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Field-based high throughput phenotyping rapidly identifies genomic regions controlling yield components in rice
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 dem...
Autores principales: | , , , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group
2017
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5318881/ https://www.ncbi.nlm.nih.gov/pubmed/28220807 http://dx.doi.org/10.1038/srep42839 |
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author | 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. |
author_facet | 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. |
author_sort | Tanger, Paul |
collection | PubMed |
description | 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. |
format | Online Article Text |
id | pubmed-5318881 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2017 |
publisher | Nature Publishing Group |
record_format | MEDLINE/PubMed |
spelling | pubmed-53188812017-02-24 Field-based high throughput phenotyping rapidly identifies genomic regions controlling yield components in rice 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. Sci Rep Article 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. Nature Publishing Group 2017-02-21 /pmc/articles/PMC5318881/ /pubmed/28220807 http://dx.doi.org/10.1038/srep42839 Text en Copyright © 2017, The Author(s) http://creativecommons.org/licenses/by/4.0/ 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/ |
spellingShingle | Article 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 |
title | Field-based high throughput phenotyping rapidly identifies genomic regions controlling yield components in rice |
title_full | Field-based high throughput phenotyping rapidly identifies genomic regions controlling yield components in rice |
title_fullStr | Field-based high throughput phenotyping rapidly identifies genomic regions controlling yield components in rice |
title_full_unstemmed | Field-based high throughput phenotyping rapidly identifies genomic regions controlling yield components in rice |
title_short | Field-based high throughput phenotyping rapidly identifies genomic regions controlling yield components in rice |
title_sort | field-based high throughput phenotyping rapidly identifies genomic regions controlling yield components in rice |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5318881/ https://www.ncbi.nlm.nih.gov/pubmed/28220807 http://dx.doi.org/10.1038/srep42839 |
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