Quantitative trait locus mapping reveals regions of the maize genome controlling root system architecture.

The quest to determine the genetic basis of root system architecture (RSA) has been greatly facilitated by recent developments in root phenotyping techniques. Methods that are accurate, high throughput, and control for environmental factors are especially attractive for quantitative trait locus mapping. Here, we describe the adaptation of a nondestructive in vivo gel-based root imaging platform for use in maize (Zea mays).

3D phenotyping and quantitative trait locus mapping identify core regions of the rice genome controlling root architecture.

Identification of genes that control root system architecture in crop plants requires innovations that enable high-throughput and accurate measurements of root system architecture through time. We demonstrate the ability of a semiautomated 3D in vivo imaging and digital phenotyping pipeline to interrogate the quantitative genetic basis of root system growth in a rice biparental mapping population, Bala × Azucena. We phenotyped >1,400 3D root models and >57,000 2D images for a suite of 25 traits that quantified the distribution, shape, extent of exploration, and the intrinsic size of root networks at days 12, 14, and 16 of growth in a gellan gum medium.

High-throughput, noninvasive imaging of root systems.

Root systems are complex structures key to plant health. The three-dimensional distribution of the root system, known as the root architecture, is important for optimal uptake of water and nutrients, as well as anchorage in the soil. Despite the importance of root systems, little is known about the genes that control root architecture, in part because of the difficulty of non-destructively viewing root systems.

GiA Roots: software for the high throughput analysis of plant root system architecture.

Background: Characterizing root system architecture (RSA) is essential to understanding the development and function of vascular plants. Identifying RSA-associated genes also represents an underexplored opportunity for crop improvement. Software tools are needed to accelerate the pace at which quantitative traits of RSA are estimated from images of root networks.