It is suggested that accuracy in predicting plant phenotypes can be improved by integrating genomic prediction with crop modelling in a single hierarchical model.
Accurate prediction of phenotypes is important for plant breeding and management. Although genomic prediction/selection aims to predict phenotypes on the basis of whole-genome marker information, it is often difficult to predict phenotypes of complex traits in diverse environments, because plant phenotypes are often influenced by genotype–environment interaction. A possible remedy is to integrate genomic prediction with crop/ecophysiological modelling, which enables us to predict plant phenotypes using environmental and management information. To this end, in the present study, we developed a novel method for integrating genomic prediction with phenological modelling of Asian rice (Oryza sativa, L.), allowing the heading date of untested genotypes in untested environments to be predicted. The method simultaneously infers the phenological model parameters and whole-genome marker effects on the parameters in a Bayesian framework. By cultivating backcross inbred lines of Koshihikari × Kasalath in nine environments, we evaluated the potential of the proposed method in comparison with conventional genomic prediction, phenological modelling, and two-step methods that applied genomic prediction to phenological model parameters inferred from Nelder–Mead or Markov chain Monte Carlo algorithms. In predicting heading dates of untested lines in untested environments, the proposed and two-step methods tended to provide more accurate predictions than the conventional genomic prediction methods, particularly in environments where phenotypes from environments similar to the target environment were unavailable for training genomic prediction. The proposed method showed greater accuracy in prediction than the two-step methods in all cross-validation schemes tested, suggesting the potential of the integrated approach in the prediction of phenotypes of plants.
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Backcross inbred line
Crop (DVR) model based on an MCMC algorithm
Crop (DVR) model based on a Nelder–Mead algorithm
Days to heading
Developmental rate model for rice heading date prediction
Extended Bayesian LASSO
Genomic best linear unbiased prediction
A hierarchical model integrating EBL with the crop (DVR) model
Leave-one-‘combination of an environment and a line’-out cross-validation
Markov chain Monte Carlo
Quantitative trait locus
Root mean squared errors
Two-step approach based on C-Bay and EBL
Two-step approach based on C-Nel and EBL
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The authors thank Seishi Ninomiya and Ryo Ohsawa for the contribution to the conception and design of this study. This study was supported by JSPS KAKENHI Grant Numbers 19208003 and 25252002 and by a Grant-in-Aid for JSPS Fellows (14J10661).
The data files, scripts and results are available at https://github.com/Onogi/HeadingDatePrediction. The data files contain heading and emergence dates and environmental information (daily mean air temperature and photoperiod).
Conflict of interest
The authors declare that they have no competing interests.
Communicated by M. J. Sillanpaa.
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Onogi, A., Watanabe, M., Mochizuki, T. et al. Toward integration of genomic selection with crop modelling: the development of an integrated approach to predicting rice heading dates. Theor Appl Genet 129, 805–817 (2016). https://doi.org/10.1007/s00122-016-2667-5
- Root Mean Square Error
- Markov Chain Monte Carlo
- Genomic Prediction
- Markov Chain Monte Carlo Algorithm
- Local Outlier Factor