Growth dynamics and heritability for plant high-throughput phenotyping studies using hierarchical functional data...

Area of Science:

• Plant Science
• Genetics
• Statistical Modeling

Background:

• High-throughput plant phenotyping generates extensive image data for trait analysis.
• Accurate recovery of plant trait trajectories and their derivatives is crucial for understanding genotype-level variation.
• Existing methods face challenges in analyzing irregular time-series data for plant growth.

Purpose of the Study:

• To develop a novel statistical framework for modeling plant trait trajectories and their derivatives.
• To introduce a time-varying measure of broad-sense heritability.
• To improve the analysis of plant growth and genetic effects in high-throughput phenotyping.

Main Methods:

• Hierarchical functional principal component analysis (HFPCA) for trajectory modeling.
• Differentiating eigenequations to estimate derivatives of eigenfunctions.
• Developing a time-dependent heritability estimation method based on HFPCA.

Main Results:

• The proposed HFPCA method accurately recovers plant trait trajectories and their derivatives from irregular time-series data.
• The new time-varying heritability measure reveals dynamic genetic influences on plant growth.
• Simulation studies demonstrate superior performance compared to existing methods.

Conclusions:

• HFPCA provides a robust framework for analyzing complex plant growth patterns and genetic contributions.
• The method enhances our understanding of plant development and heritability dynamics.
• This approach has significant implications for modern plant breeding and agricultural research.