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GWAS. The GLM doesn’t take the populace structure are not taken

in GLA model. Henceforth, GLM was utilized in populaces which

didn’t have the populace structure in faba bean, Viciafaba L. (Sallam

& Maure, 2016) and rice (Bandillo et al., 2013). The MLM, then

again, thinks about the populace structure in its model. At last, the

phenotypic and genotypic information are joined utilizing proper

programming (TASSEL) by which alleles related with a specific

characteristic can be recognized after the GWAS model was chosen.

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Stage III: Phenotyping is strongly recommendable to be led prior

to genotyping, particularly for those populaces with no earlier data.

For instance, if a populace comprised of 400 genotypes which were

gathered from various locales and the objective is to test them in a

specific climate. It is conceivable that numerous genotypes could be

lost because of helpless variation to the phenotyping climate. Along

these lines, time, and currency (for genotyping) can be saved by

testing the phenotypic variety of that populace first.

10.4 NEXT GENERATION SEQUENCING (NGS)

Initial attempts to obtain genetic sequences necessitated significant human

and financial resources. However, the development and implementation of

next-generation sequencing (NGS) technology has substantially aided the

capacity to produce genome sequences for an increasing number of plant

species. This has opened up a slew of possibilities for finding stress-related

genes and pathways that can serve as the foundation for future research or

for the purpose of crop improvement NGS technology, for example, can be

combined with high-throughput transcriptome profiling to explore transcrip­

tome-wide changes in response to stress (Molina et al., 2011). Inexpensive

technology of sequencing, often it is known as next generation sequencing

(NGS) technologies, its main feature is that at once it constructs millions of

sequencing reads (Church, 2006). NGS is a rapid technology which works in

a very cost-effective way for constructing large quantity of sequence data. In

addition, it is useful in many fields that are in comparative genomics where

we compare between genomes of species, data through NGS have higher

capability in identifying the loci under selection. The new terminologies

which define NGS are (high throughput sequencing, massively parallel

sequencing, and deep sequencing). It’s the advanced and modern technology

which came in existence last decade. NGS technologies bring a huge change