Journal of Animal Science, Vol 75, Issue 1 88-94, Copyright © 1997 by American Society of Animal Science

JOURNAL ARTICLE

Genetic analysis of discrete reproductive traits in sheep using linear and nonlinear models: II. Goodness of fit and predictive ability

C. A. Matos, D. L. Thomas, D. Gianola, M. Perez-Enciso and L. D. Young
Department of Animal Sciences, University of Wisconsin-Madison 53706, USA.

The performance of linear and nonlinear sire and animal models in the analyses of reproductive traits (fertility, litter size, and ovulation rate) in two sheep populations (Rambouillet and Finnsheep) was compared in terms of goodness of fit and predictive ability. Linear sire (LSM) and animal (LAM) models were used with all traits. Nonlinear models were the threshold, Poisson, and negative binomial. Threshold sire (TSM) and animal (TAM) models were also used with all traits. Litter size and ovulation rate were analyzed also with Poisson and negative binomial sire (PSM and NBSM, respectively) and animal (PAM and NBAM, respectively) models. Variance components were those reported in the companion article. For PAM a new set of variance components derived from estimates found with the linear animal model also was used (PAM-L). Mean squares error (MSE) and correlations between fitted and observed values were used to assess goodness of fit. Predictive ability was assessed by partitioning the data sets for the different traits into two subsets with the restriction that all levels of fixed effects were represented in each subset. Parameters from one subset were employed to predict observations in the other, and then MSE and correlations between observed and predicted values were used as criteria for model comparison. Within estimation procedure, breed, and trait, goodness of fit of sire and animal models was similar. Linear and threshold models resulted in similar fit, and both outperformed Poisson and negative binomial models. In terms of predictive ability, linear and threshold models performed only slightly better than Poisson and negative binomial models. Goodness of fit and predictive ability generally were better when models included permanent environmental effects.

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