Skip to main content
SpringerLink
Log in

Genomic Selection in Rice Breeding

  • Chapter
  • First Online:
Rice Genomics, Genetics and Breeding

Abstract

Genomic selection (GS) is a new breeding method that makes use of genome-wide DNA marker data to improve the efficiency of breeding for quantitative traits. In GS, individuals with superior breeding values are identified and selected based on prediction models built by correlating phenotype and genotype in a breeding population of interest. The potential of GS to improve rice breeding efficiency has recently been evidenced by a number of empirical and simulation studies; however efforts to implement GS in rice breeding are still limited, particularly as compared to other major grain crops such as maize and wheat. In this chapter, we discuss a variety of GS modeling methods, practical considerations for implementing GS in rice breeding programs, and the rapid evolution of GS technology. We conclude with a discussion of what this means for GS technology in the future.

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

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 229.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 299.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 299.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Similar content being viewed by others

References

  • Albrecht T, Wimmer V, Auinger HJ et al (2011) Genome-based prediction of testcross values in maize. Theor Appl Genet 123:339–350

    Article  PubMed  Google Scholar 

  • Al-Tamimi N, Brein C, Oakey H et al (2016) Salinity tolerance loci revealed in rice using high-throughput non-invasive phenotyping. Nat Commun 7:13342

    Article  PubMed  PubMed Central  Google Scholar 

  • Arruda MP, Lipka AE, Brown PJ et al (2016) Comparing genomic selection and marker-assisted selection for Fusarium head blight resistance in wheat (Triticum aestivum L.) Mol Breed 36:84

    Article  CAS  Google Scholar 

  • Asoro FG, Newell MA, Beavis WD et al (2011) Accuracy and training population design for genomic selection on quantitative traits in elite North American oats. Plant Genome 4:132

    Article  Google Scholar 

  • Asoro FG, Newell MA, Beavis WD et al (2013) Genomic, marker-assisted, and pedigree-BLUP selection methods for β-glucan concentration in elite oat. Crop Sci 53:1894–1906

    Article  CAS  Google Scholar 

  • Auinger HJ, Schönleben M, Lehermeier C et al (2016) Model training across multiple breeding cycles significantly improves genomic prediction accuracy in rye (Secale cereale L.) Theor Appl Genet 129:2043–2053

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Bassi FM, Bentley AR, Charmet G et al (2016) Breeding schemes for the implementation of genomic selection in wheat (Triticum spp.) Plant Sci 242:23–36

    Article  CAS  PubMed  Google Scholar 

  • Battenfield SD, Guzmán C, Gaynoret RC et al (2016) Genomic selection for processing and end-use quality traits in the CIMMYT spring bread wheat breeding program. Plant Genome 9. https://doi.org/10.3835/plantgenome2016.01.0005

  • Bentley AR, Scutari M, Gosman N et al (2014) Applying association mapping and genomic selection to the dissection of key traits in elite European wheat. Theor Appl Genet 127:2619–2633

    Article  CAS  PubMed  Google Scholar 

  • Bernardo R (2008) Molecular markers and selection for complex traits in plants: learning from the last 20 years. Crop Sci 48:1649–1664

    Article  Google Scholar 

  • Bernardo R (2009) Genomewide selection for rapid introgression of exotic germplasm in maize. Crop Sci 49:419–425

    Article  Google Scholar 

  • Bernardo R (2014) Genomewide selection when major genes are known. Crop Sci 54:68–75

    Article  Google Scholar 

  • Bernardo R (2016) Genomewide predictions for backcrossing a quantitative trait from an exotic to an adapted line. Crop Sci 56:1067–1075

    Article  CAS  Google Scholar 

  • Bernardo R, Yu J (2007) Prospects for genomewide selection for quantitative traits in maize. Crop Sci 47:1082–1090

    Article  Google Scholar 

  • Beyene Y, Semagn K, Mugo S et al (2015) Genetic gains in grain yield through genomic selection in eight bi-parental maize populations under drought stress. Crop Sci 55:154–163

    Article  Google Scholar 

  • Bian U, Holland JB (2017) Enhancing genomic prediction with genome-wide association studies in multiparental maize populations. Heredity 118:585–593

    Article  CAS  PubMed  Google Scholar 

  • Blondel M, Onogi A, Iwata H et al (2015) A ranking approach to genomic selection. PLoS One 10:e0128570

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  • Breiman L (2001) Random forests. Mach Learn 45:5–32

    Article  Google Scholar 

  • Buckler ES (2017) Direction of GWAS and GS. Paper presented at the plant and animal genome XXV, 14 January 2017, San Diego, CA, USA

    Google Scholar 

  • Burgueño J, de los Campos G, Weigel K et al (2012) Genomic prediction of breeding values when modeling genotype × environment interaction using pedigree and dense molecular markers. Crop Sci 52:707

    Article  Google Scholar 

  • Calus MPL, Veerkamp RF (2011) Accuracy of multi-trait genomic selection using different methods. Genet Sel Evol 43:21

    Article  Google Scholar 

  • Calus MPL, Bouwman AC, Schrooten C et al (2016) Efficient genomic prediction based on whole-genome sequence data using split-and-merge Bayesian variable selection. Genet Sel Evol 48:49

    Article  PubMed  PubMed Central  Google Scholar 

  • Charmet G, Storlie E, Oury FX et al (2014) Genome-wide prediction of three important traits in bread wheat. Mol Breed 34:1843–1852

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Cooper M, Technow F, Messina C et al (2016) Use of crop growth models with whole-genome prediction: application to a maize multienvironment trial. Crop Sci 56:2141–2156

    Article  Google Scholar 

  • Cuyabano BCD, Su G, Lund MS (2014) Genomic prediction of genetic merit using LD-based haplotypes in the Nordic Holstein population. BMC Genomics 15:1171

    Article  PubMed  PubMed Central  Google Scholar 

  • Cuyabano BCD, Su G, Lund MS (2015) Selection of haplotype variables from a high-density marker map for genomic prediction. Genet Sel Evol 47:61

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  • Dahl A, Iotchkova V, Baud A et al (2016) A multiple-phenotype imputation method for genetic studies. Nat Genet 48:466–472

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • de los Campos G, Sorensen D (2014) On the genomic analysis of data from structured populations. J Anim Breed Genet 131:163–164

    Article  PubMed  PubMed Central  Google Scholar 

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

    Article  PubMed Central  Google Scholar 

  • Desta ZA, Ortiz R (2014) Genomic selection: genome-wide prediction in plant improvement. Trends Plant Sci 19:592–601

    Article  CAS  PubMed  Google Scholar 

  • Edwards SM, Sørensen IF, Sarup P et al (2016) Genomic prediction for quantitative traits is improved by mapping variants to gene ontology categories in Drosophila melanogaster. Genetics 203:1871–1883

    Article  PubMed  PubMed Central  Google Scholar 

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

    Article  Google Scholar 

  • Fernie AR, Schauer N (2009) Metabolomics-assisted breeding: a viable option for crop improvement? Trends Genet 25:39–48

    Article  CAS  PubMed  Google Scholar 

  • Furbank RT, Tester M (2011) Phenomics – technologies to relieve the phenotyping bottleneck. Trends Plant Sci 16:635–644

    Article  CAS  PubMed  Google Scholar 

  • García-Ruiz A, Cole JB, VanRaden PM et al (2016) Changes in genetic selection differentials and generation intervals in US Holstein dairy cattle as a result of genomic selection. Proc Natl Acad Sci 113:E3995–E4004

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  • Garrick D, Dekkers J, Fernando R (2014) The evolution of methodologies for genomic prediction. Livest Sci 166:10–18

    Article  Google Scholar 

  • Gianola D (2013) Priors in whole-genome regression: the Bayesian alphabet returns. Genetics 194:573–596

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Gianola D, van Kaam JBCHM (2008) Reproducing kernel Hilbert spaces regression methods for genomic assisted prediction of quantitative traits. Genetics 178:2289–2303

    Article  PubMed  PubMed Central  Google Scholar 

  • Gianola D, Fernando RL, Stella A (2006) Genomic-assisted prediction of genetic value with semiparametric procedures. Genetics 173:1761–1776

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Gianola D, Okut H, Weigel KA et al (2011) Predicting complex quantitative traits with Bayesian neural networks: a case study with Jersey cows and wheat. BMC Genet 12:87

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Gianola D, Weigel KW, Krämer N et al (2014) Enhancing genome-enabled prediction by bagging genomic BLUP. PLoS One 9:e91693

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  • Gonzàlez-Camacho JM, de los Campos G, Pérez P et al (2012) Genome-enabled prediction of genetic values using radial basis function neural networks. Theor Appl Genet 125:759–771

    Article  PubMed  PubMed Central  Google Scholar 

  • González-Camacho JM, Crossa J, Pérez-Rodríguez P et al (2016) Genome-enabled prediction using probabilistic neural network classifiers. BMC Genomics 17:208

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  • González-Recio O, Weigel KA, Gianola D et al (2010) L2-boosting algorithm applied to high-dimensional problems in genomic selection. Genet Res (Camb) 92:227–237

    Article  CAS  Google Scholar 

  • González-Recio O, Rosa GJM, Gianola D (2014) Machine learning methods and predictive ability metrics for genome-wide prediction of complex traits. Livest Sci 166:217–231

    Article  Google Scholar 

  • Grenier C, Cao TV, Ospina Y et al (2015) Accuracy of genomic selection in a rice synthetic population developed for recurrent selection breeding. PLoS One 10:e0136594

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  • Guo Z, Tucker DM, Lu JW et al (2012) Evaluation of genome-wide selection efficiency in maize nested association mapping populations. Theor Appl Genet 124:261–275

    Article  PubMed  Google Scholar 

  • Guo Z, Tucker DM, Basten CJ et al (2014) The impact of population structure on genomic prediction in stratified populations. Theor Appl Genet 127:749–762

    Article  PubMed  Google Scholar 

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

    CAS  PubMed  PubMed Central  Google Scholar 

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

    Article  Google Scholar 

  • Haghighattalab A, Pérez LG, Mondal S et al (2016) Application of unmanned aerial systems for high throughput phenotyping of large wheat breeding nurseries. Plant Methods 12:35

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  • Hayashi T, Iwata H (2013) A Bayesian method and its variational approximation for prediction of genomic breeding values in multiple traits. BMC Bioinformatics 14:34

    Article  PubMed  PubMed Central  Google Scholar 

  • Hayes BJ, Visscher PM, Goddard ME (2009a) Increased accuracy of artificial selection by using the realized relationship matrix. Genet Res 91:47–60

    Article  CAS  Google Scholar 

  • Hayes BJ, Bowman PJ, Chamberlain AJ et al (2009b) Genomic selection in dairy cattle: progress and challenges. J Dairy Sci 92:433–443

    Article  CAS  PubMed  Google Scholar 

  • He D, Rish I, Haws D et al (2016) MINT: mutual information based transductive feature selection for genetic trait prediction. IEEE/ACM Trans Compt Biol Bioinform 13:578–583

    Article  Google Scholar 

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

    Article  CAS  Google Scholar 

  • Heffner EL, Lorenz AJ, Jannink JL et al (2010) Plant breeding with genomic selection: gain per unit time and cost. Crop Sci 50:1681–1690

    Article  Google Scholar 

  • Heffner EL, Jannink JL, Sorrells ME (2011) Genomic selection accuracy using multifamily prediction models in a wheat breeding program. Plant Genome 4:65–75

    Article  Google Scholar 

  • Heidaritabar M, Calus MPL, Megens HJ et al (2016) Accuracy of genomic prediction using imputed whole-genome sequence data in white layers. J Anim Breed Genet 133:167–179

    Article  CAS  PubMed  Google Scholar 

  • Henderson CR (1985) Best linear unbiased prediction of non-additive genetic merits. J Anim Sci 60:111–117

    Article  Google Scholar 

  • Henderson CR, Quaas RL (1976) Multiple trait evaluation using relatives’ records. J Anim Sci 43:1188–1197

    Article  Google Scholar 

  • Heslot N, Yang HP, Sorrells ME et al (2012) Genomic selection in plant breeding: a comparison of methods. Crop Sci 52:146–160

    Article  Google Scholar 

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

    Article  PubMed  Google Scholar 

  • Heslot N, Jannink JL, Sorrells ME (2015) Perspectives for genomic selection applications and research in plants. Crop Sci 55:1–12

    Article  Google Scholar 

  • Hori T, Montocho D, Agbangla C et al (2016) Multi-task Gaussian process for imputing missing data in multi-trait and multi-environment trials. Theor Appl Genet 129:2101–2115

    Article  PubMed  Google Scholar 

  • Iheshiulor OOM, Woolliams JA, Yu X et al (2016) Within- and across-breed genomic prediction using whole-genome sequence and single nucleotide polymorphism panels. Genet Sel Evol 48:15

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  • Iwata H, Jannink JL (2010) Marker genotype imputation in a low-marker-density panel with a high-marker-density reference panel: accuracy evaluation in barley breeding lines. Crop Sci 50:1269–1278

    Article  Google Scholar 

  • Iwata H, Jannink JL (2011) Accuracy of genomic selection prediction in barley breeding programs: a simulation study based on the real single nucleotide polymorphism data of barley breeding lines. Crop Sci 51:1915–1927

    Article  Google Scholar 

  • Iwata H, Ebana K, Uga Y et al (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:e0120610

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  • Jacquin L, Cao TV, Ahmadi N (2016) A unified and comprehensible view of parametric and kernel methods for genomic prediction with application to rice. Front Genet 7:145

    Article  PubMed  PubMed Central  Google Scholar 

  • Jannink JL (2010) Dynamics of long-term genomic selection. Genet Sel Evol 42:35

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  • Jannink JL, Iwata H, Bhat PR et al (2009) Marker imputation in barley association studies. Plant Genome 2:11–22

    Article  CAS  Google Scholar 

  • Jannink JL, Lorenz AJ, Iwata H (2010) Genomic selection in plant breeding: from theory to practice. Brief Funct Genomic Proteomic 9:166–177

    Article  CAS  Google Scholar 

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

    Article  PubMed  PubMed Central  Google Scholar 

  • Jiang Y, Reif JC (2015) Modeling epistasis in genomic selection. Genetics 201:759–768

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Kadarmideen HN, von Rohr P, Janss LLG (2006) From genetical genomics to systems genetics: potential applications in quantitative genomics and animal breeding. Mamm Genome 17:548–564

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Kärkkäinen HP, Sillanpää MJ (2012) Back to basics for Bayesian model building genomic selection. Genetics 191:969–987

    Article  PubMed  PubMed Central  Google Scholar 

  • Kleinknecht K, Möhring J, Singh KP et al (2013) Comparison of the performance of best linear unbiased estimation and best linear unbiased prediction of genotype effects from zoned Indian maize data. Crop Sci 53:1384

    Article  Google Scholar 

  • Kovach MJ, McCouch SR (2008) Leveraging natural diversity: back through the bottleneck. Curr Opin Plant Biol 11:193–200

    Article  CAS  PubMed  Google Scholar 

  • Kremling KA et al (2017) Large scale expression profiling reveals that rare alleles drive dysregulation and fitness loss in maize. Nature (in revision)

    Google Scholar 

  • Lau WCP, Rafii MY, Ismail MR et al (2015) Review of functional markers for improving cooking, eating, and the nutritional qualities of rice. Front Plant Sci 6:832

    Article  PubMed  PubMed Central  Google Scholar 

  • Ledford H (2017) Robots stop to smell the flower. Nature 541:445–446

    Article  CAS  PubMed  Google Scholar 

  • Lehermeier C, Krämer N, Bauer E et al (2014) Usefulness of multiparental populations of maize (Zea mays L.) for genome-based prediction. Genetics 198:3–16

    Article  PubMed  PubMed Central  Google Scholar 

  • Lehermeier C, Schon CC, de Los Campos G (2015) Assessment of genetic heterogeneity in structured plant populations using multivariate whole-genome regression models. Genetics 201:323–337

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Lian L, Jacobson A, Zhong S et al (2014) Genomewide prediction accuracy within 969 maize biparental populations. Crop Sci 54:1514–1522

    Article  Google Scholar 

  • Lopez-Cruz M, Crossa J, Bonnett D et al (2015) Increased prediction accuracy in wheat breeding trials using a marker x environment interaction genomic selection model. G3 5:569–582

    Article  PubMed  PubMed Central  Google Scholar 

  • Lorenz AJ, Chao S, Asoro FG et al (2011) Genomic selection in plant breeding: knowledge and prospects. Adv Agron 110:77–123

    Article  Google Scholar 

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

    Article  Google Scholar 

  • Lorenzana RE, Bernardo R (2009) Accuracy of genotypic value predictions for marker-based selection in biparental plant populations. Theor Appl Genet 120:151–161

    Article  PubMed  Google Scholar 

  • MacLead IM, Bowman PJ, Vander Jagt CJ et al (2016) Exploiting biological priors and sequence variants enhances QTL discovery and genomic prediction of complex traits. BMC Genomics 17:144

    Article  CAS  Google Scholar 

  • Manickavelu A, Hattori T, Yamaoka S et al (2017) Genetic nature of elemental contents in wheat grains and its genomic prediction: toward the effective use of wheat landraces from Afghanistan. PLoS One 12:e0169416

    Article  PubMed  PubMed Central  Google Scholar 

  • Marulanda JJ, Mi X, Melchinger AE et al (2016) Optimum breeding strategies using genomic selection for hybrid breeding in wheat, maize, rye, barley, rice and triticale. Theor Appl Genet 129:1901–1913

    Article  CAS  PubMed  Google Scholar 

  • Massman JM, Jung HJG, Bernardo R (2013a) Genomewide selection versus marker-assisted recurrent selection to improve grain yield and stover-quality traits for cellulosic ethanol in maize. Crop Sci 53:58–66

    Article  CAS  Google Scholar 

  • Massman JM, Gordillo A, Lorenzana RE et al (2013b) Genomewide predictions from maize single-cross data. Theor Appl Genet 126:13–22

    Article  PubMed  Google Scholar 

  • McCouch S, Baute GJ, Bradeen J et al (2013) Agriculture: feeding the future. Nature 499:23–24

    Article  CAS  PubMed  Google Scholar 

  • Meuwissen T, Goddard M (2010) Accurate prediction of genetic values for complex traits by whole-genome resequencing. Genetics 185:623–631

    Article  CAS  PubMed  PubMed Central  Google Scholar 

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

    CAS  PubMed  PubMed Central  Google Scholar 

  • Meuwissen T, Hayes B, Goddard M (2016) Genomic selection: a paradigm shift in animal breeding. Anim Front 6:6–14

    Article  Google Scholar 

  • Michel S, Ametz C, Gungor H et al (2017) Genomic assisted selection for enhancing line breeding: merging genomic and phenotypic selection in winter wheat breeding programs with preliminary yield trials. Theor Appl Genet 130:363–376

    Article  CAS  PubMed  Google Scholar 

  • Minamikawa MF, Nonaka K, Kaminuma E et al (2017) Genome-wide association study and genomic prediction in citrus: potential of genomics-assisted breeding for fruit quality traits. Sci Rep 7:4721

    Article  PubMed  PubMed Central  Google Scholar 

  • Morota G, Koyama M, Rosa GJM et al (2013) Predicting complex traits using a diffusion kernel on genetic markers with an application to dairy cattle and wheat data. Genet Sel Evol 45:17

    Article  PubMed  PubMed Central  Google Scholar 

  • Ni G, Cavero D, Fangmann A et al (2017) Whole-genome sequence-based genomic prediction in laying chickens with different genomic relationship matrices. Genet Sel Evol 49:8

    Article  PubMed  PubMed Central  Google Scholar 

  • Ohnishi T, Yoshino M, Yamakawa H et al (2011) The biotron breeding system: a rapid and reliable procedure for genetic studies and breeding in rice. Plant Cell Physiol 52:1249–1257

    Article  CAS  PubMed  Google Scholar 

  • Onogi A, Ideta O, Inoshita Y et al (2015) Exploring the area of applicability of whole-genome prediction methods for Asian rice (Oryza sativa L.) Theor Appl Genet 128:41–53

    Article  PubMed  Google Scholar 

  • Onogi A, Watanabe M, Mochizuki T et al (2016) 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

    Article  PubMed  Google Scholar 

  • Ornella L, Sukhwinder-Singh PP et al (2012) Genomic prediction of genetic values for resistance to wheat rusts. Plant Genome 5:136–148

    Article  CAS  Google Scholar 

  • Ornella L, Pérez P, Tapia E et al (2014) Genomic-enabled prediction with classification algorithm. Heredity 112:616–626

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Park T, Casella G (2008) The Bayesian LASSO. J Am Stat Assoc 103:681–686

    Article  CAS  Google Scholar 

  • Peiffer JA, Romay MC, Gore MA et al (2014) The genetic architecture of maize height. Genetics 196:1337–1356

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Pérez-Enciso M, Rincón JC, Legarra A (2015) Sequence- vs. chip-assisted genomic selection: accurate biological information is advised. Genet Sel Evol 47:43

    Article  PubMed  PubMed Central  Google Scholar 

  • Petes J (2016) KeyGene’s SBG patent upheld by the USPTO after ex parte reexamination. http://www.keygene.com/wp-content/uploads/2016/03/Press-release-KeyGenes-SBG-patent-upheld-by-theUSPTO-after-ex-parte-reexamination.pdf. Accessed 19 May

  • Poland J (2015) Breeding-assisted genomics. Curr Opin Plant Biol 24:119–124

    Article  CAS  PubMed  Google Scholar 

  • Resende MFR Jr, Moñoz P, Acosta JJ et al (2012) Accelerating the domestication of trees using genomic selection: accuracy of prediction models across ages and environments. New Phytol 193:617–624

    Article  PubMed  Google Scholar 

  • Riedelsheimer C, Endelman JB, Stange M et al (2013) Genomic predictability of interconnected biparental maize populations. Genetics 194:493–503

    Article  PubMed  PubMed Central  Google Scholar 

  • Rutkoski J, Singh RP, Huerta-Espino J et al (2015) Efficient use of historical data for genomic selection: a case study of stem rust resistance in wheat. Plant Genome 8. https://doi.org/10.3835/plantgenome2014.09.0046

  • Rutkoski J, Poland J, Mondal S et al (2016) Canopy temperature and vegetation indices from high-throughput phenotyping improve accuracy of pedigree and genomic selection for grain yield in wheat. G3 6:2799–2808

    Article  PubMed  PubMed Central  Google Scholar 

  • Sallam AH, Endelman JB, Jannink JL et al (2015) Assessing genomic selection prediction accuracy in a dynamic barley breeding population. Plant Genome 8: https://doi.org/10.3835/plantgenome2014.05.0020

  • Schopp P, Muller D, Technow F et al (2017) Accuracy of genomic prediction in synthetic populations depending on the number of parents, relatedness, and ancestral linkage disequilibrium. Genetics 205:441–454

    Article  CAS  PubMed  Google Scholar 

  • Schulz-Streeck T, Ogutu JO, Karaman Z et al (2012) Genomic selection using multiple populations. Crop Sci 52:2453–2461

    Article  Google Scholar 

  • Scutari M, Howell P, Balding DJ et al (2014) Multiple quantitative trait analysis using Bayesian networks. Genetics 198:129–137

    Article  PubMed  PubMed Central  Google Scholar 

  • Shi Y, Thomasson JA, Murray SC et al (2016) Unmanned aerial vehicles for high-throughput phenotyping and agronomic research. PLoS One 11:e0159781

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  • Sinclair TR, Seligman NG (1996) Crop modeling: from infancy to maturity. Agron J 88:698–704

    Article  Google Scholar 

  • Speed D, Balding DJ (2014) MultiBLUP: improved SNP-based prediction for complex traits. Genome Res 24:1550–1557

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Spindel JE, McCouch SR (2016) When more is better: how data sharing would accelerate genomic selection of crop plants. New Phytol 212:814–826

    Article  PubMed  Google Scholar 

  • Spindel J, Begum H, Akdemir D et al (2015) Genomic selection and association mapping 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:e1004982

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  • Spindel JE, Begum H, Akdemir D et al (2016) Genome-wide prediction models that incorporate de novo GWAS are a powerful new tool for tropical rice improvement. Heredity 116:395–408

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Su G, Christensen OF, Janss L et al (2014) Comparison of genomic predictions using genomic relationship matrices built with different weighting factors to account for locus-specific variances. J Dairy Sci 97:6547–6559

    Article  CAS  PubMed  Google Scholar 

  • Sun C, Hu Z, Zheng T et al (2017a) Rice pan-genome browser for ∼3000 rice genomes. Nucleic Acids Res 45:597–605

    Article  PubMed  Google Scholar 

  • Sun J, Rutkoski JE, Poland JA et al (2017b) Multitrait, random regression, or simple repeatability model in high-throughput phenotyping data improve genomic prediction for wheat grain yield. Plant Genome 10. https://doi.org/10.3835/plantgenome2016.11.0111

  • Sveinbjornsson G, Albrechtsen A, Zing F et al (2016) Weighting sequence variants based on their annotation increase power of whole-genome association studies. Nat Genet 48:314–318

    Article  CAS  PubMed  Google Scholar 

  • Tanaka J, Hayashi T, Iwata H (2016) A practical, rapid generation-advancement system for rice breeding using simplified biotron breeding system. Breed Sci 66:542–551

    Article  PubMed  PubMed Central  Google Scholar 

  • Tanger P, Klassen S, Mojica JP et al (2017) Field-based high throughput phenotyping rapidly identifies genomic regions controlling yield components in rice. Sci Rep 7:42839

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Tattaris M, Reynolds MP, Chapman SC (2016) A direct comparison of remote sensing approaches for high-throughput phenotyping in plant breeding. Front Plant Sci 7:1131

    Article  PubMed  PubMed Central  Google Scholar 

  • Technow F, Messina CD, Radu L et al (2015) Integrating crop growth models with whole genome prediction through approximate Bayesian computation. PLoS One 10:e0130855

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  • The 3,000 rice genomes project (2014) The 3,000 rice genomes project. GigaScience 3:7

    Article  CAS  Google Scholar 

  • van Binsbergen R, Calus MP, Bink MCAM et al (2015) Genomic prediction using imputed whole-genome sequence data in Holstein Friesian cattle. Genet Sel Evol 47:71

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  • VanRaden PM, Van Tassell CP, Wiggans GR et al (2009) Reliability of genomic predictions for North American Holstein bulls. J Dairy Sci 92:16–24

    Article  CAS  PubMed  Google Scholar 

  • Vapnik V (1995) The nature of statistical learning theory. Springer, New York

    Book  Google Scholar 

  • Veerkamp RF, Bouwman AC, Schrooten C et al (2016) Genomic prediction using preselected DNA variants from a GWAS with whole-genome sequence data in Holstein-Friesian cattle. Genet Sel Evol 48:95

    Article  PubMed  PubMed Central  Google Scholar 

  • Veroneze R, Lopes PS, Lopes MS et al (2016) Accounting for genetic architecture in single-and multipopulation genomic prediction using weights from genomewide association studies in pigs. J Anim Breed Genet 133:187–196

    Article  CAS  PubMed  Google Scholar 

  • Vitezica ZG, Varona L, Legarra A (2013) On the additive and dominant variance and covariance of individuals within the genomic selection scope. Genetics 195:1223–1230

    Article  PubMed  PubMed Central  Google Scholar 

  • Waldmann P (2016) Genome-wide prediction using Bayesian additive regression trees. Genet Sel Evol 48:42

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  • Wang H, Misztal I, Aguilar I et al (2012) Genome-wide association mapping including phenotypes from relatives without genotypes. Genet Res 94:73–83

    Article  CAS  Google Scholar 

  • Wang Y, Mette MF, Miedaner T et al (2014) The accuracy of prediction of genomic selection in elite hybrid rye populations surpasses the accuracy of marker-assisted selection and is equally augmented by multiple field evaluation locations and test years. BMC Genomics 15:556

    Article  PubMed  PubMed Central  Google Scholar 

  • Wang X, Li L, Yang Z et al (2017) Predicting rice hybrid performance using univariate and multivariate GBLUP models based on North Carolina mating design II. Heredity 118:302–310

    Article  CAS  PubMed  Google Scholar 

  • Watanabe K, Guo W, Arai K et al (2017) High-throughput phenotyping of sorghum plant height using an unmanned aerial vehicle and its application to genomic prediction modeling. Front Plant Sci 8:421

    Article  PubMed  PubMed Central  Google Scholar 

  • Windhausen VS, Atlin GN, Hickey JM et al (2012) Effectiveness of genomic prediction of maize hybrid performance in different breeding populations and environments. G3 2:1427–1436

    Article  PubMed  PubMed Central  Google Scholar 

  • Xavier A, Muir WM, Craig B et al (2016) Walking through the statistical black boxes of plant breeding. Theor Appl Genet 129:1933–1949

    Article  PubMed  Google Scholar 

  • Xie X, Jin F, Song MH et al (2008) Fine mapping of a yield-enhancing QTL cluster associated with transgressive variation in an Oryza sativa × O. rufipogon cross. Theor Appl Genet 116:613–622

    Article  PubMed  Google Scholar 

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

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Yabe S, Yamasaki M, Ebana K et al (2016) Island-model genomic selection for long-term genetic improvement of autogamous crops. PLoS One 11:e0153945

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  • Yabe S, Iwata H, Jannink JL (2017) A simple package to script and simulate breeding schemes: the breeding scheme language. Crop Sci 57:1–8

    Article  Google Scholar 

  • Yamamoto E, Matsunaga H, Onogi A et al (2017) Efficiency of genomic selection for breeding population design and phenotype prediction in tomato. Heredity 118:202–209

    Article  CAS  PubMed  Google Scholar 

  • Yang J, Benyamin B, McEvoy BP et al (2010) Common SNPs explain a large proportion of the heritability for human height. Nat Genet 42:565–569

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Yang H, Li S, Cao H et al (2016) Predicting disease trait with genomic data: a composite kernel approach. Brief Bioinform 18:591–601

    Google Scholar 

  • Zhang Z, Ober U, Erbe M et al (2014) Improving the accuracy of whole genome prediction for complex traits using the results of genome wide association studies. PLoS One 9:e93017

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  • Zhang X, Pérez-Rodríguez P, Semagn et al (2015) Genomic prediction in biparental tropical maize populations in water-stressed and well-watered environments using low-density and GBS SNPs. Heredity 114:291–299

    Article  CAS  PubMed  Google Scholar 

  • Zhang X, Lourenco D, Aguilar I et al (2016a) Weighting strategies for single-step genomic BLUP: an iterative approach for accurate calculation of GEBV and GWAS. Front Genet 7:151

    PubMed  PubMed Central  Google Scholar 

  • Zhang J, Song Q, Cregan PB et al (2016b) Genome-wide association study, genomic prediction and marker-assisted selection for seed weight in soybean (Glycine max). Theor Appl Genet 129:117–130

    Article  CAS  PubMed  Google Scholar 

  • Zhao Y, Gowada M, Liw W et al (2012) Accuracy of genomic selection in European maize elite breeding populations. Theor Appl Genet 124:769–776

    Article  PubMed  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Lawrence Berkeley National Laboratory, Joint Genome Institute, Walnut Creek, CA, USA

    Jennifer Spindel

  2. Graduate School of Agricultural and Life Sciences, The University of Tokyo, Bunkyo, Tokyo, Japan

    Hiroyoshi Iwata

Authors
  1. Jennifer Spindel
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Hiroyoshi Iwata
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Hiroyoshi Iwata .

Editor information

Editors and Affiliations

  1. NODAI Research Institute, Tokyo University of Agriculture, Setagaya, Tokyo, Japan

    Takuji Sasaki

  2. Bioscience and Biotechnology Center, Nagoya University, Nagoya, Aichi, Japan

    Motoyuki Ashikari

Rights and permissions

Reprints and permissions

Copyright information

© 2018 Springer Nature Singapore Pte Ltd.

About this chapter

Check for updates. Verify currency and authenticity via CrossMark

Cite this chapter

Spindel, J., Iwata, H. (2018). Genomic Selection in Rice Breeding. In: Sasaki, T., Ashikari, M. (eds) Rice Genomics, Genetics and Breeding. Springer, Singapore. https://doi.org/10.1007/978-981-10-7461-5_24

Download citation

Publish with us

Policies and ethics

Search

Navigation