Open Access

Genetic analysis of growth curves using the SAEM algorithm

  • Florence Jaffrézic1Email author,
  • Cristian Meza2,
  • Marc Lavielle2 and
  • Jean-Louis Foulley1
Genetics Selection Evolution200638:583

Received: 2 February 2006

Accepted: 10 August 2006

Published: 28 November 2006


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.


genetic analysis growth curves longitudinal data stochastic approximation EM algorithm

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Authors’ Affiliations

Quantitative and Applied Genetics, INRA
Laboratoire de Mathématiques, Université Paris Sud


© INRA, EDP Sciences 2006