**2.2. Model for genomic selection**

cycles, the genetic gain per selection cycle ought to be near that predicted from phenotypic or combined MAS + phenotypic selection. Progeny testing schemes have a high accuracy of selection, however the time interval is also additional, takes long term to perform a cycle of selection that decreases the genetic gain. The univariate breeder's equation was used for the GS-BPs as a result of they include just one stage of selection [3]. Selection accuracy is adequate to the correlation between selection criteria and breeding value (i.e., correlation between phenotypes or GEBVs and true breeding values [TBVs]). In oxen, Schaeffer [4] determined that the time and value savings exploitation GS with GEBV accuracy of 0.75 would increase genetic gain twofold and supply a price savings of ninety two in comparison to the present ways.

**Figure 3.** Genomic selection scheme.

98 Next Generation Plant Breeding

The basic model may be denoted as

$$Y\_{\parallel} = \mathbf{g}(\mathbf{x}\_{\parallel}) \star \mathbf{e}\_{\parallel} \tag{1}$$

where *Yi* is an observed phenotype of individual i (i = 1 … n) and *xi* is a *1 x p* vector of SNP genotypes on individual *i, g(xi)* is a function relating genotypes to phenotypes, and *ei* a residual term. The GEBV is generally equal to g (xi ). Further similarities among GS models can be seen by recognizing that they all seek to minimize a certain cost function. In least squares analysis, the well-known cost function is simply the sum of squared residuals.
