Mol Biol Rep DOI 10.1007/s11033-014-3304-5
Population structure and genetic diversity analysis of Indian and exotic rice (Oryza sativa L.) accessions using SSR markers B. Kalyana Babu • Vimla Meena • Vasudha Agarwal P. K. Agrawal
•
Received: 29 July 2013 / Accepted: 14 February 2014 Ó Springer Science+Business Media Dordrecht 2014
Abstract In order to understand the population structure and genetic diversity among a set of 82 rice genotypes collected from different parts of the Asian countries including India were characterized using 39 microsatellite loci. The Population structure analysis suggested that the optimum number of subpopulations was four (K = 4) among the rice genotypes, whereas phylogenetic analysis grouped them into three populations. The results obtained from phylogenetic and STRUCTURE analysis proved to be very powerful for the differentiation of rice genotypes based on their place of origin. The genetic diversity analysis using 39 SSR loci yielded 183 scorable alleles, out of which 182 alleles were observed to be polymorphic with an average of 4.8 alleles per locus. The Polymorphism Information Content (PIC) values for all the polymorphic primers across 82 rice genotypes varied from 0.02 to 0.77, with an average of 0.50. Gene diversity (He) was found to be in the range of 0.02 (RM484) to 0.80 (OSR13) with an average value of 0.55, while heterozygosity (Ho) was observed with an average of 0.07, ranging from 0.01 (RM334) to 0.31 (RM316). The present study resulted in identification of seven highly polymorphic SSR loci viz., OSR13, RM152, RM144, RM536, RM489, RM259 and RM271 based on the parameters like PIC value (C0.70), gene diversity (C0.71), and polymorphic alleles (C6). These seven polymorphic primers can effectively be used in further molecular breeding programs and QTL mapping studies of rice since they exhibited very high polymorphism over other loci. SSR analysis resulted in a more
B. K. Babu V. Meena V. Agarwal P. K. Agrawal (&) Division of Crop Improvement, Vivekananda Institute of Hill Agriculture VPKAS, Indian Council of Agricultural Research (ICAR), Almora 263601, Uttarakhand, India e-mail: [email protected]
definitive separation of clustering of genotypes indicating a higher level of efficiency of SSR markers for the accurate determination of relationships between accessions. Keywords Rice Population structure SSR Gene diversity Polymorphism information content (PIC)
Introduction Rice, one of the most widely cultivated crops, provides food for one-half of the world population and is the second most widely consumed cereal in the world, next to wheat. Rice breeders are increasingly challenged to meet the rapidly growing food demands for an increasing human population. India occupies second place in the rice production, next to China. In India, rice is cultivated for an area of 44 million hectare area and contributes 140 million tonnes of grain production, with a productivity of 3,124 kg/ha. The NW Himalayan region of India have 0.63 million ha area under rice cultivation producing about 1.26 million tonnes with a productivity of 1,921 kg/ha [1]. Over two billion people in Asia alone derive 80 % of their calorie needs from rice [2]. Rice protein, though small in amount, is of high nutritional value [3]. Basmati rice makes a metallothionein-like protein, rich in cystine that aids in iron absorption. Colored rice (black and red) is rich in minerals (iron and zinc), polyphenols and have anti-oxidant properties [4]. There is a requirement for increasing the production and productivity of rice in order to meet the demand of everincreasing world population. One of the key factors for the crop improvement efforts depend upon the amount and the use of genetic variability in breeding programs. Characterization and quantification of genetic diversity among various germplasm is thus a major goal for effective
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genetic enhancement for crop productivity. Genetic diversity based on the morphological characters possess several limitations as they are influenced by environmental factors, are limited in number and do not have the resolution power for the differentiation between closely related genotypes [5]. Advances in plant genetics and molecular biology have lead to the development of molecular markers. DNA markers have been used to evaluate genetic diversity in different crop species [5]. Various molecular markers are being used for fingerprinting and diversity analysis among crop plants such as Restriction Fragment Length Polymorphism (RFLP) [6], Random Amplified Polymorphic DNA (RAPD) [7], microsatellites or Simple Sequence Repeats (SSRs) [8] and Amplified Fragment Length Polymorphism (AFLP) [9]. Some of these techniques are robust and reliable, e.g., RFLP and AFLP, while some are quick, e.g., RAPD and some others are both quick and reliable, e.g., SSRs. AFLP and RFLP are highly time consuming and costly and therefore, their use is restricted. PCR based techniques, i.e., SSRs and RAPD are fast and cost effective, hence are widely used. Microsatellites are short tandem repeat motifs and offer many advantages such as high level of polymorphism [10], high accuracy and repeatability throughout the genome [11], automated analysis [12], rapidity, technical simplicity, low cost, and requirement of only a small quantity of DNA (few nanograms). Thus, they are useful as genetic markers in many plant species including Arabidopsis, rice, wheat, soybean and sweet potato [13–15]. SSR markers are also widely used in assessing genetic diversity in rice at both inter and intra-specific level [8, 16]. Structure [17] is the most extensively used software applied to detect the population genetic structure. It generates clusters based on both transient Hardy–Weinberg disequilibrium and linkage disequilibrium caused by admixture between populations. It works by clustering individuals in groups, where linkage and Hardy–Weinberg disequilibrium are both minimized, and therefore, the presence of linkage disequilibrium in the data improves clustering results [18]. Herrera et al. [19] used 48 SSR markers to assess genetic diversity among 11 Venezuelan rice cultivars. They observed that the varieties were closely related and molecular identification of seven of the cultivars could be done with nine primer pairs producing 10 genotype specific alleles. Although, the genetic diversity was observed to be low, SSRs proved to be an efficient tool in assessing the genetic variability of rice genotypes. However, no efforts have been made for the assessment of genetic diversity of rice from North-West Himalayan region of India and their comparison with other rice cultivars from other parts of India and exotic genotypes. The present study was undertaken with the objectives to assess the population structural variation among the selected 82 rice genotypes, and the level of genetic diversity among them using SSR markers to compare and analysis of genetic
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variation of NW Himalayan genotypes with the other Indian and exotic rice genotypes from different parts of the world, and to better understand the association between genetic diversity and geographic factors.
Materials and methods Plant materials and DNA extraction Seeds of 82 rice genotypes including of 27 varieties developed by Vivekananda Institute of Hill Agriculture, Almora (Uttarakhand), for the NW Himalayan region of India; 23 genotypes from Northern and Southern parts of India, and 32 exotic rice accessions including two lines from IRRI, Philippines were taken for the present study. The details of the rice genotypes along with their origin, and ecosystem are given in Table 1. The genomic DNA was isolated from the fresh young leaves following Agrawal and Katiyar [20]. Thirty nine SSR markers were used to detect polymorphism among the 82 rice genotypes spread evenly over all the chromosomes. The polymerase chain reactions and gel documentation were carried out using standard procedures, and the amplified products were resolved on 3.5 % agarose gel [Super Fine Resolution (SFR) Agarose; Amresco, USA] and scoring was carried out manually. Data analysis The SSR scores were used to create a data matrix to analyze genetic relationships using the NTSYS-pc program version 2.11a [21]. The dendrogram was constructed based on Jaccard’s similarity coefficient [22] using the marker data for all the rice genotypes following unweighted pair group method analysis (UPGMA) [23]. The Polymorphism Information Content (PIC) was determined as described by Tang et al. [24], by using the formula PIC = 1–Rfi2, where fi is the frequency of the ith allele. For genotypes showing heterozygosity at a specific SSR locus, the PIC values were calculated after considering each allele as contributing one-half instead of one, as suggested by Narvel et al. [25]. The heterozygosity, gene diversity, allele frequency and inbreeding coefficients were calculated using Power Marker V3.0 software [26]. Population structure analysis The structure of the population was studied with the structure version 2.3.4 software [27]. Clustering methods with distinctive allele frequencies were used to identify the optimum number of population (K) subgroups. Each individual can be a member of multiple subpopulations with a different coefficient, with the sum of all being equal to one [28]. The number of subgroups (K) in the population was
Mol Biol Rep Table 1 Details of the 82 rice genotypes used in the study
S. no.
Genotype
Origin (location)
Ecosystem
1.
Kba-Sawrit B2
Exotic
NA
2.
Local Ahu
Exotic
NA
3.
Milyang-15
Exotic
Irrigated
4.
Milyang-93
Exotic
Irrigated
5.
Mipun
Exotic
NA
6.
Nanglwai
Exotic
NA
7.
Naveen
India
Upland
8.
Neela
India
Upland
9.
Pusa Basmati 1
India
Irrigated
10.
Pusa Sugandha 2
India
Irrigated
11.
Pusa Sugandha 3
India
Irrigated
12.
Pusa Sugandha 5
India
Irrigated
13.
Rashi
India
Upland
14.
Ratna
India
Irrigated
15. 16.
RP 2421 Saket-4
India Exotic
Irrigated Irrigated
17.
Satabdi
India
Irrigated
18.
Satari
Exotic
Irrigated
19.
Satya
India
NA
20.
Sinsatsu
Exotic
NA
21.
Sneha
India
Irrigated
22.
Stesiara-45
Exotic
NA
23.
Sulocon 235
Exotic
NA
24.
Suweon
Exotic
Irrigated
25.
Swarna
India
Shallow rain-fed
26.
Tai-Hikawi
Exotic
NA
27.
Tangla
Exotic
NA
28.
Tapaswini
India
Irrigated
29.
Theberu
Exotic
NA
30.
TN-1
Taiwan
Irrigated
31. 32.
Vidhaya VL Dhan 82
India VPKAS, Almora, India
NA Irrigated
33.
VL Dhan 30919
VPKAS, Almora, India
Irrigated
34.
VL Dhan 31330
VPKAS, Almora, India
Irrigated
35.
VL Dhan 31331
VPKAS, Almora, India
Irrigated
36.
VL Dhan 61
VPKAS, Almora, India
Irrigated
37.
VL Dhan 65
VPKAS, Almora, India
Irrigated
38.
VL Dhan 66
VPKAS, Almora, India
Irrigated
39.
VL Dhan 81
VPKAS, Almora, India
Irrigated
40.
VL Dhan 86
VPKAS, Almora, India
Irrigated
41.
VL Dhan 154
VPKAS, Almora, India
Upland rain-fed
42.
VL Dhan 163
VPKAS, Almora, India
Upland
43.
VL Dhan 206
VPKAS, Almora, India
Upland
44.
VL Dhan 209
VPKAS, Almora, India
Upland
45.
VL Dhan 221
VPKAS, Almora, India
Upland
46. 47.
Yam-Nani Yunlen-20
Exotic Exotic
NA NA
48.
Zenith
Exotic
NA
49.
Abor-A
Exotic
NA
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Mol Biol Rep Table 1 continued
NA not available
S. no.
Genotype
Origin (location)
Ecosystem
50.
Anjali
India
Upland
51.
Barkot
Exotic
NA
52.
Basmati 370
Land race (NW India)
Irrigated
53.
Boda-271
Exotic
NA
54.
BPT 5204
India
Irrigated
55.
Catrosa
Exotic
NA
56.
Chandan
India
NA
57.
Dhalahara
India
Upland
58.
Dullo
Exotic
NA
59.
Dullo-11
Exotic
NA
60.
Dullo-A
Exotic
NA
61.
Geetanjali
India
Medium land and irrigated
62.
GGAF-BYEO
Exotic
NA
63.
Henjudo
Exotic
NA
64. 65.
IR-64 IR 3941-34
IRRI, Philippines IRRI, Philippines
Irrigated NA
66.
Janam
India
NA
67.
Jungurh
Exotic
NA
68.
Kba-Thangmaw
Exotic
NA
69.
Mujudo
Exotic
NA
70.
Vandana
India
Upland
71.
VL Dhan 7620
VPKAS, Almora, India
Upland
72.
VL Dhan 16
VPKAS, Almora, India
NA
73.
VL Dhan 85
VPKAS, Almora, India
NA
74.
VL Dhan 62
VPKAS, Almora, India
Irrigated
75.
VL Dhan 82
VPKAS, Almora, India
Irrigated
76.
S-1
NA
NA
77.
RP2421
NA
NA
78.
HPR-2143
NA
Irrigated
79.
VL Dhan207
VPKAS, Almora, India
Upland
80. 81.
VL Dhan 208 P1460
VPKAS, Almora, India NA
Upland NA
82.
P1121
NA
Irrigated
determined by running the program at different K values with K carrying from 2 to 10. Five independent runs were assessed for each K value. We used a burn-in period of 100,000 steps followed by 100,000 Monte Carlo Markov Chain replicates, as suggested by Pritchard and Wen [29].
Results Genetic variation of SSR loci among the rice genotypes The genomic DNA of the 82 rice accessions were amplified using 39 SSR markers and yielded 183 scorable alleles, out
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of which 182 alleles were found to be polymorphic, while one was monomorphic. All the 39 SSRs were spread across all the chromosomes of rice evenly. Out of the 39 primers, 38 (98 %) loci were found to be polymorphic, while one SSR locus, RM338 (2 %) was monomorphic. A total of 182 alleles were detected for the 38 polymorphic SSR markers with an average of 4.8 alleles per locus, while it was 4.69 for all the 39 SSR loci including monomorphic marker used in the study. The number of alleles generated with polymorphic primers ranged from 2 to 9 among the rice genotypes. The SSR locus RM144 was found to have maximum number of allele (9) followed by the loci RM447 and RM316 (each seven allele) (Table 2). The banding
Mol Biol Rep Table 2 Parameters for genetic analysis of 39 SSR loci across the 82 rice accessions
SSR loci
PIC
Heterozygosity
Gene diversity
Allele number
Major allele frequency
Chromosome
RM338
0.00
0.00
0.00
1.00
1.00
3
RM484
0.02
0.00
0.02
2.00
0.99
10
RM283
0.11
0.00
0.12
2.00
0.94
1
RM452
0.28
0.25
0.31
4.00
0.82
2
RM125
0.31
0.07
0.33
4.00
0.81
7
RM215
0.31
0.01
0.38
3.00
0.75
9
RM454
0.32
0.00
0.39
3.00
0.74
6
RM277
0.32
0.01
0.37
3.00
0.76
12
RM133
0.34
0.00
0.41
3.00
0.73
6
RM284
0.36
0.00
0.40
3.00
0.75
8
RM312 RM433
0.37 0.39
0.21 0.22
0.41 0.42
3.00 4.00
0.75 0.75
1 8
RM510
0.41
0.01
0.50
3.00
0.64
6
RM44
0.45
0.06
0.53
3.00
0.60
–
RM287
0.46
0.03
0.51
4.00
0.66
11
RM161
0.46
0.02
0.56
3.00
0.53
5
RM118
0.47
0.00
0.53
3.00
0.62
7
RM25
0.49
0.16
0.54
5.00
0.63
8
RM413
0.57
0.03
0.60
7.00
0.59
5
RM316
0.57
0.31
0.60
8.00
0.61
9
RM431
0.60
0.02
0.65
5.00
0.52
1
RM154
0.60
0.02
0.66
5.00
0.43
2
RM105
0.60
0.00
0.67
4.00
0.41
9
RM334
0.61
0.01
0.64
5.00
0.54
5
RM237
0.61
0.29
0.67
5.00
0.44
1
RM474
0.61
0.05
0.64
6.00
0.57
10
RM408 RM55
0.61 0.62
0.03 0.00
0.67 0.67
6.00 6.00
0.44 0.47
8 3
RM514
0.63
0.02
0.68
4.00
0.44
3
RM447
0.66
0.15
0.69
8.00
0.51
8
RM271
0.66
0.08
0.71
6.00
0.38
10
RM259
0.68
0.01
0.72
6.00
0.41
1
RM5
0.68
0.01
0.72
5.00
0.35
–
RM552
0.68
0.24
0.72
5.00
0.41
11
RM489
0.71
0.15
0.75
6.00
0.32
3
RM536
0.73
0.00
0.76
7.00
0.38
11
RM144
0.75
0.14
0.78
9.00
0.34
11
RM152
0.76
0.07
0.79
7.00
0.32
8
OSR13
0.77
0.02
0.80
7.00
0.25
–
Mean
0.50
0.07
0.55
4.7
0.58
pattern of 82 rice genotypes with SSR loci RM17 was shown in Fig. 1. The PIC values for all the polymorphic primers across 82 rice genotypes varied from 0.02 to 0.77 with an average value of 0.50. Out of the 39 primers, two primers (RM152 and OSR13) showed highest PIC values of 0.76 and 0.77 respectively, while three SSR loci, RM489, RM536 and
RM144 showed a PIC value of 0.71, 0.74 and 0.75 respectively. The lowest PIC values was observed in primers RM484 (0.02), RM 283 (0.12) (Table 2). The number of SSR loci based on PIC values with more than the average was 21 in number. Among them, SSR loci RM536, RM144, RM152 and OSR13 were noteworthy due to their relatively higher level of polymorphism. A total of
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M
1
2
3
4
5 6
7 8
9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24
200bp
100bp
M 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48
M
49
50
51
76 77 78 79 80
52
53
54
55
56
57
58
59 60
61 62 63 64
65 66
67
68 69
70 71 72 73 7475
81 82
Fig. 1 The SSR banding profile of RM17 SSR loci among the 82 rice genotypes. Lane 1, M- Marker, Lane 1–82 rice genotypes (for labels please refer to Table 1)
21 SSR loci came under the PIC range of 0.5–0.77 with an average value of 0.65, while 18 loci came within the PIC range of 0.00–0.49 with an average value of 0.33. The SSRs which had PIC value more than 0.50 also generated more number of loci with an average of 6.1, while it was 3.1 for the SSRs below the PIC value of 0.5 (Table 2). Statistical analysis of genetic diversity Gene diversity also known as ‘expected heterozygosity’ (He) was in the range of 0.02 (RM484) to 0.80 (OSR13) with an average value of 0.55. The heterozygosity, known as ‘observed heterozygosity’ (Ho) was observed with an average of 0.07 and range of 0.01 (RM334) to 0.31 (RM316). Major alleles with highest frequency were observed for the locus RM484 (80 bp allele) at 99 % followed by the locus RM283 (150 bp) at 94 %. Based on the above SSR analysis by considering the parameters of PIC value (C0.70), gene diversity (C0.71), inbreeding coefficient (C0.62) and polymorphic alleles (C6), seven most highly polymorphic SSR loci OSR13, RM152, RM144, RM536, RM489, RM259 and RM271 were observed (Table 2).
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Associations among rice genotypes Jaccard’s similarity coefficient between all the 82 rice genotypes ranged from 22 % to 95 %. The maximum similarity (88 %) was found between the genotype pairs Geetanjali with Dullo-A and Chandan and Dullo-A (each pair 88 %). The above results on similarity were also substantiated by the dendrogram generated using the software power marker (Fig. 2). The dendrogram was constructed by using both NTSYSpc2.11 and power marker software (rooted and rectangular), and both were similar. The similarity coefficients were used as input data for the cluster analysis using NTSYSpc 2.11 program. The dendrogram generated through UPGMA analysis grouped all the 82 rice genotypes into three major groups A, B, and C. The clustering of the rice genotypes was largely based on their centre of development. The cluster C comprised of all the exotic genotypes with five Indian rice accessions viz, Vandana, Satya, VL Dhan 221, Swarna and Geetanjali. All the exotic lines were grouped under the cluster C with few exceptions like Jungurh, Abor-A, Dullo, Barkot and Catrosa which were placed in the cluster A. The
Mol Biol Rep
Fig. 2 The phylogram of 82 rice genotypes as obtained from POWER MARKER software based on UPGMA analysis
major cluster A comprised of 40 genotypes, while the cluster B and C comprised of 14 and 28 genotypes respectively. The genotypes from the NW Himalayan region were grouped in the cluster A and B. Cluster ‘A’ comprised of genotypes from different centers that includes exotic and Indian genotypes, from different centers of development like CRRI, Cuttack; IARI, New Delhi; VPKAS, Almora and IRRI, Philippines. The cluster B consisted of most of the VPKAS genotypes from VPKAS along with the genotypes from IARI, New Delhi. The genotypes BPT 5204 and Swarna were from Bapatla,
Andhra Pradesh, but were scattered into two different clusters A and D respectively. The genotypes from CRRI, Cuttack were spread randomly in the cluster A. Population structure analysis The rice genotypes consisted of popular varieties of India and exotic accessions from different regions were evaluated for estimation of population structure using a panel of 39 SSR loci spread across all the 12 chromosomes. For estimation of the exact population structure (K), Ks from 1
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Fig. 3 The population structure of 82 rice genotypes as obtained from structure 2.3.4 software
to 10 (with ten iterations) were ran and the LnP(D) value was used to group all the genotypes. The maximum DK value was observed for K = 4. The inferred ancestry at K = 4 suggested that the rice genotypes were grouped into four subpopulations (Fig. 3). However power marker software was able to differentiate them into three main clusters. The subpopulation (G1) consisted of exotic rice genotypes with few Indian genotypes viz., Basmati 370, Geetanjali, VL Dhan 7620 and Anjali. This subpopulation had admixture of Anjali, Dullo and Dhalaheera. The subpopulation (G2) consisted of all the Indian accessions, where as G3 consisted of all the exotic germplasm with Satya and Swarna rice varieties of India. The subpopulations G2 and G3 did not have any admixture populations. The subpopulation (G4) consisted of mostly Indian genotypes with few exotic germplasm. Similar grouping pattern (except, exotics were under same cluster) was also observed in the case of power marker, however with the help of a structure bar plot, depicting the estimated membership of each variety in each of the populations (K = 1–10), the admixtures could easily be identified, which can explain the grouping pattern better than the dendrogram. The mean value of alpha was 0.0284, and the ancestry-inferred cluster proportions of the membership of the samples were 0.265, 0.135, 0.256 and 0.345. The average distances (expected heterozygosity) between individuals in the same cluster were 0.1728, 0.3374, 0.1387 and 0.1834 and the allele frequency [divergence among pops (Net nucleotide distance), computed using point
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Table 3 Allele frequencies, [divergence among pops (net nucleotide distance)], computed using point estimates of P Clusters
1
2
3
4
1
–
0.5327
0.2665
0.3109
2
0.5327
–
0.5359
0.5175
3
0.2665
0.5359
–
0.1936
4
0.3109
0.5175
0.1936
–
estimates of P] was 0.5327 between clusters 1 and 2, 0.2665 between clusters 1 and 3, and 0.3109 between clusters 1 and 4 (Table 3).
Discussion Microsatellite (SSR) analysis Cultivated varieties of rice were the result of several thousands of years of human selection from the available genetic diversity in various environments and human cultures. Modern breeding in the last two century has done little more than to control the process of hybridization and selection in a more efficient way and the result has been the development of varieties adapted to better controlled environments. Cultivation of few genotypes in any crop including rice leads to narrow genetic base. The crop is prone to many biotic and abiotic stresses. Improvement of
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rice breeding requires identification of highly diverse germplasm and highly polymorphic molecular markers and their effective utilization in mapping and breeding programmes. In the present study, 39 primers yielded 183 scorable alleles, out of which 182 were found to be polymorphic with an average of 4.69 alleles per locus with a range of 2–9 alleles per locus. Michael et al. [30] also found similar results where they used 30 SSR loci across the 183 Indonesian rice landraces on the islands of Borneo. They obtained 166 alleles with a range of 2–15. This high number of alleles may be due to that fact that they used land races from different parts of Indonesia, where landraces are expected to exhibit higher diversity. However, the number of alleles obtained during the present study is also quite high that could differentiate the rice genotypes matching to their centers of development largely. This showed that these markers are highly useful and can be effectively useful for genetic diversity and fingerprinting studies. Similarly, Xinghua et al. [31] used SSRs for estimating the temporal changes from 1950s to 1990s across 310 major Chinese rice varieties and obtained 221 alleles with an average of 5.7 alleles per locus. However, Chakravarthi and Rambabu [32] found 2–7 alleles per locus with 30 SSR loci on 15 elite rice genotypes which contained Indian and exotic lines from IRRI, Philippines. Out of the total 39 primers used, all the primers amplified among all the genotypes. However, 38 primers (98 %) were found to be polymorphic and one SSR was monomorphic. This high level of polymorphism existed among the selected genotypes has great potential for their use in breeding programs. In the present study, close proportionate relationship between the number of alleles and the PIC values of SSR loci was observed. PIC demonstrates the informativeness of the SSR loci and their potential to detect differences among the genotypes based on their genetic relationships. The SSR loci, RM144, RM152 and OSR13 having high number of alleles and highest PIC values, will be very useful for further genetic and mapping studies. A conclusion may be derived that the loci with more number of alleles can be highly useful for the assessment of genetic diversity. Similar results were also observed by Ravi et al. [33] where it was observed that the SSR loci RM202 produced 11 alleles with highest PIC of 0.89. The PIC values of all the polymorphic primers across 82 rice genotypes in the present study were in the range of 0.02–0.77 with an average value of 0.50. The number of SSR loci with higher than average PIC values was found to be 21. The average PIC value determined during the present investigation agreed well with the earlier findings reported based on SSR marker in rice genotypes [30, 31, 33]. Michael et al. [30] reported an average PIC value of 0.45 which was lower than the PIC value observed during the present study. This showed the polymorphic ability of the SSR loci reported
and their wide applicability for genetic analysis of rice accessions. The high level of polymorphism was due to diverse germplasm and highly polymorphic SSR loci used in the present study. Among the SSRs, RM536, RM144, RM152 and OSR13 were noteworthy due to their relatively higher polymorphism, high PIC values and more number of polymorphic alleles per locus and they can effectively be used in the genetic diversity studies. The observed and expected heterozygosity within the genotypes showed obvious deviations from Hardy–Weinberg expectations. Gene diversity (He) was in the range of 0.02–0.80 with an average value of 0.55 which was correlated with the earlier findings. Xinghau et al. [31] observed gene diversity to be 0.62 among the 310 major Chinese landraces which was slightly more than the He value observed during the present study. The heterozygosity, was observed with an average of 0.07 and ranged from 0.01 (RM334) to 0.31 (RM316) which is higher than the findings observed by Michael et al. [30] where they observed an average value of 0.03 (Table 2) emphasizing that the selection practiced during the development of hybrids, could have led to the deficit of heterozygous individuals. Based on the above SSR analysis by considering the parameters of PIC value (C0.70), gene diversity (C0.71), inbreeding coefficient (C0.62) and polymorphic alleles (C6), seven most highly polymorphic SSR loci observed were OSR13, RM152, RM144, RM536, RM489, RM259 and RM271 (Table 2). These primers can effectively be used in molecular breeding programs and QTL mapping studies since they exhibited very high level of polymorphism over other loci. Genetic diversity among the rice genotypes The ability to provide distance measures between the genotypes that reflect pedigree relatedness ensures a more stringent evaluation of the adequacy of a marker profile data. The fact that minimum genetic distance revealed during the study is a good indication confirming the power of SSR markers to distinguish between geographically similar genotypes and closely related genotypes. The average gene diversity existing among all the genotypes were relatively high (55 %), indicating existence of high levels of polymorphisms among the inbreds. These results are in close agreement with the findings reported among the Chinese rice accessions using the SSR marker system [31]. The dendrogram constructed using the UPGMA clustering algorithm grouped the rice accessions into three clusters. These groupings, in most instances, revealed evidence of associations related to their centre of development i.e., geographic origin. However, Michael et al. [30] did not get such type of correlation which may be
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because of less number of polymorphic primers used by them. Alternatively, the 82 rice genotypes were broadly grouped into exotic rice accessions, accessions from VPKAS, Almora with few exceptions. For example, VL Dhan 163 grouped with BPT 5204 and Pusa Basmati 1 which were from Bapatla, Andhra Pradesh and IARI, New Delhi respectively. It showed that during selection/evolution allele introgression has occurred from several sources. The cluster ‘A’ was comprised of mixed genotypes from different origins. This is also an indication that modern breeding programmes involved diverse genotypes in developing highly suitable genotypes adapted to their local environments by crossings from the popular varieties of different origins for important agronomic traits. The cluster B mostly composed of genotypes developed from VPKAS, Almora with exceptions like Pusa Sugandha accessions. The interesting point in our study was that all the exotic lines with some exceptions were grouped under the cluster C with Indian genotypes like Vandana, Satya, VL Dhan 221, Swarna and Geetanjali, which may be due to that exotic pedigree involved in breeding these five genotypes. This showed that these selected markers were not only able to differentiate based on geographic origin but also based on the pedigree of the genotypes. The accession VL Dhan 221 was derived from the cross between IR 2053-521-1-11/CH 1039, however Swarna was derived from Vasista and Mahsuri, and Mahsuri was derived from Taichung 65/ZxMoyzng EB05080. Likewise Sri Satya was derived from RGL1232/Phalguna/RGL1231/IR36, Vandana from C-22 and Kalakeri. Hence there was introgression of alleles from the exotic lines into these genotypes while crossings for the development of superior hybrids for this region. The rice variety Swarna is highly popular in the most parts of eastern India due to its good taste, yield, and adaptability. Population structure analysis Since some geographical grouping was observed in the phylogenetic analysis, we evaluated them to understand whether alternative clustering methods such as modelbased clustering would resolve the geographic patterning. From the phylogenetic analysis, it was observed that there were at least three population subgroups, largely corresponding to Indian, exotic and Indian with few exotic rice genotypes. However, by the structure analysis these exotic accessions were further divided into two different subgroups. The phylogenetic analysis also provided some evidence for gene flow between the subpopulations. There was good correspondence between the geographic pattern observed in the dendrogram and the population structure identified using structure. Although the population subgroups corresponded largely to geographic regions, there were some notable exceptions. In the subpopulation (G4),
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genotypes VL Dhan 221, VL Dhan 206 and VL Dhan 209 carried more than 35 % of the exotic germplasm alleles. For example, the accession VL Dhan 221 was derived from the cross between IR 2053-521-1-1-1/CH 1039. The exotic rice variety TN-1 grouped with Indian genotypes, which showed that 70 % of alleles were common with Indian germplasm. This showed that the exotic variety TN-1 was very highly used in rice breeding programmes during 1960s for developing high yielding rice varieties. Evidence of admixture was also found in exotic population where Dullo and Dhalahara accessions carried 8–10 % alleles from Indian germplasm. Thus, the present study showed that genotyping combined with phylogenetic and population structure analysis is a powerful method for characterizing germplasm. The present study was able to identify highly polymorphic SSR loci which can be effectively used in genetic analysis studies and molecular breeding programmes. Our results also showed that the polymorphic SSR loci used in the study were able to differentiate the Indian and exotic rice genotypes based on their geographical regions. There is also a great scope to derive further improved materials by hybridizing with distant lines identified during the present study and for further selection of the desired line to derive more agronomically useful lines for their commercial production.
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21. Rohlf FJ (1997) NTSYS-pc. Numerical taxonomy and multivariate analysis system, version 2.0. State University of New York, New York 22. Jaccard P (1908) Nouvelles recherches sur la distribuition florale. Bull Soc Vaud Nat 44:223–270 23. Gawel NJ, Jarret RL (1991) A modified CTAB DNA extraction procedure for Musa and Ipomoea plant. Mol Biol Rep 9:262–266 24. Tang S, Zhang Y, Zeng L, Luo L, Zhong Y, Geng Y (2010) Assessment of genetic diversity and relationships of upland rice accessions from southwest China using microsatellite markers. Plant Biosyst 144(1):85–92 25. Narvel JM, Fehr WR, Chu WC, Grant D, Shoemaker RC (2000) Simple sequence repeat diversity among soybean plant introductions and elite genotypes. Crop Sci 40:1452–1458 26. Liu K, Muse SV (2005) Powermarker: integrated analysis environment for genetic marker data. Bioinformatics 21(9):2128–2129 27. Siwach P, Jain S, Saini N, Chowdhury VK, Jain RK (2004) Allelic diversity among Basmati and non-Basmati long-grain indica rice varieties using microsatellite markers. J Plant Biochem Biotechnol 13:25–32 28. Aranzana MJ, Abbasi E, Howard W, Arus P (2010) Genetic variation, population structure and linkage disequilibrium in peach commercial varieties. BMC Genet 11:69 29. Pritchard JK, Wen W (2003) Documentation for the structure software, version 2. Department of Human Genetics, University of Chicago, Chicago. http://pritch.bsd.uchicago.edu/software 30. Michael JT, Nicholas RP, Joko PKR, Trijatmiko, Tiur SS, McCouch SusanR (2009) Genetic diversity of isolated populations of Indonesian landraces of Rice (Oryza sativa L.) collected in east Kalimantan on the Island of Borneo. Rice 2:80–92 31. Xinghua W, Yuan X, Yu H, Wang Y, Qun Xu, Tang S (2009) Temporal changes in SSR allelic diversity of major rice cultivars in China. J Genet Genom 36:363–370 32. Chakravarthi B, Rambabu N (2006) SSR marker based DNA fingerprinting and diversity study in rice (Oryza sativa. L). Afr J Biotechol 5(9):684–688 33. Ravi M, Geethanjalil S, Sameeyafarheen F, Maheswaran M (2003) Molecular marker based genetic diversity analysis in rice (Oryza sativa L.) using RAPD and SSR markers. Euphytica 133:243–252
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Population structure and genetic diversity analysis of Indian and exotic rice (Oryza sativa L.) accessions using SSR markers B. Kalyana Babu • Vimla Meena • Vasudha Agarwal P. K. Agrawal
•
Received: 29 July 2013 / Accepted: 14 February 2014 Ó Springer Science+Business Media Dordrecht 2014
Abstract In order to understand the population structure and genetic diversity among a set of 82 rice genotypes collected from different parts of the Asian countries including India were characterized using 39 microsatellite loci. The Population structure analysis suggested that the optimum number of subpopulations was four (K = 4) among the rice genotypes, whereas phylogenetic analysis grouped them into three populations. The results obtained from phylogenetic and STRUCTURE analysis proved to be very powerful for the differentiation of rice genotypes based on their place of origin. The genetic diversity analysis using 39 SSR loci yielded 183 scorable alleles, out of which 182 alleles were observed to be polymorphic with an average of 4.8 alleles per locus. The Polymorphism Information Content (PIC) values for all the polymorphic primers across 82 rice genotypes varied from 0.02 to 0.77, with an average of 0.50. Gene diversity (He) was found to be in the range of 0.02 (RM484) to 0.80 (OSR13) with an average value of 0.55, while heterozygosity (Ho) was observed with an average of 0.07, ranging from 0.01 (RM334) to 0.31 (RM316). The present study resulted in identification of seven highly polymorphic SSR loci viz., OSR13, RM152, RM144, RM536, RM489, RM259 and RM271 based on the parameters like PIC value (C0.70), gene diversity (C0.71), and polymorphic alleles (C6). These seven polymorphic primers can effectively be used in further molecular breeding programs and QTL mapping studies of rice since they exhibited very high polymorphism over other loci. SSR analysis resulted in a more
B. K. Babu V. Meena V. Agarwal P. K. Agrawal (&) Division of Crop Improvement, Vivekananda Institute of Hill Agriculture VPKAS, Indian Council of Agricultural Research (ICAR), Almora 263601, Uttarakhand, India e-mail: [email protected]
definitive separation of clustering of genotypes indicating a higher level of efficiency of SSR markers for the accurate determination of relationships between accessions. Keywords Rice Population structure SSR Gene diversity Polymorphism information content (PIC)
Introduction Rice, one of the most widely cultivated crops, provides food for one-half of the world population and is the second most widely consumed cereal in the world, next to wheat. Rice breeders are increasingly challenged to meet the rapidly growing food demands for an increasing human population. India occupies second place in the rice production, next to China. In India, rice is cultivated for an area of 44 million hectare area and contributes 140 million tonnes of grain production, with a productivity of 3,124 kg/ha. The NW Himalayan region of India have 0.63 million ha area under rice cultivation producing about 1.26 million tonnes with a productivity of 1,921 kg/ha [1]. Over two billion people in Asia alone derive 80 % of their calorie needs from rice [2]. Rice protein, though small in amount, is of high nutritional value [3]. Basmati rice makes a metallothionein-like protein, rich in cystine that aids in iron absorption. Colored rice (black and red) is rich in minerals (iron and zinc), polyphenols and have anti-oxidant properties [4]. There is a requirement for increasing the production and productivity of rice in order to meet the demand of everincreasing world population. One of the key factors for the crop improvement efforts depend upon the amount and the use of genetic variability in breeding programs. Characterization and quantification of genetic diversity among various germplasm is thus a major goal for effective
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genetic enhancement for crop productivity. Genetic diversity based on the morphological characters possess several limitations as they are influenced by environmental factors, are limited in number and do not have the resolution power for the differentiation between closely related genotypes [5]. Advances in plant genetics and molecular biology have lead to the development of molecular markers. DNA markers have been used to evaluate genetic diversity in different crop species [5]. Various molecular markers are being used for fingerprinting and diversity analysis among crop plants such as Restriction Fragment Length Polymorphism (RFLP) [6], Random Amplified Polymorphic DNA (RAPD) [7], microsatellites or Simple Sequence Repeats (SSRs) [8] and Amplified Fragment Length Polymorphism (AFLP) [9]. Some of these techniques are robust and reliable, e.g., RFLP and AFLP, while some are quick, e.g., RAPD and some others are both quick and reliable, e.g., SSRs. AFLP and RFLP are highly time consuming and costly and therefore, their use is restricted. PCR based techniques, i.e., SSRs and RAPD are fast and cost effective, hence are widely used. Microsatellites are short tandem repeat motifs and offer many advantages such as high level of polymorphism [10], high accuracy and repeatability throughout the genome [11], automated analysis [12], rapidity, technical simplicity, low cost, and requirement of only a small quantity of DNA (few nanograms). Thus, they are useful as genetic markers in many plant species including Arabidopsis, rice, wheat, soybean and sweet potato [13–15]. SSR markers are also widely used in assessing genetic diversity in rice at both inter and intra-specific level [8, 16]. Structure [17] is the most extensively used software applied to detect the population genetic structure. It generates clusters based on both transient Hardy–Weinberg disequilibrium and linkage disequilibrium caused by admixture between populations. It works by clustering individuals in groups, where linkage and Hardy–Weinberg disequilibrium are both minimized, and therefore, the presence of linkage disequilibrium in the data improves clustering results [18]. Herrera et al. [19] used 48 SSR markers to assess genetic diversity among 11 Venezuelan rice cultivars. They observed that the varieties were closely related and molecular identification of seven of the cultivars could be done with nine primer pairs producing 10 genotype specific alleles. Although, the genetic diversity was observed to be low, SSRs proved to be an efficient tool in assessing the genetic variability of rice genotypes. However, no efforts have been made for the assessment of genetic diversity of rice from North-West Himalayan region of India and their comparison with other rice cultivars from other parts of India and exotic genotypes. The present study was undertaken with the objectives to assess the population structural variation among the selected 82 rice genotypes, and the level of genetic diversity among them using SSR markers to compare and analysis of genetic
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variation of NW Himalayan genotypes with the other Indian and exotic rice genotypes from different parts of the world, and to better understand the association between genetic diversity and geographic factors.
Materials and methods Plant materials and DNA extraction Seeds of 82 rice genotypes including of 27 varieties developed by Vivekananda Institute of Hill Agriculture, Almora (Uttarakhand), for the NW Himalayan region of India; 23 genotypes from Northern and Southern parts of India, and 32 exotic rice accessions including two lines from IRRI, Philippines were taken for the present study. The details of the rice genotypes along with their origin, and ecosystem are given in Table 1. The genomic DNA was isolated from the fresh young leaves following Agrawal and Katiyar [20]. Thirty nine SSR markers were used to detect polymorphism among the 82 rice genotypes spread evenly over all the chromosomes. The polymerase chain reactions and gel documentation were carried out using standard procedures, and the amplified products were resolved on 3.5 % agarose gel [Super Fine Resolution (SFR) Agarose; Amresco, USA] and scoring was carried out manually. Data analysis The SSR scores were used to create a data matrix to analyze genetic relationships using the NTSYS-pc program version 2.11a [21]. The dendrogram was constructed based on Jaccard’s similarity coefficient [22] using the marker data for all the rice genotypes following unweighted pair group method analysis (UPGMA) [23]. The Polymorphism Information Content (PIC) was determined as described by Tang et al. [24], by using the formula PIC = 1–Rfi2, where fi is the frequency of the ith allele. For genotypes showing heterozygosity at a specific SSR locus, the PIC values were calculated after considering each allele as contributing one-half instead of one, as suggested by Narvel et al. [25]. The heterozygosity, gene diversity, allele frequency and inbreeding coefficients were calculated using Power Marker V3.0 software [26]. Population structure analysis The structure of the population was studied with the structure version 2.3.4 software [27]. Clustering methods with distinctive allele frequencies were used to identify the optimum number of population (K) subgroups. Each individual can be a member of multiple subpopulations with a different coefficient, with the sum of all being equal to one [28]. The number of subgroups (K) in the population was
Mol Biol Rep Table 1 Details of the 82 rice genotypes used in the study
S. no.
Genotype
Origin (location)
Ecosystem
1.
Kba-Sawrit B2
Exotic
NA
2.
Local Ahu
Exotic
NA
3.
Milyang-15
Exotic
Irrigated
4.
Milyang-93
Exotic
Irrigated
5.
Mipun
Exotic
NA
6.
Nanglwai
Exotic
NA
7.
Naveen
India
Upland
8.
Neela
India
Upland
9.
Pusa Basmati 1
India
Irrigated
10.
Pusa Sugandha 2
India
Irrigated
11.
Pusa Sugandha 3
India
Irrigated
12.
Pusa Sugandha 5
India
Irrigated
13.
Rashi
India
Upland
14.
Ratna
India
Irrigated
15. 16.
RP 2421 Saket-4
India Exotic
Irrigated Irrigated
17.
Satabdi
India
Irrigated
18.
Satari
Exotic
Irrigated
19.
Satya
India
NA
20.
Sinsatsu
Exotic
NA
21.
Sneha
India
Irrigated
22.
Stesiara-45
Exotic
NA
23.
Sulocon 235
Exotic
NA
24.
Suweon
Exotic
Irrigated
25.
Swarna
India
Shallow rain-fed
26.
Tai-Hikawi
Exotic
NA
27.
Tangla
Exotic
NA
28.
Tapaswini
India
Irrigated
29.
Theberu
Exotic
NA
30.
TN-1
Taiwan
Irrigated
31. 32.
Vidhaya VL Dhan 82
India VPKAS, Almora, India
NA Irrigated
33.
VL Dhan 30919
VPKAS, Almora, India
Irrigated
34.
VL Dhan 31330
VPKAS, Almora, India
Irrigated
35.
VL Dhan 31331
VPKAS, Almora, India
Irrigated
36.
VL Dhan 61
VPKAS, Almora, India
Irrigated
37.
VL Dhan 65
VPKAS, Almora, India
Irrigated
38.
VL Dhan 66
VPKAS, Almora, India
Irrigated
39.
VL Dhan 81
VPKAS, Almora, India
Irrigated
40.
VL Dhan 86
VPKAS, Almora, India
Irrigated
41.
VL Dhan 154
VPKAS, Almora, India
Upland rain-fed
42.
VL Dhan 163
VPKAS, Almora, India
Upland
43.
VL Dhan 206
VPKAS, Almora, India
Upland
44.
VL Dhan 209
VPKAS, Almora, India
Upland
45.
VL Dhan 221
VPKAS, Almora, India
Upland
46. 47.
Yam-Nani Yunlen-20
Exotic Exotic
NA NA
48.
Zenith
Exotic
NA
49.
Abor-A
Exotic
NA
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Mol Biol Rep Table 1 continued
NA not available
S. no.
Genotype
Origin (location)
Ecosystem
50.
Anjali
India
Upland
51.
Barkot
Exotic
NA
52.
Basmati 370
Land race (NW India)
Irrigated
53.
Boda-271
Exotic
NA
54.
BPT 5204
India
Irrigated
55.
Catrosa
Exotic
NA
56.
Chandan
India
NA
57.
Dhalahara
India
Upland
58.
Dullo
Exotic
NA
59.
Dullo-11
Exotic
NA
60.
Dullo-A
Exotic
NA
61.
Geetanjali
India
Medium land and irrigated
62.
GGAF-BYEO
Exotic
NA
63.
Henjudo
Exotic
NA
64. 65.
IR-64 IR 3941-34
IRRI, Philippines IRRI, Philippines
Irrigated NA
66.
Janam
India
NA
67.
Jungurh
Exotic
NA
68.
Kba-Thangmaw
Exotic
NA
69.
Mujudo
Exotic
NA
70.
Vandana
India
Upland
71.
VL Dhan 7620
VPKAS, Almora, India
Upland
72.
VL Dhan 16
VPKAS, Almora, India
NA
73.
VL Dhan 85
VPKAS, Almora, India
NA
74.
VL Dhan 62
VPKAS, Almora, India
Irrigated
75.
VL Dhan 82
VPKAS, Almora, India
Irrigated
76.
S-1
NA
NA
77.
RP2421
NA
NA
78.
HPR-2143
NA
Irrigated
79.
VL Dhan207
VPKAS, Almora, India
Upland
80. 81.
VL Dhan 208 P1460
VPKAS, Almora, India NA
Upland NA
82.
P1121
NA
Irrigated
determined by running the program at different K values with K carrying from 2 to 10. Five independent runs were assessed for each K value. We used a burn-in period of 100,000 steps followed by 100,000 Monte Carlo Markov Chain replicates, as suggested by Pritchard and Wen [29].
Results Genetic variation of SSR loci among the rice genotypes The genomic DNA of the 82 rice accessions were amplified using 39 SSR markers and yielded 183 scorable alleles, out
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of which 182 alleles were found to be polymorphic, while one was monomorphic. All the 39 SSRs were spread across all the chromosomes of rice evenly. Out of the 39 primers, 38 (98 %) loci were found to be polymorphic, while one SSR locus, RM338 (2 %) was monomorphic. A total of 182 alleles were detected for the 38 polymorphic SSR markers with an average of 4.8 alleles per locus, while it was 4.69 for all the 39 SSR loci including monomorphic marker used in the study. The number of alleles generated with polymorphic primers ranged from 2 to 9 among the rice genotypes. The SSR locus RM144 was found to have maximum number of allele (9) followed by the loci RM447 and RM316 (each seven allele) (Table 2). The banding
Mol Biol Rep Table 2 Parameters for genetic analysis of 39 SSR loci across the 82 rice accessions
SSR loci
PIC
Heterozygosity
Gene diversity
Allele number
Major allele frequency
Chromosome
RM338
0.00
0.00
0.00
1.00
1.00
3
RM484
0.02
0.00
0.02
2.00
0.99
10
RM283
0.11
0.00
0.12
2.00
0.94
1
RM452
0.28
0.25
0.31
4.00
0.82
2
RM125
0.31
0.07
0.33
4.00
0.81
7
RM215
0.31
0.01
0.38
3.00
0.75
9
RM454
0.32
0.00
0.39
3.00
0.74
6
RM277
0.32
0.01
0.37
3.00
0.76
12
RM133
0.34
0.00
0.41
3.00
0.73
6
RM284
0.36
0.00
0.40
3.00
0.75
8
RM312 RM433
0.37 0.39
0.21 0.22
0.41 0.42
3.00 4.00
0.75 0.75
1 8
RM510
0.41
0.01
0.50
3.00
0.64
6
RM44
0.45
0.06
0.53
3.00
0.60
–
RM287
0.46
0.03
0.51
4.00
0.66
11
RM161
0.46
0.02
0.56
3.00
0.53
5
RM118
0.47
0.00
0.53
3.00
0.62
7
RM25
0.49
0.16
0.54
5.00
0.63
8
RM413
0.57
0.03
0.60
7.00
0.59
5
RM316
0.57
0.31
0.60
8.00
0.61
9
RM431
0.60
0.02
0.65
5.00
0.52
1
RM154
0.60
0.02
0.66
5.00
0.43
2
RM105
0.60
0.00
0.67
4.00
0.41
9
RM334
0.61
0.01
0.64
5.00
0.54
5
RM237
0.61
0.29
0.67
5.00
0.44
1
RM474
0.61
0.05
0.64
6.00
0.57
10
RM408 RM55
0.61 0.62
0.03 0.00
0.67 0.67
6.00 6.00
0.44 0.47
8 3
RM514
0.63
0.02
0.68
4.00
0.44
3
RM447
0.66
0.15
0.69
8.00
0.51
8
RM271
0.66
0.08
0.71
6.00
0.38
10
RM259
0.68
0.01
0.72
6.00
0.41
1
RM5
0.68
0.01
0.72
5.00
0.35
–
RM552
0.68
0.24
0.72
5.00
0.41
11
RM489
0.71
0.15
0.75
6.00
0.32
3
RM536
0.73
0.00
0.76
7.00
0.38
11
RM144
0.75
0.14
0.78
9.00
0.34
11
RM152
0.76
0.07
0.79
7.00
0.32
8
OSR13
0.77
0.02
0.80
7.00
0.25
–
Mean
0.50
0.07
0.55
4.7
0.58
pattern of 82 rice genotypes with SSR loci RM17 was shown in Fig. 1. The PIC values for all the polymorphic primers across 82 rice genotypes varied from 0.02 to 0.77 with an average value of 0.50. Out of the 39 primers, two primers (RM152 and OSR13) showed highest PIC values of 0.76 and 0.77 respectively, while three SSR loci, RM489, RM536 and
RM144 showed a PIC value of 0.71, 0.74 and 0.75 respectively. The lowest PIC values was observed in primers RM484 (0.02), RM 283 (0.12) (Table 2). The number of SSR loci based on PIC values with more than the average was 21 in number. Among them, SSR loci RM536, RM144, RM152 and OSR13 were noteworthy due to their relatively higher level of polymorphism. A total of
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M
1
2
3
4
5 6
7 8
9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24
200bp
100bp
M 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48
M
49
50
51
76 77 78 79 80
52
53
54
55
56
57
58
59 60
61 62 63 64
65 66
67
68 69
70 71 72 73 7475
81 82
Fig. 1 The SSR banding profile of RM17 SSR loci among the 82 rice genotypes. Lane 1, M- Marker, Lane 1–82 rice genotypes (for labels please refer to Table 1)
21 SSR loci came under the PIC range of 0.5–0.77 with an average value of 0.65, while 18 loci came within the PIC range of 0.00–0.49 with an average value of 0.33. The SSRs which had PIC value more than 0.50 also generated more number of loci with an average of 6.1, while it was 3.1 for the SSRs below the PIC value of 0.5 (Table 2). Statistical analysis of genetic diversity Gene diversity also known as ‘expected heterozygosity’ (He) was in the range of 0.02 (RM484) to 0.80 (OSR13) with an average value of 0.55. The heterozygosity, known as ‘observed heterozygosity’ (Ho) was observed with an average of 0.07 and range of 0.01 (RM334) to 0.31 (RM316). Major alleles with highest frequency were observed for the locus RM484 (80 bp allele) at 99 % followed by the locus RM283 (150 bp) at 94 %. Based on the above SSR analysis by considering the parameters of PIC value (C0.70), gene diversity (C0.71), inbreeding coefficient (C0.62) and polymorphic alleles (C6), seven most highly polymorphic SSR loci OSR13, RM152, RM144, RM536, RM489, RM259 and RM271 were observed (Table 2).
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Associations among rice genotypes Jaccard’s similarity coefficient between all the 82 rice genotypes ranged from 22 % to 95 %. The maximum similarity (88 %) was found between the genotype pairs Geetanjali with Dullo-A and Chandan and Dullo-A (each pair 88 %). The above results on similarity were also substantiated by the dendrogram generated using the software power marker (Fig. 2). The dendrogram was constructed by using both NTSYSpc2.11 and power marker software (rooted and rectangular), and both were similar. The similarity coefficients were used as input data for the cluster analysis using NTSYSpc 2.11 program. The dendrogram generated through UPGMA analysis grouped all the 82 rice genotypes into three major groups A, B, and C. The clustering of the rice genotypes was largely based on their centre of development. The cluster C comprised of all the exotic genotypes with five Indian rice accessions viz, Vandana, Satya, VL Dhan 221, Swarna and Geetanjali. All the exotic lines were grouped under the cluster C with few exceptions like Jungurh, Abor-A, Dullo, Barkot and Catrosa which were placed in the cluster A. The
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Fig. 2 The phylogram of 82 rice genotypes as obtained from POWER MARKER software based on UPGMA analysis
major cluster A comprised of 40 genotypes, while the cluster B and C comprised of 14 and 28 genotypes respectively. The genotypes from the NW Himalayan region were grouped in the cluster A and B. Cluster ‘A’ comprised of genotypes from different centers that includes exotic and Indian genotypes, from different centers of development like CRRI, Cuttack; IARI, New Delhi; VPKAS, Almora and IRRI, Philippines. The cluster B consisted of most of the VPKAS genotypes from VPKAS along with the genotypes from IARI, New Delhi. The genotypes BPT 5204 and Swarna were from Bapatla,
Andhra Pradesh, but were scattered into two different clusters A and D respectively. The genotypes from CRRI, Cuttack were spread randomly in the cluster A. Population structure analysis The rice genotypes consisted of popular varieties of India and exotic accessions from different regions were evaluated for estimation of population structure using a panel of 39 SSR loci spread across all the 12 chromosomes. For estimation of the exact population structure (K), Ks from 1
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Fig. 3 The population structure of 82 rice genotypes as obtained from structure 2.3.4 software
to 10 (with ten iterations) were ran and the LnP(D) value was used to group all the genotypes. The maximum DK value was observed for K = 4. The inferred ancestry at K = 4 suggested that the rice genotypes were grouped into four subpopulations (Fig. 3). However power marker software was able to differentiate them into three main clusters. The subpopulation (G1) consisted of exotic rice genotypes with few Indian genotypes viz., Basmati 370, Geetanjali, VL Dhan 7620 and Anjali. This subpopulation had admixture of Anjali, Dullo and Dhalaheera. The subpopulation (G2) consisted of all the Indian accessions, where as G3 consisted of all the exotic germplasm with Satya and Swarna rice varieties of India. The subpopulations G2 and G3 did not have any admixture populations. The subpopulation (G4) consisted of mostly Indian genotypes with few exotic germplasm. Similar grouping pattern (except, exotics were under same cluster) was also observed in the case of power marker, however with the help of a structure bar plot, depicting the estimated membership of each variety in each of the populations (K = 1–10), the admixtures could easily be identified, which can explain the grouping pattern better than the dendrogram. The mean value of alpha was 0.0284, and the ancestry-inferred cluster proportions of the membership of the samples were 0.265, 0.135, 0.256 and 0.345. The average distances (expected heterozygosity) between individuals in the same cluster were 0.1728, 0.3374, 0.1387 and 0.1834 and the allele frequency [divergence among pops (Net nucleotide distance), computed using point
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Table 3 Allele frequencies, [divergence among pops (net nucleotide distance)], computed using point estimates of P Clusters
1
2
3
4
1
–
0.5327
0.2665
0.3109
2
0.5327
–
0.5359
0.5175
3
0.2665
0.5359
–
0.1936
4
0.3109
0.5175
0.1936
–
estimates of P] was 0.5327 between clusters 1 and 2, 0.2665 between clusters 1 and 3, and 0.3109 between clusters 1 and 4 (Table 3).
Discussion Microsatellite (SSR) analysis Cultivated varieties of rice were the result of several thousands of years of human selection from the available genetic diversity in various environments and human cultures. Modern breeding in the last two century has done little more than to control the process of hybridization and selection in a more efficient way and the result has been the development of varieties adapted to better controlled environments. Cultivation of few genotypes in any crop including rice leads to narrow genetic base. The crop is prone to many biotic and abiotic stresses. Improvement of
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rice breeding requires identification of highly diverse germplasm and highly polymorphic molecular markers and their effective utilization in mapping and breeding programmes. In the present study, 39 primers yielded 183 scorable alleles, out of which 182 were found to be polymorphic with an average of 4.69 alleles per locus with a range of 2–9 alleles per locus. Michael et al. [30] also found similar results where they used 30 SSR loci across the 183 Indonesian rice landraces on the islands of Borneo. They obtained 166 alleles with a range of 2–15. This high number of alleles may be due to that fact that they used land races from different parts of Indonesia, where landraces are expected to exhibit higher diversity. However, the number of alleles obtained during the present study is also quite high that could differentiate the rice genotypes matching to their centers of development largely. This showed that these markers are highly useful and can be effectively useful for genetic diversity and fingerprinting studies. Similarly, Xinghua et al. [31] used SSRs for estimating the temporal changes from 1950s to 1990s across 310 major Chinese rice varieties and obtained 221 alleles with an average of 5.7 alleles per locus. However, Chakravarthi and Rambabu [32] found 2–7 alleles per locus with 30 SSR loci on 15 elite rice genotypes which contained Indian and exotic lines from IRRI, Philippines. Out of the total 39 primers used, all the primers amplified among all the genotypes. However, 38 primers (98 %) were found to be polymorphic and one SSR was monomorphic. This high level of polymorphism existed among the selected genotypes has great potential for their use in breeding programs. In the present study, close proportionate relationship between the number of alleles and the PIC values of SSR loci was observed. PIC demonstrates the informativeness of the SSR loci and their potential to detect differences among the genotypes based on their genetic relationships. The SSR loci, RM144, RM152 and OSR13 having high number of alleles and highest PIC values, will be very useful for further genetic and mapping studies. A conclusion may be derived that the loci with more number of alleles can be highly useful for the assessment of genetic diversity. Similar results were also observed by Ravi et al. [33] where it was observed that the SSR loci RM202 produced 11 alleles with highest PIC of 0.89. The PIC values of all the polymorphic primers across 82 rice genotypes in the present study were in the range of 0.02–0.77 with an average value of 0.50. The number of SSR loci with higher than average PIC values was found to be 21. The average PIC value determined during the present investigation agreed well with the earlier findings reported based on SSR marker in rice genotypes [30, 31, 33]. Michael et al. [30] reported an average PIC value of 0.45 which was lower than the PIC value observed during the present study. This showed the polymorphic ability of the SSR loci reported
and their wide applicability for genetic analysis of rice accessions. The high level of polymorphism was due to diverse germplasm and highly polymorphic SSR loci used in the present study. Among the SSRs, RM536, RM144, RM152 and OSR13 were noteworthy due to their relatively higher polymorphism, high PIC values and more number of polymorphic alleles per locus and they can effectively be used in the genetic diversity studies. The observed and expected heterozygosity within the genotypes showed obvious deviations from Hardy–Weinberg expectations. Gene diversity (He) was in the range of 0.02–0.80 with an average value of 0.55 which was correlated with the earlier findings. Xinghau et al. [31] observed gene diversity to be 0.62 among the 310 major Chinese landraces which was slightly more than the He value observed during the present study. The heterozygosity, was observed with an average of 0.07 and ranged from 0.01 (RM334) to 0.31 (RM316) which is higher than the findings observed by Michael et al. [30] where they observed an average value of 0.03 (Table 2) emphasizing that the selection practiced during the development of hybrids, could have led to the deficit of heterozygous individuals. Based on the above SSR analysis by considering the parameters of PIC value (C0.70), gene diversity (C0.71), inbreeding coefficient (C0.62) and polymorphic alleles (C6), seven most highly polymorphic SSR loci observed were OSR13, RM152, RM144, RM536, RM489, RM259 and RM271 (Table 2). These primers can effectively be used in molecular breeding programs and QTL mapping studies since they exhibited very high level of polymorphism over other loci. Genetic diversity among the rice genotypes The ability to provide distance measures between the genotypes that reflect pedigree relatedness ensures a more stringent evaluation of the adequacy of a marker profile data. The fact that minimum genetic distance revealed during the study is a good indication confirming the power of SSR markers to distinguish between geographically similar genotypes and closely related genotypes. The average gene diversity existing among all the genotypes were relatively high (55 %), indicating existence of high levels of polymorphisms among the inbreds. These results are in close agreement with the findings reported among the Chinese rice accessions using the SSR marker system [31]. The dendrogram constructed using the UPGMA clustering algorithm grouped the rice accessions into three clusters. These groupings, in most instances, revealed evidence of associations related to their centre of development i.e., geographic origin. However, Michael et al. [30] did not get such type of correlation which may be
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because of less number of polymorphic primers used by them. Alternatively, the 82 rice genotypes were broadly grouped into exotic rice accessions, accessions from VPKAS, Almora with few exceptions. For example, VL Dhan 163 grouped with BPT 5204 and Pusa Basmati 1 which were from Bapatla, Andhra Pradesh and IARI, New Delhi respectively. It showed that during selection/evolution allele introgression has occurred from several sources. The cluster ‘A’ was comprised of mixed genotypes from different origins. This is also an indication that modern breeding programmes involved diverse genotypes in developing highly suitable genotypes adapted to their local environments by crossings from the popular varieties of different origins for important agronomic traits. The cluster B mostly composed of genotypes developed from VPKAS, Almora with exceptions like Pusa Sugandha accessions. The interesting point in our study was that all the exotic lines with some exceptions were grouped under the cluster C with Indian genotypes like Vandana, Satya, VL Dhan 221, Swarna and Geetanjali, which may be due to that exotic pedigree involved in breeding these five genotypes. This showed that these selected markers were not only able to differentiate based on geographic origin but also based on the pedigree of the genotypes. The accession VL Dhan 221 was derived from the cross between IR 2053-521-1-11/CH 1039, however Swarna was derived from Vasista and Mahsuri, and Mahsuri was derived from Taichung 65/ZxMoyzng EB05080. Likewise Sri Satya was derived from RGL1232/Phalguna/RGL1231/IR36, Vandana from C-22 and Kalakeri. Hence there was introgression of alleles from the exotic lines into these genotypes while crossings for the development of superior hybrids for this region. The rice variety Swarna is highly popular in the most parts of eastern India due to its good taste, yield, and adaptability. Population structure analysis Since some geographical grouping was observed in the phylogenetic analysis, we evaluated them to understand whether alternative clustering methods such as modelbased clustering would resolve the geographic patterning. From the phylogenetic analysis, it was observed that there were at least three population subgroups, largely corresponding to Indian, exotic and Indian with few exotic rice genotypes. However, by the structure analysis these exotic accessions were further divided into two different subgroups. The phylogenetic analysis also provided some evidence for gene flow between the subpopulations. There was good correspondence between the geographic pattern observed in the dendrogram and the population structure identified using structure. Although the population subgroups corresponded largely to geographic regions, there were some notable exceptions. In the subpopulation (G4),
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genotypes VL Dhan 221, VL Dhan 206 and VL Dhan 209 carried more than 35 % of the exotic germplasm alleles. For example, the accession VL Dhan 221 was derived from the cross between IR 2053-521-1-1-1/CH 1039. The exotic rice variety TN-1 grouped with Indian genotypes, which showed that 70 % of alleles were common with Indian germplasm. This showed that the exotic variety TN-1 was very highly used in rice breeding programmes during 1960s for developing high yielding rice varieties. Evidence of admixture was also found in exotic population where Dullo and Dhalahara accessions carried 8–10 % alleles from Indian germplasm. Thus, the present study showed that genotyping combined with phylogenetic and population structure analysis is a powerful method for characterizing germplasm. The present study was able to identify highly polymorphic SSR loci which can be effectively used in genetic analysis studies and molecular breeding programmes. Our results also showed that the polymorphic SSR loci used in the study were able to differentiate the Indian and exotic rice genotypes based on their geographical regions. There is also a great scope to derive further improved materials by hybridizing with distant lines identified during the present study and for further selection of the desired line to derive more agronomically useful lines for their commercial production.
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