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Phage Phenomics: Physiological Approaches to Characterize Novel Viral Proteins
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Published on: June 11, 2015

Genetic analysis of growth curves using the SAEM algorithm.

Florence Jaffrézic1, Cristian Meza, Marc Lavielle

  • 1Quantitative and Applied Genetics, INRA 78352 Jouy-en-Josas Cedex, France. florence.Jaffrezic@jouy.inra.fr

Genetics, Selection, Evolution : GSE
|November 30, 2006
PubMed
Summary

This study introduces the SAEM algorithm for faster genetic analysis of nonlinear growth traits. It improves upon existing methods by accelerating convergence and enhancing robustness in mixed-effects models.

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Area of Science:

  • Genetics
  • Statistical modeling
  • Animal breeding

Background:

  • Nonlinear function-valued characters are crucial in genetic studies, particularly for growth traits in various species.
  • Inference in nonlinear mixed-effects models presents significant complexity, often relying on approximations or Bayesian techniques.

Purpose of the Study:

  • To present an efficient stochastic EM (Expectation-Maximization) procedure, the SAEM algorithm, for analyzing nonlinear function-valued characters.
  • To demonstrate the SAEM algorithm's superior convergence speed and robustness compared to traditional methods.

Main Methods:

  • The study proposes the SAEM algorithm, which recycles simulated values across iterations to accelerate convergence.
  • A simulation study was conducted to validate the algorithm's performance in genetic analysis.
  • The SAEM algorithm was applied to real-world datasets of growth measurements in beef cattle and chickens.

Main Results:

  • The SAEM algorithm demonstrated significantly faster convergence than classical Monte Carlo EM and Bayesian procedures.
  • The algorithm is robust to the choice of starting values and does not require prior distribution specifications.
  • Application to real data confirmed the advantages of the SAEM procedure for genetic analysis of growth traits.

Conclusions:

  • The SAEM algorithm offers an efficient and robust method for inference in nonlinear mixed-effects models for genetic studies.
  • The procedure provides significance tests and model comparison criteria, facilitating comparisons with other longitudinal methods.