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

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dc.contributor.author Tanger, P.
dc.contributor.author Klassen, S.
dc.contributor.author Mojica, J. P.
dc.contributor.author Lovell, J. T.
dc.contributor.author Moyers, B. T.
dc.contributor.author Baraoidan, M.
dc.contributor.author Naredo, M. E. B.
dc.contributor.author McNally, K. L.
dc.contributor.author Poland, Jesse A.
dc.contributor.author Bush, D. R.
dc.contributor.author Leung, H.
dc.contributor.author Leach, J. E.
dc.contributor.author McKay, J. K.
dc.date.accessioned 2017-11-30T21:53:39Z
dc.date.available 2017-11-30T21:53:39Z
dc.date.issued 2017-02-21
dc.identifier.uri http://hdl.handle.net/2097/38402
dc.description Citation: Tanger, P., Klassen, S., Mojica, J. P., Lovell, J. T., Moyers, B. T., Baraoidan, M., . . . McKay, J. K. (2017). Field-based high throughput phenotyping rapidly identifies genomic regions controlling yield components in rice. Scientific Reports, 7, 8. doi:10.1038/srep42839
dc.description.abstract 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. Here 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. 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.
dc.relation.uri https://doi.org/10.1038/srep42839
dc.rights © The Author(s) 2017. This work is licensed under a Creative Commons Attribution 4.0 International License. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in the credit line; if the material is not included under the Creative Commons license, users will need to obtain permission from the license holder to reproduce the material. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/
dc.rights.uri https://creativecommons.org/licenses/by/4.0/
dc.subject Quantitative Trait Loci
dc.subject Green-Revolution
dc.subject Crosses
dc.subject Science & Technology - Other Topics
dc.title Field-based high throughput phenotyping rapidly identifies genomic regions controlling yield components in rice
dc.type Article
dc.date.published 2017
dc.citation.doi 10.1038/srep42839
dc.citation.epage 8
dc.citation.issn 2045-2322
dc.citation.issue 1
dc.citation.jtitle Scientific Reports
dc.citation.spage 1
dc.citation.volume 7
dc.citation Tanger, P., Klassen, S., Mojica, J. P., Lovell, J. T., Moyers, B. T., Baraoidan, M., . . . McKay, J. K. (2017). Field-based high throughput phenotyping rapidly identifies genomic regions controlling yield components in rice. Scientific Reports, 7, 8. https//doi.org/10.1038/srep42839
dc.contributor.authoreid jpoland
dc.description.version Article: Version of Record
dc.contributor.kstate Poland, Jesse A.


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© The Author(s) 2017. This work is licensed under a Creative Commons Attribution 4.0 International License. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in the credit line; if the material is not included under the Creative Commons license, users will need to obtain permission from the license holder to reproduce the material. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/ Except where otherwise noted, the use of this item is bound by the following: © The Author(s) 2017. This work is licensed under a Creative Commons Attribution 4.0 International License. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in the credit line; if the material is not included under the Creative Commons license, users will need to obtain permission from the license holder to reproduce the material. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/

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