Allele frequencies of κ-CN, β-CN, αs1-CN, αs2-CN and β-LG in cows of the Estonian Holstein breed
Cheese production is of substantial economic importance in most European countries, where an increasing amount of the produced milk is used for manufacturing cheese (Eurostat 2010a). In Estonia 60% (Statistics Estonia, 2010), in Italy more than 75% (De Marchi et al., 2008), and in Scandinavian countries 33% (Wedholm et al., 2006) of milk is used for cheese production. Recent trends indicate that per capita consumption of cheese is also increasing (Eurostat 2010b).
Milk quality is an essential factor to the dairy industry due to its economic impact. Milk coagulation ability is one of the most important factors affecting cheese yield and quality and has been reviewed (Jakob & Puhan, 1992; Johnson et al., 2001), and therefore is becoming more important as an increasing percentage of milk is used for cheese manufacturing. Milk coagulation properties (MCP) are commonly defined by milk coagulation time (RCT) and curd firmness (A30). It is feasible to design raw milk according to its specific technological use.
Improving cheese yield and quality, through the direct selection of breeding animals on the basis of milk coagulation property traits, is an option due to genetic variation of MCP traits (Ojala et al., 2005). MCP are heritable, quantitative traits; up to 40% of the variation among animals is caused by genetic factors (Ikonen et al., 2004). Estimates of heritability for MCP traits are from 0.30 to 0.40 (Bittante et al., 2002; Ikonen et al., 1999a), and from 0.25 for RCT, and 0.15 for A30 (Cassandro et al., 2008) to 0.28 for RCT to 0.41 for A30 (Vallas et al., 2010). Predictions of MCP provided by mid-infrared spectroscopy (MIR) techniques have been proposed as indicator traits for the genetic enhancement of MCP (Cecchinato et al., 2009; De Marchi et al., 2009). The expected response of RCT and A30 ensured by the selection using MIR predictions as indicator traits was equal to, or slightly less than, the response achievable through a single measurement of these traits. Accordingly, breeding strategies for the enhancement of MCP based on MIR predictions as indicator traits could be easily and immediately implemented for dairy cattle populations where the routine acquisition of spectra from individual milk samples is already measured (Cecchinato et al., 2009). Nevertheless, MCP traits analyzed with different methodologies have significantly different values, due to the diversity of the instruments used and the coagulant activity (Pretto et al., 2011). The type of coagulant could also have an effect, since different coagulants have been used. The method proposed for the prediction of non-coagulation probability of milk samples showed that non coagulating samples from one methodology were highly predictable based on the rennet coagulation time measured with another methodology (Pretto et al., 2011). A standard definition of MCP traits analysis is needed to enable reliable comparisons between MCP traits recorded in different laboratories, and in different animal populations and breeds.
More than 95% of the proteins contained in ruminant milk are coded by six structural genes (Martin et al., 2002). The four casein genes (
In the Dutch Holstein-Friesian population selection for
The objective of this study was to estimate the contribution of the aggregate β-κ-CN and β-LG genotypes on first lactation milk coagulation and quality traits of Estonian Holstein cows. A parallel objective was to identify the variation in genetic polymorphism of milk proteins with the aim to improve the protein composition in milk by selecting for variants of specific genes.
2. Material and methods
2.1. Performance of Estonian cattle populations
In Estonia there are three breeds of dairy cattle – Estonian Red (ER), Estonian Holstein (EHF) and Estonian Native (EN). Distribution of breeds has come to favour EHF (Fig.1). In Estonia, 93.0% of cows are enrolled in an official milk recording programme (Results of Animal Recording in Estonia 2010, 2011) (Fig. 2). Since 1995, average milk yield in Estonia has risen 3947 kg (48%, Fig. 3). The 2010 average actual production for Estonian Holstein herd that were enrolled in producing-testing programs and eligible for genetic evaluation was 7778 kg milk, 317 kg of fat and 260 kg of protein per year. Holstein dairy cattle dominate in Estonian milk production industry because of their excellent production and greater income.
In the evaluation programme for young bulls of Estonian Holstein breed, ca 25 bulls are tested each year, parallel testing is carried out on 10-12 foreign bulls. The selection of bulls is made from imported American and Canadian embryos, the Estonian Holstein best bull dams and imported young bulls. Often the sons of imported cows are used whose sires are world-known top bulls. The bulls come mainly from the USA, Canada, Germany and the Netherlands.
Currently, estimated breeding values (EBV) for production, conformation and udder health traits for bulls and cows in Estonia are computed by the Animal Recording Centre four times per year (Pentjärv & Uba, 2004). Breeding value estimation is carried out separately for the EHF and the Estonian Red breed (ER), using the best linear unbiased predictor (BLUP) test day animal model for production and udder health traits and the BLUP animal model for conformation traits. The EBV for each production trait – milk (kg), fat (kg) and protein (kg) – is the mean breeding value of the first, second and third lactations, adjusted by the mean average breeding value of the cows born in a defined base year (currently, 1995).
The milk production index (SPAV) is expressed as relative breeding value (RBV) with a mean of 100 and a standard deviation of 12 points for base animals, combining breeding values for milk, fat and protein yield weighted by relative economic values of 0:1:4 for EHF and 0:1:6 for ER (Pentjärv & Uba, 2004).
The information source for breeding value estimation of udder health traits is somatic cell count (SCC) in one millilitre of milk, transformed into a somatic cell score (SCS) using the formula SCS = log2 (SCC/100000) + 3 (Pentjärv & Uba, 2004).
The udder health index SSAV is calculated as the sum of EBVs of the first, second and third lactations with index weights 0.26, 0.37 and 0.37, respectively, and is expressed as RBV for genetic evaluation of conformation traits. Data from first lactation cows are used to compute RBVs for 16 linear traits for EHF and 14 linear traits for ER, as well as for three general traits. The conformation index SVAV is expressed as RBV, combining relative breeding values for type, udder and feet by relative economic weights of 0.3:0.5:0.2 for ER and 0.3:0.4:0.3 for EHF.
2.2. Data collection and laboratory milk analysis
First lactation milk samples were collected during routine milk recording as part of a development project for the Bio-Competence Centre of Healthy Dairy Products in Estonia during the period April 2005 – June 2008. The herds had twice-a-day or thrice-a-day milkings. The individual milk samples collected from the cows were either bulked test-day milkings or separate samples from each of the milkings on the test-day. Milk samples were immediately preserved after collection with Bronopol (2-bromo-2-nitropropane-1,3-diol, Knoll Pharmaceuticals, Nottingham, UK) and stored at 4˚C during transportation and analyzing periods. Milk samples with a pH lower than 6.5, indicative of colostrum, and non-coagulated milk samples (n=33) were excluded from the analysis. Furthermore, farms with less than 10 cows, and cows with fewer than three test-day records, were removed. The final dataset used for analyses consist of 11,437 test-day records from 2,769 Estonian Holstein cows which were located in 66 herds across the country and were daughters of 169 sires. The number of daughters per sire ranged from 1 to 267. Each cow had from 3 – 6 measurements collected during the different stages (7 – 305 DIM) of the first lactation. Information about the cows, herds and pedigree was obtained from the Estonian Animal Recording Centre (EARC), the Animal Breeders’ Association of Estonia and a database, COAGEN®, was produced. The test-day milk yield was recorded and individual milk samples were analyzed for fat percentage, protein percentage and urea content using the MilkoScan 4000 and MilcoScan FT6000, and for SCC using the Fossomatic 4000 and Fossomatic 5000 cell counter at the Milk Analysis Laboratory of the EARC, using methods suggested by the International Committee for Animal Recording (2009). Values of SCC were log-transformed to SCS.
The pH and milk coagulation properties were determined at the Laboratory of Milk Quality of the Estonian University of Life Sciences, usually three days after sampling. The proportion of milk samples with a maximum storage age of seven days was very small, less than 1%. The pH level of the milk was determined using a pH meter (Seven Multi; Mettler Toledo GmbH, Greifensee, Switzerland) at a temperature of 20˚C before analyzing the milk coagulation properties. The latter were milk coagulation time in minutes and firmness of curd in volts. Prior to the assessment of the milk coagulation properties, milk samples were heated to the renneting temperature (35˚C). The rennet (Milase MRS 750 IMCU/ml; CSK Food Enrichment B.V., The Netherlands) used in the analyses was diluted 1:100 (v/v) with distilled water and 0.2 ml of the solution was added to 10 ml milk. The milk coagulation properties were determined using the Optigraph (Ysebaert, Frepillon, France), which was developed by YDD (Ysebaert Dairy Division) in partnership with the INRA (LGMPA, lab. G. CORRIEU) to define coagulation characteristics in the laboratory, specifically to answer the needs of cheese makers (Ysebaert Dairy Division, 2009).
Measurements made with the Optigraph are not based on a rheological method but on an optical signal in the near-infrared spectrum. During a coagulation test, the light transmitted through the milk gradually weakens because of changes in the micellar structure of casein. The Optigraph then calculates the coagulation parameters (coagulation time, curd firmness, speed of aggregation) by means of particular feature points extracted from the optical information acquired in real time (Optigraph User’s Manual).
2.3. Data collection and laboratory blood analysis
Blood samples were collected as part of a development project for the Bio-Competence Centre of Healthy Dairy Products in Estonia during the period of June 2005 to December 2007. Blood samples (n=2,959) were stored in tubes containing K3EDTA. DNA was extracted from whole blood according to the method described by (Miller et al., 1998) or by using a commercial Puregene Gentra Blood kit (Minneapolis, USA). The quantity of template DNA was approximately 40 to 100 ng for Allele-specific oligonucleotide (ASO) PCR and PCR-RFLP, respectively. Polymorphisms of five milk protein genes were analyzed, four from the casein cluster
2.4. Statistical analyses
Preliminary analyses for testing the significance of fixed effects and single genotype effects were carried out on the SAS System® (SAS, Cary, NC, USA) using the MIXED procedure. Aggregate β-κ-CN genotypes were formed for further analysis. The genotypes with relative frequencies of less than 1% were grouped together into rare β-κ-CN genotype (A1A1-BB, A1A1-BE, A1A1-EE, A1B-BB, A1B-BE, A2A2-AE, A2A2-BE, A2A3-AA, A2B-AA, A2B-BB, BB-AB, BB-BB). Further statistical analysis was carried out using ASReml (VSN International Ltd., Hemel Hempstead, UK), using the following univariate repeatability animal model:
Sample age was included as a covariate in the model only for milk coagulation traits. Sampling year-season and calving year-season were grouped into 3-month classes, 14 classes from April 2005 to June 2008 and 11 classes from December 2004 to August 2007, respectively. Three generations of ancestors with a total number of 17,185 animals in the relationship matrix were included in the analysis.
The class with the largest number of observations, genotype A2A2 -AA was used as the class of comparison. It is also homozygous for both loci. Accordingly, the standard errors of the genotype effects are standard errors of the differences between each genotype and the most frequent A2A2 -AA
3. Results and discussion
3.1. Genetic structure of milk proteins
Allelic variants of casein and
This finding, of a high frequency of the A2 allele, confirms the advantage of the Estonian Holstein breed that their milk naturally might lower health risks associated with the occurence of the β-CN A1 allele. The other advantage of the β-CN A2 allele is its positive association with protein yield (Olenski et al., 2010). The positive effect of the rare A2A2-BB
Expected frequencies of the β-κ-CN genotypes were calculated by multiplying the expected frequencies of the β-CN and κ-CN genotypes. Some alleles at one locus were associated only with certain alleles at the other locus, causing distinct differences between observed and expected frequencies of certain β-κ-CN genotypes (Table 3).
|AA||4.0 (117)||23.1 (683)||0.0||27.1 (801)||0.2 (5)||0.1 (2)||0.0|
|AB||1.6 (48)||10.2 (301)||1.5 (45)||12.5 (369)||2.8 (82)||0.1 (3)|
|AE||2.5 (73)||7.2 (214)||0.0||0.2 (7)||0.0||0.0|
|BB||0.1 (3)||1.2 (34)||0.4 (12)||1.2 (36)||0.8 (24)||0.2 (6)|
|BE||0.7 (20)||1.7 (51)||0.3 (9)||0.0 (1)||0.0|
Some genotypes were observed two to fourfold more frequently than expected (A1A1-BE, A1B-BE, A1B-BB etc) and A2B-BB at eightfold more frequently than expected. All cows carrying the κ-CN EE genotype had association only with the β-CN A1A1 genotype as has also been reported for Finnish Ayrshire cows (Ikonen et al., 1999a). Some genotypes were less frequent than expected (A1A1-BB a quarter and A2A2-AE one-twentieth of the expected frequency). Linkage disequilibrium in the casein
3.2. Associations between milk protein genotypes and milk coagulation and quality traits
The associations of β-CN and κ-CN genotypes with milk coagulation (RCT, A30), quality traits (SCS, fat and protein contents), and milk yield was investigated (Table 4).
3.2.1. Milk coagulation traits
Milk coagulation traits (RCT and A30) were affected by aggregate β-κ-CN genotypes (p<0.001, Table 4). The most favourable β-κ-CN genotypes for RCT included the B allele at both loci, as has also been reported elsewhere (Comin et al., 2008) for Italian Holstein cows. Favourable aggregate genotypes for RCT were A1B-AB and A2B-AB. The best aggregate genotypes for A30 had two B alleles κ-CN, A1A2-BB, and the second best had the genotype A2A2-BB. κ-CN
|RCT* (min)||A30 (V)||MILK (kg)||PROTEIN ( %)||FAT (%)||SCS***|
|A1A2 - BB||34||-0.073||0.023||4.357||0.422||-2.013||0.705||0.130||0.031||0.246||0.077||-0.560||0.235|
|A1A2 - BE||49||-0.052||0.019||2.214||0.355||-0.963||0.595||0.039||0.026||-0.008||0.065||0.084||0.197|
|A2A2 - BB||34||-0.072||0.023||3.865||0.418||-0.460||0.699||0.076||0.031||-0.042||0.077||-0.237||0.233|
As for the impact of
3.2.2. Milk yield and protein and fat percentage, SCS
Milk yield and protein and fat contents were affected by aggregate β-κ-CN genotype (p<0.05, Table 4). β-κ-CN genotype A1B-AB is favourable for milk yield. Similarly to A30, the most favourable β-κ-CN genotypes for milk protein content were homozygous for the B allele for κ-CN, A1A2-BB and A2A2-BB. The most favourable aggregate genotype for fat content was also A1A2-BB and unfavourable genotype for fat percentage, containing E allele in κ-CN locus, A1A2-AE, but also genotype A2B-AB.
The most favourable for protein content was BB for κ-CN and A1A2 for β-CN (the second best was A2A2, where the A2A2 genotype of β-CN had a slight advantage over the A1A1 genotype). These results were in agreement with those previously reported (Heck, 2009), that the κ-CN genotype was associated with protein content (B>A). Milk with the aggregate genotype A1A2-BB had the best firmness of curd and also the best protein and fat contents. This is in agreement with another investigation (Vallas et al., 2010), where curd firmness had the highest genetic correlation with milk protein percentage (0.48), suggesting that a high protein percentage results in a favourable curd firmness. It has been reported (Cassandro et al., 2008) that there is a correlation coefficient of 0.44 between curd firmness and protein percentage, which is in agreement with the results found in this experiment. The genetic correlations of −0.24 and −0.07 reported (Ikonen et al., 1999a, 2004) for the same traits, however, are different. These inconsistencies indicate that different methodologies used for the investigations may influence the results (Pretto et al., 2011). Curd firmness showed a weak positive genetic correlation (Vallas et al., 2010) with milk fat percentage (0.25) and a weak negative genetic correlation with milk yield (−0.29). Therefore, selection for improved curd firmness may be associated with a somewhat higher protein and fat percentage and slightly reduced milk yield. Genetic correlations for curd firmness with milk yield and fat percentage were negligible in previous studies (Cassandro et al., 2008; Ikonen et al., 1999a). As for the impact of the CSN2 A1 and A2 alleles on milk production, the A2 allele seems to have slight advantage over A1 in the aggregate β-κ-CN genotype. Genotypes of β-LG were associated with both milk coagulation traits (p<0.001), but had no a significant effect on either milk yield (p=0.462), protein percentage (p=0.648), nor fat percentage (p=0.356) and SCS (p=0.571).
The β-κ-CN locus had a strong effect on protein and fat content and milk coagulation properties. Milk with the β-κ-CN aggregate genotype A1A2-BB had the best firmness of curd and also the best protein and fat contents. The aggregate genotype A2A2-BB, haplotype
The research leading to these results is co-financed by the European Community`s Regional Development Fund in the framework of the Competence Centre Programme of the Enterprise Estonia under project No EU22868; EU27789; EU28662; EU30002 of Bio-Competence Centre Of Healthy Dairy Products (Tervisliku Piima Biotehnoloogiate Arenduskeskus OÜ) and by the Targeted Finance Project 1080045s07.