Detection of quantitative trait loci for rice root systems grown in paddies based on nondestructive phenotyping using X-ray computed tomography.

Plant roots are essential for water and nutrient uptake, as well as resistance to abiotic stresses. While measuring root systems under field conditions is labor-intensive, most quantitative trait loci (QTLs) related to root traits have been detected under artificial conditions. However, QTLs identified under artificial conditions may not always manifest the expected effects that are observed under field conditions. To address this issue, we developed RSApaddy3D, a rapid phenotyping method for rice root systems, using X-ray computed tomography (CT) volumes of soil blocks collected from paddies. RSApaddy3D employs 2-dimensional kernel filters tailored to extract disk-shaped fragments from the CT volumes. Tubular root fragments are expected to exhibit disk-shaped cross-sections along the x-, y-, or z-axes. By applying these filters along all three axes and integrating the results, 3-dimensional root fragments can be accurately extracted. Furthermore, vectorizing the root system enables geometrical removal of the roots of neighboring individuals. We conducted a genome-wide association study (GWAS) of root diameter, number, and growth angle in 133 Japanese rice varieties and detected three QTLs (qNCR1, qNCR2, and qRGA1) that were associated with each trait. This process was completed within 10 person-days from soil monolith collection in the paddy to the GWAS. Without RSApaddy3D, roots would need to be washed from the soil monolith and measured, which is estimated to require >500 person-days. Therefore, RSApaddy3D was approximately 50× more labor-saving. In summary, we have demonstrated that RSApaddy3D is an efficient method for phenotyping rice root systems under field conditions.