gene that encodes for ß-lactoglobulin (ß-LG), and only α(S1)-CN-9P was affected by a region on BTA14 that contains the diacylglycerol acyltransferase 1 (DGAT1) gene. Estimated effects of ß-LG protein genotypes showed that only α(S1)-CN-8P was associated with the ß-LG A/B polymorphism (g.1772G>A and g.3054C>T); the AA genotype of ß-LG was associated with a lower concentration of α(S1)-CN-8P (-0.32% wt/wt) than the BB genotype (+0.41% wt/wt). Estimated effects of DGAT1 K232A genotypes showed that only α(S1)-CN-9P was associated with the DGAT1 gene polymorphism; DGAT1 AA genotype was associated with a higher α(S1)-CN-9P concentration (+0.53% wt/wt) than the DGAT1 KK genotype (-0.44% wt/wt). The results give insight in phosphorylation of α(S1)-CN-8P and α(S1)-CN-9P, which seem to be regulated by a different set of genes.

Influence of C16:0 and long-chain saturated fatty acids on normal variation of bovine milk fat triacylglycerol structure.

Fatty acids (FA) are nonrandomly distributed within milk fat triacylglycerols (TAG). Moreover, the structure of milk fat TAG differs with feeding regimens. So far, nothing is known about the variation of milk fat TAG structure among individual cows. A deep understanding of the normal variation of TAG structures and the relationships between milk fat FA profile and its TAG structure could help to better control functional and compositional differences between milk fats from various sources and to increase the knowledge on milk fat synthesis. The focus of the present study was to determine the regiospecific TAG structure of individual samples of winter milk fat from Dutch Holstein-Friesian cows with a wide variation of FA profiles and with 2 diacylglycerol acyltransferase 1 (DGAT1) genotypes: DGAT1 K232A genotype AA and DGAT1 K232A genotype KK. From an initial set of 1,918 individual milk fat samples, 24 were selected. The selected samples had a wide range of FA composition and had either DGAT1 K232A genotype AA or KK. The structure analysis was done with a regiospecific approach. This analysis is based on the acyl degradation of TAG by a Grignard reagent and further isolation of sn-2 monoacylglycerols by thin-layer chromatography. An intra- and interpositional approach was used to study the structural variation. With the intrapositional approach, the amount of an FA at the secondary (sn-2) and primary (sn-1,3) positions was related to its total amount in the TAG. With the interpositional approach, the proportion of C8:0, C10:0, C14:1 cis-9, C16:1 cis-9, and C18:1 cis-9 at sn-2 was positively correlated with the amount of C16:0 in the triacylglycerol; in contrast, saturated C14:0, C16:0, and long-chain saturated FA (C14:0-C18:0) were negatively correlated. These observations suggest that the amount of long-chain saturated FA in TAG influences the positioning of other FA in the TAG. With an interpositional approach, the DGAT1 polymorphism had a significant effect on the proportional positioning of C16:0 at sn-2. These results provide a new direction to controlling functional and compositional differences between milk fats.

Concentrations of n-3 and n-6 fatty acids in Dutch bovine milk fat and their contribution to human dietary intake.

Weekly samples representative of Dutch milk were analyzed for concentrations of n-3 and n-6 fatty acids (FA). Concentrations of the n-3 FA α-linolenic acid (ALA), eicosatetraenoic acid, eicosapentaenoic acid, and docosapentaenoic acid were 0.495±0.027, 0.041±0.004, 0.067±0.005, and 0.086±0.008g per 100g of fat, respectively, whereas docosahexaenoic acid was absent or present in concentrations lower than 0.020g per 100g of fat. Concentrations of the n-6 FA linoleic acid (LeA), γ-linoleic acid, dihomo-γ-linoleic acid, and arachidonic acid were 1.428±0.068, 0.070±0.007, 0.066±0.004, and 0.089±0.004g per 100g of fat, respectively; adrenic acid was present in concentrations lower than 0.020g per 100g of fat, whereas docosapentaenoic acid was absent in all samples. The concentrations of ALA and LeA were significantly higher in spring and summer, compared with autumn and winter. The concentrations of all other ALA- and LeA-derived n-3 and n-6 FA were not significantly different between seasons. The contribution of milk fat to the daily intake of eicosapentaenoic acid, docosapentaenoic acid and docosahexaenoic acid was calculated for human consumption levels in different countries. Milk fat contributed between 10.7 and 14.1% to the daily intake of eicosapentaenoic acid and between 23.5 and 34.2% to the intake of docosapentaenoic acid; whereas docosahexaenoic acid contribution was marginal. Arachidonic acid from milk fat contributed between 10.5 and 18.8% to the human intake of n-6 FA.

Protein, casein, and micellar salts in milk: current content and historical perspectives.

The protein and fat content of Dutch bulk milk has been monitored since the 1950s and has increased considerably, by 11 and 20%, respectively, whereas milk yield has more than doubled. The change in protein and fat content of milk is advantageous for the dairy industry, as these are the 2 most economically valuable constituents of milk. Increases in protein and fat content of milk have allowed increases in the yield of various products such as cheese and butter. However, for cheese and other applications where casein micelles play a crucial role in structure and stability, it is not only casein content, but also the properties of the casein micelles that determine processability. Of particular importance herein is the salt partition in milk, but it is unknown whether increased protein content has affected the milk salts and their distribution between casein micelles and milk serum. It was, therefore, the objective of this research to determine the salt composition and protein content for individual cow milk and bulk milk over a period of 1 yr and to compare these data to results obtained during the 1930s, 1950s, and 1960s in the last century. Calcium, magnesium, sodium, potassium, and phosphorus content were determined by inductively coupled plasma atomic emission spectrometry and inorganic phosphate, citrate, chloride, and sulfate content by anion-exchange chromatography in bulk milk and milk ultracentrifugate. In addition, ionic calcium and ionic magnesium concentration were determined by the Donnan membrane technique. We concluded that historical increase in milk yield and protein content in milk have resulted in correlated changes in casein content and the micellar salt fraction of milk. In addition, the essential nutrients, calcium, magnesium, and phosphorus in milk have increased the past 75yr; therefore, the nutritional value of milk has improved.

Linseed oil supplementation and DGAT1 K232A polymorphism affect the triacylglycerol composition and crystallization of milk fat.

We studied the effect of dietary linseed oil (LSO) supplementation and DGAT1 K232A (DGAT1) polymorphism on the triacylglycerol composition and crystallization of bovine milk fat. LSO supplementation increased unsaturated triacylglycerols, notably in the C52-C54 carbon range, while reducing the saturated C29-C49 triacylglycerols. These changes were associated with an increase in the low-melting fraction and the crystal lamellar thickness, as well as a reduction in the medium and high-melting fractions and the formation of the most abundant crystal type at 20 °C (ß'-2 polymorph). Furthermore, DGAT1 KK was associated with higher levels of odd-chain saturated triacylglycerols than DGAT1 AA, and it was also associated with an increase in the high-melting fraction and the endset melting temperature. An interaction between diet and DGAT1 for the unsaturated C54 triacylglycerols accentuated the effects of LSO supplementation with DGAT1 AA. These findings show that genetic polymorphism and cows' diet can have considerable effects on milk fat properties.

Short communication: A new bovine milk-protein variant: α-lactalbumin variant D.

Capillary zone electrophoresis of 1,948 Holstein-Friesian cows suggested the presence of an unknown protein variant of α-lactalbumin (α-LA) in the milk of 1 cow. Sequencing genomic DNA of this cow showed a polymorphism in the α-LA gene (LAA) that appears to be responsible for this protein variant. This single nucleotide polymorphism g.600G > T was located in exon 2 of LAA and causes the amino acid change 65Gln > His in the α-LA protein. This α-LA protein variant is a new protein variant and should be called α-LA protein variant D. This amino acid change is not expected to affect protein function. Genomic DNA of 156 bulls of various dairy cattle breeds was screened to examine the presence of the new α-LA protein variant D. Single nucleotide polymorphism g.600G > T, responsible for α-LA protein variant D, was not found in any of the 156 bulls. However, 10 other polymorphisms in the coding and promoter regions of LAA were detected that were used to construct haplotypes.

Association of bovine ß-casein protein variant I with milk production and milk protein composition.

The aim of this study was to detect new polymorphisms in the bovine ß-casein (ß-CN) gene and to evaluate association of (new) ß-CN protein variants with milk production traits and milk protein composition. Screening of the ß-CN gene in genomic DNA from 72 Holstein Friesian (HF) bulls resulted in detection of 19 polymorphisms and revealed the presence of ß-CN protein variant I in the Dutch HF population. Studies of association of ß-CN protein variants with milk composition usually do not discriminate protein variant I from variant A2. Association of ß-CN protein variants with milk composition was studied in 1857 first-lactation HF cows and showed that associations of protein variants A2 and I were quite different for several traits. ß-CN protein variant I was significantly associated with protein percentage and protein yield, and with αs1 -casein (αs1 -CN), αs2 -casein (αs2 -CN), κ-casein (κ-CN), α-lactalbumin (α-LA), ß-lactoglobulin (ß-LG), casein index and casein yield. Inferring ß-κ-CN haplotypes showed that ß-CN protein variant I occurred only with κ-CN variant B. Consequently, associations of ß-κ-CN haplotype IB with protein percentage, κ-CN, α-LA, ß-LG and casein index are likely resulting from associations of κ-CN protein variant B, while associations of ß-κ-CN haplotype IB with αs1 -CN and αs2 -CN seem to be resulting from associations of ß-CN variant I.

Seasonal variation in the Dutch bovine raw milk composition.

In this study, we determined the detailed composition of and seasonal variation in Dutch dairy milk. Raw milk samples representative of the complete Dutch milk supply were collected weekly from February 2005 until February 2006. Large seasonal variation exists in the concentrations of the main components and milk fatty acid composition. Milk lactose concentration was rather constant throughout the season. Milk true protein content was somewhat more responsive to season, with the lowest content in June (3.21 g/100 g) and the highest content in December (3.38 g/100 g). Milk fat concentration increased from a minimum of 4.10 g/100 g in June to a maximum of 4.57 g/100 g in January. The largest (up to 2-fold) seasonal changes in the fatty acid composition were found for trans fatty acids, including conjugated linoleic acid. Milk protein composition was rather constant throughout the season. Milk unsaturation indices, which were used as an indication of desaturase activity, were lowest in spring and highest in autumn. Compared with a previous investigation of Dutch dairy milk in 1992, the fatty acid composition of Dutch raw milk has changed considerably, in particular with a higher content of saturated fatty acids in 2005 milk.

The influence of incubation on the formation of volatile bacterial metabolites in mastitis milk.

The possibility to detect mastitis-causing pathogens based on their volatile metabolites was previously studied. In that study, the mastitis samples were incubated overnight. To minimize the total analysis time, no incubation, or a short incubation, would be preferred. We therefore investigated the effect of the incubation time on the formation of volatile metabolites in mastitis samples. A selection of 6 volatile metabolites with the highest impact on the prediction model for identifying the mastitis-causing pathogen, was compared at different incubation times between 0 and 24 h. Identification of the pathogens was not possible without incubation. The minimum incubation time for detection of most of the 6 metabolites was 4 to 8 h. Although a longer incubation time increased the differences between pathogens, after 8 h all metabolites could be detected and the pathogens could be differentiated. Eight hours was therefore selected as the optimal incubation time. This optimal incubation time was evaluated with a set of 25 mastitis samples, of which 88% were correctly classified after 8 h of incubation. The total analysis time for this method is therefore considerably shorter than current microbiological culturing.

Predicting bovine milk fat composition using infrared spectroscopy based on milk samples collected in winter and summer.

It has recently been shown that Fourier transform infrared spectroscopy has potential for the prediction of detailed milk fat composition, even based on a limited number of observations. Therefore, there seems to be an opportunity for improvement by means of using more observations. The objective of this study was to verify whether the use of more data would add to the accuracy of predicting milk fat composition. In addition, the effect of season on modeling was quantified because large differences in milk fat composition between winter and summer samples exist. We concluded that the use of 3,622 observations does increase predictability of milk fat composition based on infrared spectroscopy. However, for fatty acids with low concentrations, the use of many observations does not increase predictability to a level at which application of the model becomes obvious. Furthermore, the effect of season on validation r-square was limited but was occasionally large on prediction bias. For fatty acids that show large differences in level and standard deviation between winter and summer, a representative sample that includes observations collected in various seasons is critical for unbiased prediction. This research shows that all major fatty acids, combined groups of fatty acids, and the ratio of saturated to unsaturated fatty acids can be predicted accurately.

Genetic parameters for major milk proteins in Dutch Holstein-Friesians.

The objective of this study was to estimate genetic parameters for major milk proteins. One morning milk sample was collected from 1,940 first-parity Holstein-Friesian cows in February or March 2005. Each sample was analyzed with capillary zone electrophoresis to determine the relative concentrations of the 6 major milk proteins. The results show that there is considerable genetic variation in milk protein composition. The intraherd heritabilities for the relative protein concentrations were high and ranged from 0.25 for beta-casein to 0.80 for beta-lactoglobulin. The intraherd heritability for the summed whey fractions (0.71) was higher than that for the summed casein fractions (0.41). Further, there was relatively more variation in the summed whey fraction (coefficient of variation was 11% and standard deviation was 1.23) compared with the summed casein fraction (coefficient of variation was 2% and standard deviation was 1.72). For the caseins and alpha-lactalbumin, the proportion of phenotypic variation explained by herd was approximately 14%. For beta-lactoglobulin, the proportion of phenotypic variation explained by herd was considerably lower (5%). Eighty percent of the genetic correlations among the relative contributions of the major milk proteins were between -0.38 and +0.45. The genetic correlations suggest that it is possible to change the relative proportion of caseins in milk. Strong negative genetic correlations were found for beta-lactoglobulin with the summed casein fractions (-0.76), and for beta-lactoglobulin with casein index (-0.98). This study suggests that there are opportunities to change the milk protein composition in the cow's milk using selective breeding.

Effects of milk protein variants on the protein composition of bovine milk.

The effects of beta-lactoglobulin (beta-LG), beta-casein (beta-CN), and kappa-CN variants and beta-kappa-CN haplotypes on the relative concentrations of the major milk proteins alpha-lactalbumin (alpha-LA), beta-LG, alpha(S1)-CN, alpha(S2)-CN, beta-CN, and kappa-CN and milk production traits were estimated in the milk of 1,912 Dutch Holstein-Friesian cows. We show that in the Dutch Holstein-Friesian population, the allele frequencies have changed in the past 16 years. In addition, genetic variants and casein haplotypes have a major impact on the protein composition of milk and explain a considerable part of the genetic variation in milk protein composition. The beta-LG genotype was associated with the relative concentrations of beta-LG (A >> B) and of alpha-LA, alpha(S1)-CN, alpha(S2)-CN, beta-CN, and kappa-CN (B > A) but not with any milk production trait. The beta-CN genotype was associated with the relative concentrations of beta-CN and alpha(S2)-CN (A(2) > A(1)) and of alpha(S1)-CN and kappa-CN (A(1) > A(2)) and with protein yield (A(2) > A(1)). The kappa-CN genotype was associated with the relative concentrations of kappa-CN (B > E > A), alpha(S2)-CN (B > A), alpha-LA, and alpha(S1)-CN (A > B) and with protein percentage (B > A). Comparing the effects of casein haplotypes with the effects of single casein variants can provide better insight into what really underlies the effect of a variant on protein composition. We conclude that selection for both the beta-LG genotype B and the beta-kappa-CN haplotype A(2)B will result in cows that produce milk that is more suitable for cheese production.

Genetic parameters for major milk fatty acids and milk production traits of Dutch Holstein-Friesians.

The objective of this study was to estimate genetic parameters for major milk fatty acids and milk production traits. One morning milk sample was collected from 1,918 Holstein-Friesian heifers located in 398 commercial herds in The Netherlands. Each sample was analyzed for total percentages of fat and protein, and for detailed fatty acid percentages (computed as fatty acid weight as a proportion of total fat weight). Intraherd heritabilities were high for C4:0 to C16:0, ranging from 0.42 for C4:0 to 0.71 for C10:0. Saturated and unsaturated C18 fatty acids had intraherd heritability estimates of approximately 0.25, except for C18:2 cis-9, trans-11, which was 0.42. Standard errors of the heritabilities were between 0.07 and 0.12. Genetic correlations were high and positive among C4:0 to C14:0, as well as among unsaturated C18, but correlations of C4:0 to C14:0 with unsaturated C18 were generally weak. The genetic correlation of C16:0 with fat percentage was positive (0.65), implying that selection for fat percentage should result in a correlated increase of C16:0, whereas unsaturated C18 fatty acids decreased with increasing fat percentage (-0.74). Milk fat composition can be changed by means of selective breeding, which offers opportunities to meet consumer demands regarding health and technological aspects.