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Genomic Selection in Hybrid Breeding

Chapter

Abstract

This chapter aims to outline the basic concepts underlying genomic selection (GS) in hybrid breeding. First, the concepts of dominance, heterosis, combining ability and heterotic groups are presented as a special feature of hybrid breeding, giving special attention to the breeding method of recurrent reciprocal selection. Subsequently, the cross-validated predictability is introduced as an evaluation criterion for the performance of GS and the relatedness between estimation and prediction sets is presented as its fundamental influential factor in hybrid breeding. Consequently, cross-validation schemes which consider different levels of relatedness according to particular breeding scenarios are illustratively explained. Later, classical mixed models and Bayesian GS approaches modeling dominance and additive effects receive special treatment in this chapter. Even though classical mixed models are in principle not suited for all genetic architectures, it seems they are preferred because of their relatively straightforward understanding and implementation plus their considerable robust performance. Moreover, modeling dominance in addition to additive effects seems to be beneficial when dominance effects are expected to have an important influence on predicted traits. GS models efficiently accommodating epistasis are available, but they have not received the attention needed to properly evaluate their advantages and limitations for hybrid performance prediction. Furthermore, other GS approaches are briefly introduced. Finally, the implementation of GS as a tool to assist hybrid breeding is dissected as an optimization problem, giving later emphasis to the model recalibration after implementing GS for the early stages of a breeding program.

Keywords

Hybrid breeding Genomic selection Dominance Heterosis Combining ability Cross-validation Relatedness Predictability Implementation 

Abbreviations

BLUP

Best linear unbiased prediction

e-Bayes

Empirical Bayes method

GCA

General combining ability

GS

Genomic selection

LD

Linkage disequilibrium

MAS

Marker assisted selection

PS

Phenotypic selection

RE

Relative efficiency

REML

Restricted maximum likelihood

RKHS

Reproducing kernel Hilbert space

RR-BLUP

Ridge regression best linear unbiased prediction

RRS

Recurrent reciprocal selection

SCA

Specific combining ability

SNP

Single nucleotide polymorphism

W-BLUP

Weighted best linear unbiased prediction

References

  1. Akdemir D, Sanchez JI, Jannink JL (2015) Optimization of genomic selection training populations with a genetic algorithm. Genet Sel Evol 47:38CrossRefPubMedPubMedCentralGoogle Scholar
  2. Albrecht T, Wimmer V, Auinger HJ, Erbe M, Knaak C, Ouzunova M, Simianer H, Schön CC (2011) Genome-based prediction of testcross values in maize. Theor Appl Genet 123:339–350CrossRefPubMedGoogle Scholar
  3. Albrecht T, Auinger HJ, Wimmer V, Ogutu JO, Knaak C, Ouzunova M, Piepho HP, Schön CC (2014) Genome-based prediction of maize hybrid performance across genetic groups, testers, locations, and years. Theor Appl Genet 127:1375–1386CrossRefPubMedGoogle Scholar
  4. Becker H (2011) Pflanzenzüchtung (in German). Auflagennr. 2. Verlag Eugen Ulmer, StuttgartGoogle Scholar
  5. Bernardo R (1994) Prediction of maize single-cross performance using RFLPs and information from related hybrids. Crop Sci 34:20–25CrossRefGoogle Scholar
  6. Bernardo R (1996) Best linear unbiased prediction of maize single-cross performance. Crop Sci 36:50–56CrossRefGoogle Scholar
  7. Bernardo R (2010) Breeding for quantitative traits in plants. Stemma Press, WoodburyGoogle Scholar
  8. Bernardo R (2014) Genomewide selection when major genes are known. Crop Sci 54:68–75CrossRefGoogle Scholar
  9. Bos I, Caligari P (2008) Selection methods in plant breeding, 2nd edn. Springer, DordrechtCrossRefGoogle Scholar
  10. Bruce AB (1910) The Mendelian theory of heredity and the augmentation of vigor. Science 32:627–628CrossRefPubMedGoogle Scholar
  11. Burrows PM (1975) Expected selection differentials for directional selection. Biometrics 28:1091–1100CrossRefGoogle Scholar
  12. Clark SA, Hickey JM, Daetwyler HD, Van der Werf JHJ (2012) The importance of information on relatives for the prediction of genomic breeding values and implications for the makeup of reference populations in livestock breeding schemes. Genet Sel Evol 44:4CrossRefPubMedPubMedCentralGoogle Scholar
  13. Collins GN (1921) Dominance and vigor of first generation hybrids. Am Nat 55:116–133CrossRefGoogle Scholar
  14. Comstock RE, Robinson HF, Harvey PH (1949) A breeding procedure designed to make maximum use of both general and specific combining ability. Agron J 41:360–367CrossRefGoogle Scholar
  15. Crossa J, Pérez-Rodríguez P, Hickey J, Burgueño J, Ornella L, Cerón-Rojas J et al (2013) Genomic prediction in CIMMYT maize and wheat breeding programs. Heredity 112:48–60CrossRefPubMedPubMedCentralGoogle Scholar
  16. Crow JF (1948) Alternative hypotheses of hybrid vigor. Genetics 33:477–487PubMedPubMedCentralGoogle Scholar
  17. Da Y, Wang C, Wang S, Hu G (2014) Mixed model methods for genomic prediction and variance component estimation of additive and dominance effects using SNP markers. PLoS One 9:e87666CrossRefPubMedPubMedCentralGoogle Scholar
  18. Daetwyler HD, Villanueva B, Woolliams JA (2008) Accuracy of predicting the genetic risk of disease using a genome-wide approach. PLoS One 3:e3395CrossRefPubMedPubMedCentralGoogle Scholar
  19. Desta ZA, Ortiz R (2014) Genomic selection: genome-wide prediction in plant improvement. Trends Plant Sci 19:592–601CrossRefPubMedGoogle Scholar
  20. East EM (1936) Heterosis. Genetics 21:375–397PubMedPubMedCentralGoogle Scholar
  21. Endelman JB, Atlin GN, Beyene Y, Semagn K, Zhang X, Sorrells ME, Jannink JL (2014) Optimal design of preliminary yield trials with genome-wide markers. Crop Sci 54:48–59CrossRefGoogle Scholar
  22. Falconer DS, Mackay TFC (1996) Introduction to quantitative genetics, 4th edn. Ronald Press Company, New YorkGoogle Scholar
  23. Feher K, Lisec J, Römisch-Margl L, Selbig J, Gierl A, Piepho HP, Nikiloski Z, Willmitzer L (2014) Deducing hybrid performance from parental metabolic profiles of young primary roots of maize by using a multivariate Diallel approach. PLoS One 9:e85435CrossRefPubMedPubMedCentralGoogle Scholar
  24. Gianola D, van Kaam JB (2008) Reproducing kernel Hilbert spaces regression methods for genomic assisted prediction of quantitative traits. Genetics 178:2289–2303CrossRefPubMedPubMedCentralGoogle Scholar
  25. Gianola D, Fernando RL, Stella A (2006) Genomic-assisted prediction of genetic value with semiparametric procedures. Genetics 173:1761–1776CrossRefPubMedPubMedCentralGoogle Scholar
  26. Gowda M, Zhao Y, Maurer HP, Weissmann EA, Würschum T, Reif JC (2013) Best linear unbiased prediction of triticale hybrid performance. Euphytica 191:223–230CrossRefGoogle Scholar
  27. Gowda M, Zhao Y, Würschum T, Longin CFH, Miedaner T, Ebmeyer E, Schachschneider R, Kazman E, Schacht J, Martinant JP, Mette MF, Reif JC (2014) Relatedness severely impacts accuracy of marker-assisted selection for disease resistance in hybrid wheat. Heredity 112:552–561CrossRefPubMedGoogle Scholar
  28. Guo T, Li H, Yan J, Tang J, Li J, Zhang Z, Zhang L, Wang J (2013) Performance prediction of F1 hybrids between recombinant inbred lines derived from two elite maize inbred lines. Theor Appl Genet 126:189–201CrossRefPubMedGoogle Scholar
  29. Guo G, Zhao F, Wang Y, Zhang Y, Du L, Su G (2014) Comparison of single-trait and multiple-trait genomic prediction models. BMC Genet 15:30CrossRefPubMedPubMedCentralGoogle Scholar
  30. Habier D, Fernando RL, Dekkers JCM (2007) The impact of genetic relationship information on genome-assisted breeding values. Genetics 177:2389–2397PubMedPubMedCentralGoogle Scholar
  31. Habier D, Fernando R, Kizilkaya K, Garrick D (2011) Extension of the bayesian alphabet for genomic selection. BMC Bioinf 12:186CrossRefGoogle Scholar
  32. Hallauer AR, Carena MJ, Miranda Filho JB (2010) Quantitative genetics in maize breeding. Iowa State University Press, AmesGoogle Scholar
  33. Hayashi T, Iwata H (2013) A Bayesian method and its variational approximation for prediction of genomic breeding values in multiple traits. BMC Bioinf 14:34CrossRefGoogle Scholar
  34. Hayes BJ, Visscher PM, Goddard ME (2009) Increased accuracy of artificial selection by using the realized relationship matrix. Genet Res 91:47–60CrossRefGoogle Scholar
  35. Henderson CR (1984) Applications of linear models in animal breeding. University of Guelph, GuelphGoogle Scholar
  36. Henderson CR (1985) Best linear unbiased prediction of non-additive genetic merits. J Anim Sci 60:111–117CrossRefGoogle Scholar
  37. Heslot N, Akdemir D, Sorrells M, Jannink JL (2014) Integrating environmental covariates and crop modeling into the genomic selection framework to predict genotype by environment interactions. Theor Appl Genet 127:463–480CrossRefPubMedGoogle Scholar
  38. Hillier FS, Lieberman GJ (2001) Introduction to operations research, 7nd edn. McGraw Hill, New YorkGoogle Scholar
  39. Hjorth JSU (1994) Computer intensive statistical methods. Validation model selection and bootstrap. Chapman & Hall, LondonGoogle Scholar
  40. Hofheinz N, Borchardt D, Weissleder K, Frisch M (2012) Genome-based prediction of test cross performance in two subsequent breeding cycles. Theor Appl Genet 125:1639–1645CrossRefPubMedGoogle Scholar
  41. Holland JB, Nyquist WE, Cervantes-Martińex CT (2003) Estimating and interpreting heritability for plant breeding: an update. In: Janick J (ed) Plant breeding reviews, vol 22. Wiley, New York, pp 9–112Google Scholar
  42. Hull FH (1945) Recurrent selection for specific combining ability in corn. J Am Soc Agron 37:134–145CrossRefGoogle Scholar
  43. Jacobson A, Lian L, Zhong S, Bernardo R (2014) General combining ability model for genomewide selection in a biparental cross. Crop Sci 54:895–905CrossRefGoogle Scholar
  44. Jannink JL, Lorenz AJ, Iwata H (2010) Genomic selection in plant breeding: from theory to practice. Brief Funct Genomics 9:166–177CrossRefPubMedGoogle Scholar
  45. Jia Y, Jannink JL (2012) Multiple-trait genomic selection methods increase genetic value prediction accuracy. Genetics 192:1513–1522CrossRefPubMedPubMedCentralGoogle Scholar
  46. Jiang Y, Reif JC (2015) Modeling epistasis in genomic selection. Genetics 201:759–768CrossRefPubMedPubMedCentralGoogle Scholar
  47. Jones DF (1917) Dominance of linked factors as a means of accounting for heterosis. Genetics 2:466–479PubMedPubMedCentralGoogle Scholar
  48. Keeble F, Pellew C (1910) The mode of inheritance of stature and of time of flowering in peas (Pisum sativum). J Genet 1:47–56CrossRefGoogle Scholar
  49. Krchov LM, Bernardo R (2015) Relative efficiency of genomewide selection for testcross performance of doubled haploid lines in a maize breeding program. Crop Sci 55:2091–2099CrossRefGoogle Scholar
  50. Krchov LM, Gordillo GA, Bernardo R (2015) Multienvironment validation of the effectiveness of phenotypic and genomewide selection within biparental maize populations. Crop Sci 55:1068–1075CrossRefGoogle Scholar
  51. Lande R, Thompson R (1990) Efficiency of marker-assisted selection in the improvement of quantitative traits. Genetics 124:743–756PubMedPubMedCentralGoogle Scholar
  52. Lehermeier C, Schön CC, de los Campos G (2015) Assessment of genetic heterogeneity in structured plant populations using multivariate whole-genome regression models. Genetics. doi: 10.1534/genetics.115.177394
  53. Longin CFH, Mühleisen J, Maurer HP, Zhang H, Gowda M, Reif JC (2012) Hybrid breeding in autogamous cereals. Theor Appl Genet 125:1087–1096CrossRefPubMedGoogle Scholar
  54. Longin CFH, Mi X, Würschum T (2015) Genomic selection in wheat: optimum allocation of test resources and comparison of breeding strategies for line and hybrid breeding. Theor Appl Genet 128:1297–1306CrossRefPubMedGoogle Scholar
  55. Lorenz AJ (2013) Resource allocation for maximizing prediction accuracy and genetic gain of genomic selection in plant breeding: a simulation experiment. G3 3:481–491Google Scholar
  56. Lorenzana RE, Bernardo R (2009) Accuracy of genotypic value predictions for marker-based selection in biparental plant populations. Theor Appl Genet 120:151–161CrossRefPubMedGoogle Scholar
  57. Massman JM, Gordillo A, Lorenzana RE, Bernardo R (2013) Genomewide predictions from maize single-cross data. Theor Appl Genet 126:13–22CrossRefPubMedGoogle Scholar
  58. Melchinger AE, Gumber RK (1998) Overview of heterosis and heterotic groups in agronomic crops. In: Lamkey KR, Staub JE (eds) Concepts and breeding of heterosis in crop plants. ASACSSA-SSSA Publication, Madison, pp 29–44Google Scholar
  59. Meuwissen THE (2009) Accuracy of breeding values of ‘unrelated’ individuals predicted by dense SNP genotyping. Genet Sel Evol 41:35CrossRefPubMedPubMedCentralGoogle Scholar
  60. Meuwissen THE, Hayes BJ, Goddard ME (2001) Prediction of total genetic value using genome-wide dense marker maps. Genetics 157:1819–1829PubMedPubMedCentralGoogle Scholar
  61. Miedaner T, Zhao Y, Gowda M, Longin CFH, Korzun V, Ebmeyer E, Kazman E, Reif JC (2013) Genetic architecture of resistance to Septoria Tritici blotch in European wheat. BMC Genomics 14:858CrossRefPubMedPubMedCentralGoogle Scholar
  62. Mirdita V, Liu G, Zhao Miedaner T, Longin CFH, Gowda M, Mette MF, Reif JC (2015) Genetic architecture is more complex for resistance to Septoria Tritici blotch than to Fusarium head blight in central European winter wheat. BMC Genet 16:430CrossRefGoogle Scholar
  63. Mrode RA (2005) Linear models for the prediction of animal breeding values, 2nd edn. CABI Publishing, WallingfordCrossRefGoogle Scholar
  64. Nishio M, Satoh M (2014) Including dominance effects in the genomic BLUP method for genomic evaluation. PLoS One 9:e85792CrossRefPubMedPubMedCentralGoogle Scholar
  65. Patti GJ, Yanes O, Siuzdak G (2012) Innovation: metabolomics: the apogee of the omics trilogy. Nat Rev Mol Cell Biol 13:263–269CrossRefPubMedPubMedCentralGoogle Scholar
  66. Piepho HP (2009) Ridge regression and extensions for genomewide selection in maize. Crop Sci 49:1165–1176CrossRefGoogle Scholar
  67. Piepho HP, Möhring J (2007) Computing heritability and selection response from unbalanced plant breeding trials. Genetics 177:1881–1888CrossRefPubMedPubMedCentralGoogle Scholar
  68. Reif JC, Gumpert F, Fischer S, Melchinger AE (2007) Impact of genetic divergence on additive and dominance variance in hybrid populations. Genetics 176:1931–1934CrossRefPubMedPubMedCentralGoogle Scholar
  69. Reif JC, Zhao YS, Würschum T, Gowda M, Hahn V (2013) Genomic prediction of sunflower hybrid performance. Plant Breed 132:107–114CrossRefGoogle Scholar
  70. Richey FD (1942) Mock-dominance and hybrid vigor. Science 96:280–281CrossRefPubMedGoogle Scholar
  71. Riedelsheimer C, Melchinger AE (2013) Optimizing the allocation of resources for genomic selection in one breeding cycle. Theor Appl Genet 126:2835–2848CrossRefPubMedGoogle Scholar
  72. Riedelsheimer C, Czedik-Eysenberg A, Grieder C, Lisec J, Technow F, Sulpice R, Altmann T, Stitt M, Willmitzer L, Melchinger AE (2012) Genomic and metabolic prediction of complex heterotic traits in hybrid maize. Nat Genet 44:217–220CrossRefPubMedGoogle Scholar
  73. Rincent R, Laloë D, Nicolas S, Altmann T, Brunel D, Revilla P, Rodriguez VM, Moreno-Gonzalez J, Melchinger A, Bauer E (2012) Maximizing the reliability of genomic selection by optimizing the calibration set of reference individuals: comparison of methods in two diverse groups of maize in breds (Zea mays L.) Genetics 192:715–728CrossRefPubMedPubMedCentralGoogle Scholar
  74. Schnell FW, Cockerham CC (1992) Multiplicative vs. Arbitrary gene action in heterosis. Genetics 131:461–469PubMedPubMedCentralGoogle Scholar
  75. Schrag TA, Frisch M, Dhillon BS, Melchinger AE (2009) Marker-based prediction of hybrid performance in maize single-crosses involving doubled haploids. Maydica 54:353–362Google Scholar
  76. Schulthess AW, Wang Y, Miedaner T, Wilde T, Reif JC, Zhao Y (2016) Multiple-trait- and selection indices-genomic predictions for grain yield and protein content in rye for feeding purposes. Theor Appl Genet 129:273–287CrossRefPubMedGoogle Scholar
  77. Schulz-Streeck T, Ogutu JO, Gordillo A, Karaman Z, Knaak C, Piepho HP (2013) Genomic selection allowing for marker-by-environment interaction. Plant Breed 132:532–538CrossRefGoogle Scholar
  78. Sorensen D, Gianola D (2002) Likelihood, Bayesian, and MCMC methods in quantitative genetics. Springer, New YorkCrossRefGoogle Scholar
  79. Stuber CW, Cockerham CC (1966) Gene effects and variances in hybrid populations. Genetics 54:1279–1286PubMedPubMedCentralGoogle Scholar
  80. Su G, Christensen OF, Ostersen T et al (2012) Estimating additive and non-additive genetic variances and predicting genetic merits using genome-wide dense single nucleotide polymorphism markers. PLoS One 7:e45293CrossRefPubMedPubMedCentralGoogle Scholar
  81. Technow F, Riedelsheimer C, Ta S, Melchinger AE (2012) Genomic prediction of hybrid performance in maize with models incorporating dominance and population specific marker effects. Theor Appl Genet 125:1181–1194CrossRefPubMedGoogle Scholar
  82. Technow F, Schrag TA, Schipprack W, Bauer E, Simianer H, Melchinger AE (2014) Genome properties and prospects of genomic prediction of hybrid performance in a breeding program of maize. Genetics 197:1343–1355CrossRefPubMedPubMedCentralGoogle Scholar
  83. Tracy WF, Chandler MA (2006) The historical and biological basis of the concept of heterotic patterns in corn belt dent maize. In: Lamkey KR, Lee M (eds) Plant breeding: the Arnel R Hallauer international symposium. Blackwell Publishing, Ames, pp 219–233Google Scholar
  84. VanRaden PM (2007) Genomic measures of relationship and inbreeding. Interbull Bull 37:33–36Google Scholar
  85. VanRaden PM (2008) Efficient methods to compute genomic predictions. J Dairy Sci 91:4414–4423CrossRefPubMedGoogle Scholar
  86. Wang Y, Mette MF, Miedaner T, Gottwald M, Wilde P, Reif JC, Zhao Y (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 15:556CrossRefPubMedPubMedCentralGoogle Scholar
  87. Ward J, Rakszegi M, Bedo Z, Shewry P, Mackay I (2015) Differentially penalized regression to predict agronomic traits from metabolites and markers in wheat. BMC Genet 16:19CrossRefPubMedPubMedCentralGoogle Scholar
  88. Whitford R, Fleury D, Reif JC, Garcia M, Okada T, Korzun V, Langridge P (2013) Hybrid breeding in wheat: technologies to improve hybrid wheat seed production. J Exp Bot 64:5411–5428CrossRefPubMedGoogle Scholar
  89. Whittaker JC, Thompson R, Denham MC (2000) Marker-assisted selection using ridge regression. Genet Res 75:249–252CrossRefPubMedGoogle Scholar
  90. Windhausen VS, Atlin GN, Hickey JM, Crossa J, Jannink JL, Sorrels ME, Raman B, Cairns JE, Tarekegne A, Semagn K, Beyene Y, Grudloyma P, Technow F, Riedelsheimer C, Melchinger AE (2012) Effectiveness of genomic prediction of maize hybrid performance in different breeding populations and environments. G3 2:1427–1436CrossRefPubMedPubMedCentralGoogle Scholar
  91. Wricke G, Weber WE (1986) Quantitative genetics and selection in plant breeding. Gruyter, BerlinCrossRefGoogle Scholar
  92. Würschum T, Reif JC, Kraft T, Janssen G, Zhao Y (2013) Genomic selection in sugar beet breeding populations. BMC Genet 14:85CrossRefPubMedPubMedCentralGoogle Scholar
  93. Xu S, Zhu D, Zhang Q (2014) Predicting hybrid performance in rice using genomic best linear unbiased prediction. Proc Natl Acad Sci U S A 111:12456–12461CrossRefPubMedPubMedCentralGoogle Scholar
  94. Zhang X, Pérez-Rodríguez P, Semagn K, Beyene Y, Babu R, López-Cruz MA, San Vicente F, Olsen M, Buckler E, Jannink JL, Prasanna BM, Crossa J (2015) Genomic prediction in biparental tropical maize populations in water-stressed and well-watered environments using low-density and GBS SNPs. Heredity 114:291–299CrossRefPubMedGoogle Scholar
  95. Zhao Y, Gowda M, Liu W, Würschum T, Maurer HP, Longin CFH, Ranc N, Reif JC (2012a) Accuracy of genomic selection in European maize elite breeding populations. Theor Appl Genet 124:769–776CrossRefPubMedGoogle Scholar
  96. Zhao Y, Gowda M, Longin CFH, Würschum T, Ranc N, Reif JC (2012b) Impact of selective genotyping in the training population on accuracy and bias of genomic selection. Theor Appl Genet 125:707–713CrossRefPubMedGoogle Scholar
  97. Zhao Y, Gowda M, Würschum T, Longin CFH, Korzun V, Kollers S, Schachschneider R, Zeng J, Fernando R, Dubcovsky J (2013a) Dissecting the genetic architecture of frost tolerance in Central European winter wheat. J Exp Bot 64:4453–4460CrossRefPubMedPubMedCentralGoogle Scholar
  98. Zhao Y, Zeng J, Fernando R, Reif JC (2013b) Genomic prediction of hybrid wheat performance. Crop Sci 53:802–810CrossRefGoogle Scholar
  99. Zhao Y, Mette MF, Gowda M, Longin CFH, Reif JC (2014a) Bridging the gap between marker-assisted and genomic selection of heading time and plant height in hybrid wheat. Heredity 112:638–645CrossRefPubMedPubMedCentralGoogle Scholar
  100. Zhao Y, Mette MF, Reif JC (2014b) Genomic selection in hybrid breeding. Plant Breed. doi: 10.1111/pbr.12231
  101. Zhao Y, Li Z, Liu G, Jiang Y, Maurer HP, Würschum T, Mock HP, Matros A, Ebmeyer E, Schachschneider R, Kazman E, Schacht J, Gowda M, Longin CFH, Reif JC (2015) Genome-based establishment of a high-yielding heterotic pattern for hybrid wheat breeding. Proc Natl Acad Sci U S A. doi: 10.1073/pnas.1514547112

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Authors and Affiliations

  1. 1.Department of Breeding ResearchLeibniz Institute of Plant Genetics and Crop Plant Research (IPK)GaterslebenGermany

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