Advance Sustainable Science, Engineering and Technology (ASSET)
Vol. 2, No.2, October 2020, pp. 0200202-1 ~ 0200202-14
ISSN: 2715-4211 DOI: https://doi.org/10.26877/asset.v2i2.6279

Development of Single Nucleotide Polymorphism (SNP)
Markersin Tropical Crops
Yheni Dwiningsih*, Miranti Rahmaningsih, Jawaher Alkahtani
Department of Crop, Soil and Environmental Sciences, University of Arkansas,
Fayetteville, Arkansas, USA
*ydwining@uark.edu

Abstract. Understanding genetic diversity, association studies, evolution analysis, quantitative
trait loci, marker-assisted selection and genome-wide association in tropical crops are important
for improving plant characteristics in order to increase food sustainability in tropical countries.
Single nucleotide polymorphism (SNP) marker is becoming the most popular molecular marker
for those studies. By using SNP marker, genes associated with important traits can be identified
efficiently compared to the other molecular markers. This review describes about how SNP can
be discovered in the plant genomes and the application of SNP in plant breeding, especially in
tropical crops such as rice, maize, peas, potato, tomato, cassava, taro, etc.
Keywords: food sustainability, plant breeding, SNP marker, tropical crops

(Received 2020-10-06, Accepted 2020-10-26, Available Online by 2020-10-31)

1. Introduction
Gene position or genomic regions that regulate important traits in plants are discovered by using
molecular markers. These markers are Single Nucleotide Polymorphisms (SNPs), Simple Sequence
Repeats (SSRs), Amplified Fragment Length Polymorphisms (AFLPs), Restriction Fragment Length
Polymorphisms (RFLPs), and Random Amplified Polymorphic DNAs (RAPDs). SNPs are widely used
DNA markers to identify genomic regions for important traits that effectively to speed up the plant
breeding. These SNPs characterized as the most abundant variations in the plant genome that are very
useful in the high-resolution genotyping and produce the highest map resolution [1]. Additionally, using
SNPs are more efficient and cost effective [2]. SNPs become the most popular DNA markers in the 21st
century due to the development of genotyping by sequencing (GBS) technique [3]. Furthermore,
important traits for plants including agronomic traits (grain yield, seed size, maturity day, etc.),
morphological traits (plant height, seed color, root length, etc.), physiological traits (photosynthetic

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activity, stomatal conducting, etc.), abiotic (drought, heat, flooding, salt resistance, etc.) and biotic
resistance (bacterial blight, mosaic virus, root-knot resistance).
Genomic information of plants obtained from genetic diversity, association studies, evolution
analysis, quantitative trait loci (QTL), marker-assisted selection (MAS) and genome-wide association
studies (GWAS) are very useful for increasing food sustainability. To achieve food sustainability in this
changing climate condition, plants with superior characteristics must be developed by using genetic
information. Since 1900s, plant breeding has made a positive impact in increasing food production by
developing superior cultivars [4, 5]. Furthermore, these superior cultivars have been developed through
application effective molecular breeding by using MAS for desirable genes. These superior cultivars
including increasing yield, adaptation in unstable environment, resistance to biotic and abiotic stresses,
and quality improvement.

2. SNP discovery
SNP markers discovered by two methods, including SNP discovery from PCR and SNP discovery from
High Throughput-Next generation sequencing (NGS) – RNA-Seq, RAD-Seq, GBS (Genome by
Sequencing), WGS (Whole-Genome Sequencing), WGR (Whole-Genome Regression), etc. These
approaches reduce complexity to lower the cost and simplify the discovery of SNP markers. In the past
few years, NGS technologies have led to the discovery of thousands, even millions of SNPs. Moreover,
there are many tools for SNP discovery such as BioEdit, DNASTAR Lasergene Genomics Suite,
SAMtools, SOAPsnp, Stacks, Ddocent, Py RAD and GATK. SNPs are usually biallelic and thus easily
assayed. In theory, a SNP is identified when a nucleotide from an accession read differs from the
reference genome at the same nucleotide position. In the absence of a reference genome, this is achieved
by comparing reads from different genotypes using de novo assembly strategies. Read assembly files
generated by mapping programs are used to perform SNP calling. In practice, various empirical and
statistical criteria are used to call SNPs, such as a minimum and maximum number of reads considering
the read depth, the quality score and the consensus base ratio for examples. Moreover, SNP discovery
is more robust when multiple and divergent genotypes are used simultaneously, creating the necessary
basis to capture the genetic variability of a species.
Three kinds of SNP Genotyping platform: single SNP genotyping (using PCR with Taqman – Life
Technologies or KASP genotyping – LGC Genomics), multiple SNP genotyping (with SNP chip –
Illumina and Multiplex - Sequenom), and SNP genotyping by next generation sequencing (Genotyping
by Sequencing/GBS and Restriction site associated DNA sequencing/RADSeq). In single SNP
genotyping with Taqman – Life Technologies use forward primer, reverse primer and allele specific
probes (VIC® labeled and 6-FAMTM labeled). Furthermore, in single SNP genotyping with
Kompetitive Allele Specific PCR (KASP) genotyping – LGC Genomics are based in competitive allelespecific PCR and enable bi-allelic scoring of SNPs and insertions and deletions (Indels) at specific loci.
The process in KASP genotyping is the SNP-specific KASP Assay mix and the universal KASP Master
mix are added to DNA samples, a thermal cycling reaction in then performed, followed by an end-point
fluorescent read. Moreover, in SNP genotyping by next generation sequencing with Genotyping by
Sequencing/GBS, DNA sample info entered to webform, if the project approved, DNA samples shipped
then GBS libraries made, followed by DNA sequencing data then compare with reference genome and
getting data file SNPs. SNP identification in crops with GBS is more effective due to low cost and
having simple methodology [6]. In SNP genotyping by next generation sequencing with Restriction site
associated DNA sequencing/RADSeq can identify and score thousands of SNPs, randomly distributed
across the target genome, from a group of individuals using Illumina technology (the use of restriction
enzymes to cut DNA into fragments) and molecular identifiers (MID) to associate sequence reads to
particular individuals. RADSeq can be used to detect restriction site presence-absence polymorphisms
(by identifying a SNP that is present in one set of individuals but absent in another, indicating a variation
in the restriction site).
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Recent emergence of NGS technologies such as 454 Life Sciences (Roche Applied Science,
Indianapolis, IN), HiSeq (Illumina, San Diego, CA), SOLiD and Ion Torrent (Life Technologies
Corporation, Carlsbad, CA) has eliminated the problems associated with low throughput and high cost
of SNP discovery. In addition, transcriptome resequencing using NGS technologies allows rapid and
inexpensive SNP discovery within genes and avoids highly repetitive regions of a genome.
Unlike the above-mentioned techniques, recently developed genome complexity reduction
technologies such as Complexity Reduction of Polymorphic Sequences (CRoPS) (Keygene N.V.,
Wageningen, The Netherlands) and Restriction Site Associated DNA (RAD) (Floragenics, Eugene, OR,
USA) are computationally well equipped and capable of filtering out duplicated SNPs. These systems
were successfully applied to discover SNPs in crops with and without reference genome sequences.
Although GBS has the potential to discover several million SNPs, one of the major drawbacks of this
technique is large numbers of missing data. To solve this problem, computational biologists developed
data imputation models such as BEAGLE v3.0.2 and IMPUTE v2, to bring imputed data as close as
possible to the real data.

Figure 1. The procedure of SNP Discovery by PCR

Table 1. Summary of commercially available SNP genotyping platforms [7]
Platform
Taqman

Assay type
PCR

Technology
Taqman probe

Throughput Multiplexing
~1536/day
~256

PCR

~1536/3
days
~96/3 h

KASPar

LGC

PCR

Capillary
electrophoresis
Microfluid-based
chips
FRET quenching
oligos

~48

BioMark HD

Provider
Applied
Biosystem
Applied
Biosystem
Fluidigm

~96/day

Not vailable

SNPlex

PCR

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~96

Axiom
Biobank
Infinium II
GoldenGate

Affymetrix

iPlex

Sequenome

Illumina
Illumina

Hybridization Oligo nucleotide
array
Hybridization Bead array
Primer
Bead array
extension
Primer
Mass
extension
spectrometry

~96/5 days

~650K

~128/5 days ~700K
~172/3 days ~1536
~3840/2.5
days

~40

Table 2. Representative GBS protocols published in peer-reviewed journals
Method

Restriction
Enzyme
Sbf1 or
EcoR1

Insert
size
Sizeselection

Barcodes Sequencing Sequencing Ref.
platform
mode
~96
Illumina
Paired[8]
end

MSG
(Multiplex shotgun
genotyping)

MseI

Sizeselection

~384

Illumina

Singleend

[9]

GBS
(Genotypeby
sequencing)

ApeKI

<350bp

~384

Illumina

Pairedend

[10]

Double-digested
RAD-seq

EcoR1 and
Msp1

Sizeselection

~48

Illumina

Pairedend

[11]

DoubledigestedGBS

Pst1 and
Msp1

<350bp

~384

Illumina

Pairedend

[12]

Ion TorrentGBS

Pst1 and
Msp1
EcoR1 and
Mse1
Pst1 and
Mse1
Taq1 and
Tru1

<350bp

~384

Ion Torrent

[13]

Sizeselection

~32

Illumina

Pairedend
Pairedend

Sizeselection

~305

Ion Torrent

Pairedend

[15]

Mse1 or
NlaIII

Sizeselection

~96

Illumina

Pairedend

[16]

RAD-seq (Restriction
association DNA
sequencing)

SBG
(Sequence-based
genotyping)
REST-seq (Restriction
fragment
sequencing)
Restrictionenzyme
sequence
comparativeanalysis

3. SNP Validation
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[14]

The discovered SNPs must be validated to identify the true SNPs and get an idea of the percentage of
potentially false SNPs resulting from an SNP discovery exercise. Validation can serve as an iterative
and informative process to modify and optimize the SNP filtering criteria to improve SNP calling. For
example, a subset of 144 SNPs from a total of 2,113,120 SNPs were validated using the Ilumina
GoldenGate assay on 160 accessions in apple.

4. SNP Genotyping
SNP genotyping is the downstream application of SNP discovery to identify genetic variations. SNP
applications including phylogenic analysis, marker-assisted selection, genetic mapping of quantitative
trait loci (QTL), bulked segregant analysis, genome selection, and genome-wide association studies
(GWAS).

Figure 2. Overview of SNP discovery in plants through genotyping by sequencing (GBS) system [6]
In addition, analytical methods to discover novel SNPs and detect known SNPs include: capillary
electrophoresis; mass spectrometry; single-strand conformation polymorphism (SSCP); single-base
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extension; electrochemical analysis; denaturating HPLC (High-performance liquid chromatography)
and gel electrophoresis; restriction fragment length polymorphism; and hybridization analysis.

5. SNP discovery in tropical crops
In potato (Solanum tuberosum), 575,340 SNPs were identified within three cultivars: ‘Atlantic’,
‘Premier Russet’ and ‘Snowden’ using Illumina sequencing. DNA was extracted from 248 potato lines
using the Qiagen Qiaxtractor DX system (Qiagen Inc., Valencia, CA). Samples were loaded at 50 ng/μl
on an Illumina BeadXpress Analyzer (Illumina inc., San Diego, CA) and data were analyzed using the
Illumina GenomeStudio software. Cluster positions for three marker classes (AA, AB, and BB) were
manually determined for each marker within the Illumina GenomeStudio software. The SNPs identified
in this study will enable more efficient marker-assisted breeding efforts in potato.

Figure 3. The workflow of SNP discovery in potato transcriptomes and design of the BeadXpressSNP
array [17]
The discovery of ~10,000 SNPs and nearly 1,000 indels in eggplant (Solanum melongena L.), equivalent
to a SNP frequency of 0.8 per Kb and an indel frequency of 0.07 per Kb using RAD tag sequencing and
the Illumina GoldenGate assay. This is the workflow for eggplant RAD tags sequencing and gene
annotation based on Barchi et al. (2011) [18] (Figure 4).

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Figure 4. The workflow for eggplant RAD tags sequencing and gene annotation based on Barchi et al.
(2011) [18]
A set of 100,000 to 250,000 SNPs were discovered in sorghum (Sorghum bicolor L.) by High
Throughput-Next generation sequencing (NGS) – RAD-Seq [19]. In common bean (Phaseolus vulgaris
L.), 827 non-genic SNPs were discovered using the GoldenGate technology of Illumina [20]. Moreover,
in chickpea (Cicer arietinum L.), 1022 SNPs were identified based on Illumina GoldenGate Genotyping
Technology [21]. About 533 SNPs were discovered in Camelina sativa by Illumina GoldenGate [22]. In
quinoa (Chenopodium quinoa Willd.), 14,178 SNPs were identified based on KASPar genotyping
chemistry and were detected using the Fluidigm dynamic array platform [23]. In rice (Oryza sativa),
more than four million SNPs from around 500 rice landraces were identified by Yu et al. (2014) [24]
using GBS. In Nipponbare genome 0.64 SNP was found per one kb, while in Dongjin genome
contains0.45 SNP/kb. Moreover, Huang et al. (2009) [25] detected 122,791 SNPs in indica cv.”9311”
and japonica cv. “Nipponbare” that was average 3.2 SNPs/kb.
6. SNP Identification for a trait
SNPs are involving in genetic and genome mapping, association studies, genetic diversity analysis, and
tagging important genes. In figure 5 is one of the example of association SNPs with a trait (plant height).
In this example, each SNP conducts a test of association with a trait (plant height). In here, SNP (A/G)
associated with plant height. In this significant SNP/trait association suggests SNP has direct biological
function (functional polymorphism) and SNP in Linkage Disequilibrium with functional
polymorphisms. Furthermore, association studies can determine whether a genetic variant is associated
with a trait (for example, disease). Based on Shi et al. (2011) [26], SNPs have been discovered and
verified in tomato and successfully used in selection of resistance to viruses, bacterial speck and bacterial
spot. Below is the example of four tomatoes that resistance and susceptible to root-knot nematode (figure
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6). SNP (G/C) associated with root-knot nematode. Mi is root-knot nematode resistance and mi is rootknot nematode susceptible.

Figure 5. SNP identification for plant height
Motella/LA2823
Mogeor/LA3471
464
LA3130

Mi
AAGTAGACGAGGTTAGTAAAAT
Mi
AAGTAGACGAGGTTAGTAAAAT NY07mi
AAGTAGACGACGTTAGTAAAAT
mi
AAGTAGACGACGTTAGTAAAAT

Figure 6. SNP identification for resistance and susceptible to root-knot nematode in tomatoes
[26]

7. Application of SNP markers in molecular breeding
SNP is becoming to be the most useful as molecular marker in genome mapping, comparative mapping,
framework mapping, varietal/line identification, map-based cloning, genome selection, association
studies, diversity analysis, genetic variation, population structure, evolution analysis, bulk segregant
analysis, tagging genes for economically important traits, accelerated cloning of gene/QTL of interest,
marker-assisted selection (MAS) and genome-wide association studies (GWAS) in crops because of their
abundance and automated high throughput genotyping. MAS has many advantages than conventional
phenotypic selection because MAS is simpler than phenotypic screening which can save time, resources
and effort; selection can be carried out at the seedling stage; and single plants can be accurately selected.
Moreover, SNP markers can be used in molecular breeding in population genetics, such as disease
association and pathogen detection.
In tomato (Solanuum lycopersicum L. syn Lycopersicon esculentum Mill.) breeding, SNP markers
were used in selection Tomato Mosaic Virus (ToMV) and Tomato spotted wilt virus (TSWV) resistance
genes [11]. In this research, SNP markers were used in association study, to create a genetic test that will
screen for a disease in which the disease-causing gene had already been identified. Then, collect leaves
samples from a group of plants affected by the disease and analyze their DNA for SNP patterns.
Furthermore, compare these patterns to patterns obtained by analyzing the DNA from group of plants
unaffected by the disease. This comparison can detect differences between the SNP patterns of the two
groups of plants. Therefore, SNP marker has become important and useful in the selection of disease
resistance genes. These markers will provide breeders with a tool in selection of Tm-2 and Tm-22
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resistance genes of ToMV in tomato breeding program. SNPs can differentiate resistance and susceptible
allele at Tm-2 locus. In addition, SNP markers also can differentiate Sw5-b and Sw5-a resistance genes
and sw5-b susceptible gene of TSWV.
Furthermore, in molecular breeding, SNP markers also can be used in quantitative trait locus
(QTL)/gene discovery. For example, in maize (Zea mays), SNP markers have facilitated the dissectionof
complex traits such as flowering time by using 5000 Recombinant Inbred Lines (RILs) andgenotyping
with 1,200 SNP markers. Next, the genetic architecture of flowering time was discovered and controlled
by small additive QTL rather than a single large-effect QTL [27]. In addition, based on Poland et al.
(2011) [28], by using 5000 RILs and 1.6 million SNP markers, 29 QTL were discovered and candidate
genes for northern leaf blight disease were identified. Moreover, a study from Pioneer Hi- Bred
International Inc. (a private breeding program) by using their proprietary SNP markers developedby them
self, reported identifying a high-oil QTL (qHO6) affecting maize seed oil and oleic acid contents. This
QTL encodes an acyl-CoA:diacylglycerol acyltransferase (DGAT1-2), which catalyzes the final step of
oil synthesis [30]. According to Kump et al. (2011) [30], the genetic structure of northern leaf blight,
southern leaf blight, and leaf architecture was studied using ∼1.6 million SNPs in maize. Additionally,
five SNP primers which are associated with northern leaf blight resistant gene (Ht1) wereidentified by
Junta et al. (2020) [31]. These SNP primers are MZSNP-0055106, MZSNP-0065744, MZSNP-0070164,
MZSNP0063922, and MZSNP-0073150 that located on chromosome 2.
In rice (Oryza sativa) breeding, a GWAS was performed using ∼3.6 million SNPs from ~50,000 rice
accessions identified genomic regions associated with 14 agronomic traits [32]. These traits including
morphological characteristics (tiller number and leaf angle), yield components (grain width, grain length,
grain weight and spikelet number), grain quality (gelatinization temperature and amylose content),
coloration (apiculus color, pericarp color and hull color) and physiological features (heading date, drought
tolerant and degree of seed shattering). Furthermore, according to McNally et al. (2009) [33], SNPs
revealed the breeding history and relationships among the 20 rice varieties; some SNPs are associated
with agronomic traits that used in rice improvement. These comprehensive SNP data providea foundation
for deep exploration of rice diversity and gene–trait relationships and their use for future rice
improvement. Based on Ayres (2000) [34], SNP markers were used to identify Waxy gene (control
amylose synthesis by coding starch synthase enzyme) and sd-1 (semi dwarfing gene). In addition, 1536
SNP markers used for identify genetic diversity on Malaysian rice varieties [35]. Meanwhile, only 932
SNPs that have high quality alleles and amplified across the rice varieties.
In wheat (Triticum aestivum), SNP markers were mapped between the known flanking markers for
Fhb1 (Fusarium head blight resistance gene). These new markers would be useful for MAS and fine
mapping towards cloning Fhb1 gene [36]. Moreover, SNP markers have also been used to study the
evolution of genes such as WAG-2 in wheat [37]. Identification of Pin b gene (Puroindolin b) for grain
hardiness [38] and Rht1 and Rht2 (semi dwarfing gene) also using SNP markers [39].
In soybean (Glycine max), SNP markers were used to increase the efficiency and cost effectiveness
through MAS and enhance the resolution within the target locus. The gene Rag1 (Soybean aphid
resistance gene) was mapped between two SNP markers that corresponded to a physical distance of
115kb and identified several candidate genes [40]. In another aphid resistance gene, Rag2, originally
mapped to a 10cM interval, was fine mapped to a 54kb interval using SNP markers that were developed
by resequencing of target intervals and sequence-tagged sites [41]. Ha et al. (2007) [42] identified SNP
markers tightly linked to a QTL conferring resistance to southern root-knot nematode by developing
these SNP markers from the bacterial artificial chromosome (BAC) ends and SSR-containing genomic
DNA clones. According to Shi et al. (2009) [43], SNP markers were useful in identification and selection
of Soybean Mosaic Virus (SMV) resistance genes. SMV is the most destructive viral disease in soybean.
Then, genetic resistance is the primary method of controlling this disease. These genes are Rsv1, Rsv3,
and Rsv4. Furthermore, SNP markers were developed for those genes. There are 2 SNP markers were
identified from the 3gG2 gene (the resistance allele at Rsv1 locus and the gene has been cloned and
sequenced) at Rsv1 locus, 4 SNPs near Rsv1, 5 near Rsv3, and 5 near Rsv4 were validated. SNP markers
also were used to identify Rhg1 and Rhg4 (Soybean Cyst Nematode resistance allele) [44].
In sugar beet (Beta vulgaris), SNP markers were developed to map QTL for Beet necrotic yellow
vein virus resistance genes, Rz4 [45] and Rz5 I [46]. Additionally, in cowpea (Vigna unguiculata), a
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consensus genetic map was developed based on EST-derived SNPs that a very important resource for
genomic and QTL mapping studies [47]. In Arabidopsis, SNP markers were utilized to clone the VTC2
gene based on the fine mapping and map based cloning approaches. Jander et al. (2002) [48] fine mapped
the gene interval from ~980kb region to a 20kb interval and additional nine candidate genes were
identified in that interval and subsequently the underlying mutation was discovered.
In oil palm (Elaeis guineensis), SNP markers were used for genetic diversity [49]. The genetic
evaluation of oil palm germplasm collections is important to insight into the variability among
populations. The information obtained is also useful for incorporating new genetic materials into current
breeding programs. Furthermore, genetic diversity information among populations is important to
identify selected palms with economically important traits which are being incorporated into the current
breeding programs; these include high oil yield, low height increment, large kernel, long stalk, low levels
of lipase, and high levels of carotene, vitamin E, iodine, and oleic acid. In this research, 219 oil palms
from two natural Angolan populations are used and a total of 62 SNP markers were designed from oil
palm genomic sequences and converted to cleaved amplified polymorphic sequence (CAPS). Based on
cluster analysis using unweighted pair group method with arithmetic, the 219 oil palms fell into two
clusters or genetic groups that do not coincide with the geographic populations. Cluster I consisted of
eight palms and cluster II included 211 palms. Furthermore, molecular variance analysis revealed that
only 7% of the total genetic variation was explained by the variation between populations and 93% of
the total genetic variation was attributed to variation within populations. Moreover, levels of genetic
variation in plants are directly associated with breeding system. This information can be applied in
selecting a sampling strategy for genetic conservation. In addition, SNP markers also use for identifying
fumarate hydratase 1 (FUM1) gene located on chromosome 4, which associated with nitrogen uptake in
oil palm [50].
Genetic diversity study in cassava (Manihot esculenta Crantz) was done by using 5600 informative
SNP markers obtained from GBS analysis [51]. The result showed three main clusters from 96 cassava
genotypes, at similarity of 0.41. This genetic diversity suggests that cassava genotypes might contains
alleles with high additive genetic variance that very important for the genetic improvement,
conservation, and provide database for parental selection in order to develop hybrid vigor cassava.
Furthermore, genetic diversity study of 70 taro (Colocasia esculenta) accessions of Hawaiian, South
Pacific, Pulauan, and mainland Asian origins was done by using 2400 SNP markers [52]. The disease
resistant gene in taro was revealed by using this genetic diversity study.
In pea (Pisum sativum), SNP markers were applied in fine mapping within chosen QTL confidence
intervals and marker assisted breeding for important traits in pea improvement [53]. Moreover, in
eggplant (Solanum melongena), the genetic diversity revealed by SNP markers [17]. In this research,
384 of the 2,201 highest quality SNPs (score > 0.6) were applied to genotype 23 eggplant germplasms
that have variation in fruit shape and skin color. About 94 SNP markers in common bean (Phaseolus
vulgaris L.), were used in genetic diversity. These markers were evaluated from 70 cultivated and wild
accessions according to their gene pool, race and country of origin [20]. In Chickpea (Cicer arietinum),
1022 SNPs were used to make high-resolution genetic linkage map [21]. Furthermore, in Camelina
sativa, 533 SNPs were applied to mapped potential candidate genes and to assess genetic variation
among a collection of 175 accessions. The SNPs will provide useful tools for future crop improvement
of C. sativa as an industrial oilseed [22].
SNP markers were used on GWAS and candidate gene association (CGA) studies. A GWAS
approach was applied to understand the genetic architecture of complex traits, for example complex
diseases of northern and southern corn leaf blights [30]. As GWAS requires large number of molecular
markers, the utility of GWAS in dissection of molecular basis of traits in polyploid crops such as wheat,
and cotton has been fairly limited due to the insufficient number of polymorphic markers and the absence
of reference genome.
8. Conclusion
Genomic information of plants obtained from genetic diversity, association studies, evolution analysis,
quantitative trait loci (QTL), marker-assisted selection (MAS) and genome-wide association studies
0200203-10

(GWAS) are very useful for increasing food sustainability. To achieve food sustainability in this
changing climate condition, plants with superior characteristics must be developed by using genomic
information. Genes associated with important traits can be identified efficiently with SNP markers.
Genomic information of many tropical crops have been identified in order to improve their
characteristics.
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