A framework for genomics-informed ecophysiological modeling in plants.

Area of Science:

• Plant physiology
• Computational biology
• Genomics

Background:

• Process-based plant models simulate physiological responses, offering insights into phenotypes and potential for crop improvement.
• Applying these models to large, genetically diverse populations is challenging due to complexity.
• Linking natural genetic variation to first-principles modeling is key for varietal crop improvement.

Purpose of the Study:

• To develop and evaluate a new canopy growth submodel for Brassica rapa within the TREES model.
• To assess parameter estimation for direct phenotypic and indirect genomic prediction.
• To explore the model's performance in silico under varying water conditions.

Main Methods:

• Developed a new canopy growth submodel for Brassica rapa integrated into the TREES model.
• Tested parameter estimation using observed phenotypes and genomic prediction on a recombinant inbred line population.
• Evaluated model performance on an in silico population under non-stressed and mild water-stressed conditions.

Main Results:

• The updated whole-plant model effectively distills genotype by environment interactions (G×E) into manageable components.
• Evidence suggests the model can link genetic variation to environment-modulated plant responses.
• The framework facilitates prediction of unphenotyped, related individuals in novel environments.

Conclusions:

• The developed framework provides a pathway for large-scale, process-based modeling of intraspecific variation in Brassica rapa.
• This approach serves as a crucial step towards predicting crop performance across diverse environments and genetic backgrounds.
• The study lays the groundwork for future applications in breeding and crop management through integrated modeling and genomics.