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Modeling soybean growth: A mixed model approach.

Maud Delattre1, Yusuke Toda2, Jessica Tressou2,3

  • 1Université Paris-Saclay, INRAE, MaIAGE, Jouy-en-Josas, France.

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|July 11, 2024
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This summary is machine-generated.

This study introduces a novel approach combining nonlinear mixed-effects models and SAEM algorithm to analyze genetic and environmental factors influencing plant growth. The method enhances understanding of soybean growth patterns and aids in genomic prediction.

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Area of Science:

  • Plant Biology
  • Genetics
  • Bioinformatics

Background:

  • Understanding genetic and environmental influences on plant growth is crucial for genetic improvement.
  • Existing methods for analyzing growth patterns are rarely applied in plant biology.

Purpose of the Study:

  • To extend nonlinear mixed-effects modeling (NLMEM) and stochastic approximation of the Expectation-Maximization algorithm (SAEM) for analyzing genetic and environmental effects on plant growth.
  • To integrate genetic relationships using a genetic relationship matrix.
  • To provide an efficient alternative to Markov Chain Monte Carlo (MCMC) methods.

Main Methods:

  • A nonlinear function was used to model growth curves.
  • Random effects were incorporated to account for genetic and environmental variability.
  • The SAEM algorithm was implemented for parameter estimation using maximum-likelihood and maximum-a-posteriori approaches.

Main Results:

  • The proposed NLMEM-SAEM approach was validated using simulated data and real-world soybean growth data.
  • Daily plant height measurements from drone data for approximately 200 soybean cultivars were analyzed.
  • The model successfully inferred population growth patterns and individual cultivar growth curves.

Conclusions:

  • The NLMEM-SAEM approach enhances the understanding of soybean growth determinants.
  • This method can be effectively utilized for genomic prediction of growth patterns.
  • The approach offers a powerful tool for plant biology research and crop improvement.