Euphytica 140: 133–146, 2004. C 2004 Kluwer Academic Publishers. Printed in the Netherlands.
133
Assessment of genetic diversity within and among Basmati and non-Basmati rice varieties using AFLP, ISSR and SSR markers Navinder Saini1,‡ , Neelu Jain1,‡ , Sunita Jain2 & Rajinder K. Jain1,∗ 1
Department of Biotechnology and Molecular Biology, CCS Haryana Agricultural University, Hisar 125004, India; Department of Biochemistry, CCS Haryana Agricultural University, Hisar 125004, India; (∗ author for correspondence: e-mail:
[email protected])
2
Received 29 October 2003; accepted 15 June 2004
Key words: AFLP, Basmati rice, DNA fingerprinting, genetic diversity, ISSR, Oryza sativa, SSR
Summary Molecular markers provide novel tools to differentiate between the various grades of Basmati rice, maintain fair-trade practices and to determine its relationship with other rice groups in Oryza sativa. We have evaluated the genetic diversity and patterns of relationships among the 18 rice genotypes representative of the traditional Basmati, cross-bred Basmati and non-Basmati (indica and japonica) rice varieties using AFLP, ISSR and SSR markers. All the three marker systems generated higher levels of polymorphism and could distinguish between all the 18 rice cultivars. The minimum number of assay-units per system needed to distinguish between all the cultivars was one for AFLP, two for ISSR and five for SSR. A total of 171 (110 polymorphic), 240 (188 polymorphic) and 160 (159 polymorphic) bands were detected using five primer combinations of AFLP, 25 UBC ISSR primers and 30 well distributed, mapped SSR markers, respectively. The salient features of AFLP, ISSR and SSR marker data analyzed using clustering algorithms, principal component analysis, Mantel test and AMOVA analysis are as given below: (i) the two traditional Basmati rice varieties were genetically distinct from indica and japonica rice varieties and invariably formed a separate cluster, (ii) the six Basmati varieties developed from various indica × Basmati rice crosses and backcrosses were grouped variably depending upon the marker system employed; CSR30 and Super being more closer to traditional Basmati followed by HKR228, Kasturi, Pusa Basmati 1 and Sabarmati, (iii) AFLP, ISSR and SSR marker data-sets showed moderate levels of positive correlation (Mantel test, r = 0.42–0.50), and (iv) the partitioning of the variance among and within rice groups (traditional Basmati, cross-bred Basmati, indica and japonica) using AMOVA showed greater variation among than within groups using SSR data-set, while reverse was true for both ISSR and AFLP data-sets. The study emphasizes the need for using a combination of different marker systems for a comprehensive genetic analysis of Basmati rice germplasm. The high-level polymorphism generated by SSR, ISSR and AFLP assays described in this study shall provide novel markers to differentiate between traditional Basmati rice supplies from cheaper cross-bred Basmati and long-grain non-Basmati varieties at commercial level. Abbreviations: PCR, polymerase chain reaction; AFLP, amplified restriction fragment polymorphism; AMOVA, analysis of molecular variance; Hav , average heterozygosity; ISSR, inter simple sequence repeats; EMR, effective multiplex ratio, MI, marker index; PCA, principal component analysis; PIC, polymorphism information content; SSR, simple sequence repeat; TB, traditional Basmati; UPGMA, un-weighted pair-group method with an arithmetic average
‡ The
first two authors have equal contribution.
134 Introduction Basmati rice traditionally grown in north-western states of Indian sub-continent, commands a premium price in domestic as well as international markets due to its exquisite aroma, superfine grain characteristics and excellent cooking (extra elongation, soft and flaky texture) qualities (Bhasin, 2000; Singh et al., 2000a; Khush & dela Kruz, 2002). Basmati rice has its origin in the foothills of Himalayas and is the result of centuries of selection and cultivation by farmers (Khush, 2000). Traditional Basmati rice varieties have several undesirable agronomic traits including tall plant stature, long crop duration, sensitivity to photoperiod, and poor response to fertilizer application resulting in to low yield potentials (Singh et al., 2000b). Breeding efforts have been made to genetically improve Basmati rice by crossing with novel high yielding indica rice varieties but the progresses has been limited due to higher degree of divergence between Basmati and indica rice varieties, hybrid sterility and polygenic nature of aroma and grain/cooking quality traits (Khush & Juliano, 1991). Several semi-dwarf, high yielding Basmati rice varieties have been developed but these cross-bred varieties fall short of the quality features of traditional Basmati rice varieties, which is reflected in price differences between traditional and crossbred Basmati varieties (Bhasin, 2000; Singh et al., 2000b). The study of genetic diversity in Basmati gene pool and vis-`a-vis other rice types is necessary for varietal identification, classification, proper purity maintenance, conservation, and Basmati rice breeding. In view of the implementation of plant variety protection rights and export under WTO regulations, increasing attention is being paid towards comprehensive characterization of elite Indian Basmati quality rice germplasm, supplementing the existing morphological descriptors with reliable and repeatable DNAbased marker profiles. Genetic analysis is important to ensure the export quality of Basmati rice, for maintaining the ’distinctiveness’ of Basmati varieties, and to differentiate between the various grades of Basmati rice (Bligh et al., 1999; Bligh, 2000; Aggarwal et al., 2002; Nagaraju et al., 2002). The latter is particularly important to differentiate between Indian traditional Basmati from other cheaper cross-bred Basmati or long-grain rice varieties, which can be traded as traditional Basmati or used for adulteration. Historically, genetic diversity and intraspecific classification in Asian rice has been studied using
morphological, serological and hybrid fertility parameters in combination (Kato et al., 1928), morphogeographical (Matsuo, 1952; Oka, 1958), hybrid sterility (Terao & Mizushima, 1942), isozymatic data (Glaszmann, 1987) and more recently using the DNA markers (Aggarwal et al., 1999; Blair et al., 1999, McCouch et al., 2001; Blair et al., 2002). Kato (1928) recognized two rice varietal groups, indica and japonica. Basmati rice varieties of Indian subcontinent have been conventionally classified as aromatic indica. Glaszmann (1987, 1988) used the isozyme markers based polymorphism to classify the Asian rice germplasm into six varietal groups. The Basmati rice varieties were clustered in a distinct Group V different than those for indica (Group I) and japonica (Group VI) rice varieties. PCR-based molecular markers including micro-satellite DNA markers (simple sequence repeats, SSRs) (Bligh et al., 1999; Bligh, 2000; Blair et al., 2002; Nagaraju et al., 2002; Jain et al., 2004), AFLPs (Cho et al., 1996; Maheshwaran et al., 1997; Aggarwal et al., 1999, 2002;) and ISSRs (Blair et al., 1999; Qian et al., 2001; Nagaraju et al., 2002) have been successfully used for genotype identification and diversity analysis in rice including Basmati rice. In most of these studies, Basmati types clustered into a separate group distinct from that of indica and japonica rice varieties (Bligh et al., 1999; Aggarwal et al., 2002; Nagaraju et al., 2002; Jain et al., 2004). The results in fact indicated that Indian Basmati germplasm may have a long, independent and complex pattern of evolution that distinguishes it from other groups within Oryza sativa (Jain et al., 2004; unpublished data). Plant genome analysis in a number of plant species including rice have shown that genetic relationships determined between different varieties/species using different marker systems can vary significantly (Parsons et al., 1997; Powell et al., 1996; Virk et al., 2000). Most widely adopted marker technologies such as AFLP, ISSR, RAPD, RFLP and SSR may amplify different regions of the genome, have their own advantages and disadvantages and needs careful evaluation before effectively deployed for diversity analysis. We have used AFLP, ISSR and SSR markers for evaluating genetic diversity among the representatives of traditional Basmati, cross-bred Basmati and non-Basmati (indica and japonica) rice varieties. The objectives were to compare their effectiveness for detecting variation, to determine if each marker system revealed similar or different patterns of relationships or whether any one or a combination of marker systems should be
135 recommended for genetic analysis and varietal identification in Basmati rice. The utility of each marker system in terms of technical and economic considerations is also discussed.
Nurseries of all these genotypes were raised in a nethouse at CCS Haryana Agricultural University, Hisar, India. DNA extraction
Material and methods Plant material A total of 18 rice varieties representing the commercially important premium traditional Basmati and cross-bred Basmati rice varieties and indica and japonica rice groups, were selected for the molecular polymorphism studies; the characteristics, origin, source and accession numbers of these genotypes are given in Table 1. Varieties IR36, IR64,Azucena and Nipponbare also served as controls for determining allele mw in this study because they had previously been assayed using the same SSR markers (Cho et al., 2000).
Genomic DNA from each rice genotype was isolated from bulked leaf samples (∼20 mg each) from five plants (one month-old) using CTAB method described by Saghai-Maroof et al. (1984). The DNA was spooled out, washed twice with 70% ethanol and dissolved in TE (10 mM Tris, 0.1 mM EDTA, pH 8.0) containing 25 µg/ml RNase-A, incubated at 37 ◦ C for 30 min and extracted with chloroform:iso-amyl alcohol (24:1 v/v). DNA was re-precipitated and dissolved in TE buffer. DNA was checked for its quality and quantity by 1% agarose gel electrophoresis using a standard containing 100 ng/µl genomic λ DNA.
Table 1. A brief description of rice varieties used for diversity analysis. Rice types
Varieties
Abbreviation
Remarks
Traditional Basmati
Basmati 370
Bas370
Traditional Basmati rice variety
Taraori Basmati accession HBC19
HBC19
Premium traditional Basmati rice variety, a pure line selection from Taraori Basmati
Super
Super
Selection from IR-661*/Basmati 370
Cross-bred Basmati
Indica
Japonica
Sabarmati
Sabar
Selection fromT (N) 1*/Basmati 370
CSR30
CSR30
Salt tolerant, obtained from BR4-10*/ Pakistan Basmati.
Pusa Basmati
PB1
Selection from Pusa-167* /Karnal local Basmati.
Kasturi
Kasturi
Selection from CR-88-17-1-5*/Basmati 370
Haryana Basmati 1 (HKR228) CSR10
HKR228
Selection Sona*/Basmati 370
CSR10
Salt tolerant selection from CSRI (Damodar)*/Jaya*
IR24
IR24
Indica rice variety developed at IRRI
IR36
IR36
Indica rice variety developed at IRRI
IR64
IR64
Indica rice variety developed at IRRI
Pokkali
Pok
Salt tolerant landrace (indica) from South India (Kerala)
HKR120
HKR120
High yielding indica variety
Sharbati
Sharbati
A local selection from Uttar Pradesh
Azucena
Azu
Aromatic, tropical japonica rice variety from Phillippines
Nipponbare
Nippon
Temperate Japonica rice variety
New Plant Type II (IR68552-100-1-2-2)
NPTII
Tropical japonica rice accession developed at IRRI
*Indica; DRR, HAU, CCS Haryana Agricultural Univesity Rice Research Station, Kaul – 132021, India; CSSRI, Central Soil Salinity Research Institute, Karnal – 132001, India; IRRI, International Rice Research Institute, Los Ba˜nos, Phillippines; GBPU, G.B. Pant University of Agriculture and Technology, Pantnagar – 261145, Uttaranchal, India; IARI, Indian Agricultural Research Institute, New Delhi, India.
136 Table 2. Data on AFLP fragments and polymorphism obtained using five primer combinations in 18 rice genotypes
Primer combinations
Total no. of bands
No. of monomorphic bands
No. of polymorphic bands
Polymorphism (%)
Eco-AG/ Mse- CAA
46
14
32
69.0
Eco-AC/ Mse- CAG
41
13
28
68.4
Eco-AG/Mse- CAT
23
7
16
69.0
Eco-AG/Mse- CAG
26
8
18
69.0
Eco-AC/Mse- CAA
35
19
16
45.0
171
61
110
Total Mean
34.2 ± 4.35
Molecular marker analysis AFLP The AFLP analysis was done using the GIBCO BRLTM AFLP analysis system II (Cat. No. 10717-015) following the manufacturer’s instructions using the silver staining procedure (Cho et al., 1996). Genomic DNA (500 ng) was double digested with restriction enzymes, EcoRI (rare cutter) and MseI (frequent cutter) (Vos et al., 1995). Resultant fragments were ligated to adapters specific for the EcoRI and MseI restriction sites. A pre-selective amplification was carried out with EcoRI and MseI specific primer core sequences plus one selective nucleotide (Eco-A, Mse-C). Selective amplification was conducted using five primer combinations, each with three nucleotides for MseI and two nucleotides for EcoRI specific primers (Eco-AG Mse-CAA, Eco-ACMse-CAG, Eco-AGMse-CAT,EcoAG Mse-CAA and Eco-ACMse-CAA; Table 2) as per cycling conditions described by the manufacturer. Amplification products were denatured at 95 ◦ C for 2 min and resolved on 4% denatured polyacrylamide gel, using Aluminum Backed sequencing System Model # 535 (Owl Scientific, Woburn, MA, U.S.A.) essentially as described by Chen et al. (1997). Electrophoresis was performed at constant power of 100 Watt for 3.5 h including a 1 h pre-run to warm the gel to 50–60 ◦ C. Following electrophoresis, DNA bands were visualized using the Silver SequenceTM DNA Sequencing System (Promega, Madison, WI, U.S.A.). ISSR A total of 100 ISSR primers (UBC primer set # 9, John Hobbs, NAPS Unit, University of British Columbia, Vancouver, V6T 1Z3 Canada) were tested for DNA amplification; 25 of these which produced sharp and
22 ± 3.3
– 64.0
clear banding profile were used for genotyping of 18 rice genotypes (Table 3). The PCR reaction was carried out using a single primer at a time, in 20 µl reaction mixture containing 50 ng of template DNA, 1× buffer, 200 µM of each of the four dNTPs, 1 unit Taq DNA polymerase, 2.5 mM MgCl2 and 0.4 µM of primer. PCR amplifications were performed in 96-well plates on a PTC100TM 96 V thermocycler (MJ Research, Watertown, MA, U.S.A.) with initial denaturation at 95 ◦ C for 5 min; followed by 40 amplification cycles of denaturing at 94 ◦ C for 1 min, annealing at 50 ◦ C for 1 min, extension at 72 ◦ C for 2 min and final extension at 72 ◦ C for 15 min. The PCR products were resolved by electrophoresis on 1.5% (w/v) agarose+SynergelTM (Diversified Biotech, 1208, V.F.W. Parkway, Boston, MA 02132) gels stained with ethidium bromide, photographed under UV light. Molecular weight of bands was estimated using a wide range ladder from Sigma (Direct LoadTM Wide Range DNA marker 50–10,000 bp). SSR Thirty rice SSR (microsatellite) primer pairs well distributed on all the 12 chromosomes were chosen on the basis of the published rice microsatellite framework (Temnykh et al., 2000) (Table 4). The original sources and repeat motifs for these markers can be found in Temnykh et al. (2000) and in RiceGenes database (http://www.gramene.org/microsat/RM primers.html). Microsatellite primer pairs were obtained from the Research Genetics, (Huntsville, AL, U.S.A.). The PCR reaction was conducted in a reaction volume of 20 µl containing 1× PCR buffer, 100 µM dNTPs, 0.4 µM of each primer, 1.2 mM MgCl2 , 1 unit Taq DNA polymerase and 50 ng template DNA. PCR amplification was performed with initial denaturation
137 Table 3. ISSR primers: their sequence, anchored end, repeat motif and data on DNA profile and polymorphism generated in 18 rice genotypes using 25 ISSR markers
UBC primer type/ number
Sequence (5 → 3 )
Total no. of bands
No. of monomorphic bands
No. of polymorphic bands
Polymorphism (%)
Di-nucleotide, 3 anchored 807
AGAGAGAGAGAGAGAGT
13
6
7
808
AGAGAGAGAGAGAGAGC
809
AGAGAGAGAGAGAGAGG
810 811
53.8
21
3
18
9
0
9
100
GAGAGAGAGAGAGAGAT
4
0
4
100
GAGAGAGAGAGAGAGAC
13
2
11
84.6
812
GAGAGAGAGAGAGAGAA
11
1
10
90.9
815
CTCTCTCTCTCTCTCTG
6
1
5
83.3
823
TCTCTCTCTCTCTCTCC
11
2
9
81.8
824
TCTCTCTCTCTCTCTCG
9
1
8
88.9
825
ACACACACACACACACT
12
4
8
66.7
827
ACACACACACACACACG
13
2
11
84.6
828
TGTGTGTGTGTGTGTGA
10
1
9
90.0
836
AGAGAGAGAGAGAGAGYA
9
3
6
66.7
840
GAGAGAGAGAGAGAGAYT
10
4
6
60.0
853
TCTCTCTCTCTCTCTCRT
4
1
3
75.0
857
ACACACACACACACACYG
9
3
6
74.4
884
HBHAGAGAGAGAGAGAG
10
3
7
70.0
885
BHBGAGAGAGAGAGAGA
4
2
2
50.0
886
VDVCTCTCTCTCTCTCT
17
2
15
88.2
890
VHVGTGTGTGTGTGTGT
16
1
15
93.5
891
HVHTGTGTGTGTGTGTG
7
1
6
85.7
864
ATGATGATGATGATGATG
4
2
2
50.0
866
CTCCTCCTCCTCCTCCTC
5
2
3
60.0
GGGTGGGGTGGGGTG
8
1
7
87.5
85.7
Di-nucleotide, 5 anchored
Tri-nucleotide, non-anchored
Pentanucleotide 881 Mixed repeat motifs 899
CATGGTGTTGGTCATTGTTCCA
Total Mean
5
4
1
240
52
188
9.6 ± 0.87
7.5 ± 0.8
20.0 – 78.33
Single letter abbreviations for mixed base positions: R = (A,G); Y = (C,T); B = (C,G,T i.e. not A); D = (A,G,T i.e. not C); H = (A,C,T i.e. not G); V = (A,C,G i.e., not T).
at 94 ◦ C for 5 min followed by 35 cycles of 94 ◦ C for 1 min, 55 ◦ C for 1 min, 72 ◦ C for 2 min and final extension at 72 ◦ C for 7 min before cooling at 4 ◦ C. Amplification products were denatured and resolved on 4% denatured polyacrylamide gel, as described earlier for AFLP.
Scoring and data analysis DNA profile data for each of marker systems has been presented by using the term ’assay unit’. Assay unit is defined as one reaction involving one primer for ISSR or one set of primers for AFLP and SSR. The PCR
138 Table 4. Data on the number of alleles, number of unique alleles, number of genotypes with multiple alleles, alleles size range, highest frequency allele and polymorphism information content (PIC) obtained using 30 SSR markers in 18 rice genotypes. Allele size (bp)
High freq. Allele
Range Difference
Size(bp) %
Marker RM144
6
2
0
218-280
62
236,218
33.4
0.75
RM169
5
2
0
163-196
33
165
39.4
0.70
RM180
5
1
0
108-202
94
112
50.0
0.67
RM201
4
1
0
144-159
15
144
50.0
0.62
RM208
4
2
0
164-178
14
164
50.0
0.59
RM210
6
3
1
133-157
24
143
39.0
0.72
RM223
4
2
0
146-168
22
151
50.0
0.59
RM228
7
3
0
108-153
45
108
44.5
0.74
RM232
6
3
0
148-166
18
158
56.0
0.62
RM237
6
0
0
128-137
9
135,133
28.0
0.80
RM241
5
3
0
104-149
45
135
67.0
0.52
RM247
7
3
0
127-178
51
136
28.0
0.80
RM249
5
3
0
123-150
27
125
50.0
0.63
RM250
4
0
0
149-173
24
155
39.0
0.72
RM251
7
4
0
115-151
36
119
33.4
0.77
RM256
5
3
1
105-143
38
107
78.0
0.37
RM270
4
1
0
105-115
10
109,107
39.0
0.67
RM278
5
3
0
133-150
17
144
50.0
0.63
RM287
7
1
1
98-118
20
106
39.0
0.76
RM303
7
3
0
158-230
72
202
33.4
0.80
RM304
5
0
0
138-170
32
162
50.0
0.70
RM310
7
2
0
87-123
36
113
33.0
0.80
RM312
5
2
0
98-105
7
103
50.0
0.65
RM314
5
1
1
109-122
13
122
39.0
0.73
RM325
1
0
0
210
0
210
RM332
4
2
0
159-183
24
169,172
44.4
RM335
7
1
1
105-155
50
155,107,148
22.3
0.81
RM336
8
4
1
144-195
51
164
33.4
0.79
RM339
5
3
0
143-189
46
150
50.0
0.63
RM340
4
0
1
105-166
61
166,158
39.4
0.66
58
7
87-280 46.9
0.66
Total Mean
5.34
No. of unique alleles*
Multiple alleles
No. of alleles
0.234
33.2
PIC values
100
0 0.60
*Present in one of the 18 rice genotypes only.
products from AFLP, ISSR and SSR analyses were scored qualitatively for presence or absence (Ghosh et al., 1997). Only clear and apparently unambiguous bands were scored for AFLP and ISSR. Genetic similarities between the cultivars were measured by the Dice similarity coefficient based on the proportion of shared alleles using ‘simqual’ sub-program of software
NTSYS-PC version 1.8 (Exeter Software, Setauket, NY, U.S.A.) software package (Rohlf, 1993). The resultant distance matrix data was used to construct dendrograms by using the un-weighted pair-group method with an arithmetic average (UPGMA) subprogram of NTSYS-PC (Rohlf, 1993). In case of SSRs, polymorphism information content (PIC) was also calculated
139 according to Anderson et al. (1993) using the following equation: PICi = 1 −
n
Pi2j
j=1
where Pi j is the frequency of the jth allele for ith marker and summation extends over n alleles. To measure the utility of the marker systems, average heterozygosity (Hav ), effective multiplex ratio (EMR) and marker index (MI) were calculated for each marker systems according to the Powell et al. (1996) and Milbourne et al. (1997). Partitioning of variation within and among the groups by each marker system was achieved by analysis of molecular variance (AMOVA) using the Arlequin ver 2000 software as described by Excoffier et al. (1992). The Mantel test of significance (Mantel, 1967) was also used to compare each pair of similarity matrices produced above. Student’s t-test was performed in order to determine the level of significance of differences obtained. Results DNA fingerprint database has been prepared using the three different PCR-based marker (SSR, AFLP and ISSR) systems for 18 rice genotypes (SSR database available at http://hau.ernet.in/basmati1.htm). All the three molecular approaches used in this study were able to generate sufficient polymorphisms and unique DNA fingerprints to identify each of the 18 rice varieties. Salient features of fingerprint database obtained using different markers are given below: AFLP analysis AFLP analysis revealed a large number of distinct, scorable fragments per primer pair and in total, 171 bands, both polymorphic and monomorphic, were obtained using the five primer combinations in 18 rice genotypes (Table 2). The number of amplified fragments varied from 23 to 46, with an average of 34.2 ± 4.35 bands (electromorphs) per primer combination. Overall size of PCR amplified fragments using five primer sets ranged from 100 to 700 bp. Out of 171 bands, 110 bands were polymorphic and thus, the polymorphism percentage averaged to 64.0 across all the varieties. Maximum number of polymorphic bands (32 of the 171 bands) was obtained for MseI–EcoRI (CAA–AG) primer combination. A significant correlation (r = 0.872; P < 0.01, analysis of variance) was
observed between the total number of bands and the number of polymorphic bands.
ISSR analysis A total of 240 bands were detected using 25 ISSR markers, out of which 52 were monomorphic and 188 were polymorphic (Table 3). ISSR DNA bands varied between 4 (UBC primer #810, 853, 864, 885) and 21 (UBC primer #808), with an average of 9.6 ± 0.87 bands per primer. The maximum number of polymorphic bands (18 bands) were obtained using UBC primer #808; the average number of polymorphic bands was 7.5 ± 0.87 per primer. Percent polymorphism ranged from 20% to as high as 100% with an average polymorphism of 78.3% across all the varieties. A significant correlation (0.916, P < 0.01) was observed between the total number of bands and the number of polymorphic bands amplified by 25 ISSR primers. ISSR primers with di-nucleotide repeat motifs on an average amplified greater number of bands (10.4 ± 0.9 bands per primer) compared to the primers with tri(4.5 ± 0.50) and penta- (8.0 ± 0.6) nucleotide repeat motifs. The average number of bands produced by ISSR primers with different motifs were negatively correlated (r = −0.2691) with the increasing number of nucleotides in repeat motif.
SSR analysis A total of 160 alleles were detected among 18 rice varieties using 30 SSR markers (Table 4). Number of alleles ranged from one (RM325A) to eight (RM336), with an average of 5.33 alleles per locus. The overall size of amplified products ranged from 87 bp (RM310) to 280 bp (RM144). The size difference between the smallest and largest allele at a given SSR locus varied from 0 (RM325A) to 94 (RM180). A total of 58 unique alleles (36.2%) were observed at 25 of the 30 SSR loci. Maximum number of unique alleles was observed at RM251 and RM336 loci (4 alleles each). Null allele was observed only in rice variety Basmati 370 at RM304 locus. Multiple alleles were observed in one or more genotypes at 7 of the 30 SSR loci. The frequency of most common allele ranged from 22.2% (RM335) to 100% (RM325). PIC values ranged from 0 to 0.81, with an average value of 0.661 per locus. PIC values showed a positive correlation (r = 0.77; P < 0.01, analysis of variance) with number of alleles at SSR locus.
140 CSR30 Super HBC19 Bas370 PB1 HKR228 Kasturi Sabarmati Pokkali HKR120 IR24 Sharbati CSR10 IR64 IR36 Nippon Azucena NPTII
A
0.82
0.85
0.89
0.92
CSR30 HBC19 Super Bas370 HKR228 Kasturi Nippon Azucena NPTII HKR120 Sabarmati IR64 IR36 IR24 Sharbati PB1 Pokkali CSR10
C
0.70
0.95
0.77
0.85
0.93
1.00
Similarity coefficient
CSR30 HBC19 HKR228 Super Bas370 Kasturi Sabarmati HKR120 PB1 CSR10 IR64 IR36 Sharbati IR24 Pokkali NPTII Nippon Azucena
B
0.68
0.74
0.79
0.84
CSR30 HBC19 Super Bas370 HKR228 Kasturi Sabarmati HKR120 PB1 IR24 Sharbati IR64 IR36 Pokkali CSR10 Nippon Azucena NPTII
D
0.71
0.90
0.76
0.80
0.85
0.90
Similarity coefficient
Figure 1. Dendrograms (UPGMA, NTSYS-PC) showing genetic relationships among 18 rice genotypes based on genetic distance matrix data obtained using five sets of AFLP primer combinations (A), 25 ISSR primers (B), 30 SSR primers (C) and pooled allelic profile (D).
Genetic diversity analysis All the three marker systems were able to uniquely discriminate between 18 rice varieties. The level of polymorphism generated by ISSR markers (78.3%) was higher compared to the SSR (67.8%) and AFLP (64.0%) markers. Genetic relationships as determined by cluster and PCA analysis of SSR, AFLP, ISSR and/or pooled allelic diversity data of 18 rice genotypes, are shown in Figures 1 and 2. These dendrograms obtained using SSR, AFLP and ISSR data (Figures 1A–C) were quite similar and most of the varieties were placed in their respective groups, which also match their known pedigrees. The two traditional Basmati rice varieties (Basmati 370 and HBC19) and three (CSR30, Super and HKR228) of the cross-bred
Basmati varieties, shared higher degree of similarity (SSR 0.86%, AFLP 0.88% and ISSR 0.76%) and are clustered together irrespective of the marker system. Indica rice varieties HKR120, IR24, IR64, IR36, Pokkali, CSR10 and Sharbati were grouped together in all the three dendrograms. IR36 and IR64 had >80% similarity and formed a separate sub-group within the major indica group. Cross-bred variety Sabarmati always grouped with indica rice varieties. The status of crossbred varieties, Pusa Basmati 1 and Kasturi varied with the type of marker systems used. Kasturi formed a sub-group with Sabarmati, which clustered with indica group in dendrograms constructed using AFLP and ISSR data. But in case of SSR-dendrogram, Kasturi did not show any such association with Sabarmati and clustered with traditional Basmati group instead of indica
141 0.40
HBC19
Bas370
CSR30 Super 0.27
HKR228 0.13
Kasturi Dim-2 -0.00
Nippon PB1
Azucina NPTII
-0.13
IR24 Pokkali CSR10 Sharbati Sabarmati IR64 IR36
HKR120
-0.27
-0.40 0.84
0.85
0.87
0.89
0.90
Dim-1 Figure 2. Two-dimensional scaling of 18 rice genotypes by principal component analysis (PCA) using the pooled genetic distance matrix data.
group. Pusa Basmati 1 clustered with indica varieties in SSR and ISSR data based dendrograms, but it merged with Basmati group in case of AFLP-dendrogram. Of the three japonica varieties (Azucena, Nipponbare and NPTII), Azucena and Nipponbare were invariably clustered together in a sub-group, but NPTII clustered with Azucena-Nipponbare sub-group only in dendrograms based on SSR and AFLP allelic data. Japonica group was placed closer to traditional/cross-bred Basmati group in SSR-dendrogram, equi-distant from indica and Basmati sub-groups in case of AFLP-dendrogram and closer to indica subgroups in ISSR-dendrogram. Dendrogram obtained using the pooled allelic diversity data shows the grouping of 18 rice varieties into three major groups at a similarity coefficient value of 0.75 (Figure 1D). Group I had two traditional Basmati rice varieties (Basmati 370 and HBC19) and three cross-bred rice varieties (Super, HKR228 and CSR30). Group II had all the indica rice varieties (HKR120, IR24, IR64, Sharbati, IR36, Pokkali and CSR10) and remaining three cross-bred Basmati varieties (Kasturi,
Sabarmati and Pusa Basmati1). Group III contained the three japonica varieties (Azucena, Nipponbare and NPTII), and placed closer to Group II than Group I. Molecular markers for varietal differentiation A number of SSR, ISSR and AFLP markers were identified that distinguished between different Basmati rice varieties, and Basmati and non-Basmati rice varieties (Table 5). At three of the 30 SSR loci, Basmati rice varieties amplified different allele(s) than the indica and japonica rice varieties. Traditional Basmati varieties could be differentiated from the cross-bred Basmati rice varieties by polymorphism specifically at RM144 and RM303 SSR loci, by using the ISSR primer UBC812 and Eco-AGMse-CAT AFLP marker combination. While many of the traditional Basmati specific alleles were shared by the cross-bred Basmati rice varieties, the DNA fingerprint database developed in this study shows enough polymorphism to differentiate different Basmati rice varieties.
142 Table 5. SSR, ISSR and AFLP markers that distinguish between Basmati, cross-bred Basmati and/or non-Basmati rice varieties Molecular markers that can be used for varietal differentiation Rice types
SSR (RM series)
ISSR (UBC)
AFLP (Eco- Mse)
Basmati and non-Basmati
169, 324, 340.
807,808,812,815,824,827, 828,836,853,881,884, 886,8890,891
AG-CAA, AG-CAT, AC-CAG, AG-CAG, AC-CAA
Between traditional Basmati (Basmati 370 and HBC19)
144, 180, 228, 232, 237, 241, 246, 250, 251, 270, 312, 335
807, 808, 809, 810, 811, 812, 815, 823, 825, 827, 827, 840, 881, 886, 890
AG-CAA, AG-CAT, AC-CAG, AG-CAG, AC-CAA
Traditional Basmati and cross-bred Basmati
144, 303, 247*, 287*, 335*
812, 807*, 808*, 810*, 811*, 823*, 827*, 857*, 864*, 886*, 890*
AG-CAA, AG-CAT*, AC-CAG*, AG-CAG*, AC-CAA*
Traditional Basmati and Sharbati
51, 144, 152, 169, 180, 201, 208, 210, 232, 237, 246, 247, 249, 251, 278, 287, 303, 304, 314, 324, 332, 335, 336, 340 144,152, 169, 180, 208, 287, 335,336, 340
807, 808, 810, 815, 823, 824, 825, 836, 866, 881, 866
AG-CAA, AG-CAT, AC-CAG, AG-CAG, AC-CAA
807, 808, 815, 823, 824, 825, 828, 836, 840, 886, 890
AG-CAA, AG-CAT, AC-CAG, AG-CAG, AC-CAA
144, 169, 210, 228, 247, 250, 270, 278, 303, 314, 324, 336, 340
807, 808, 809, 815, 823, 824, 825, 828, 836, 840, 853, 886, 890
AG-CAA, AG-CAT, AC-CAG, AG-CAG, AC-CAA
Traditional Basmati and indica Traditional Basmati and japonica
*Differentiate between one or more cross-bred Basmati varieties from traditional Basmati rice varieties.
Correlation between the similarity values measured using three marker systems The values of the Mantel test correlation showed a positive correlation between the three marker types. The correlation coefficient (r ) was 0.45 between SSRs and ISSRs, 0.50 (significant at P > 0.01) between SSRs and AFLPs and 0.42 between AFLPs and ISSRs. The analysis of molecular variation (AMOVA) revealed apparent differences in partitioning of variation within and among groups (traditional Basmati, crossbred Basmati, indica, and japonica) accomplished by three different marker systems (Table 6). All the three marker systems showed greater variance within rice groups than among various groups. SSR marker analysis showed relatively greater variance among the various rice groups. The best variance within groups was obtained by ISSRs (91.2%) followed by AFLPs (86.6%) and SSRs (76.9%). As described by Powell et al. (1996) and Milbourne et al. (1997), mean expected heterozygosity (Hav ) and marker indices (MI) were estimated as a measure of polymorphism and utility of different marker systems used in this study (Table 7). The average heterozygosity obtained using SSR (Hav = 0.66) was significantly higher (P > 0.01) than estimated using AFLP
Table 6. Partitioning of variance within and between the different groups derived from the analysis of molecular variance for data derived from different markers AFLP
ISSR
SSR
Percent variance: Among groups
13.42
8.83
23.07
Within groups
86.58
91.17
76.93
17.39
33.45
22.26
Total variance
Table 7. A summary of average heterozygosity (Hav ), mean EMR for each marker type, calculated as the mean of the effective multiplex ratios of individual assays (SSRs are assigned an EMR of 1.0) and marker index for each marker type, calculated as the mean of the product of the Hav and EMR for each assay Marker system
Hav
EMR
MI
AFLP
0.45
10.59
4.74
ISSR
0.57
5.63
3.20
SSR
0.66*
1.0
0.66
*Significantly different from the corresponding AFLP value at P > 0.01 level.
(Hav = 0.45). As expected, mean EMR (effective multiplex ratio), for the individual assays was maximum for the AFLP followed by ISSR and minimum for SSR.
143 Consequently the marker index, which is the mean of the product of Hav and EMR for each assay was maximum for AFLP followed by ISSR and SSR. Discussion For genetic analysis, it is of utmost importance to know which type of marker(s) and how many of them truly represent variation in the entire genome and should be used in order to derive reliable estimates of diversity. In this study, we have compared the marker data-sets produced using three different marker systems, AFLP, ISSR and SSR, to define genetic relationships within a set of 18 genotypes representing Basmati, indica and japonica rices, and to know if marker system other than SSR can be effectively used and/or to complement identification of Indian Basmati rice accessions. The minimum number of assay-units per system needed to distinguish between all the cultivars was one for AFLP, two for ISSR and five for SSR. It is evident that AFLPs generated the largest number of fragments and polymorphic fragments per assay than multilocus ISSRs or single locus SSRs. As far as number of genotypes differentiated per assay is concerned, AFLP had the maximum discriminatory power, followed by ISSRs and SSRs. However, ISSRs showed more percent polymorphism (78.3%) than SSRs (67.8%) and AFLPs (64.0%). This was not unexpected because the ISSR technique amplifies at least two microsatellite regions (regarded as highly polymorphic) as well as unique regions in between, and our results are in conformity with earlier published reports in potato (McGregor et al., 2000), rice (Blair et al., 1999; Nagaraju et al., 2002) and oilseed rape (Charters et al., 1996). ISSR primers 808, 886, 809 and 889 generated higher levels of polymorphism and any two of them can be used to differentiate between the 18 rice genotypes. Of the 30 SSR primer pairs, 12 primer pairs amplified 6–8 alleles in this small range of germplasm comprising of 8 Basmati and 10 non-Basmati rice varieties. A number of SSR markers have been identified, which can be used to differentiate among the varieties belonging to different rice groups (Table 5). ISSR and SRR markers involving di-nucleotide repeats motifs particularly those with GA repeats amplified relatively more number of bands as reported earlier in rice (Chen et al., 1997; Cho et al., 2000). All AFLP primer combinations were more or less were equally polymorphic and any one of them can be used to differentiate all 18 rice varieties. Our results also by and large substantiate
the inferences of Zhu et al. (1998) and Aggarwal et al. (2002) that AFLP data sets generated from two or three primer combinations are sufficient for robust estimates and that additional datasets do not change the relationships among the 18 rice genotypes (data not shown). Genetic relationships and diversity data generated using each of three marker systems and pooled diversity data, were in agreement with the Glaszmann (1987) classification of Asian rice germplasm. The study shows that Group V traditional Basmati rice landraces/varieties (Basmati 370, HBC19) are genetically distinct from Group 1 indica (HKR120, CSR10, IR24, IR36, IR64) and Group VI japonica (Nipponbare, Azucena and NPTII) rice varieties. Higher levels of genetic diversity between Basmati and non-Basmati (indica and japonica) suggest that former may have a long history of independent evolution and diverged from non-Basmati rices a long time ago through conscious selection and patronage (Nagaraju et al., 2002; Jain et al., 2004; unpublished data). There were, however, differences between the marker techniques in terms of degree of relationships between Indian Basmati, japonica and indica rices. SSR markers placed traditional Basmati varieties closer to japonica than indica, which is in conformity with Glaszmann (1987) isozyme markers-based classification. AFLP data classified Basmati as closer to indica rice, which in fact supports conventional classification of Basmati as aromatic indica on the basis of phenotypic traits and place of origin. ISSR, on the other hand, located Basmati at equal distance from indica and japonica groups. These observations do not match Glaszmann (1987) classification, which revealed closer association between varieties of Groups V (Basmati) and Group VI (japonica) compared to Group I (indica). It is interesting to note marker system based differences in classification of Basmati rice in relation to indica and japonica, which may be attributed to the different types of target genomic sequences involved in three marker systems (Zietkiewicz et al., 1994; Cho et al., 2000; Zhu et al., 1998). SSR markers used in this study are well distributed on 12 chromosomes, and are located in both coding and non-coding segments of the genome (Cho et al., 2000; Temnykh et al., 2000, 2001). ISSR have been reported to amplify multiple loci across the genome (Zietkiewicz et al., 1994; Blair et al., 1999) and from the centromeric region (Parsons et al., 1997). Though markers generated by most AFLP primer combinations depends on the restriction enzymes used, but are more or less randomly distributed throughout the genome (Zhu et al., 1998; Aggarwal et al., 2002).
144 Among the six cross-bred (indica × Basmati) Basmati rice varieties, there were three categories: first (CSR30 and super) that invariably clustered with traditional Basmati group, second (Sabarmati) closer to indica and the third (HKR228, Pusa Basmati 1, and Kasturi) that grouped variably depending upon the marker system employed (Figure 1). The variable degree of diversity between traditional Basmati and crossbred Basmati rice varieties may be due to the complex parentage of cross-bred varieties involving several recombination events and is the indicative of different levels of genomic fractions from their respective indica rice parent(s) (Singh et al., 2000b; Nagaraju et al., 2002). Marker-dependent differences in the grouping status of HKR228, Pusa Basmati 1, and Kasturi are quite interesting but remain unclear. It is interesting to note that all the three (AFLP, ISSR and SSR) data-sets showed moderate levels positive correlation (Mantel test; r = 0.42–0.50) only; correlation value (r = 0.50) being statistically significant only between AFLP and SSR. This is not surprising as these markers are known to target different genomic fractions involving repeat and/or unique sequences, which may have been differentially evolved or preserved in due course of natural or human selection. A study on winter wheat cultivars did not generate a common pattern of genetic relationships using RFLP, AFLP and SSR markers (Bohn et al., 1999); in barley, classification pattern were similar when using RFLP and AFLP but not SSR (Russell et al., 1997). Virk et al. (2000) reported differences between AFLP and ISSR marker techniques in terms of classification of 42 rice accessions belonging to varietal groups I, II and VI. The seemingly anomalous classification of rice varieties using ISSR markers has also been reported by Parsons et al. (1997). Parsons et al. (1997) reported that ISSRs may target greater number of repeat sequences particularly in the centromeric region which may heavily influence the classification pattern. Marker-based differences in the genetic relationships between rice genotypes do emphasize the need of using a combination of different marker systems for a comprehensive genetic analysis. All the three marker systems invariably showed large variation within groups than among groups. This is not unexpected keeping in view low number and diverse nature of genotypes analyzed for each group. Several of the rice genotypes including Basmati, NPTII and IR lines used in this study have complex parentages/pedigrees (Table 1). As discussed earlier, the six cross-bred Basmati varieties are derived from differ-
ent indica × Basmati rice crosses. To give an example, Pusa Basmati 1 has been developed from several crosses involving six indica and two Basmati rice varieties (Singh et al., 2000b). The best discrimination among the groups was obtained using SSRs. This clearly indicates that SSRs are more efficient for discriminating between the rice groups compared to AFLPs and ISSRs including Basmati rice germplasm. Virk et al. (2000) in a study comprising of 42 rice accessions reported greater variance among the rice groups using AFLPs; ISSRs and RAPDs displayed large variation within the groups. A more objective comparison of overall efficiency of the three marker systems was provided by the marker index (MI) and its components. Comparison of average heterozygosity values highlights the high-level polymorphism generated by SSRs in comparison to AFLPs and ISSRs, which is in concurrence with the earlier reports in many plant species (Powell et al., 1996; Milbourne et al., 1997; McGregor et al., 2000). Hypervariability observed at SSR loci could be due to ‘replication-slippage’ mechanism, which is thought to occur more frequently than the point mutation and insertion/deletion events responsible for generation of polymorphism detectable by AFLP and ISSR analyses (Tautz et al., 1986). As expected multi-locus marker systems (AFLP and ISSR) had high EMR and MI than the single-locus SSRs. Recent developments in fluorescence-based marker technology offer possibility of increasing EMR and MI of SSRs via multiplexing (the use of eight or more primer sets labeled with different fluorophores, which produces a range of allele sizes) using automated DNA sequencer (Blair et al., 2002; McCouch et al., 2002). Though this technology shall require additional capital outlay for equipment, but it shall greatly increase the usefulness of co-dominant, already mapped and publicly available SSRs, allowing greater automation and efficiency. Several other factors such as robustness of the assay, levels of skills required, prior sequence information/developmental cost and cost per assay of the marker system without compromising the quality would also be critical for any agency planning to use it for varietal identification. SSRs and AFLPs are relatively expensive but proven to be more reliable and reproducible than the ISSRs. McGregor et al. (2000) compared the reproducibility factor of different marker systems and reported SSR (100%) to be highly reproducible followed by AFLP (99.6%), ISSR (87%) and RAPD (84.4%). While systematic experiments have not been carried to compare the reproducibility of
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