title: Genetic evaluation of mastitis liability and recovery through longitudinal analysis of transition probabilities.
abstract: BACKGROUND
  Many methods for the genetic analysis of mastitis use a cross-sectional approach, which omits information on, e.g., repeated mastitis cases during lactation, somatic cell count fluctuations, and recovery process. Acknowledging the dynamic behavior of mastitis during lactation and taking into account that there is more than one binary response variable to consider, can enhance the genetic evaluation of mastitis.
  
  
  METHODS
  Genetic evaluation of mastitis was carried out by modeling the dynamic nature of somatic cell count (SCC) within the lactation. The SCC patterns were captured by modeling transition probabilities between assumed states of mastitis and non-mastitis. A widely dispersed SCC pattern generates high transition probabilities between states and vice versa. This method can model transitions to and from states of infection simultaneously, i.e. both the mastitis liability and the recovery process are considered. A multilevel discrete time survival model was applied to estimate breeding values on simulated data with different dataset sizes, mastitis frequencies, and genetic correlations.
  
  
  RESULTS
  Correlations between estimated and simulated breeding values showed that the estimated accuracies for mastitis liability were similar to those from previously tested methods that used data of confirmed mastitis cases, while our results were based on SCC as an indicator of mastitis. In addition, unlike the other methods, our method also generates breeding values for the recovery process.
  
  
  CONCLUSIONS
  The developed method provides an effective tool for the genetic evaluation of mastitis when considering the whole disease course and will contribute to improving the genetic evaluation of udder health.
authors.affiliation: Department of Animal Breeding and Genetics, Swedish University of Agricultural Sciences, Uppsala, Sweden. jessica.franzen@stat.su.se
authors.forename: Jessica
authors.initials: J
authors.lastname: Franzén
authors.forename: Daniel
authors.initials: D
authors.lastname: Thorburn
authors.forename: Jorge I
authors.initials: JI
authors.lastname: Urioste
authors.forename: Erling
authors.initials: E
authors.lastname: Strandberg
mesh_terms: D000818:Animals; D001947:Breeding; D002417:Cattle; D005260:Female; D020022:Genetic Predisposition to Disease; D007774:Lactation; D008137:Longitudinal Studies; D008414:Mastitis, Bovine / Q000235:genetics* / Q000503:physiopathology; D008957:Models, Genetic; D015233:Models, Statistical; D011336:Probability