**7. Conclusions**

Currently, the lion's share of research on GS has been performed in livestock breeding, where effective population size, extent of LD, breeding objectives, experimental design, and other characteristics of populations and breeding programs are quite different from those of crop species. Nevertheless, a great number of findings within this literature are very illuminating for GS in crops and should be studied and built upon by crop geneticists and breeders. The application of powerful, relatively new statistical methods to the problem of high dimensional marker data for GS has been nearly as important to the development of GS as the creation of high-density marker platforms and greater computing power. The methods can be classified by what type of genetic architecture they try to capture. Somewhat surprisingly, RR-BLUP, which makes the ostensibly unrealistic assumption that genetic effects are uniformly spread across the genome, often performs as well as more sophisticated models. Exceptions do exist, though, and there is abundant evidence that BayesB is superior for traits with strong QTL effects. Additionally, since BayesB better identifies markers in strong LD with QTL than RR-BLUP, it maintains accuracy for more generations. Finally, the question of whether or not to model epistasis remains open. If epistasis is important for a particular trait in a particular population, the kernel methods and machine-learning techniques such as SVM may be preferable. It is important for the practitioner to consider such issues or test methods on a relevant data set before a method for GEBV calculation is chosen. Although the increasing marker density, training population size, and trait heritability are obvious ways to improve GEBV accuracy; these options add cost to the program. Implementing algorithms for markers imputation and training population design holds the potential for essentially free additional accuracy, leading to greater overall GS efficiency.

Clearly, exciting times are ahead of us as public breeding programs launch GS efforts. This review compiles several immediately useful results for breeders wanting to maximize gains through GS. Knowledge of breeding program parameters (effective population size, extent of LD, and trait heritability) allows marker density and training population size to be determined using analytical formulae. The greatest impact of GS on gain per unit time will come from shortening the breeding cycle [11]. Therefore, redesigning crossing and population development schemes incorporate GS as early as possible will likely be the most effective. Consequently, phenotyping resources will need to be shifted from early generation, evaluation for selection to evaluation for model training. The importance of epistasis will need to be assessed for each trait. A major paradigm in plant breeding since the availability of molecular marker technology is that mapping and characterizing the genetic loci that control a trait will lead to improved breeding. Often, one of the rationales for cloning of QTL is to develop the "perfect market" for MAS, perhaps based on a functional polymorphism. In contrast, an advantage of the GS is precisely its black box approach to exploiting genotyping technology to expedite genetic progress. This is an advantage in our view because it does not rely on a "breeding by design" engineering approach to cultivar development requiring knowledge of biological function before the creation of phenotypes. Breeders can therefore use GS without the large upfront cost of obtaining that knowledge. In addition, GS can maintain the creative nature of phenotypic selection which couples random mutation and recombination to sometimes arrive at solu-

The Usage of Genomic Selection Strategy in Plant Breeding

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

107

Support from the teaching section Department of plant breeding and Genetics, Punjab

tions outside the engineer's scope.

Agricultural University, Ludhiana India.

MAS marker-assisted selection

GEBV genomic estimated breeding value

GS genomic selection

BV breeding value

CV cross-validation

TP training population BP Breeding population LD linkage disequilibrium

**Acknowledgements**

**Nomenclature**

The current drops in genotyping costs, while phenotyping costs remain constant or increase, suggest that efforts to understand how to choose which lines to phenotype on the basis of their genotype, that is, how to design training populations, will be rewarding. Combining training populations from different populations is another way to boost accuracy when individual populations lack sufficient size and assuming that the marker densities required are available. With respect to maximizing long-term selection, we discussed several promising approaches that strive to retain favorable, low-frequency alleles while minimizing loss of short-term gain. Both simulation and empirical results for GS have been quite impressive. Empirical results of GS accuracy in crops, however, are not yet available for the public sector, except in the form of CV within families. Further empirical studies of the effects of statistical models, marker density, TP size and composition, and different selection criteria for the effectiveness of GS in breeding populations are urgently needed. In addition, while the CV approach can be instructive, an important caveat should be mentioned. In CV, the training and validation sets belong to the same population. But in GS, the selected candidates will rarely belong to the same population as the training set and may well be several generations removed from it. Recombination during meiosis between generations erodes the association between marker and QTL, systematically reducing accuracy. The effect of selection on allele frequencies and the Bulmer effect can also have detrimental effects on accuracy. In order to realistically evaluate GS for crops, studies designed for this purpose should be performed.

Clearly, exciting times are ahead of us as public breeding programs launch GS efforts. This review compiles several immediately useful results for breeders wanting to maximize gains through GS. Knowledge of breeding program parameters (effective population size, extent of LD, and trait heritability) allows marker density and training population size to be determined using analytical formulae. The greatest impact of GS on gain per unit time will come from shortening the breeding cycle [11]. Therefore, redesigning crossing and population development schemes incorporate GS as early as possible will likely be the most effective. Consequently, phenotyping resources will need to be shifted from early generation, evaluation for selection to evaluation for model training. The importance of epistasis will need to be assessed for each trait. A major paradigm in plant breeding since the availability of molecular marker technology is that mapping and characterizing the genetic loci that control a trait will lead to improved breeding. Often, one of the rationales for cloning of QTL is to develop the "perfect market" for MAS, perhaps based on a functional polymorphism. In contrast, an advantage of the GS is precisely its black box approach to exploiting genotyping technology to expedite genetic progress. This is an advantage in our view because it does not rely on a "breeding by design" engineering approach to cultivar development requiring knowledge of biological function before the creation of phenotypes. Breeders can therefore use GS without the large upfront cost of obtaining that knowledge. In addition, GS can maintain the creative nature of phenotypic selection which couples random mutation and recombination to sometimes arrive at solutions outside the engineer's scope.
