La Trobe
57076_Pryce,J_2016.pdf (502.79 kB)

Invited review: Opportunities for genetic improvement of metabolic diseases

Download (502.79 kB)
journal contribution
posted on 2023-03-28, 22:38 authored by Jennie PryceJennie Pryce, KLP Gaddis, A Koeck, C Bastin, Mary Abdelsayed, N Gengler, F Miglior, B Heringstad, C Egger-Danner, KF Stock, AJ Bradley, JB Cole
Metabolic disorders are disturbances to one or more of the metabolic processes in dairy cattle. Dysfunction of any of these processes is associated with the manifestation of metabolic diseases or disorders. In this review, data recording, incidences, genetic parameters, predictors, and status of genetic evaluations were examined for (1) ketosis, (2) displaced abomasum, (3) milk fever, and (4) tetany, as these are the most prevalent metabolic diseases where published genetic parameters are available. The reported incidences of clinical cases of metabolic disorders are generally low (less than 10% of cows are recorded as having a metabolic disease per herd per year or parity/lactation). Heritability estimates are also low and are typically less than 5%. Genetic correlations between metabolic traits are mainly positive, indicating that selection to improve one of these diseases is likely to have a positive effect on the others. Furthermore, there may also be opportunities to select for general disease resistance in terms of metabolic stability. Although there is inconsistency in published genetic correlation estimates between milk yield and metabolic traits, selection for milk yield may be expected to lead to a deterioration in metabolic disorders. Under-recording and difficulty in diagnosing subclinical cases are among the reasons why interest is growing in using easily measurable predictors of metabolic diseases, either recorded on-farm by using sensors and milk tests or off-farm using data collected from routine milk recording. Some countries have already initiated genetic evaluations of metabolic disease traits and currently most of these use clinical observations of disease. However, there are opportunities to use clinical diseases in addition to predictor traits and genomic information to strengthen genetic evaluations for metabolic health in the future.


J. E. Pryce acknowledges financial support from the Australian Government (Department of Agriculture and Water Resources) Rural R&D for Profit Programme (MIRprofit). A. Koeck was supported by the Dairy Research Cluster Initiative [Dairy Farmers of Canada (Ottawa, ON), Agriculture and Agri-Food Canada (Ottawa, ON), the Canadian Dairy Network (Guelph, ON), and Canadian Dairy Commission (Ottawa, ON)]. B. Heringstad was supported by FFL/JA project number 207792 (Norway). C. Bastin and N. Gengler were supported by the National Fund for Scientific Research (FNRS), Brussels, Belgium, through different projects and the European Commission under the Seventh Framework Program through the GplusE project, grant agreement FP7-KBBE-613689; the content of the paper reflects only the view of the authors; the community is not liable for any use that may be made of the information contained in this publication. J. B. Cole was supported by appropriated project 1265-31000-096-00, "Improving Genetic Predictions in Dairy Animals Using Phenotypic and Genomic Information," of the Agricultural Research Service of the USDA, and J. B. Cole and K. L. Parker Gaddis were supported by Agriculture and Food Research Initiative Competitive Grant No. 2013-68004-20365, "Improving Fertility of Dairy Cattle Using Translational Genomics."


Publication Date



Journal of Dairy Science






19p. (p. 6855-6873)


FASS and Elsevier



Rights Statement

© 2016, THE AUTHORS. Published by FASS and Elsevier Inc. on behalf of the American Dairy Science Association®. This is an open access article under the CC BY-NC-ND license (

Usage metrics

    Journal Articles


    No categories selected


    Ref. manager