Springer Nature is making SARS-CoV-2 and COVID-19 research free. View research | View latest news | Sign up for updates

Toward integration of genomic selection with crop modelling: the development of an integrated approach to predicting rice heading dates

Abstract

Key message

It is suggested that accuracy in predicting plant phenotypes can be improved by integrating genomic prediction with crop modelling in a single hierarchical model.

Abstract

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.

This is a preview of subscription content, log in to check access.

Fig. 1

Abbreviations

BIL:

Backcross inbred line

C-Bay:

Crop (DVR) model based on an MCMC algorithm

C-Nel:

Crop (DVR) model based on a Nelder–Mead algorithm

DTH:

Days to heading

DVR:

Developmental rate model for rice heading date prediction

DVS:

Developmental stage

EBL:

Extended Bayesian LASSO

GBLUP:

Genomic best linear unbiased prediction

IM:

A hierarchical model integrating EBL with the crop (DVR) model

LOEO:

Leave-one-environment-out cross-validation

LOELO:

Leave-one-‘combination of an environment and a line’-out cross-validation

LOLO:

Leave-one-line-out cross-validation

MCMC:

Markov chain Monte Carlo

QTL:

Quantitative trait locus

RMSE:

Root mean squared errors

T-Bay:

Two-step approach based on C-Bay and EBL

T-Nel:

Two-step approach based on C-Nel and EBL

References

  1. Bishop CM (2006) Pattern recognition and machine learning. Section 10.2.1 Variational distribution. New York: Springer

  2. Bogard M, Ravel C, Paux E, Bordes J, Balfourier F, Chapman SC, Le GJ, Allard V (2014) Predictions of heading date in bread wheat (Triticum aestivum L.) using QTL-based parameters of an ecophysiological model. J Exp Bot 65:5849–5865

  3. Breunig MM, Kriegel HP, Ng RT, Sander J (2000) LOF: identifying density-based local outliers. In: Chen W, Naughton JF, Bernstein PA (eds) Proceedings of the ACM SIGMOD International Conference on Management Data, ACM, pp 93–104

  4. Burgueno J, de los Campos G, Weigel K, Crossa J (2012) Genomic prediction of breeding values when modeling genotype × environment interaction using pedigree and dense molecular markers. Crop Sci 52:707–719

  5. Crossa J, Deloscampos G, Perez P, Gianola D, Burgueno J, Araus JL, Makumbi D, Singh RP, Dreisigacker S, Yan J, Arief V, Banziger M, Braun HJ (2010) Prediction of genetic values of quantitative traits in plant breeding using pedigree and molecular markers. Genetics 186:713–724

  6. Ghahramani Z, Beal MJ (2001) Propagation algorithms for variational Bayesian learning. In: Leen TK, Dietterich TG, Tresp V (eds) Advances in neural information processing systems 13, MIT press, pp 507–513

  7. Gu J, Yin X, Zhang C, Wang H, Struik PC (2014) Linking ecophysiological modelling with quantitative genetics to support marker-assisted crop design for improved yields of rice (Oryza sativa) under drought stress. Ann Bot 114:499–511

  8. Hammer G, Cooper M, Tardieu F, Welch S, Walsh B, van Eeuwijk F, Chapman S, Podlich D (2006) Models for navigating biological complexity in breeding improved crop plants. Trends Plant Sci 11:587–593

  9. Heslot N, Akdemir D, Sorrells ME, Jannink JL (2014) Integrating environmental covariates and crop modeling into the genomic selection framework to predict genotype by environment interactions. Theor Appl Genet 127:463–480

  10. Horie T, Nakagawa H (1990) Modelling and prediction of developmental process in rice. I. Structure and method of parameter estimation of a model for simulating developmental process toward heading. Jpn J Crop Sci 59:687–695

  11. Iwata H, Hayashi T, Terakami S, Takada N, Sawamura Y, Yamamoto T (2013) Potential assessment of genome-wide association study and genomic selection in Japanese pear Pyrus pyrifolia. Breed Sci 63:125–140

  12. Li Z, Sillanpaa MJ (2012) Estimation of quantitative trait locus effects with epistasis by variational Bayes algorithms. Genetics 190:231–249

  13. Li Z, Hallingback HR, Abrahamsson S, Fries A, Gull BA, Sillanpaa MJ, Garcia-Gil MR (2014) Functional multi-locus QTL mapping of temporal trends in Scots Pine wood traits. G3 (Bethesda) 4:2365–2379

  14. Ly D, Hamblin M, Rabbi I, Melaku G, Bakare M, Gauch HG, Okechukwu R, Dixon AGO, Kulakow P, Jannink JL (2013) Relatedness and genotype × environment interaction affect prediction accuracies in genomic selection: a study in Cassava. Crop Sci 53:1312–1325

  15. Ma JF, Shen R, Zhao Z, Wissuwa M, Takeuchi Y, Ebitani T, Yano M (2002) Response of rice to Al stress and identification of quantitative trait Loci for Al tolerance. Plant Cell Physiol 43:652–659

  16. Malosetti M, Visser RG, Celis-Gamboa C, van Eeuwijk FA (2006) QTL methodology for response curves on the basis of non-linear mixed models, with an illustration to senescence in potato. Theor Appl Genet 113:288–300

  17. Matsubara K, Hori K, Ogiso-Tanaka E, Yano M (2014) Cloning of quantitative trait genes from rice reveals conservation and divergence of photoperiod flowering pathways in Arabidopsis and rice. Front Plant Sci 5:193

  18. Metropolis N, Rosenbluth AW, Rosenbluth MN, Teller AH, Teller E (1953) Equation of state calculations by fast computing machines. J Chem Phys 21:1087–1092

  19. Meuwissen TH, Hayes BJ, Goddard ME (2001) Prediction of total genetic value using genome-wide dense marker maps. Genetics 157:1819–1829

  20. Monna L, Lin X, Kojima S, Sasaki T, Yano M (2002) Genetic dissection of a genomic region for a quantitative trait locus, Hd3, into two loci, Hd3a and Hd3b, controlling heading date in rice. Theor Appl Genet 104:772–778

  21. Mutshinda CM, Sillanpaa MJ (2010) Extended Bayesian LASSO for multiple quantitative trait loci mapping and unobserved phenotype prediction. Genetics 186:1067–1075

  22. Nakagawa H, Yamagishi J, Miyamoto N, Motoyama M, Yano M, Nemoto K (2005) Flowering response of rice to photoperiod and temperature: a QTL analysis using a phenological model. Theor Appl Genet 110:778–786

  23. Onogi A (2015) Documents for VIGoR. https://github.com/Onogi/VIGoR

  24. Onogi A, Ideta O, Inoshita Y, Ebana K, Yoshioka T, Yamasaki M, Iwata H (2015) Exploring the areas of applicability of whole-genome prediction methods for Asian rice (Oryza sativa L.). Theor Appl Genet 128:41–53

  25. Park T, Casella G (2008) The Bayesian lasso. Amer Stat Assoc 103:681–686

  26. Quilot B, Kervella J, Genard M, Lescourret F (2005) Analysing the genetic control of peach fruit quality through an ecophysiological model combined with a QTL approach. J Exp Bot 56:3083–3092

  27. R Development Core Team (2011) R: A language and environment for statistical computing. R Foundation for Statistical Computing. Vienna, Austria. ISBN 3-900051-07-0. http://www.R-project.org/

  28. Resende MFJ, Munoz P, Acosta JJ, Peter GF, Davis JM, Grattapaglia D, Resende MD, Kirst M (2012) Accelerating the domestication of trees using genomic selection: accuracy of prediction models across ages and environments. New Phytol 193:617–624

  29. Reymond M, Muller B, Leonardi A, Charcosset A, Tardieu F (2003) Combining quantitative trait Loci analysis and an ecophysiological model to analyze the genetic variability of the responses of maize leaf growth to temperature and water deficit. Plant Physiol 131:664–675

  30. Sillanpaa MJ, Pikkuhookana P, Abrahamsson S, Knurr T, Fries A, Lerceteau E, Waldmann P, Garcia-Gil MR (2012) Simultaneous estimation of multiple quantitative trait loci and growth curve parameters through hierarchical Bayesian modeling. Heredity (Edinb) 108:134–146

  31. Soltani A, Sinclair TR (2012) Modeling physiology of crop development, growth and yield. Chapter 6 phenology–temperature. CABI, MA, USA

  32. Takahashi Y, Shomura A, Sasaki T, Yano M (2001) Hd6, a rice quantitative trait locus involved in photoperiod sensitivity, encodes the alpha subunit of protein kinase CK2. Proc Natl Acad Sci USA 98:7922–7927

  33. Tardieu F (2003) Virtual plants: modelling as a tool for the genomics of tolerance to water deficit. Trends Plant Sci 8:9–14

  34. Technow F, Messina CD, Totir LR, Cooper M (2015) Integrating crop growth models with whole genome prediction through approximate Bayesian computation. PLoS ONE 10:e0130855. doi:10.1371/journal.pone.0130855

  35. Thornton PK, Ericksen PJ, Herrero M, Challinor AJ (2014) Climate variability and vulnerability to climate change: a review. Glob Chang Biol 20:3313–3328

  36. Uptmoor R, Schrag T, Stützel H, Esch E (2008) Crop model based QTL analysis across environments and QTL based estimation of time to floral induction and flowering in Brassica oleracea. Mol Breed 21:205–216

  37. Wei GCG, Tanner MA (1990) A Monte Carlo implementation of the EM algorithm and the poor man’s data augmentation algorithms. J Amer Statist Assoc. 85:699–704

  38. Yano M, Harushima Y, Nagamura Y, Kurata N, Minobe Y, Sakaki T (1997) Identification of quantitative trait loci controlling heading date in rice using a high-density linkage map. Theor Appl Genet 95:1025–1032

  39. Yano M, Katayose Y, Ashikari M, Yamanouchi U, Monna L, Fuse T, Baba T, Yamamoto K, Umehara Y, Nagamura Y, Sasaki T (2000) Hd1, a major photoperiod sensitivity quantitative trait locus in rice, is closely related to the Arabidopsis flowering time gene CONSTANS. Plant Cell 12:2473–2484

  40. Yin X, Kropff MJ, Horie T, Nakagawa H, Centeno HG, Zhu D, Goudriaan J (1997) A model for photothermal responses of flowering in rice I. Model description and parameterization. Field Crop Res 51:189–200

  41. Yin X, Chasalow SD, Dourleijn CJ, Stam P, Kropff MJ (2000) Coupling estimated effects of QTLs for physiological traits to a crop growth model: predicting yield variation among recombinant inbred lines in barley. Heredity (Edinb) 85:539–549

  42. Yin X, Stam P, Kropff MJ, Schapendonk AH (2003) Crop modeling, QTL mapping, and their complementary role in plant breeding. Agron J 95:90–98

  43. Yin X, Struik PC, Kropff MJ (2004) Role of crop physiology in predicting gene-to-phenotype relationships. Trends Plant Sci 9:426–432

  44. Yin X, Struik PC, van Eeuwijk FA, Stam P, Tang J (2005) QTL analysis and QTL-based prediction of flowering phenology in recombinant inbred lines of barley. J Exp Bot 56:967–976

Download references

Acknowledgments

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).

Data archiving

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).

Author information

Correspondence to Hiroyoshi Iwata.

Ethics declarations

Conflict of interest

The authors declare that they have no competing interests.

Additional information

Communicated by M. J. Sillanpaa.

Electronic supplementary material

Below is the link to the electronic supplementary material.

Supplementary material 1 (PDF 688 kb)

Supplementary material 2 (PDF 899 kb)

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

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

Download citation

Keywords

  • Root Mean Square Error
  • Markov Chain Monte Carlo
  • Genomic Prediction
  • Markov Chain Monte Carlo Algorithm
  • Local Outlier Factor