Genetic parameters for various random regression models to describe the weight data of pigs

Abstract

Various random regression models have been advocated for the fitting of covariance structures. It was suggested that a spline model would fit better to weight data than a random regression model that utilizes orthogonal polynomials. The objective of this study was to investigate which kind of random regression model fits best to weight data of pigs. Two random regression models that described weight of individual pigs, one using orthogonal polynomials, and the other using splines, were compared. A comparison with a multivariate model, Akaike's information criterion, and the Bayesian-Schwarz information criterion were used to select the best model. Genetic, permanent environmental, and total variances increased with age. Heritabilities for the multivariate model ranged from 0.14 to 0.19, and for both random regression models the heritabilities were fluctuating around 0.17. Both genetic and phenotypic correlations decreased when the interval between measurements increased. The spline model needed fewer parameters than the multivariate and polynomial models. Akaike's information criterion was least for the spline model and greatest for the multivariate model. The Bayesian-Schwarz information criterion was least for the polynomial model and greatest for the multivariate model. Residuals of all models were normally distributed. Based on these results, it is concluded that random regression models provide the best fit to pig weight data.