Mapping and Predicting Non-Linear Brassica rapa Growth Phenotypes Based on Bayesian and Frequentist Complex Trait...

Area of Science:

• Plant genetics
• Developmental biology
• Quantitative genetics

Background:

• Understanding plant performance requires knowledge of developmental processes and genomic architecture.
• Complex traits influence plant growth trajectories and overall performance.

Purpose of the Study:

• To characterize leaf development in Brassica rapa using a Function-Valued Trait approach.
• To investigate genetic variation in phenotypic plasticity of leaf growth.
• To compare Bayesian and frequentist methods for quantitative trait loci (QTL) mapping.

Main Methods:

• Applied a hierarchical Bayesian Function-Valued Trait (FVT) approach to logistic growth curves for leaf data (length, width).
• Analyzed Brassica rapa genotypes across multiple densities and seasons.
• Constructed QTL-based predictive models for leaf growth rate and final size.

Main Results:

• Identified genetic variation in phenotypic plasticity for leaf growth rate and duration influenced by growing season.
• Observed limited plasticity in maximum leaf size, indicating distinct environmental sensitivities for growth dynamics versus final size.
• Bayesian QTL mapping yielded more significant QTL with higher LOD scores and greater variance explained compared to frequentist methods.
• QTL-by-year interactions were significant for growth rate and duration, but not for leaf size.

Conclusions:

• Leaf growth dynamics exhibit distinct environmental sensitivities compared to final leaf size.
• Bayesian FVT analysis enhances QTL mapping accuracy and predictive power for plant developmental phenotypes.
• QTL-based models can successfully predict non-linear developmental traits across different environmental conditions, such as plant density.