- Research
- Open access
- Published:
Genetic analysis of growth curves using the SAEM algorithm
Genetics Selection Evolution volume 38, Article number: 583 (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.
(To access the full article, please see PDF)
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
Open Access This article is published under license to BioMed Central Ltd. This is an Open Access article is distributed under the terms of the Creative Commons Attribution License ( https://creativecommons.org/licenses/by/2.0 ), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
About this article
Cite this article
Jaffrézic, F., Meza, C., Lavielle, M. et al. Genetic analysis of growth curves using the SAEM algorithm. Genet Sel Evol 38, 583 (2006). https://doi.org/10.1186/1297-9686-38-6-583
Received:
Accepted:
Published:
DOI: https://doi.org/10.1186/1297-9686-38-6-583