Genetics Selection Evolution

, 38:583

Genetic analysis of growth curves using the SAEM algorithm

  • Florence Jaffrézic
  • Cristian Meza
  • Marc Lavielle
  • Jean-Louis Foulley
Open Access
Research

DOI: 10.1186/1297-9686-38-6-583

Cite this article as:
Jaffrézic, F., Meza, C., Lavielle, M. et al. Genet Sel Evol (2006) 38: 583. doi:10.1186/1297-9686-38-6-583
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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.

Keywords

genetic analysis growth curves longitudinal data stochastic approximation EM algorithm 

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Copyright information

© INRA, EDP Sciences 2006

Authors and Affiliations

  • Florence Jaffrézic
    • 1
  • Cristian Meza
    • 2
  • Marc Lavielle
    • 2
  • Jean-Louis Foulley
    • 1
  1. 1.Quantitative and Applied GeneticsINRAJouy-en-Josas CedexFrance
  2. 2.Laboratoire de MathématiquesUniversité Paris SudOrsayFrance

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