3D phenotyping and quantitative trait locus mapping identify core regions of the rice genome controlling root architecture
Proceedings of the National Academy of Sciences of the United States of America
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. From these data we identified 89 quantitative trait loci, some of which correspond to those found previously in soil-grown plants, and provide evidence for genetic tradeoffs in root growth allocations, such as between the extent and thoroughness of exploration. We also developed a multivariate method for generating and mapping central root architecture phenotypes and used it to identify five major quantitative trait loci (r 2 = 24-37%), two of which were not identified by our univariate analysis. Our imaging and analytical platform provides a means to identify genes with high potential for improving root traits and agronomic qualities of crops. Oryza sativa | QTL | three-dimensional R oot systems are high-value targets for crop improvement because of their potential to boost or stabilize yields in saline, dry, and acid soils, improve disease resistance, and reduce the need for unsustainable fertilizers (1-7). Root system architecture (RSA) describes the spatial organization of root systems, which is critical for root function in challenging environments (1-10). Modern genomics could allow us to leverage both natural and engineered variation to breed more efficient crops, but the lack of parallel advances in plant phenomics is widely considered to be a primary hindrance to developing "next-generation" agriculture (3, 11, 12). Root imaging and analysis have been particularly intractable: Decades of phenotyping efforts have failed to identify genes controlling quantitative RSA traits in a crop species. Several factors confound RSA gene identification, including polygenic inheritance of root traits, soil opacity, and a complex 3D morphology that can be influenced heavily by the environment. Most phenotyping efforts have relied on small numbers of basic measurements to extrapolate system-wide traits. For example, given the length and mass of a few sample roots and the excavated root system mass, one can estimate the total root length, volume, and average root width of the entire root system (13, 14) . Other common measurements involve measuring the root surface exposed on a soil core or pressed against a transparent surface to estimate root coverage at a certain soil horizon. In these cases, the choices of sample roots and phenotyping standards, the size and shape of the container, and the limitations of 2D descriptions of 3D structure are sources of bias. Methods to image the unimpeded growth of entire root systems in 3D could circumvent these problems (15) (16) (17) (18) (19) . Live 3D imaging can capture complete spatial and developmental aspects of RSA and results in value-added digital data that can be phenotyped repeatedly for any number of traits. To date, such efforts have been limited by a throughput insufficient for quantitative genetic studies. Here Q:12 we describe the use of a 3D imaging and phenotyping system to reveal the genetic basis of root architecture. The integrated system leverages prior advances in the areas of hardware, imaging, software, and analysis (17, (20) (21) (22) . We combined these methods into a semiautomated pipeline to reconstruct and phenotype a well-studied rice mapping population on days 12, 14, and 16 after planting in gellan gum medium. We identified 89 quantitative trait loci (QTLs) at 13 clusters among 25 RSA traits. Several clusters correspond to QTLs previously identified under field and greenhouse conditions; others do not. Apparent tradeoffs at some clusters are consistent with genetic limitations on "ideal" RSA phenotypes. We also used a multivariate-composite QTL approach to extract central RSA phenotypes and identify five large effect QTLs (r 2 = 24-37%) that control multiple root traits.