Comparison of models for genetic evaluation of survival traits in dairy cattle: a simulation study.
Three models for the analysis of functional survival data in dairy cattle were compared using stochastic simulation. The simulated phenotype for survival was defined as a month after the first calving (from 1 to 100) in which a cow was involuntarily removed from the herd. Parameters for simulation were based on survival data of the Canadian Jersey population. Three different levels of heritability of survival (0.100, 0.050 and 0.025) and two levels of numbers of females per generation (2000 or 4000) were considered in the simulation. Twenty generations of random mating and selection (on a second trait, uncorrelated with survival) with 20 replicates were simulated for each scenario. Sires were evaluated for survival of their daughters by three models: proportional hazard (PH), linear multiple-trait (MT), and random regression (RR) animal models. Different models gave different ranking of sires with respect to survival of their daughters. Correlations between true and estimated breeding values for survival to five different points in a cow's lifetime after the first calving (120 and 240 days in milk after first, second, third and fourth calving) favoured the PH model, followed by the RR model evaluations. Rankings of models were independent of the heritability level, female population size and sire progeny group size (20 or 100). The RR model, however, showed a slight superiority over MT and PH models in predicting the proportion of sire's daughters that survived to the five different end-points after the first calving.
Jamrozik
J
J
Centre for Genetic Improvement of Livestock, Animal and Poultry Science, University of Guelph, Guelph, ON, Canada. jjamrozi@uoguelph.ca
Fatehi
J
J
Schaeffer
L R
LR
D000818:Animals; D002417:Cattle / Q000235:genetics*; D005260:Female; D016014:Linear Models; D008136:Longevity / Q000235:genetics*; D008297:Male; D008957:Models, Genetic; D011247:Pregnancy; D016016:Proportional Hazards Models; D012044:Regression Analysis; D013269:Stochastic Processes