**6. Limitations of genomic selection**

**2.** Each simulation and empirical studies reveal that GS produces larger gains per unit time than constitution selection. For instance, a simulation study in maize showed GS to be superior to MARS, notably for traits having low heritability. Further, GS will predict the performance of breeding lines additional accurately than that supported pedigree data,

**3.** The selection index approach integrates appropriately weighted data from multiple traits into an index that is the premise for concurrent selection for the concerned traits. The genome-wide marker data is integrated into a range index either alone or in conjunction with phenotype data on one or additional traits. Simulation studies show that the on top of combined selection index approach of GS can increase the effectiveness of selection,

**4.** GS would tend to cut back the speed of inbreeding and also the loss of genetic variability as compared to selection based on breeding values calculable from phenotype data; this may be achieved while not sacrificing selection gains. This might be notably vital in species that

**5.** Genomic selection scheme consist of phenotyping for each selection cycle within the breeding population is not needed. This greatly reduces the length of breeding cycle, notably in perennial species. For instance, GS was calculable to reduce the selection cycle time from 19 years to simply 6 years just in case of oil palm (*Elaeis guineensis*). Further, GS was calculable to outperform MARS and phenotypic selection even with a population size of fifty once selection gain was considered on per unit time and price, however not on per selection cycle, basis. (The selection the choice) cycle is reduced as a result of GS does not need analysis of interbreeding performance of the plants being subjected to selection that is critical within the case of phenotypic selection. In perennial species, GS is anticipated to facilitate commercialization of improved genotypes at abundant shorter intervals of time

**6.** Genomic selection would possibly enable breeders to pick out parents for crossbreeding programs from among those lines that have not been evaluated within the target environment. This selection would be supported GEBVs of these lines estimated for their adaptation to the target environment. This could facilitate germplasm exchange and their

**7.** Genotype X environment interaction could be a vital a part of phenotype and its estimation is sort of demanding. GS can utilize information on marker genotype and trait phenotype accumulated over time in varied analysis programs covering a variety of environments and integrate an identical in GEBV estimates of the various individuals/lines. This could

**8.** Theoretically, GEBV estimates is employed for the selection of parents for crossing programs and, possibly, for the development of hybrid varieties. These applications, however,

enable GEBV estimation even for traits that they have never been tested.

ought to expect validation of the concept in apply.

and GS appears to be an efficient tool for rising the potency of rice breeding.

considerably for low heritability traits.

104 Next Generation Plant Breeding

show severe inbreeding depression.

than phenotypic selection.

utilization in breeding programs.

