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Model-based QTL detection is sensitive to slight modifications in model formulation.

Caterina Barrasso1,2, Mohamed-Mahmoud Memah2, Michel Génard2

  • 1GAFL, INRA, 84143, Montfavet, France.

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|October 4, 2019
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Summary

Choosing different crop growth models and optimization methods significantly impacts the detection of Quantitative Trait Locus (QTL) controlling crop traits. Comparing various models is crucial for effective QTL-based ecophysiological modeling.

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Area of Science:

  • Plant Physiology and Genetics
  • Computational Biology and Bioinformatics
  • Agricultural Science

Background:

  • Classical crop models predict yield using physiological and environmental data.
  • Integrating genetic variation into crop models is essential for genotype-dependent predictions.
  • Quantitative Trait Locus (QTL)-based ecophysiological modeling shows promise but faces integration challenges.

Purpose of the Study:

  • To assess the impact of different crop growth model equations and optimization methods on detecting QTLs for crop growth parameters.
  • To investigate strategies for handling parameter correlations in QTL analysis.
  • To validate detected QTLs using observed dry mass data.

Main Methods:

  • Parameterized various growth equations using a genetic population.
  • Analyzed parameter correlations and applied strategies to address them.
  • Performed QTL analysis on optimized growth parameters and observed dry mass data.

Main Results:

  • Different models and strategies detected varying sets of QTLs, with differences in peak significance and position.
  • Closely related growth models yielded distinct QTL detection outcomes.
  • Model complexity and goodness-of-fit were insufficient for selecting the optimal model; parameter independence from genotype is recommended.

Conclusions:

  • The choice of model formulation and optimization significantly influences QTL detection in crop growth modeling.
  • Current algorithms struggle with the complexity of large-scale optimization and parameter correlations.
  • Testing diverse model formulations and comparing detected QTLs is recommended before adopting a specific QTL-based ecophysiological model.