Optical topometry and machine learning to rapidly phenotype stomatal patterning traits for maize QTL mapping.

Area of Science:

• Plant biology
• Genetics
• Agricultural science

Background:

• Stomata regulate crucial plant functions like CO2 uptake and water loss.
• Limited methods for phenotyping epidermal cells hinder genetic studies of stomatal patterning.

Purpose of the Study:

• To develop a high-throughput phenotyping pipeline for maize epidermal cells.
• To use this pipeline for quantitative trait loci (QTL) mapping of stomatal traits in field-grown maize.

Main Methods:

• Automated determination of stomatal complex and pavement cell locations and sizes from optical topometer images.
• Quantitative trait loci (QTL) mapping using the high-throughput phenotyping data.
• Analysis of genetic correlations between epidermal traits and leaf gas exchange.

Main Results:

• The pipeline accurately estimated stomatal complex density (SCD) and stomatal complex area (SCA).
• Leaf gas exchange traits showed genetic correlations with stomatal complex dimensions but not SCD.
• Moderate to high heritability was observed for epidermal traits, with 36 consistently identified QTL across two years.

Conclusions:

• The developed pipeline significantly accelerates the discovery of genetic factors controlling stomatal patterning in maize.
• Identified QTL and potential pleiotropy provide insights into the genetic architecture of stomatal development.
• This work lays the foundation for understanding stomatal regulation in C4 plants like maize.