| Project/Area Number |
22380010
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| Research Category |
Grant-in-Aid for Scientific Research (B)
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| Allocation Type | Single-year Grants |
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| Section | 一般 |
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| Research Field |
Breeding science
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| Research Institution | The University of Tokyo |
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Principal Investigator |
IWATA Hiroyoshi 東京大学, 大学院・農学生命科学研究科, 准教授 (00355489)
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| Co-Investigator(Kenkyū-buntansha) |
EBANA Kaworu 独立行政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)
HAYASHI Takeshi 独立行政法人農業・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)
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| Project Period (FY) |
2010 – 2012
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| Project Status |
Completed (Fiscal Year 2012)
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| Budget Amount *help |
¥16,250,000 (Direct Cost: ¥12,500,000、Indirect Cost: ¥3,750,000)
Fiscal Year 2012: ¥3,770,000 (Direct Cost: ¥2,900,000、Indirect Cost: ¥870,000)
Fiscal Year 2011: ¥4,680,000 (Direct Cost: ¥3,600,000、Indirect Cost: ¥1,080,000)
Fiscal Year 2010: ¥7,800,000 (Direct Cost: ¥6,000,000、Indirect Cost: ¥1,800,000)
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| Keywords | 植物育種 / 遺伝 / ゲノム育種 / ゲノミックセレクション / 量的遺伝子座(QTL) / 一塩基多型(SNP) / 育種シミュレーション / 作物育種 / 選抜方式 / 量的遺伝子座（QTL） / イネ / 一塩基多型（SNP） / 機械学習 / 量的形質遺伝子座(QTL) / 選抜方法 |
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| Research Abstract |
Genomic selection (GS) is a new breeding technology for selecting individuals/lines based on their genetic potential predicted based on genome-wide markers. We evaluated the potential of GS in crop plant breeding through (1) the development of novel methods for building a prediction model, (2) the collection and analysis of real marker and phenotypic data of rice varieties/lines, (3) simulation studies of breeding programs using GS. In (1), we conducted the development of novel methods for predicting multiple traits simultaneously, trait segregation in a progeny population, and the pattern of genetic by environmental interaction. In (2), we collected genome-wide SNP data and phenotypic data for rice germplasm collection, and used the collected data to assess the prediction accuracy of GS through simulations and cross-validations. In (3), we performed studies simulating breeding programs in allogamous and autogamous plants. Through the researches, we developed various methods for prediction based on genomic information, and revealed proper ways to utilize GS in a crop plant-breeding program.
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