**4.1. SNP chips**

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 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 SNP and direct resequencing with next-generation.

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

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

measure of marker density relative to decay of linkage disequilibrium. They found that for a

Genetic drift is an important cause in generating LD, the non-independence of alleles at different loci. This non-independence allows marker alleles to predict the allelic state of nearby QTL, enabling marker genotypes to predict the phenotype. The LD intensity decays with greater distance between two markers. Decay rates which vary widely across species, populations, and genomes due to forces of mutation, recombination, population size, population mating marker density must increase with increases in Ne\*c, where Ne is the effective population size and c is the recombination rate between loci. LD patterns. Marker density can be inferred by the rate of LD decay across the genome as inferred by the relationship b/w

determine target marker densities for GS at equilibrium, drift generating LD is balanced by recombination, causing it to decay, such that nearby loci are expected to be in higher LD than faraway loci. LD has a major effect on the operability of GS, so it has to be well understood before performing GS. LD is defined as the non-random association of alleles at different loci.

Since, then marker of choice is very important to accurate estimate GEBVBs, different plat-

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

It has been found that for high heritability trait average adjacent marker r2

between adjacent markers as a

of 0.15 was sufficient, but for a low heri-

to 0.20 improved prediction accuracy. Heritability dramatically

less than 0.40 [3].

and genetic distance. LD estimates can be used to

value to 0.2 improve accuracy of GEBV predictions.

of 0.15 is sufficient

trait heritability. Calus and Veerkamp [9] used the average r2

affects TP sizes required for successful GS, especially at h<sup>2</sup>

highly heritability trait, average adjacent marker r2

inter marker coefficient of determination r<sup>2</sup>

but for low heritability trait increasing r2

**4. Types of marker platforms**

forms are available

**4.1. SNP chips**

tability trait, increasing the r2

102 Next Generation Plant Breeding

**3.2. Linkage disequilibrium**

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.
