| 1. |
- Krappmann, K, et al.
(författare)
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Short communication : evaluation of bovine milk residues from routine milk testing programs as DNA source for genotyping
- 2012
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Ingår i: Journal of Dairy Science. - New York, USA : Elsevier. - 0022-0302 .- 1525-3198. ; 95:9, s. 5436-41
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Tidskriftsartikel (refereegranskat)abstract
- Genome-wide association studies and genomic evaluation using a dense set of genetic markers both require a large number of genotyped individuals. Collection of the respective samples contributes substantially to the cost of the approach. In dairy cattle research, the use of residues from routine milk recording would be a cost-saving alternative to obtain samples for an appropriate number of individuals with specific phenotypes in a very short time. To assess the suitability of milk recording residues, we concurrently investigated milk residues obtained after standardized milk recording procedures and blood samples from 115 cows originating from 3 farms with different milking systems by genotyping 15 microsatellite markers. We found that 4% of the milk samples were possibly assigned to the wrong animal (i.e., conflicts) and that at least 27% of the milk residues were contaminated, as indicated by an extra allele not present in the blood sample. These additional alleles primarily originated from a sample with a higher somatic cell score that went through the milk sample analyzer in the milk laboratory before the target sample. Furthermore, additional allele carryover was observed across more than one sample, when the difference in somatic cell count between samples exceeded 100,000 cells/mL. Finally, in several samples, the extra allele could not be traced back to previous samples passing through the milk sample analyzer. One source of those contaminations might be sample collection on-farm due to milk traces from the previously milked cow in the hose. No correlation was found between the farm management and conflicts or contaminations. We conclude that residues from routine milk recording are not suitable for genomic evaluation or genome-wide association studies because of the high prevalence of contamination generated at several steps during the collection and processing of milk residual samples.
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| 2. |
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| 3. |
- Melzer, Nina, et al.
(författare)
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Investigating associations between milk metabolite profiles and milk traits of Holstein cows
- 2013
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Ingår i: Journal of Dairy Science. - New York, USA : Elsevier. - 0022-0302 .- 1525-3198. ; 96:3, s. 1521-34
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Tidskriftsartikel (refereegranskat)abstract
- In the field of dairy cattle research, it is of great interest to improve the detection and prevention of diseases (e.g., mastitis and ketosis) and monitor specific traits related to the state of health and management. During the standard milk performance test, traditional milk traits are monitored, and quality and quantity are screened. In addition to the standard test, it is also now possible to analyze milk metabolites in a high-throughput manner and to consider them in connection with milk traits to identify functionally important metabolites that can also serve as biomarker candidates. We present a study in which 190 milk metabolites and 14 milk traits of 1,305 Holstein cows on 18 commercial farms were investigated to characterize interrelations of milk metabolites between each other, to milk traits from the milk standard performance test, and to influencing factors such as farm and sire effect (half-sib structure). The effect of influencing factors (e.g., farm) varied among metabolites and traditional milk traits. The investigations of associations between metabolites and milk traits revealed groups of metabolites that show, for example, positive correlations to protein and casein, and negative correlations to lactose and pH. On the other hand, groups of metabolites jointly associated with the investigated milk traits can be identified and functionally discussed. To enable a multivariate investigation, 2 machine learning methods were applied to detect important metabolites that are highly correlated with the investigated traditional milk traits. For somatic cell score, uracil, lactic acid, and 9 other important metabolites were detected. Lactic acid has already been proposed as a biomarker candidate for mastitis in the recent literature. In conclusion, we found sets of metabolites eligible to predict milk traits, enabling the analysis of milk traits from a metabolic perspective and discussion of the possible functional background for some of the detected associations.
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| 4. |
- Wittenburg, Dörte, et al.
(författare)
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Milk metabolites and their genetic variability
- 2013
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Ingår i: Journal of Dairy Science. - New York, USA : Elsevier. - 0022-0302 .- 1525-3198. ; 96:4, s. 2557-69
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Tidskriftsartikel (refereegranskat)abstract
- The composition of milk is crucial to evaluate milk performance and quality measures. Milk components partly contribute to breeding scores, and they can be assessed to judge metabolic and energy status of the cow as well as to serve as predictive markers for diseases. In addition to the milk composition measures (e.g., fat, protein, lactose) traditionally recorded during milk performance test via infrared spectroscopy, novel techniques, such as gas chromatography-mass spectrometry, allow for a further analysis of milk into its metabolic components. Gas chromatography-mass spectrometry is suitable for measuring several hundred metabolites with high throughput, and thus it is applicable to study sources of genetic and nongenetic variation of milk metabolites in dairy cows. Heritability and mode of inheritance of metabolite measurements were studied in a linear mixed model approach including expected (pedigree) and realized (genomic) relationship between animals. The genetic variability of 190 milk metabolite intensities was analyzed from 1,295 cows held on 18 farms in Mecklenburg-Western Pomerania, Germany. Besides extensive pedigree information, genotypic data comprising 37,180 single nucleotide polymorphism markers were available. Goodness of fit and significance of genetic variance components based on likelihood ratio tests were investigated with a full model, including marker- and pedigree-based genetic effects. Broad-sense heritability varied from zero to 0.699, with a median of 0.125. Significant additive genetic variance was observed for highly heritable metabolites, but dominance variance was not significantly present. As some metabolites are particularly favorable for human nutrition, for instance, future research should address the identification of locus-specific genetic effects and investigate metabolites as the molecular basis of traditional milk performance test traits.
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