Correlation between breeding values for milk fatty acids and Nordic Total Merit index traits for Danish Holstein and Danish Jersey.

Milk fatty acid composition is gaining interest in the Danish dairy industry both to develop new dairy products and as a management tool. To be able to implement milk fatty acid (FA) composition in the breeding program, it is important to know the correlations with the traits in the breeding goal. To estimate these correlations, we measured milk fat composition in Danish Holstein (DH) and Danish Jersey (DJ) cattle breeds using mid-infrared spectroscopy. Breeding values were estimated for specific FA and for groups of FA. Correlations with the estimated breeding values (EBV) underlying the Nordic Total Merit index (NTM) were calculated within breed. For both DH and DJ, we showed that FA EBV had moderate correlations with the NTM and production traits. For both DH and DJ, the correlation of FA EBV and NTM were in the same direction, except for C16:0 (0 in DH, 0.23 in DJ). A few correlations differed between DH and DJ. The correlation between claw health index and C18:0 was negative in DH (-0.09) but positive in DJ (0.12). In addition, some correlations were not significant in DH but were significant in DJ. The correlations between udder health index and long-chain FA, trans FA, C16:0, and C18:0 were not significant in DH (-0.05 to 0.02), but were significant in DJ (-0.17, -0.15, 0.14, and -0.16, respectively). For both DH and DJ, the correlations between FA EBV and nonproduction traits were low. This implies that it is possible to breed for a different fat composition in the milk without affecting the nonproduction traits in the breeding goal.

Estimation of heritability for milk urea and genetic correlations with milk production traits in 3 Danish dairy breeds.

The aim of this study was to estimate genetic parameters for milk urea (MU) content in 3 main Danish dairy breeds. As a part of the Danish milk recording system, milk samples from cows on commercial farms were analyzed for MU concentration (mmol/L) and the percentages of fat and protein. There were 323,800 Danish Holstein, 70,634 Danish Jersey, and 27,870 Danish Red cows sampled with a total of 1,436,580, 368,251, and 133,922 test-day records per breed, respectively, included in the data set. Heritabilities for MU were low to moderate (0.22, 0.18, and 0.24 for the Holstein, Jersey, and Red breeds, respectively). The genetic correlation was close to zero between MU and milk yield in Jersey and Red, and -0.14 for Holstein. The genetic correlations between MU and fat and protein percentages, respectively, were positive for all 3 dairy breeds. Herd-test-day explained 51%, 54%, and 49% of the variation in MU in Holstein, Jersey, and Red, respectively. This indicates that MU levels in milk can be reduced by farm management. The current study shows that there are possibilities to influence MU by genetic selection as well as by farm management.

Genetic and phenotypic variation and consistency in cow preference and circadian use of robotic milking units.

Many dairy herds use automatic milking stations (AMS), with cows in large herds often having access to 2 or more AMS, and must choose between them when they go for milking. Individual cows acquire routines of either consistently using a specific milking box or consistently using any available milking box. Here, we hypothesized that the degree of use of the same milking box was an expression of preference, and quantified it as preference consistency score (PCS). The PCS was calculated as a ratio between the excess frequencies of the first choice over the base frequency of "not first choice" over 15-d segments of lactation. This ratio was 0 if all choices were taken equally, and became 1.0 if only the first choice was taken in all events. We investigated the consistency of milking box preference in 2 cohorts (one Holstein and one Jersey) across 6 commercial dairy herds in Denmark (n = 4,665 cows total). In addition to PCS, we recorded and analyzed associated milking and behavior traits, including a time profile index showing use of specific clock hours when cows were milked (Time_profile, based on excess use of specific clock hours), milking frequency, time spent in the milking box, and milk yield. Records from each milking event were condensed into 15-d segments based on days in milk. The data were analyzed using a linear mixed model, with random genetic and individual cow effects, to estimate heritability (h2), repeatability (t), and individual level correlations (ri) between traits. The average PCS was 0.43 and 0.41 in Holstein and Jersey, respectively, showing that cows developed routines for consistently using the same milking box; however, some cows had lower preference (i.e., greater flexibility in use). The Time_profile indicated that some cows were milked in a few hour-bins, whereas others were more flexible. The PCS and Time_profile traits had low heritability (h2, PCS/Time_profile = 0.07 ± 0.02/0.11 ± 0.02 Holstein, 0.13 ± 0.03/0.04 ± 0.02 Jersey) and moderate repeatability (t, PCS/Time_profile = 0.47/0.40 Holstein, 0.50/0.42 Jersey). The 2 traits were weakly correlated with each other (ri = 0.18 and 0.17), and were weakly correlated with milk yield (ri range: 0.0 to -0.10). However, the time profile was strongly correlated with milking frequency (ri range: -0.81 to -0.73), and was moderately correlated with daily box time (ri range: -0.43 to -0.35). In general, Holstein and Jersey parameter estimates were of similar size, and thus in good agreement. Overall, individual cows covered a broad spectrum of preference consistency, both regarding the use of specific milking boxes and time profiles, with these 2 traits representing different aspects or dimensions of milking behavior. The findings that some cows have strong preferences for specific AMS may be most useful in herd management and farm design. The weak correlation to milk yield indicated that yield minimally affected these 2 milking associated behavior traits. In conclusion, although the traits were repeatable, heritability was low; thus, genetic selection for milk yield might minimally affect these 2 traits.

Genetic analysis on infrared-predicted milk minerals for Danish dairy cattle.

A group of milk components that has shown potential to be predicted with milk spectra is milk minerals. Milk minerals are important for human health and cow health. Having an inexpensive and fast way to measure milk mineral concentrations would open doors for research, herd management, and selective breeding. The first aim of this study was to predict milk minerals with infrared milk spectra. Additionally, milk minerals were predicted with infrared-predicted fat, protein, and lactose content. The second aim was to perform a genetic analysis on infrared-predicted milk minerals, to identify QTL, and estimate variance components. For training and validating a multibreed prediction model for individual milk minerals, 264 Danish Jersey cows and 254 Danish Holstein cows were used. Partial least square regression prediction models were built for Ca, Cu, Fe, K, Mg, Mn, Na, P, Se, and Zn based on 80% of the cows, selected randomly. Prediction models were externally validated with 8 herds based on the remaining 20% of the cows. The prediction models were applied on a population of approximately 1,400 Danish Holstein cows with 5,600 infrared spectral records and 1,700 Danish Jersey cows with 7,200 infrared spectral records. Cows from this population had 50k imputed genotypes. Prediction accuracy was good for P and Ca, with external R2 ≥ 0.80 and a relative prediction error of 5.4% for P and 6.3% for Ca. Prediction was moderately good for Na with an external R2 of 0.63, and a relative error of 18.8%. Prediction accuracies of milk minerals based on infrared-predicted fat, protein, and lactose content were considerably lower than those based on the infrared milk spectra. This shows that the milk infrared spectrum contains valuable information on milk minerals, which is currently not used. Heritability for infrared-predicted Ca, Na, and P varied from low (0.13) to moderate (0.36). Several QTL for infrared-predicted milk minerals were observed that have been associated with gold standard milk minerals previously. In conclusion, this study has shown infrared milk spectra were good at predicting Ca, Na, and P in milk. Infrared-predicted Ca, Na, and P had low to moderate heritability estimates.

Genetic analysis of orotic acid predicted with Fourier transform infrared milk spectra.

Fourier transform infrared spectral analysis is a cheap and fast method to predict milk composition. A not very well studied milk component is orotic acid. Orotic acid is an intermediate in the biosynthesis pathway of pyrimidine nucleotides and is an indicator for the metabolic cattle disorder deficiency of uridine monophosphate synthase. The function of orotic acid in milk and its effect on calf health, health of humans consuming milk or milk products, manufacturing properties of milk, and its potential as an indicator trait are largely unknown. The aims of this study were to determine if milk orotic acid can be predicted from infrared milk spectra and to perform a large-scale phenotypic and genetic analysis of infrared-predicted milk orotic acid. An infrared prediction model for orotic acid was built using a training population of 292 Danish Holstein and 299 Danish Jersey cows, and a validation population of 381 Danish Holstein cows. Milk orotic acid concentration was determined with nuclear magnetic resonance spectroscopy. For genetic analysis of infrared orotic acid, 3 study populations were used: 3,210 Danish Holstein cows, 3,360 Danish Jersey cows, and 1,349 Dutch Holstein Friesian cows. Using partial least square regression, a prediction model for orotic acid was built with 18 latent variables. The error of the prediction for the infrared model varied from 1.0 to 3.2 mg/L, and the accuracy varied from 0.68 to 0.86. Heritability of infrared orotic acid predicted with the standardized prediction model was 0.18 for Danish Holstein, 0.09 for Danish Jersey, and 0.37 for Dutch Holstein Friesian. We conclude that milk orotic acid can be predicted with moderate to good accuracy based on infrared milk spectra and that infrared-predicted orotic acid is heritable. The availability of a cheap and fast method to predict milk orotic acid opens up possibilities to study the largely unknown functions of milk orotic acid.

Genome-wide association study on Fourier transform infrared milk spectra for two Danish dairy cattle breeds.

BACKGROUND: Infrared spectral analysis of milk is cheap, fast, and accurate. Infrared light interacts with chemical bonds present inside the milk, which means that Fourier transform infrared milk spectra are a reflection of the chemical composition of milk. Heritability of Fourier transform infrared milk spectra has been analysed previously. Further genetic analysis of Fourier transform infrared milk spectra could give us a better insight in the genes underlying milk composition. Breed influences milk composition, yet not much is known about the effect of breed on Fourier transform infrared milk spectra. Improved understanding of the effect of breed on Fourier transform infrared milk spectra could enhance efficient application of Fourier transform infrared milk spectra. The aim of this study is to perform a genome wide association study on a selection of wavenumbers for Danish Holstein and Danish Jersey. This will improve our understanding of the genetics underlying milk composition in these two dairy cattle breeds. RESULTS: For each breed separately, fifteen wavenumbers were analysed. Overall, more quantitative trait loci were observed for Danish Jersey compared to Danish Holstein. For both breeds, the majority of the wavenumbers was most strongly associated to a genomic region on BTA 14 harbouring DGAT1. Furthermore, for both breeds most quantitative trait loci were observed for wavenumbers that interact with the chemical bond C-O. For Danish Jersey, wavenumbers that interact with C-H were associated to genes that are involved in fatty acid synthesis, such as AGPAT3, AGPAT6, PPARGC1A, SREBF1, and FADS1. For wavenumbers which interact with -OH, associations were observed to genomic regions that have been linked to alpha-lactalbumin. CONCLUSIONS: The current study identified many quantitative trait loci that underlie Fourier transform infrared milk spectra, and thus milk composition. Differences were observed between groups of wavenumbers that interact with different chemical bonds. Both overlapping and different QTL were observed for Danish Holstein and Danish Jersey.

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.

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.

Genetic analysis of Fourier transform infrared milk spectra in Danish Holstein and Danish Jersey.

Fourier transform infrared milk spectral data are routinely used for milk quality control and have been revealed to be driven by genetics. This study aimed to (1) estimate heritability for 1,060 wavenumbers in the infrared region from 5,008 to 925 cm-1, (2) estimate genomic correlations between wavenumbers with increased heritability, and (3) compare results between Danish Holstein and Danish Jersey cows. For Danish Holstein, 3,275 cows and 19,656 milk records were available. For Danish Jersey, 3,408 cows and 20,228 milk records were available. We used a hierarchical mixed model, with a Bayesian approach. Heritability of individual wavenumbers ranged from 0.00 to 0.31 in Danish Holstein, and from 0.00 to 0.30 in Danish Jersey. Genomic correlation was calculated between 15 selected wavenumbers, and varied from weak to very strong, in both Danish Holstein and Danish Jersey (0.03 to 0.97, and -0.11 to -0.97). Within the 15 selected wavenumbers, a subdivision into 2 groups of wavenumbers was observed, where genomic correlations were negative between groups, and positive within groups. Heritability and genomic correlations were higher in Danish Holstein compared with Danish Jersey, but followed a similar pattern in both breeds. Breed differences were most pronounced in the mid-infrared region that interacts with lactose and the spectral region that interacts with protein. In conclusion, heritability for individual wavenumbers of Fourier transform milk spectra was moderate, and strong genomic correlations were observed between wavenumbers across the spectrum. Heritability and genomic correlations were higher in Danish Holstein, with the strongest breed differences showing in spectral regions interacting with protein or lactose.

Genetic analysis of predicted fatty acid profiles of milk from Danish Holstein and Danish Jersey cattle populations.

The objective of this study was to assess the genetic variability of the detailed fatty acid (FA) profiles of Danish Holstein (DH) and Danish Jersey (DJ) cattle populations. We estimated genetic parameters for 11 FA or groups of FA in milk samples from the Danish milk control system between May 2015 and October 2016. Concentrations of different FA and FA groups in milk samples were measured by mid-infrared spectroscopy. Data used for parameter estimation were from 132,732 first-parity DH cows and 21,966 first-parity DJ cows. We found the highest heritabilities for test day measurements in both populations for short-chain FA (DH = 0.16; DJ = 0.16) and C16:0 (DH = 0.14; DJ = 0.16). In DH, the highest heritabilities were also found for saturated FA and monounsaturated FA (both populations: 0.15). Genetic correlations between the fatty acid traits showed large differences between DH and DJ for especially short-chain FA with the other FA traits measured. Furthermore, genetic correlations of total fat with monounsaturated FA, polyunsaturated FA, short-chain FA, and C16:0 differed markedly between DH and DJ populations. In conclusion, we found genetic variation in the mid-infrared spectroscopy-predicted FA and FA groups of the DH and DJ cattle populations. This finding opens the possibility of using genetic selection to change the FA profiles of dairy cattle.

Predicting methane emissions of lactating Danish Holstein cows using Fourier transform mid-infrared spectroscopy of milk.

Enteric methane (CH4), a potent greenhouse gas, is among the main targets of mitigation practices for the dairy industry. A measurement technique that is rapid, inexpensive, easy to use, and applicable at the population level is desired to estimate CH4 emission from dairy cows. In the present study, feasibility of milk Fourier transform mid-infrared (FT-IR) spectral profiles as a predictor for CH4:CO2 ratio and CH4 production (L/d) is explained. The partial least squares regression method was used to develop the prediction models. The models were validated using different random test sets, which are independent from the training set by leaving out records of 20% cows for validation and keeping records of 80% of cows for training the model. The data set consisted of 3,623 records from 500 Danish Holstein cows from both experimental and commercial farms. For both CH4:CO2 ratio and CH4 production, low prediction accuracies were found when models were obtained using FT-IR spectra. Validated coefficient of determination (R2Val) = 0.21 with validated model error root mean squared error of prediction (RMSEP) = 0.0114 L/d for CH4:CO2 ratio, and R2Val = 0.13 with RMSEP = 111 L/d for CH4 production. The important spectral wavenumbers selected using the recursive partial least squares method represented major milk components fat, protein, and lactose regions of the spectra. When fat and protein predicted by FT-IR were used instead of full spectra, a low R2Val of 0.07 was obtained for both CH4:CO2 ratio and CH4 production prediction. Other spectral wavenumbers related to lactose (carbohydrate) or additional wavenumbers related to fat or protein (amide II) are providing additional variation when using the full spectral profile. For CH4:CO2 ratio prediction, integration of FT-IR with other factors such as milk yield, herd, and lactation stage showed improvement in the prediction accuracy. However, overall prediction accuracy remained modest; R2Val increased to 0.31 with RMSEP = 0.0105. For prediction of CH4 production, the added value of FT-IR along with the aforementioned traits was marginal. These results indicated that for CH4 production prediction, FT-IR profiles reflect primarily information related to milk yield, herd, and lactation stage rather than individual milk fatty acids related to CH4 emission. Thus, it is not feasible to predict CH4 emission based on FT-IR spectra alone.

Prediction and validation of residual feed intake and dry matter intake in Danish lactating dairy cows using mid-infrared spectroscopy of milk.

The present study explored the effectiveness of Fourier transform mid-infrared (FT-IR) spectral profiles as a predictor for dry matter intake (DMI) and residual feed intake (RFI). The partial least squares regression method was used to develop the prediction models. The models were validated using different external test sets, one randomly leaving out 20% of the records (validation A), the second randomly leaving out 20% of cows (validation B), and a third (for DMI prediction models) randomly leaving out one cow (validation C). The data included 1,044 records from 140 cows; 97 were Danish Holstein and 43 Danish Jersey. Results showed better accuracies for validation A compared with other validation methods. Milk yield (MY) contributed largely to DMI prediction; MY explained 59% of the variation and the validated model error root mean square error of prediction (RMSEP) was 2.24kg. The model was improved by adding live weight (LW) as an additional predictor trait, where the accuracy R2 increased from 0.59 to 0.72 and error RMSEP decreased from 2.24 to 1.83kg. When only the milk FT-IR spectral profile was used in DMI prediction, a lower prediction ability was obtained, with R2=0.30 and RMSEP=2.91kg. However, once the spectral information was added, along with MY and LW as predictors, model accuracy improved and R2 increased to 0.81 and RMSEP decreased to 1.49kg. Prediction accuracies of RFI changed throughout lactation. The RFI prediction model for the early-lactation stage was better compared with across lactation or mid- and late-lactation stages, with R2=0.46 and RMSEP=1.70. The most important spectral wavenumbers that contributed to DMI and RFI prediction models included fat, protein, and lactose peaks. Comparable prediction results were obtained when using infrared-predicted fat, protein, and lactose instead of full spectra, indicating that FT-IR spectral data do not add significant new information to improve DMI and RFI prediction models. Therefore, in practice, if full FT-IR spectral data are not stored, it is possible to achieve similar DMI or RFI prediction results based on standard milk control data. For DMI, the milk fat region was responsible for the major variation in milk spectra; for RFI, the major variation in milk spectra was within the milk protein region.

Detection of genetic variation affecting milk coagulation properties in Danish Holstein dairy cattle by analyses of pooled whole-genome sequences from phenotypically extreme samples (pool-seq).

Rennet-induced milk coagulation is an important trait for cheese production. Recent studies have reported an alarming frequency of cows producing poorly coagulating milk unsuitable for cheese production. Several genetic factors are known to affect milk coagulation, including variation in the major milk proteins; however, recent association studies indicate genetic effects from other genomic regions as well. The aim of this study was to detect genetic variation affecting milk coagulation properties, measured as curd-firming rate (CFR) and milk pH. This was achieved by examining allele frequency differences between pooled whole-genome sequences of phenotypically extreme samples (pool-seq).. Curd-firming rate and raw milk pH were measured for 415 Danish Holstein cows, and each animal was sequenced at low coverage. Pools were created containing whole genome sequence reads from samples with "extreme" values (high or low) for both phenotypic traits. A total of 6,992,186 and 5,295,501 SNP were assessed in relation to CFR and milk pH, respectively. Allele frequency differences were calculated between pools and 32 significantly different SNP were detected, 1 for milk pH and 31 for CFR, of which 19 are located on chromosome 6. A total of 9 significant SNP, which were selected based on the possible function of proximal candidate genes, were genotyped in the entire sample set ( = 415) to test for an association. The most significant SNP was located proximal to , explaining 33% of the phenotypic variance. , coding for κ-casein, is the most studied in relation to milk coagulation due to its position on the surface of the casein micelles and the direct involvement in milk coagulation. Three additional SNP located on chromosome 6 showed significant associations explaining 7, 3.6, and 1.3% of the phenotypic variance of CFR. The significant SNP on chromosome 6 were shown to be in linkage disequilibrium with the SNP peaking proximal to ; however, after accounting for the genotype of the peak SNP within this QTL, significant effects (-value < 0.1) could still be detected for 2 of the SNP accounting for 2 and 1% of the phenotypic variance. These 2 interesting SNP were located within introns or proximal to the candidate genes-solute carrier family 4 (sodium bicarbonate cotransporter), member 4 () and LIM and calponin homology domains 1 (), respectively-making them interesting targets for further analysis.

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.

Short communication: Multi-trait estimation of genetic parameters for milk protein composition in the Danish Holstein.

Genetic parameters were estimated for the major milk proteins using bivariate and multi-trait models based on genomic relationships between animals. The analyses included, apart from total protein percentage, αS1-casein (CN), αS2-CN, ß-CN, κ-CN, α-lactalbumin, and ß-lactoglobulin, as well as the posttranslational sub-forms of glycosylated κ-CN and αS1-CN-8P (phosphorylated). Standard errors of the estimates were used to compare the models. In total, 650 Danish Holstein cows across 4 parities and days in milk ranging from 9 to 481d were selected from 21 herds. The multi-trait model generally resulted in lower standard errors of heritability estimates, suggesting that genetic parameters can be estimated with high accuracy using multi-trait analyses with genomic relationships for scarcely recorded traits. The heritability estimates from the multi-trait model ranged from low (0.05 for ß-CN) to high (0.78 for κ-CN). Genetic correlations between the milk proteins and the total milk protein percentage were generally low, suggesting the possibility to alter protein composition through selective breeding with little effect on total milk protein percentage.

Joint genome-wide association study for milk fatty acid traits in Chinese and Danish Holstein populations.

The identification of causal genes or genomic regions associated with fatty acids (FA) will enhance our understanding of the pathways underlying FA synthesis and provide opportunities for changing milk fat composition through a genetic approach. The linkage disequilibrium between adjacent markers is highly consistent between the Chinese and Danish Holstein populations, such that a joint genome-wide association study (GWAS) can be performed. In this study, a joint GWAS was performed for 16 milk FA traits based on data of 784 Chinese and 371 Danish Holstein cows genotyped by a high-density bovine single nucleotide polymorphism (SNP) array. A total of 486,464 SNP markers on 29 bovine autosomes were used. Bonferroni corrections were applied to adjust the significance thresholds for multiple testing at the genome- and chromosome-wide levels. According to the analysis of either the Chinese or Danish data individually, the total numbers of overlapping SNP that were significant at the chromosome level were 94 for C14:1, 208 for the C14 index, and 1 for C18:0. Joint analysis using the combined data of the 2 populations detected greater numbers of significant SNP compared with either of the individual populations alone for 7 and 10 traits at the genome- and chromosome-wide significance levels, respectively. Greater numbers of significant SNP were detected for C18:0 and the C18 index in the Chinese population compared with the joint analysis. Sixty-five significant SNP across all traits had significantly different effects in the 2 populations. Ten FA were influenced by a quantitative trait loci (QTL) region including DGAT1. Both C14:1 and the C14 index were influenced by a QTL region including SCD1 in the combined population. Other QTL regions also showed significant associations with the studied FA. A large region (14.9-24.9 Mbp) in BTA26 significantly influenced C14:1 and the C14 index in both populations, mostly likely due to the SNP in SCD1. A QTL region (69.97-73.69 Mbp) on BTA9 showed a significantly different effect on C18:0 between the 2 populations. Detection of these important SNP and the corresponding QTL regions will be helpful for follow-up studies to identify causal mutations and their interaction with environments for milk FA in dairy cattle.

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.

Phenotypic and genetic associations of milk traits with milk coagulation properties.

The aim of this study was to examine milk composition and rennet-induced coagulation properties of milk from 892 individual Danish Holstein and Danish Jersey cows and determine the genetic influences on these properties by determining heritability and genomic correlations with single nucleotide polymorphisms identified by the bovine HD Beadchip (Illumina Inc., San Diego, CA). Despite no signs of clinical mastitis, milk from cows with somatic cell counts >500,000 cells/mL showed altered milk composition, indicating impaired barrier between the milk and the blood. Curd-firming rate (CFR) and rennet coagulation time (RCT) were used to describe milk coagulation properties (MCP). These traits describe the second phase of milk coagulation and were mutually negatively correlated, but only to some extent associated with the same compositional traits. In both breeds, CFR were highly correlated with protein content, whereas longer RCT were primarily associated with lower milk pH. Estimated heritabilities for milk production and compositional traits ranged from 0.09 for yield to 0.82 for citric acid in Danish Jersey cows, and from 0.21 for yield to 0.59 for citric acid in Danish Holstein cows. Heritabilities for MCP traits varied considerably between breeds, and were estimated to be 0.28 for RCT and 0.75 for CFR in Danish Holstein cows and 0.45 for RCT and 0.15 for CFR in Danish Jersey cows. This difference was further reflected in the genomic correlations between RCT and CFR which was -0.90 in Danish Holstein and 0.06 in Danish Jersey. These data suggest that potential for changing MCP through breeding exists, but the genetic background of the MCP traits might be different in different breeds; therefore, using Danish Holstein as background for Danish Jersey is not trivial. Thereby, the study underlines the need for breed-specific models.

Effects of breed and casein genetic variants on protein profile in milk from Swedish Red, Danish Holstein, and Danish Jersey cows.

In selecting cows for higher milk yields and milk quality, it is important to understand how these traits are affected by the bovine genome. The major milk proteins exhibit genetic polymorphism and these genetic variants can serve as markers for milk composition, milk production traits, and technological properties of milk. The aim of this study was to investigate the relationships between casein (CN) genetic variants and detailed protein composition in Swedish and Danish dairy milk. Milk and DNA samples were collected from approximately 400 individual cows each of 3 Scandinavian dairy breeds: Swedish Red (SR), Danish Holstein (DH), and Danish Jersey (DJ). The protein profile with relative concentrations of α-lactalbumin, ß-lactoglobulin, and α(S1)-, α(S2)-, κ-, and ß-CN was determined for each milk sample using capillary zone electrophoresis. The genetic variants of the α(S1)- (CSN1S1), ß- (CSN2), and κ-CN (CSN3) genes for each cow were determined using TaqMan SNP genotyping assays (Applied Biosystems, Foster City, CA). Univariate statistical models were used to evaluate the effects of composite genetic variants, α(S1)-ß-κ-CN, on the protein profile. The 3 studied Scandinavian breeds differed from each other regarding CN genotypes, with DH and SR having similar genotype frequencies, whereas the genotype frequencies in DJ differed from the other 2 breeds. The similarities in genotype frequencies of SR and DH and differences compared with DJ were also seen in milk production traits, gross milk composition, and protein profile. Frequencies of the most common composite α(S1)-ß-κ-CN genotype BB/A(2)A(2)/AA were 30% in DH and 15% in SR, and cows that had this genotype gave milk with lower relative concentrations of κ- and ß-CN and higher relative concentrations of αS-CN, than the majority of the other composite genotypes in SR and DH. The effect of composite genotypes on relative concentrations of the milk proteins was not as pronounced in DJ. The present work suggests that a higher frequency of BB/A(1)A(2)/AB, together with a decrease in BB/A(2)A(2)/AA, could have positive effects on DH and SR milk regarding, for example, the processing of cheese.

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 αS1- (CSN1S1), ß- (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 A(2), 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.