Investigating associations between milk metabolite profiles and milk traits of Holstein cows

• Published in: Journal of dairy science Vol. 96; no. 3; pp. 1521 - 1534
• Main Authors: Melzer, N., Wittenburg, D., Hartwig, S., Jakubowski, S., Kesting, U., Willmitzer, L., Lisec, J., Reinsch, N., Repsilber, D.
• Format: Journal Article
• Language: English
• Published: United States Elsevier Inc 01.03.2013
• Subjects: Animals; artificial intelligence; biomarker candidate; biomarkers; Biomarkers - metabolism; casein; Cattle - metabolism; commercial farms; cows; dairy cattle; Dairying - methods; disease prevention; Energy Metabolism; Female; Food Quality; Holstein; ketosis; Lactation - metabolism; lactic acid; lactose; mastitis; metabolite profile; metabolites; milk; Milk - chemistry; milk analysis; milk trait; Quantitative Trait, Heritable; sires; somatic cells; uracil; milk trait; metabolite profile; biomarker candidate
• ISSN: 0022-0302; 1525-3198; 1525-3198
• DOI: 10.3168/jds.2012-5743

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.