**1. Introduction**

Marker-assisted selection (MAS) is an important scheme in plant breeding since the 1990s, after promising analysis results for tagging genes or mapping QTL [1]. Marker assisted selection and molecular breeding have been used in the identification of underlying major genes in gene pools and their transfer to desirables traits of major plant breeding programs. Using of MAS have shown some shortcomings due to long selection schemes and also the look for vital marker-QTL associations being unable to capture "minor" gene effects. Thus marker-assisted selection (MAS) is difficult to improve traits having complex inheritance such as grain yield and abiotic stresses.

Using whole-genome prediction models, the genomic selection (GS) strategy has paved the way to over-come these limitations. High-density molecular markers using is one of the main features of genomic selection. Therefore, each of the trait loci has the likelihood of being in linkage disequilibrium (LD) with a minimum of one marker locus within the entire breeding population. Genome selection strategy removes the need to mapping of genes and search for linked QTL–marker loci associated individually. Rather, Genomic selection accounts for bunches of predictors simultaneously and is characterized by constraining random estimates towards zero. Moreover, Genomic selection helpful for accelerate breeding cycles in such a way that the rate of annual genetic gain per unit of time and cost can be decreased. Genomic selection has been well established in the field of animal breeding, but is in its beginning in crops plants and forest tree breeding.

Genome-wide selection or genomic selection estimates marker effects across the full ordering of the breeding population (BP) supported the prediction model developed within the training population (TP). Training population could be a group of related individuals (such as half-sibs or lines) that are each phenotypes and genotypes. Breeding population typically is just genotyped not phenotypes. Hence, Genomic selection depends on the degree of genetic similarity between training population and breeding population within the Linkage disequilibrium between marker and trait loci. Breeding values have not been a preferred index in plant breeding, however it is in animal breeding. Once plan of genomic estimated breeding value (GEBV) was planned, it had been considered an unrealistic approach due to lack of enormous scale genotyping technologies. However, currently, it has been a possible approach with recent advances in high throughput genotyping platforms (3rd generation platforms). Generally processes of genomic selection and marker assisted selection used for Quantitative Traits are shown in **Figure 1**.

between the two approaches: (1) within the training section, quantitative trait loci (QTLs) are known in marker assisted selection whereas formulae for genetic estimation of breeding value prediction are generated in genomic selection, called genomic selection models; (2) within the breeding section, genotype data are solely needed for targeted regions in marker assisted selection, whereas genomic selection genotype data are considered to be mandatory in genomic selection (3) within the breeding phase, favorable individuals are selected on the bases of the linked markers in marker assisted selection, whereas GEBVs are used for selection in GS. Thus, GS collectively analyses all the genetic variance of every individual by summing the marker impacts of GEBV and it is expected to deal with little effect genes that cannot be captured

The Usage of Genomic Selection Strategy in Plant Breeding

http://dx.doi.org/10.5772/intechopen.76247

95

**Figure 1.** Self-pollinated crop genomic selection vs. phenotypic/MAS selection timeline.

The statistical ways employed by GS are comparatively new the plant-breeding community. The ways of marker-assisted selection (MAS) or marker-assisted recurrent selection (MARS) assume that the user is aware of that alleles are favorable, and what their average effects on the phenotype are. This assumption is viable for major-gene traits however not for quantitative traits that are influenced by several loci of little impact and the environment. To deal with

by traditional MAS.

The main schemes of the two approaches are similar, wherever each marker assisted selection and genomic selection consist of breeding and training phases. In the training phase, phenotypes and genome-wide (GW) genotypes are investigated in an exceedingly set of a population, i.e., the mapping population in marker assisted selection and also the training population in genomic selection. Among populations, important relationships between phenotypes and genotypes are expected utilizing statistical models. Within the breeding phase, genotype data are obtained in an exceedingly breeding population, on the basis of genotypic information favorable individuals are selected. There are three prominent variations

**Figure 1.** Self-pollinated crop genomic selection vs. phenotypic/MAS selection timeline.

**1. Introduction**

94 Next Generation Plant Breeding

abiotic stresses.

crops plants and forest tree breeding.

Traits are shown in **Figure 1**.

Marker-assisted selection (MAS) is an important scheme in plant breeding since the 1990s, after promising analysis results for tagging genes or mapping QTL [1]. Marker assisted selection and molecular breeding have been used in the identification of underlying major genes in gene pools and their transfer to desirables traits of major plant breeding programs. Using of MAS have shown some shortcomings due to long selection schemes and also the look for vital marker-QTL associations being unable to capture "minor" gene effects. Thus marker-assisted selection (MAS) is difficult to improve traits having complex inheritance such as grain yield and

Using whole-genome prediction models, the genomic selection (GS) strategy has paved the way to over-come these limitations. High-density molecular markers using is one of the main features of genomic selection. Therefore, each of the trait loci has the likelihood of being in linkage disequilibrium (LD) with a minimum of one marker locus within the entire breeding population. Genome selection strategy removes the need to mapping of genes and search for linked QTL–marker loci associated individually. Rather, Genomic selection accounts for bunches of predictors simultaneously and is characterized by constraining random estimates towards zero. Moreover, Genomic selection helpful for accelerate breeding cycles in such a way that the rate of annual genetic gain per unit of time and cost can be decreased. Genomic selection has been well established in the field of animal breeding, but is in its beginning in

Genome-wide selection or genomic selection estimates marker effects across the full ordering of the breeding population (BP) supported the prediction model developed within the training population (TP). Training population could be a group of related individuals (such as half-sibs or lines) that are each phenotypes and genotypes. Breeding population typically is just genotyped not phenotypes. Hence, Genomic selection depends on the degree of genetic similarity between training population and breeding population within the Linkage disequilibrium between marker and trait loci. Breeding values have not been a preferred index in plant breeding, however it is in animal breeding. Once plan of genomic estimated breeding value (GEBV) was planned, it had been considered an unrealistic approach due to lack of enormous scale genotyping technologies. However, currently, it has been a possible approach with recent advances in high throughput genotyping platforms (3rd generation platforms). Generally processes of genomic selection and marker assisted selection used for Quantitative

The main schemes of the two approaches are similar, wherever each marker assisted selection and genomic selection consist of breeding and training phases. In the training phase, phenotypes and genome-wide (GW) genotypes are investigated in an exceedingly set of a population, i.e., the mapping population in marker assisted selection and also the training population in genomic selection. Among populations, important relationships between phenotypes and genotypes are expected utilizing statistical models. Within the breeding phase, genotype data are obtained in an exceedingly breeding population, on the basis of genotypic information favorable individuals are selected. There are three prominent variations between the two approaches: (1) within the training section, quantitative trait loci (QTLs) are known in marker assisted selection whereas formulae for genetic estimation of breeding value prediction are generated in genomic selection, called genomic selection models; (2) within the breeding section, genotype data are solely needed for targeted regions in marker assisted selection, whereas genomic selection genotype data are considered to be mandatory in genomic selection (3) within the breeding phase, favorable individuals are selected on the bases of the linked markers in marker assisted selection, whereas GEBVs are used for selection in GS. Thus, GS collectively analyses all the genetic variance of every individual by summing the marker impacts of GEBV and it is expected to deal with little effect genes that cannot be captured by traditional MAS.

The statistical ways employed by GS are comparatively new the plant-breeding community. The ways of marker-assisted selection (MAS) or marker-assisted recurrent selection (MARS) assume that the user is aware of that alleles are favorable, and what their average effects on the phenotype are. This assumption is viable for major-gene traits however not for quantitative traits that are influenced by several loci of little impact and the environment. To deal with quantitative traits, new statistical approaches that might account for this uncertainty were required to get the most effective predictions potential. Finding problem with locus identification, entailed that the consequences for all marker loci be at the same time estimated. Once a prediction based on allele effects, the allele becomes the unit of analysis. Alleles are so the units that need to be replicated inside and across environments. However that replication will occur in spite of the particular lines carrying the alleles such lines themselves no longer need to be replicated. Within the breeding context, removing the requirement for line replication opens the likelihood of dramatically increasing the amount of lines pushed through the pipeline of a breeding program, and successively of accelerating selection intensity.
