title: Toward integration of genomic selection with crop modelling: the development of an integrated approach to predicting rice heading dates.
abstract: 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.
authors.affiliation: Department of Agricultural and Environmental Biology, Graduate School of Agricultural and Life Sciences, The University of Tokyo, 1-1-1 Yayoi, Bunkyo-ku, Tokyo, 113-8657, Japan.
authors.forename: Akio
authors.initials: A
authors.lastname: Onogi
authors.affiliation: Department of Agricultural and Environmental Biology, Graduate School of Agricultural and Life Sciences, The University of Tokyo, 1-1-1 Yayoi, Bunkyo-ku, Tokyo, 113-8657, Japan.
authors.forename: Maya
authors.initials: M
authors.lastname: Watanabe
authors.affiliation: Faculty of Agriculture, Kyushu University, Fukuoka, 812-8581, Japan.
authors.forename: Toshihiro
authors.initials: T
authors.lastname: Mochizuki
authors.affiliation: National Agriculture and Food Research Organization Agricultural Research Center, Tsukuba, Ibaraki, 305-8666, Japan.
authors.forename: Takeshi
authors.initials: T
authors.lastname: Hayashi
authors.affiliation: National Agriculture and Food Research Organization Agricultural Research Center, Tsukuba, Ibaraki, 305-8666, Japan.
authors.forename: Hiroshi
authors.initials: H
authors.lastname: Nakagawa
authors.affiliation: National Institute for Agro-Environmental Sciences, Tsukuba, Ibaraki, 305-8604, Japan.
authors.forename: Toshihiro
authors.initials: T
authors.lastname: Hasegawa
authors.affiliation: Department of Agricultural and Environmental Biology, Graduate School of Agricultural and Life Sciences, The University of Tokyo, 1-1-1 Yayoi, Bunkyo-ku, Tokyo, 113-8657, Japan. aiwata@mail.ecc.u-tokyo.ac.jp.
authors.forename: Hiroyoshi
authors.initials: H
authors.lastname: Iwata
mesh_terms: D001499:Bayes Theorem; D004777:Environment; D023281:Genomics / Q000379:methods*; D005838:Genotype; D008390:Markov Chains; D008957:Models, Genetic; D009010:Monte Carlo Method; D012275:Oryza / Q000235:genetics* / Q000502:physiology; D010641:Phenotype; D000069600:Plant Breeding