Full Text: 1 Introduction Rice (Oryza sativa L.) is an annual grass of the family Poaceae (graminae) with chromosome number 2n=24. Rice is being consumed by more than half of the world’s population and serves as a major source of carbohydrates. Having more amounts of carbohydrate, it provides instant energy, and is a staple food that is consumed by the majority of India’s population. Rice grain contains about 75-80% starch, 12% water and 7% protein (Hossain et al., 2015). It is also an important source of vitamins like thiamine, riboflavin and niacin and minerals like magnesium, phosphorus and calcium. It is enormously diverse both in the way it is cultivated and its usage by the humans. Having the smallest genome of all cultivated cereals, being diploid and self pollinating, it is the most extensively studied species among cereals (Pooja et al., 2019). Among very few options for increasing yield potential of rice, improvement of the genetic potential of the crop cultivars is one of the best options. Rice has endowed with rich natural genetic diversity and there is tremendous scope to exploit diversity for improvement of the genetic base. To encounter the existing food requirements, genetic diversity is a natural source for rice breeding (Reig-Valiente et al., 2016). The higher level of genetic variation present in a population, the more valuable resource, is used for enlarging the genetic base of the breeding program (Nachimuthu et al., 2015). This would help in development of transgressive segregants which will perform better than both the parents. In addition, Haritha et al., 2016 postulated that genetic diversity obtained precious information for both basic studies and practical applications. Mahalanobis’s D² statistic is a powerful tool in measuring the degree of divergence between biological populations at genetic level and provides a quantitative measure of association between geographic and genetic diversity based on generalized distance. For the assessment of genetic diversity, microsatellite or simple sequence repeat (SSR) markers are considered as most amenable, as they are multi- allelic in nature, highly informative, highly reproducible, have co-dominant inheritance and provide extensive genomic coverage (McCouch et al., 2002). SSR markers are able to detect great level of allelic diversity and they have been extensively used to identify genetic variation among rice subspecies. Keeping this in view, the present experiment was conducted using 29 local rice genotypes to characterize the morphological and molecular genetic diversity. 2 Materials and Methods 2.1 Plant material: A total of twenty nine local rice genotypes including 3 checks were grown in an augmented block design with three blocks having repeated checks during Kharif 2017. The list of genotypes used in this experiment is presented in table 1. 2.2 Morphological observations: Data was recorded on sixteen quantitative traits viz. days to first flowering, days to 50% flowering, tillers per plant, panicle length, plant height (cm), fertile spikelets, sterile spikelets, total grains per panicle, kernel length(cm), kernel breadth(cm), L/B ratio,1000 grain weight(g), plot yield(kg/ha), grain yield (kg/ha), biomass(kg/ha) and Harvest Index. The data for days to first flowering, days to 50% flowering and plot yield was collected on plot basis whereas for other yield and yield attributing traits, data was recorded on five randomly selected plants of each genotype in the respective block. 2.3 Molecular analysis: DNA was extracted following CTAB extraction method according to Doyle & Doyle, (1987) with few modifications and the DNA quality was estimated using Biophotometer plus. 21 simple sequence repeat (SSR) markers were used for molecular analysis. The details of SSRs used are listed in table 2 along with the forward and reverse sequence. Polymerase chain reaction (PCR) was performed using a thermal cycler (SureCycler 8800) in vitro to amplify a specific segment of the total genomic DNA to a billion fold (Mullis et al., 1986). The amplified DNA products generated through SSR primers were resolved through electrophoresis in 2.5 per cent agarose gel prepared in TAE buffer and the gels were visualized under a UV light source in a gel documentation system (Gel DocTM XR+, BIO-RAD, USA). The images of amplified products were used for the evaluation of genetic diversity between the genotypes. A binary data matrix was utilized to generate genetic similarity data among the 29 lines of rice genotypes. 2.4 Statistical analysis The quantitative trait mean values computed based on data on five randomly tagged plants in each genotype and checks were used for statistical analysis. Analysis of variance was performed to partition the total variation among the genotypes and check entries into sources attributable to ‘genotypes+ checks entries, ‘genotype check entries’ and ‘genotype vs. check entries’ following Augmented design (Federer, 1956) WINDOSTAT version 9.0. Adjusted trait mean of each of the genotype accession was estimated (Federer, 1956) and the same were used for all subsequent statistical analysis. In the present study, Mahalanobis’ generalized distance as described by Rao (1952) was used to estimate the genetic divergence. Data Analysis for the SSRs used in the present study was done using the software NTSYSpc version 2.02 (Rohlf, 1998). The binary data matrix generated by polymorphic SSR markers were subjected to further analysis using NTSYS-pc version 2.11W (Rohlf, 1997). The SIMQUAL programme was used to calculate the Jackard’s dissimilarity coefficient. To measure the in formativeness of the markers, the polymorphism information content (PIC) for each SSR marker was calculated according to the formula suggested by (Weir, 1996) and it is written as PIC=1-(P_i2) Where, ‘i’ is the total number of alleles detected for each SSR marker, ‘P_i’ is the frequency of the i^th plus allele in the set of 29 genotypes studied. PIC value is used to estimate the discriminatory power of the SSR marker. 3 Results and Discussion The result of ANOVA revealed significant difference among the genotypes with respect to all the characters under study and this indicates that there is an inherent genetic difference among the genotypes for all the traits studied. 3.1 Diversity analysis of morphological data Mahalanobis’ D² analysis grouped the total genotypes into 6 clusters (table 3) on the basis of inter-cluster genetic distances. Clustering pattern indicated that 17 out of 29 genotypes belong to cluster I indicating their close relationship among themselves as compared to others. Genotypes grouped in one cluster are less diverse than the genotypes located in a different cluster. Further, 8 genotypes belong to the cluster II and 1 genotype each in cluster III, IV, V, & VI. The intra and inter cluster distances i.e. D² values are presented in table 4 and the diagrammatic representation of clusters with inter and intra cluster D² values have been presented in figure 1. Highest intra-cluster distance was observed in the cluster I (22.431) with 17 genotypes followed by cluster II (11.657) with 8 genotypes. Highest inter-cluster distance was found between clusters III and VI (99.21), indicating that the hybridization between the genotypes of these clusters would yield desirable segregates with the accumulation of favorable genes in the segregating generations.  This is followed by cluster I and VI (63.13) and cluster III and IV (61.74). The smallest inter-cluster distance (22.44) was observed between cluster I and II followed by cluster II and V (23.37). Similar results were found by Awasthi et al. (2005), Rajesh et al. (2010) and Devi et al., (2019). Information about genetically diverse genotypes would help in selection of parents while planning for a hybridization programme which would yield useful segregants. However, while selecting parents for hybridization programme their yield potential should not be overlooked (Ramya & Kumar 2008). Based on the results of Mahalanobis D² analysis, the genotypes grouped in cluster III are having maximum genetic distance with the genotypes placed in the cluster VI. This indicates that crosses between the genotypes present in these two clusters are expected to be much heterotic. It should be kept in mind that, along with the genetic distance, per se yield and yield contributing characters are to be taken into consideration while selecting the genotypes. In this respect, genotypes like Bahubali and Golden 105 are having highest inter cluster distance and high per se yield can be considered suitable for hybridization programme. 3.2 Diversity analysis of molecular data: Molecular diversity analysis with the help of markers is based on the naturally occurring polymorphism of the individuals which is not affected by the environment. In the present study, all the 29 genotypes were analysed with 21 SSR markers and the results obtained are explained in the following subheads. 3.2.1 Scoring of SSR band and PIC value: The presence or absence of each band in all genotype was scored manually by binary data matrix with 1 and 0 for presence and absence respectively. Level of polymorphism in rice genotypes was evaluated by calculating allelic number and PIC values for each of twenty one polymorphic SSR markers. 21 polymorphic markers run across 29 rice genotypes obtained a total of 65 alleles with an average of 3 alleles. Among the 21 SSRs, 1 marker produced 2 alleles, 3 markers have produced 4 alleles each and 17 markers have produced 3 alleles each. Similar results were shown by Pachauri et al. (2013) and Anh et al. (2018). The PIC (Polymorphic information content) was assessed for each locus to evaluate the information of each marker and this PIC value is an evidence of allele diversity and frequency among genotypes and its values ranged from 0.701 (RM 277) followed by 0.693 (RM514) to 0.346 (RM237), with a mean value of 0.571. Hossain et al. (2012) reported similar range of PIC value (0.239 to 0.765) with an average of 0.508. The PIC values obtained for all the markers are shown in table 2. In another study conducted by Singh et al. (2015), PIC values ranged from 0.265 to 0.65 with an average of 0.47. Similar results were further obtained by Roy et al. (2015), Krupa et al. (2017) and Anh et al. (2018). Higher PIC value of a locus detected higher alleles value.  From this data, it is shown that, RM 277 was found to be the most appropriate marker among the rice genotypes owing to the highest PIC value of 0.701 can be employed to enlarge the genetic foundation of the current genotypes (Anupam et al., 2017). The other markers with PIC value above 0.5 also play a significant role in studying the genetic divergence of the rice genotypes (Anh et al., 2018) Gel pictures obtained from analysis of 29 genotypes with some of the SSR markers is shown in figure 2 (figure 2a to 2c). 3.2.2 Dendrogram analysis A dendrogram (figure 3) based on Jackard’s dissimilarity coefficient was constructed using UPGMA (Unweighted Pair Group Method with Arithmetic Averages) method and the 29 rice genotype were grouped into two main clusters i.e. cluster I  and cluster II with dissimilarity coefficient 0.36. Cluster I was further divided into two groups IA and IB with dissimilarity coefficient 0.46. Cluster IA was further divided into two sub- groups IA-1 and IA-2 with dissimilarity coefficient 0.56. Cluster IA-1 contains 6 genotypes and IA-2 contains 3 genotypes. Cluster IB further divided into IB-1 and IB-2 with dissimilarity coefficient 0.58. Cluster IB-1 consists of 8 genotypes and I B-2 contains 2 genotypes. Cluster II was divided into two main sub-groups II-A and II-B with dissimilarity coefficient 0.50. Cluster IIA had 6 and II B had 4 genotypes. Grouping of the clusters along with the name of genotypes included in each cluster is shown in table 5. Similar results were obtained by Sonkar et al., 2016, Vengadessan et al., 2016, Anh et al., 2018 and Pooja et al., 2019. 3.2.3 Jackard dissimilarity coefficient To determine the level of relatedness among the genotypes the dissimilarity coefficient was used. The dissimilarity coefficient varies from zero to one, closer to one shows a higher dissimilarity, whereas, closer to zero shows a higher similarity. The average dissimilarity ranged from 0.6536 to 0.7262. The total average of dissimilarity coefficient of all the 29 genotypes is 0.714. The dissimilarity coefficient varied from the largest value 0.88 between the cultivar Pusa 1121 and HUR-1301 followed by RK-2 Lal kasturi and RK-8 Gold (0.86) and pan 815 and RK-8 Gold (0.86) which shows high dissimilarity between them showing that they are highly dissimilar with each other. The lowest value (0.48) was found between Red long and HUR-97 PB 1-S followed by 0.50 between Vishnu bhog black and Pusa 1121. Similar results were obtained by Pooja et al., 2019. Finally, according to the dendrogram and Jackard dissimilarity coefficient values, the most diverse cultivars among the 29 local rice genotypes studied are Pusa 1121 and HUR-1301 followed by RK-2 Lal kasturi and RK-8 Gold, Pan815 and RK-8 Gold as they showed greater genetic distances which means a greater chance of getting useful segregants when the above said genotypes used in a hybridization programme and having lower relatedness making them genetically diverse which is of prime importance for any breeding programme. Similar results were obtained and explained by Ahn et al., 2018 and Pooja et al., 2019. Conclusion Study of genetic diversity at both morphological and molecular level gives a reliable information regarding the genotypes included in the study. From the above study, It is observed that both morphological and molecular variations exists among the 29  local rice genotypes evaluated and differences in the grouping of the genotypes in different clusters was observed. Considering all the aspects, the genotypes, Bahubali, Golden 105, Pusa 1121, HUR-1301, RK-2 Lal kasturi and Pan 815 have the advantage of both greater genetic distance from each other and have higher per se yield. Hence, these genotypes can be used by the breeders for planning a successful hybridization programme and for creation of more variability in rice. Acknowledgement The authors are highly thankful for the support given by Molecular drought laboratory, Dept of genetics and Plant Breeding for providing the genotypes used in the study and Niche area lab, Central Laboratory, Institute of Agricultural sciences, BHU for valuable support in the conduct of molecular analysis.