Abstract
Marker-assisted selection (MAS) is well suited for handling oligogenes and quantitative trait loci (QTLs) with large effects. MAS has been extensively used mainly for backcross breeding, including pyramiding of useful genes/QTLs, and for marker-assisted recurrent selection (MARS). The expression of most quantitative traits is governed by one or few QTLs with relatively large effects along with several QTLs with small effects. Thus, MAS and MARS schemes are not well suited for the improvement of quantitative traits since they cannot handle QTLs with small effects. The genomic selection (GS) scheme was proposed to rectify this deficiency of MAS and MARS schemes. The GS scheme is a specialized form of MAS that utilizes information from genome-wide marker data whether or not their associations with the concerned trait(s) are significant. GS scheme uses a training population to estimate the effects associated with the marker on the target trait and to train the GS model. The GS model is then used to calculate the genomic estimated breeding values (GEBVs) of the plants/lines of the breeding population on the basis of their marker genotypes. These GEBV estimates are then used as the basis of the selection of the superior plants/lines. The GS scheme can make effective use of off-season nursery and greenhouse facilities to accelerate the breeding program and is quite effective in selection for the complex traits. This chapter describes the various aspects of the GS scheme and discusses its applications for the improvement of both cross- and self-pollinated species.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Albrecht T, Wimmwe V, Auinger H-J et al (2011) Genome-based prediction of test-cross values in maize. Theor Appl Genet 123:339–350
Bernardo R (2009) Genome-wide selection for rapid introgression of exotic germplasm in maize. Crop Sci 49:419–425
Bernardo R (2010) Genome-wide selection with minimal crossing in self-pollinated crops. Crop Sci 50:624–627
Bernardo R, Yu J (2007) Prospects for genome-wide selection for quantitative traits in maize. Crop Sci 41:1–4
Crossa J, Campos G, Perez P et al (2010) Prediction of genetic values of quantitative traits in plant breeding using pedigree and molecular markers. Genetics 186:713–724
Crossa J, Perez P, Campos G et al (2011) Genomic selection and prediction in plant breeding. J Crop Improv 25:239–261
de los Campos G, Perez P (2010) BLR: Bayesian linear regression. R Package Version 1.2. http://cran.r-project.org/web/packages/BLR/index.html
Dekkers JCM (2007) Prediction of response to marker-assisted genomic selection using selection index theory. J Anim Breed Genet 124:331–341
Gianola D, Fernando R, Stella A (2006) Genomic-assisted prediction of genetic values with semiparametric procedures. Genetics 173:1761–1776
Goddard M (2009) Genomic selection: prediction of accuracy and maximisation of long term response. Genetica 136:245–257
Gonzalez-Camacho JM, de los Campos G, Perez P et al (2012) Genome-enabled prediction of genetic values using radial basis function neural networks. Theor Appl Genet 125:759–771
Guo Z, Tucker DM, Lu J et al (2012) Evaluation of genome-wide selection efficiency in maize nested association mapping populations. Theor Appl Genet 124:261–275
Habier D, Fernando RL, Dekkers JCM (2007) The impact of genetic relationship information on genome assisted breeding values. Generics 177:2389–2397
Heffner EL, Sorrels ME, Jannink J-L (2009) Genomic selection for crop improvement. Crop Sci 49:1–12
Heffner EL, Lorenz AJ, Jannink J-L et al (2010) Plant breeding with genomic selection: gain per unit time and cost. Crop Sci 50:1681–1690
Jannink J-L, Lorenz AJ, Iwata H (2010) Genomic selection in plant breeding: from theory to practice. Brief Funct Genomics 9:166–177
Lorenzana RE, Bernardo R (2009) Accuracy of genotypic value predictions for marker-based selection in biparental plant populations. Theor Appl Genet 120:151–161
Meuwissen THE (2009) Accuracy of breeding values of ‘unrelated’ individuals predicted by dense SNP genotyping. Genet Select Evol 41:35. doi:10.1186/1297-9686-41-35
Meuwissen THE, Hayes BJ, Goddard ME (2001) Prediction of total genetic value using genome-wide dense marker maps. Genetics 157:1819–1829
Nakaya A, Isobe SN (2012) Will genomic selection be a practical method for plant breeding? Annals Bot. doi:10.1093/aob/mcs109
Park T, Casella G (2008) The Bayesian Lasso. J Amer Stat Assoc 103:681–686
Piepho HP (2009) Ridge regression and extensions for genome-wide selection in maize. Crop Sci 49:1165–1176
Rutkoski JE, Heffner EL, Sorrels ME (2010) Genomic selection for durable stem rust resistance in wheat. Euphytica 179:161–173
Solberg TR, Sonesson AK, Woolliams JA et al (2008) Genomic selection using different marker types and densities. J Anim Sci 86:2447–245
Whittaker JC, Thompson R, Denham MC (2000) Marker assisted selection using ridge regression. Genet Res 75:249–252
Wong CK, Bernardo R (2008) Genome wide selection in oil palm: increasing selection gain per unit time and cost with small populations. Theor Appl Genet 116:815–824
Zhao Y, Gowda M, Liu W et al (2012a) Accuracy of genomic selection in European maize elite breeding populations. Theor Appl Genet 124:769–776
Zhao Y, Gowda M, Longin FH (2012b) Impact of selective genotyping in the training population on accuracy and bias of genomic selection. Theor Appl Genet 125:707–713
Zhao Y, Gowda M, Liu W et al (2013) Choice of shrinkage parameter and prediction of genomic breeding values in maize elite breeding populations. Plant Breed 132:99–106
Zhong S, Dekkers JCM, Fernando RL et al (2009) Factors affecting accuracy from genomic selection in populations derived from multiple inbred lines: a barley case study. Genetics 182:355–364
Author information
Authors and Affiliations
Rights and permissions
Copyright information
© 2015 Author(s)
About this chapter
Cite this chapter
Singh, B.D., Singh, A.K. (2015). Genomic Selection. In: Marker-Assisted Plant Breeding: Principles and Practices. Springer, New Delhi. https://doi.org/10.1007/978-81-322-2316-0_10
Download citation
DOI: https://doi.org/10.1007/978-81-322-2316-0_10
Publisher Name: Springer, New Delhi
Print ISBN: 978-81-322-2315-3
Online ISBN: 978-81-322-2316-0
eBook Packages: Biomedical and Life SciencesBiomedical and Life Sciences (R0)