Genetic evaluation of mastitis liability and recovery through longitudinal analysis of transition probabilities

Genetics Selection Evolution

Research

Erling Strandberg¹  Jorge I Urioste²  Jessica Franzén³  Daniel Thorburn⁴ 

[1] Department of Animal Breeding and Genetics, Swedish University of Agricultural Sciences, Box 7023, 750 07, Uppsala, Sweden;Department of Animal Breeding and Genetics, Swedish University of Agricultural Sciences, Box 7023, 750 07, Uppsala, Sweden;Departamento de Producción Animal y Pasturas, Facultad de Agronomía, UDELAR, Garzón 780, 12900, Montevideo, Uruguay;Department of Animal Breeding and Genetics, Swedish University of Agricultural Sciences, Box 7023, 750 07, Uppsala, Sweden;Department of Statistics, Stockholm University, 106 91, Stockholm, Sweden;Department of Statistics, Stockholm University, 106 91, Stockholm, Sweden;

关键词: Markov Chain Monte Carlo;    Genetic Correlation;    Mastitis;    Somatic Cell Count;    Genetic Evaluation;   

DOI  :  10.1186/1297-9686-44-10

 received in 2011-06-14, accepted in 2012-04-04,  发布年份 2012

来源: Springer

【 摘 要 】

BackgroundMany 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.MethodsGenetic 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.ResultsCorrelations 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.ConclusionsThe 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.

【 授权许可】

CC BY   
© Franzén et al; licensee BioMed Central Ltd. 2012