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. 2015 Sep 26;8:30. doi: 10.1186/s12284-015-0062-5

Analysis of Population Structure and Genetic Diversity in Rice Germplasm Using SSR Markers: An Initiative Towards Association Mapping of Agronomic Traits in Oryza Sativa

Vishnu Varthini Nachimuthu 1,✉, Raveendran Muthurajan 3, Sudhakar Duraialaguraja 3, Rajeswari Sivakami 2, Balaji Aravindhan Pandian 2, Govinthraj Ponniah 5, Karthika Gunasekaran 4, Manonmani Swaminathan 2, Suji K K 3, Robin Sabariappan 6
PMCID: PMC4583558  PMID: 26407693

Abstract

Background

Genetic diversity is the main source of variability in any crop improvement program. It serves as a reservoir for identifying superior alleles controlling key agronomic and quality traits through allele mining/association mapping. Association mapping based on LD (Linkage dis-equilibrium), non-random associations between causative loci and phenotype in natural population is highly useful in dissecting out genetic basis of complex traits. For any successful association mapping program, understanding the population structure and assessing the kinship relatedness is essential before making correlation between superior alleles and traits. The present study was aimed at evaluating the genetic variation and population structure in a collection of 192 rice germplasm lines including local landraces, improved varieties and exotic lines from diverse origin.

Results

A set of 192 diverse rice germplasm lines were genotyped using 61 genome wide SSR markers to assess the molecular genetic diversity and genetic relatedness. Genotyping of 192 rice lines using 61 SSRs produced a total of 205 alleles with the PIC value of 0.756. Population structure analysis using model based and distance based approaches revealed that the germplasm lines were grouped into two distinct subgroups. AMOVA analysis has explained that 14 % of variation was due to difference between with the remaining 86 % variation may be attributed by difference within groups.

Conclusions

Based on these above analysis viz., population structure and genetic relatedness, a core collection of 150 rice germplasm lines were assembled as an association mapping panel for establishing marker trait associations.

Electronic supplementary material

The online version of this article (doi:10.1186/s12284-015-0062-5) contains supplementary material, which is available to authorized users.

Keywords: Rice, Genetic diversity, Population structure, Polymorphism information content, Molecular variance, Association mapping

Background

Rice, being the staple food crop for more than 50 % of the world population is cultivated in 163 million hectares with the production of 491 million tonnes. About 90 % of the world’s rice is produced in Asia and India contributes 20 % of the world’s production. This record level production and productivity is due to the availability and exploitation of rich genetic diversity existing in rice germplasm of India. For precise genetic manipulation of complex quantitative traits like, yield, tolerance against biotic/abiotic stresses, quality etc., understanding the genetic/molecular basis of target traits needs to be investigated thoroughly.

The genetic basis of important agronomic traits has been unraveled through Quantitative Trait Loci (QTL) mapping either through linkage mapping (bi-parental mapping populations) or through LD mapping (natural populations). Although traditional linkage based QTL-mapping has become an important tool in gene tagging of crops, it has few limitations viz., 1) classical linkage mapping involves very high cost; 2) it has low resolution as it can resolve only a few alleles and 3) it has limitations towards fine mapping of QTLs as it needs BC-NILs. These limitations can be overcome by the LD based approach of “Association Mapping” using the natural populations. Association mapping serves as a tool to mine the elite genes by structuring the natural variation present in a germplasm. It was successfully exploited in various crops such as rice, maize, barley, durum wheat, spring wheat, sorghum, sugarcane, sugarbeet, soybean, grape, forest tree species and forage grasses (Abdurakhmonov and Abdukarimov 2008).

Before performing an association analysis in a population, it is essential to determine the population structure which can reduce type I and II errors in association mapping due to unequal allele frequency distribution between subgroups that causes spurious association between molecular markers and trait of interest (Pritchard et al. 2000). Similar attempts were recently undertaken to define population structure in rice using different germplasm lines and by developing core collection from national collections and international collections (Ebana et al. 2008; Jin et al. 2010; Zhang et al. 2011; Agrama et al. 2010 and Liakat Ali et al. 2011). Simple Sequence repeat (SSR) markers have been commonly used in genetic diversity studies in rice because of high level of polymorphism which helps to establish the relationship among the individuals even with less number of markers (McCouch et al. 1997). For similar studies, SSR markers were used alone by Jin et al. (2010); Hesham et al. (2008); Sow et al. (2014); Das et al. (2013) and Choudhury et al. (2013) or along with SNP markers by Courtois et al. (2012) and Zhao et al. (2011). The objectives of this present study were to evaluate the genetic variation and to examine the population structure of 192 rice germplasm accessions that comprises of local landraces, improved varieties and exotic lines from diverse origin.

Results

Genetic Diversity

All the 192 rice germplasm lines were genotyped using 61 SSR (microsatellite) markers which produced a total of 205 alleles (Additional file 1: Figure S1). Among these 205 alleles, 5 % were considered as rare (showed an allele frequency of < 5 %). The number of alleles per loci varied from 2 to 7 with an average of 3 alleles per locus. The highest number of alleles were detected for the loci RM316 (7) and the lowest was detected for a group of markers viz., RM171, RM284, RM455, RM514, RM277, RM 5795, HvSSR0247, RM 559, RM416 and RM1227. PIC value represents the relative informativeness of each marker and in the present study, the average PIC value was found to be 0.468. The highest genetic diversity is explained by the landraces included in this study with the mean PIC value of 0.416. PIC values ranged between 0.146 for RM17616 to 0.756 for RM316. Heterozygosity was found to be very low which may be due to autogamous nature of rice. Expected heterozygosity or Gene diversity (He) computed according to Nei (1973) varied from 0.16 (RM17616) to 0.75 (RM287) with the average of 0.52 (Table 1).

Table 1.

Details of SSR loci used for genotyping in the 192 rice accessions and their genetic diversity parameters

S. no Marker Chromosome no. SSR MOTIF Min molecular weight Maximum molecular weight Number of alleles Gene diversity Heterozygosity PIC value
1 RM237 1 (CT)18 110 143 4 0.61 0.89 0.545
2 RM1 1 (GA)26 70 105 3 0.63 0.12 0.552
3 RM5 1 (GA)14 105 115 3 0.64 0.6 0.557
4 RM312 1 (ATTT)4(GT)9 95 105 3 0.3 0.03 0.281
5 RM283 1 (GA)18 149 155 3 0.42 0.02 0.377
6 RM452 2 (GTC)9 195 245 3 0.54 0.83 0.448
7 HvSSR0247 2 395 400 2 0.5 0.18 0.373
8 RM555 2 (AG)11 135 145 3 0.59 0.04 0.517
9 RM211 2 (TC)3A(TC)18 140 160 3 0.52 0.08 0.463
10 RM324 2 (CAT)21 135 180 5 0.74 0.06 0.695
11 RM514 3 (AC)12 245 252 2 0.19 0 0.171
12 RM55 3 (GA)17 220 225 3 0.44 0.07 0.4
13 RM231 3 (CT)16 170 200 3 0.59 0.12 0.511
14 RM416 3 (GA)9 110 115 2 0.42 0.01 0.335
15 RM442 3 (AAG)10 260 275 3 0.5 0.03 0.448
16 RM 16643 4 (GGGA)5 165 200 5 0.73 0.05 0.685
17 RM 559 4 (AACA)6 160 165 2 0.39 0.01 0.311
18 RM17377 4 (AG)25 140 175 4 0.67 0.04 0.625
19 RM7585 4 (TCTT)6 140 160 4 0.46 0.02 0.422
20 RM17616 4 (TC)14 165 180 3 0.16 0 0.146
21 RM413 5 (AG)11 75 100 4 0.59 0.25 0.548
22 RM178 5 (GA)5(AG)8 110 115 3 0.39 0.04 0.35
23 RM 161 5 (AG)20 160 180 3 0.29 0.04 0.258
24 RM7293 5 (ATGT)6 140 150 3 0.64 0.1 0.558
25 RM1024 5 (AC)13 125 140 3 0.32 0.02 0.298
26 RM 162 6 (AC)20 220 240 3 0.37 0.03 0.34
27 RM7434 6 (GTAT)10 120 145 5 0.66 0.19 0.614
28 RM19620 6 (GTG)7 160 177 3 0.21 0.03 0.204
29 RM5963 6 (CAG)9 160 175 3 0.48 0.15 0.38
30 RM11 7 (GA)17 120 150 4 0.71 0.72 0.661
31 RM118 7 (GA)8 155 185 4 0.62 0.77 0.543
32 RM125 7 (GCT)8 105 130 4 0.61 0.89 0.544
33 RM455 7 (TTCT)5 130 135 2 0.24 0.02 0.208
34 HvSSR0740 7 340 400 4 0.7 0.21 0.65
35 RM44 8 (GA)16 95 107 4 0.62 0.77 0.559
36 RM433 8 (AG)13 235 270 3 0.55 0.81 0.446
37 RM447 8 (CTT)8 105 120 4 0.64 0.16 0.572
38 RM284 8 (GA)8 140 145 2 0.21 0.02 0.189
39 RM408 8 (CT)13 120 125 3 0.52 0.01 0.465
40 RM25 8 (GA)18 120 140 4 0.73 0.37 0.679
41 RM256 8 (CT)21 125 140 4 0.73 0 0.681
42 RM105 9 (CCT)6 100 140 3 0.41 0.48 0.37
43 RM107 9 (GA)7 280 300 3 0.48 0 0.425
44 RM 215 9 (CT)16 140 150 3 0.6 0.01 0.528
45 RM 316 9 (GT)8-(TG)9(TTTG)4(TG)4 160 235 7 0.79 0.75 0.756
46 RM205 9 (CT)25 110 140 4 0.72 0 0.665
47 RM171 10 (GATG)5 320 330 2 0.24 0.02 0.211
48 RM271 10 (GA)15 90 99 3 0.66 0.19 0.588
49 RM590 10 (TCT)10 120 140 4 0.57 0.04 0.516
50 RM474 10 (AT)13 240 280 3 0.61 0 0.537
51 RM222 10 (CT)18 200 220 3 0.63 0.02 0.557
52 RM144 11 (ATT)11 160 240 5 0.69 0.18 0.644
53 RM287 11 (GA)21 95 110 5 0.75 0.2 0.706
54 RM 536 11 (CT)16 240 270 5 0.74 0.06 0.701
55 RM224 11 (AAG)8(AG)13 120 155 5 0.65 0.07 0.617
56 RM206 11 (CT)21 130 145 4 0.34 0 0.319
57 RM277 12 (GA)11 115 120 2 0.45 0.08 0.35
58 RM 5795 12 (AGC)8 140 145 2 0.5 0.03 0.374
59 RM1227 12 (AG)15 160 180 2 0.31 0.02 0.262
60 RM20A 12 (ATT)14 220 240 3 0.54 0 0.476
61 RM2197 12 (AT)23 135 140 2 0.44 0 0.341
Average 3 0.52 0.18 0.468
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STRUCTURE Analysis

Population structure of the 192 germplasm lines was analysed by Bayesian based approach. The estimated membership fractions of 192 accessions for different values of k ranged between 2 and 5 (Fig. 1). The log likelihood revealed by structure showed the optimum value as 2 (K = 2). Similarly the maximum of adhoc measure ΔK was found to be K = 2 (Fig. 2), which indicated that the entire population can be grouped into two subgroups (SG1 and SG2). Based on the membership fractions, the accessions with the probability of ≥ 80 % were assigned to corresponding subgroups with others categorized as admixture (Fig. 3).

Fig. 1.

Fig. 1

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Pattern of variation of 192 accessions based on 61 SSR markers. The K values are based on the run with highest likelihood. Bar length represent the membership probability of accessions belonging to different subgroups

Fig. 2.

Fig. 2

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Population structure of 192 accessions based on 61 SSR markers (K = 2) and Graph of estimated membership fraction for K = 2. The maximum of adhoc measure ΔK determined by structure harvester was found to be K = 2, which indicated that the entire population can be grouped into two subgroups (SG1 and SG2)

Fig. 3.

Fig. 3

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Population structure of 192 accessions arranged based on inferred ancestry. Based on the membership fractions, the accessions with the probability of ≥ 80 % were assigned to corresponding subgroups with others categorized as admixture

SG1 consisted of 134 accessions with most of the landraces and varieties of Indian origin and SG2 consisted of 38 accessions which composed of non Indian accessions. Twenty accessions were retained to be admixture. The subgroup SG1 was dominated by indica subtype whereas the subgroup SG2 consisted mostly of japonica group. When the number of subgroups increased from two to five, the accessions in both the subgroups were classified into sub-sub groups (Table 2). As SG1 consisted of 134 accessions mostly of Indian origin, an independent STRUCTURE analysis was performed for this subgroup. ΔK showed its maximum value for K =3 which indicated that SG1 could be further classified into three sub-sub groups (Fig. 4). The differentiation in origin and seasonal differentiation of rice varieties contributed for this clustering.

Table 2.

Population structure group of accessions based on Inferred ancestry values

G. no. Genotypes Inferred ancestry Structure group Subtype
Q1 Q2
RG1 Mapillai samba 0.977 0.023 SG1 Indica
RG2 CK 275 0.991 0.009 SG1 Indica
RG3 Senkar 0.992 0.008 SG1 Indica
RG4 Murugankar 0.964 0.036 SG1 Indica
RG5 CHIR 6 0.811 0.189 SG1 Indica
RG6 CHIR 5 0.989 0.011 SG1 Indica
RG7 Kudai vazhai 0.975 0.025 SG1 Indica
RG8 CHIR 8 0.759 0.241 SG1 Indica
RG9 Kuruvai kalanjiyam 0.971 0.029 SG1 Indica
RG10 Nava konmani 0.99 0.01 SG1 Indica
RG11 CHIR 10 0.869 0.131 SG1 Indica
RG12 Vellai chithiraikar 0.802 0.198 SG1 Indica
RG13 CHIR 2 0.983 0.017 SG1 Indica
RG14 Jothi 0.992 0.008 SG1 indica
RG15 Palkachaka 0.962 0.038 SG1 indica
RG16 Thooyala 0.934 0.066 SG1 indica
RG17 Chivapu chithiraikar 0.994 0.006 SG1 indica
RG18 CHIR 11 0.976 0.024 SG1 indica
RG19 Koolavalai 0.99 0.01 SG1 indica
RG20 Kalvalai 0.982 0.018 SG1 indica
RG21 Mohini samba 0.963 0.037 SG1 indica
RG22 IR 36 0.989 0.011 SG1 indica
RG23 Koombalai 0.975 0.025 SG1 indica
RG24 Tadukan 0.674 0.326 AD indica
RG25 Sorna kuruvai 0.986 0.014 SG1 indica
RG26 Rascadam 0.637 0.363 AD indica
RG27 Muzhi karuppan 0.991 0.009 SG1 indica
RG28 Kaatukuthalam 0.828 0.172 SG1 indica
RG29 Vellaikattai 0.987 0.013 SG1 indica
RG30 Poongar 0.987 0.013 SG1 indica
RG31 Chinthamani 0.985 0.015 SG1 indica
RG32 Thogai samba 0.975 0.025 SG1 indica
RG33 Malayalathan samba 0.701 0.299 AD indica
RG34 RPHP 125 0.986 0.014 SG1 indica
RG35 CK 143 0.993 0.007 SG1 indica
RG36 Kattikar 0.913 0.087 SG1 indica
RG37 Shenmolagai 0.994 0.006 SG1 indica
RG38 Velli samba 0.887 0.113 SG1 indica
RG39 Kaatu ponni 0.975 0.025 SG1 indica
RG40 kakarathan 0.989 0.011 SG1 indica
RG41 Godavari samba 0.941 0.059 SG1 indica
RG42 Earapalli samba 0.978 0.022 SG1 indica
RG43 RPHP 129 0.01 0.99 SG2 indica
RG44 Mangam samba 0.968 0.032 SG1 indica
RG45 RPHP 105 0.943 0.057 SG1 indica
RG46 IG 4(EC 729639- 121695) 0.977 0.023 SG1 indica
RG47 Machakantha 0.976 0.024 SG1 indica
RG48 Kalarkar 0.992 0.008 SG1 indica
RG49 Valanchennai 0.972 0.028 SG1 indica
RG50 Sornavari 0.957 0.043 SG1 indica
RG51 RPHP 134 0.909 0.091 SG1 indica
RG52 ARB 58 0.987 0.013 SG1 indica
RG53 IR 68144-2B-2-2-3-1-127 0.708 0.292 AD indica
RG54 PTB 19 0.981 0.019 SG1 indica
RG55 IG 67(EC 729050- 120988) 0.957 0.043 SG1 indica
RG56 RPHP 59 0.031 0.969 SG2 Aromatic
RG57 RPHP 103 0.656 0.344 AD Aromatic
RG58 Kodaikuluthan 0.828 0.172 SG1 indica
RG59 RPHP 68 0.981 0.019 SG1 indica
RG60 Rama kuruvaikar 0.985 0.015 SG1 indica
RG61 Kallundai 0.939 0.061 SG1 indica
RG62 Purple puttu 0.994 0.006 SG1 indica
RG63 IG 71(EC 728651- 117588) 0.823 0.177 SG1 aus
RG64 Ottadaiyan 0.994 0.006 SG1 indica
RG65 IG 56(EC 728700- 117658 0.435 0.565 AD Aromatic
RG66 Jeevan samba 0.876 0.124 SG1 indica
RG67 RPHP 106 0.915 0.085 SG1 indica
RG68 IG 63(EC 728711- 117674) 0.049 0.951 SG2 Tropical Japonica
RG69 RPHP 48 0.025 0.975 SG2 Aromatic
RG70 Karthi samba 0.987 0.013 SG1 indica
RG71 IG 27(IC 0590934- 121255) 0.444 0.556 AD indica
RG72 Aarkadu kichili 0.99 0.01 SG1 indica
RG73 Kunthali 0.969 0.031 SG1 indica
RG74 ARB 65 0.83 0.17 SG1 indica
RG75 IG 21(EC 729334- 121355) 0.091 0.909 SG2 japonica
RG76 Matta kuruvai 0.934 0.066 SG1 indica
RG77 Karuthakar 0.994 0.006 SG1 indica
RG78 RPHP 165 0.99 0.01 SG1 indica
RG79 Manavari 0.704 0.296 AD indica
RG80 IG 66(EC 729047- 120985) 0.992 0.008 SG1 indica
RG81 CB-07-701-252 0.977 0.023 SG1 indica
RG82 Thooyamalli 0.994 0.006 SG1 indica
RG83 RPHP 93 0.153 0.847 SG2 indica
RG84 Velsamba 0.99 0.01 SG1 indica
RG85 RPHP 104 0.898 0.102 SG1 indica
RG86 RPHP 102 0.993 0.007 SG1 indica
RG87 IG 40(EC 728740- 117705) 0.98 0.02 SG1 indica
RG88 Saranga 0.988 0.012 SG1 indica
RG89 IR 83294-66-2-2-3-2 0.125 0.875 SG2 japonica
RG90 IG 61(EC 728731- 117696) 0.843 0.157 SG1 indica
RG91 IG 23(EC 729391- 121419) 0.852 0.148 SG1 Aus
RG92 IG 49(EC 729102- 121052) 0.945 0.055 SG1 indica
RG93 uppumolagai 0.987 0.013 SG1 indica
RG94 Karthigai samba 0.993 0.007 SG1 indica
RG95 Jeeraga samba 0.685 0.315 SG1 indica
RG96 RP-BIO-226 0.833 0.167 SG1 indica
RG97 Varigarudan samba 0.975 0.025 SG1 indica
RG98 IG 5(EC 729642- 121698) 0.012 0.988 SG2 japonica
RG99 IG 31(EC 728844- 117829) 0.813 0.187 SG1 indica
RG100 IG 7(EC 729598- 121648) 0.008 0.992 SG2 japonica
RG101 RPHP 52 0.991 0.009 SG1 indica
RG102 Varakkal 0.958 0.042 SG1 indica
RG103 Mattaikar 0.732 0.268 AD indica
RG104 IG 53(EC 728752- 117719) 0.005 0.995 SG2 Temperate japonica
RG105 IG 6(EC 729592- 121642) 0.204 0.796 SG2 Temperate japonica
RG106 Katta samba 0.872 0.128 SG1 indica
RG107 RH2-SM-1-2-1 0.606 0.394 AD indica
RG108 Red sirumani 0.93 0.07 SG1 indica
RG109 Vadivel 0.977 0.023 SG1 indica
RG110 Norungan 0.991 0.009 SG1 indica
RG111 IG 20(EC 729293- 121310) 0.113 0.887 SG2 indica
RG112 IG 35(EC 728858- 117843) 0.027 0.973 SG2 japonica
RG113 IG 45(EC 728768- 117736) 0.017 0.983 SG2 japonica
RG114 RPHP 159 0.008 0.992 SG2 aromatic rice
RG115 IG 43(EC 728788- 117759) 0.992 0.008 SG1 indica
RG116 RPHP 27 0.52 0.48 AD Tropical Japonica
RG117 IG 65(EC 729024- 120958) 0.974 0.026 SG1 indica
RG118 Ponmani samba 0.973 0.027 SG1 indica
RG119 Ganthasala 0.993 0.007 SG1 indica
RG120 Thattan samba 0.949 0.051 SG1 indica
RG121 IG 74(EC 728622- 117517) 0.16 0.84 SG2 japonica
RG122 Kaliyan samba 0.245 0.755 AD indica
RG123 IG 2(EC 729808-121874) 0.56 0.44 AD japonica
RG124 IG 29(EC 728925- 117920) 0.059 0.941 SG2 Tropical Japonica
RG125 RPHP 55 0.963 0.037 SG1 indica
RG126 Kallimadayan 0.984 0.016 SG1 indica
RG127 IG 10(EC 729686- 121743) 0.066 0.934 SG2 aromatic
RG128 IG 75(EC 728587- 117420) 0.008 0.992 SG2 japonica
RG129 IG 38(EC 728742 - 117707) 0.02 0.98 SG2 Tropical japonica
RG130 IG 39(EC 728779- 117750) 0.012 0.988 SG2 indica
RG131 RPHP 90 0.991 0.009 SG1 indica
RG132 IG 33(EC 728938- 117935) 0.162 0.838 SG2 Tropical Japonica
RG133 IG 42(EC 728798- 117774) 0.495 0.505 AD indica
RG134 IG 9(EC 729682- 121739) 0.019 0.981 SG2 indica
RG135 RPHP 161 0.849 0.151 SG1 indica
RG136 IG 8(EC 729601- 121651) 0.883 0.117 SG1 indica
RG137 IG 37(EC 728715- 117678) 0.005 0.995 SG2 Tropical Japonica
RG138 Sigappu kuruvikar 0.979 0.021 SG1 indica
RG139 RPHP 138 0.917 0.083 SG1 indica
RG140 Raja mannar 0.989 0.011 SG1 indica
RG141 IG 44(EC 728762- 117729) 0.134 0.866 SG2 indica
RG142 Sasyasree 0.989 0.011 SG1 indica
RG143 IG 46(IC 471826- 117647) 0.073 0.927 SG2 indica
RG144 Chetty samba 0.993 0.007 SG1 indica
RG145 IG 60(EC 728730- 117695) 0.033 0.967 SG2 indica
RG146 IR 75862-206 0.013 0.987 SG2 Tropical Japonica
RG147 IG 58(EC 728725- 117689) 0.011 0.989 SG2 japonica
RG148 Chinna aduku nel 0.798 0.202 SG1 indica
RG149 RH2-SM-2-23 0.296 0.704 AD indica
RG150 IG 14(IC 517381- 121422) 0.775 0.225 AD indica
RG151 IG 32(EC 728838- 117823) 0.065 0.935 SG2 japonica
RG152 RPHP 47 0.989 0.011 SG1 indica
RG153 Sembilipiriyan 0.933 0.067 SG1 indica
RG154 IG 48(EC 729203- 121195) 0.006 0.994 SG2 indica
RG155 Sona mahsuri 0.889 0.111 SG1 indica
RG156 IG 12(EC 729626- 121681) 0.405 0.595 AD indica
RG157 Karungan 0.602 0.398 AD indica
RG158 IG 13(EC 729640- 121696) 0.143 0.857 SG2 indica
RG159 Sembala 0.934 0.066 SG1 indica
RG160 IG 72(EC 728650- 117587) 0.992 0.008 SG1 indica
RG161 Panamarasamba 0.978 0.022 SG1 indica
RG162 IR 64 0.995 0.005 SG1 indica
RG163 Mikuruvai 0.992 0.008 SG1 indica
RG164 Thillainayagam 0.939 0.061 SG1 indica
RG165 ARB 64 0.843 0.157 SG1 indica
RG166 RPHP 140 0.959 0.041 SG1 indica
RG167 IG 70(EC 729045- 120983) 0.989 0.011 SG1 indica
RG168 Haladichudi 0.993 0.007 SG1 indica
RG169 IG 24(EC 728751- 117718) 0.725 0.275 AD Aus
RG170 RPHP 42 0.981 0.019 SG1 indica
RG171 RPHP 44 0.951 0.049 SG1 indica
RG172 IG 25(EC 729728- 121785) 0.903 0.097 SG1 Tropical Japonica
RG173 IG 73(EC 728627- 117527) 0.991 0.009 SG1 indica
RG174 IG 51(EC 728772- 117742) 0.008 0.992 SG2 Tropical Japonica
RG175 Vellai kudaivazhai 0.786 0.214 SG1 indica
RG176 Kodai 0.906 0.094 SG1 indica
RG177 Kallundaikar 0.951 0.049 SG1 indica
RG178 IG 17(EC 728900- 117889) 0.993 0.007 SG1 indica
RG179 Avasara samba 0.939 0.061 SG1 indica
RG180 IG 59(EC 728729- 117694) 0.093 0.907 SG2 Tropical Japonica
RG181 IG 52(EC 728756- 117723) 0.026 0.974 SG2 Tropical Japonica
RG182 ARB 59 0.779 0.221 SG1 indica
RG183 RPHP 163 0.995 0.005 SG1 indica
RG184 IG 18(EC 728892- 117880) 0.994 0.006 SG1 indica
RG185 RPHP 36 0.915 0.085 SG1 indica
RG186 IG 28(EC 728920- 117914) 0.009 0.991 SG2 Tropical Japonica
RG187 Vadakathi samba 0.986 0.014 SG1 indica
RG188 RPHP 80 0.986 0.014 SG1 indica
RG189 IG 41(EC 728800- 117776) 0.016 0.984 SG2 Tropical japonica
RG190 IG 26(IC 0590943- 121899) 0.422 0.578 SG2 aromatic
RG191 IG 15(EC 728910- 117901) 0.755 0.245 AD indica
RG192 Nootri pathu 0.943 0.057 SG1 indica
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Fig. 4.

Fig. 4

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Population structure of 134 accessions in sub group-1 and membership probability of assigning genotypes of sub group-1 (K = 3)

Clustering analysis based on Unweighted Pair Group Method with Arithmetic Mean (UPGMA) method using DARwin separated the accessions into two main groups which showed similar results as STRUCTURE analysis. The group I in UPGMA tree consists of both indigenous and agronomically improved varieties whereas the other group consists of exotic accessions. In UPGMA tree, the accessions within group 1 and 2 clustered into smaller sub groups based on their origin and types. Most of the landraces and varieties have been clustered in upper branches of the tree whereas the exotic accessions have been clustered in lower branches of the tree (Fig 5). Hence the clustering analysis by two classification methods revealed high level of similarity in clustering the genotypes. PCoA was used to characterize the subgroups of the germplasm set. A two- dimensional scatter plot involving all 192 accessions has shown that the first two PCA axes accounted for 12.6 and 4.9 % of the genetic variation among populations (Fig 6).

Fig. 5.

Fig. 5

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Unrooted neighbour joining tree of 192 rice varieties. The landraces and varieties used in the study has clustered in the upper branches of the tree whereas the exotic accessions has positioned in the lower branches of the tree

Fig. 6.

Fig. 6

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Principal Coordinates of 192 accessions based on 61 SSR loci. Coord 1 and Coord 2 represent first and second coordinates, respectively. The two PCA axes accounted for 12.6 and 4.9 % of the genetic variation among populations

Genetic Variance Analysis

The hierarchial distribution of molecular variance by AMOVA and pair-wise analysis revealed highly significant genetic differentiation among the groups. It revealed that 14 % of the total variation was between the groups, while 86 % was among individuals within groups (Tables 3 and 4). Calculation of Wright’s F statistic at all SSR loci revealed that FIS was 0.50 and FIT was 0.56. Determination of FST for the polymorphic loci across all accessions has shown FST as 0.14 which implies high genetic variation (Table 4). The pairwise FST estimate among sub-groups has indicated that the two groups are significantly different from each other (Table 3).

Table 3.

AMOVA between groups and Pair wise comparison using Fst values (GenAlEx)

Source df SS MS Est. var. Percent
Among the population 2 971.922 485.961 9.631 14 %
Within Pops 189 10961.256 57.996 57.996 86 %
Total 191 11933.177 67.627 100 %
Pairwise population Fst values
SG2 AD
SG1 0.128 0.040
SG2 0.061
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Table 4.

AMOVA between groups and accessions and Fixation indices (Arlequin software)

Source of variation d.f. Sum of squares Variance components Percentage of variation
Among Populations 2 200.013 1.01840 Va 13.82
Among individuals within Populations 189 1794.771 3.14391 Vb 42.65
Within Individuals 192 616 3.20833 Vc 43.53
383 2610.784 7.37064
Fixation Indices
FIS 0.49493
FST 0.13817
FIT 0.56471
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Discussion

Genetic diversity is the key determinant of germplasm utilization in crop improvement. Population with high level of genetic variation is the valuable resource for broadening the genetic base in any breeding program. The panel of 192 accessions in this study with landraces, varieties as well as breeding lines has different salient agronomic traits. Few landraces included in this study i.e., Mappillai samba (Krishnanunni et al. 2015), Jyothi, Njavara (Deepa et al. 2008), Kavuni (Valarmathi et al. 2015) derived breeding line has therapeutic properties. Many lines included in this study are drought tolerant (Nootripathu, Norungan, Vellaikudaivazhai, kallundaikar, kodai, kalinga 3, Kinandang patong, azucena, mattaikar, IR65907-116-1, karuthakar, mattakuruvai, manavari, kallundai, kodaikulathan, kattikar, poongar, thogai samba, vellaikattai, kattukuthalam, kalvalai, chivapu chithiraikar, vellai chithiraikar, kudaivazhai and murugankar). Few lines have significant level of micronutrients in it (Nachimuthu et al. 2014). This panel has its importance because of its major component as traditional landraces with valuable agronomic traits that are cultivated in the small pockets of Tamil Nadu, India.

Molecular markers help us to understand the level of genetic diversity that exists among traditional races, varieties and exotic accessions which can be exploited in rice breeding programs. The genetic architecture of diverse germplasm lines can be precisely estimated by assessing the STRUCTURE of the population using molecular markers viz., SSRs or SNPs etc., (Horst and Wenzel 2007; Powell et al. 1996; Varshney et al. 2007). In this study, the genetic diversity among the accessions was evaluated by model based clustering and distance based clustering approach using the SSR genotypic data.

Regarding genetic divergence of the population consisting of local landraces, exotic cultivars and breeding lines, 61 polymorphic markers have detected a total of 205 alleles across 192 individuals. The number of alleles varied from 2 to 7 per locus and the average was 3 alleles per locus. Several previous reports have indicated the number of alleles per locus, polymorphic information content and gene diversity of 4.8–14.0, 0.63–0.70 and 6.2–6.8 respectively (Garris et al. 2005; Ram et al. 2007). In the current study, the average number of alleles (3 alleles/locus) is slightly lesser than the average number of alleles (3.88 alleles/ locus) reported by Zhang et al. (2011) in rice core collection with 150 rice varieties from south Asia and Brazil and Jin et al. (2010) who has reported the average alleles per locus as 3.9 in 416 rice accessions collected from China. Using three sets of germplasm lines (Thai (47), IRRI germplasm (53) amd other Oryza species (5)), Chakhonkaen et al. (2012) has reported 127 alleles for all loci, with a mean of 6.68 alleles per locus, and a mean Polymorphic Information Content (PIC) of 0.440 by screening with 19 InDel markers.

Chen et al. (2011) has reported the average gene diversity of 0.358 and polymorphic information content of 0.285 from 300 rice accessions from different rice growing areas of the world with 372 SNP markers. The gene diversity detected in this study (0.52) is comparable to overall gene diversity of rice core collection (0.544) from China, North Korea, Japan, Philippines, Brazil, Celebes, Java, Oceanina and Vietnam (Zhang et al. 2011) and it is higher than US accession panel with average gene diversity of 0.43 (Agrama and Eizenga 2008) and Chinese rice accession panel by Jin et al. (2010) with the average gene diversity of 0.47. The gene diversity reported in our study is lesser than gene diversity (0.68) reported by (Liakat Ali et al. 2011). Most of the diversity panel with global accessions has the gene diversity of 0.5 to 0.7 (Garris et al. 2005; Liakat Ali et al. 2011; Ni et al. 2002). These results on global accessions help to infer that this diversity panel of 192 germplasm lines represents a large proportion of the genetic diversity that exists in major rice growing Asian continent.

The PIC value was 0.468 which varied from 0.146 for RM17616 with only 2 two alleles to 0.756 for RM316 that allowed the amplification of 7 alleles. The PIC value was found to be 0.418 for SG1 which had the majority of indica accessions. The subgroup SG2 dominated by japonica accessions had the PIC value of 0.414. Hence, both the subgroups contribute in a major way for population diversity. As this population encompass different rice materials i.e., landraces, varieties and breeding lines, the molecular diversity is contributed majorly by landraces. These values are similar to those found by Courtois et al. (2012) who reported the PIC value from 0.16 to 0.78 with the average of 0.49 in European rice germplasm collection and in Chinese rice collection of 416 accessions by Jin et al. (2010), who has given similar PIC value of 0.4214. It is also consistent with PIC value (0.48) attained by Zhang et al. (2011). In this study, significant amount of rare alleles was identified which indicates that these rare alleles contribute well to the overall genetic diversity of the population.

Model based approach by STRUCTURE is implemented frequently for studying population structure by various researchers (Agrama et al. 2007, Agrama and Eizenga 2008; Garris et al. 2005; Zhang et al. 2007, 2011; Jin et al. 2010; Liakat Ali et al. 2011, Chakhonkaen et al. 2012 Courtois et al. 2012, Das et al. 2013). Courtois et al. (2012) has successfully detected two subgroups in their study population and assigned rice varieties into two groups with few admixture lines. Jin et al. (2010) has identified seven sub populations among 416 rice accessions from China. Das et al. (2013) has grouped a collection of 91 accessions of rice landraces from eastern and north eastern India into four groups.

Assigning of genotypes to the subgroups based on ancestry threshold vary between different research groups. Zhao et al. (2010) and Courtois et al. (2012) used an ancestry threshold of 80 % to identify accessions belonging to a specific subpopulation. Liakat Ali et al. (2011) has steup the threshold as 60 % and identified 33 accessions as admixtures as the threshold of 80 % consider more genotypes as admixtures. In the current study, a stringent threshold of 80 % ancestry value leaves only 20 genotypes as admixtures.

Population structure analysis in different rice diversity panel has indicated the existence of two to eight sub population in rice (Zhang et al. 2007, Zhang et al. 2009, Zhang et al. 2011, Garris et al. 2005, Agrama et al. 2007, Liakat Ali et al. 2011, Chakhonkaen et al. 2012 and Das et al. 2013). In the current rice diversity panel of 192 accessions based on the criterion of maximum membership probabilities, 134 accessions were assigned to SG1 which is dominated by indica subtype with most of the landraces and varieties of Indian origin and SG2 consisted of 38 accessions which composed mostly of japonica accessions of exotic origin. Similar population structure of two subgroups was observed in previous research by Zhang et al. (2009) in a collection of 3024 rice landraces in China. Zhang et al. (2011) has reported two distinct subgroups in a rice core collection. Courtois et al. (2012) has successfully classified two subgroups as japonica and non japonica accessions in European core collection of rice. The results indicated that two subgroups are due to the different adaptation behavior of accessions to different ecological environment as indica and japonica accessions has independent evolution frame and the origin of Indian rice accessions from indica cultivars. Hence the major criterion for population structure in this panel is indica – japonica subtype. This study includes large number of traditional landraces and varieties from Indian Subcontinent and few exotic accessions randomly selected from IRRI worldwide collection. It clarifies the relationship between Indian germplasm and exotic accessions which indicates that germplasm lines varies based on its ecology and also shows higher level of genetic diversity exists within this population.

Further structure analysis of SG1 that consisted of 134 lines indicated that it can be further subdivided in to three sub sub-groups. The three sub sub-groups classification has the factor of ecosystem and seasonal variation as the major factors for population structure. This results is in accordance with the inference that indica group has higher genetic diversity than japonica accessions which was given by various researchers (Gao et al. 2005; Lu et al. 2005; Lapitan et al. 2007; Caicedo et al. 2007; Liakat Ali et al. 2011; Garris et al. 2005; Qi et al. 2006; Qi et al. 2009); as this subgroup has indica accessions. Liakat Ali et al. (2011) has substantiated this statement with the reason of the indica subpopulation occupying the largest rice growing region which has a varied environments, ecological conditions and soil type.

The result of model based analysis is in accordance with the clustering pattern of Neighbour joining tree and Principal Coordinate Analysis. The first two principal coordinates explained 12.6 and 4.8 % of the molecular variance. Similar pattern of molecular variance explanation was observed by Zhang et al. (2011) for two population subgroups.

Calculation of Wright’s F Statistic at all loci revealed the deviation from Hardy- Weinberg law for molecular variation within the population. The result of Fst indicates higher divergence existing between subgroups of the population. Higher FIT, which is measured at subgroup level in whole population, has indicated lack of equilibrium across the groups and lack of heterozygosity most likely due to the inbreeding nature of rice.

The present study revealed that several unexploited landraces of Tamil Nadu, India which is widely cultivated by the farmers in different parts of the state. Ecological and evolutionary history contributes for the genetic diversity maintained in a population. The varieties with diverse ecosystems and wide eco-geographical conditions contribute for the genetic diversity among rice varieties in this population.

For establishing a core collection for association studies, two step approach followed by Breseghello and Sorrells (2006) and Courtois et al. (2012) was used. This approach involves the determination of population structure and then sampling can be done based on the relatedness of the accessions in the population. Those accessions that show high magnitude of genetic relatedness can be eliminated to develop core collection with diverse representatives. Based on this idea, out of 192 accessions, 150 (Table 5) were selected to form association mapping panel which can be utilized either by genome wide or candidate gene specific association mapping for linking the genotypic and phenotypic variation.

Table 5.

Genotypes selected for association mapping panel

G. no Genotypes G. no Genotypes G. no Genotypes G. no Genotypes G. no Genotypes G. no Genotypes
RG1 Mapillai samba RG58 Kodaikuluthan RG113 IG 45(EC 728768- 117736) RG154 IG 48(EC 729203- 121195) RG39 Kaatu ponni RG95 Jeeraga samba
RG2 CK 275 RG59 RPHP 68 RG114 RPHP 159 RG156 IG 12(EC 729626- 121681) RG41 Godavari samba RG96 RP-BIO-226
RG3 Senkar RG60 Rama kuruvaikar RG115 IG 43(EC 728788- 117759) RG157 Karungan RG42 Earapalli samba RG98 IG 5(EC 729642- 121698)
RG4 Murugankar RG62 Purple puttu RG116 RPHP 27 RG158 IG 13(EC 729640- 121696) RG43 RPHP 129 RG99 IG 31(EC 728844- 117829)
RG5 CHIR 6 RG63 IG 71(EC 728651- 117588) RG117 IG 65(EC 729024- 120958) RG159 Sembala RG44 Mangam samba RG100 IG 7(EC 729598- 121648)
RG6 CHIR 5 RG65 IG 56(EC 728700- 117658 RG118 Ponmani samba RG160 IG 72(EC 728650- 117587) RG45 RPHP 105 RG101 RPHP 52
RG7 Kudai vazhai RG66 Jeevan samba RG120 Thattan samba RG161 Panamarasamba RG46 IG 4(EC 729639- 121695) RG102 Varakkal
RG8 CHIR 8 RG67 RPHP 106 RG121 IG 74(EC 728622- 117517) RG162 IR 64 RG48 Kalarkar RG103 Mattaikar
RG9 Kuruvai kalanjiyam RG68 IG 63(EC 728711- 117674) RG122 Kaliyan samba RG163 Mikuruvai RG50 Sornavari RG104 IG 53(EC 728752- 117719)
RG12 Vellai chithiraikar RG69 RPHP 48 RG123 IG 2(EC 729808-121874) RG164 Thillainayagam RG51 RPHP 134 RG105 IG 6(EC 729592- 121642)
RG14 Jothi RG70 Karthi samba RG124 IG 29(EC 728925- 117920) RG165 ARB 64 RG52 ARB 58 RG106 Katta samba
RG15 Palkachaka RG71 IG 27(IC 0590934- 121255) RG126 Kallimadayan RG166 RPHP 140 RG53 IR 68144-2B-2-2-3-1-127 RG107 RH2-SM-1-2-1
RG17 Chivapu chithiraikar RG72 Aarkadu kichili RG127 IG 10(EC 729686- 121743) RG168 Haladichudi RG54 PTB 19 RG108 Red sirumani
RG18 CHIR 11 RG74 ARB 65 RG128 IG 75(EC 728587- 117420) RG169 IG 24(EC 728751- 117718) RG55 IG 67(EC 729050- 120988) RG109 Vadivel
RG20 Kalvalai RG76 Matta kuruvai RG129 IG 38(EC 728742 - 117707) RG170 RPHP 42 RG56 RPHP 59 RG110 Norungan
RG22 IR 36 RG77 Karuthakar RG130 IG 39(EC 728779- 117750) RG172 IG 25(EC 729728- 121785) RG57 RPHP 103 RG112 IG 35(EC 728858- 117843)
RG25 Sorna kuruvai RG80 IG 66(EC 729047- 120985) RG131 RPHP 90 RG173 IG 73(EC 728627- 117527) RG143 IG 46(IC 471826- 117647) RG184 IG 18(EC 728892- 117880)
RG26 Rascadam RG81 CB-07-701-252 RG132 IG 33(EC 728938- 117935) RG174 IG 51(EC 728772- 117742) RG145 IG 60(EC 728730- 117695) RG185 RPHP 36
RG31 Chinthamani RG82 Thooyamalli RG133 IG 42(EC 728798- 117774) RG175 Vellai kudaivazhai RG146 IR 75862-206 RG186 IG 28(EC 728920- 117914)
RG32 Thogai samba RG83 RPHP 93 RG134 IG 9(EC 729682- 121739) RG176 Kodai RG147 IG 58(EC 728725- 117689) RG187 Vadakathi samba
RG33 Malayalathan samba RG85 RPHP 104 RG135 RPHP 161 RG178 IG 17(EC 728900- 117889) RG148 Chinna aduku nel RG188 RPHP 80
RG34 RPHP 125 RG86 RPHP 102 RG136 IG 8(EC 729601- 121651) RG180 IG 59(EC 728729- 117694) RG149 RH2-SM-2-23 RG189 IG 41(EC 728800- 117776)
RG35 CK 143 RG89 IR 83294-66-2-2-3-2 RG137 IG 37(EC 728715- 117678) RG181 IG 52(EC 728756- 117723) RG150 IG 14(IC 517381- 121422) RG190 IG 26(IC 0590943- 121899)
RG36 Kattikar RG91 IG 23(EC 729391- 121419) RG141 IG 44(EC 728762- 117729) RG182 ARB 59 RG151 IG 32(EC 728838- 117823) RG191 IG 15(EC 728910- 117901)
RG37 Shenmolagai RG92 IG 49(EC 729102- 121052) RG142 Sasyasree RG183 RPHP 163 RG152 RPHP 47 RG192 Nootri pathu
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Conclusion

This study analyze the pattern of divergence exists in a population of 192 rice accessions that constitute our rice diversity panel for association mapping. Based on various statistical methods, we identified two sub groups within 192 rice accessions selected for establishing association mapping panel. The average number of alleles per locus and gene diversity has indicated the existence of broad genetic base in this collection. The result of structure analysis is in accordance with clustering method of neighbor joining tree and principal coordinate analysis. Thus, the results of this study which indicates the genetic diversity of the accessions can be utilized to predict approaches such as association analysis, classical mapping population development; parental line selection in breeding programs and hybrid development for exploiting the natural genetic variation exists in this population.

Methods

Plant Material

A collection consisting of 192 rice accessions was used in this study, which consist of land races and varieties collected from nine different states of India as well as from Argentina, Bangladesh, Brazil, Bulgaria, China, Colombia, Indonesia, Philippines, Taiwan, Uruguay, Venezuela and United States (Table 6).

Table 6.

Germplasm accessions used in the study

G. no. Genotype Parentage Origin Type – traditional/Improved Subtype Ecosystem IR = irrigated, RL = rainfed lowland; UP = upland Maturity class: E = early, M = medium, L = late; Donors/Original providing country
RG1 Mapillai samba Landrace Tamil Nadu, India T indica IR L India
RG2 CK 275 CO50 X KAVUNI Tamil Nadu, India I indica IR L India
RG3 Senkar Landrace Tamil Nadu, India T indica IR M India
RG4 Murugankar Landrace Tamil Nadu, India T indica UP L India
RG5 CHIR 6 Improved chinsurah West Bengal I indica IR E India
RG6 CHIR 5 Improved chinsurah West Bengal I indica IR E India
RG7 Kudai vazhai Landrace Tamil Nadu, India T indica UP E India
RG8 CHIR 8 Improved chinsurah West Bengal I indica IR E India
RG9 Kuruvai kalanjiyam Landrace Tamil Nadu, India T indica IR E India
RG10 Nava konmani Landrace Tamil Nadu, India T indica RL M India
RG11 CHIR 10 Improved chinsurah West Bengal I indica IR M India
RG12 Vellai chithiraikar Landrace Tamil Nadu, India T indica RL E India
RG13 CHIR 2 Improved chinsurah West Bengal I indica IR M India
RG14 Jyothi Variety Kerala, India T indica IR E India
RG15 Palkachaka Landrace Tamil Nadu, India T indica IR M India
RG16 Thooyala Landrace Tamil Nadu, India T indica IR E India
RG17 Chivapu chithiraikar Landrace Tamil Nadu, India T indica RL E India
RG18 CHIR 11 Improved chinsurah West Bengal I indica IR M India
RG19 Koolavalai Landrace Tamil Nadu, India T indica RL M India
RG20 Kalvalai Landrace Tamil Nadu, India T indica RL E India
RG21 Mohini samba Landrace Tamil Nadu, India T indica IR M India
RG22 IR 36 IR 1561 X IR 24 X Oryza nivara x CR 94 IRRI, Philippines I indica IR E Philippines
RG23 Koombalai Landrace Tamil Nadu, India T indica IR M India
RG24 Tadukan Landrace Philippines T indica UP M Philippines
RG25 Sorna kuruvai Landrace Tamil Nadu, India T indica IR M India
RG26 Rascadam Landrace Tamil Nadu, India T indica IR M India
RG27 Muzhi karuppan Landrace Tamil Nadu, India T indica IR E India
RG28 Kaatukuthalam Landrace Tamil Nadu, India T indica RL M India
RG29 Vellaikattai Landrace Tamil Nadu, India T indica RL M India
RG30 Poongar Landrace Tamil Nadu, India T indica RL L India
RG31 Chinthamani Landrace Tamil Nadu, India T indica RL M India
RG32 Thogai samba Landrace Tamil Nadu, India T indica RL M India
RG33 Malayalathan samba Landrace Tamil Nadu, India T indica IR E India
RG34 RPHP125 NDR 2026 (RICHA) UTTAR PRADHESH I indica IR E India
RG35 CK 143 CO50 X KAVUNI Tamil Nadu, India I indica IR L India
RG36 Kattikar Landrace Tamil Nadu, India T indica RL M India
RG37 Shenmolagai Landrace Tamil Nadu, India T indica IR M India
RG38 Velli samba Landrace Tamil Nadu, India T indica IR M India
RG39 Kaatu ponni Landrace Tamil Nadu, India T indica IR M India
RG40 kakarathan Landrace Tamil Nadu, India T indica IR M India
RG41 Godavari samba Landrace Tamil Nadu, India T indica IR M India
RG42 Earapalli samba Landrace Tamil Nadu, India T indica IR M India
RG43 RPHP 129 Kamad JAMMU & KASHMIR T indica Scented E India
RG44 Mangam samba Landrace Tamil Nadu, India T indica IR M India
RG45 RPHP 105 Moirang phou MANIPUR T indica IR E India
RG46 IG 4(EC 729639- 121695) TD2: :IRGC 9148-1 IRRI, Philippines I indica IR M Philippines
RG47 Machakantha Landrace Orissa, India T indica scented E India
RG48 Kalarkar Landrace Tamil Nadu, India T indica RL E India
RG49 Valanchennai Landrace Tamil Nadu, India T indica RL E India
RG50 Sornavari Landrace Tamil Nadu, India T indica RL E India
RG51 RPHP 134 NJAVARA Kerala T indica RL E India
RG52 ARB 58 Variety Karnataka I indica IR E India
RG53 IR 68144-2B-2-2-3-1-127 IR 72 X ZAWA BONDAY IRRI, Philippines I indica E Philippines
RG54 PTB 19 Variety Kerala, India I indica IR M India
RG55 IG 67(EC 729050- 120988) IR 77384-12-35-3-12-l-B::IRGC 117299-1 IRRI, Philippines I indica IR E Philippines
RG56 RPHP 59 Taroari Basmati/karnal local HARYANA T Aromatic scented L India
RG57 RPHP 103 Pant sugandh dhan -17 UTTARKHAND I Aromatic scented L India
RG58 Kodaikuluthan Landrace Tamil Nadu, India T indica RL E India
RG59 RPHP 68 Subhdra Orissa, India I indica RL E India
RG60 Rama kuruvaikar Landrace Tamil Nadu, India T indica IR E India
RG61 Kallundai Landrace Tamil Nadu, India T indica RL E India
RG62 Purple puttu Landrace Tamil Nadu, India T indica IR E India
RG63 IG 71(EC 728651- 117588) TEPI BORO::IRGC 27519-1 IRRI, Philippines I aus IR E Philippines
RG64 Ottadaiyan Landrace Tamil Nadu, India T indica RL M India
RG65 IG 56(EC 728700- 117658 BICO BRANCO Brazil T Aromatic UP E Philippines
RG66 Jeevan samba Landrace Tamil Nadu, India T indica IR M India
RG67 RPHP 106 akut phou MANIPUR I indica IR M India
RG68 IG 63(EC 728711- 117674) CAAWA/FORTUNA IRRI, Philippines I Tropical Japonica IR M Philippines
RG69 RPHP 48 Bindli UTTARKHAND T Aromatic Scented L India
RG70 Karthi samba Landrace Tamil Nadu, India T indica IR M India
RG71 IG 27(IC 0590934- 121255) ARC 11345::IRGC 21336-1 IRRI, Philippines I indica IR M Philippines
RG72 Aarkadu kichili Landrace Tamil Nadu, India T indica IR M India
RG73 Kunthali Landrace Tamil Nadu, India T indica IR E India
RG74 ARB 65 Variety Karnataka I indica IR E India
RG75 IG 21(EC 729334- 121355) HONGJEONG::IRGC 73052-1 IRRI, Philippines I japonica IR E Philippines
RG76 Matta kuruvai Landrace Tamil Nadu, India T indica IR E India
RG77 Karuthakar Landrace Tamil Nadu, India T indica RL E India
RG78 RPHP 165 Tilak kachari West Bengal T indica IR E India
RG79 Manavari Landrace Tamil Nadu, India T indica U E India
RG80 IG 66(EC 729047- 120985) IR 71137-243-2-2-3-3::IRGC 99696-1 IRRI, Philippines I indica IR E Philippines
RG81 CB-07-701-252 White ponni X Rasi Tamil Nadu, India I indica IR E India
RG82 Thooyamalli Landrace Tamil Nadu, India T indica IR M India
RG83 RPHP 93 Type-3 (Dehradooni Basmati) UTTARKHAND I indica Scented M India
RG84 Velsamba Landrace Tamil Nadu, India T indica IR M India
RG85 RPHP 104 Kasturi (IET 8580) UTTARKHAND I indica IR M India
RG86 RPHP 102 Kanchana Kerala, India I indica Semi Deep Water L India
RG87 IG 40(EC 728740- 117705) DEE GEO WOO GEN TAIWAN T Indica IR M Philippines
RG88 Saranga Landrace Tamil Nadu, India T indica IR E India
RG89 IR 83294-66-2-2-3-2 DAESANBYEO X IR65564-44-5-1 IRRI, Philippines I japonica RL M Philippines
RG90 IG 61(EC 728731- 117696) CRIOLLO LA FRIA Venezuela I Indica IR E Philippines
RG91 IG 23(EC 729391- 121419) MAHA PANNITHI::IRGC 51021-1 IRRI, Philippines I Aus IR M Philippines
RG92 IG 49(EC 729102- 121052) MENAKELY ::IRGC 69963-1 Madagascar I Indica RL M Philippines
RG93 Uppumolagai Landrace Tamil Nadu, India T Indica IR M India
RG94 Karthigai samba Landrace Tamil Nadu, India T Indica RL M India
RG95 Jeeraga samba Landrace Tamil Nadu, India T Indica IR M India
RG96 RP-BIO-226 IMPROVED SAMBHA MAHSURI ANDHRA PRADESH I Indica IR M India
RG97 Varigarudan samba Landrace Tamil Nadu, India T Indica IR M India
RG98 IG 5(EC 729642- 121698) IR 65907-116-1-B::C1 IRRI, Philippines I japonica UP E Philippines
RG99 IG 31(EC 728844- 117829) ORYZICA LLANOS 5 Colombia T Indica IR M Philippines
RG100 IG 7(EC 729598- 121648) VARY MAINTY::IRGC 69910-1 Madagascar I japonica IR M Philippines
RG101 RPHP 52 SEBATI Orissa, India I Indica IR M India
RG102 Varakkal Landrace Tamil Nadu, India T Indica UP E India
RG103 Mattaikar Landrace Tamil Nadu, India T Indica RL L India
RG104 IG 53(EC 728752- 117719) CAROLINA RINALDO BARSANI URUGUAY I Temperate japonica IR E Philippines
RG105 IG 6(EC 729592- 121642) SOM CAU 70 A::IRGC 8227-1 Vietnam I Temperate japonica IR E Philippines
RG106 Katta samba Landrace Tamil Nadu, India T Indica RL L India
RG107 RH2-SM-1-2-1 SWARNA X MOROBERAKAN Tamil Nadu, India I Indica IR E India
RG108 Red sirumani Landrace Tamil Nadu, India T Indica RL E India
RG109 Vadivel Landrace Tamil Nadu, India T Indica IR M India
RG110 Norungan Landrace Tamil Nadu, India T Indica RL E India
RG111 IG 20(EC 729293- 121310) CHIGYUNGDO::IRGC 55466-1 South Korea I Indica UP E Philippines
RG112 IG 35(EC 728858- 117843) PATE BLANC MN 1 Cote D’Ivoire I japonica UP M Philippines
RG113 IG 45(EC 728768- 117736) FORTUNA Puerto Rico T japonica IR M Philippines
RG114 RPHP 159 Radhuni Pagal BANGLADESH I aromatic rice Scented L India
RG115 IG 43(EC 728788- 117759) IR-44595 IRRI, Philippines I indica IR E Philippines
RG116 RPHP 27 Azucena IRRI, Philippines T Tropical Japonica RL E India
RG117 IG 65(EC 729024- 120958) GODA HEENATI::IRGC 31393-1 SRILANKA I indica IR E Philippines
RG118 Ponmani samba Landrace Tamil Nadu, India T indica IR M India
RG119 Ganthasala Landrace Tamil Nadu, India T indica IR M India
RG120 Thattan samba Landrace Tamil Nadu, India T indica IR E India
RG121 IG 74(EC 728622- 117517) KINANDANG PATONG::IRGC 23364-1 IRRI, Philippines I japonica RL M Philippines
RG122 Kaliyan samba Landrace Tamil Nadu, India T indica IR M India
RG123 IG 2(EC 729808-121874) BLUEBONNET 50::IRGC 1811-1 IRRI, Philippines I japonica UP M Philippines
RG124 IG 29(EC 728925- 117920) TOX 782-20-1 NIGERIA T Tropical Japonica IR E Philippines
RG125 RPHP 55 Kalinga -3 Orissa I indica RL E India
RG126 Kallimadayan Landrace Tamil Nadu, India T indica RL E India
RG127 IG 10(EC 729686- 121743) HASAN SERAI IRRI, Philippines I aromatic IR E Philippines
RG128 IG 75(EC 728587- 117420) AEDAL::IRGC 55441-1 Korea T japonica IR E Philippines
RG129 IG 38(EC 728742 - 117707) DELREX UNITED STATES Tropical japonica IR M Philippines
RG130 IG 39(EC 728779- 117750) HONDURAS HONDURAS indica IR M Philippines
RG131 RPHP 90 182(M) Andhra Pradesh I indica IR E India
RG132 IG 33(EC 728938- 117935) WC 3397 JAMAICA Tropical Japonica IR E Philippines
RG133 IG 42(EC 728798- 117774) KALUBALA VEE SRILANKA T indica IR E Philippines
RG134 IG 9(EC 729682- 121739) GEMJYA JYANAM::IRGC 32411-C1 IRRI, Philippines I indica IR E Philippines
RG135 RPHP 161 Champa Khushi Vietnam T indica UP E India
RG136 IG 8(EC 729601- 121651) XI YOU ZHAN::IRGC 78574-1 China I indica IR E Philippines
RG137 IG 37(EC 728715- 117678) CENIT ARGENTINA T Tropical Japonica IR L Philippines
RG138 Sigappu kuruvikar Landrace Tamil Nadu, India T indica RL E India
RG139 RPHP 138 EDAVANKUDI POKKALI Kerala, India T indica Deep water L India
RG140 Raja mannar Landrace Tamil Nadu, India T indica IR M India
RG141 IG 44(EC 728762- 117729) EDITH UNITED STATES T indica IR E Philippines
RG142 Sasyasree TKM 6 x IR 8 West Bengal I indica IR E India
RG143 IG 46(IC 471826- 117647) BABER INDIA I indica IR E India
RG144 Chetty samba Landrace Tamil Nadu, India T indica IR E India
RG145 IG 60(EC 728730- 117695) CREOLE Belize T indica IR M Philippines
RG146 IR 75862-206 IR 75083 X IR 65600 -81-5-3-2 IRRI, Philippines I Tropical Japonica IR M Philippines
RG147 IG 58(EC 728725- 117689) CI 11011 UNITED STATES japonica IR M Philippines
RG148 Chinna aduku nel Landrace Tamil Nadu, India T indica IR L India
RG149 RH2-SM-2-23 SWARNA X MOROBERAKAN Tamil Nadu, India I indica IR M India
RG150 IG 14(IC 517381- 121422) MALACHAN::IRGC 54748-1 India I indica UP E Philippines
RG151 IG 32(EC 728838- 117823) NOVA United States I japonica IR M Philippines
RG152 RPHP 47 Pathara (CO-18 x Hema) India I indica IR E India
RG153 Sembilipiriyan Landrace Tamil Nadu, India T indica RL M India
RG154 IG 48(EC 729203- 121195) DINOLORES::IRGC 67431-1 IRRI, Philippines I indica UP M Philippines
RG155 Sona mahsuri Landrace Tamil Nadu, India T indica IR E India
RG156 IG 12(EC 729626- 121681) SHESTAK::IRGC 32351-1 Iran I indica IR E Philippines
RG157 Karungan Landrace Tamil Nadu, India T indica IR E India
RG158 IG 13(EC 729640- 121696) CURINCA::C1 BRAZIL I indica IR E Philippines
RG159 Sembala Landrace Tamil Nadu, India T indica IR L India
RG160 IG 72(EC 728650- 117587) TD 25::IRGC 9146-1 Thailand I indica IR M Philippines
RG161 Panamarasamba Landrace Tamil Nadu, India T indica IR M India
RG162 IR 64 IR-5857-33-2-1 x IR-2061-465-1-5-5 IRRI, Philippines I indica IR E Philippines
RG163 Mikuruvai Landrace Tamil Nadu, India T indica RL E India
RG164 Thillainayagam Landrace Tamil Nadu, India T indica IR M India
RG165 ARB 64 Variety Karnataka I indica IR E India
RG166 RPHP 140 VYTILLA ANAKOPON Kerala T indica IR E India
RG167 IG 70(EC 729045- 120983) IR43::IRGC 117005-1 IRRI, Philippines I indica IR M Philippines
RG168 Haladichudi Landrace Orissa, India T indica IR E India
RG169 IG 24(EC 728751- 117718) DNJ 140 BANGLADESH I Aus IR E Philippines
RG170 RPHP 42 Salimar Rice -1 JAMMU & KASHMIR I indica IR M India
RG171 RPHP 44 BR- 2655 KARNATAKA I indica IR L India
RG172 IG 25(EC 729728- 121785) LOHAMBITRO 224::GERVEX 5144-C1 Madagascar I Tropical Japonica IR E Philippines
RG173 IG 73(EC 728627- 117527) MAKALIOKA 34::IRGC 6087-1 IRRI, Philippines I indica IR E Philippines
RG174 IG 51(EC 728772- 117742) GOGO LEMPUK Indonesia Tropical Japonica IR M Philippines
RG175 Vellai kudaivazhai Landrace Tamil Nadu, India T indica RL M India
RG176 Kodai Landrace Tamil Nadu, India T indica RL E India
RG177 Kallundaikar Landrace Tamil Nadu, India T indica UP M India
RG178 IG 17(EC 728900- 117889) SIGADIS INDONESIA T indica RL L Philippines
RG179 Avasara samba Landrace Tamil Nadu, India T indica IR E India
RG180 IG 59(EC 728729- 117694) COPPOCINA BULGARIA I Tropical Japonica IR M Philippines
RG181 IG 52(EC 728756- 117723) DOURADO AGULHA BRAZIL I Tropical Japonica IR M Philippines
RG182 ARB 59 Variety Karnataka I indica IR E India
RG183 RPHP 163 Seeta sail West Bengal T indica Scented M India
RG184 IG 18(EC 728892- 117880) SERATOES HARI INDONESIA T indica IR E Philippines
RG185 RPHP 36 TKM-9 Tamil Nadu, India I indica IR E India
RG186 IG 28(EC 728920- 117914) TIA BURA INDONESIA T Tropical Japonica IR M Philippines
RG187 Vadakathi samba Landrace Tamil Nadu, India T indica IR M India
RG188 RPHP 80 24(K) Andhra Pradesh I indica IR E India
RG189 IG 41(EC 728800- 117776) KANIRANGA Indonesia T Tropical japonica IR M Philippines
RG190 IG 26(IC 0590943- 121899) BASMATI 370::IRGC 3750-1 IRRI, Philippines I aromatic IR E Philippines
RG191 IG 15(EC 728910- 117901) SZE GUEN ZIM CHINA I indica IR E Philippines
RG192 Nootri pathu Landrace Tamil Nadu, India T indica RL L India
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IRRI lines - The number after hyphen inside brackets represent IRGC number

Microsatellite Genotyping

DNA Isolation and PCR Amplification

DNA was extracted from leaf tissue by grinding with liquid nitrogen using CTAB method (Saghai-Maroof et al. 1984.). It was diluted to a final concentration of 30 ng μl−1 for enabling polymerase chain reactions. DNA amplification parameters such as specificity, efficiency and fidelity are strongly influenced by the components of the PCR reaction and by thermal cycling conditions (Caetano-Anolles and Brant 1991). Therefore, the careful optimization of reaction components and conditions will ultimately result in more reproducible and efficient amplification. The concentrations of primers, template DNA, Master Mix, and annealing temperature was optimized on eight diverse accessions for 156 SSR markers distributed on the 12 chromosomes by modified Taguchi method (Cobb and CIarkson 1994). Microsatellite primer sequences, annealing temperature and chromosomal locations are obtained from GRAMENE database (http://archive.gramene.org/markers/microsat/). Sixty one SSR primer pairs which produce polymorphic allele amplification were chosen to genotype the entire set of germplasm collection.

The volume of the PCR reaction system was 10 μl. The PCR reaction mixture of 10 μl had 0.4 mM dNTPs, 4 mM of MgCl2, 150 mM of Tris–HCl, 10 pmoles of forward and reverse primer and 0.05 U Taq polymerase with 30 ng of DNA. Polymerase chain reaction was performed in BIORAD THERMAL CYCLER using the following program: 94 °C for 2 min, 35 cycles of 94 °C for 45 sec, 50–60 °C for 1 min, 72 °C for 2 min with a final extension of 72 °C for ten min.

Polyacrylamide Gel Electrophoresis

Amplified products were size separated in native polyacrylamide gel electrophoresis using 6 % (w/v) polyacrylamide gel according to Sambrook et al. (2001) in vertical electrophoresis tank with 1X TBE at 150 V. The gel size was determined using standard molecular weight size markers after the bands were detected by silver staining.

Allele Scoring

The bands were visualized in a cluster of two to six in the stained gels for most of the markers. Based on the expected product size given in the GRAMENE website (Additional file 2: Table S1), the size of the most intensely amplified bands around the expected product size for each microsatellite marker was identified using standard molecular weight size markers (20 bp DNA ladder, GeNeI Company). Then the stained gel was dried and documented using light box. Allele score was given based on the presence of a particular size allele in each of the germplasm. The presence was denoted as 1 and absence of an allele as 0 and it was rechecked manually (Additional file 3: Table S2).

Data Analysis

A 1/0 matrix was constructed based on the presence and absence of alleles for the set of 61 markers. This SSR genotype data was analyzed for genetic diversity and population structure.

Genetic Diversity

For a set of accessions, genetic diversity parameters such as number of alleles per locus, allele frequency, heterozygosity and polymorphic information index (PIC) was estimated using the program POWERMARKER Ver3.25 (Liu and Muse 2005). Allele frequency represents the frequency of particular allele for each marker. Heterozygosity is the proportion of heterozygous individuals in the population. Polymorphic information content that represent the amount of polymorphism within a population was estimated based on Botstein et al. (1980).

To assess genetic structure, model based approach and distance based approach were used. Model based approach was utilized with Structure ver 2.3.4 software (Pritchard et al. 2000). The actual number of subpopulation which is denoted by K was identified by this method. For that, the project was run with the following parameter set: the possibility of admixture and allele frequency correlated. Run length was given as 150,000 burning period length followed by 150,000 Markov Chain Monte Carlo (MCMC) replication. Each k value was run for 10 times with k value varying from 1 to 10. The optimum k value was determined by plotting the mean estimate of the log posterior probability of the data (L (K) against the given K value. True number of subpopulation was identified using the maximal value of L (K). An adhoc quantity ΔK proposed by (Evanno et al. 2005) based on second order rate of change of the likelihood function with respect to K estimated using Structure Harvester (Earl 2012) has also shown a clear peak at the optimal K value.

Distance based approach which is based on calculating pair wise distance matrix was computed by calculating a dissimilarity matrix using a shared allele index with DARwin software (Perrier and Jacquemoud-Collet 2006). An unweighted neighbor joining tree was constructed using the calculated dissimilarity index. The genetic distance between accessions was estimated using NEI coefficient (Nei 1972) with bootstrap procedure of resampling (1000) across markers and individuals from allele frequencies. To determine the association among the accessions, unweighted pair group method with arithmetic mean (UPGMA) tree was also drawn using Powermarker and viewed in MEGA 6.0 software (Tamura et al. 2013).

The presence of molecular variance within and between hierarchical population structure estimated by Structure was assessed via Analysis of molecular variance (AMOVA) by Arlequin (Excoffier et al. 2005). F statistics which include FIT, deviations from Hardy- Weinberg expectation across the whole population, FIS deviation from Hardy- Weinberg expectation within a population and FST, correlation of alleles between subpopulation was calculated using AMOVA approach in Arlequin. AMOVA and Principal Coordinate analysis of the germplasm set was performed based on Nei (Nei 1973) distance matrix using GenAlEx 6.5 (Peakall and Smouse 2012).

Acknowledgement

This work was supported by a grant from Department of Biotechnology, Government of India under Rice biofortification with enhanced iron and zinc in high yielding non basmati cultivars through marker assisted breeding and transgenic approaches- Phase II (E28SO) scheme. I thank Dr. Yasodha from Institute of Forest Genetics and Tree Breeding, Coimbatore for helping in the analysis.

Additional files

Additional file 1: Figure S1. (1MB, jpg)

Allelic pattern of different SSR markers used in this study. (JPG 1.03 MB)

Additional file 2: Table S1. (170.5KB, xls)

Expected product size obtained from Gramene and observed product size for the SSR markers used in this study. (XLS 170 kb)

Additional file 3: Table S2. (10.6KB, xlsx)

Allele matrix of 192 accessions x 61 SSRs. (XLSX 10 kb)

Footnotes

Competing interests

The authors declare that they have no competing interests.

Authors’ contributions

RoS, VVN, RS and SKK prepared the samples. VVN carried out the genotyping, data analysis and drafted the manuscript. BAP, GP, KG participated in genotyping and data preparation. RoS designed the experiment and revised the manuscript. SD, RS, RM, SM participated in study design and revised the manuscript. All authors read and approve the final manuscript.

Contributor Information

Vishnu Varthini Nachimuthu, Email: popvarun@gmail.com.

Raveendran Muthurajan, Email: raveendrantnau@gmail.com.

Sudhakar Duraialaguraja, Email: dsudhakar@hotmail.com.

Rajeswari Sivakami, Email: rajisundar93@gmail.com.

Balaji Aravindhan Pandian, Email: aaron.agri@gmail.com.

Govinthraj Ponniah, Email: sanjay.626@gmail.com.

Karthika Gunasekaran, Email: karthi.tnau07@gmail.com.

Manonmani Swaminathan, Email: swamimano@yahoo.co.in.

Suji K K, Email: krishika_suji@yahoo.co.in.

Robin Sabariappan, Email: robin.tnau@gmail.com.

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