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Journal Article

Milk metabolites and their genetic variability


Willmitzer,  L.
Small Molecules, Department Willmitzer, Max Planck Institute of Molecular Plant Physiology, Max Planck Society;


Lisec,  J.
Small Molecules, Department Willmitzer, Max Planck Institute of Molecular Plant Physiology, Max Planck Society;


Repsilber,  D.
BioinformaticsCRG, Cooperative Research Groups, Max Planck Institute of Molecular Plant Physiology, Max Planck Society;

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Wittenburg, D., Melzer, N., Willmitzer, L., Lisec, J., Kesting, U., Reinsch, N., et al. (2013). Milk metabolites and their genetic variability. Journal of Dairy Science, 96(4), 2557-2569. doi:10.3168/jds.2012-5635.

Cite as: https://hdl.handle.net/11858/00-001M-0000-0014-1D70-8
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.