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Use of genomic selection in breeding rice (Oryza sativa L.) for resistance to rice blast (Magnaporthe oryzae)

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Abstract

Rice blast (RB), caused by the fungal pathogen Magnaporthe oryzae, is a major disease in rice (Oryzae sativa L.) with resistance controlled by major and minor genes. Genomic selection (GS) is a breeding technology applicable for selecting traits controlled by many genes. Our objective was to assess the utility of GS in improving RB resistance. A population of 161 accessions from Africa and another population of 162 accessions from the USA were evaluated for resistance to six and eight RB isolates, respectively. Each rice population was genotyped with single nucleotide polymorphism (SNP) markers. The accuracy of GS was determined using seven models: genomic best linear unbiased prediction (gBLUP), gBLUP with some markers as fixed effects (fgBLUP), gBLUP model with population structure as a covariate (sgBLUP), multitrait gBLUP (mgBLUP), Bayesian (BayesA and BayesC) models, and a multiple linear regression model using significant markers (MLR). Each set of population had accessions with good resistance to multiple isolates. Using cross-validation, the accuracy of gBLUP ranged from 0.15 to 0.72; the gBLUP, sgBLUP, mgBLUP, and Bayesian methods had similar accuracy, while fgBLUP gave the greatest accuracy. Without cross-validation, gBLUP, sgBLUP, fgBLUP, and Bayesian methods were similar and were superior to mgBLUP and MLR. In general, a GS model built on data from one isolate was able to predict the phenotypes generated from other isolates, suggesting common genes controlling resistance across isolates. Our results demonstrate that GS may be a very useful method to improve RB resistance. The fgBLUP model could be used to effectively select for both durable and resistance traits conferred by major genes.

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References

  • Arruda MP, Brown PJ, Lipka AE, Krill AM, Thurber C, Kolb FL (2015) Genomic selection for predicting Fusarium head blight resistance in a wheat breeding program. Plant Genome 8(3):1–2

    Article  CAS  Google Scholar 

  • Arruda MP, Lipka A, Brown P, Krill A, Thurber C, Brown-Guedira G, Dong Y, Foresman B, Kolb F (2016) Comparing genomic selection and marker-assisted selection for Fusarium head blight resistance in wheat (Triticum aestivum L.). Mol Breed 36(7):1–11

    Article  CAS  Google Scholar 

  • Ballini E, Morel J-B, Droc G, Price A, Courtois B, Notteghem J-L, Tharreau D (2008) A genome-wide meta-analysis of rice blast resistance genes and quantitative trait loci provides new insights into partial and complete resistance. Mol Plant Microbe In 21(7):859–868

    Article  CAS  Google Scholar 

  • Bates D, Mächler M, Bolker B, Walker S (2014) Fitting linear mixed-effects models using lme4. J Stat Softw 67(1):1–48

    Google Scholar 

  • Butler D, Cullis BR, Gilmour A, Gogel B (2009) ASReml-R reference manual. The State of Queensland, Department of Primary Industries and Fisheries, Brisbane, Australia

    Google Scholar 

  • Chaudhary RC (1996) Standard evaluation system for rice. International Rice Research Institute, Manila

    Google Scholar 

  • Crossa J, Gauch H, Zobel RW (1990) Additive main effects and multiplicative interaction analysis of two international maize cultivar trials. Crop Sci 30(3):493–500

    Article  Google Scholar 

  • de los Campos G, Hickey JM, Pong-Wong R, Daetwyler HD, Calus MP (2013) Whole-genome regression and prediction methods applied to plant and animal breeding. Genetics 193(2):327–345

    Article  PubMed Central  Google Scholar 

  • Doyle JJ, Doyle JL (1987) A rapid DNA isolation procedure for small quantities of fresh leaf tissue. Phytochem 19:11–15

    Google Scholar 

  • Endelman JB (2011) Ridge regression and other kernels for genomic selection with R package rrBLUP. Plant Genome 4(3):250–255

    Article  Google Scholar 

  • Fairhurst T, Dobermann A (2002) Rice in the global food supply. World 5 (7,502):454,349-511,675

  • Gauch HG (2006) Statistical analysis of yield trials by AMMI and GGE. Crop Sci 46(4):1488–1500

    Article  Google Scholar 

  • Gowda M, Das B, Makumbi D, Babu R, Semagn K, Mahuku G, Olsen MS, Bright JM, Beyene Y, Prasanna BM (2015) Genome-wide association and genomic prediction of resistance to maize lethal necrosis disease in tropical maize germplasm. Theor Appl Genet 128(10):1957–1968

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Grenier C, Cao T-V, Ospina Y, Quintero C, Châtel MH, Tohme J, Courtois B, Ahmadi N (2015) Accuracy of genomic selection in a rice synthetic population developed for recurrent selection breeding. PLoS One 10(8):e0136594

    Article  PubMed  PubMed Central  Google Scholar 

  • Habier D, Fernando RL, Dekkers JC (2007) The impact of genetic relationship information on genome-assisted breeding values. Genetics 177(4):2389–2397

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Habier D, Fernando RL, Kizilkaya K, Garrick DJ (2011) Extension of the Bayesian alphabet for genomic selection. BMC Bioinformatics 12(1):186

    Article  PubMed  PubMed Central  Google Scholar 

  • Heffner EL, Sorrells ME, Jannink J-L (2009) Genomic selection for crop improvement. Crop Sci 49(1):1–12

    Article  CAS  Google Scholar 

  • Hoffstetter A, Cabrera A, Huang M, Sneller C (2016) Optimizing training population data and validation of genomic selection for economic traits in soft winter wheat. G3: Genes, Genom, Genet 6(9):2919–2928

    Article  Google Scholar 

  • Huang M, Cabrera A, Hoffstetter A, Griffey C, Van Sanford D, Costa J, McKendry A, Chao S, Sneller C (2016) Genomic selection for wheat traits and trait stability. Theor Appl Genet 129(9):1697–1710

    Article  CAS  PubMed  Google Scholar 

  • Huang M, Ward B, Griffey C, Van Sanford D, McKendry A, Brown-Guedira G, Tyagi P, Sneller C (2018) The accuracy of genomic prediction between environments and populations for soft wheat traits. Crop Sci 58:2274–2288

    Article  Google Scholar 

  • Iwata H, Ebana K, Uga Y, Hayashi T (2015) Genomic prediction of biological shape: elliptic Fourier analysis and kernel partial least squares (PLS) regression applied to grain shape prediction in rice (Oryza sativa L.). PLoS One 10(3):e0120610

    Article  PubMed  PubMed Central  Google Scholar 

  • Jannink J-L, Lorenz AJ, Iwata H (2010) Genomic selection in plant breeding: from theory to practice. Briefings in Functional Genomics 9(2):166–177

    Article  CAS  PubMed  Google Scholar 

  • Jia Y, Jannink J-L (2012) Multiple-trait genomic selection methods increase genetic value prediction accuracy. Genetics 192(4):1513–1522

    Article  PubMed  PubMed Central  Google Scholar 

  • Kafiriti E, Dondeyne S, Msomba S, Deckers J, Raes D (2003) Coming to grips with farmers’ variety selection—the case of new improved rice varieties under irrigation in South East Tanzania. Tropicultura 21(4):211–217

    Google Scholar 

  • Kihoro J, Bosco NJ, Murage H, Ateka E, Makihara D (2013) Investigating the impact of rice blast disease on the livelihood of the local farmers in greater Mwea region of Kenya. SpringerPlus 2(1):308

    Article  PubMed  PubMed Central  Google Scholar 

  • Kiyosawa S (1982) Genetics and epidemiological modeling of breakdown of plant disease resistance. Annu Rev Phytopathol 20(1):93–117

    Article  Google Scholar 

  • Liu W, Liu J, Triplett L, Leach JE, Wang G-L (2014) Novel insights into rice innate immunity against bacterial and fungal pathogens. Annu Rev Phytopathol 52:213–241

    Article  CAS  PubMed  Google Scholar 

  • Lorenz AJ, Chao S, Asoro FG, Heffner EL, Hayashi T, Iwata H, Smith KP, Sorrells ME, Jannink J-L (2011) Genomic selection in plant breeding: knowledge and prospects. In: Advances in agronomy, vol 110. Elsevier, pp 77–123

  • Lorenz AJ, Smith KP, Jannink J-L (2012) Potential and optimization of genomic selection for Fusarium head blight resistance in six-row barley. Crop Sci 52(4):1609–1621

    Article  Google Scholar 

  • Marulanda JJ, Mi X, Melchinger AE, Xu J-L, Würschum T, Longin CFH (2016) Optimum breeding strategies using genomic selection for hybrid breeding in wheat, maize, rye, barley, rice and triticale. Theor Appl Genet 129(10):1901–1913

    Article  CAS  PubMed  Google Scholar 

  • Mendiburu FD (2015) Agricolae: statistical procedures for agricultural research. R Package Version 1. 2 - 3. http://CRAN.R-project.org/package=agricolae

  • Meuwissen THE, Hayes BJ, Goddard ME (2001) Prediction of total genetic value using genome-wide dense marker maps. Genetics 157(4):1819–1829

    CAS  PubMed  PubMed Central  Google Scholar 

  • Mgonja EM, Balimponya EG, Kang H, Bellizzi M, Park CH, Li Y, Mabagala R, Sneller C, Correll J, Opiyo S, Talbot NJ, Mitchell T, Wang G-L (2016) Genome-wide association map** of rice resistance genes against Magnaporthe oryzae isolates from four African countries. Phytopathology 106(11):1359–1365

    Article  CAS  PubMed  Google Scholar 

  • Mgonja EM, Park CH, Kang H, Balimponya EG, Opiyo S, Bellizzi M, Mutiga SK, Rotich F, Ganeshan VD, Mabagala R, Sneller C, Correll J, Zhou B, Talbot NJ, Mitchell T, Wang G-l (2017) Genoty**-by-sequencing-based genetic analysis of African rice cultivars and association map** of blast resistance genes against Magnaporthe oryzae populations in Africa. Phytopathology 107(9):1039–1046

    Article  CAS  PubMed  Google Scholar 

  • Nazarian A, Gezan SA (2016) GenoMatrix: a software package for pedigree-based and genomic prediction analyses on complex traits. J Hered 107(4):372–379

    Article  PubMed  PubMed Central  Google Scholar 

  • Onaga G, Wydra K, Koopmann B, Séré Y, von Tiedemann A (2015) Population structure, pathogenicity, and mating type distribution of Magnaporthe oryzae isolates from East Africa. Phytopathology 105(8):1137–1145

    Article  CAS  PubMed  Google Scholar 

  • 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(1):41–53

    Article  PubMed  Google Scholar 

  • Onogi A, Watanabe M, Mochizuki T, Hayashi T, Nakagawa H, Hasegawa T, Iwata H (2016) Toward integration of genomic selection with crop modelling: the development of an integrated approach to predicting rice heading dates. Theor Appl Genet 129(4):805–817

    Article  PubMed  Google Scholar 

  • Pérez P, de los Campos G (2014) Genome-wide regression and prediction with the BGLR statistical package. Genetics 198(2):483–495

    Article  PubMed  PubMed Central  Google Scholar 

  • R Core Team (2018) R: a language and environment for statistical computing. R Foundation for Statistical Computing, Vienna. https://www.R-project.org/

  • Resende MF, Muñoz P, Resende MD, Garrick DJ, Fernando RL, Davis JM, Jokela EJ, Martin TA, Peter GF, Kirst M (2012) Accuracy of genomic selection methods in a standard data set of loblolly pine (Pinus taeda L.). Genetics 190(4):1503–1510

    Article  PubMed  PubMed Central  Google Scholar 

  • Rutkoski JE, Heffner EL, Sorrells ME (2011) Genomic selection for durable stem rust resistance in wheat. Euphytica 179(1):161–173

    Article  Google Scholar 

  • Rutkoski J, Benson J, Jia Y, Brown-Guedira G, Jannink J-L, Sorrells M (2012) Evaluation of genomic prediction methods for Fusarium head blight resistance in wheat. Plant Genome 5(2):51–61

    Article  CAS  Google Scholar 

  • Rutkoski JE, Poland JA, Singh RP, Huerta-Espino J, Bhavani S, Barbier H, Rouse MN, Jannink J-L, Sorrells ME (2014) Genomic selection for quantitative adult plant stem rust resistance in wheat. Plant Genome 7(3)

  • Sharma T, Rai A, Gupta S, Vijayan J, Devanna B, Ray S (2012) Rice blast management through host-plant resistance: retrospect and prospects. Agribiol Res 1(1):37–52

    Article  Google Scholar 

  • Spindel J, Iwata H (2018) Genomic selection in rice breeding. In: Rice genomics, genetics and breeding. Springer, pp 473–496

  • Spindel J, Begum H, Akdemir D, Virk P, Collard B, Redona E, Atlin G, Jannink J-L, McCouch SR (2015) Genomic selection and association map** in rice (Oryza sativa): effect of trait genetic architecture, training population composition, marker number and statistical model on accuracy of rice genomic selection in elite, tropical rice breeding lines. PLoS Genet 11(2):e1004982

    Article  PubMed  PubMed Central  Google Scholar 

  • Spindel J, Begum H, Akdemir D, Collard B, Redoña E, Jannink J, McCouch S (2016) Genome-wide prediction models that incorporate de novo GWAS are a powerful new tool for tropical rice improvement. Heredity 116(4):395–408

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Thomson MJ (2014) High-throughput SNP genoty** to accelerate crop improvement. Plant Breed and Biotech 2(3):195–212

    Article  Google Scholar 

  • Wang Y, Wang D, Deng X, Liu J, Sun P, Liu Y, Huang H, Jiang N, Kang H, Ning Y (2012) Molecular map** of the blast resistance genes Pi2-1 and Pi51 (t) in the durably resistant rice ‘Tian**gyeshengdao’. Phytopathology 102(8):779–786

    Article  CAS  PubMed  Google Scholar 

  • Wang X, Lee S, Wang J, Ma J, Bianco T, Jia Y (2014) Current advances on genetic resistance to rice blast disease. In: Rice—germplasm, genetics and improvement. InTech, Rijeka. 195–217

    Google Scholar 

  • Wang X, Li L, Yang Z, Zheng X, Yu S, Xu C, Hu Z (2017) Predicting rice hybrid performance using univariate and multivariate GBLUP models based on North Carolina mating design II. Heredity 118(3):302–310

    Article  CAS  PubMed  Google Scholar 

  • Ward JH (1963) Hierarchical grou** to optimize an objective function. J Am Stat Assoc 58(301):236–244

    Article  Google Scholar 

  • Xu S, Zhu D, Zhang Q (2014) Predicting hybrid performance in rice using genomic best linear unbiased prediction. Proc Natl Acad Sci 111(34):12456–12461

    Article  CAS  PubMed  Google Scholar 

  • Yu ZH, Mackill DJ, Bonman JM (1987) Inheritance of resistance to blast in some traditional and improved rice cultivars. Genetics 77:323–326

    Google Scholar 

  • Zhao K, Tung C-W, Eizenga GC, Wright MH, Ali ML, Price AH, Norton GJ, Islam MR, Reynolds A, Mezey J (2011) Genome-wide association map** reveals a rich genetic architecture of complex traits in Oryza sativa. Nat Commun 2:467

    Article  PubMed  PubMed Central  Google Scholar 

  • Zobel RW, Wright MJ, Gauch HG (1988) Statistical analysis of a yield trial. Agron J 80(3):388–393

    Article  Google Scholar 

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Acknowledgments

The authors are also grateful to Dr. Sneller’s and Wang’s labs at the Ohio State University (OSU) and Dr. Bo Zhou’s Lab at the International Rice Research Institute (IRRI) for their technical assistance.

Funding

The authors are grateful to the Norman E. Borlaug Leadership Enhancement in Agriculture Program (Borlaug LEAP) and Innovative Agricultural Research Initiative (iAGRI) project of the USAID for providing grants to support this project.

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Authors and Affiliations

  1. Department of Horticulture and Crop Science, The Ohio State University, 1680 Madison Ave, Wooster, OH, 44691, USA

    Mao Huang & Clay H. Sneller

  2. Tanzania Official Seed Certification Institute, Mtwara, Tanzania

    Elias G. Balimponya

  3. Department of Plant Pathology, The Ohio State University, 2021 Coffey Road, Columbus, OH, 43210, USA

    Emmanuel M. Mgonja & Guo-Liang Wang

  4. Department of Horticulture and Crop Science, The Ohio State University, 2021 Coffey Road, Columbus, OH, 43210, USA

    Leah K. McHale

  5. Department of Crop Science and Horticulture, Sokoine University of Agriculture, P.O. Box 3005, Morogoro, Tanzania

    Ashura Luzi-Kihupi

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  1. Mao Huang
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  3. Emmanuel M. Mgonja
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Correspondence to Clay H. Sneller.

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Huang, M., Balimponya, E.G., Mgonja, E.M. et al. Use of genomic selection in breeding rice (Oryza sativa L.) for resistance to rice blast (Magnaporthe oryzae). Mol Breeding 39, 114 (2019). https://doi.org/10.1007/s11032-019-1023-2

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