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Investigating associations between milk metabolite profiles and milk traits of Holstein cows
Research Unit Genetics and Biometry, Leibniz Institute for Farm Animal Biology, Dummerstorf, Germany.
Research Unit Genetics and Biometry, Leibniz Institute for Farm Animal Biology, Dummerstorf, Germany.
Landeskontrollverband für Leistungs-und Qualitatsprufung Mecklenburg-Vorpommern e.V. (LKV), Güstrow, Germany.
Landeskontrollverband für Leistungs-und Qualitatsprufung Mecklenburg-Vorpommern e.V. (LKV), Güstrow, Germany.
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2013 (English)In: Journal of Dairy Science, ISSN 0022-0302, E-ISSN 1525-3198, Vol. 96, no 3, 1521-34 p.Article in journal (Refereed) Published
Abstract [en]

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.

Place, publisher, year, edition, pages
New York, USA: Elsevier, 2013. Vol. 96, no 3, 1521-34 p.
Keyword [en]
metabolite profile, milk trait, biomarker candidate
National Category
Bioinformatics and Systems Biology
Identifiers
URN: urn:nbn:se:oru:diva-40719DOI: 10.3168/jds.2012-5743ISI: 000315061700019PubMedID: 23438684Scopus ID: 2-s2.0-84874342054OAI: oai:DiVA.org:oru-40719DiVA: diva2:778527
Available from: 2015-01-10 Created: 2015-01-10 Last updated: 2015-03-20Bibliographically approved

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