論文

査読有り
2016年3月

Improvement of Rice Biomass Yield through QTL-Based Selection

PLOS ONE
  • Kazuki Matsubara
  • Eiji Yamamoto
  • Nobuya Kobayashi
  • Takuro Ishii
  • Junichi Tanaka
  • Hiroshi Tsunematsu
  • Satoshi Yoshinaga
  • Osamu Matsumura
  • Jun-ichi Yonemaru
  • Ritsuko Mizobuchi
  • Toshio Yamamoto
  • Hiroshi Kato
  • Masahiro Yano
  • 全て表示

11
3
開始ページ
e0151830
終了ページ
記述言語
英語
掲載種別
研究論文(学術雑誌)
DOI
10.1371/journal.pone.0151830
出版者・発行元
PUBLIC LIBRARY SCIENCE

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 alpha = 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 alpha = 0.05). These results demonstrate that QTL-based selection is effective in improving rice biomass yield.

リンク情報
DOI
https://doi.org/10.1371/journal.pone.0151830
PubMed
https://www.ncbi.nlm.nih.gov/pubmed/26986071
Web of Science
https://gateway.webofknowledge.com/gateway/Gateway.cgi?GWVersion=2&SrcAuth=JSTA_CEL&SrcApp=J_Gate_JST&DestLinkType=FullRecord&KeyUT=WOS:000372580300137&DestApp=WOS_CPL
ID情報
  • DOI : 10.1371/journal.pone.0151830
  • ISSN : 1932-6203
  • PubMed ID : 26986071
  • Web of Science ID : WOS:000372580300137

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