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Analysis of Maize (Zea mays L.) Seedling Roots with the High-Throughput Image Analysis Tool ARIA (Automatic Root Image Analysis)
PLoS ONE
  • Jordan Pace, Iowa State University
  • Nigel Lee, Iowa State University
  • Hsiang Sing Naik, Iowa State University
  • Baskar Ganapathysubramanian, Iowa State University
  • Thomas Lubberstedt, Iowa State University
Document Type
Article
Publication Version
Published Version
Publication Date
9-1-2014
DOI
10.1371/journal.pone.0108255
Abstract
The maize root system is crucial for plant establishment as well as water and nutrient uptake. There is substantial genetic and phenotypic variation for root architecture, which gives opportunity for selection. Root traits, however, have not been used as selection criterion mainly due to the difficulty in measuring them, as well as their quantitative mode of inheritance. Seedling root traits offer an opportunity to study multiple individuals and to enable repeated measurements per year as compared to adult root phenotyping. We developed a new software framework to capture various traits from a single image of seedling roots. This framework is based on the mathematical notion of converting images of roots into an equivalent graph. This allows automated querying of multiple traits simply as graph operations. This framework is furthermore extendable to 3D tomography image data. In order to evaluate this tool, a subset of the 384 inbred lines from the Ames panel, for which extensive genotype by sequencing data are available, was investigated. A genome wide association study was applied to this panel for two traits, Total Root Length and Total Surface Area, captured from seedling root images from WinRhizo Pro 9.0 and the current framework (called ARIA) for comparison using 135,311 single nucleotide polymorphism markers. The trait Total Root Length was found to have significant SNPs in similar regions of the genome when analyzed by both programs. This high-throughput trait capture software system allows for large phenotyping experiments and can help to establish relationships between developmental stages between seedling and adult traits in the future.
Comments

This article is published as Pace J, Lee N, Naik HS, Ganapathysubramanian B, Lübberstedt T (2014) Analysis of Maize (Zea mays L.) Seedling Roots with the High-Throughput Image Analysis Tool ARIA (Automatic Root Image Analysis). PLoS ONE 9(9): e108255. DOI:10.1371/journal.pone.0108255. Posted with permission.

Creative Commons License
Creative Commons Attribution 4.0
Copyright Owner
The Authors
Language
en
File Format
application/pdf
Citation Information
Jordan Pace, Nigel Lee, Hsiang Sing Naik, Baskar Ganapathysubramanian, et al.. "Analysis of Maize (Zea mays L.) Seedling Roots with the High-Throughput Image Analysis Tool ARIA (Automatic Root Image Analysis)" PLoS ONE Vol. 9 Iss. 9 (2014)
Available at: http://works.bepress.com/baskar-ganapathysubramanian/31/