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

Theoretical and Applied Genetics

Volumn 129, Issue 4, 2016, Pages 805-817

  Onogi, Akio ^a   Watanabe, Maya ^a   Mochizuki, Toshihiro ^b   Hayashi, Takeshi ^c   Nakagawa, Hiroshi ^c   Hasegawa, Toshihiro ^d   Iwata, Hiroyoshi ^a  

^a UNIVERSITY OF TOKYO   (Japan)

^b KYUSHU UNIVERSITY   (Japan)

^c FOOD RESEARCH INSTITUTE   (Japan)

^d NATIONAL INSTITUTE FOR AGRO ENVIRONMENTAL SCIENCES   (Japan)

Author keywords

[No Author keywords available]

Indexed keywords

CROPS; GENES; HIERARCHICAL SYSTEMS; INFORMATION MANAGEMENT; INTEGRATED CONTROL; MARKOV PROCESSES; MONTE CARLO METHODS; PLANTS (BOTANY);

ACCURATE PREDICTION; BAYESIAN FRAMEWORKS; GENOMIC PREDICTIONS; INTEGRATED APPROACH; MANAGEMENT INFORMATION; MARKOV CHAIN MONTE CARLO ALGORITHMS; PHENOLOGICAL MODEL; TOWARD INTEGRATION;

FORECASTING;

BAYES THEOREM; BIOLOGICAL MODEL; COMPARATIVE STUDY; ENVIRONMENT; GENETICS; GENOMICS; GENOTYPE; MARKOV CHAIN; MONTE CARLO METHOD; ORYZA; PHENOTYPE; PHYSIOLOGY; PLANT BREEDING; PROCEDURES;

BAYES THEOREM; ENVIRONMENT; GENOMICS; GENOTYPE; MARKOV CHAINS; MODELS, GENETIC; MONTE CARLO METHOD; ORYZA; PHENOTYPE; PLANT BREEDING;

EID: 84979090952     PISSN: 00405752     EISSN: None     Source Type: Journal    
DOI: 10.1007/s00122-016-2667-5     Document Type: Article

References (44)

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