Quantitative genetics and functional-structural plant growth models: simulation of quantitative trait loci detection...

Area of Science:

• Plant genetics and breeding
• Computational biology and modeling
• Agricultural science

Background:

• Predicting crop performance across environments is key for breeding strategies.
• Existing models often lack architectural plasticity, limiting genotype x environment interaction studies.
• Integrating genetics into functional-structural models offers a more comprehensive approach.

Purpose of the Study:

• To introduce genetics into a functional-structural growth model for enhanced quantitative trait loci (QTL) detection.
• To develop tools for optimizing crop yield through simulation.
• To explore genotype x environment interactions using a novel modeling framework.

Main Methods:

• Utilized the GREENLAB model to link growth parameters with QTL.
• Developed a virtual genetic model with virtual genes and chromosomes.
• Employed QTL Cartographer for simulated trait analysis and a genetic algorithm for ideotype definition.

Main Results:

• Simulated ideal case scenarios with constant environmental factors and large virtual populations.
• Found QTL detection to be more accurate for model parameters than for phenotypic traits like cob weight.
• Identified parameters and genotypes for yield optimization in a GREENLAB maize model.

Conclusions:

• The integrated model accurately simulates genotype x environment interactions, particularly via biomass supply/demand ratio.
• This approach provides a powerful tool for quantitative trait loci (QTL) discovery and crop yield optimization.
• The study demonstrates the potential of functional-structural models in advancing plant breeding.