Abstract
Knowledge of genetic diversity is necessary for designing future breeding programs and deriving desired genotypes. The current study was designed to explore the genetic diversity between 37 coarse and basmati rice genotypes by using 15 agro-morphological traits and 35 SSR markers. Mahalanobis’ generalized distance (D2) and principal component analysis (PCA) were used to evaluate the data for agronomical traits. Of the information in the raw data for the yield-related traits, 67.28% was represented by two principal components. Five different clusters were revealed by cluster analysis (D2): Cluster I had up to 6 genotypes, followed by 17, 5, 8, and 1 genotypes in clusters II, III, IV, and V, respectively. Greater genetic diversity among the genotypes was signified by a greater inter-cluster than intra-cluster distance. The maximum inter-cluster distance was observed between clusters II and V (80.88). The highest (33.080) and lowest (12.745) intra-cluster distances were observed for Cluster IV and Cluster II, respectively. Tall-growing and long grain basmati genotypes were grouped into Cluster II, while Cluster IV contained all the coarse rice genotypes. The minimum intra-cluster distance (12.745) of Cluster II indicated a narrow genetic base for the basmati rice. Molecular-based exploration of genetic diversity produced genetic similarity coefficients and clustered the genotypes into two major clusters. The total number of polymorphic alleles was 69, with an average of 1.97 alleles per SSR locus. In this study, a maximum of 5 alleles were revealed by marker RM16. The highest and lowest polymorphic information content (PIC) values were observed for markers RM6 (0.92) and RM10 (0.36), respectively. The coefficient of genetic similarity ranged between 0.45 and 1 for all basmati and coarse rice genotypes. Two pairs of coarse rice genotypes, Nagina/RD25 and Nagina/SUB-1, showed maximum divergence (0.42), with a similarity index of 0.58 for both pairs. In contrast, the maximum divergence (0.18) between three pairs of basmati rice genotypes—EF52/sup-23 and Super basmati, Sup/1138-2 and Lpa-56-3, and Sup/1138-2 and Basmati515—had a similarity index of 0.82. The similarity coefficient ranges showed a narrower genetic base for basmati rice genotypes as compared to coarse rice genotypes. Clustering based on agronomic and molecular analysis showed the clear differentiation of coarse and basmati rice into different groups, except in a few lines—Lpa-66-3-1, KSK-133, Nagina, SUB-4, and Sup1138/2—which showed some deviation from the trend. The study of genetic differences concluded that the genotypes of Cluster II and Cluster V are complementary for maximum desirable traits and could be selected for use in hybridization programs to develop promising F1 hybrids or transgressive segregants in subsequent generations.
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References
Agrama H, Tuinstra M (2003) Phylogenetic diversity and relationships among sorghum accessions using SSRs and RAPDs. Afr J Biotech 2:334–340
Akagi H, Yokozeki Y, Inagaki A, Fujimura T (1996) Microsatellite DNA markers for rice chromosomes. Theor Appl Genet 93:1071–1077
Akhtar J, Ashraf M, Akram M, Hameed A (2012) A multivariate analysis of rice genetic resources. Pak J Bot 44:1335–1340
Bartlett MS (1937) Properties of sufficiency and statistical tests. Proc Roy Soc Lond Ser A 160:268–282
Bhattacharjee P, Singhal RS, Kulkarni PR (2002) Basmati rice: a review. Int J Food Sci Technol 37:1–12
Bohra A, Jha R, Pandey G, Patil PG, Saxena RK, Singh IP et al (2017) New hypervariable SSR markers for diversity analysis, hybrid purity testing and trait mapping in Pigeonpea [Cajanus cajan (L.) Millspaugh]. Front Plant Sci 8:377
Botstein D, White RL, Skolnick M, Davis RW (1980) Construction of a genetic linkage map in man using restriction fragment length polymorphisms. Am J Hum Genet 32:314–331
Chakraborty S, Patel D, Parmar H, Dhaduk H, Sasidharan N (2018) Genetic diversity analysis in soybean (Glycine max (L.) Merrill.) using SSR markers. J Pharmacogn Phytochem 7:2380–2384
Chakravarthi BK, Naravaneni R (2006) SSR marker-based DNA fingerprinting and diversity study in rice (Oryza sativa. L). Afr J Biotechnol 5(9):684–688
De Datta SK (1981) Principles and practices of rice production: Int Rice Res Inst
Frei M, Becker K (2005) On rice, biodiversity & nutrients. Institute of animal production in the tropics and subtropics. Stuttgart: University of Hohenheim. www. greenpeace. org/international/en/publications/rep
Gasim S, Abuanja I, Abdalla A-W (2019) Genetic diversity of rice (Oryza sativa L.) accessions collected from Sudan and IRRI using SSR markers. Afr J Agric Res 14(3):143
Gour L, Koutu G, Singh S, Mishra D (2017) Genetic potency identification of indigenous rice (Oryza sativa L.) lines using quality assessment and SSR marker analysis. IJCS 5:97–101
Gracia AAF, Benchimol LL, Antonica MM, Geraldi IO, Deuza AP (2004) Comparison of RAPD, RFLP, AFLP and SSR marker for diversity studies in tropical maize inbred lines. Euphytica 108:53–63
Islam MR, Gregorio GB, Salam MA, Collard BCY, Singh RK, Hassan L (2012) Validation of SalTol linked markers and haplotype diversity on chromosome 1 of rice. Mol Plant Breed 3(10):103–114
Jagadev P, Samal K, Lenka D (1991) Genetic divergence in rape mustard. Indian J Genet Plant Breed 51:465–467
Joshi SP, Gupta VS, Aggarwal RK, Ranjekar PK, Brar DS (2000) Genetic diversity and phylogenetic relationship as revealed by inter simple sequence repeat (ISSR) polymorphism in the genus Oryza. Theor Appl Genet 100:1311–1320
Kumar H, Dikshit HK, Singh A, Jain N, Kumari J, Singh AM et al (2014) Characterization of grain iron and zinc in lentil (“Lens culinaris” Medikus’ culinaris’) and analysis of their genetic diversity using SSR markers. Aust J Crop Sci 8:1005
Lapitan VC, Brar DS, Abe T, Redofia ED (2007a) Assessment of genetic diversity of Philippine rice cultivars carrying good quality traits using SSR markers. Breed Sci 57:263–270
Lapitan VC, Brar DS, Abe T, Redoña ED (2007b) Assessment of genetic diversity of Philippine rice cultivars carrying good quality traits using SSR markers. Breed Sci 57:263–270
Lassois L, Denancé C, Ravon E, Guyader A, Guisnel R, Hibrand-Saint-Oyant L et al (2016) Genetic diversity, population structure, parentage analysis, and construction of core collections in the French apple germplasm based on SSR markers. Plant Mol Biol Rep 34:827–844
Latif M, Yusop MR, Rahman MM, Talukdar MB (2011) Microsatellite and minisatellite markers based DNA fingerprinting and genetic diversity of blast and ufra resistant genotypes. CR Biol 334:282–289
Lefort-Buson M, Guillot-Lemoine B, Dattee Y (1987) Heterosis and genetic distance in rapeseed (Brassica napus L.): crosses between European and Asiatic selfed lines. Genome 29:413–418
Murgai R, Ali M, Byerlee D (2001) Productivity growth and sustainability in post–green revolution agriculture: the case of the Indian and Pakistan Punjabs. World Bank Res Obs 16:199–218
Nachimuthu VV, Muthurajan R, Duraialaguraja S, Sivakami R, Pandian BA, Ponniah G et al (2015) Analysis of population structure and genetic diversity in rice germplasm using SSR markers: an initiative towards association mapping of agronomic traits in Oryza sativa. Rice 8:30
Ni J, Colowit PM, Mackill DJ (2002) Evaluation of genetic diversity in rice sub species using microsatellite markers. Crop Sci 42:601–607
Powell W, Morgante M, Andre C, Hanafey M, Vogel J, Tingey S et al (1996) The comparison of RFLP, RAPD, AFLP and SSR (microsatellite) markers for germplasm analysis. Mol Breed 2:225–238
Pradhan K, Roy A (1990) Genetic divergence in rice. Oryza 27:415–418
Rabbani MA, Masood MS, Shinwari ZK, Shinozaki K (2010) Genetic analysis of basmati and non-basmati Pakistani rice (Oryza sativa L.) cultivars using microsatellite markers. Pak J Bot 42:2551–2564
Ram BJ, Babu GS, Singh O, Lavanya GR, Kumar KM (2018) Molecular characterization of rice genotypes using microsatellite markers. J Pharmacogn Phytochem 7:3065–3069
Röder MS, Plaschke J, König SU, Börner A, Sorrells ME, Tanksley SD et al (1995) Abundance, variability and chromosomal location of microsatellites in wheat. Mol Gen Genet MGG 246:327–333
Rohlf F (2005) Numerical taxonomy and multivariate analysis system version 2.2 Exeter software, New York, USA
Shah SM, Naveed SA, Arif M (2013) Genetic diversity in basmati and non-basmati rice varieties based on microsatellite markers. Pak J Bot 45:423–431
Sharma R, Goossens B, Heller R, Rasteiro R, Othman N, Bruford MW et al (2018) Genetic analyses favor an ancient and natural origin of elephants on Borneo. Sci Rep 8:880
Singh N, Choudhury DR, Tiwari G, Singh AK, Kumar S, Srinivasan K et al (2016) Genetic diversity trend in Indian rice varieties: an analysis using SSR markers. BMC Genet 17:127
Thompson JA, Nelson RL, Vodkin LO (1998) Identification of diverse soybean germplasm using RAPD markers. Crop Sci 38:1348–1355
Vikram P, Wamy BPM, Dixit S, Cruz S, Ahmed HU, Singh AK, Kumar A (2011) qDTY11 a major QTL for rice grain yield under reproductive-stage drought stress with a consistent effect in multiple elite genetic backgrounds. BMC Genet 12:89
Zafar Y (2015) Genetic diversity of rice in Pakistan, National institute of genomics and advanced biotechnology (NIGAB), Islamabad, Pakistan
Zeng L, Shannon M, Grieve C (2002) Evaluation of salt tolerance in rice genotypes by multiple agronomic parameters. Euphytica 127:235–245
Zheng K, Huang N, Bennett J, Khush GS (1995) PCR-based markerassisted selection in rice breeding. IRRI Discussion paper series no 12, International rice research institute, Manila, Philippines, pp 9–11
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The authors extend their appreciation to the Researchers Supporting Project number (RSP-2023R369), King Saud University, Riyadh, Saudi Arabia.
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Conceptualization, TL and ZQ; Data curation, TN, AT and AH; Formal analysis, TL and ZM; Methodology, TL; Resources, ZQ, WHE and IK; Writing—original draft, KA, TN, SF, MAN, SA and AT; Writing—review & editing, KA, ZM, AH and ZQ. All authors have read and agreed to the published version of the manuscript.
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Luqman, T., Qamar, Zu., Tabasum, A. et al. Genetic characterization of coarse and basmati rice (Oryza sativa L.) through microsatellite markers and morpho-agronomic traits. Genet Resour Crop Evol 70, 2307–2320 (2023). https://doi.org/10.1007/s10722-023-01620-w
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DOI: https://doi.org/10.1007/s10722-023-01620-w