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

Author(s): Topp, Christopher N; Iyer-Pascuzzi, Anjali S; Anderson, Jill T; Lee, Cheng-Ruei; Zurek, Paul R; Symonova, Olga; Zheng, Ying; Bucksch, Alexander; Mileyko, Yuriy; Galkovskyi, Taras; Moore, Brad T; Harer, John; Edelsbrunner, Herbert; Mitchell-Olds, Thomas ; Weitz, Joshua S; Benfey, Philip N
Article Title: 3D phenotyping and quantitative trait locus mapping identify core regions of the rice genome controlling root architecture
Affiliation IST Austria
Abstract: 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 x 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 (r2 = 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.
Keywords: Multivariate Analysis; Qtl; Live root imaging; Oryza sativa; Three-dimensional
Journal Title: PNAS
Volume: 110
Issue 18
ISSN: 1091-6490
Publisher: National Academy of Sciences  
Date Published: 2013-04-30
Start Page: E1695
End Page: E1704
DOI: 10.1073/pnas.1304354110
Notes: This work was supported by US Department of Agriculture Agriculture and Food Research Initiative Grant 2011-67012-30773 (to C.N.T.), National Institutes of Health (NIH)-National Research Service Award GM799993 (to A.S.I.-P.), National Science Foundation (NSF) Doctoral Dissertation Improvement Grant 1110445 (to C.-R.L.), NIH Grant GM086496 and NSF Grant EF-0723447 (to T.M.-O.), by the Burroughs Wellcome Fund (J.S.W.), and by NSF-Division of Biological Infrastructure Grant 0820624 (to J.H., H.E., J.S.W., and P.N.B.). This research is funded in part by the Howard Hughes Medical Institute and the Gordon and Betty Moore Foundation (through Grant GBMF3405) to P.N.B.
Open access: yes (repository)