Topological data analysis expands the genotype to phenotype map for 3D maize root system architecture.

Area of Science:

• Plant biology
• Genetics
• Computational biology

Background:

• Understanding genotype-to-phenotype (G to P) maps is crucial in biology.
• Plant phenomes are complex and influenced by development and environment.
• Current root phenotyping often uses simplified 2D measurements, limiting understanding of genetic influences on root architecture.

Purpose of the Study:

• To explore 3D root system architecture in maize using advanced topological methods.
• To identify novel genotype-to-phenotype (G to P) relationships in root traits.
• To assess the effectiveness of different phenotyping approaches for capturing root system variation.

Main Methods:

• Applied persistent homology, a topological data analysis technique, to 3D root system architectures.
• Utilized 3D branching methods and multivariate trait vectors.
• Analyzed a maize mapping population for genotype-to-phenotype (G to P) mapping.

Main Results:

• 3D topological analysis and multivariate trait vectors captured distinct information about root system variation.
• Each method identified unique quantitative trait loci (QTL), indicating non-overlapping phenotypic information.
• Root phenotypic trait space is more complex than typically captured by univariate traits.

Conclusions:

• Advanced 3D phenotyping methods, including topological data analysis, offer a more comprehensive view of root system architecture.
• Non-canonical phenotypes are important for a more accurate genotype-to-phenotype (G to P) map.
• This data-driven approach enhances the assessment of 3D root structure and its genetic basis.