Genetic analysis of protein composition of bovine milk

This thesis is part of the Dutch Milk Genomics Initiative, and the general aim was to obtain more insight into the genetic background of bovine milk protein composition. Morning milk samples from roughly 2000 cows were analyzed for the six major milk proteins (αS1-casein, αS2-casein, β-casein, κ-casein, α-lactalbumin and β-lactoglobulin) using capillary zone electrophoresis. The estimated genetic parameters for milk protein composition showed that there was considerable genetic variation for milk protein composition and that the genetic correlations among the six major milk proteins were low. There was a strong negative genetic correlation between β-lactoglobulin and total casein in milk. The presence of genetic variation justified the performance of in-depth genetic analyses such as linkage and association mapping. A linkage study was performed to screen the whole bovine genome to identify chromosomal regions affecting milk protein composition. This study resulted in ten chromosomal regions, of which regions on BTA6, 11 and 14 showed the largest effect on milk protein composition. The confidence intervals of these regions were large, in general. Therefore, an association study was performed to narrow down these chromosomal regions and to detect new chromosomal regions affecting milk protein composition. The association study resulted in four main regions on BTA5, 6, 11 and 14, and also new regions were detected. These new regions may, in addition to the four main regions, play a role in the genetic regulation of milk protein synthesis. The milk protein composition is important for technological properties of milk. An increase in casein index is preferable for the cheese production. Therefore, four scenario’s, to increase casein index in milk, were discussed. The first scenario has been termed genetic differentiation, the second scenario was genetic selection based on estimated breeding values, the third scenario was genetic selection based on genotypes, and the last scenario was genomic selection. These four scenarios illustrated that there are opportunities to utilize genetic variation in milk protein composition. <br/

Simultaneous deregression of cow and bull breeding values

The next step to increase the accuracy of genomic prediction is to extend reference populations with cows next to daughter proven bulls. Cows typically have estimated breeding values (EBV) with considerably lower reliabilities compared to bulls. This suggests that commonly used (approximate) deregression procedures for bulls may not be appropriate for cows. The objective of this study was to test an alternative approach to simultaneously de-regress EBV of cows and bulls, and to derive appropriate weights for those de-regressed EBV. First, the appropriate weights of the de-regressed EBV were derived, and then the de-regressed EBV were computed using those weights. The analyses showed that the methods were well able to accurately de-regress EBV and compute their weights, both for bulls and cows. Despite observed discrepancies between intermediate results and simulated values, final EBV and reliabilities correlated very well with original values

Fokken voor hogere kaasproductie : niet voer maar met name fokkerij beïnvloedt eiwitsamenstelling in melk

De hoeveelheid melkeiwit per lactatie per koe is meer dan verdubbeld: van 148 kilo in 1960 tot 330 kilo in 2009. Er is echter weinig kennis over de samenstelling van het melkeiwit en de mogelijkheden om deze met behulp van fokkerij te verbeteren. Fokken op het aandeel caseïnes in de melk blijkt haalbaar en dat is lucratief voor de kaasproductie

Integrate cow and bull data in genomic evaluation for conformation traits and claw health

The two objectives of this study were to investigate and find methods to successfully integrate cow data in the bull reference population for genomic evaluation and to investigate the effect of adding reference cows on the DGV reliability for conformation traits and claw health. Information from about 25,000 bulls and about 15,000 cows was available. Bulls were genotyped with the Illumina 50K SNP chip and the cows with the Illumina 10K SNP chip. All animals were imputed to an equal 50K SNP set. After SNP edits 37,995 SNP remain for all animals. As phenotypes, yield deviations, deregressed proofs (DRPs) with adjustments for cows and DRPs calculated based on matrix deregression will be used. The three kinds of phenotypes will be validated to investigate the effect on the reliability of direct genomic value for conformation traits and claw health

Comparison of information content for microsatellites and SNPs in poultry and cattle

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

Comparison of information content for microsatellites and SNPs in poultry and cattle

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

Whole genome scan to detect quantitative trait loci for bovine milk protein composition

The 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 aS1-casein, aS2-casein, ß-casein and ¿-casein. The QTL on BTA11 was found at 124 cM, and affected ß-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 ß-casein and 7.9% for ¿-casein on BTA6, 28.3% for ß-lactoglobulin on BTA11, and 8.6% for protein percentage on BTA14. The QTL affecting aS2-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 ß-casein, ¿-casein, ß-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