AJCS 10(5):637-645 (2016)
DOI: 10.21475/ajcs.2016.10.05.p7331

ISSN:1835-2707

Genetic diversity and association analysis among Egyptian barley (Hordeum vulgare L.)
genotypes with different adaptations to saline conditions analyzed by SSR markers
Ammar Elakhdar1, 2, 3, Mohamed Abd El-sattar2, Khairy Amer3 and Toshihiro Kumamaru1*
1

Laboratory of plant Genetic Resources, Institute of Genetic Resources, Faculty of Agriculture, Kyushu
University, Hakozaki 6-10-1, Fukuoka 812-8581, Japan
2
Crops Science Department, Faculty of Agriculture, Alexandria University, Elshatby 21545, Aflaton St, Egypt
3
Barley Research Department, Field Crops Research Institute, Agricultural Research Centre, 9 Gama St., Giza,
Egypt*Corresponding author: kumamaru@agr.kyushu-u.ac.jp
Abstract
The success of salt tolerance breeding programs employing traditional screening and selection has been limited in the past decades.
This study was designed to characterize the genetic diversity within a subset of barley germplasm using microsatellite markers under
different adaptations to salt conditions. Twenty-five microsatellites (SSR) representative of the barley genome, were used in 30
barley accessions and cultivars. A total of 59 alleles were generated, the mean number of alleles per locus was 2.4 and the PIC was
0.22. The four genomic Locus Bmac0030, Bmag0125, EBmac0701 and EBmac0871 were sufficient to differentiate the diversity of
all genotypes since they generated a high number of allele with high PIC values. Cluster analysis UPGMA based on SSRs dataclearly the most differentiate genotypes according to their Salinity tolerance and the PCA recorded 58.8 % of the total variance. Fivemarkers pairs were found to be associated with salinity and showed linkage disequilibrium with r 2 values higher than 0.05.
Association analysis along with specific significant alleles of grain yield showed a significant association of the marker Bmag749(2H, 176 bp) with salinity. Our results shown a small sample and limited markers were used in this study and the results need to be
confirmed using linkage mapping with a large association population. However, the results are credible because many of the loci thatwere identified were associated with traits that were common with previous reports of linkage or association mapping. These will be
useful for molecular marker assist selection and molecular design breeding.
Keywords: Barley, salinity tolerance, DNA markers, Linkage Disequilibrium, Microsatellites, principal Components analysis.
Abbreviations: EC _ Electric conductivity; SSR_ simple sequence repeat; PCA _ Principal Components Analysis.
Introduction
Barley (Hordeum vulgare L.) has a long history as adomesticated crop, as one of the first crops adopted for
cultivation. Migration of people together with their crop
seeds led to a major diversification and adaptation to newareas, and the crop is now virtually found worldwide.
Conscious selection of desired genotypes by farmers at any
early stage, together with natural selection, increased thediversity and created the rich gene pool source of variation
found today in local varieties. The development of newtechnology and methods increased the genetic diversity evenfurther and turned barley into the universal, highly diverse
crop Harlan, (1976).
Salinity is one of the major obstacles to increasing crop
productivity. Some of the most severe problems in soil
salinity occur in arid and semiarid regions of the world.
Besides these regions, salinity also affects agriculture incoastal regions and areas affected by low-quality irrigationwater. Salt tolerance of barley plants is affected by soil
conditions. In dry saline soils, the primary limiting factors forplant growth and development are high pH and sodicity(Barrett-Lennard, 2003; Colmer et al., 2005).
Germination and seedling growth under saline environment
are the screening criteria which are widely used to select thesalt tolerance genotype (Adjel et al., 2013; El Goumi et al.,2014; Yousofinia et al., 2012). Salt tolerance of most species

including barley varies with plant growth stage. Barley ismost sensitive to salinity at germination and young seedlingstage, and exhibits increased tolerance with age. Salt stressfor barley at seedling stage has been mainly attributed toionic effects rather than to osmotic effects (Storey and Wyn,
1978).
Genetic diversity between parental genotypes is usuallyestimated by measurements of physiological andmorphological differences of quantitative and economicallyimportant traits. The disadvantages of this conventional
approach are the cost of time and labour during themeasurements, and the influences of environmental factors.
Often, these disadvantages are exacerbated in salt-tolerance
breeding. For example any change in the environment such astemperature, light or humidity can dramatically change thetranspiration driving forces and, subsequently, ion uptake
Jaiwal, (1997), Such changes may alter salt tolerance among
genotypes. It is important to note that morphologicalcharacters are often limited in their numbers and may notadequately represent actual genetic relationships among
genotypes.
Conversely, identified genetic variations based on DNA
polymorphism are abundant and independent of theenvironmental factors Garland et al. (1999). Microsatellite
markers are very useful for plant breeding and genetic637

diversity studies for several reasons. They require only small
amounts of sample DNA, easy to amplify by polymerase
chain reaction (PCR), amenable to high-throughput analysis,largely co-dominantly inherited, multi-allelic, highly
informative and abundant in plant genomes Powell et al.
(1994).
In barley, more than 775 microsatellites have been
published Varshney et al. (2007), and genetic maps based on
microsatellites for all seven barley chromosomes are publicly
available (Saghai Maroof et al., 1994; Ramsay et al., 2000).
Novel association mapping or linkage disequilibrium
approaches have recently been introduced in plant genetic
studies (Mackay and Powell 2007; Cockram et al., 2010).
Association mapping studies in a much broader germplasm
are now possible due to fast and affordable genotyping andsequencing technologies (Zhu et al. 2008). Association
mapping relies on linkage disequilibrium between markers
and QTLs present in collections of diverse germplasm
Pritchard et al. (2000). It exploits the recombination events
that have occurred during the long evolutionary history(Nordborg and Tavare 2002) of a crop species, producing
shorter linkage blocks than found in bi-parental QTL
mapping studies.
The objective of this study was to investigate the genetic
diversity of 30 barley accessions. These lines, werecomposed of lines tested for salinity tolerance, elite lines andvarieties with good agronomic performance. Additionally,
association trait analysis was conducted for grain yield,
number of grain per spike, 1000-grain weight, Leaf area andchlorophyll content under salinity condition.

seasonal conditions for genotypes coded 13 and 20 whereas;
its contribution was indefinite for the rest of cultivars.
Plant culture and salinity treatments
The mean squares of germination percent (G %) and seedlingcharacters for twelve selected genotypes (representing
tolerant and sensitive) showed in (Table 2). Characters of
seedlings were recorded to trace the effect of salinity.
Genotypes expressed significant differences in germination
percentage, Shoot length (SL), shoot dry weight (SDW) androot dry weight (RDW). Also, significant salinities ×
genotype interaction had detected in all measured characters,
except for, root fresh weight (RFW).
The salinity levels and genotypes mean are presented in(Supplementary Table 3) Significant substantial reduction in(G %) was obtained with salinity level increase. The rate of G
% reduction over genotypes reached 16.24%, 27.43% and43.1% for levels 12, 16, and 20 dSm-1, respectively. Seedlingcharacters expressed by SL, RL, SFW, RFW, SDW and
RDW, exhibit significant substantial reduction with salinity
level increase from 12 dSm-1 until 20 dSm-1. Attained
growth reduction was ≥ 80% at 20 dSm-1for shoot and root
dry weights. While, RFW had severely affected by salinity
levels increase relative to SFW (reductions of 26, 57, 40, 70
and 45, 80% for shoot and root fresh weights at salinity levels
12, 16 and 20 dSm-1, respectively).
The interaction effects of (salinity levels × genotypes) for
twelve barley genotypes were presented in (Supplemental
Table 4). Genotypes coded 4, 5, 15, 27 and 30 enjoyed over
90% (G %) under the level 12 and 16 dSm-1 except for,genotype coded 27 and 30 under 16 dSm-1. Also, over 70%(G %) had recorded by the aforementioned genotypes under
the high salinity level 20 dSm-1 except for genotype coded
30 was 52.43%. Genotype coded 13, showed sensitivity tosalinity levels, where, recorded (G %) of 76.14, 52.43 and29.57 under the three studied levels respectively, in spite ofgenotype coded 8 exhibited a good performance undercontrol specially (RL), but quickly degradation in mostcharacters under the three salinity levels (Supplemental Fig.
1).

Results
Analysis of variance (ANOVA) and mean performance
Analysis of variance for five traits of thirty barley genotypes(Supplementary Table 1) in each study season, revealed
accepted homogeneity of errors Bartlett, (1937). The
pertinent mean squares for those characters across seasons
were illustrated in (Table 1). The tested barley genotypes
significantly differed in all five tested traits. Also, genotypes
had characters of different magnitude or ranked in each study
season (significance season × genotype interaction). The only
measured character that had not significantly varied withseasons was the No. grains/ spike.
The combined mean performance for all genotypes during
two seasons was presented in (Supplementary Table 2). The
overall GY (t/ha) of the second season for all genotypes was
about 0.77 the recorded values (4.93 vs. 3.81 t/ha for the first
and the second seasons, respectively. data not shown) withmean values 4.41 t/ha over two season. The superiority of
GY under salt-affected soil for each studied genotype had
expressed differently. Stable superior productivity across thetwo seasons of the study had expressed by the genotypes
coded 4, 11, 12, 14, 16, 18, 20, 22 and 23. Whereas, thegenotypes coded 3, 6, 8, 13, 15, 19, 25, 26, 27, 28, 29 and 30
had showed elastic response to growing season conditions,since yield superiority was only confined to the first season.
The combined yield average un-discussable since thegenotype × season interaction was significant.
No. grain over all the two study seasons, explained thestability of yield superiority for genotypes coded 17, 18, 22,
23, 25, 26, 28 and 30, whereas, it's influence to GY
superiority for the rest of genotypes was unclear, 1000-GW(g) supported the elasticity of genotype response to variable

Microsatellite markers analysis
Most of the primer pairs used in this study generated
polymorphic bands among the genotypes. A total of 59
alleles were detected among the 30 barley genotypes with anaverage of 2.4 alleles per locus (Table 3). The number ofalleles per locus ranged from 1 to 6 in Bmac0030 on
chromosome 4H. The average genetic diversity andpolymorphism information content (PIC) values observed
were 0.24 and 0.22, respectively. The PIC values for the
microsatellite loci ranged from 0.033 for (Bmac0096, 5H and
EBmac0603, 7H) to 0.67 for (Bmac0030, 4H). The mean
heterozygosity for the 25 SSR primer pairs was about 0.059.
The components of genetic diversity of each marker obtainedwith an average data availability of 91 % are shown in (Table3). The major allele frequency had an average of 0.823 with arange extended from 0.40 to 1.0 (data not shown).
Cluster analysis of DNA polymorphism
The genetic relationships among 30 barley genotypes basedon SSR data are presented in a UPGMA dendrogram (Fig 1).
All genotypes clearly grouped into four major branches(clusters). The genetic similarity was ranged from 0.22 %638

Table 1. The analysis of variance for five traits of thirty barley genotypes combined over the study seasons.
Mean squares
S.O.V
DF
GY
No. Grain
1000-G.W
LA
Season (S)1
55.78**
56.67n.s2228.1**30950**
Rep × season
4
2.331
12.11
75.16
360.10
Genotypes (G)29
1.971**
268.68**65.42**
673.06**
S×G
29
1.629**
23.98n.s
22.50*
10.95**
Combined error
116
0.328
31.37
14.04
73.6
CV%
13.10
10.14
8.23
17.20

CHL
814.9**
18.20
40.85**
37.07**
16.94
10.80

GY; grain yield (ton/h), No. Grain; Number of grains per spike, 1000-G.W; 1000 grain weight, LA; flag leaf area and CHL; total chlorophyll content.
*, **; indicate significance at 0.05 and 0.01 levels, respectively, n.s; not significantly different Inter.; interaction.

Fig 1. UPGMA tree describing genetic relationships among the thirteen barley accessions based on SSR markers. Genetic distanceswere calculated based on Nei/Li genetic distances. Consistency of the tree topology was evaluated by boot- strapping over loci
(10,000 reps).
and reached to 0.85 % between lines 14 and 15 in C1 cluster
according to Jaccard similarity index, indicating magnitude
of genetic diversity among the elite genotypes. Most oftolerant and moderate genotypes were located in C1 and C4
clusters. On the other hand, the sensitive genotypes 23, 26
and 27 were located in the C3 cluster with genetic similarity
0.82 % between line 23 and line 26. While, the remaining
sensitive genotype; Lines 2, 8, 18 were distributed in C2
cluster. Most results of UPGMA cluster analysis was inagreement with field evaluation, which indicated that thesegenotypes were closely related to each other and in general,
this is reflected from their response to salt stress performance
during the field evolution. The principal coordinate analysis(PCA) for all genotypes was performed based on genetic
distance using 25 SSR markers. The PCA 1 recorded 51.53 %of the total variance, meanwhile PCA 2 showed 7.27 % (Fig2), which was a good informative and agree with the
UPGMA cluster results (Fig 1.)

Table 2. The mean square of germination percentage and seedling characters for twelve barley genotypes under four salinity levels.
Mean squares
S.O.V
DF
G%
SL
RL
SFW
RFW
SDW
RDW
Treatments (salt)
358.40**
426.8**
95.6**
0.073*
0.140**
0.012**
0.005**
Rep × Salt error a
8
0.30
0.46
0.64
0.015
0.018
0.00010.00001
Genotypes (G)11
6.09**
14.2**
5.60n.s.
0.015n.s.
0.006 n.s.
0.002**
0.001**
S×G
33
1.63**
5.1**
2.01**
0.023*
0.009 n.s.
0.001**
0.001**
Error b
88
0.40
0.29
0.34
0.013
0.011
0.00001
0.00001
C.V. %
11.38
5.39
10.71
2.09
12.73
2.27
3.87
G %; germination %, SL; Shoot length, RL; root length, SFW; Shoot fresh weight, RFW; root fresh weight, SDW; shoot dry weight and RDW; root dry weight.
*and **; indicate significance at 0.05 and 0.01levels, respectively and n.s; not significantly different.

639

linkage disequilibrium LD were assessed for the 25 SSR loci.
Significant LD between markers existing in the same linkage
group and intra-chromosomal loci (P value < 0.05) areindicated as well as their r2 values (Supplementary Table 5).

Association analysis
Based on the GLM model, a total of 16 markers- trait
associations were identified for the five yield-related traits, (Table 4). Among the 16 significant associations, six werecorrelated with GY, six with No. grain, three with 1000-GW,
one with LA and five with CHL. The six SSR-trait
associations related to the GY; two SSR loci; Bmag0120 and
AF022725A on chromosome 7H with common significant
specific alleles 248 bp and 124 bp, respectively. The six SSRtrait associations related to No. grain involved two SSR loci;
Bmag0011 and Bmag0120 on chromosome 7H. The three
SSR-trait associations related to 1000-GW involved 2 SSRloci; EBmac0602 (198, 210 bp) and Bmac0316 (316 bp) on
chromosome 7H. The five SSR-trait associations related to
CHL involved three SSR markers; Bmac0030, HVMLOH1A
and EBmac0701on chromosome 4H. Moreover, Five SSRtrait associations; Bmag0120, Bmac0316, Bmac0030,
EBmac0501 and EBmac0701 were detected in the most traits
over two years.

Discussion
In the present study, No. grain of barley genotypes showedelastic response to changing seasonal conditions where,
insignificant season × genotype interaction had obtained(Table 1). Commonly, the anticipation of the studied yieldcomponent characters, i. e; No. grain and 1000-GW, to GY
superiority stability or plasticity was not independent of theunstudied characters(Amer et al., 2012b; Abd El-Aty et al.,2011), especially the number of spike- bearing tillers. Our
results also suggest that, estimates of grain yield might bringanother complexity to the salinity response, not just because
the crops must be grown in controlled environments for long
periods of time, but also due to complexity of the conversionof shoot biomass into the grain (Sadeghi, 2011). The fact thatgrain yield may not decrease until a given (‘threshold’)salinity is reached (Maas and Hoffman, 1977). Regarding thevegetative characters, i.e.; LA and CHL, barley genotypes
expressed a responsive value to seasonal changes. In themeantime, barley genotypes that showed larger LA or high
CHL were not essentially those of high grain yield,
Superiority in grain yield might depend on other plantcharacters related to root functionality (Amer et al., 2012a;
Eleuch et al., 2008).

Linkage disequilibrium (LD)
Differences in effective population size and breeding history
are expected to generate different patterns of LD, which
would affect the design of association studies using thismaterial. In Fig 3, we show the decay of average LD (r2) withrecombination distance across the genome. The squared
allele-frequency correlations r2 and the P value representing

Fig 2. Principal components analysis of 30 elite accessions based on the analysis of 25 SSRs. Genetic similarities among accessions
have been calculated as the proportion of loci with shared alleles. The accessions, have been chosen to represent the genetic diversity
present in Egypt; local and exotic materials. Where, L numbers; genotypes name, C number; clusters numbers, S; salt sensitive
genotypes.

640

The reduction in shoot length is due to excessive
accumulation of salts in the cell wall elasticity. Further,secondary cell appears sooner and wall becomes rigid as aconsequence the turgor pressure efficiency in cellenlargement decreases (Taghipour and Mohammad, 2008).
Germination is complicate phenomenon comprisingphysiological and biochemical variation due to embryo
activation. Salinity as a non-live stress makes many hardships
for seed in germination period. Salinity decrease water
availability for the seed by taking down osmotic potential andin second stage cause to toxicity and change in enzyme
activity Massai et al. (2004). The aforementioned seedling
results were true for shoot and root length where, the attainedlevels of growth reduction corresponding to salinity of 12, 16
and 20 dSm-1 were; 26; 38, 41; 48 and 56; 55 % for SL and
RL, respectively (Supplementary Table 3). Reduction in
growth with increased salinity levels might be due to limited
supply of metabolites to young growing tissue as a result ofeither metabolic disturbance or low water uptake along with
toxic effects of sodium Adjel et al. (2013). High external
concentrations of Cl‒ had similar adverse effects as highconcentrations of Na+, suggesting that Cl‒ toxicity may
reduce growth Tavakkoli et al. (2010). Fundamental
differences in salinity responses appeared between soil andsolution culture, and the importance of the different
mechanisms of damage varies according to the severity andduration of the salt stress Ehsan et al. (2010). Saline soils
have many dissolved salts including cation and anion. The
most prevailing cation and anion are Na+, Mg2+, K+, Cl‒and
So24. Plant growth and development in saline soil is scanty
due to high osmotic pressure that finally cause to reduction inwater availability. Also direct toxicity resulted from abundantpresence of this ions have a preventive effect on plant and atlast this interaction put some limitation for plant growth
Homaee et al. (2002). Shoot length was indicative togermination salinity tolerance for genotypes coded 4, 5 and
30. Meanwhile, RL was independent from germination
salinity tolerance, since was not associated with the obtained
G% that was true for shoot fresh weight, root fresh weightand root dry weight (Supplementary Table 4), (El Goumi etal., 2014;Yousofinia et al., 2012; Mahmood 2011). Salt stressdetermines a diversion of root metabolism towards thesynthesis of osmolytes, such as glycine betaine and proline,
and increased levels of reduced glutathione Cardi et al.
(2015). The increase of salt concentration had a negative
effect on germination for which the rate decreased; this result
has also been reported by El Madidi et al. (2004).
In the present study, SSR markers were able to discriminate
between the 30 barley genotypes studied. Primers (Bmac0030
and EBmac0701 on chromosome 4H, got a high value of PIC
0.673 and 0.558, respectively. Furthermore, primer
Bmag0125 on chromosome 2H with PIC value 0.567 (Table3), these markers were sufficient to differentiate all of the
genotypes since they generated a high number of allele withhigh PIC values. The results indicated that this region ofchromosome 4H is important for salt tolerance in barley it
may be assumed that there is a QTL cluster for salt tolerance
in the region of chromosome 4H, and thus the region may be
used as an important target for improving salt tolerance ofbarley Xue et al. (2009). The PIC value (0.38) obtained by
Pillen et al. (2000), who used 22 microsatellite markers and aset of 28 mainly German barley cultivars and two wild forms.
The observed alteration in the DNA fragments of barleycultivars exposed to salinity stress may be attributed to theactivation of the defence responsive genes, whose transcripts
and expression are controlled under salinity stress. Markers
validation in independent genotypes of different genetic

background is essential in determining the effectiveness andreliability of the markers to predict phenotypic similarity(Collins et al., 2003; Cakir et al., 2003), which indicated that,
SSR marker could be used in routine screening for markerassisted selection (MAS). Markers should also be validated
by testing for the presence of the markers on arrange ofcultivars and other important genotypes. Therefore, markerassisted selection for salinity tolerance could be genotype
tolerant specific. Interestingly enough, our findings indicatedthe potential efficacy of highly informative SSR markers for
efficient screening of brewing barley genotypes.
All the 30 genotypes were clustered to four groups with thesame genotypes. Genetic distance (Fig. 1) was ranged from
42% to 85% (data not shown), indicating magnitude ofgenetic diversity among the elite genotypes. Obviously, theC1 cluster which represented showed higher genetic diversity
85 % followed by C3 cluster 82 % (data not shown). It is
obvious that the adaptation of barley to saline soils isdifferent among the genotypes with diverse genetic
backgrounds. In addition, the physiological and molecularmarkers would be useful in screening different cultivars fortheir tolerance against salt stress during breeding programs ofbarley Abdel-Hamid, (2014). In barley, important traits suchas salt tolerance are controlled by polygene with additive anddominant effects that were described by quantitative trait loci
(QTLs) Ellis et al. (2000). Genetic relationships between
barley genotypes revealed by genetic similarity at SSR levels
were in agreement with their roles in agricultural production
and breeding Karakousis et al. (2003) argued the usefulnessof polymorphic SSR markers for the discrimination of barley.
The PCA was performed to compare the distribution of the
genotypes based on genetic distance. Four possible groups ofgenotypes were found and agree with UPGMA cluster
analysis (Fig. 1 and Fig. 2). Eleuch et al. (2008), showed that
Cluster and principal coordinate analysis allowed a clear
grouping between 48 barley genotypes were analyzed with 22(SSR) markers. Principal Components Analyses is also used
to remove correlation among independent variables that are
to be used in multivariate regression analysis.
LD and association analysis
The molecular marker data set in combination withphenotype evaluation was used to examine linkage-related
marker-trait associations (LD). The markers pairs (Bmag770
and EBmac0501, in chromosome 1H); (EBmac0701 and
Bmac0030 4H); (Bmac0316 and Bmag0009 6H) and(AF022725A and Bmag0120, 7H in addition to, EBmac0603
and AF022725A, 7H), were found to be associated withsalinity and showed linkage disequilibrium with r2 values
higher than 0.05, Zhang et al. (2009) (Supplementary Table
5). Based on the GLM model, the marker-trait associations
were identified for the five yield-related traits during two
season and were subjected to elevated salinity in the soil
(Table 4). Association analysis along with specific significant
alleles of grain yield (GY) showed a significant association of
the marker Bmag749 (2H,176bp) with salinity, Eleuch et al.
(2008) reported association analysis for grain yield undersalinity conditions revealed a close association of the marker
Bmag749 (2H, bin 13) in two different environments with
common significant alleles (175, 177bp). Markers that havebeen reported to be associated with salinity like HVCMA and
HvLTPPB (Rostokset al. 2005) and EBmac0415,
EBmac0871 and Bmac0096 (Bahieldin et al., 2005;
Heshamet al., 2005) did not show any association withsalinity tolerance in this study. Salt tolerance is a complex
inherited trait and it is likely that several QTLs but also

641

Table 3. Summary of the diversity analysis components revealed by simple sequence repeat (SSR) markers.
Size
Availability
Gene
Heterozygosity
No SSR Locus
Motif
Ch. L
Allele No.
abbp
Diversity
1
EBmac0501 (AC) 13
1H
151
3
0.93
0.43880.00
2
Bmac0154(AT)19 (AC)6
1H
130
3
1.00
0.1561
0.10
3
Bmag770(GT)13 (AG)19
1H
158
4
1.00
0.1861
0.20
4
Bmag382(AG)7 AA(AG)7
1H
109
1
0.97
0.00000.00
5
Bmac0213(AC)23
1H
168
1
0.97
0.00000.00
6
Bmag0125(AG)19
2H
134
3
1.00
0.64000.00
7EBmac0415 (AC)17
2H
247
1
0.97
0.00000.00
8
Bmag749(AG)11
2H
166
1
0.93
0.00000.00
9EBmac0871 (TG)13
3H
180
3
0.87
0.57690.00
10
HvLTPPB(AC)10(AT)5
3H
221
1
0.87
0.00000.00
11
Bmac0209(AC)13
3H
176
2
0.97
0.36620.00
12
Bmac0030(AC)22
4H
155
6
1.00
0.71890.90
13EBmac0701 (AC) 23
4H
149
3
0.77
0.6314
0.00
14
HVMLOH1A (GA)6
4H
175
1
0.87
0.00000.00
15
Bmac0096(AT)6 (AC)16
5H
173
2
0.97
0.03390.03
16
Bmac0113(AT) 7 (AC)18
5H
187
1
0.90
0.0000
0.00
17
HvLOX
(AG) 95H/6H 150
30.43
0.14500.16
18
EBmac0602 (AC)9AT(AC)7(AG)9
6H
205
3
0.97
0.53750.00
19
Bmag0009(AG)13
6H
172
2
0.97
0.18550.00
20
Bmac0316(AC) 19
6H
135
3
0.97
0.49050.03
21
Bmag0120(AG)15
7H
230
4
1.00
0.4883
0.03
22
HVCMA
(AT) 9
7H
141
2
0.Z3
0.07680.00
23AF022725A (TG) 8
7H
136
2
0.90
0.13720.00
24
Bmag0011(AG) 25
7H
147
2
0.97
0.32820.00
25
EBmac0603 (CA) 10
7H
149
2
0.97
0.0339
0.03
Mean
2.4
0.92
0.24680.0596
Total
59- Ch. L; Chromosomal Location, a; Percentage of available data, b; Percentage of heterozygote genotypes; PIC: Polymorphism information content.

PIC
0.38
0.15
0.18
0.00
0.00
0.57
0.00
0.00
0.50
0.00
0.30
0.70
0.56
0.00
0.03
0.00
0.14
0.50
0.17
0.44
0.43
0.07
0.13
0.27
0.03
0.22

Annealing
*
A
A
B
A
A
B
B
A
B
B
A
A
B
B
A
A
A
A
A
B
A
A
A
A
A

- *A Annealing temp., 58°C. - * B Annealing temp., 55.

Fig 3. Linkage disequilibrium matrix. Pair-wise LD values of polymorphic sites displaying r2 above the diagonal and thecorresponding p-values from rapid1000 shuffle permutation test below the diagonal. Each cell represents the comparison of two pairsof marker sites with the colour codes for the presence of significant LD.
several different mechanisms are involved Mazzucotelli et al.
(2008). Although a large number of SSR markers areavailable in barley, they have been developed and mapped indifferent mapping populations. Ideally, all markers should bemapped in the same mapping population Varshney et al.
(2007). However, the limited polymorphism in current study

has not allowed all possible SSR markers to be mapped onto
a single genetic map. An alternative way to prepare a dense
SSR genetic map is to combine the different and available
genetic maps by exploiting common bridging markers.
Consensus maps including various types of molecularmarkers have been developed before in several species, e.g.

642

Table 4. SSR loci significantly associated with five traits and the significance [–Log(P-value)] based on the GLM model.
Trait
Loci
Chr.
Position (cM)
Significant alleles (bp)GY
Bmac0030
4H
58.60
110
Bmag0120
7H
97.00
248
Bmag7492H
117.86176
Bmag382
1H
81.40
105
AF022725A
7H
45.80
124
HvLOX
5H
122.34
145
No. Grain
Bmag0011
7H
81.78
188
EBmac0501
1H
64.84
148
Bmac0316
6H
7.16
180
EBmac0701
4H
96.17
154
Bmag0125
2H
89.83
122
Bmag0120
7H
97.00
248
1000-GW
EBmac0602
6H
75.42
198 - 210
Bmac0316
6H
7.16
156
Bmac0213
1H
30.81
178
LA
Bmac0154
1H
87.83
111 - 305
CHL
Bmac0030
4H
58.60
93
EBmac0501
1H
64.84
130
HVMLOH1A
4H
102.27
180
Bmag770
1H
54.60
233
EBmac0701
4H
96.17
133
Table 5. The chemical and physical characteristics of soil samples during the two seasons.
Soil samples properties
Soluble cationsmeq/LseasonpH
ECe (dsm-1) CaCO3 %
ESP %
SAR %
Ca++
Mg++
Na++
K+
1st
7.83
10.90
3.95
52.9
8.9
20.70
40.30
47.05
0.95
2nd
8.41
9.5
2.32
51.7
8.4
51
30.6
53.5
0.4
Soluble anions meq/L
Physical properties
Silt
seasonSO4ClHCO3
CO3
Sand %
Clay %
Texture class
%
1st
38.87
67.00
3.13
23.80
24.90 51.30
Clayey
2nd
72.5
60
3
16.7
33.27 50.03
Clayeybarley (Langridge et al., 1995; Qi et al., 1996; Karakousisetal., 2003).

Water samples propertiespH
ECe (dsm-1)
5.3
2.3
5.5
2.1

A field trial was conducted at Sakha Agricultural Researchstation, Egypt on thirty Egyptian elite hulls barley genotypes
during the two successive winter seasons 2012/2013 and2013/2014 (Supplementary Table 1). The experimental
design was a randomized complete block design (RCBD)
with three replications. Genotypes have divided to two "sets"
each of 15 genotypes. Plot size was four rows, 2 m long and
0.2 m apart. Data had collected random from the central rows
in each plot. The recorded characters included; total
chlorophyll content (CHL), flag leaf area (LA), number ofgrains per spike (No. Grain), 1000- grain weight (1000-GW)
and grain yield (GY) (ton/ha-1).

Agriculture, Kyushu University, Japan. Based on the field
evaluation, the best performing genotypes under saline soil
condition (ten genotypes, i.e.; coded 12, 29, 13, 15, 4, 5, 7,
30, 14 and 20) along with the most sensitive genotypes (twogenotypes, i.e.; coded 8 and 27) were selected. The selected
genotypes were germinated under controlled conditions, at
20-25 0C, relative humidity of 55-60% and 16 hours lightperiod under four concentrations of sodium chloride (NaCl)
and Calcium chloride (CaCl2.2H2O) (W/W). The fourcombinations were; Lo= distilled water (control), L1= 7680
ppm (Ec =12 dSm-1 and pH=5.40), L2= 10240 ppm (Ec= 16
dSm-1 and pH=5.55), and L3=12800 ppm (Ec20 dSm-1 and
pH=5.58). Each plot was represented by five seeds sown to aplastic pot (2 cm diameter) filled with artificial soil. A
factorial arrangement of twelve genotypes by four irrigationwater salinity levels (total of 48 treatments) were carried out
in a completely randomized design (CRD) with three
replicates. Data of shoot and root length, fresh and dry weight
were recorded for the five seedling in each plot.

Soil characteristics

Statistical analysis

At harvesting, soil samples were taken from the upper soil
layer (0-30 cm) to conduct the chemical and physical analysis(Table 5) according to Black et al. (1965).

Data were statistically analyzed following the analysis ofvariance procedure (ANOVA) by using statisticalprogrammed MSTAT-C Russel, (1996). Least Significant
Difference test was used to compare means at 0.05 and 0.01
levels.

Materials and Methods
Plant genetic materials and phenotyping

Growth chamber conditions
The trials were conducted in a growth chamber and molecularanalysis at the Institute of Genetic Resources, Faculty of643

large association population. However, the results are
credible because many of the loci that were identified wereassociated with traits that were common with previous
reports of linkage or association mapping. These will be
useful for molecular marker assist selection and molecular
design breeding.

Polymorphism of microsatellite markers
Leaf tissues (100 -150 mg) were ground to a fine powder byusing a grinding mill "MULF BEADS SHOCKER® and usedfor DNA extraction. The sequences of microsatellite primer
pairs were downloaded from Grain Genes data base
http://wheat.pw.usda .gov/cgi-bin/graingenes/report.cgi. The
markers were selected based on; responsive to abiotic stress(Varshney et al., 2007; Rostoks et al., 2005; Ramsay et al.,2000) and their uniform distribution in the barley genome.

Acknowledgments
This work was supported by cultural Affairs& Mission sectorin Egypt and Laboratory of Plant Genetic Resources, Instituteof Genetic Resource, Faculty of Agriculture, Kyushu
University, Japan.

PCR amplification
DNA was isolated by CTAB method (Doyle and Doyle
1990). The PCR amplification was performed in a total
volume 10 μL and the bands were detected with Ethidium
Bromide staining and visualized under UV light, thenphotographed on Gel Documentation.

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