This document summarizes a case study on the draft genome sequence of chickpea. Key points include: - Researchers sequenced and assembled the ~738Mb genome of a kabuli chickpea variety, identifying an estimated 28,269 genes. - The genome provides resources for molecular breeding through identification of candidate genes for traits like disease resistance. - Resequencing of elite varieties provided insights into genome diversity and domestication. - Analysis found the draft captured over 90% of the gene space through mapping of transcriptome data, and contained homologs for over 98% of core eukaryotic genes.

2. Genomic Aided Selection for Crop
Improvement
Tanvi Chauhan
Ph.D Agri.Biotech.

3. Content
1) Introduction.
2) Background of Genomic Aided Selection for Crop Improvement.
3) Molecular and Genetic Markers in Plant Breeding.
4) Development of Genetic Markers.
5) Potential of cereal genomics
6) Progress in crop genome sequencing and analysis.
7) The impact of whole genome sequencing on breeding.
8) Genomic assisted features for future breeding.
9) Challenges in genomics-assisted crop improvement.
10)Case studies.
11)Conclusion.
12)References.

4. What is Genetics?
Study of Heredity and its Variation – heredity itself is meaningful
only ‘coz of variation.
Unit of study?
Species; Populations (natural/random mating/genetic cross
generated);Individuals
Components of variation?
Vp= Vg + Ve ; Interaction between genotype and environment also
matters.
How does variation arise?
Mutation; gene flow; recombination; ploidy level changes; gene
duplication; transposons
Who is interested?
Crop improvement biologists and evolutionary biologists

5. Genome is an organism’s complete set of DNA and is organized into
chromosomes containing genes that encode for hereditary traits. The
differences that distinguish one plant from another are encoded in the
plant’s genetic material, the DNA. DNA is packaged in chromosome
pairs (strands of genetic material), one coming from each parent. The
genes, which control a plant’s characteristics, are located on specific
segments of each chromosome. Together, all of a plant’s genes make
up its Genome.

6. Introduction
•In recent years advances in genetics and genomics have greatly enhanced our
understanding of the structural and functional aspects of plant genomes. Several
novel genetic and genomics approaches are expended.
•At only two Decades ago that plant scientists would have access to nearly the
complete genetic code of numerous plants species, including major crops species.
•Since the advent of modern crop improvement, breeders have dreamed of
applying marker-based selection in their populations.
•Morphological markers have long been known but are too few to be of any use.
• Protein markers, e.g. isozymes, are not of much use either also.
•Advances in genomics have given us microsatellite and SNP markers in such
high number that molecular marker technology for crop improvement and
eventually for human welfare has become a reality.

7. In past few years, Genomics research is generating new tools,
such as functional molecular markers and informatics, as well
as new knowledge about statistics and inheritance
phenomena that could increase the efficiency and precision of
crop improvement :
• A truly impressive number of advances in genetics and genomics
greatly enhanced our understanding of structural and functional
aspects of plant genomes but at the same time have challenged us
with many compelling avenues of investigation.
•Numerous transcription factors and even non coding sequences are
being implicated as the basis of considerable genetic variation found
to be very useful in revealing gene-trait associations.
Background of Genomic Aided Selection for
Crop Improvement

8. A variety of approaches
(cars)
 MAS: MARKER-ASSISTED SELECTION
- Plants are selected for one or more (up to 8-10) alleles
 MABC: MARKER-ASSISTED BACKCROSSING
 One or more (up to 6-8) donor alleles are transferred
to an elite line
 MARS: MARKER-ASSISTED RECURRENT SELECTION
 Selection for several (up to 20-30) mapped QTLs relies
on index (genetic) values computed for each individual
based on its target QTLs
 GWS: GENOME-WIDE SELECTION
 Selection of genome-wide several loci that confer
tolerance/resistance/ superiority to traits of interest
using based on genome-wide marker profiling

9. The complete genome sequences of Arabidopsis, rice, sorghum
and popular as well as an enormous number of plant expressed
sequence tags (ESTs) have become available.

10. Molecular Markers in Plant Breeding
Molecular markers are now widely used to track loci and genome
regions in several crop-breeding programmes, as molecular markers
tightly linked with a large number of agronomic and disease
resistance traits are available in major crop species (Phillips and Vasil
2001, Jain et al. 2002, Gupta and Varshney 2004)
Markers can exhibit two modes of
inheritance i.e.
(a) Dominant/recessive
(b) Co-dominant.
If the genetic pattern of
homozygotes can be
distinguished from that of
heterozygotes, then a marker is
said to be co-dominant. Generally
co-dominant markers are more
informative than the dominant
markers.

11. In issue of the journal Nature , researchers who decoded the
human genome concluded that people have only 20,000 to
25,000 genes ,a drop from the 30,000 to 40,000 estimated
in 2001.

12. Genetic Markers in Plant Breeding
A large number of genes have been
identified through ‘wet lab’ as well as
in silico studies and a wealth of
sequence data have been accumulated
in public databases in the form of BAC
clones, ESTs, full length cDNA clones
and genes.
The availability of enormous amount
of sequence data from complete or
partial genes has made it possible to
develop the molecular markers directly
from the parts of genes. These markers
are referred as “genic” molecular
markers (GMM).

13. What is EST
Expressed sequence tag (EST) have generated from a vast
amount of publicly available sequence data from plant species;
these data can be mined for simple sequence repeats (SSRs).
• They have been developed and mapped in several crop species
and could prove useful for marker-assisted selection, especially
when the markers reside in the genes responsible for a phenotypic
trait.
GMMs developed from coding sequences like ESTs or fully
characterized genes frequently have been assigned known
functions. Based on the site of polymorphism and later’s effect on
phenotypic variation

14. These EST- SSRS are useful as molecular markers because
 Their development is inexpensive,
 They represent transcribed genes
 A putative function can often be deduced by a homology
search.
 Derived from transcripts,
 Assaying the functional diversity in natural populations or
germplasm collections.
 Valuable because of their higher level of transferability to
related species,
 They can often be used as anchor markers for comparative
mapping and evolutionary studies.

15. Potential of cereal genomics
•Cereals, including rice, maize, wheat, barley, rye, sorghum,
oats and millets, have constituted the staple food of the world
since their domestication 10 000 years ago.
•In the past, cereals have been the subject of intensive
cytogenetic investigations, and these are now further
extended using the powerful tools of molecular biology in
the genomics era.
•The structural and functional genomics research on cereal
genomes , which during the past two decades has covered
both basic and applied aspects, deepens our understanding
about gene networks for cereal development and agronomy
•More interestingly genomics is revolutionizing breeding
methodology through marker-assisted selection (MAS) and
directed mutagenesis, which are significantly enhancing
the efficiency of breeding for the improvement of
agronomic traits.

16. The cloned genes, containing their own exons,
introns and regulatory elements, are good
candidates for transformation into other varieties of
the same crop, or into other cereals, without
additional modification.

17. GMMs have been classified into two groups by:
(i) Gene-targeted markers (GTMs): are associated with
anonymous gene segments and derived from polymorphisms
within genes, however not necessarily involved in phenotypic trait
variation, e.g. untranslated regions (UTRs) of EST sequences.
(ii) Functional markers (FMs): associated with a gene of known
function and derived from polymorphic sequences or sites within genes
and, thus, more likely to be causally involved in phenotypic trait
variation (e.g. candidate gene-based molecular markers).

18. Development of Genetic Markers
•ESTs represent a basic commodity within the analysis of genomes and their
genes for a species.
•Developing molecular markers from ESTs, it is essential to define the
“unigenes” after cluster analysis of random ESTs using appropriate
computer programmes such as stackPack.
STS

19. Once the unigene sequence data from EST analysis or non-redundant set of
genes are available, molecular markers can be developed using two main
approaches:
(1) Direct mapping: ESTs of interest can be used as RFLP probe or
the PCR primers can be designed for the EST/gene. Direct mapping
approach should be undertaken with the unigene set of ESTs or
genes only.

20. 2) In silico mining: In this approach, the SSR or SNP
identification software tools are used to screen the
sequence data for ESTs/genes. For identification of
SNPs, the redundant set of EST data, generated from
more than one genotype of a given species, are used

21. Characteristics of Genetic Molecular Markers
The development of GMMs, now permits a targeted approach for detection of
nucleotide diversity in genes controlling agronomic traits in plant populations
1. Trait Mapping
•Their use as diagnostic markers for the trait in the selection
2. Functional Diversity
•GMMs, especially genic SSRs, have been found useful for estimating genetic
relationship on one hand while at the same time these have provided
opportunities to examine functional diversity in relation to adaptive variation
3. Interspecific or Intergeneric Transferability
•GMMs is that these markers provide high degree of transferability among
distantly related species
•Transferability of GMM markers to related species or genera has now been
demonstrated in several studies

23. Progress in crop genome sequencing and analysis
•Advances in sequencing crop genomes have mirrored the development of
sequencing technologies.
•Until 2010, Sanger sequencing of bacterial artificial chromosome (BAC)-based
physical maps was the predominant approach used to access crop genomes such as
rice, poplar and maize.
•The sorghum genome was the first crop genome to be sequenced completely by
the exclusive use of WGS sequence assemblies, which were then assessed for
integrity using high-density genetic maps and physical maps.
•Since 2012, analyses of the sequences of 12 crop genomes have been
published, accounting for nearly half of the total published. This explosion of
data has been driven by cheaper and more effective sequencing technologies
(primarily the Illumina and Roche 454 methods) coupled with increasingly
sophisticated sequence and assembly strategies, which are generally
delivered by large genome centers.

24. •NGS speed and capacity enable over
100 published plant genomes.
•Underserved specialty and orphan crop
genomic resources grow due to low cost
NGS.
•Double haploid and diploid ancestors
key to sequence complex plant
genomes.
•Polyploidy, heterozygosity and repeats
complicate plant genome assembly but
also underlie key agronomic traits.
•More than 100 plant genomes have
been sequenced since 2000, 63% of
which are crop species.

27. The impact of whole genome sequencing
on breeding.

28. Initial genetic maps consisted of few and sparse markers, many of which were
anonymous markers (simple sequence repeats (SSR)) or markers based on
restriction fragment length polymorphisms (RFLP).
if a phenotype of interest was affected by genetic variation within the SSR1-SSR2
interval
the complete region would be selected with little information about its gene
content or allelic variation
the Whole genome sequencing of a closely related species enabled projection of
gene content onto the target genetic map
This allowed breeders to postulate the presence of specific genes on the basis of
conserved gene order across species (synteny), although this varies between
species and regions
Complete genome sequence in the target species provides breeders with an
unprecedented wealth of information that allows them to access and identify
variation that is useful for crop improvement

29. You have just
cloned a gene
Evolutionary
relationship?
-Phylogenetic
tree
-Accession #?
-Annotation?
Is it already in
databases?
-Sub-localization
-Soluble?
-3D fold
Protein
characteristics?
-% identity?
-Family member?
Is there similar
sequences?
-Alignments?
-Domains?
Is there conserved
regions?
Other
information?
-Expression profile?
-Mutants?
A critical failure of current bioinformatics is the lack of a single software
package that can perform all of these functions.
Applying algorithms to analyze genomics data

30. DNA (nucleotide sequences) databases
They are big databases and searching either one should produce
similar results because they exchange information routinely.
-GenBank (NCBI): http://www.ncbi.nlm.nih.gov
-DDBJ (DNA DataBase of Japan): http://www.ddbj.nig.ac.jp
-Yeast: http://yeastgenome.org
-Microbes: http://img.jgi.doe.gov/cgi-bin/pub/main.cgi
Specialized databases:Tissues, species…
-ESTs (Expressed Sequence Tags)
~at NCBI http://www.ncbi.nlm.nih.gov/dbEST
~at TIGR http://tigr.org/tdb/tgi
- ...many more!

31. They are big databases too:
-Swiss-Prot (very high level of annotation)
http://au.expasy.org/
-PIR (protein identification resource) the world's most
comprehensive catalog of information on proteins
http://www.pir.uniprot.org/
Translated databases:
-TREMBL (translated EMBL): includes entries that have
not been annotated yet into Swiss-Prot.
http://www.ebi.ac.uk/trembl/access.html
-GenPept (translation of coding regions in GenBank)
-pdb (sequences derived from the 3D structure
Brookhaven PDB) http://www.rcsb.org/pdb/
Protein (amino acid) databases

32. Cost - a major obstacle
 Cost-efficiency has rarely been calculated
but GAS is more expensive for most traits
 Exceptions include quality traits
 Determined by:
 Trait and method for phenotypic screening
 Cost of glasshouse/field trials
 Labour costs
 Type of markers used

33. Genomic assisted features for future
breeding
Genomics has radically altered the scope of genetics by providing a
landscape of :-
 their epigenetic states.
 access to an enormous range of genetic variation.
 the potential to measure gene expression directly.
 with high precision and accuracy.
 Superior breeding lines
 Molecular markers associated with resistance to
biotic stresses and tolerance to abiotic stresses.
 Alleles and haplotype information available on
germplasm set so that breeders can use informative
lines.
 Set of well characterized disease resistance and
abiotic stress tolerance genes
 Breeder-friendly genome database of Crops.

34. Challenges in genomics-
assisted crop improvement
 Narrow genetic base in the primary gene
pool
 Very few molecular (SSR) markers
 Non-availability of appropriate germplasm
such as mapping populations
 Intraspecific genetic map with low marker
density
 Non-availability of trait-associated markers
in breeding
 Issues of costs and expertise in molecular
breeding
 Data Management

35. Case Study 1

36. Draft genome sequence of chickpea (Cicer arietinum) provides
a resource for trait improvement (Varshney RK.,et al,2013)
Chickpea (Cicer arietinum) is the
second most widely grown legume
crop after soybean, accounting for a
substantial proportion of human
dietary nitrogen intake and playing
a crucial role in food security in
developing countries.
It is reported that the ~738Mb
draft whole genome shotgun
sequence, a kabuli chickpea variety,
which contains an estimated 28,269
genes.

38. By taking Candidate genes for disease resistance and agronomic traits are
highlighted, including traits that distinguish the two main market classes of
cultivated chickpea—desi and kabuli. These data comprise a resource for chickpea
improvement through molecular breeding and provide insights into both genome
diversity and domestication.
To understand the genetic history among chickpea accessions, They resequenced
29 elite varieties from both desi and kabuli genotypes grown around the world,
and conducted genotyping by sequencing of 61 Cicer accessions from ten
countries.
To assess the proportion of the gene space captured in this draft genome
assembly , for this they mapped a 454/Roche transcriptome data set (>500 bp
read length), produced from the same CDC Frontier line and comprising 60,802
reads, to the genome assembly
On the basis of this analysis , they estimate gene coverage to be 90.8%.
Analysis of the draft genome assembly for core eukaryotic genes reveals
homologs for >98% of conserved genes in the assembly.

39. To evaluate the conservation of chickpea gene models in other plant species, used
BLAST to query the chickpea proteome against the proteomes of A. thaliana, M.
truncatula, G. max, Cajanus cajan and L. japonicus.
Using this analysis, proteins predicted for chickpea were most similar to those from
M. truncatula (89.7% of predicted chickpea proteins were similar to M. truncatula
proteins) and
 A.thaliana (79.2% had similarity with A. thaliana proteins).
They also observed five instances in which organelle genome segments of >10 kb
had been integrated into chickpea pseudomolecules, consistent with findings in
both plant and animal genomes.
Genome organization and evolution
Approximately half (49.41%) of the chickpea genome is composed of transposable
elements and unclassified repeats, which is comparable to other sequenced
legumes:
M. truncatula (30.5%),
Pigeonpea (C. cajan, 51.6%)
Soybean (G. max,59%).

40. Fig. Shared and unique gene families in legume species chickpea, M. truncatula,
L. japonicus, soybean, pigeonpea; in millettioid and galegoids, and in legumes, A. thaliana
and grape.
Long-terminal repeat (LTR) retrotransposons are the most abundant
transposable element class, and cover >45% of the total nuclear genome

41. Genetic diversity among cultivated varieties and germplasm
With the objective of understanding genetic diversity in chickpea , they used
whole genome resequencing (WGRS) of 29 (17 desi and 12 kabuli) chickpea
breeding lines and released varieties collected from the leading chickpea-growing
countries. WGRS yielded 204.52 Gb of high-quality sequence data with an
average coverage of 9.5×, from which calculated the average pair wise nucleotide
diversity within population
This draft whole genome sequence of chickpea (CDC Frontier, a kabuli chickpea
variety) adds to the genomic resources available for legume research.
The Papilionaceae subfamily now has the draft or complete genome sequences of
two model species (M. truncatula and L. japonicus) and
 three crop legume species (chickpea, soybean and pigeonpea).
The availability of these genome sequences should facilitate de novo assembly of
the genomes of other important but less-studied galegoid legume crops such as pea
(Pisum sativum), lentil (Lens culinaris) and faba bean (Vicia faba).

42. Furthermore, analyses by this objective such as
population structure,
Diversity, and
phylogenetic analyses
not only document the mixed use of desi and kabuli genotypes in
breeding, but also serve to identify regions (and candidate genes) across
the genome that might have been greatly affected by selection during
domesticationor breeding.
Fig. Phylogenetic
tree of seven
species.

43. Case Study 2

44. Achievements and prospects of genomics-assisted breeding in three
legume crops of the semi-arid tropics(Mohan SM.,Gaur PM.,
Varshney RK., et al, 2013)
Legumes form an important constituent of food crops consumed globally and
complement cereal crops as a source of dietary protein. In addition to
providing important micronutrients to human beings, they also fix
atmospheric nitrogen, which consequently increase soil fertility and
production of other cereal crops.
Among several food crops, chickpea (Cicer
arietinum), pigeonpea (Cajanus cajan) and
groundnut or peanut (Arachis hypogaea)
are the leading legume crops to feed
underprivileged living in the semi-arid
tropics (SAT), which is also called “habitat
of the hungry”.Chickpea (Cicer arietinum
L.)

45. Genomic resources
Molecular markers and genotyping
platforms
Genomics-assisted breeding (GAB)
Marker-assisted recurrent
selection (MARS)
Genomic selection (GS)
Draft genome sequences,large-scale molecular markers,
construction of comprehensive genetic maps,
establishment of various marker-trait associations and
initiation of molecular breeding in these three crops.
Range of marker systems
hybridization-based Diversity Array Technology
(DArT)
sequence based markers (SNPs)
Integration and use of genomic tools in breeding
practices for developing superior lines with enhanced
biotic or abiotic stress tolerance and improved yield.
Involves estimation of marker effects from
genotyping F2 or F3 population and
phenotyping F2 derived F4 or F5 progenies,
followed by two or three recombination cycles
based on presence of marker alleles for small
effect QTLs .
Targets identification of superior lines with
higher breeding value in a breeding program
based on genome-wide marker profile data.

46. Fig. A coordinated and collaborative efforts on development and application of
genomic resources for crop improvement of SAT legumes.

47. This figure shows coordinated and collaborative efforts of ICRISAT and its partners, using
high-throughput sequencing and genotyping and modern breeding approaches, to develop
and apply genomic resources in crop improvement programs of three legume crops.
genomic and transcriptomic resources such as molecular markers, ESTs, genes,
transcriptome assemblies and genome sequences have been developed using Sanger and
next generation sequencing technology platforms
cost-effective,
low- capillary electrophoresis (SSR genotyping), ,
medium- iScan or BeadXpress systems and KASPar assays (SNP genotyping)
and high-throughput DArT genotyping platforms (panel on top).
A range of genetic resources (panel on right) are used using different genotyping
platforms to develop genetic map (center top left) as well as
for multi-location phenotyping for traits of interest to breeders (center top right).
Genotyping data and phenotyping data are used to identify the QTLs/markers associated
with target traits using QTL mapping or association mapping approaches (center down
left).
Genetic and QTL information subsequently are used in breeding program using modern
breeding programs such as marker-assisted back crossing (MABC) (center down right).
By using these modern breeding approaches, superior lines for target traits with enhanced
crop productivity are generated (panel on bottom).
‘orphan
legumes’
‘genomic resource
rich’

48. Case Study 3

49. The peanut or groundnut (Arachis
hypogaea) is a species in
the family Fabaceae (Leguminosae) is the
third largest angiosperm family, containing
ca. 18,000 species attributed to 650 genera
(Zhu et al. 2005). Legumes are important to
world agriculture, as they provide biological
fixed nitrogen, break the cereal disease
cycles, and contribute to locally grown food
and feed, including forage (Stoddard et al.
2009).
Development and deployment of genomic resources for
groundnut improvement.(Varshney RK., etal., 2012)

50. Fig. A technical program for breeding

51. With an objective to increase breeding efficiency for accelerated development of
superior genotypes, by ICRISAT and its partners have developed and deployed
thousands of genetic markers such as
SSRs (>6000),
DArTs (>15,000 features)
SNPs (90 KASP assays) in several genetic applications.
The above efforts resulted in development of many
genetic maps (82-198 loci),
consensus maps (>3600 loci) and
identification of QTLs for drought tolerance related traits (153
QTLs) and
foliar disease resistance (143 QTLs).
Efforts are underway to conduct genetic and QTL mapping for oil content,
oil/nutritional quality, seed dormancy, nodulation, disease resistance, drought
tolerance and pod yield.

52. Association mapping conducted using large scale genotyping
(4597 DArT features and 154 SSRs) and
multiple season phenotyping data (51 traits) identified
a total of 524 highly significant marker-trait associations (MTAs).
Further efforts are in progress to identify MTAs at high genetic resolution
through comprehensive genome-wide association studies (GWAS) in diverse
groundnut global germplasm sets (ICRISAT reference set, minicore collections
of USA and China).
Three elite cultivars (TAG 24, JL 24 and ICGV 91114) were improved through
MABC approach by introgressing a major QTL for rust resistance.
MABC efforts to develop lines with high oleate trait individually as well as
pyramiding with genes/QTLs for two foliar diseases (LLS and rust) are
underway. In addition, efforts have been initiated to deploy genomic selection
for improvement of complex traits such as yield under drought stress.

53. References
Aggarwal RK, Hendre PS, Varshney RK, Bhat PR, Krishna KV, Singh L (2007) Identification,
characterization and utilization of EST-derived genic microsatellite markers for genome
analyses of coffee and related species. Theor Appl Genet 114:359–372
Andersen JR, Lubberstedt T (2003) Functional markers in plants. Trends Plant Sci 8:554–
560 Arnold C, Rossetto M, McNally J, Henry RJ (2002) The application of SSRs
characterized for grape (Vitis vinifera) to conservation studies in Vitaceae. Am J Bot 89:22–
28
Bandopadhyay R, Sharma S, Rustgi S, Singh R, Kumar A, Balyan HS, Gupta PK (2004)
DNA polymorphism among 18 species of Triticum–Aegilops complex using wheat EST-
SSRs. Plant Sci 166:349–356
Bevan and Uauy (2013), Genomics reveals new landscapes for crop improvement 14:206
Cato SA, Gardner RC, Kent J, Richardson TE (2001) A rapid PCR based method for
genetically mapping ESTs. Theor Appl Genet 102:296–306
Varshney Rk, Mahendar T, Aggarwal RK, Börnera A( 2007) Genic Molecular Markers In
Plants: Development And Applications. Book_Varshney&Tuberosa
Varshney RK , Song C, Saxena RK (2013) Draft genome sequence of chickpea (Cicer
arietinum) provides a resource for trait improvement, nature biotech. Vol. 31

54. Varshney RK, Graner A, Sorrells ME (2005a) Genic microsatellite markers in plants:
features and applications. Trends Biotechnol 23:48–55
Varshney RK, Graner A, Sorrells ME (2005b) Genomic-assisted breeding for crop
improvement. Trends Plant Sci 10:621–630
Varshney RK, Grosse I, Hahnel U, Siefken R, Prasad M, Stein N, Langridge P,
Altschmied L, Graner A (2006) Genetic mapping and BAC assignment of EST-derived
SSR markers shows non-uniform distribution of genes in the barley genome. Theor Appl
Genet 113:239–250
Varshney RK, Korzun V, Börner A (2004) Molecular maps in cereals: Methodology and
progress. In: Gupta PK, Varshney RK (eds) Cereal genomics. Kluwer Academic
Publishers, The Netherlands, p35
Woodhead M, Russell J, Squirrell J, Hollingsworth PM, Cardle L, Ramsay L, Gibby M,
Powell W (2003) Development of EST-SSRs from the alpine lady-fern, Athyrium
distentifolium. Mol Eco Notes 3:287–290
Yu JK, La Rota M, Kantety RV, Sorrells ME (2004) EST derived SSR markers for
comparative mapping in wheat and rice. Mol Genet Genomics 271:742–751

55. Conclusion
Now a days, genomics-assisted breeding has shown its tremendous
successful for crop improvement in several crops, however these successes
have been largely restricted to temperate cereal and legume crops, and
others such as Eucalyptus, sugarcane, tomato and other vegetables crops.
Although it is clear that present stage of plant genomics research has great
potential to revolutionize the discipline of plant breeding, high costs invested
in associated with genomics research currently limit the implementation of
genomics-assisted crop improvement, especially for inbreeding or minor
crops. Marker-assisted breeding and selection will gradually domesticated
into ‘genomics-assisted breeding’ for crop improvement. Being a agriculture
rich country one should utilize this research technique keeping in mind about
how it will help full to a our farmers or Breeders.