Abstract

Mastitis, a bacterial intramammary infection, is one of the recently prioritized research thematic areas in the dairy cattle breeding programs. Genetic evaluation of mastitis is performed either with crosssectional or longitudinal models. The objective of this study was to develop better longitudinal models through the analysis of simulated somatic cell count (SC C) which is often used as a proxy to label clinical mastitis. Data were simulated for 2 traits: intramammary infection and recovery, for two scenarios (28% and 95% cases/lactation) and for two daughter groups of 60 and 150 per sire distributed over 1200 herds. Weekly observations for SCC were simulated assuming a baseline curve for non-mastitis cows and deviations in the case of a mastitis event. Binary data were created to define presence or absence of mastitis as 1 if the SCC was above pre-specified boundary (200000 cells/mL), and 0 otherwise. The boundary was allowed to vary along the lactation curve modeled by a spline function with a multiple of 10 or 15. The dynamic nature of the SCC was taken into consideration with the longitudinal approach; and the patterns were captured by modelling transition probabilities of moving across the boundary. Thus, a transition from below to above the boundary is an indicator of the probability to contract mastitis, and a transition from above to below the boundary is an indicator of the recovery process. Sire model with mean, fixed herd and random sire effects was fitted to calculate the estimated breeding values for intramammary infection and recovery using Bayesian inference and MCMC simulations in DMU, a statistical package for analyzing multivariate mixed models. Our preliminary results showed that the estimation accuracy or the correlation between true and estimated breeding value for the simulated intramammary infection mastitis was 0.72, which is as high as the estimations based on clinical mastitis. The estimation accuracy for recovery (0.42) was not as high as for getting infection. However, the transition probability model enables us to generate breeding values for the recovery process. The MCMC nonlinear and longitudinal approach leads to more precise genetic evaluation. This is because the MCMC fits well to the binary nature of getting infection; and the longitudinal approach uses more available information for the analysis.

Published in

Title: 15th International Conference on Production Diseases in Farm Animals, Book of Abstracts
ISBN: 978-91-576-9150-7
Publisher: SLU

SLU Authors

• Welderufael, Berihu

  • Department of Animal Biosciences, Swedish University of Agricultural Sciences
    
  • Aarhus University

• de Koning, Dirk-Jan

  • Department of Animal Biosciences, Swedish University of Agricultural Sciences
    

• Fikse, Freddy

  • Department of Animal Biosciences, Swedish University of Agricultural Sciences
    

• Strandberg, Erling

  • Department of Animal Biosciences, Swedish University of Agricultural Sciences
    

UKÄ Subject classification

Genetics and Breeding


Permanent link to this page (URI)

https://res.slu.se/id/publ/51775