Introduction

Rice is one of the most important staple crop for a large-scale human population over worldwide and India both. As a primary food source, it can meet the nutritional demands of many people. With the ever rising population and unfavorably changing climate there must be a keen step to follow to increase rice productivity with high quality. India has already been victim of such climate change problems so far. Some the abiotic stresses reducing rice crop productivity includes, drought, salinity, temperature, pollution etc. (Singha et al. 2021). The high yield and productivity have always been a concern to breeders for genetic improvement with desirable nutritional quality traits in rice (Sreenivasulu et al. 2015; Sun et al. 2018). In any genetic improvement program, the availability and knowledge about genetic diversity play important role for safeguarding competent use and also for choosing efficient breeding strategies (Chakravarthi and Naravaneni 2006). The overall success of breeding program depends on the consequence of genetic variability and how the beneficial make-up is heritable. Diversity in genes of rice accessions creates an option for the breeder to select desirable traits and use them in making new combinations (Garris et al. 2005). Many techniques support to evaluate genetic variation at both phenotypic and genotypic levels. For the genotypic level, one of the best methods for analyzing diversity in rice is by molecular markers which can tell huge differences among accessions at the DNA level, providing a more reliable, and well-planned aid in accession characterization, and genetic make-up. Many such methods are there, like RAPD, SSR, AFLP, ISSR, etc. The SSR is one of the widely and most commonly used, powerful, and low-cost methods for genetic characterization of germplasm. SSR markers can efficiently facilitate the establishment of genetic relationships due to their co-dominant and specific nature, high level of allelic diversity, relative polymorphic abundance, and extensive distribution across the genome (Powell et al. 1996; Mondini et al. 2009; Hoshino et al. 2012). SSR markers even in less number can give a better genetic diversity spectrum due to their multi-allelic and highly polymorphic nature (Singh et al. 2016). SSR markers have pivotal role in detecting high level of allelic diversity and genetic polymorphisms, and it is broadly used to know genetic variation among rice accessions at a finer level of variation among closely related accessions. The objective of this study was to understand and analyze the extent of genetic diversity and differences among 25 accessions of rice using SSR markers because they give favourable genetic information, help in grouping of accessions according to similarity and dissimilarity to their genetic relationship and identification of diverse accessions for future breeding programs which has the main aim of enhancing rice production balanced with adequate increase in rice quality.

Materials and methods

Plant material and DNA extraction

The plant materials for the present study consisted of 25 rice accessions of indica sub-species collected from Chandra Shekhar Azad University of Agriculture and Technology (CSAUA&T), Kanpur, Uttar Pradesh, and Acharya Narendra Dev University, Faizabad, Uttar Pradesh. The details of accessions are provided in Table 1. The plants were grown in experimental field at CSIR, NBRI, Lucknow during cropping season 2018–2019. The fresh and young leaves from 1 month old plant were collected and total genomic DNA extracted using CTAB method (Murray and Thompson, 1980). In brief, the leaf sample was ground with help of liquid nitrogen and powder was added in pre heated 2% extraction buffer (20 mM EDTA, 1.5 M NaCl, 100 mM Tris HCL, 2% CTAB & 1% BME). The mixture was then incubated in water bath at 65 °C for 45 min and shaken softly in between followed by treatment with Chloroform: Isoamyl alcohol (25:1), 100 mg/ml RNase, and 70% ethanol, then dissolved the pellet in 1X TE buffer. The isolated genomic DNA was quantified by Spectrophotometer Nanodrop (Thermo Scientific, Wilmington, DE, USA), and its quality was checked on 0.8% agarose gel. Finally, the DNA was diluted to 20 ng/µl in TE buffer for PCR amplification.

Table 1 Details of Polymorphism and other information on 21 SSR marker used for diversity analysis in rice
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SSR genotyping and data analysis

A set of 30 SSR primers were selected from the panel of 50 standard SSR markers defined for rice diversity analysis (https://archive.gramene.org/markers/microsat/50_ssr.html). Initially, 4 genomic DNA were amplified with 30 SSR primers to check the amplification and appropriateness of each primer for further genotyping of remaining accessions. Out of 30 SSR primers, 21 primers were selected based on polymorphic amplified products. The PCR amplification was carried out in 10 µl reaction volume containing 20 ng DNA, 1X PCR master mix (Genei Labs, India), 5 pmol each forward and reverse primer. The amplification was carried in C1000 thermal cycler (Bio-Rad Laboratories Inc. USA) with following conditions: pre denaturation at 95 ºC for 5 min followed by 39 cycles of denaturation at 95 ºC for 30 s, annealing at 53–58 ºC (primer specific) for 45 s, extension at 72 ºC for 1 min and final extension at 72 ºC for 10 min. The amplified PCR product was separated on 6% non-denaturing PAGE and visualized by silver staining. The fragment size was calculated by fragment size calculator (http://biotools.nubic.northwestern.edu/SizeCalc.html). The SSR genotyping data were scored both as allele size and as binary matrix (0–1). The allelic data were subjected to calculation of the polymorphic information content (PIC) value, major allele frequency, number of alleles per locus, and gene heterozygosity using Power Marker Software (Liu and Muse 2005). The binary data matrix were subjected to calculate the distance matrix following Jaccard similarity coefficient using DARwin Software (version 6.0.021) (Perrier et al., 2003). The resultant distance matrix was used to construct neighbour joining dendrogram with 1000 bootstrap. To determine the number of subpopulation among the accessions, Bayesian clustering was carried out using software package STRUCTURE v 2.3.3 (Pritchard et al., 2000). The STRUCTURE program was run using admixture model with independent allele frequencies. The presumed population (K) was set from 2 to 10 with 3 independent runs per K and each run was carried out using 30,000 burn-in period and 100,000 iterations. The optimal value of K was determined by examining Delta K statistic and L (K) according to Evanno et al., 2005 using structure harvester (Earl and Holdt 2012). The number of observed alleles (Na), number of effective alleles (Ne), Shannon’s information index (I) and molecular variance (AMOVA) (Excoffier et al. 2009) was calculated with GenAlex 6.5 software.

Results

The 21 SSR primer produced a total of 87 alleles across the 25 rice accessions with an average of 4 alleles per SSR that ranged from 2 (RM507, RM237, RM283, and RM133) to 9 alleles (RM514) per SSR (Table 1). The maximum number of alleles were shown by RM514 (9 alleles) followed by RM125 (7 alleles) and RM271, RM447 (6 alleles). The gene diversity was in between 0.08 (RM507) to 0.84 (RM514) with an average of 0.51 ± 0.22. The 19 SSRs showed observed heterozygosity below 0.20. The observed heterozygosity ranged from 0.00 to 0.36 (RM125) with an average of 0.03 ± 0.09. The PIC value ranged from 0.07 (RM507) to 0.83 (RM514) with an average of 0.46 ± 0.21. Out of 21 SSRs, 23.8% viz. 5 SSRs showed moderate PIC value ranged between 0.25 to 0.50 while 4 SSRs (19.04%) showed lower PIC value (0.07–0.25) and 12 SSRs (57.1%) were highly informative with PIC value ranging from 0.50 to 0.83 Table 1. To understand the genetic dissimilarity among 25 accessions the allelic data were used to calculate a pairwise genetic distance based on the Jaccard coefficient. The pairwise genetic dissimilarity determines the level of relatedness among the accessions, which varied from 0.10 to 0.89. The maximum genetic dissimilarity (0.89) was noticed between accessions Dub Raj & IPB—1 (0.89), and Pant—12 & IPB—1 (0.89) while minimum (0.10) was noticed between Kalanamak and CSR—10. The genetic dissimilarity of one accession with respect to the rest 24 accession revealed that IPB—1 (0.77 ± 0.12) was found to be highly diverse followed by, NDR—8002 (0.74 ± 0.11), BPT—5204 (0.74 ± 0.08), NDR—2065 (0.73 ± 0.09), PB—1 (0.71 ± 0.07), PUSA Basmati (0.71 ± 0.09), & NDR—359 (0.71 ± 0.09) (Table 2). The NJ based dendrogram divided all the 25 accessions into 3 major clusters. The cluster I accommodated 9 (36%) accessions namely Pant–12, Azad Basmati, Kalmuhi, Tarvari Basmati, CSR—10, Kalanamak, CSR—27, Dub Raj and Shakkarchini, cluster II with 7 accessions (28%), NDR—2065, NDR—359, Pusa Basmati, PB—1, BPT—5204, DRR—42 and Sobhini, and cluster III with 9 accessions (36%), NDR—2064, NDR—3112, Swarna Sub–1, MTU – 7029, Sarjoo–52, NDR–8002, IPB—1, Ram Raj and CSR—43 (Fig. 1). Further, the clustering pattern was supported by Bayesian based population structure which also revealed 3 subpopulation based on ∆K value as it showed maximum for K = 3 (Fig. 2). The clustering pattern based on Bayesian statistics depicts consistency with NJ clustering. The bar plot cluster shows 10 accessions in subpopulation I, 6 accessions in subpopulation II and 9 accessions in subpopulation III and of which 7 accessions tend to be pure and 3 showed admixture nature in subpopulation I. All accessions of subpopulation II were pure while 2 accessions of subpopulation III were admixture type (Fig. 3). The cluster composition of NJ tree and bar plot was almost of similar types except the accession Sobhini which was clustered in II cluster in NJ tree and subpopulation I in bar plot. The structure analysis revealed that out of 25, five accessions (Pant 12, Dub Raj, Sobhini, NDR 2064, Ram Raj) were found to be of admixture types and rest have pure genetic makeup based on the present investigation using 21 SSRs. The AMOVA helps in knowing the significance of genetic structure by separating accessions based on genetic variation within and among population using 3 population based on NJ dendrogram. It showed that the 85% of the total genetic variation was found to be within the population and 15% among the population (Table 3) with an average pairwise ϕpt (similar to Fst) being 0.154. The maximum gene diversity (He) was noticed for population 1 (0.19 ± 0.01), followed by Population 3 (0.15 ± 0.02), and population 2 (0.13 ± 0.02) with an average of 0.15 ± 0.01 (Table 4). The other parameters, such as number of different alleles (Na), effective number of alleles (Ne) and Shannon’s information index (I) was noticed higher for population 1 having a mean value of 1.16 ± 0.09, 1.31 ± 0.03 and 0.31 ± 0.02 respectively (Table 4).

Table 2 Minimum, maximum and mean of the Jaccard's similarity coefficient of 25 accessions of rice
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Fig. 1
figure 1

Dendrogram of 25 rice accessions constructed by NJ cluster analysis of genetic dissimilarity based on 21 SSR data

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Fig. 2
figure 2

Estimation of subpopulation by using ∆K (k = 2 to 10)

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Fig. 3
figure 3

Bar plot clustering performed in Structure for K = 3 populations of rice accessions

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Table 3 Analysis of molecular variance (AMOVA) for 25 rice accession
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Table 4 Different genetic diversity estimates for 3 Populations of rice-based on 21 SSR loci
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Discussion

The understanding of genetic diversity is feasible in adopting and incorporating suitable breeding strategies for developing high yielding varieties of rice to fulfill its increasing requirement. Genetic improvement is constantly progressing procedure so there must be a need of the knowledge and understanding of genetic variablity to perform genetic improvement strategy. For identification of diverse accessions there must be evaluation of different set of genetic material with appropriate tool which helps in utilization of these accession in breeding programs. The efficiency of any DNA marker for diversity analysis is mainly based on its polymorphism rate among the accessions to be evaluated. The knowledge and existence of genetic diversity in any other crop plant pave the foundation for breeders to devise suitable breeding strategies towards genetic improvement for developing high-yielding varieties. Many types of molecular markers such as RAPD, ISSR, SNP etc. apart from SSR marker can be used to access genetic variability in wide range of crop plants. The SSR markers are highly informative, co-dominant, multi–allelic, reproducible and transferable in nature (Mason 2015) and thus very useful for population genetics studies. The SSR provides the best tool in identification and characterization of genetically distinct accessions, it serves adequate power of resolution to discriminate among accessions. In rice, several set of genetic materials have been assessed for genetic variability and identification of potential accessions (Embate et al. 2021; Rohmawati et al. 2021). However, the informations obtained from one set of genetic materials could not be implemented on the other set, therefore, it is important to evaluate the genetic materials for variability afresh. A set of well characterized 50 SSRs have been reported for their utilization to evlauate the new sets of accessions (https://archive.gramene.org/markers/microsat/50_ssr.html). We have utilized 21 SSRs from the same set of SSRs to access the genetic diversity among 25 accessions of rice mainly cultivated in Indo-gangetic plains of Uttar Pradesh. A fair number of alleles (87 alleles) have been observed among the studied accessions. Similar range of allelic variability has also been reported earlier in rice with SSRs (Hossain et al. 2007; and Sajib et al. 2012; Ahmed et al. 2019). The average number of alleles detected were 4.14 with a range of 2 (RM507, RM237, RM283, RM133) to 9 (RM514). These findings are similar to those of Ahmed et al. (2019). According to Zhu et al. (2011) an allele observed in less than 5 percent are considered as rare allele. Rare alleles are very instructive in rice accessions fingerprinting (Jain et al. 2004). RM514 and RM125 have higher number of alleles which might be due to wider diversity in the accessions evaluated. The SSRs RM_514, RM_125, RM_447, RM_271 and RM_495 have been identified as potential ones as these showed considerable allelic variability and higher PIC values. These SSRs could be further utilized for diversity analysis in new set of accessions of rice. Gene diversity is the most suitable method of evaluating genetic variability within genotype (Toro and Caballero 2005). According to Rana et al. (2018), marker showing higher number of alleles has high genetic variability and so on. The Polymorphism information content (PIC) values shown by some of the SSRs in the present investigation was found to be higher than the previously reported (Borba et al. 2009; Upadhyay et al. 2011; Singha et al. 2021). However, similar range of PIC value was reported by Islam et al. (2018). The results of our study give out that RM514 is among screening in 25 rice accessions followed by RM125 and RM447. In general the SSRs which showed wide allelic range also showed higher PIC value indicated that the PIC value is an outcome of allele diversity and frequency among the varieties (Rashid et al. 2018). Similar observation have also been reported earlier by Yu et al. (2003) and Onaga et al. (2013). Considering the lowest range of pair-wise genetic similarity coefficient, the accessions PB—1, BPT—5204, PUSA Basmati were found to most diverse. These accessions were also distributed across the 3 cluster of NJ tree and bar plot of population structure. The neighbour-joining (NJ) based clustering and model based population structure analysis was done to understand the extent of genetic divergence which revealed similar pattern of accessions distribution except the accession Sobhini in both. This indicated the robustness of two clustering methods and suggests that any one could be utilized for diversity analysis. Apart from clustering pattern structure analysis also revealed admixture nature of accessions and here a total of 5 accessions showed different level of admixture indicating genetic relatedness and sharing of alleles among themselves. The genetic structure of populations previously reported in rice by Zhao et al. (2009), Jin et al. (2010), Huang et al. (2010). The present study exhibited 3 subpopulation having 5 admixture accessions (Pant 12, Dub Raj, Sobhini, NDR 2064, Ram Raj). The similar level of admixture in several rice accessions have also been reported earlier by Upadhyay et al. (2012). Considering each cluster as one population allowed the estimation of partitioning of genetic diversity as variation within population and variation between populations. The variance within population was much higher than the variance among population. Similar pattern of population variance was also reported by Jasim et al. (2018), Yogi et al. (2020). The main focus of the study is to screen out the diverse accessions of rice for maintaining the important yield attributing trait by selecting most diverse accessions as parents to perform breeding trials and is essential for commercial rice production.

Conclusion

The present investigation propose that the genetics of most of the popular rice accessions in India in the region of Indo Gangetic Plains is narrow, so there must be characterization of large set of commercial rice varieties which helps in its improvement. By evaluating data from clustering, dissimilarity and admixture nature the most diverse accessions were IPB—1, Pant—12, Dub Raj, Ram Raj, NDR 2064 & Sobhini. The accessions IPB—1, NDR—8002, BPT—5204, NDR—2065, PB—1, PUSA Basmati, & NDR—359 were found to be highly diverse as these accessions showed highest dissimilarity with respect to rest of the accessions. The identified potential SSR such as RM514, RM125, RM447, RM271 and RM495 could be utilized to investigate genetic diversity in new set of rice accessions and accessions identified here could be utilized in various breeding programs to create new lines. Microsatellite marker (SSR) offers good opportunity for assessing genetic diversity.