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Title: Field-based high throughput phenotyping rapidly identifies genomic regions controlling yield components in rice

Abstract

In order to ensure food security in the face of population growth, decreasing water and land for agriculture, and increasing climate variability, crop yields must increase faster than the current rates. Increased yields will require implementing novel approaches in genetic discovery and breeding. We demonstrate the potential of field-based high throughput phenotyping (HTP) on a large recombinant population of rice to identify genetic variation underlying important traits. We find that detecting quantitative trait loci (QTL) with HTP phenotyping is as accurate and effective as traditional labor- intensive measures of flowering time, height, biomass, grain yield, and harvest index. Furthermore, genetic mapping in this population, derived from a cross of an modern cultivar (IR64) with a landrace (Aswina), identified four alleles with negative effect on grain yield that are fixed in IR64, demonstrating the potential for HTP of large populations as a strategy for the second green revolution.

Authors:
 [1];  [2];  [3];  [4];  [1];  [2];  [2];  [2];  [5];  [6];  [2];  [1];  [1]
  1. Colorado State Univ., Fort Collins, CO (United States). Bioagricultural Sciences and Pest Management
  2. International Rice Research Inst. (IRRI), Los Banos (Phillippines)
  3. Colorado State Univ., Fort Collins, CO (United States). Bioagricultural Sciences and Pest Management; Duke Univ., Durham, NC (United States). Dept. of Biology
  4. Univ. of Texas, Austin, TX (United States). Dept. of Integrative Biology
  5. Kansas State Univ., Manhattan, KS (United States). Dept. of Plant Pathology
  6. Colorado State Univ., Fort Collins, CO (United States). Dept. of Biology
Publication Date:
Research Org.:
Colorado State Univ., Fort Collins, CO (United States)
Sponsoring Org.:
USDOE Office of Science (SC), Biological and Environmental Research (BER)
OSTI Identifier:
1366463
Grant/Contract Number:  
FG02-08ER64629
Resource Type:
Accepted Manuscript
Journal Name:
Scientific Reports
Additional Journal Information:
Journal Volume: 7; Journal ID: ISSN 2045-2322
Publisher:
Nature Publishing Group
Country of Publication:
United States
Language:
English
Subject:
60 APPLIED LIFE SCIENCES; agriculture genetics; plant breeding

Citation Formats

Tanger, Paul, Klassen, Stephen, Mojica, Julius P., Lovell, John T., Moyers, Brook T., Baraoidan, Marietta, Naredo, Maria Elizabeth B., McNally, Kenneth L., Poland, Jesse, Bush, Daniel R., Leung, Hei, Leach, Jan E., and McKay, John K. Field-based high throughput phenotyping rapidly identifies genomic regions controlling yield components in rice. United States: N. p., 2017. Web. doi:10.1038/srep42839.
Tanger, Paul, Klassen, Stephen, Mojica, Julius P., Lovell, John T., Moyers, Brook T., Baraoidan, Marietta, Naredo, Maria Elizabeth B., McNally, Kenneth L., Poland, Jesse, Bush, Daniel R., Leung, Hei, Leach, Jan E., & McKay, John K. Field-based high throughput phenotyping rapidly identifies genomic regions controlling yield components in rice. United States. doi:10.1038/srep42839.
Tanger, Paul, Klassen, Stephen, Mojica, Julius P., Lovell, John T., Moyers, Brook T., Baraoidan, Marietta, Naredo, Maria Elizabeth B., McNally, Kenneth L., Poland, Jesse, Bush, Daniel R., Leung, Hei, Leach, Jan E., and McKay, John K. Tue . "Field-based high throughput phenotyping rapidly identifies genomic regions controlling yield components in rice". United States. doi:10.1038/srep42839. https://www.osti.gov/servlets/purl/1366463.
@article{osti_1366463,
title = {Field-based high throughput phenotyping rapidly identifies genomic regions controlling yield components in rice},
author = {Tanger, Paul and Klassen, Stephen and Mojica, Julius P. and Lovell, John T. and Moyers, Brook T. and Baraoidan, Marietta and Naredo, Maria Elizabeth B. and McNally, Kenneth L. and Poland, Jesse and Bush, Daniel R. and Leung, Hei and Leach, Jan E. and McKay, John K.},
abstractNote = {In order to ensure food security in the face of population growth, decreasing water and land for agriculture, and increasing climate variability, crop yields must increase faster than the current rates. Increased yields will require implementing novel approaches in genetic discovery and breeding. We demonstrate the potential of field-based high throughput phenotyping (HTP) on a large recombinant population of rice to identify genetic variation underlying important traits. We find that detecting quantitative trait loci (QTL) with HTP phenotyping is as accurate and effective as traditional labor- intensive measures of flowering time, height, biomass, grain yield, and harvest index. Furthermore, genetic mapping in this population, derived from a cross of an modern cultivar (IR64) with a landrace (Aswina), identified four alleles with negative effect on grain yield that are fixed in IR64, demonstrating the potential for HTP of large populations as a strategy for the second green revolution.},
doi = {10.1038/srep42839},
journal = {Scientific Reports},
number = ,
volume = 7,
place = {United States},
year = {2017},
month = {2}
}

Journal Article:
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Cited by: 34 works
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Figures / Tables:

Figure 1 Figure 1: (A) Tractor based HTP platform with eight sets of canopy sensors mounted on a 24 m boom. (B) Experimental design: each horizontal bar represents a repeated planting cohort from sowing to harvest, with the range of plot heading dates (80% of plants flowered) represented by thicker bars. Verticalmore » lines indicate HTP sampling dates. These are plotted over mean daily air temperature (second y axis) during the growing season. (C) Genetic correlations (r2) between two pairs of manual and HTP traits over time. Correlations for each HTP sampling date (hash marks on x-axis) and cohort are represented by loess-smoothed lines and shaded 95% confidence intervals. See Fig. S2 for correlations between each HTP and manual traits, for each sampling date for every HTP trait, in every cohort.« less

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Works referenced in this record:

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    Works referencing / citing this record:

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    dataset, March 2017

    • Tanger, Paul; Klassen, Stephen; Mojica, Julius P.
    • Dryad Digital Repository-Supplementary information for journal article at DOI: 10.1038/srep42839, 1 ZIP file (74.79 Mb)
    • DOI: 10.5061/dryad.53bj8

    High-fidelity detection of crop biomass quantitative trait loci from low-cost imaging in the field
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    • DOI: 10.1002/pld3.41

    The Potential of Using Spectral Reflectance Indices to Estimate Yield in Wheat Grown Under Reduced Irrigation
    journal, August 2006


    Automated measurement of canopy stomatal conductance based on infrared temperature
    journal, December 2009


    Variation in root system architecture and drought response in rice (Oryza sativa): Phenotyping of the OryzaSNP panel in rainfed lowland fields
    journal, January 2011

    • Henry, Amelia; Gowda, Veeresh R. P.; Torres, Rolando O.
    • Field Crops Research, Vol. 120, Issue 2
    • DOI: 10.1016/j.fcr.2010.10.003

    Estimating crop yield potential at regional to national scales
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    Phenotyping and beyond: modelling the relationships between traits
    journal, April 2014


    Field high-throughput phenotyping: the new crop breeding frontier
    journal, January 2014


    Physiological phenotyping of plants for crop improvement
    journal, March 2015


    Monitoring rice reflectance at field level for estimating biomass and LAI
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    Visible and near-infrared reflectance techniques for diagnosing plant physiological status
    journal, April 1998


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    Breeding crops to feed 10 billion
    journal, June 2019


    Development and evaluation of a field-based high-throughput phenotyping platform
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    • DOI: 10.1071/fp13126

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    journal, June 2002

    • Spielmeyer, W.; Ellis, M. H.; Chandler, P. M.
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    • DOI: 10.1073/pnas.132266399

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    journal, May 2003


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    journal, July 2005


    Making Hunger Yield
    journal, May 2014


    Improvement of the Oryza sativa Nipponbare reference genome using next generation sequence and optical map data
    journal, January 2013

    • Kawahara, Yoshihiro; de la Bastide, Melissa; Hamilton, John P.
    • Rice, Vol. 6, Issue 1
    • DOI: 10.1186/1939-8433-6-4

    Development of High-Density Genetic Maps for Barley and Wheat Using a Novel Two-Enzyme Genotyping-by-Sequencing Approach
    journal, February 2012


    Yield Trends Are Insufficient to Double Global Crop Production by 2050
    journal, June 2013


    Unmanned Aerial Vehicles for High-Throughput Phenotyping and Agronomic Research
    journal, July 2016


    Field-Based High-Throughput Plant Phenotyping Reveals the Temporal Patterns of Quantitative Trait Loci Associated with Stress-Responsive Traits in Cotton
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    A Novel Remote Sensing Approach for Prediction of Maize Yield Under Different Conditions of Nitrogen Fertilization
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    Harmonizing technological advances in phenomics and genomics for enhanced salt tolerance in rice from a practical perspective
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    Intercomparison of Unmanned Aerial Vehicle and Ground-Based Narrow Band Spectrometers Applied to Crop Trait Monitoring in Organic Potato Production
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      Figures/Tables have been extracted from DOE-funded journal article accepted manuscripts.