Integration of Genomics with Crop Modeling for Predicting Rice Days to Flowering: A Multi-Model Analysis
文献类型：期刊论文
| 作者 | Yang, Yubin2; Wilson, Lloyd T.2; Li, Tao3; Paleari, Livia4; Confalonieri, Roberto4; Zhu, Yan5; Tang, Liang5; Qiu, Xiaolei5; Tao, Fulu6,7; Chen, Yi6 |
| 刊名 | FIELD CROPS RESEARCH
 |
| 出版日期 | 2022-02-01 |
| 卷号 | 276页码:15 |
| 关键词 | Genome-wide association Genomic prediction Crop model Rice Days to flowering Photoperiod sensitivity Temperature response |
| ISSN号 | 0378-4290 |
| DOI | 10.1016/j.fcr.2021.108394 |
| 通讯作者 | Yang, Yubin(yyang@aesrg.tamu.edu) |
| 英文摘要 | The ability of crop models to decompose complex traits and integrate the underlying processes enables them to capture genotype-environment interactions in diverse environments. Integrating genomics with biophysical crop models represents a potential breakthrough technology for improving our understanding of genotypeenvironment interactions across the biological organization. We present the results of a multi-model analysis on integrating crop modeling with genomic prediction. Seven rice models were evaluated on their ability to predict days to flowering in ten environments from parameters estimated through genome-wide association and genomic prediction, using a 13-fold cross-validation scheme. Phenotypic data were based on a rice diversity panel of 169 accessions with 700k markers. Significant associations with known flowering genes were identified for several model parameters. Although high accuracy was achieved for genomic prediction of model parameters in calibration, prediction accuracy was low for untested genotypes. We observed divergent model performance using genomic-predicted model parameters, which was attributed to photoperiod and temperature response curves, and number of calibrated model parameters. Several areas were identified for further research that could lead to better understanding the genetic control of complex trait formation and improved integration of genomics with crop modeling. |
| WOS关键词 | WIDE ASSOCIATION ; ORYZA-SATIVA ; PHYSIOLOGICAL TRAITS ; GENETIC ARCHITECTURE ; NATURAL VARIATION ; COMMON DISEASES ; SIMULATION ; SELECTION ; YIELD ; GROWTH |
| 资助项目 | Texas A&M AgriLife Research Crop Improvement Program ; National Natural Science Foundation of China[31561143003] ; National Natural Science Foundation of China[31761143006] ; Japan Science and Technology Agency CREST[JPMJCR17O3] |
| WOS研究方向 | Agriculture |
| 语种 | 英语 |
| WOS记录号 | WOS:000742588200006 |
| 出版者 | ELSEVIER |
| 资助机构 | Texas A&M AgriLife Research Crop Improvement Program ; National Natural Science Foundation of China ; Japan Science and Technology Agency CREST |
| 源URL | [http://ir.igsnrr.ac.cn/handle/311030/169756]  |
| 专题 | 中国科学院地理科学与资源研究所 |
| 通讯作者 | Yang, Yubin |
| 作者单位 | 1.Univ Montpellier, Montpellier SupAgro, INRIA, INRA,AGAP,CIRAD, Montpellier, France 2.Texas A&M AgriLife Res Ctr Beaumont, Beaumont, TX 79106 USA 3.DNDC Applicat Res & Training, 87 Packers Falls Rd, Durham, NC USA 4.Univ Milan, Cassandra Lab, ESP, Milan, Italy 5.Nanjing Agr Univ, MARA Key Lab Crop Syst Anal & Decis Making, Jiangsu Key Lab Informat Agr, Nanjing 210095, Jiangsu, Peoples R China 6.Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, Beijing, Peoples R China 7.Nat Resources Inst Finland Luke, Helsinki, Finland 8.Univ Florida, Gainesville, FL USA 9.Ryukoku Univ, Fac Agr, Dept Plant Life Sci, Lab Biol Data Sci, Otsu, Shiga, Japan 10.Natl Agr & Food Res Org, Inst Agroenvironm Sci, Tsukuba, Ibaraki, Japan |
| 推荐引用方式 GB/T 7714 | Yang, Yubin,Wilson, Lloyd T.,Li, Tao,et al. Integration of Genomics with Crop Modeling for Predicting Rice Days to Flowering: A Multi-Model Analysis[J]. FIELD CROPS RESEARCH,2022,276:15. |
| APA | Yang, Yubin.,Wilson, Lloyd T..,Li, Tao.,Paleari, Livia.,Confalonieri, Roberto.,...&Wang, Jing.(2022).Integration of Genomics with Crop Modeling for Predicting Rice Days to Flowering: A Multi-Model Analysis.FIELD CROPS RESEARCH,276,15. |
| MLA | Yang, Yubin,et al."Integration of Genomics with Crop Modeling for Predicting Rice Days to Flowering: A Multi-Model Analysis".FIELD CROPS RESEARCH 276(2022):15. |
入库方式： OAI收割
来源：地理科学与资源研究所
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