Morphological and molecular characterization of Saudi ... - CiteSeerX

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Journal of Food, Agriculture & Environment Vol.8 (1) : 220-228. 2010

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Morphological and molecular characterization of Saudi wheat genotypes under drought stress Mohamed Najeb Barakat 1*, Abdullah Abdlulaziz Al-Doss 1, 2, Khaled Ahmed Moustafa Ahmed 2 and Adel Ahmed Elshafei 1 1

1, 2

, Eid Ibrahim

Plant Genetic Manipulation and Genomic Breeding, Center of Excellence in Biotechnology Research, King Saud University, P.O. Box 2460-Riyadh 11451, Kingdom of Saudi Arabia. 2 Plant Production Department, College of Food Sciences and Agriculture, King Saud University, Riyadh, Saudi Arabia. *e-mail: [email protected]

Received 17 May 2009, accepted 20 December 2009.

Abstract The objectives of the present study were to compare the application and utility of random amplified polymorphic DNA (RAPD) and inter-simple sequence repeat (ISSR) marker techniques for analysis of genetic diversity among Saudi wheat genotypes under drought stress, compare genetic diversity estimated using molecular markers with agronomic performance under water stress to establish the degree of association between these techniques and develop drought tolerance-associated DNA markers. Twelve wheat genotypes were used in this study. They were evaluated phenotypically for drought tolerance and were planted under four irrigation treatments over two seasons to expose genotypes to different levels of drought stress during the grain filling period. The UPGMA dendrogram generated from the standardized agronomic data separated the twelve wheat genotypes into three main groups, which diverged at a similarity index of 0.42. The average genetic similarity among the twelve wheat genotypes was 0.50, with value ranging from 0.34 to 0.68. Two types of molecular markers, RAPD and ISSR, were assayed to determine the genetic diversity of 12 wheat genotypes. A high level of polymorphism was found with both RAPD and ISSR markers. In RAPD analyses, a total of 322 fragments were produced by the 30 primers. Of these 322 amplified fragments, 18.63% were not polymorphic; whereas, the remaining bands (81.37%) were polymorphic in one or more in the twelve genotypes. In ISSR analyses, 192 out of 238 bands (80.67%) were polymorphic. The dendrogram based on RAPD markers was not in accord with the dendrogram based on ISSR markers. The combined dendrogram agreed better with the groups of the wheat genotypes based on pedigree analysis than the dendrogram generated by ISSR or RAPD data alone. The correlation coefficient between RAPD and ISSR matrix was highly significant (0.534**, p > 0.001). Additionally, both RAPD and ISSR matrices showed significantly positive correlation (r = 0.94**and r = 0.77**, respectively) with RAPD+ISSR matrix. Specific RAPD and ISSR markers were developed successfully to identify drought tolerance genotype (Ksu103 and Ksu105) from drought sensitive genotype (Yecora Rojo). Thus, the markers identified in this study should be applicable for marker–assisted selection for the drought tolerance in wheat breeding programs. Key words: Drought tolerance, genetic diversity, ISSR markers, RAPD markers, wheat.

Introduction In a breeding program, knowledge of the degree of genetic diversity among parental materials for key selection traits will facilitate the development of high yielding, stress tolerant wheat cultivars. Thus, the correct choice of parents employed in the development of the basic population can influence the final result of the artificial selection and promote a better allocation of financial resources during the whole process of adjusting genotypes to a given environment 3. However, to confirm such expectations, it is necessary that the parents combine high means with an increase in variability for the characters under selection. Molecular and morphological analysis is among the most used tools for the estimation of genetic distances within a group of genotypes. Molecular markers provide an excellent tool for obtaining genetic information and their use in the assessment of genetic diversity in wheat (Triticum aestivum L.) has increased in the last few years 4-8. Molecular markers are a useful complement to morphological and physiological characterization of cultivars because they are plentiful, independent of tissue or environmental 220

effects and allow cultivar identification early in plant development. Molecular characterization of cultivars is also useful to evaluate potential genetic erosion, defined here as a reduction of genetic diversity in time 4. Better understanding of the genetic basis of phenotypic variability will improve the efficiency of wheat improvement for drought tolerance. The objectives of the present study were to (1) compare the application and utility of RAPD and ISSR marker techniques for analysis of genetic diversity among Saudi wheat genotypes under water stress and (2) compare genetic diversity estimated using molecular markers with agronomic performance under water stress to establish the degree of association between these techniques. Materials and Methods Field trials and traits evaluation: Twelve wheat genotypes were used in this study. These included the two recommended cultivars (Yecora Rojo and WestBred911) as well as ten advanced lines (F8) (Table 1) selected from the wheat breeding program at the Plant Journal of Food, Agriculture & Environment, Vol.8 (1), January 2010

Production Department, College of Food and Agriculture Sciences, King Saud University, Saudi Arabia. They were evaluated phenotypically for drought tolerance using four irrigation treatments over two seasons to expose genotypes to different levels of drought stress during the grain filling period. The four irrigation treatments were formed by irrigation scheduled at cumulative pan evaporation (CPE) of T1 50, T2 100, T3 150 and T4 200 mm during the entire irrigation interval. The CPE was calculated as sum of daily recorded evaporation from USWB open pan. The pan was located at the Meteorological Station adjacent the experimental site. A split-plot design with three replications was used. The mainplot was the water treatments and the sub-plot was the genotypes. Five agronomic traits were scored on the 12 bread wheat genotypes. These were grain yield (GY), biological yield (BY), spike number per m2 (SN) and 1000-kernel weight (KW). Grain and biological yields were determined from the central rows and converted to grain yield per hectare. Harvest index (HI) was calculated as grain yield/biological yield. DNA extraction: Frozen young leaves (500 mg) were ground to powder in a mortar with liquid nitrogen. The powder was poured into tubes containing 9.0 ml of warm (65°C) CTAB extraction buffer 9. The tubes were incubated at 65°C for 60-90 min, 4.5 ml chloroform/octanol (24:1) was added and tubes were rocked to mix for 10 min. and centrifuged for 10 min. at 3200 rpm. The supernatants were pipetted off into new tubes and 6 ml isopropanol was added. After 60 min., the tubes were centrifuged for 10 min. and the pellets obtained were put in sterile Eppendorf tubes, containing 400 µl of TE buffer of a pH 8.0 (10 mM Tris-HCl, pH 8.0 + 1.0 mM EDTA, pH 8.0). The DNA’s from genotypes were then extracted and stored at -20ºC until use. RAPD and ISSR analysis: A total of thirty 10-mer oligonucleotides with arbitrary sequence were used in RAPD analysis, and 25 primers based on dinucleotide, trinucleotide and tetranucleotide repeats (Amersham Pharmacia Biotech UK Limited, England HP79 NA) were used in ISSR analysis. The PCR reaction mixture consisted of 20-50 ng genomic DNA, 1×PCR buffer, 2.0 mM MgCl2, 100 µM of each dNTP, 0.1 µM primer and 1U Taq polymerase in a 25 µl volume. The amplification protocol was 94ºC for 4 min to pre-denature, followed by 45 cycles of 94ºC for 1 min, 36ºC (for RAPD analysis) or 50ºC (for ISSR analysis) for 1 min and 72ºC for 1 min, with a final extension at 72ºC for 10 min. Amplification products were fractionated on 1% (for RAPD analysis) or 2% (for ISSR analysis) agarose gel. Statistical analysis: Analysis of variance was performed for all measured traits (agronomic traits) in order to test the significance of variance among genotypes 10. To determine a data matrix of pairwise similarities between genotypes, the standardized traits mean values (mean of each traits was subtracted from the data values and the result divided by the standard division) were used, according to Jaccard coefficient 11. RAPD and ISSR data were scored for presence (1), absence (0) or as a missing observation 12, and each band was regarded as a locus. Two matrices, one for each marker, were generated. Pairwise comparisons of genotypes, based on the presence or absence of unique and shared polymorphic products, were used to determine Journal of Food, Agriculture & Environment, Vol.8 (1), January 2010

a data matrix of pairwise similarities between cultivars, according to Jaccard coefficient 11. All matrices (based on agronomic traits and molecular markers) were used to obtain the respective dendrograms using the algorithm UPGMA (Unweighed Pair Group Method with Arithmetic Average) 13 employed the SAHN (Aequential, Agglomerative, Hierarchical, and Nested clustering) from the software NTSYS-pc (Numerical Taxonomy and Multivariate Analysis System, version 1.80 (Applied Biostatics Program 14 ). The correlation coefficients between the Jaccard distance matrix based on agronomic traits and genetic distance matrix obtained with molecular markers were analyzed according to Mantel 15 using NTSYS-pc. Results and Discussion Morphological analysis: The analysis of variance indicated that for all the characters evaluated there were statistically significant differences (p = 0.05) among the wheat genotypes studied and, for most of the characters evaluated, for years and the genotype times year interaction. The interaction between water treatment and genotypes for grain yield (ton\ha) over two seasons is presented in Table 2. Discussion was focused on grain yield because of its importance as main objective in the breeding program. The highest grain yield was achieved from the wheat genotypes Ksu103 and Ksu105 (7.05 and 6.04 ton/ha, respectively) across the four water treatments, without significant differences between these two genotypes. The Ksu103 genotype had the highest yield at water stress condition (CPE of T4:200 mm), yielding 5.93 ton/ha over the two seasons, out-yielding the recommended cultivars Yecora Rojo and West Bred911 (4.88 and 4.74 ton/ha, respectively). Ksu103 and Ksu105 should be recognized as drought tolerant potential varieties. A dendrogram generated from the standardized agronomic data is presented in Fig. 1. The UPGMA dendrogram separated the twelve wheat genotypes into three main groups, which diverged at similarity index of 0.42. The larger group contained 9 wheat genotypes and consisted of two subgroups and one genotype (Ksu104) which did not fall into a subgroup. The first subgroup included Ksu107 and Ksu130. The second subgroup included Ksu103, Ksu124, Ksu102, Ksu106, Ksu122 and Ksu105. The Ksu103 and Ksu105 genotypes had one parent in common (L9, Table 1), a line obtained from CIMMYT and characterized as drought tolerant. The other parent for Ksu 103 was Er2, which is early flowering. The other parent for Ksu105 was RI474, characterized as drought tolerant (Table 1). The second group consisted of Ksu128 and the commercial wheat Yecora Rojo. The commercial wheat variety WestBred 911 clustered separately into the third group (Fig. 1). The average genetic similarity among the twelve wheat genotypes was 0.50, with value ranging from 0.34 to 0.68. The Ksu103 and Ksu124 genotypes showed a very high degree of similarity (0.68) indicating that these two genotypes had similar agronomic traits under water stress. On the other hand, West Bred and Ksu102 cultivars showed a very low degree of similarity (0.34) indicated that this pair is not closely related genotypes and had different agronomic traits under water stress. Identification and evaluation of RAPD and ISSR markers for diversity estimates: Thirty RAPD primers were screened for their 221

Table 1. Name and origin of the 12 genotypes used in the study. Genotypes Ksu122 Ksu124 Ksu102 Ksu103 Ksu128 Ksu104 Ksu130 Ksu107 Ksu105 Yecora Rojo West Bred911 Ksu106

Pedigree Er4/WB-24-1-2 YR/E2-94-5-30-16-4 YR/E2-94-2-5-30-19-5 L9/Er2-31-15-6 YR/WB-8 L9/Sak69-9 YR/E2-94-2-6-43-10-10 YR/E2-94-2-6-60-20-11 L9/RI474-1-3-161-10-10

Origin Plant Production ,, ,, ,, ,, ,, ,, ,, ,, USA USA, , WestBred LLC Plant Production

Barouk/RI474-75-3-53-3-3

Table 2. The interaction between water treatment and genotypes for grain yield (ton\ha) over two seasons. Genotype

Water treatment 100 mm 150 mm 6.73 5.18 7.09 6.03 7.00 5.93 6.95 6.36 6.13 5.48 6.78 5.76 5.90 5.49 5.87 4.58 7.18 5.84 5.98 6.03 5.99 4.43 6.50 5.98 6.49b 5.59c

50 mm 6.99 8.35 8.11 8.96 6.73 8.44 6.84 7.12 9.58 7.43 7.38 7.48 7.78a

Ksu122 Ksu124 Ksu102 Ksu103 Ksu128 Ksu104 Ksu130 Ksu107 Ksu105 Yecora Rojo West Bred Ksu106 Mean

Mean

200 mm 5.27 5.22 5.55 5.93 5.45 5.30 5.17 4.78 5.15 4.88 4.74 4.98 5.20d

6.04 fe 6.67 bc 6.65 bc 7.05 a 5.59 feg 6.57 cd 5.85 fhg 5.59 h 6.94 ab 6.03 fe 5.63 hg 6.23 de

Ksu107

Ksu130

Ksu103

Ksu124

Ksu102

Ksu106

Ksu122

Ksu105

Ksu104

Ksu128

West Bread

Yecora Rojo

LSD0.05 for water treatment x genotype interaction = 0.7

1

0.9

0.8

0.7

Similarity

0.6

0.5

0.4

0.3

0.2

0.1

0 0

1.6

3.6

4.8

6.4

8

9.6

11.2

12.8

Figure 1. Dendrogram based on Jaccard similarity coefficient of 12 wheat genotypes, generated by five agronomic traits over two seasons under water stress condition. 222

Journal of Food, Agriculture & Environment, Vol.8 (1), January 2010

ability to amplify the genomic DNA from twelve wheat genotypes. The number of DNA fragments amplified ranged from 5.0 to 20.0 depending on the primer and the DNA sample with a mean value of 10.7 bands per primer (Table 3). These values are rather high for RAPD amplification, compared to the average numbers of amplified bands recorded in other crops; namely, three fragments in Triticum turgidum L. 16, 4.3 fragments in Solanum tuberosum L. 17, 6.7 in Zea mays L. 18 and 4.4 in barley 19. Previously, Barakat et al.20 reported that the same primers were highly polymorphic among wheat genotypes. In the present investigation, the size of fragments ranged from 150 to 1500 bp. A total of 322 fragments were produced by the 30 primers. Of these 322 amplified fragments, 18.63% were not polymorphic and 81.37% were polymorphic among the twelve genotypes. Primer OPJ10 generated the greatest polymorphism (100%) while the lowest level of polymorphism (37.5%) was obtained by Primer 13. Out of the 30 primers, 24 revealed more than 80% polymorphism (Table 3). Fig. 2 shows the amplification profiles, generated by primer OPC15 (5‘ GACGGATCAG 3’) across the twelve wheat. All wheat genotypes were distinguishable by their band patterns. Polymorphism between genotypes can arise through nucleotide changes that prevent amplification by introducing a mismatch at one priming site, deletion of a priming site, insertions that render priming sites too distant to support amplification and insertions or deletions that change the size of the amplified product 21. Recently, RAPD markers have been used to detect the genetic diversity of some South Tunisian barley races 22. Forty-five ISSR primers were used to amplify DNA segments from 12 wheat genotypes. The number of amplified bands per primer varied between 0 and 17. Twenty five primers out of 45

were selected for further analysis based on the intensity, size and number of amplified products (Table 4). A total of 238 bands were observed, with 9.52 bands per primer (Table 4). One hundred ninety-two out of 238 bands (80.67%) were polymorphic. The number of amplification bands per primer varied between 5 and 17. Example of polymorphism is shown in Fig. 2. The dinucleotide repeats (AC)n primer had more bands as average than (GA)n (CA)n (CA)n and (CT)n primers and trinucleotide repeats primers (Table 4). The dinucleotide repeats primer generated a maximum of bands (12) with 6 polymorphic ISSR markers in wheat 23. On the other hand, tetranucleotide repeats did not amplify with DNA of wheat lines. This might indicate that di- and trinucleotide-based ISSRPCR markers could provide potential marker for wheat genome mapping. Genetic diversity of molecular markers: The relationships among wheat genotypes were estimated by a UPGMA cluster analysis of genetic similarity matrices. The composition of clusters obtained using RAPD markers alone (Fig. 3), ISSR markers alone (Fig. 4) and using both RAPD and ISSR markers together (Fig. 5) revealed similar groupings in some cases. Cluster analysis using RAPD data grouped the 12 wheat genotypes into three main clusters with Jaccard’s similarity coefficient ranging from 0.41 to 0.76 (Fig. 3). The highest similarity was found between Ksu122 and Ksu124 (0.76) and the lowest was between Ksu122 and Ksu130 (0.41). Although, the wheat genotypes Ksu124 and Ksu130 were more closely related with each other and derived from the cross YR X E2 (Table 1), they were different at DNA level. The first cluster contained three subgroups, with the first subgroup only including one wheat

Table 3. Number of amplifications and polymorphic products of 30 RAPD primers. Primer Pr1 Pr3 Pr4 Pr5 Pr6 Pr7 Pr8 Pr9 Pr10 Pr11 Pr12 Pr13 Pr14 Pr15 Pr16 Pr17 Pr19 Pr20 OPA02 OPA07 OPB09 OPB13 OPC04 OPC15 OPE20 OPF15 OPJ04 OPU06 OPJ10 OPZ03

Nucleotide sequence (5' 3') CAGGCCCTTC AGTCAGCCAC AATCGGGCTG AGGGGTCTTG GGTCCCTGAC GAAACGGGTG GTGACGTAGG GGGTAACGCC GTGATCGCAG CAATCGCCGT TCGGCGATAG CAGCACCCAC TCTGTGCTGG TTCCGAACCC AGCCAGCGAA GACCGCTTGT CAAACGTCGG GTTGCGATCC TGCCGAGCTG GAAACGGGTG TGGGGGACTC TTCCCCCGCT CCGCATCTAC GACGGATCAG AACGGTGACC CCAGTACTCC CCGAACACGG ACCTTTGCGG AAGCCCGAGG CAGCACCGCA

No. of amplification products 9 7 7 20 5 11 12 10 12 12 12 8 8 11 18 9 10 12 8 8 6 7 20 14 8 11 9 16 14 8

No. of polymorphic products 5 7 4 18 4 8 11 7 11 10 10 3 8 9 17 9 5 12 7 7 6 6 18 14 7 11 9 14 14 8


Polymorphism (%) 55.6 100.0 57.1 90.0 80.0 72.7 91.7 70.0 91.7 83.3 83.3 37.5 100 .0 81.8 94.4 100.0 50.0 100.0 87.5 87.5 100.0 85.7 90.0 100.0 87.5 100 .0 100.0 87.5 100.0 100.0

223

Table 4. Number of amplifications and polymorphic products of 25 ISSR primers.

W West Bread

Yecora YecoraRojo Rojo

No. of polymorphic products 6 4 8 5 7 5 10 4 3 6 4 5 9 9 9 4 5 9 11 7 12 16 13 9 7

K KSU 106

Y Yecora Rojo

K KSU 105

KSU 105

K KSU 107

K KSU 104

KSU 104

K KSU 130

K KSU 128

No. of amplification products 8 7 8 6 12 6 11 6 5 7 6 5 13 12 11 6 8 11 15 7 15 17 17 11 8

KSU 128

KSU 103 K

KSU 102 K

M

KSU 124 K

AD1 AD2 AD3 AD5 AD6 AD9 M-1 M-6 M-7 M-8 M-11 M-12 ISSR-1 ISSR-3 ISSR-4 M-11 ISSR-5 ISSR-8 ISSR-11 ISSR-808 ISSR-811 ISSR-816 ISSR-817 ISSR-821 ISSR-827

KSU 122 K

Nucleotide sequence (5' 3') (GA)9C (AGC)6G (ACC)6G (CA)10C GT(CAC)7 (AC)9G (AC)8CG (CAC)5 (CAG)5 (GTG)5 (CA)6A/G (CA)6RY (GA)8T (CT)8A (CT)8G (CA)6A/G (TC)8A (GA)8YT (GGGGT)3G A(GA)7GC G(AG)7AC C(AC)7AT C(AC)7AA G(TG)7TT A(CA)7CG

Primer

KSU KSU 106 106

West West Bread Bread

KSU KSU 107 107

KSU 130

KSU 103

KSU 102 102 KSU

KSU 124 124 KSU

KSU 122

M M

KSU 122

OPC15

ISSR-817

Figure 2. Polymorphism revealed using RAPD primer OPC15 (5‘ GACGGATCAG 3’) and ISSR primer ISSR-817 (5‘ C (AC) 7AA 3’) to amplify genomic DNA purified from wheat genotypes. M: Molecular weight, followed by wheat genotypes. 224

Polymorphism (%) 75.0 57.1 100 83.3 58.3 83.3 91.0 66.6 60.0 85.7 66.6 100 69.2 75.0 81.8 66.6 62.5 81.8 73.3 100 80.0 94.1 76.4 81.8 87.5

genotype (Ksu130). The second subgroup consisted of 4 wheat genotypes (Ksu107, Ksu106, Ksu105 and Yecora Rojo). The third subgroup only included one wheat genotype (WestBred911). The second cluster contained Ksu102, Ksu103, Ksu124 and Ksu 122. These wheat genotypes were more closely related with each other within the second cluster. The third cluster contained wheat genotypes Ksu128 and Ksu104. Comparisons of the agronomic relationships of wheat genotypes with RAPD clustering showed that the drought tolerant genotype (Ksu105) in Fig. 1 was located in the larger cluster as in the RAPD dendrogram in Fig. 3. However, the drought tolerant genotype (Ksu103) in the RAPD dendrogram in Fig. 3 was located in the second cluster. The dendrogram generated from ISSR data clearly indicated three main clusters (Fig. 4). Jaccard similarity coefficient ranged from 0.51 to 0.73. Maximum dissimilarity was found between Ksu103 (drought tolerant) and WestBred 911 (drought sensitive). The first cluster included wheat genotypes WestBred911 and Ksu 106. The second cluster included wheat genotypes Ksu 128 and Ksu 130. The third cluster contained 8 wheat genotypes which consisted of two subgroups. First subgroup included Ksu102, Ksu107, Ksu105, Yecora Rojo and Ksu104. A second subgroup included Ksu124, Ksu103 and Ksu105. It should be noted that the third cluster included wheat genotype Ksu 105 and Ksu103 which were drought tolerance genotypes. Previously, ISSR have been successfully used to estimate the genetic diversity in wheat 23, 24, rice 25, 26, barley 12, 19 and maize 27. RAPD and ISSR data were combined to produce a dendrogram. The similarity coefficient among the wheat genotypes varied from 0.48 to 0.73 with the highest being between Ksu124 and Ksu122 and the lowest being between Ksu130 and Ksu122 as revealed by earlier RAPD alone. Cluster analysis revealed three main clusters (Fig. 5). The first cluster contained 6 wheat genotypes, namely Ksu 130, Ksu 107, Ksu105, Yecora Rojo, WestBred 911 and Ksu 106. The second cluster contained two subgroups, with the first Journal of Food, Agriculture & Environment, Vol.8 (1), January 2010


225

1.6

Ksu106

3.6

Yecora Rojo

4.8

West Bread

6.4

8

Ksu103

Ksu102

Ksu105

Ksu107

Ksu130

9.6

11.2

12.8

0.75

0.8

0.85

0.9

0.95

1

Similarity

Figure 3. Dendrogram based on Jaccard similarity coefficient of 12 wheat genotypes, generated using RAPD markers.

0

0

1.6

Ksu128 3.6

Ksu102 4.8

Ksu107 6.4

Yecora Rojo 8

9.6

11.2

Ksu103

Ksu124

Ksu104

Ksu105

Ksu130

Ksu106

West Bread

12.8

Figure 4. Dendrogram based on Jaccard similarity coefficient of 12 wheat genotypes, generated using ISSR markers.

0.55

0.6

Ksu124

0.1

Ksu122

0.65

0

Ksu128

0.2

Ksu104

0.7

Similarity

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

Ksu122

Ksu104

Ksu128

Ksu122

Ksu124

Ksu103

Ksu102

Ksu106

West Bread

Yecora Rojo

Ksu105

Ksu107

Ksu130

1.02

0.96

0.9

Similarity

0.84

0.78

0.72

0.66

0.6

0.54

0

1.6

3.6

4.8

6.4

8

9.6

11.2

12.8

Figure 5. Dendrogram constructed from similarity coefficients and showing the clustering of the 12 wheat genotypes using RAPD and ISSR markers.

subgroup including wheat genotypes Ksu 102 and Ksu 103. The second subgroup consisted of two wheat genotypes Ksu 124 and Ksu122. The combined dendrogram agrees better with the grouping of these wheat genotypes depending on pedigree and the dendrogram generated by RAPD data alone than the dendrogram generated by ISSR data alone. Correlation between agronomic traits and molecular markers: In order to compare the extent of agreement among dendrograms, derived from morphology and molecular markers, a distance matrix was constructed for each assay and compared, using the Mantel matrix correspondence test. Comparison of matrices of either RAPD or ISSR or RAPD+ISSR matrix and morphological data showed a moderate correlation among dendrograms but significant correlation (r = 0.36, 0.39 and 0.41, respectively). Agrama and Tuinstra 30 reported that genetic diversity of sorghum measured using SSR and RAPD markers exhibited highly significant association with geographic origin and race classification. The correlation of pairwise distances between all pairs of genotypes for SSRs compared to geographical and race was r = 0.51; the correlation for RAPDs with geographical and race data was r = 0.43. The correlation of pairwise distances among all pairs of sorghum genotypes for SSRs compared to RAPDs was r = 0.79. Also, significant and positive correlation between distance matrices generated using morphological traits, end-use quality and molecular markers in wheat were reported 31. Recently, Vieira et al.32 reported that the matrices obtained by morphological and molecular marker data analyses in wheat revealed a significant 226

but moderate correlation (r = 0.47), indicating that such techniques sample distinct genome regions. The moderate association between genetic distances estimated using molecular and phenotypic markers can be explained by a range of factors. Molecular analysis provides a wider genome sampling than the morphological analysis, since a study comparing both techniques rarely evaluates the same or even a similar number of morphological and molecular markers. The association between estimates is also influenced by the fact that a large portion of the variation detected by molecular markers is non-adaptive and, therefore, not subject to either natural or artificial selection. On the other hand, the phenotypic characters are subject to both natural and artificial selection, aside from their high environmental dependence. Moreover, it is not always the case that two identical phenotypes are determined by the same genes, i.e. distinct genes may lead to similar phenotypes. Thus, it is clear that such estimates are closer when there is an association between the loci controlling the targeted morphological traits (quantitative trait loci, or QTLs) and the evaluated bands and when a large number of morphological traits are evaluated 7, 8. The correlation coefficient between RAPD and ISSR matrix was highly significant (0.54**, p > 0.001). Additionally, both RAPD and ISSR matrices showed significantly positive correlation (r = 0.95** and r = 0.77**, respectively) with RAPD+ISSR matrix. Development of drought tolerance-associated DNA markers: The potential of using markers generated in the current study to develop drought tolerance–associated DNA markers is presented Journal of Food, Agriculture & Environment, Vol.8 (1), January 2010

was used. Moreover, specific DNA bands, generated from RAPD primers (Table 5), could be used to characterize between Ksu103 and Ksu105 (drought tolerance) and Yecora Rojo (drought sensitive). For the ISSR analysis, polymorphic DNA fragments of 950 bp and 740 bp were identified in Ksu103 as well as Ksu105 and were absent in Yecora Rojo, when primer ISSR-811was used (Fig.6, Table 5). These fragments appear to be linked to drought tolerance genes. On the other hand, specific DNA bands at 1200 bp and 1040 bp are present in Yecora Rojo as drought sensitive, but not in the Ksu103 and Ksu105 as drought tolerant. In addition, specific DNA bands generated from ISSR primers (Table 5) could be used to characterize M between Ksu103 as well as Ksu105 (drought tolerance) and Yecora Rojo (drought sensitive). The 1400 bp reproducibility of these variety-specific markers was 1200 bp confirmed in RAPD and ISSR analyses for which DNA 700 bp OPE20 isolation, PCR amplification, and gel electrophoresis were carried out separately. Molecular marker technology has allowed the 310 bp identification and genetic characterization of QTLs with significant effects on stress tolerance during different stages of plant development and facilitated determination of genetic relationships among tolerance to different stresses 33. Comparatively, however, limited M research has been conducted to identify genetic markers associated with drought tolerance in different plant species. 1200 bp 1040 bp 950 bp In the present investigation, the characterized wheat 740 bp genotypes were mainly classified according to agronomic traits under water stress conditions, which ISSR-811 were complex and multigenic characters. Such characters were environmentally affected and, therefore, liable to subjective evaluation. In this sense, the molecular characterization was more efficient in the generation of an unbiased picture of diversity than an agronomic approach. However, the agronomic characterization was still important in wheat germplasm management, and determination of molecular diversity Figure 6. Polymorphic DNA fragments associated with heat tolerance genes, should not be seen as replacing traditional generated by RAPD primer OPE20 (5‘ AACGGTGACC 3’) and generated by ISSR primer ISSR-811 (5‘ G (AG) 7AC 3’) M: Molecular weight, followed by characterization but rather as a complement to it. KSU 106

West Bread

Yecora Rojo

KSU 106

Yecora Rojo

West Bread

KSU 105

KSU 105

KSU 107

KSU 130

KSU 107

KSU 130

KSU 104

KSU 104

KSU 128

KSU 128

KSU 102

KSU 102

KSU 103

KSU 124

KSU 124

KSU 103

KSU 122

KSU 122

in Table 5. For the RAPD analysis presented here some wheat genotypes reported to be drought tolerant/sensitive (on the basis of field performance) were used. Fig. 6 and Table 5 indicate that a DNA band at about 310 bp is present in Ksu103 as drought tolerant, but not in Yecora Rojo as drought sensitive, when primer OPE20 is used. On the other hand, specific DNA bands at 1400 bp and 1200 bp are present in Yecora Rojo as drought sensitive but not in the Ksu103 and Ksu105 as drought tolerant, when primer OPE20

wheat genotypes.

Table 5. Specific DNA fragments generated from RAPD and ISSR analysis to develop drought tolerance–associated DNA markers between Yecora Rojo (drought-sensitive) and Ksu 105& Ksu 103 (drought-tolerant). Primer

Marker

Pr10 Pr16

680 750 500 250 300 630 1400 1200 700 310 620 410 330 420 320 1400

OPC04 OPC15 OPE20

OPE15

OPU06 OPZ03

Ksu105 + + + + + + + + -

RAPD Yokora Rojo + + + + + + +

Ksu103 + + + + + + + + + +

Primer

Marker

ISSR-811

1200 1040 950 740 500 540 520 280 900 850 320

ISSR-816 ISSR-817 ISSR-11 ISSR-821


Ksu105 + + + + + -

ISSR Yokora Rojo + + + + + +

Ksu103 + + + + + -

227

In conclusion, these results indicated that both morphological analysis and molecular markers showed a high degree of variation among the wheat genotypes analyzed. These genotypes should represent an important source of genetic diversity in wheat and could be used in future breeding programs. These distinct genotypes can be crossed to produce mapping population for detection of quantitative trait loci against various important agronomical traits. Future study will be conducted to develop wheat cultivars that are highly tolerant to drought stress in addition to other desirable wheat characters. Production of these wheat lines can minimize yield losses due to growing drought–sensitive wheat cultivars under stress conditions. This consequently can lead to maximizing farm income of wheat grower. References 1

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