f. Model used

Several published GS studies compared the accuracy of various statistical models. The disparity in prediction accuracies is negligible. However, some studies have found that the prediction accuracies of various models vary greatly, as seen in rice hybrid breeding [17–20].

#### **3.4 Approaches to improve genetic gain and GS accuracies**

### *3.4.1 Using biparental populations*

With no group structure, biparental populations have a high level of LD between markers and trait alleles. Three shorter GS cycles can be completed in one year by using full-sib families. Genetic gain per unit time was improved in biparental populations from rapid selection cycles (C), according to studies made in maize [21]. Under drought conditions, maize hybrids generated from C3 cycles yielded 7.3 percent more than C0, according to Beyene et al. [21]. In winter wheat biparental populations, Lozada et al. [4] found a 10% increase in responsiveness to selection using genomic section relative to phenotypic selection. Similar studies have been conducted in oat by Asoro et al. [22] and wheat by Rutkoski et al. [23].

### *3.4.2 Using multi-parental populations and multi-environment models*

GS rapid cycles of multi-parental crosses were performed in diallelic fashion to form cycle 0 in the CIMMYT maize breeding program. With two selection cycles each year and two location experiments, it suggested an improvement in genetic gain in which the study predicted a 0.1 t/h per year yield gain over a period of 4.5 years [24]. However, when comparing the C4 cycle to the C0 cycle, a decrease in genetic diversity was seen [24].

#### *3.4.3 Combining GS with high-throughput phenotyping*

In addition to genotyping data, accurate phenotypic data is required for genomic prediction model training to achieve the desired accuracy. For large-scale field-based accurate phenotypic data collection, a number of high-throughput phenotyping technologies have been built. These platforms are based on image and distant or proximal sensor technologies. Infrared thermometry and thermal imaging; visible/near-infrared spectroradiometry; and red, green, and blue light color digital photography are the three types of technologies in use for high throughput phenotyping. Their deployment is determined by the trait of interest and experimental design in the field. These technologies' data can be used as the primary input for model training. It is feasible to quantify high-density phenotypes over time and space using distant or proximal sensing by applying high throughput phenotyping such as canopy hyperspectral reflectance in a large number of breeding lines. This can improve the precision and intensity of selection, as well as the selection response, while lowering the phenotyping expenses. Lozada et al. [4] have proved in wheat that combining GS with high throughput phenotyping results in the highest accuracy for grain yield. The advantage of this imaging method is that large numbers of phenotypes can be screened for complex phenotypic expression and secondary traits that are genetically connected with grain yield at a low cost during early-generation testing. Juliana et al. [25] claim that by utilizing high throughput phenotyping, they were able to achieve a 60 percent increase in genetic gain for wheat yield and secondary characteristics.

#### *3.4.4 Using historical data*

Predicting the performance of new lines can be done by using phenotypic data from relatives and ancestors for model training that accounts for GxE interaction in multilocation research [26]. Historical data from breeding programs can be used effectively to increase genomic selection accuracy, particularly when the training set is adjusted to include only the most informative individuals from the target testing set [27].

#### *3.4.5 Genotype imputation*

For genotyping, genomic selection uses a high sample size and a dense marker set. In such data sets, missing data is a problem. Missing data were dealt with in one of four ways: (1) repeating genotyping in missing regions, (2) adapting analysis methods to accommodate missing data, (3) eliminating SNPs and/or samples with missing data, or (4) inferring the missing data (imputation).

Imputation of genotype is useful in a variety of situations. First, genotyping by sequencing, which is regarded as a low-cost genotyping method, typically yields a large number of markers at a low cost, but with a high proportion of missing data due to the poor genome sequencing depth. As a result of the imputation, the data set is full and ready for further study [28]. Second, utilizing low density genotyping and a closely similar reference panel genotyped at high density, imputation can enable GEBV prediction without a significant loss of accuracy. Using this in silico genotyping technique, low density genotyping in GS can be done without sacrificing accuracy. Imputation, on the other hand, may pose the danger of biases and inaccuracies [29].

Haplotype tagging is the simplest technique for genotype imputation [30]. In this strategy, a tag from the reference panel was chosen so that the majority of known (common SNPs) have a r<sup>2</sup> of less than 0.8 with the tag SNP. To identify shared haplotypes, the sequences of the reference panel haplotypes were compared to the genotyped markers. The missing genotypes were then filled in by copying alleles found in a matching reference haplotype (called FILLIN method) [29].

*Genomic Selection: A Faster Strategy for Plant Breeding DOI: http://dx.doi.org/10.5772/intechopen.105398*

For imputation, a number of statistical approaches have been developed. These include the expectation maximization, Bayesian, LinkImpute, LD k-nearest neighbor imputation (LD KNNi) and entropy methods. These methods integrate models of recombination by partitioning markers into haplotype blocks. The tree-based imputation infers on the basis of perfect phylogeny and pairwise haplotype dissimilarity rather than haplotype structure [31].

#### **3.5 Applications of genomic selection**

#### *3.5.1 GS for breeding of quality traits and yield*

Grain yield is a crucial economic feature that has been studied in most GS studies of crops such as wheat. Grain yield is a complicated quantitative trait that is impacted by interactions between genes and surroundings and is regulated by a number of genes with little effects. GS has been shown to be important in grain yield studies in cereals. Prediction accuracies have been improved by include GxE effects in models [32]. Furthermore, GS aided in the cost reduction of phenotyping for malt quality in barley breeding [33].

#### *3.5.2 GS and breeding for disease resistance*

In terms of boosting intricate quantitative disease resistance, the GS method has a lot of potential for crop breeders. Pathogens find it difficult to overcome quantitative disease resistance because it is governed by a large number of genes with minor effects. Wheat rust, fusarium head blight, and rice blast resistance are three of the most well-studied diseases using the GS approach [18, 34].

#### *3.5.3 GS for germplasm enhancement*

Alleles for cultivar development can be found in abundance in gene bank accessions. Identification of these alleles is costly and time-consuming, and it necessitates extensive pre-breeding operations. Germplasm augmentation initiatives can begin with landraces by crossing them with elite testers. High genome-enabled prediction accuracy may be attained with GS, which may aid breedings in introducing valuable genetic variants. This supports the use of GS to introduce landrace accessions into elite germplasm and create gene pools and populations suited for pre-breeding and germplasm improvement [35].

#### *3.5.4 GS for hybrid breeding*

In hybrid breeding, parental selection is a critical issue. The performance of prospective crosses of a given parent set with genotyped parents and a small number of crosses examined in the field can be improved by employing whole genome markers in GS. This lowers the expense of hybridization and field testing of all possible hybrids. GS can also be used to predict hybrid performance and assist in hybrid selection. The predicted hybrids can be tested in the field and released as superior hybrids if they pass the test. There are just a few papers indicating the use of genomic selection for hybrid breeding in maize and rice [17, 20].
