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Improvement of Rice Biomass Yield through QTL-Based Selection
Biomass yield of rice (Oryza sativa L.) is an important breeding target, yet it is not easy to improve because the trait is complex and phenotyping is laborious. Using progeny derived from a cross between two high-yielding Japanese cultivars, we evaluated whether quantitative trait locus (QTL)-based...
Autores principales: | , , , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Public Library of Science
2016
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4795639/ https://www.ncbi.nlm.nih.gov/pubmed/26986071 http://dx.doi.org/10.1371/journal.pone.0151830 |
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author | Matsubara, Kazuki Yamamoto, Eiji Kobayashi, Nobuya Ishii, Takuro Tanaka, Junichi Tsunematsu, Hiroshi Yoshinaga, Satoshi Matsumura, Osamu Yonemaru, Jun-ichi Mizobuchi, Ritsuko Yamamoto, Toshio Kato, Hiroshi Yano, Masahiro |
author_facet | Matsubara, Kazuki Yamamoto, Eiji Kobayashi, Nobuya Ishii, Takuro Tanaka, Junichi Tsunematsu, Hiroshi Yoshinaga, Satoshi Matsumura, Osamu Yonemaru, Jun-ichi Mizobuchi, Ritsuko Yamamoto, Toshio Kato, Hiroshi Yano, Masahiro |
author_sort | Matsubara, Kazuki |
collection | PubMed |
description | Biomass yield of rice (Oryza sativa L.) is an important breeding target, yet it is not easy to improve because the trait is complex and phenotyping is laborious. Using progeny derived from a cross between two high-yielding Japanese cultivars, we evaluated whether quantitative trait locus (QTL)-based selection can improve biomass yield. As a measure of biomass yield, we used plant weight (aboveground parts only), which included grain weight and stem and leaf weight. We measured these and related traits in recombinant inbred lines. Phenotypic values for these traits showed a continuous distribution with transgressive segregation, suggesting that selection can affect plant weight in the progeny. Four significant QTLs were mapped for plant weight, three for grain weight, and five for stem and leaf weight (at α = 0.05); some of them overlapped. Multiple regression analysis showed that about 43% of the phenotypic variance of plant weight was significantly explained (P < 0.0001) by six of the QTLs. From F(2) plants derived from the same parental cross as the recombinant inbred lines, we divergently selected lines that carried alleles with positive or negative additive effects at these QTLs, and performed successive selfing. In the resulting F(6) lines and parents, plant weight significantly differed among the genotypes (at α = 0.05). These results demonstrate that QTL-based selection is effective in improving rice biomass yield. |
format | Online Article Text |
id | pubmed-4795639 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2016 |
publisher | Public Library of Science |
record_format | MEDLINE/PubMed |
spelling | pubmed-47956392016-03-23 Improvement of Rice Biomass Yield through QTL-Based Selection Matsubara, Kazuki Yamamoto, Eiji Kobayashi, Nobuya Ishii, Takuro Tanaka, Junichi Tsunematsu, Hiroshi Yoshinaga, Satoshi Matsumura, Osamu Yonemaru, Jun-ichi Mizobuchi, Ritsuko Yamamoto, Toshio Kato, Hiroshi Yano, Masahiro PLoS One Research Article Biomass yield of rice (Oryza sativa L.) is an important breeding target, yet it is not easy to improve because the trait is complex and phenotyping is laborious. Using progeny derived from a cross between two high-yielding Japanese cultivars, we evaluated whether quantitative trait locus (QTL)-based selection can improve biomass yield. As a measure of biomass yield, we used plant weight (aboveground parts only), which included grain weight and stem and leaf weight. We measured these and related traits in recombinant inbred lines. Phenotypic values for these traits showed a continuous distribution with transgressive segregation, suggesting that selection can affect plant weight in the progeny. Four significant QTLs were mapped for plant weight, three for grain weight, and five for stem and leaf weight (at α = 0.05); some of them overlapped. Multiple regression analysis showed that about 43% of the phenotypic variance of plant weight was significantly explained (P < 0.0001) by six of the QTLs. From F(2) plants derived from the same parental cross as the recombinant inbred lines, we divergently selected lines that carried alleles with positive or negative additive effects at these QTLs, and performed successive selfing. In the resulting F(6) lines and parents, plant weight significantly differed among the genotypes (at α = 0.05). These results demonstrate that QTL-based selection is effective in improving rice biomass yield. Public Library of Science 2016-03-17 /pmc/articles/PMC4795639/ /pubmed/26986071 http://dx.doi.org/10.1371/journal.pone.0151830 Text en © 2016 Matsubara et al http://creativecommons.org/licenses/by/4.0/ This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. |
spellingShingle | Research Article Matsubara, Kazuki Yamamoto, Eiji Kobayashi, Nobuya Ishii, Takuro Tanaka, Junichi Tsunematsu, Hiroshi Yoshinaga, Satoshi Matsumura, Osamu Yonemaru, Jun-ichi Mizobuchi, Ritsuko Yamamoto, Toshio Kato, Hiroshi Yano, Masahiro Improvement of Rice Biomass Yield through QTL-Based Selection |
title | Improvement of Rice Biomass Yield through QTL-Based Selection |
title_full | Improvement of Rice Biomass Yield through QTL-Based Selection |
title_fullStr | Improvement of Rice Biomass Yield through QTL-Based Selection |
title_full_unstemmed | Improvement of Rice Biomass Yield through QTL-Based Selection |
title_short | Improvement of Rice Biomass Yield through QTL-Based Selection |
title_sort | improvement of rice biomass yield through qtl-based selection |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4795639/ https://www.ncbi.nlm.nih.gov/pubmed/26986071 http://dx.doi.org/10.1371/journal.pone.0151830 |
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