**1.1 Phases of genomic selection**

For large effect alleles, molecular marker technology has aided QTL identification, marker-assisted introgression, and selection, but not for low heritability quantitative traits, which still require considerable field testing. In the case of low heritability quantitative traits, locus identification and effect estimation are difficult to predict. New statistical methods that account for such uncertainty in genomic selection were used to make the best predictions. When classical marker-assisted selection and genomic selection are compared, the core framework is similar, including both breeding and training phases [3, 4]. In genomic selection, there are three crucial processes [3]:

1.Prediction model training and validation. Some lines in a population under selection are referred to as training sets. The training population is made up of germplasm that has both phenotypic and genome-wide marker data and is used to estimate marker effect and cross-validate results (**Figure 1**). These data were used to develop a statistical model that links variance in detected genotypes marker loci to variation in individual phenotypes. The training set's statistical

#### **Figure 1.**

*Phases of genomic selection. Genome wide genotypic and phenotypic information from training population allows GS model optimization and breeding value estimation.*

analysis evaluates allele effect in all loci at the same time. The second group of lines, known as selection candidates (breeding materials), are genotyped in order to calculate their GEBV.

