Genomic Selection in Rice Breeding



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.


Whole-genome selection Genomic prediction Breeding values Prediction models Statistical models Implementation in rice breeding Omics-aided breeding 


  1. Albrecht T, Wimmer V, Auinger HJ et al (2011) Genome-based prediction of testcross values in maize. Theor Appl Genet 123:339–350PubMedCrossRefGoogle Scholar
  2. Al-Tamimi N, Brein C, Oakey H et al (2016) Salinity tolerance loci revealed in rice using high-throughput non-invasive phenotyping. Nat Commun 7:13342PubMedPubMedCentralCrossRefGoogle Scholar
  3. 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 36:84CrossRefGoogle Scholar
  4. 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 4:132CrossRefGoogle Scholar
  5. 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 53:1894–1906CrossRefGoogle Scholar
  6. 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 129:2043–2053PubMedPubMedCentralCrossRefGoogle Scholar
  7. Bassi FM, Bentley AR, Charmet G et al (2016) Breeding schemes for the implementation of genomic selection in wheat (Triticum spp.) Plant Sci 242:23–36PubMedCrossRefGoogle Scholar
  8. 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.
  9. 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 127:2619–2633PubMedCrossRefGoogle Scholar
  10. Bernardo R (2008) Molecular markers and selection for complex traits in plants: learning from the last 20 years. Crop Sci 48:1649–1664CrossRefGoogle Scholar
  11. Bernardo R (2009) Genomewide selection for rapid introgression of exotic germplasm in maize. Crop Sci 49:419–425CrossRefGoogle Scholar
  12. Bernardo R (2014) Genomewide selection when major genes are known. Crop Sci 54:68–75CrossRefGoogle Scholar
  13. Bernardo R (2016) Genomewide predictions for backcrossing a quantitative trait from an exotic to an adapted line. Crop Sci 56:1067–1075CrossRefGoogle Scholar
  14. Bernardo R, Yu J (2007) Prospects for genomewide selection for quantitative traits in maize. Crop Sci 47:1082–1090CrossRefGoogle Scholar
  15. 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 55:154–163CrossRefGoogle Scholar
  16. Bian U, Holland JB (2017) Enhancing genomic prediction with genome-wide association studies in multiparental maize populations. Heredity 118:585–593PubMedCrossRefGoogle Scholar
  17. Blondel M, Onogi A, Iwata H et al (2015) A ranking approach to genomic selection. PLoS One 10:e0128570PubMedPubMedCentralCrossRefGoogle Scholar
  18. Breiman L (2001) Random forests. Mach Learn 45:5–32CrossRefGoogle Scholar
  19. Buckler ES (2017) Direction of GWAS and GS. Paper presented at the plant and animal genome XXV, 14 January 2017, San Diego, CA, USAGoogle Scholar
  20. 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 52:707CrossRefGoogle Scholar
  21. Calus MPL, Veerkamp RF (2011) Accuracy of multi-trait genomic selection using different methods. Genet Sel Evol 43:21CrossRefGoogle Scholar
  22. 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 48:49PubMedPubMedCentralCrossRefGoogle Scholar
  23. Charmet G, Storlie E, Oury FX et al (2014) Genome-wide prediction of three important traits in bread wheat. Mol Breed 34:1843–1852PubMedPubMedCentralCrossRefGoogle Scholar
  24. 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 56:2141–2156CrossRefGoogle Scholar
  25. Cuyabano BCD, Su G, Lund MS (2014) Genomic prediction of genetic merit using LD-based haplotypes in the Nordic Holstein population. BMC Genomics 15:1171PubMedPubMedCentralCrossRefGoogle Scholar
  26. Cuyabano BCD, Su G, Lund MS (2015) Selection of haplotype variables from a high-density marker map for genomic prediction. Genet Sel Evol 47:61PubMedPubMedCentralCrossRefGoogle Scholar
  27. Dahl A, Iotchkova V, Baud A et al (2016) A multiple-phenotype imputation method for genetic studies. Nat Genet 48:466–472PubMedPubMedCentralCrossRefGoogle Scholar
  28. de los Campos G, Sorensen D (2014) On the genomic analysis of data from structured populations. J Anim Breed Genet 131:163–164PubMedPubMedCentralCrossRefGoogle Scholar
  29. 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 193:327–345PubMedCentralCrossRefGoogle Scholar
  30. Desta ZA, Ortiz R (2014) Genomic selection: genome-wide prediction in plant improvement. Trends Plant Sci 19:592–601PubMedCrossRefGoogle Scholar
  31. 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 203:1871–1883PubMedPubMedCentralCrossRefGoogle Scholar
  32. Endelman JB (2011) Ridge regression and other kernels for genomic selection with R package rrBLUP. Plant Genome 4:250–255CrossRefGoogle Scholar
  33. Fernie AR, Schauer N (2009) Metabolomics-assisted breeding: a viable option for crop improvement? Trends Genet 25:39–48PubMedCrossRefGoogle Scholar
  34. Furbank RT, Tester M (2011) Phenomics – technologies to relieve the phenotyping bottleneck. Trends Plant Sci 16:635–644PubMedCrossRefGoogle Scholar
  35. 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–E4004PubMedPubMedCentralCrossRefGoogle Scholar
  36. Garrick D, Dekkers J, Fernando R (2014) The evolution of methodologies for genomic prediction. Livest Sci 166:10–18CrossRefGoogle Scholar
  37. Gianola D (2013) Priors in whole-genome regression: the Bayesian alphabet returns. Genetics 194:573–596PubMedPubMedCentralCrossRefGoogle Scholar
  38. Gianola D, van Kaam JBCHM (2008) Reproducing kernel Hilbert spaces regression methods for genomic assisted prediction of quantitative traits. Genetics 178:2289–2303PubMedPubMedCentralCrossRefGoogle Scholar
  39. Gianola D, Fernando RL, Stella A (2006) Genomic-assisted prediction of genetic value with semiparametric procedures. Genetics 173:1761–1776PubMedPubMedCentralCrossRefGoogle Scholar
  40. 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 12:87PubMedPubMedCentralCrossRefGoogle Scholar
  41. Gianola D, Weigel KW, Krämer N et al (2014) Enhancing genome-enabled prediction by bagging genomic BLUP. PLoS One 9:e91693PubMedPubMedCentralCrossRefGoogle Scholar
  42. 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 125:759–771PubMedPubMedCentralCrossRefGoogle Scholar
  43. González-Camacho JM, Crossa J, Pérez-Rodríguez P et al (2016) Genome-enabled prediction using probabilistic neural network classifiers. BMC Genomics 17:208PubMedPubMedCentralCrossRefGoogle Scholar
  44. González-Recio O, Weigel KA, Gianola D et al (2010) L2-boosting algorithm applied to high-dimensional problems in genomic selection. Genet Res (Camb) 92:227–237CrossRefGoogle Scholar
  45. González-Recio O, Rosa GJM, Gianola D (2014) Machine learning methods and predictive ability metrics for genome-wide prediction of complex traits. Livest Sci 166:217–231CrossRefGoogle Scholar
  46. 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:e0136594PubMedPubMedCentralCrossRefGoogle Scholar
  47. Guo Z, Tucker DM, Lu JW et al (2012) Evaluation of genome-wide selection efficiency in maize nested association mapping populations. Theor Appl Genet 124:261–275PubMedCrossRefGoogle Scholar
  48. Guo Z, Tucker DM, Basten CJ et al (2014) The impact of population structure on genomic prediction in stratified populations. Theor Appl Genet 127:749–762PubMedCrossRefGoogle Scholar
  49. Habier D, Fernando RL, Dekkers JC (2007) The impact of genetic relationship information on genome-assisted breeding values. Genetics 177:2389–2397PubMedPubMedCentralGoogle Scholar
  50. Habier D, Fernando RL, Kizilkaya K (2011) Extension of the Bayesian alphabet for genomic selection. BMC Bioinfo 12:186CrossRefGoogle Scholar
  51. 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 12:35PubMedPubMedCentralCrossRefGoogle Scholar
  52. Hayashi T, Iwata H (2013) A Bayesian method and its variational approximation for prediction of genomic breeding values in multiple traits. BMC Bioinformatics 14:34PubMedPubMedCentralCrossRefGoogle Scholar
  53. Hayes BJ, Visscher PM, Goddard ME (2009a) Increased accuracy of artificial selection by using the realized relationship matrix. Genet Res 91:47–60CrossRefGoogle Scholar
  54. Hayes BJ, Bowman PJ, Chamberlain AJ et al (2009b) Genomic selection in dairy cattle: progress and challenges. J Dairy Sci 92:433–443PubMedCrossRefGoogle Scholar
  55. 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 13:578–583CrossRefGoogle Scholar
  56. Heffner EL, Sorrells ME, Jannink JL (2009) Genomic selection for crop improvement. Crop Sci 49:1–12CrossRefGoogle Scholar
  57. Heffner EL, Lorenz AJ, Jannink JL et al (2010) Plant breeding with genomic selection: gain per unit time and cost. Crop Sci 50:1681–1690CrossRefGoogle Scholar
  58. Heffner EL, Jannink JL, Sorrells ME (2011) Genomic selection accuracy using multifamily prediction models in a wheat breeding program. Plant Genome 4:65–75CrossRefGoogle Scholar
  59. 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 133:167–179PubMedCrossRefGoogle Scholar
  60. Henderson CR (1985) Best linear unbiased prediction of non-additive genetic merits. J Anim Sci 60:111–117CrossRefGoogle Scholar
  61. Henderson CR, Quaas RL (1976) Multiple trait evaluation using relatives’ records. J Anim Sci 43:1188–1197CrossRefGoogle Scholar
  62. Heslot N, Yang HP, Sorrells ME et al (2012) Genomic selection in plant breeding: a comparison of methods. Crop Sci 52:146–160CrossRefGoogle Scholar
  63. 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 127:463–480PubMedCrossRefGoogle Scholar
  64. Heslot N, Jannink JL, Sorrells ME (2015) Perspectives for genomic selection applications and research in plants. Crop Sci 55:1–12CrossRefGoogle Scholar
  65. 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 129:2101–2115PubMedCrossRefGoogle Scholar
  66. 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 48:15PubMedPubMedCentralCrossRefGoogle Scholar
  67. 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 50:1269–1278CrossRefGoogle Scholar
  68. 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 51:1915–1927CrossRefGoogle Scholar
  69. 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:e0120610PubMedPubMedCentralCrossRefGoogle Scholar
  70. 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 7:145PubMedPubMedCentralCrossRefGoogle Scholar
  71. Jannink JL (2010) Dynamics of long-term genomic selection. Genet Sel Evol 42:35PubMedPubMedCentralCrossRefGoogle Scholar
  72. Jannink JL, Iwata H, Bhat PR et al (2009) Marker imputation in barley association studies. Plant Genome 2:11–22CrossRefGoogle Scholar
  73. Jannink JL, Lorenz AJ, Iwata H (2010) Genomic selection in plant breeding: from theory to practice. Brief Funct Genomic Proteomic 9:166–177CrossRefGoogle Scholar
  74. Jia Y, Jannink JL (2012) Multiple-trait genomic selection methods increase genetic value prediction accuracy. Genetics 192:1513–1522PubMedPubMedCentralCrossRefGoogle Scholar
  75. Jiang Y, Reif JC (2015) Modeling epistasis in genomic selection. Genetics 201:759–768PubMedPubMedCentralCrossRefGoogle Scholar
  76. Kadarmideen HN, von Rohr P, Janss LLG (2006) From genetical genomics to systems genetics: potential applications in quantitative genomics and animal breeding. Mamm Genome 17:548–564PubMedPubMedCentralCrossRefGoogle Scholar
  77. Kärkkäinen HP, Sillanpää MJ (2012) Back to basics for Bayesian model building genomic selection. Genetics 191:969–987PubMedPubMedCentralCrossRefGoogle Scholar
  78. 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 53:1384CrossRefGoogle Scholar
  79. Kovach MJ, McCouch SR (2008) Leveraging natural diversity: back through the bottleneck. Curr Opin Plant Biol 11:193–200PubMedCrossRefGoogle Scholar
  80. 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
  81. 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 6:832PubMedPubMedCentralCrossRefGoogle Scholar
  82. Ledford H (2017) Robots stop to smell the flower. Nature 541:445–446PubMedCrossRefGoogle Scholar
  83. Lehermeier C, Krämer N, Bauer E et al (2014) Usefulness of multiparental populations of maize (Zea mays L.) for genome-based prediction. Genetics 198:3–16PubMedPubMedCentralCrossRefGoogle Scholar
  84. Lehermeier C, Schon CC, de Los Campos G (2015) Assessment of genetic heterogeneity in structured plant populations using multivariate whole-genome regression models. Genetics 201:323–337PubMedPubMedCentralCrossRefGoogle Scholar
  85. Lian L, Jacobson A, Zhong S et al (2014) Genomewide prediction accuracy within 969 maize biparental populations. Crop Sci 54:1514–1522CrossRefGoogle Scholar
  86. 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 5:569–582PubMedPubMedCentralCrossRefGoogle Scholar
  87. Lorenz AJ, Chao S, Asoro FG et al (2011) Genomic selection in plant breeding: knowledge and prospects. Adv Agron 110:77–123CrossRefGoogle Scholar
  88. Lorenz AJ, Smith KP, Jannink JL (2012) Potential and optimization of genomic selection for Fusarium head blight resistance in six-row barley. Crop Sci 52:1609–1621CrossRefGoogle Scholar
  89. Lorenzana RE, Bernardo R (2009) Accuracy of genotypic value predictions for marker-based selection in biparental plant populations. Theor Appl Genet 120:151–161PubMedCrossRefGoogle Scholar
  90. 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 17:144CrossRefGoogle Scholar
  91. 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:e0169416PubMedPubMedCentralCrossRefGoogle Scholar
  92. 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 129:1901–1913PubMedCrossRefGoogle Scholar
  93. 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 53:58–66CrossRefGoogle Scholar
  94. Massman JM, Gordillo A, Lorenzana RE et al (2013b) Genomewide predictions from maize single-cross data. Theor Appl Genet 126:13–22PubMedCrossRefGoogle Scholar
  95. McCouch S, Baute GJ, Bradeen J et al (2013) Agriculture: feeding the future. Nature 499:23–24PubMedCrossRefGoogle Scholar
  96. Meuwissen T, Goddard M (2010) Accurate prediction of genetic values for complex traits by whole-genome resequencing. Genetics 185:623–631PubMedPubMedCentralCrossRefGoogle Scholar
  97. Meuwissen T, Hayes BJ, Goddard ME (2001) Prediction of total genetic value using genome-wide dense marker maps. Genetics 157:1819–1829PubMedPubMedCentralGoogle Scholar
  98. Meuwissen T, Hayes B, Goddard M (2016) Genomic selection: a paradigm shift in animal breeding. Anim Front 6:6–14CrossRefGoogle Scholar
  99. 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 130:363–376PubMedCrossRefGoogle Scholar
  100. 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 7:4721PubMedPubMedCentralCrossRefGoogle Scholar
  101. 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 45:17PubMedPubMedCentralCrossRefGoogle Scholar
  102. 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 49:8PubMedPubMedCentralCrossRefGoogle Scholar
  103. 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 52:1249–1257PubMedCrossRefGoogle Scholar
  104. 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 128:41–53PubMedCrossRefGoogle Scholar
  105. 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 129:805–817PubMedCrossRefGoogle Scholar
  106. Ornella L, Sukhwinder-Singh PP et al (2012) Genomic prediction of genetic values for resistance to wheat rusts. Plant Genome 5:136–148CrossRefGoogle Scholar
  107. Ornella L, Pérez P, Tapia E et al (2014) Genomic-enabled prediction with classification algorithm. Heredity 112:616–626PubMedPubMedCentralCrossRefGoogle Scholar
  108. Park T, Casella G (2008) The Bayesian LASSO. J Am Stat Assoc 103:681–686CrossRefGoogle Scholar
  109. Peiffer JA, Romay MC, Gore MA et al (2014) The genetic architecture of maize height. Genetics 196:1337–1356PubMedPubMedCentralCrossRefGoogle Scholar
  110. Pérez-Enciso M, Rincón JC, Legarra A (2015) Sequence- vs. chip-assisted genomic selection: accurate biological information is advised. Genet Sel Evol 47:43PubMedPubMedCentralCrossRefGoogle Scholar
  111. Petes J (2016) KeyGene’s SBG patent upheld by the USPTO after ex parte reexamination. Accessed 19 May
  112. Poland J (2015) Breeding-assisted genomics. Curr Opin Plant Biol 24:119–124PubMedCrossRefGoogle Scholar
  113. 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 193:617–624PubMedCrossRefGoogle Scholar
  114. Riedelsheimer C, Endelman JB, Stange M et al (2013) Genomic predictability of interconnected biparental maize populations. Genetics 194:493–503PubMedPubMedCentralCrossRefGoogle Scholar
  115. 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.
  116. 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 6:2799–2808PubMedPubMedCentralCrossRefGoogle Scholar
  117. Sallam AH, Endelman JB, Jannink JL et al (2015) Assessing genomic selection prediction accuracy in a dynamic barley breeding population. Plant Genome 8:
  118. 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 205:441–454PubMedCrossRefGoogle Scholar
  119. Schulz-Streeck T, Ogutu JO, Karaman Z et al (2012) Genomic selection using multiple populations. Crop Sci 52:2453–2461CrossRefGoogle Scholar
  120. Scutari M, Howell P, Balding DJ et al (2014) Multiple quantitative trait analysis using Bayesian networks. Genetics 198:129–137PubMedPubMedCentralCrossRefGoogle Scholar
  121. Shi Y, Thomasson JA, Murray SC et al (2016) Unmanned aerial vehicles for high-throughput phenotyping and agronomic research. PLoS One 11:e0159781PubMedPubMedCentralCrossRefGoogle Scholar
  122. Sinclair TR, Seligman NG (1996) Crop modeling: from infancy to maturity. Agron J 88:698–704CrossRefGoogle Scholar
  123. Speed D, Balding DJ (2014) MultiBLUP: improved SNP-based prediction for complex traits. Genome Res 24:1550–1557PubMedPubMedCentralCrossRefGoogle Scholar
  124. Spindel JE, McCouch SR (2016) When more is better: how data sharing would accelerate genomic selection of crop plants. New Phytol 212:814–826PubMedCrossRefGoogle Scholar
  125. 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:e1004982PubMedPubMedCentralCrossRefGoogle Scholar
  126. 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 116:395–408PubMedPubMedCentralCrossRefGoogle Scholar
  127. 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 97:6547–6559PubMedCrossRefGoogle Scholar
  128. Sun C, Hu Z, Zheng T et al (2017a) Rice pan-genome browser for ∼3000 rice genomes. Nucleic Acids Res 45:597–605PubMedCrossRefGoogle Scholar
  129. 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.
  130. 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 48:314–318PubMedCrossRefGoogle Scholar
  131. Tanaka J, Hayashi T, Iwata H (2016) A practical, rapid generation-advancement system for rice breeding using simplified biotron breeding system. Breed Sci 66:542–551PubMedPubMedCentralCrossRefGoogle Scholar
  132. 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 7:42839PubMedPubMedCentralCrossRefGoogle Scholar
  133. Tattaris M, Reynolds MP, Chapman SC (2016) A direct comparison of remote sensing approaches for high-throughput phenotyping in plant breeding. Front Plant Sci 7:1131PubMedPubMedCentralCrossRefGoogle Scholar
  134. Technow F, Messina CD, Radu L et al (2015) Integrating crop growth models with whole genome prediction through approximate Bayesian computation. PLoS One 10:e0130855PubMedPubMedCentralCrossRefGoogle Scholar
  135. The 3,000 rice genomes project (2014) The 3,000 rice genomes project. GigaScience 3:7CrossRefGoogle Scholar
  136. 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 47:71PubMedPubMedCentralCrossRefGoogle Scholar
  137. VanRaden PM, Van Tassell CP, Wiggans GR et al (2009) Reliability of genomic predictions for North American Holstein bulls. J Dairy Sci 92:16–24PubMedCrossRefGoogle Scholar
  138. Vapnik V (1995) The nature of statistical learning theory. Springer, New YorkCrossRefGoogle Scholar
  139. 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 48:95PubMedPubMedCentralCrossRefGoogle Scholar
  140. 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 133:187–196PubMedCrossRefGoogle Scholar
  141. Vitezica ZG, Varona L, Legarra A (2013) On the additive and dominant variance and covariance of individuals within the genomic selection scope. Genetics 195:1223–1230PubMedPubMedCentralCrossRefGoogle Scholar
  142. Waldmann P (2016) Genome-wide prediction using Bayesian additive regression trees. Genet Sel Evol 48:42PubMedPubMedCentralCrossRefGoogle Scholar
  143. Wang H, Misztal I, Aguilar I et al (2012) Genome-wide association mapping including phenotypes from relatives without genotypes. Genet Res 94:73–83CrossRefGoogle Scholar
  144. 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 15:556PubMedPubMedCentralCrossRefGoogle Scholar
  145. 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 118:302–310PubMedCrossRefGoogle Scholar
  146. 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 8:421PubMedPubMedCentralCrossRefGoogle Scholar
  147. Windhausen VS, Atlin GN, Hickey JM et al (2012) Effectiveness of genomic prediction of maize hybrid performance in different breeding populations and environments. G3 2:1427–1436PubMedPubMedCentralCrossRefGoogle Scholar
  148. Xavier A, Muir WM, Craig B et al (2016) Walking through the statistical black boxes of plant breeding. Theor Appl Genet 129:1933–1949PubMedCrossRefGoogle Scholar
  149. 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 116:613–622PubMedCrossRefGoogle Scholar
  150. Xu S, Zhu D, Zhang Q (2014) Predicting hybrid performance in rice using genomic best liner unbiased prediction. Proc Natl Acad Sci 111:12456–12461PubMedPubMedCentralCrossRefGoogle Scholar
  151. Yabe S, Yamasaki M, Ebana K et al (2016) Island-model genomic selection for long-term genetic improvement of autogamous crops. PLoS One 11:e0153945PubMedPubMedCentralCrossRefGoogle Scholar
  152. Yabe S, Iwata H, Jannink JL (2017) A simple package to script and simulate breeding schemes: the breeding scheme language. Crop Sci 57:1–8CrossRefGoogle Scholar
  153. Yamamoto E, Matsunaga H, Onogi A et al (2017) Efficiency of genomic selection for breeding population design and phenotype prediction in tomato. Heredity 118:202–209PubMedCrossRefGoogle Scholar
  154. Yang J, Benyamin B, McEvoy BP et al (2010) Common SNPs explain a large proportion of the heritability for human height. Nat Genet 42:565–569PubMedPubMedCentralCrossRefGoogle Scholar
  155. Yang H, Li S, Cao H et al (2016) Predicting disease trait with genomic data: a composite kernel approach. Brief Bioinform 18:591–601Google Scholar
  156. 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:e93017PubMedPubMedCentralCrossRefGoogle Scholar
  157. 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 114:291–299PubMedCrossRefGoogle Scholar
  158. 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 7:151PubMedPubMedCentralGoogle Scholar
  159. 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 129:117–130PubMedCrossRefGoogle Scholar
  160. Zhao Y, Gowada M, Liw W et al (2012) Accuracy of genomic selection in European maize elite breeding populations. Theor Appl Genet 124:769–776PubMedCrossRefGoogle Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2018

Authors and Affiliations

  1. 1.Lawrence Berkeley National LaboratoryJoint Genome InstituteWalnut CreekUSA
  2. 2.Graduate School of Agricultural and Life SciencesThe University of TokyoBunkyo, TokyoJapan

Personalised recommendations