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.
KeywordsBreeding Population Genomic Selection Marker Density Ridge Regression Reproduce Kernel Hilbert Space
- 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
- Habier D, Fernando RL, Dekkers JCM (2007) The impact of genetic relationship information on genome assisted breeding values. Generics 177:2389–2397Google Scholar