Assessing Genetic Diversity and Population Structure of Rice Genotypes using ISSR Markers

Rice serves as the primary dietary staple for the majority of the global population and its worldwide production is 517 million tonnes during 2022-2023 (FAO 2023). The remarkable genetic diversity of rice is evident from the wide range climatic conditions under which it is cultivated. However, the productivity of rice is limited by various biotic and abiotic stresses which may affect the global production and the livelihood of rice farmers in the under-developed countries (Kumar et al., 2021). Hence it is crucial to investigate the wide range of stress-resistant genes from the extensive gene reservoirs of rice and its indigenous counterparts. The initial step in this process is to assess the genetic diversity of various rice germplasm to understand the similarities and differences in their genetic composition (Dale and Schantz, 2002). Comparison of complete genome sequences of germplasm lines enables the detection of a comprehensive spectrum of potential variations.  However, considering the extent of time and effort, for regular investigations, the methods focusing on discrete genomic alterations are favoured (Dale and Schantz, 2002). In this context, molecular markers stand out as a valuable tool, facilitating the estimation of genetic variability across and within numerous species. The technique of ISSR-PCR is swiftly employed by the research community across multiple domains of plant improvement (Godwin et al., 1997).

Plant materials
 
A set of 30 rice accessions including 16 cultivated varieties and 14 landraces were collected from Tamil Nadu Agricultural University. The details of the materials are furnished in Table 1.
 

 
ISSR primers
 
For this study, a set of 49 ISSR primers, selected randomly to encompass the repeats of di-nucleotide, tri-nucleotide and tetra-nucleotide were subjected to screening. Within this set, 22 ISSR markers which displayed polymorphism and distinctive patterns were selected to determine the genetic diversity (Table 2).
 

 
DNA extraction
 
The Genomic DNA from the leaf samples was extracted using the protocol detailed by McCouch et al., (1988).
 
PCR and determination of polymorphism information content
 
The DNA amplification was conducted within a reaction volume of10 μl with 20-30 ng of genomic DNA, 0.5 μM each of forward and reverse primers, 1.0mM dNTPs, 1.0 mM assay buffer and 0.03 units of Taq DNA polymerase. The PTC Thermal Cycler (MJ Research Inc.,) was employed with the program involving initial denaturation at 94°C for 1.5 min, 35 cycles of 40-seconds denaturation at 94°C, primer annealing at 45°C for 45 s, final extension at 72°C for 1.5 min. and then hold at 4°C. The computation of the Polymorphic Information Content (PIC) value for each ISSR marker was accomplished using the formula Hn = 1 - Spi 2, where pi represents the allele frequency of the ith allele, as outlined by Nei in 1973.
 
Cluster Analysis
 
The Sequential Agglomerative Hierarchical Non-overlapping (SAHN) clustering technique was applied to a similarity matrix, with the dice co-efficient for binary data generated by the Unweighted Pair Group Method with Arithmetic Averages (UPGMA) method. The clustering of the 60 accessions was done with NTSYSpc version 2.02 (Rohlf, 1994).

The landraces and cultivars used in this study are regularly employed as one of the parents in the rice breeding programmes.
 
Determination of PIC values
 
The polymorphic potential of 49 ISSR markers was evaluated among the set of 30 rice genotypes. Out of these, 22 markers exhibited polymorphism. On the average, there were 16.36 alleles ranging from 7 to 28 for ISSR 829 and ISSR 808 respectively. In cases where an amplification product could not be detected for a specific genotype-marker combination, a variety was designated to have a null allele at the corresponding ISSR locus. The PIC values, which are indicative of allele diversity and frequency among the varieties, displayed variations among the tested ISSR loci. With an average of 0.666, the PIC value ranged from 0.359 (ISSR 890) to 0.846 (ISSR 826). The most informative marker based on the PIC value was ISSR 826 which is composed of (AC)_n repeats. ISSR 808 wih (AG)_n recorded the second maximum PIC value. The higher number of alleles were detected from primers ISSR 808, 840, 885 and 807 which comprised of either (AG)_n or (GA)_n repeats. This is in correspondence with reports of Sarla et al., (2005) and Reddy et al., (2009) wherein the primers comprising (AG)_n or (GA)_n repeats demonstrated the highest PIC and Rp values in distinguishing rice germplasm lines. The polymorphism percentage and the PIC values obtained are similar to those observed by Khumbar et al., (2015) and Zayed et al., (2023) in their study on the landraces and improved varieties of rice and on hybrids respectively. Details regarding the ISSR primers employed in the determination of genetic diversity including the allele count for each ISSR locus and their corresponding PIC values are presented in Table 2. The allele distribution ISSR loci viz. 889 and 842 across a section of 30 rice cultivars is shown in Fig 1.
 

 
DNA marker-based diversity across rice accessions
 
Genetic relationships among the thirty genotypes were determined by computing Dice’s similarity coefficient through the assessment of shared bands proportions generated by the primers (Dice, 1945) and the dendrogram is represented in Fig 2. All 30 rice accessions were classified using 22 primers. Three distinct groups emerged from the analysis, characterized by the similarity coefficient of 0.66. The Cluster I contained Bharathi, BG367-2, PTB33, ASD9, ASD16, ASD20, Rathu Heenati and Columbia-2. Notably, Cluster II appeared to be the largest cluster with 20 accessions. Most of the cultivated genotypes used in the study were grouped together in this cluster indicating the common genetic background of their parents. Cluster III was found to possess two accessions, Jeeraga Samba and Basmati370. Major cluster I have three sub-clusters, in which Rathu Heenati and Columbia-2 formed separate group. Cluster II had four sub-clusters. Among four sub-clusters, Pusa basmati fell in separate sub-cluster and the set of accessions comprising TN1, Veerdangan, White Ponni and N22, Rascadam grouped into separate sub-clusters. Table 3. Similarly, the traditional rice varieties were divided into three major clusters using ISSR markers by Nahar et al., 2020, indicating moderate genetic diversity among the genotypes studied. The efficiency of ISSR markers in determining the genetic diversity of rice was evidenced in the study by Taratima et al., (2019) as compared to the RAPD markers.
 

 

 
Population structure
 
In rice, the population structure analysis is carried out in different panels of genotypes and under various growth and stress conditions like salinity and drought using various molecular markers and genomic tools (Bhattarai et al., 2019; Warraich et al., 2021). In this study, the population structure of 30 germplasm lines was determined using the Bayesian approach which has been used to identify sub-populations in various crops including rice, wheat, chickpea, carrot and bamboo (Seyedimoradi et al., 2020; Chaitra et al., 2020; Li et al., 2019). The ideal number of populations was determined through the examination of correlated allele frequencies as 3 (K = 3). Likewise, the maximum of adhoc measure DK was also found to be K = 3 (Fig 3), thereby indicating the possibility of categorizing the entire population into three sub-groups denoted as SG1, SG2 and SG3. It is interesting to note that the cluster analysis based on the similarity coefficients, also divided the genotypes into three major clusters. Similar coherence among the grouping of genotypes using clustering and population structure analysis has been reported in many crops. Khumbar et al., (2015) reported similar results in the genetic diversity analysis of rice using SSR and ISSR markers. The ability of structure analysis to determine the extent of admixture and the unique genotypes within and among various races and species of rice has been reported in a multitude of research investigations (Haritha et al., 2016; Zhou et al., 2020). The membership fractions were utilized to assign the accessions to different sub-gruops. Those with the probability of ³70 per cent were designated to the respective subgroups while the rest were labelled   as admixture (Table 4). Within SG1, there were 5 accessions including 3 landraces and 2 varieties of Indian origin and SG2 was constituted by 12 accessions, of which 4 were landraces and 8 were varieties of Indian origin. SG3 contained 6 accessions viz.,1 landrace and 5 varieties of Indian and Sri Lankan origin respectively. The grouping of landraces and the commonly cultivated varieties together in a group denotes the extent of shared alleles between them. Hence the landraces which are grouped with the cultivars can be used as donors in breeding schemes as they might result in lesser linkage drag (Mazumder et al., 2020). Seven accessions were retained to be admixture. It is observed that the genotypes were placed in distinct groups based on their membership fractions and there are only fewer admixture genotypes. This could be attributed to the autogamous nature of the crop resulting in restricted gene flow and allele sharing (Gao and Innan, 2008; Choudhry et al., 2013). Rice genotypes including Basmati 370, Jeeraga Samba, N22, Kathanellu and Rascadam formed the components of SG1. SG2 possessed BPT5204, IR50, ADT38, ADT39, CO43, CO50, Nootripathu, Purple Puttu, TN 1, Mattaikar, SR26B and White Ponni. SG 3 comprised Bharathi, BG 367-2, PTB33, ASD9, ASD16 and ASD20. The ancestry values inferred from the structure analysis were utilized for the clustering of rice accessions. Rhathu Heenati, Colambia-2, Kallurandaikar, Sivappu Chithraikar, GEB24, Pusa Basmati and Veeradangan were found to be not in any of the distinct populations based on their inferred ancestry values. Despite the incomplete genome coverage, the allele frequencies derived from these markers yielded valuable insights into relationships among accessions, reflecting the allele sharing pattern. Owing to the limitations posed by the marker’s scope and the number of genotypes examined, the additional parameters associated with the structure analysis were disregarded, yet the analysis notably highlighted the presence of three distinct sub-populations within the cohort of 30 accessions of the present investigation.
 

 

The genetic diversity assessment of the set of rice accessions comprising landraces and cultivars using ISSR markers classified them into three groups. The ISSR markers with (GA) and parentheses (AG) repeats were highly informative in determining the genetic composition. The genetic relatedness among the genotypes was well characterized using similarity co-efficient-based clustering and Bayesian approach-based population structure analysis and were coherent with each other. It is observed from the study that this set of genotypes possess moderate to high genetic diversity and the landraces and cultivars analysed in this study sharing similar alleles can be crossed to produce improved cultivars.

The author acknowledges the Department of Biotechnology, New Delhi for providing the Junior Research Fellowship to carry out the research work through its project ‘Functional Genomics for BPH resistance in Rice closing apostrophe.