Dynamic QTL-based ecophysiological models to predict phenotype from genotype and environment data.

Area of Science:

• Plant biology
• Genetics
• Computational biology

Background:

• Predicting plant phenotype from genotype is a major biological challenge, amplified by environmental factors.
• Existing crop simulation models lack robust genetic frameworks to capture genotypic effects.
• Plant development is complex, occurring post-embryonically under fluctuating environmental conditions.

Purpose of the Study:

• To develop a novel mixed-effects dynamic model for predicting time-to-flowering in common beans.
• To integrate genotype and environment effects for real-time prediction of plant development.
• To improve upon traditional crop simulation and static models in predicting plant traits.

Main Methods:

• Construction of a mixed-effects dynamic model.
• Application of a developmental approach similar to traditional crop simulation models.
• Incorporation of direct observational data to capture Genotype (G), Environment (E), and GxE effects.

Main Results:

• The developed dynamic model accurately predicts time-to-flowering in common beans.
• The model demonstrates advantages over traditional crop simulation and static models.
• The model captures Genotype, Environment, and Genotype-by-Environment interactions in real time.

Conclusions:

• The gene-based dynamic model is adaptable to other plant species and processes.
• Modular integration into existing crop models (e.g., BeanGro within DSSAT) is feasible.
• These models can accelerate precision breeding, aid climate change adaptation, and support food supply policy decisions.