Pace Jordon, Lee Nigel, Naik Hsiang Sing, Ganapathysubramanian Baskar, Lübberstedt Thomas
Department of Agronomy, Iowa State University, Ames, Iowa, United States of America.
Department of Mechanical Engineering, Iowa State University, Ames, Iowa, United States of America.
PLoS One. 2014 Sep 24;9(9):e108255. doi: 10.1371/journal.pone.0108255. eCollection 2014.
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.
玉米根系对于植株的定植以及水分和养分的吸收至关重要。根系结构存在大量的遗传和表型变异，这为选择提供了机会。然而，根系性状尚未被用作选择标准，主要是由于测量困难以及它们的数量遗传模式。与成年根系表型分析相比，幼苗根系性状为研究多个个体以及每年进行重复测量提供了机会。我们开发了一个新的软件框架，用于从幼苗根系的单张图像中获取各种性状。该框架基于将根系图像转换为等效图形的数学概念。这使得通过简单的图形操作就可以自动查询多个性状。此外，该框架可扩展到三维断层扫描图像数据。为了评估这个工具，我们研究了艾姆斯小组384个自交系中的一个子集，该子集有大量的测序基因型数据。利用135311个单核苷酸多态性标记，对该小组进行了全基因组关联研究，以比较从WinRhizo Pro 9.0获取的幼苗根系图像和当前框架（称为ARIA）中的两个性状，即总根长和总表面积。当用这两个程序进行分析时，发现总根长性状在基因组的相似区域有显著的单核苷酸多态性。这个高通量性状捕获软件系统允许进行大规模的表型分析实验，并有助于在未来建立幼苗与成年性状发育阶段之间的关系。
