Genetic analysis of growth curves using the SAEM algorithmFlorence Jaffrézica, Cristian Mezab, Marc Lavielleb and Jean-Louis Foulleya
a Quantitative and Applied Genetics, INRA 78352 Jouy-en-Josas Cedex, France
b Laboratoire de Mathématiques, Université Paris Sud, 91400 Orsay, France
(Received 2 February 2006; accepted 10 August 2006; published online 28 November 2006)
Abstract - The analysis of nonlinear function-valued characters is very important in genetic studies, especially for growth traits of agricultural and laboratory species. Inference in nonlinear mixed effects models is, however, quite complex and is usually based on likelihood approximations or Bayesian methods. The aim of this paper was to present an efficient stochastic EM procedure, namely the SAEM algorithm, which is much faster to converge than the classical Monte Carlo EM algorithm and Bayesian estimation procedures, does not require specification of prior distributions and is quite robust to the choice of starting values. The key idea is to recycle the simulated values from one iteration to the next in the EM algorithm, which considerably accelerates the convergence. A simulation study is presented which confirms the advantages of this estimation procedure in the case of a genetic analysis. The SAEM algorithm was applied to real data sets on growth measurements in beef cattle and in chickens. The proposed estimation procedure, as the classical Monte Carlo EM algorithm, provides significance tests on the parameters and likelihood based model comparison criteria to compare the nonlinear models with other longitudinal methods.
Key words: genetic analysis / growth curves / longitudinal data / stochastic approximation EM algorithm
Correspondence and reprints: florence.Jaffrezic@jouy.inra.fr
© INRA, EDP Sciences 2006