| Project/Area Number |
25252002
|
|---|
| Research Category |
Grant-in-Aid for Scientific Research (A)
|
| Allocation Type | Single-year Grants |
|---|
| Section | 一般 |
|---|
| Research Field |
Science in genetics and breeding
|
|---|
| Research Institution | The University of Tokyo |
|---|
Principal Investigator |
Iwata Hiroyoshi 東京大学, 農学生命科学研究科, 准教授 (00355489)
|
|---|
| Co-Investigator(Kenkyū-buntansha) |
HASEGAWA Toshihiro 国立研究開発法人農業環境技術研究所, 大気循環研究領域, 上席研究員 (10228455)
EBANA Kaworu 国立研究開発法人農業生物資源研究所, 遺伝資源センター多様性活用研究ユニット, 主任研究員 (00370643)
YAMASAKI Masanori 神戸大学, (連合)農学研究科(研究院), 准教授 (00432550)
NAKAGAWA Hiroshi 国立研究開発法人農業・食品産業技術総合研究機構, 中央農業総合研究センター, 上席研究員 (90207738)
HAYASHI Takeshi 国立研究開発法人農業・食品産業技術総合研究機構, 中央農業総合研究センター, 上席研究員 (70370674)
|
|---|
| Project Period (FY) |
2013-04-01 – 2016-03-31
|
| Project Status |
Completed (Fiscal Year 2015)
|
| Budget Amount *help |
¥29,770,000 (Direct Cost: ¥22,900,000、Indirect Cost: ¥6,870,000)
Fiscal Year 2015: ¥9,360,000 (Direct Cost: ¥7,200,000、Indirect Cost: ¥2,160,000)
Fiscal Year 2014: ¥9,490,000 (Direct Cost: ¥7,300,000、Indirect Cost: ¥2,190,000)
Fiscal Year 2013: ¥10,920,000 (Direct Cost: ¥8,400,000、Indirect Cost: ¥2,520,000)
|
|---|
| Keywords | イネ / 出穂 / 収量構成要素 / ゲノミックセレクション / 作物生育モデル / 環境応答 / 予測モデル / 組換え近交系 / 育種学 / ゲノム / 遺伝子 / モデル化 / 気候変動 / ゲノム育種 / 作物モデル |
|---|
| Outline of Final Research Achievements |
The earliness (or lateness) of heading is a major factor responsible for environmental response in rice. In this study, we worked on the modeling of the pattern of environmental response in heading date and yield related traits, which are influenced by the heading date. For the earliness (or lateness) of heading, we successfully developed the model predicting the heading date of untested varieties/lines based on the genotypes of DNA polymorphisms in heading date related genes and the temperature and day length of cultivation environments, by combining a crop model and a genomic prediction model. For the experiment using recombinant inbred lines, we developed a model predicting biomass by modeling the relationship between environmental conditions and the phenotypes of morphological traits that were measured in various growth stages.
|
