Journal
PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA
Volume 110, Issue 18, Pages E1695-E1704Publisher
NATL ACAD SCIENCES
DOI: 10.1073/pnas.1304354110
Keywords
Oryza sativa; QTL; three-dimensional; live root imaging; multivariate analysis
Categories
Funding
- US Department of Agriculture Agriculture and Food Research Initiative Grant [2011-67012-30773]
- National Institutes of Health (NIH)-National Research Service Award [GM799993]
- National Science Foundation (NSF) Doctoral Dissertation Improvement Grant [1110445]
- NIH [GM086496]
- NSF [EF-0723447]
- Burroughs Wellcome Fund
- NSF-Division of Biological Infrastructure Grant [0820624]
- Howard Hughes Medical Institute
- Gordon and Betty Moore Foundation [GBMF3405]
- Direct For Biological Sciences
- Division Of Environmental Biology [1110445] Funding Source: National Science Foundation
- Division Of Integrative Organismal Systems
- Direct For Biological Sciences [0820624] Funding Source: National Science Foundation
- NIFA [579286, 2011-67012-30773] Funding Source: Federal RePORTER
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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 (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.
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