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.

Our objective was to perform a genome-wide association study for content in bovine milk of α(S1)-casein (α(S1)-CN), α(S2)-casein (α(S2)-CN), β-casein (β-CN), κ-casein (κ-CN), α-lactalbumin (α-LA), β-lactoglobulin (β-LG), casein index, protein percentage, and protein yield using a 50K single nucleotide polymorphism (SNP) chip. In total, 1,713 Dutch Holstein-Friesian cows were genotyped for 50,228 SNP and a 2-step association study was performed. The first step involved a general linear model and the second step used a mixed model accounting for all family relationships. Associations with milk protein content and composition were detected on 20 bovine autosomes. The main genomic regions associated with milk protein composition or protein percentage were found on chromosomes 5, 6, 11, and 14. The number of chromosomal regions showing significant (false discovery rate <0.01) effects ranged from 3 for β-CN and 3 for β-LG to 12 for α(S2)-CN. A genomic region on Bos taurus autosome (BTA) 6 was significantly associated with all 6 major milk proteins, and a genomic region on BTA 11 was significantly associated with the 4 caseins and β-LG. In addition, regions were detected that only showed a significant effect on one of the milk protein fractions: regions on BTA 13 and 22 with effects on α(S1)-CN; regions on BTA 1, 9, 10, 17, 19, and 28 with effects on α(S2)-CN; a region on BTA 6 with an effect on β-CN; regions on BTA 13 and 21 with effects on κ-CN; regions on BTA 1, 5, 9, 16, 17, and 26 with effects on α-LA; and a region on BTA 24 with an effect on β-LG. The proportion of genetic variance explained by the SNP showing the strongest association in each of these genomic regions ranged from <1% for α(S1)-CN on BTA 22 to almost 100% for casein index on BTA 11. Variation associated with regions on BTA 6, 11, and 14 could in large part but not completely be explained by known protein variants of β-CN (BTA 6), κ-CN (BTA 6), and β-LG (BTA 11) or DGAT1 variants (BTA 14). Our results indicate 3 regions with major effects on milk protein composition, in addition to several regions with smaller effects involved in the regulation of milk protein composition.

Heritability of susceptibility to Johne's disease in cattle has been shown to vary from 0.041 to 0.159. Although the presence of genetic variation involved in susceptibility to Johne's disease has been demonstrated, the understanding of genes contributing to the genetic variance is far from complete. The objective of this study was to contribute to further understanding of genetic variation involved in susceptibility to Johne's disease by identifying associated chromosomal regions using a genome-wide association approach. Log-transformed ELISA test results of 265,290 individual Holstein-Friesian cows from 3,927 herds from the Netherlands were analyzed to obtain sire estimated breeding values for Mycobacterium avium subspecies paratuberculosis (MAP)-specific antibody response in milk using a sire-maternal grandsire model with fixed effects for parity, year of birth, lactation stage, and herd; a covariate for milk yield on test day; and random effects for sire, maternal grandsire, and error. For 192 sires with estimated breeding values with a minimum reliability of 70%, single nucleotide polymorphism (SNP) typing was conducted by a multiple SNP analysis with a random polygenic effect fitting 37,869 SNP simultaneously. Five SNP associated with MAP-specific antibody response in milk were identified distributed over 4 chromosomal regions (chromosome 4, 15, 18, and 28). Thirteen putative SNP associated with MAP-specific antibody response in milk were identified distributed over 10 chromosomes (chromosome 4, 14, 16, 18, 19, 20, 21, 26, 27, and 29). This knowledge contributes to the current understanding of genetic variation involved in Johne's disease susceptibility and facilitates control of Johne's disease and improvement of health status by breeding.

Data were available for 12 poultry microsatellites and 29 poultry single nucleotide polymorphisms (SNPs), and for 34 cattle microsatellites and 36 cattle SNPs. Stochastic permutation was used to determine the number of SNPs needed to obtain the same average information content as a given number of microsatellites. For poultry, the information content averaged 0.71 for the 12 microsatellites compared to 0.72 for the 29 SNPs. For cattle, the information content averaged 0.92 for the 34 microsatellites compared with 0.79 for the 36 SNPs. This study shows that, for each microsatellite, three SNPs are needed to obtain the same average information content.

SummaryThe objective of this study was to perform a whole genome scan to detect quantitative trait loci (QTL) for milk protein composition in 849 Holstein–Friesian cows originating from seven sires. One morning milk sample was analysed for the major milk proteins using capillary zone electrophoresis. A genetic map was constructed with 1341 single nucleotide polymorphisms, covering 2829 centimorgans (cM) and 95% of the cattle genome. The chromosomal regions most significantly related to milk protein composition (Pgenome < 0.05) were found on Bos taurus autosomes (BTA) 6, 11 and 14. The QTL on BTA6 was found at about 80 cM, and affected αS1‐casein, αS2‐casein, β‐casein and κ‐casein. The QTL on BTA11 was found at 124 cM, and affected β‐lactoglobulin, and the QTL on BTA14 was found at 0 cM, and affected protein percentage. The proportion of phenotypic variance explained by the QTL was 3.6% for β‐casein and 7.9% for κ‐casein on BTA6, 28.3% for β‐lactoglobulin on BTA11, and 8.6% for protein percentage on BTA14. The QTL affecting αS2‐casein on BTA6 and 17 showed a significant interaction. We investigated the extent to which the detected QTL affecting milk protein composition could be explained by known polymorphisms in β‐casein, κ‐casein, β‐lactoglobulin and DGAT1 genes. Correction for these polymorphisms decreased the proportion of phenotypic variance explained by the QTL previously found on BTA6, 11 and 14. Thus, several significant QTL affecting milk protein composition were found, of which some QTL could partially be explained by polymorphisms in milk protein genes.

Recently, selective breeding was proposed as a means of changing the fatty acid composition of milk to improve its nutritional quality. Before implementing such breeding objectives, effects on other economically important traits should be investigated. The objectives of this study were to examine 1) the effect of milk fat composition, and 2) the effect of polymorphisms of DGAT1 and SCD1 genes on female fertility in commercial Dutch Holstein-Friesian cattle. Data on 1,745 first-lactation cows were analyzed by fitting linear mixed models. We found that higher concentrations of trans fatty acids within total milk fat negatively affected reproductive performance. Furthermore, results suggested a potential effect of the DGAT1 polymorphism on nonreturn rates for insemination 28 and 56 d after the first service. Our results can be used to assess the correlated effects of breeding for improved milk fat composition on reproduction, thereby allowing for better evaluation of breeding programs before implementation.

Studies have reported genetic variation in milk urea nitrogen (MUN) between cows, suggesting genetic differences in nitrogen efficiency between cows. In this paper, the results of a genome-wide scan to identify quantitative trait loci (QTL) that contribute to genetic variation in MUN and MUN yield are presented. Two to 3 morning milk samples were taken from 1,926 cows, resulting in 5,502 test-day records. Test-day records were corrected for systematic environmental effects using a repeatability animal model. Averages of corrected phenotypes of 849 cows, belonging to 7 sire families, were used in an across-family multimarker regression approach to detect QTL. Animals were successfully genotyped for 1,341 single nucleotide polymorphisms. The QTL analysis resulted in 4 chromosomal regions with suggestive QTL: Bos taurus autosomes (BTA) 1, 6, 21, and 23. On BTA 1, 2 suggestive QTL affecting MUN were detected at 60 and 140 cM. On BTA 6, 1 suggestive QTL affecting both MUN and MUN yield was detected at 103 cM. On BTA 21, 1 suggestive QTL affecting MUN yield was detected at 83 cM. On BTA 23, 1 suggestive QTL affecting MUN was detected at 54 cM. Quantitative trait loci for MUN and MUN yield were suggestive and each explained between 2 and 3% of the phenotypic variance.

The objectives of this study were to make subsets of high-density (HD) loci based on localized haplotype clusters, without loss of genomic information, to reduce computing time compared with the use of all HD loci and to investigate the effect on the reliability of the direct genomic value (DGV) when using this HD subset based on localized haplotype clusters in the genomic evaluation for Holstein-Friesians. The DNA was isolated from semen samples of 548 bulls (key ancestors) of the EuroGenomics Consortium, a collaboration between 4 European dairy cattle breeding organizations and scientific partners. These bulls were genotyped with the BovineHD BeadChip [~777,000 (777K) single nucleotide polymorphisms (SNP); Illumina Inc., San Diego, CA] and used to impute all 30,483 Holstein-Friesians from the BovineSNP50 BeadChip [~50,000 (50K) SNP; Illumina Inc.] to HD, using the BEAGLE software package. The final data set consisted of 30,483 animals and 603,145 SNP. For each locus, localized haplotype clusters (i.e., edges of the fitted graph model) identifications were obtained from BEAGLE. Three subsets [38,000 (38K), 116,000 (116K), and 322,000 (322K) loci] were made based on deleting obsolete loci (i.e., loci that do not give extra information compared with the neighboring loci). A fourth data set was based on 38K SNP, which is currently used for routine genomic evaluation at the Cattle Improvement Cooperative (CRV, Arnhem, the Netherlands). A validation study using the HD loci subsets based on localized haplotype clusters was performed for 9 traits (production, conformation, and functional traits). Error of imputation from 50K to HD averaged 0.78%. Three thresholds (0.17, 0.05, and 0.008%) were used for the identification of obsolete HD loci based on localized haplotype clusters to obtain a desired number of HD loci (38K, 116K, and 322K). On average, 46% (using threshold 0.008%) to 93% (using threshold 0.17%) of HD loci were eliminated. The computing time was about 9 d for 38K loci, 15.5d for 116K loci, 21d for 322K loci, and 7.5 d for 38K SNP. The increase in reliability of DGV compared with pedigree-based estimated breeding values for kilograms of protein was similar for 322K and 116K loci (30.7%), but was 1.5 to 2% higher compared with 38K loci and 38K SNP. Averaged over 9 traits, subset 116K loci resulted in a higher increase in reliability compared with 38K loci and 38K SNP. Eliminating obsolete loci enormously decreased the amount of data to be analyzed for genomic evaluations. The more HD loci used in a genomic evaluation, the higher the increase in reliability of DGV. It is possible to increase the reliability of DGV by 1 to 2% compared with the SNP currently used for routine genomic evaluation.