**3. Factors responsible for the estimation accuracy of GS models**

The response of genomic selection is that the output of varied factors responsible for estimation accuracy of GEBVs. These factors are interconnected in an exceedingly advanced and comprehensive manner. They include model performances, sample size and relatedness, marker density, gene effects, heritability and genetic design, and therefore the extent and distribution of linkage disequilibrium between markers and QTL.

## **3.1. Population size**

environment. In that case, genotype by environment (G.E) interaction would generate a common component of error in both GEBV and EBV, biasing their correlation upward. Thus, training and validation data should be collected in different environments to ensure sound estimates of GEBV prediction accuracy. The correction, r(EBV:TBV), accounts for the fact that the EBV in the validation set is not free of error. When the EBVs are phenotypes, r(EBV:TBV)

Accurate GEBV predictions offer the possibility that future elite and parental lines will be selected on GEBV rather than phenoypic data from extensive field testing. Immediate impact would be a great increase in speed of breeding cycle increasing selection gains per unit time. Thus, GS could radically change the practice of field evaluation for breeders. Of course, regardless of the breeding method used, final field evaluations of varieties across the target

Breeding cycle time is shortened by removing phenotypic evaluation of lines before selection as parents for the next cycle. Model training and line development cycle length will be crop and breeding program specific. In a GS breeding schema, genome-wide DNA markers are used to predict which individuals in a breeding population are most valuable as parents of the next generation of offspring. The prediction model is additionally continuously rejuvenated as genotypical and phenotypic data from elite lines derived from the collaborating breeding programs is incorporated into the prediction models. In this manner, new germplasm may be infused into the system at any point. As lines derived from the recently infused germplasm advance within the breeding process, their genotypical and phenotypic data may be incorpo-

The purpose of phenotyping now is to pick the best lines from a segregating population and to judge fewer lines with larger replication in every cycle of selection. However during a GS driven breeding cycle, the aim of phenotyping is to estimate or re-estimate marker effects. It is far from clear at this point whether or not it will be advantageous to evaluate solely the best lines or to evaluate few lines with high replication. So separates the germplasm improvement cycle from the prediction model improvement cycle. Indeed, if we tend to use the rules for optimum QTL linkage mapping, analysis should include not just the best however the best and the the} worst lines **Figure 3** also emphasizes the requirement for model updating and re-evaluation. Marker effects might amendment as a results of allomorph frequency changes [6] or of epistatic gene action. Model updating with every breeding cycle should mitigate reduced gains from GS caused by these mechanisms. Thus, GS may radically change the practice of field evaluation for breeders. Of course, despite the breeding technique used, final field evaluations of varieties across the target environments are going to be needed before they are

Accuracy declines as generation number between the last model update and selection candidates increases [4–6], because selection causes changes in variances, allele frequencies, and LD relationships between markers and QTL [4]. Under random mating, simulations have shown model accuracy to decrease by about 5% per generation [5, 6], but accuracy decrease

is equal to the square root of heritability (h) within the validation set [2].

environments will be needed before they are distributed to farmers.

rated into the prediction models.

100 Next Generation Plant Breeding

distributed to farmers.

was much more rapid under selection.

The most important characteristic of the population is its effective size. An obvious measure of population size is its census: how many individuals it contains. But populations with the same census size can behave quite differently. For a population of a given rate of inbreeding, the effective size is equal to the census size of a randomly mating ("ideal") population that would have that same rate.

Accuracy due to genetic relationships can represent from a small minority to a large majority proportion of the overall accuracy. The combination of long-distance LD due to pedigree relatedness (e.g., full sibs and half sibs) and short-distance ancestral LD due to small effective population size are among the key features of our training population. With improved marker technology, large TPs that use a representative sample of germplasm in a given breeding program may be a good strategy for long-term accuracy over a broad range of families. It has been observed to be monotonic increase in the prediction accuracy for grain yield with increasing population size without any substantial decrease in the slope (**Figure 4**). Studies in this the size of the training population is of crucial importance in genomic selection. The impact of the population size on the accuracy of genomic selection is less pronounced for fewer characters like grain moisture, which might be due to presence of larger variance among populations

**Figure 4.** Relation between number of plants in the training population and accuracy of genomic selection for traits with different heritabilities.

that can be efficiently utilized by few individuals per population. Parameters such as effective population size and QTL number strongly influence marker densities and TP sizes required for acceptable accuracy. Indeed, simulations similar to those of Meuwissen et al. [6] have shown that marker density needs to scale with effective population size [7]. Until very low marker densities were reached, marker number had very little, if any, effect on prediction accuracies within families from various plant species [8]. Likewise, GEBV accuracy of several traits in cattle, including net merit, was hardly affected when as many as 75% of the original markers were masked. Adequate marker density and TP size depend on QTL number and trait heritability. Calus and Veerkamp [9] used the average r2 between adjacent markers as a measure of marker density relative to decay of linkage disequilibrium. They found that for a highly heritability trait, average adjacent marker r2 of 0.15 was sufficient, but for a low heritability trait, increasing the r2 to 0.20 improved prediction accuracy. Heritability dramatically affects TP sizes required for successful GS, especially at h<sup>2</sup> less than 0.40 [3].

SNP analysis [10]. Recent breakthroughs in next generation sequencing (NGS) technologies enabled millions of sequences reads to be generated from a single run at a more affordable cost. The ability to perform GS requires routine genotyping at a high number of loci. Single nucleotide polymorphisms (SNPs) differentiate individuals based on variations detected at the level of a single nucleotide base in the genome. SNPs have become the marker of choice for crop genetics and breeding applications because of their high abundance in genomes, and the availability of a wide array of genotyping platforms with various multiplex capabilities for SNP analysis [10]. Recent breakthroughs in next generation sequencing (NGS) technologies enabled millions of sequences reads to be generated from a single run at a more affordable cost. The resulting large amount of data provided sequence depth adequate for de novo sequence assembly, which has made the SNP discovery on a large scale a feasible task, particularly for species without completed genome sequences. Successful results on largescale discovery of SNPs based on NGS methods have been reported in several plant species, including both and polyploid species, and more are on the way. The development of highly parallel SNP assays, such as Illumina's Golden Gate assay with 1536-plex platform enabled the genome-wide studies previously not feasible for economically important crops. Using these techniques, SNP-based high-density genetic maps are now available in several crop plants such as soybean, maize, barley and wheat. Thus, genotyping lines for use in GS using

The Usage of Genomic Selection Strategy in Plant Breeding

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

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Advances in next generation technologies have driven the costs of DNA sequencing down to the point that genotyping-by sequencing (GBS) is now feasible for high diversity, large genome species. GBS is a highly multiplexed approach is based on high-throughput, next-generation sequencing of genomic subsets targeted by restriction enzymes (REs). Genotyping by sequencing (GBS) in any large genome species requires reduction of genome complexity. Genotypingby-sequencing can be applied to different populations or even different species without any prior genomic knowledge as marker discovery is simultaneous with the genotyping of the population. GBS sequence allows access to any sequence within low copy genomic regions or regulatory regions controlling the expression of plant genes responsible for agronomically important phenotypes are often located in non-coding DNA. The use of GBS for GS, therefore, should be applicable to a range of model and non-model crop species to implement genomicsassisted breeding. Genotyping-by sequencing combines marker discovery and genotyping of large populations, making it a superb marker platform for breeding applications even within the absence of a reference genome sequence or previous polymorphism discovery. Additionally, the pliability and low price of GBS create this an ideal approach for genomics-assisted breeding.

**1.** The marker effects are calculable from the training population and used directly for GS within the involved breeding population, and QTL discovery, mapping, etc., are not needed.

SNP and direct resequencing with next-generation.

**4.2. Genotyping by sequencing (GBS)**

**5. Advantages of genomic selection**
