Genetic evaluation of mastitis liability and recovery through longitudinal analysis of transition probabilities
| Autor: | Erling Strandberg, Daniel Thorburn, Jessica Franzén, Jorge I Urioste |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2012 |
| Předmět: |
lcsh:QH426-470
Biology Breeding 01 natural sciences Genetic correlation 010104 statistics & probability Statistics medicine Genetics Animals Lactation Genetics(clinical) Genetic Predisposition to Disease Longitudinal Studies 0101 mathematics Udder Mastitis Bovine Economic consequences Dairy cattle Survival analysis Ecology Evolution Behavior and Systematics Probability lcsh:SF1-1100 Binary response Models Statistical Models Genetic business.industry Research 0402 animal and dairy science 04 agricultural and veterinary sciences General Medicine medicine.disease 040201 dairy & animal science Mastitis Biotechnology lcsh:Genetics medicine.anatomical_structure Animal Science and Zoology Cattle Female lcsh:Animal culture business Somatic cell count |
| Zdroj: | Genetics Selection Evolution Genetics Selection Evolution, Vol 44, Iss 1, p 10 (2012) Genetics, Selection, Evolution : GSE |
| ISSN: | 1297-9686 |
| DOI: | 10.1186/1297-9686-44-10 |
| Popis: | 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. Background Mastitis is a common disease in dairy cattle with severe economic consequences [1]. It has been shown that susceptibility to the disease varies between breeds and individuals, with heritabilities ranging from 0.07 to 0.12 [2,3]. Genetic evaluation of the disease is an important issue and a wide range of methods is available. Methods can be divided into cross-sectional or longitudinal approaches. Cross-sectional methods consider each lactation as a static process, whereas longitudinal methods model changes in disease states during the lactation. The variables mostly used in mastitis analyses are recorded cases of clinical mastitis (CM) or somatic cell counts (SCC). |
| Databáze: | OpenAIRE |
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