Current Address: Via de acesso Paulo Donato Castellane s/n. Departamento de Zootecnia. Prédio 2. CEP: 14884-900, Jaboticabal, SP. Brazil.
Random regression analyses using B-splines functions to model growth from birth to adult age in Canchim cattle*
Version of Record online: 28 OCT 2010
© 2010 Blackwell Verlag GmbH
Journal of Animal Breeding and Genetics
Volume 127, Issue 6, pages 433–441, December 2010
How to Cite
Baldi, F., Alencar, M.M. and Albuquerque, L.G. (2010), Random regression analyses using B-splines functions to model growth from birth to adult age in Canchim cattle. Journal of Animal Breeding and Genetics, 127: 433–441. doi: 10.1111/j.1439-0388.2010.00873.x
This work was supported by Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq) and Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP).
- Issue online: 16 NOV 2010
- Version of Record online: 28 OCT 2010
- Received: 31 August 2009; accepted: 24 March 2010
- Beef cattle;
- covariance function;
- genetic parameters;
- piece-wise polynomials;
- random regression
The objective of this work was to estimate covariance functions using random regression models on B-splines functions of animal age, for weights from birth to adult age in Canchim cattle. Data comprised 49 011 records on 2435 females. The model of analysis included fixed effects of contemporary groups, age of dam as quadratic covariable and the population mean trend taken into account by a cubic regression on orthogonal polynomials of animal age. Residual variances were modelled through a step function with four classes. The direct and maternal additive genetic effects, and animal and maternal permanent environmental effects were included as random effects in the model. A total of seventeen analyses, considering linear, quadratic and cubic B-splines functions and up to seven knots, were carried out. B-spline functions of the same order were considered for all random effects. Random regression models on B-splines functions were compared to a random regression model on Legendre polynomials and with a multitrait model. Results from different models of analyses were compared using the REML form of the Akaike Information criterion and Schwarz′ Bayesian Information criterion. In addition, the variance components and genetic parameters estimated for each random regression model were also used as criteria to choose the most adequate model to describe the covariance structure of the data. A model fitting quadratic B-splines, with four knots or three segments for direct additive genetic effect and animal permanent environmental effect and two knots for maternal additive genetic effect and maternal permanent environmental effect, was the most adequate to describe the covariance structure of the data. Random regression models using B-spline functions as base functions fitted the data better than Legendre polynomials, especially at mature ages, but higher number of parameters need to be estimated with B-splines functions.