ARS | Publication request: Discrete Time Survival Analysis of Longevity in a Colony of Dog Guides

Submitted to: Journal of Animal Science
Publication Type: Abstract
Publication Acceptance Date: February 28, 2005
Publication Date: July 24, 2005
Citation: Cole, J.B., Southey, B.R., Franke, D.E., Leighton, E.A. 2005. Discrete time survival analysis of longevity in a colony of dog guides [abstract]. Journal of Animal Science. 83(Suppl. 1):103.

Technical Abstract: Working life for 1,177 German Shepherd (GS) and 1,724 Labrador Retriever (LR) dogs that worked as guides for the blind was studied using discrete time survival analysis. Total years worked after graduation, total months worked after graduation, months worked between graduation and 18 mo (EWL), and months worked beyond 18 mo (LWL) were analyzed using complementary log-log animal and maternal effects models. Animals working 10 or more years were combined in a single group. Censoring rates were 91.76% (44.87%) and 94.90% (48.90%) for EWL (LWL) in GS and LR, respectively. Explanatory variables were duration of time interval (months or years), contemporary group, sex, and inbreeding coefficient. Estimates of explanatory variables obtained within the same period across models and different time intervals were similar. No sex differences were observed and the hazard of culling increased with increasing inbreeding coefficient. Maternal effects were small and non-significant in both breeds for all traits. Heritability estimates ranged from 0.05 to 0.12 in GS and 0.04 to 0.15 in LR and were lowest for later working life, intermediate for total working life in months or years, and highest for early working life. These estimates were higher than the previously reported estimates of 0.03 (0.02) and 0.05 (0.03) for EWL (LWL) in GS and LR, respectively, that were obtained with a Weibull sire model. Pearson's product-moment correlations among sire estimated breeding values for EWL and LWL were 0.92 and 0.83 for GS and LR, respectively, suggesting that EWL and LWL are biologically different traits. These results suggest that there is sufficient genetic variability that can be exploited to genetically improve working life.