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Int.J.Curr.Microbiol.App.Sci (2021) 10(03): 1748-1759 International Journal of Current Microbiology and Applied Sciences ISSN: 2319-7706 Volume 10 Number 03 (2021) Journal homepage: http://www.ijcmas.com Original Research Article https://doi.org/10.20546/ijcmas.2021.1003.218 Molecular Characterization of Cotton (Gossypium hirsutum L.) Genotypes Using SSR Markers for Fiber Quality Traits D. K. Sarode1*, K. M. Sharma1, N. A. Shinde1, A. R. Gaikwad2, R. L. Chavhan1 and P. A. Pimpale1 1 Vilasrao Deshmukh College of Agricultural Biotechnology, Latur, (M.S.), (Under Vasantrao Naik Marathwada Krishi Vidyapeeth, Parbhani), India 2 Cotton Research Station, VNMKV, Nanded, MS, India *Corresponding author ABSTRACT Keywords Gossipium hirsutum, Fiber quality traits, SSR, Polymorphism, Dendrogram Article Info Accepted: 17 February 2021 Available Online: 10 March 2021 To assess the genetic diversity and relationships among 25 cotton (Gossypium hirsutum L.) genotypes were analyzed using morphological and molecular markers. Morphological analysis was performed based on 3 fiber quality traits including fiber length, strength and fineness the mean values of each trait for each genotype were calculated. The results showed that most of the genotypes have long fiber length, very high fiber strength and average fiber fineness. The molecular profiling of 25 cotton genotypes were carried out by using 42 SSR markers reported to be linked to quantitative trait loci (QTLs) for fiber quality traits (Fiber length, Fiber strength and Micronaire). Twenty five SSR markers related with fiber quality traits ware found to be polymorphic produced a total of 53 amplicon with an average of 2.12 alleles per locus ranging from 130 bp to 370 bp products, Markers showing 100% polymorphism with average polymorphism information content (PIC) value of 0.290. The BNL-1059 (PIC-0.481), CIR-244 (PIC-0.538), CIR-354 (PIC-0.660), CIR-381 (PIC-0.784), CIR-246 (PIC-0.749) and JESPR153 (PIC-0.639) marker were found to be most informative which indicates significantly diversity between all genotypes for fiber quality traits. Similarity matrix was used to depict dendrogram through UPGMA cluster analysis based on SSR similarity coefficient ranged from 0.61 to 0.91 indicating the fact that large proportions of genotype were dissimilar. The present study revealed that genotypes BN-1, PH-348, and ARB-908 for fiber quality were found to be the most diverse having a broad genetic base for the concerned fiber traits. Introduction Cotton as an annual crop is the world’s leading natural fiber crop and an important crop for bio energy production (Lusas and Jividen, 1987; Chen et al., 2007). It belongs to the genus Gossypium of the family Malvaceae and consists of approximately 50 species, including 45 diploids (2n =26) and 5 allotetraploids (2n = 52). The four species of cotton cultivated in india are G. hirsutum L., G. arborenum L., G. herbaceum L. and G. barbadense L. (Gillham et al., 1995). The geographic center of origin for G. hirsutum is 1748 Int.J.Curr.Microbiol.App.Sci (2021) 10(03): 1748-1759 North and Central America and Mexico. Conventional breeding methods generally aim to improve agronomically important traits by combining characters present in different parental lines of cultivated species or their wild relatives. The prediction of genetic similarities among genotypes is very important for crop improvement and accurate selection of parental combinations and the maintenance of sufficient diversity in breeding programs is necessary (Lacape et al., 2010). Different physical characteristics of cotton fibers are measured ranging from fiber length and length uniformity, strength, elongation (degree of extensibility), maturity (extent of cell wall thickening), micronaire (resistance to air flow across a plug of fibers) and fineness (linear density, a function of diameter and thickness) to color indices (reflectance and yellowness) (Lacape et al., 2010). These mentioned characteristics are associated with the proficient spinning and weaving processes that alter the fiber into fabrics. Therefore, it is very important to improve fiber quality i`n locally dominating cotton genotypes to accomplish the requirements of the growing textile industry, processing and end use (Ali et al., 2008). Molecular breeding programme in cotton using the molecular marker to improve the efficiency of introgression of fiber quality traits in to the favorable genetic background. This effort relies on, development of genetic map in cotton genome, the identification of fiber quality and the marker assisted introgression of favorable genomic region into an adequate genetic background. SSR markers are very practical for integrating different linkage maps, QTL mapping, and comparative mapping for evolutional study, especially for species with a large genome size like cotton. SSRs can be also employed as anchors to combine linkage maps produced in different laboratories or with different populations to generate a more comprehensive genetic linkage representation. Identification of the molecular markers linked to fiber quality traits may accelerate the selection and breeding of traits. The aim of the present study was to determine the genetic diversity of commercial cotton genotypes for the fiber length and fiber strength through the molecular markers profiling. The present study evaluated the genetic profile of cotton genotypes using SSR markers linked to fiber quality traits. Materials and Methods Parental materials Twenty five cotton genotypes (Gossypium hirsutum L.) were used as experimental material comprised of 19 parents and 6 hybrid (G. hirsutum x G. hirsutum) were collected from Cotton Research Station, Nanded (M.S.) under Vasantrao Naik Marathwada Krishi Vidyapeeth, Parbhani (Table 1). Morphological analysis Field experiments The seeds of 25 genotypes were sown in the field under randomized complete block design with 3 replications during the 2018 and 2019 growing seasons in the field of cotton research station, Nanded (M.S.). All of the agronomic and plant protection practices were followed from sowing to harvesting of the cotton crop. Fiber quality analysis The collected boll samples were ginned with a single roller electrical gin on an individual plant basis to obtain lint for fiber analysis. Before fiber quality analysis, lints were 1749 Int.J.Curr.Microbiol.App.Sci (2021) 10(03): 1748-1759 conditioned at 21 ± 1 °C and 65 ± 2% relative humidity for 48 h in a controlled room. An HVI 1000 (Uster, Switzerland) was used to analyze fiber quality traits and the most important cotton fiber properties, i.e. fiber length (mm), fiber strength (g tex–1), fiber fineness (micronaire) were examined. Molecular analysis Genomic DNA extraction The DNA was extracted from young leaves by cetyl trimethyl ammonium bromide (CTAB) extraction method (Doyle and Doyle, 1987) with a few modifications. For each 100 mg of tissue, 500 μL of CTAB isolation buffer (2% hexadecyltrimethylammonium bromide, 1.5 M NaCl, 10 mM EDTA, 100 mM Tris-HCl, 0.2% β-ME, pH 8) was added to each tube, and homogenized. More CTAB extraction buffer (300 µL) was added to each tube and the samples were incubated at 65 °C for 60 min with occasional mixing. Due to the high content of polyphenolic compounds in cotton tissues, 700 µL of phenol/chloroform/isoamyl alcohol (25:24:1 v/v) was added to each sample and the samples were vortexed and then centrifuged. The supernatants were transferred to a new tube and 500 µL of chloroform/isoamyl alcohol (24:1 v/v) solution was added. Next, 500 µL of ice-cold isopropanol was added to each tube and the tubes were incubated for 30 min at room temperature. The samples were centrifuged and the supernatants were discarded. The pellets were air-dried and then resuspended in 50 µL of 10 mM Tris, pH 8.0, 1 mM EDTA buffer. Nucleic acids were measured quantitatively and qualitatively by spectrophotometer NanoDrop 2000c (Thermo Fisher Scientific). The extracted DNA was stored at –20 °C. PCR analysis The 42 SSR (Microsatellite) primer pairs (BNL, CIR and JESPR primer sets) linked to QTLs for important fiber quality traits as length, strength and fineness were used for PCR analysis (Table 2). Amplification reaction mixture was prepared in 0.2 ml PCR tubes, containing 1 X PCR buffer A (2.5µl ), 0.2 mM each dNTP (0.2µl), 1 unit of Taq DNA polymerase enzyme (0.2µl, 5U/µl), 10 picomoles of each primer (0.5µl), 30ng template DNA (2µl) in each 20µl reaction volume. The amplification was carried out on a thermal cycler (Eppendorf, Master cycler gradient). Reactions incubated at 94 °C for 2 min and following 35 amplification cycles (30 s at 95 °C, 50 s at 50–60 °C, and 60 s at 72 °C) were performed. The final PCR products were visualized under UV light after electrophoresis on ethidium bromide-stained 2.5% Agarose gels. Data analysis Morphological data analysis was performed based on the three fiber quality traits’ HVI measurements, i.e. length (mm), strength (g tex–1) and fineness (mic) and the mean values of each trait for each genotype were calculated. To standardize the assessments of numerical mean values of genotypes, they were scaled based on Bradow and Davidonis’ (2000) index and USTER Technologies indices Switzerland, (2010) (Table 3). For the molecular data analysis, polymorphism information content (PIC) values of molecular markers were calculated according to the following formula: PIC = 1 − Σ P2 i, where Pi is the frequency of the ith allele (Anderson et al., 1993) (Fig. 2). For genetic analysis based on molecular data, each amplified band was scored based on the presence (1) and absence (0) of bands. The binary qualitative data matrix was used to 1750 Int.J.Curr.Microbiol.App.Sci (2021) 10(03): 1748-1759 construct similarity matrices based on Jaccard similarity coefficients (Jaccard, 1908) and to construct dendrograms using UPGMA on NTSYS-pc software (Numerical Taxonomy System, Version 2.02i, Rohlf, 1998) and the polymorphic percentage was calculated by formulae: No. of polymorphic amplicons --------------------------------------------x 100 Total No. of amplicons Polymorphic %= genotypes for polymorphism. Out of them 25 primers were found polymorphic, 10 were found monomorphic and remaining 7 primers had produced no amplification or did not show clear band. The polymorphic information content (PIC) value of SSR loci was calculated for the 25 polymorphic primers for fiber traits across 25 cotton genotypes is presented in Table 4. The 25 polymorphic primers amplified a total of 53 alleles. Results and Discussion Fiber quality characterization The fiber length analysis, based on Bradow and Davidonis (2000) index, showed that genotype BN 1 has short (22.00 mm) fiber length, and genotypes NH-452, NH-625, PH93, PH-325, PH-348 and PKV RAJAT have medium (22.0-26.0 mm) fiber length. Moreover, NH-111, NH-545, NH-615, NH630, NH-635, PH-1009, ARB-757, ARB-908, AC738, AKH-8828, PHULE-0688, PHULE YAMUNA, NHH-44, NHH-206, NHH-225, NHH-250, NHH-324 and PHH-316 has long fiber length (26.1-32.00 mm). According to the USTER Technologies (Switzerland, 2010) the most of genotypes have average fiber strength (average fiber strength with 24.0026.08 g tex–1). The analysis of fiber fineness showed that Phule Yamuna, AKH 8828, NHH 44, NHH 206 and NH 635 has fine fiber fineness (3.7 mic), while the genotype AC 738 have coarse or average (19 genotypes) fiber fineness (Table 1). Molecular characterization SSR marker profiling for fiber quality traits In the present study total 42 QTL associated simple sequence repeat (SSR) primers of BNL, CIR and JESPR series linked with fiber traits were used to evaluate 25 cotton The average numbers of alleles per marker were found to be 2.12. Amplified alleles ranged from 1 to 4 with PIC value in the range of 0.072 (JESPR220) to 0.784 (CIR381) with an average of 0.290 per primer. These SSR markers with higher PIC values can be used for assessing genetic diversity and selecting parental lines for molecular mapping. Said et al., 2013 reported 721 QTLs distributed across all 26 chromosomes controlling fiber quality traits in tetraploid cotton. The JESPR-153 primers amplified a higher number of alleles i.e. 4, followed by primers BNL-3627, CIR-354, CIR-413, CIR-320 and JESPR-220 with 3 numbers of alleles (Table 4). While primers BNL-1059, BNL-1395, BNL-1672, BNL-2575, BNL-3255, BNL2131, BNL-1122, CIR-244, CIR-246, CIR305, CIR-253, CIR-381, JESPR-127, JESPR298 and JESPR-307 amplified 2 number of alleles (Table 4) and primer BNL-1047, BNL1064, BNL-3140, and CIR-307 amplified lowest number of alleles i.e. 2. Primer JESPR-153 amplified 4 allelic bands among 25 genotypes having PIC value 0.639 with 100 per cent polymorphism. However, primers BNL-1059 (PIC-0.481), CIR-244 (PIC-0.538), CIR-354 (PIC-0.660), CIR-381 (PIC-0.784), CIR-246 (PIC-0.749) and JESPR153 (PIC-0.639) as presented in Fig.1. having high PIC value for fiber quality traits 1751 Int.J.Curr.Microbiol.App.Sci (2021) 10(03): 1748-1759 among 25 genotypes with 100 % polymorphism. Similar results were reported in Gossypium spp. was observed by few earlier workers (Kumbhalkar, et al., 2018; Ghuge, et al., 2017 and Mishra and Fougat, 2014) and found that primer gave high polymorphism and they are also abundant in both diploid G. herbaceum and teraploid G. hirsutum genotypes and suggested that it is the best marker for marker assisted selection of cotton genotypes related to fiber quality traits. Table.1 Mean performance of 25 cotton genotypes for 3 different fiber quality parameters Sr. No. 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 Genotype Source Fibre quality parameters Fiber strength (g/tex)) Micronaire Values(µ/inch) 4.6 20.60 4.1 24.2 4.01 26.2 4.10 26.8 4.00 24.1 4.10 23.8 3.8 25.8 4.1 23.7 4.2 24.8 4.2 22.7 4.3 25.3 4.4 25.0 4.3 25.1 4.81 24.4 3.90 24.6 NH 111 NH 452 NH 545 NH 615 NH 630 NH 625 NH 635 PH 93 PH 325 PH 348 PH 1009 ARB 757 ARB 908 Phule 0688 Phule Yamuna AKH 8828 PKV Rajat BN 1 AC 738 NHH 44 NHH 206 NHH 225 NHH 250 PHH 316 VNMKV Parbhani VNMKV Parbhani VNMKV Parbhani VNMKV Parbhani VNMKV Parbhani VNMKV Parbhani VNMKV Parbhani VNMKV Parbhani VNMKV Parbhani VNMKV Parbhani VNMKV Parbhani MPKV Rahuri MPKV Rahuri Fiber length (mm) 26.40 25.80 27.80 27.8 26.4 25.6 27.00 24.8 25.1 25.9 27.5 26.8 26.8 26.7 26.5 Dr. PDKV Akola Dr. PDKV Akola VNMKV Parbhani VNMKV Parbhani VNMKV Parbhani VNMKV Parbhani VNMKV Parbhani 29.8 25.9 22.00 26.2 27.1 26.8 27.9 27.5 26.3 3.8 5.00 4.12 5.10 3.8 3.9 4.1 4.4 4.3 NHH 324 VNMKV Parbhani 27.3 4.3 1752 25.3 23.8 21.8 22.4 29.9 23.7 26.7 26.3 25.0 25.8 Int.J.Curr.Microbiol.App.Sci (2021) 10(03): 1748-1759 Table.2 Details of SSR Primers used in molecular analysis SN 1. 2. 3. 4. 5. 6. 7. 8. 9. 10. 11. 12. 13. 14. 15. 16. 17. 18. 19. 20. 21. 22. 23. 24. 25. 26. 27. 28. 29. 30. 31. 32. 33. 34. 35. 36. 37. 38. 39. 40. 41. 42. Primer Code BNL-1047 BNL-1059 BNL-1064 BNL-1231 BNL-1395 BNL-1672 BNL-2572 BNL-3140 BNL-3255 BNL-3627 BNL1030 BNL1317 BNL3994 BNL580 \ BNL1421 BNL1227 BNL3359 BNL1122 BNL1017 BNL1034 CIR-244 CIR-246 CIR-305 CIR-307 CIR-354 CIR-381 CIR-413 CIR320 CIR328 CIR253 CIR45 CIR70 CIR78 JESPR65 JESPR220 JESPR298 JESPR119 JESPR29 JESPR208 JESPR127 JESPR153 JESPR307 Primer sequence (Forward) Primer sequence (Reverse) GCTTGTCATCTCCATTGCTG TGTATTCTCTTCTTTTCCTTATACTTTT CCTTCTCTGACACTCTGCCC TGTATTCTCTTCTTTTCCTTATACTTTT TTTGCGGGTAATCCTATTGC TGTCTATGGGACATTTCGCA TAATAAAAGGGAAAGGAAAGAGTT TATGGCTCTAGAATATTCCCTCG AAGCAGCCAAGAAATACCGA TCGATAACGGCTACAGTAATCT TGGATTTGTCCCTCTGTGTG AACCAACTTTTCCAACACCG GTCCTATTACTAAAATTGTTAATTTAGCC CGATGTTAAATCAATCAGGTCA CACCATTGTGGCAACTGAGT GGAAAAGGGAAAGCCATTGT GACAGTCAAACAGAACAGATATGC TTACACGACTTGTTCCCACG TATGGGCCTGTCCACCTAAG CAAAGCAACATGCACACACA TTTGGAGCCATTTACATGCA AAACCACTTCTGCATCTGGA AAAAATCAGCCAAATTGGGA CGTCAACAATTGTCCCAAGA TTGAGGGCATCCAAATCCAT CCTCCACCATACACGTGCTA CTATGTTTGGCCTTGGCATT TAGTGACAGATATCCCCGGC TGAAGATTTGGAGGCAATTG GAAATCAAGCCTCAATTCGG CATCAAGATCTATCTCTCTCTATACCG TTTACCCTCCGATCTCAACG TTGTTGTTGGGAATGATGGA TGACCCTTCACCGACTTTCT TCGATAACGGCTATAGTAATCTCTC CAACAAATAAGCAGCCAAGAAA AGAAAAAAACTTCCTCATGAACC GTTTCTCTCAGAATTTGTAGGCC TTGCTTTCAATGGAAAACCC CGTCGCAAAGTTGAGAATCA TGGAAGGTGATGTTCTAA GATCAAAGAGCAAACTAATC TTAGGGTTTAGTTGAATGG ATGAACACACGCACG TTTCCAGCAAAAGAAGT GAATTTTGAAGTGTCCTG GACTTGAAAAGATTACACAC GAATTTGCTGGCTCT CACAATCCTCAGCCA AGAGAAGGAAAGAGGAAA TTTCCATCCTTTTGTGA AAGGAGAAGAACAAGCAA TTAAAGCTCACACACACA CAACAGTAACGAAGAACAAT CCTCCATAAACCCTCTT TCACATACGAAGACAACC ATCCCTATGCTTGTCATC ATTACCATTCATTCACCAC CCAACCAAGAAACCAG GTAAGCATGGGCATTT ACTAGCAGTGCGAATACA TGGTTAAGGGTTGGG AACCACCAACCATTCA TGGGACTCGGTCATC TGCATGATGAAGTTAGA ACATAAATCCCAAGAAC CCACCCAATTTAAGAAGAAATTG GGTTAGTTGTATTAGGGTCGTTG CGAGGAAGAAATGAGGTTGG CTAAGAACCAACATGTGAGACC GATGCCCTCGTGTTAAAG GGACCTTCGGAATAATTACC CTCAGGGAACTATTTGTAGTAGC GATCCACAAGAAACTGAAACTAG CACCGTTTCCAAGTAAGATT GGTTAATCTTAGTTGAGGTC CGCAACCAAACATATACTTCACAC CCCTTTCCATCCATAGAACG GATTTGGGTAACATTGGCTC CTGCAGTGTTGTGTTGGGTAGA GATTACCTTCATAGGCCACTG GAAAACATGAGCATCCTGTG CTTGGCCATGTATTCCTTCA GAAAGACACTAAGCTGAGGC 1753 Int.J.Curr.Microbiol.App.Sci (2021) 10(03): 1748-1759 Table.3 The scale of fiber quality traits used in this study Fiber length (mm) Short fiber length Medium fiber length Long fiber length Fiber Strength (g/tex) Fiber fineness (mic) (µg/inch) 0.95-22.0 22.0-26.0 Average High 25.0-27.0 28.0-30.0 Fine Average 3.1-3.9 4.0-4.9 26.1-32.0 Very high >30.0 Coarse 5.0-5.9 Table.4 Characteristics of the amplification products with polymorphic SSR primer SN. 1. 2. 3. 4. 5. 6. 7. 8. 9. 10. 11. 12. 13. 14. 15. 16. 17. 18. 19. 20. 21. 22. 23. 24. 25. Primer Code BNL-1059 BNL-1395 BNL-1672 BNL-2572 BNL-3255 BNL-1064 BNL-1231 BNL-3140 BNL-3627 BNL-1047 BNL1122 CIR-244 CIR-354 CIR-381 CIR-413 CIR-246 CIR-305 CIR-307 CIR320 CIR253 JESPR-153 JESPR-127 JESPR-307 JESPR220 JESPR298 Total Average Allele size (bp) Total number of allele 190-230 110-130 130-150 110-130 270-370 190 210-290 230 150-190 170 170-210 150-210 150-210 170-190 170-210 150-170 150-170 190 150-290 110-170 110-290 170-290 210-290 170-210 130-190 2 2 2 2 2 1 2 1 3 1 2 2 3 2 3 2 2 1 3 2 4 2 2 3 2 53 2.12 1754 Number of Polymorphic allele 2 2 2 2 2 1 2 1 3 1 2 2 3 2 3 2 2 1 3 2 4 2 2 3 2 53 2.12 Percent Polymorphism 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 2500 100 PIC 0.481 0.078 0.264 0.153 0.375 0.073 0.375 0.073 0.357 0.078 0.163 0.538 0.660 0.784 0.073 0.749 0.073 0.294 0.068 0.158 0.639 0.375 0.078 0.072 0.240 7.271 0.290 Int.J.Curr.Microbiol.App.Sci (2021) 10(03): 1748-1759 0.77 0.67 0.87 0.81 0.1.0 0.74 0.75 0.80 0.81 0.74 0.74 0.75 0.86 0.73 1.0 0.78 0.76 0.78 0.78 0.73 0.74 0.77 0.72 0.77 0.78 0.78 0.70 0.76 0.78 0.77 0.79 0.79 0.75 0.76 0.72 0.73 0.78 0.75 0.80 0.81 0.85 0.73 0.81 0.81 0.82 0.78 0.78 0.78 0.80 0.73 0.78 0.77 0.74 0.77 0.78 0.80 0.68 0.82 0.78 0.77 0.83 0.81 0.81 0.81 0.78 0.77 0.76 0.79 0.78 0.77 0.79 0.73 0.71 0.83 0.80 0.86 0.84 0.84 0.86 0.83 0.82 0.81 0.80 0.83 0.78 0.82 0.70 0.72 0.78 0.75 0.61 0.66 0.68 0.66 0.64 0.63 0.66 0.70 0.64 0.68 0.74 0.70 0.74 0.78 0.79 0.83 0.85 0.79 0.81 0.76 0.71 0.70 0.77 0.78 0.77 0.81 0.69 0.71 0.77 0.76 0.86 0.84 0.84 0.88 0.83 0.76 0.75 0.78 0.85 0.82 0.88 0.78 0.78 0.80 0.81 0.81 0.83 0.73 0.77 0.72 0.75 0.76 0.69 0.78 0.75 0.79 0.71 0.67 0.75 0.74 0.80 0.80 0.76 0.78 0.75 0.72 0.71 0.74 0.77 0.76 0.80 0.72 0.76 0.78 0.79 Maximum: 0.91 PHH-316 0.80 NHH-250 0.75 NHH-324 0.78 NHH-225 0.69 NHH-206 1.0 NHH-44 0.85 PH-1009 0.70 NH-111 0.84 PH-325 0.83 PH-93 0.80 ARB-908 0.83 ARB-757 0.78 NH-625 1.0 AC-738 1.0 0.64 BN-1 PKV RAJAT PHULE YAMUN PH-348 1.0 0.65 0.83 AKH-8828 NH-635 1.0 0.83 0.73 0.80 NH-630 1.0 0.81 0.80 0.72 0.75 NH-615 1.0 0.79 0.76 0.83 0.71 0.80 NH-545 1.0 0.87 0.76 0.77 0.86 0.66 0.79 NH-452 NH-452 NH-545 NH-615 NH-630 NH-635 PH-348 PKV Rajat AKH8828 PHULE 0688 Phule Yamun BN-1 AC-738 NH-625 ARB-757 ARB-908 PH-93 PH-325 NH-111 PH-1009 NHH-44 NHH-206 NHH-225 NHH-324 NHH-250 PHH-316 PHULE 0688 Table.5 Similarity matrix of SSR marker analysis based on Jaccard’s similarity coefficient 1.0 0.88 0.80 0.82 0.77 0.74 0.77 0.78 0.81 0.74 0.78 0.74 0.70 0.76 0.75 1.0 0.80 0.84 0.79 0.80 0.79 0.87 0.81 0.80 0.80 0.76 0.74 0.80 0.77 1.0 0.90 0.83 0.78 0.71 0.76 0.79 0.80 0.80 0.74 0.80 0.80 0.73 1.0 0.91 0.84 0.81 0.82 0.85 0.86 0.88 0.76 0.80 0.84 0.77 1.0 0.83 0.84 0.75 0.82 0.83 0.83 0.69 0.73 0.77 0.78 0.1.0 0.85 0.76 0.85 0.80 0.76 0.66 0.74 0.74 0.67 1.0 0.79 0.84 0.77 0.77 0.67 0.75 0.77 0.78 1.0 0.85 0.80 0.82 0.76 0.74 0.82 0.75 1.0 0.83 0.87 0.75 0.75 0.79 0.72 1.0 0.88 0.80 0.82 0.84 0.77 1.0 0.80 0.82 0.88 0.83 1.0 0.74 0.80 0.77 1.0 0.82 0.77 1.0 0.91 1.0 Average: 0.76 1755 Minimum: 0.61 Int.J.Curr.Microbiol.App.Sci (2021) 10(03): 1748-1759 Fig.1 Fig. 1. Representative PCR amplification profile with SSR primers BNL 3255, CIR 244 and JESPR 153 1 : NH-452 2 : NH-545 3 : NH-615 4 : NH-630 5 : NH-635 6 : PH-348 7 : PKV Rajat 8 : AKH-8828 9 : PHULE 0688 10 : Phule Yamuna 11 : BN-1 12 : AC-738 13 : NH-625 14 :ARB-757 15 16 17 18 19 20 21 : ARB-908 : PH-93 : PH-325 : NH-111 : PH-1009 : NHH-44 : NHH-206 22 : NHH-225 23 : NHH-324 24 : NHH-250 25 : PHH-316 Ladder – 100 bp Fig.2 Frequency of PIC value of all primers for different fibre quality traits 1756 Int.J.Curr.Microbiol.App.Sci (2021) 10(03): 1748-1759 Fig.3 Dendrogram showing clustering pattern in 25 cotton genotypes studied based on SSR analysis Clustering analysis of cotton genotypes on the basis of SSR analysis The Jaccard’s similarity coefficient showed the genetic relationships among the cotton genotypes. The similarity matrix based on the SSR marker profiling for fiber traits is depicted in (Table 5) and dendrogram depicted in Fig. 3. As concerned to fiber traits, 25 cotton genotypes showed similarity coefficient value ranged from 0.61 to 0.91 indicating more variation in respect of genetic similarity at studied loci. This ultimately means that large range of genetic diversity for fiber traits existed among the studied genotypes. Minimum similarity coefficient i.e. 0.61 was observed in genotypes PH-348 and BN-1. Maximum similarity coefficient of 0.91 was observed in genotype ARB-757, ARB-908, NHH- 2250 and PHH-316. The cluster analysis revealed that the four cluster contains one major, three minor cluster and three outgroups. The cluster-II being the major cluster with 80% similarity amongst the thirteen genotypes. The Cluster-III was minor cluster with two genotypes having 77% genetic similarity. The cluster-I and clusterIV contained four and three genotypes sharing 77% and 76% similarity within their respective cluster. The dendrogram also generated three out-group namely, Phule0688 (sharing 76% similarity with rest of cluster), NH-225 (shearing 75% similarity with rest of cluster) and PH-348 (sharing 69% similarity with rest of cluster). The Major cluster-II was further divided in two subcluster sharing 81% similarity with each other. The two subcluster share 80% similarity with remaining one out group (NH111). The sub cluster II-A contains 5 genotypes namely (NH-630, NH- 635, BN-1, AC-738 and PKV Rajat) having long fiber length except one genotypes i.e. BN-1 while sub cluster II-B contains 7 genotypes (AKH8828, NHH-206, PH- 1009, NHH-44, NH625, ARB-757 and ARB-908) having long fiber length. The NH- 111 was out grouped in this mojor cluster II having long fiber length. The Cluster-III has minor cluster with two genotypes (PH-93 and PH-325) having medium fiber length sharing 77% genetic similarity each other. The cluster-I contained four genotypes (NH-452, NH-545, NHH-250 and PHH-316) having medium to high fiber length they have 77% genetic similarity with each other. The cluster IV containing three genotypes (NH-615, NHH-324 and Phule Yamuna) having long fiber length and sharing 76 % genetic similarity with each other 1757