Molecular genetic diversity in relation to the quantitative and qualitative (fiber quality) traits in upland cotton (Gossypium hirsutum L.)

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Rani Chapara, K. V. Siva Reddy, M. Sudha Rani, K. Sudhamani, A. D.G. Diana Grace, and 4 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-3327039/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract An attempt has been made to assess molecular genetic diversity at Regional Agricultural Research Station, Lam, Guntur using fifty-four tetraploid cotton ( Gossypium hirsutum L.) genotypes with forty-four SSR markers related to various fibre quality traits. A total number of 24 alleles, with an average of 1.75 effective alleles per locus, were generated by these markers. The polymorphism information content (PIC) value ranged from 0.23 to 0.50 with a mean of 0.44 indicating lesser variation for various fibre quality traits within the investigated material. Using principal coordinate analysis (PCOORDA), cotton genotypes were separated by the first three principal coordinates (PC1, PC2, and PC3) accounting for 11.5, 8.6, and 7.2% of the total genetic variance, respectively. The SSR markers revealed a genetic similarity of 63.21 among the varieties studied. Cotton Cluster analysis Genetic diversity Molecular diversity SSR markers PCOORDA Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 1. Introduction Cotton is the most important fiber crops and the second most important oilseed crops in the world (Yu and Kohel, 2001). China, India, the United States, Pakistan, and Brazil are the top five cotton-producing nations, together accounting for around two-thirds of global cotton production (Townsend, 2020). Cotton contains around 50 species, which are distributed in tropics and subtropical regions. Among these species, upland cotton ( Gossypium hirsutum L .) is the world's major fibre producing crop, grown in over 80 countries. G. hirsutum currently accounts for 95% of global cotton production (Chen et al., 2007). On the other hand, this domesticated upland cotton has narrow genetic base (Tyagi et al., 2014). Genetic diversity of cotton plays an important role in sustainable development and food security, as it allows the cultivation of crops in the presence of various biotic and abiotic stresses (Begna and Begna, 2021). It is also important for the selection of parents that can be used in plant breeding programs (Sun et al., 2019). Characterizing genetic diversity and degree of association between and within varieties is the first step toward developing germplasm and crop cultivars (Swarup et al., 2021). Successful crop improvement depends on genetic variability that arises from genetic diversity and lack of genetic diversity may limit breeding progress and gain from selection (Govindaraj et al., 2015). Molecular markers have been used to measure genetic diversity and relationship within species and between their wild relatives in cotton (Abdellatif et al., 2012; Santhy et al., 2019; Kushanov et al., 2021). Among all molecular markers, Simple Sequence Repeats (SSRs) have been involved in many genetic diversity studies (Zhao et al., 2015; Shi et al., 2023). The present study was aimed to analyze the genetic diversity among the fifty-four tetraploid cotton genotypes using DNA-based markers (SSRs) in order to study the cotton varietal development and evolution. 2. Materials and Methods Plant material In the present study, a set of 50 tetraploid cotton advanced breeding lines (ABLs) were included. In addition, 4 known local check varieties for various fiber quality traits have also included (Table 1 ). Table 1 List of cotton genotypes included in the study and their pedigree S.No. Genotype Pedigree Source 1 GP-2 SCS 1002 AICRP on cotton trials 2 GP-5 CNH-19 AICRP on cotton trials 3 GP-6 DSC-54 AICRP on cotton trials 4 GP-9 ARBH 1352 AICRP on cotton trials 5 GP-11 ARBC 1301 OT AICRP on cotton trials 6 GP-19 L 1008 X L 788-1-2-1-2-1-1 AICRP on cotton trials 7 GP-20 TSH 0533-1 AICRP on cotton trials 8 GP-28 GISV 298 AICRP on cotton trials 9 GP-32 CSH 3622 AICRP on cotton trials 10 GP-37 SHC 374 AICRP on cotton trials 11 GP-42 CCH 16 − 5 AICRP on cotton trials 12 GP-45 CSH 820 AICRP on cotton trials 13 GP-46 GSHV 164 AICRP on cotton trials 14 GP-49 RS 2514 AICRP on cotton trials 15 GP-52 L 762 AICRP on cotton trials 16 GP-55 NA 606 AICRP on cotton trials 17 GP-56 PBH 174 AICRP on cotton trials 18 GP-61 H1519 AICRP on cotton trials 19 GP-65 CSH 820 AICRP on cotton trials 20 GP-66 CSH 3088 AICRP on cotton trials 21 GP-73 L 801 AICRP on cotton trials 22 GP-75 PBH 174 AICRP on cotton trials 23 GP-76 GBHV 185 AICRP on cotton trials 24 GP-81 LH 2298 OT AICRP on cotton trials 25 GP-86 F 2383 AICRP on cotton trials 26 GP-88 ARBC 1401 AICRP on cotton trials 27 GP-90 GSHV-01/1338 AICRP on cotton trials 28 GP-92 GSHV 160 AICRP on cotton trials 29 GP-95 L 1801 AICRP on cotton trials 30 GP-99 DSC 1402 OT AICRP on cotton trials 31 GP-101 RAHC 1011 AICRP on cotton trials 32 GP-105 CSH − 73 AICRP on cotton trials 33 GP-106 CCH 13 − 3 AICRP on cotton trials 34 GP-109 CCH 19 − 4 AICRP on cotton trials 35 GP-121 RAH 1076 AICRP on cotton trials 36 GP-123 GTHV 18117 AICRP on cotton trials 37 GP-129 BS 8–19 AICRP on cotton trials 38 GP-130 RHCHD 1312 AICRP on cotton trials 39 GP-131 Suraj (C) AICRP on cotton trials 40 GP-139 NDLH 1938 AICRP on cotton trials 41 GP-140 RAH 1076 AICRP on cotton trials 42 GP-144 SHS 234 AICRP on cotton trials 43 GP-145 BS 7–19 AICRP on cotton trials 44 GP-148 CCH 19 − 2 AICRP on cotton trials 45 GP-151 TSH 383 AICRP on cotton trials 46 GP-152 GTHV 15134 AICRP on cotton trials 47 GP-155 CPD 1902 AICRP on cotton trials 48 GP-157 RB 615 AICRP on cotton trials 49 GP-158 RAT 1047 AICRP on cotton trials 50 GP-159 F 2383 AICRP on cotton trials 51 LHDP 1 Local Check Station trials, RARS, Lam 52 NDLH 2005-4 Check AICRP on cotton 53 NDLH 1938 Check AICRP on cotton 54 NDLH 2051-1 Check AICRP on cotton Phenotyping for various fiber quality and yield related traits 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 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., GOT- Ginning Out Turn, UHML- Upper Half Mean Length (inches), UI- Fibre Uniformity (%), MIC-Micronaire, STR- Fibre Strength (g/tex), ELG- Elongation (or Fibre Elasticity, %), LI-Lint Index. Besides fiber quality traits, yield related traits such as PH-Plant height (cm), NM-No. of Monopodia, NS-No. of Sympodia, NBP-No. of Bolls / Plant, BW-Boll weight (g) and SI- Seed Index (g) were also evaluated. DNA extraction and genotyping Genomic DNA was isolated from leaf samples using CTAB method (Murray and Thompson, 1980) and the quantification along with relative purity were estimated with nanodrop (ND-1000, Thermo Scientific, Nanodrop Technologies, U.S.A). The final concentration of each DNA sample was adjusted to 50 ng/µl and 44 SSR primers linked to fiber quality traits were used for the molecular profiling. The genomic DNA has been amplified in a polymerase chain reaction (PCR) (Eppendorf, Hamburg, Germany). The PCR was performed in a final volume of 10 µl, containing 2 µl of 50 ng template DNA, 10X PCR buffer (1 µl), 25mM MgCl2 (1 µl), 2.5 mM dNTP (0.5 µl), 0.5 µl of each 10 pmol primer and 0.2 µl of 5U/ µl of Taq-DNA polymerase. The temperature profile for DNA amplification was 5 min at 94°C for initial denaturation, 30 s at 94°C for denaturation, 45s at 55–61°C for primer annealing (vary as per primer annealing temperature), and 60s at 72°C for primer extension for 35 cycles. The PCR reaction was completed with 5 min incubation at 72°C for a final extension. Finally, the PCR products were run on 2.5% agarose gel in 0.5X Tris-Borate EDTA (TBE) buffer for gel electrophoresis, and the amplicons were visualized under a gel documentation system (iBright1500, invitrogen, US). Statistical analysis The amplified products for marker analysis were scored visually based on the presence ( 1 ) or absence (0) of the band for each marker. Each marker fragment was treated as a unit character and only unambiguous bands were scored. The total number of bands and the number of polymorphic bands were calculated as well as the polymorphic information content (PIC) which was calculated according to Anderson et al. (1993) using the following simplified formula: where i = 1 to n and P ij is the frequency of the j th allele for the i th band scored for a particular marker. Genetic distances were estimated among the genotypes using simple matching coefficients by bootstrapping 1000 times and all the genotypes were clustered using a neighbor-joining method based on a dissimilarity matrix obtained using Darwin 6.0 (Perrier and Jacquemond, 2006) software. Principal coordinate analysis was performed to highlight the resolving power and the first three components were used to represent the genetic variability among the genotypes. PCOORDA was performed using the dissimilarity matrix constructed using Darwin version 6.0. The phenotypic data recorded was subjected to various statistical analyses. 3. Results and Discussion Phenotyping of fiber quality and yield related traits The population mean values of most of the traits were lower or no-par with the average trait values of local check varieties (Table 2 ). Wide data range has been recorded for all the fiber quality and yield-related traits. The frequency distribution for all the fiber quality and yield-related traits was depicted in Fig. 1 . All the recorded traits except PH and NBP seem to follow normal distribution, with skewness values lying in between − 1 and + 1 and kurtosis values between − 3 and + 3. This indicates that polygenic inheritance accounts for the majority of fiber quality and yield-related traits, which might be controlled by both major and minor genes. Table 2 Mean performance of various cotton genotypes for various fiber quality and yield-related traits Trait Check varieties Genotypes LHDP 1 NDLH 2005-4 NDLH 1938 NDLH 2051-1 Range Mean ± SE PH 115.6 148.8 110.7 140.4 91.3–197 117.86 ± 2.38 NM 2.6 3 2 2.2 1–3 1.53 ± 0.09 NS 18.2 21.2 17 15.6 10-24.3 16.53 ± 0.43 NBP 53.2 43.2 56 47.4 22–116 44.78 ± 2.1 BW 3.8 3.8 3.7 3.7 2.4–4.1 3.41 ± 0.06 SI 7.8 7.5 14.7 8 9.3–17.1 13.08 ± 0.28 GOT 35.5 34.2 36.5 33.9 28.6–39.4 34.94 ± 0.4 UHML 28.0 30.9 27.8 27.9 25.4–30.8 28.26 ± 0.2 UI 82 82 82 82 81–84 82.67 ± 0.11 MIC 4.7 3.9 4.0 4.3 2.9–5.1 4.19 ± 0.07 STR 29.4 27.4 28.8 27.0 24.3–32.2 28.47 ± 0.22 ELG 6.1 6.8 6.7 6.3 5.3–7.2 6.32 ± 0.06 LI 4.3 3.9 5.4 4.1 3.3–6.1 4.54 ± 0.09 PH-Plant height (cm), NM-No. of Monopodia, NS-No. of Sympodia, NBP-No. of Bolls / Plant, BW-Boll weight (g), SI- Seed Index (g), GOT- Ginning Out Turn, UHML- Upper Half Mean Length (inches), UI- Fibre Uniformity (%), MIC-Micronaire, STR- Fibre Strength (g/tex), ELG- Elongation (or Fibre Elasticity (%)), LI-Lint Index. Character associations Understanding the relationships between fiber quality and yield-related traits can help researchers to make informed decisions about which traits to prioritize in their breeding programs. For instance, a positive correlation among fiber quality and yield suggests that plants with greater fiber quality are more likely to have higher yields. On the other hand, if negative correlations are found among these variables, it indicates trade-offs among fiber quality and yield-related traits, and that efforts to improve one of these traits may negatively impact the others. Correlation analysis for fiber quality and yield-related traits in F 5:6 generation was represented in Fig. 2 . Among the yield-related traits, significant positive correlation was recorded between PH and NBP (0.292). Among the fiber quality traits, GOT showed significant negative correlations with UHML (-0.535) and UI (-0.485). Similar results were observed by Nikhil et. al., (2018). UHML recorded highly significant and positive correlations with UI (0.921), STR (0.41) and ELG (0.391). UI recorded significant positive correlations with STR (0.429) and ELG (0.415). STR recorded positive significant correlation with ELG (0.342) and ELG showed positive correlation with LI (0.46). Interestingly, inter correlations among fiber quality and yield traits has been recorded. PH showed positive correlation with ELG. NM recorded positive correlation with GOT and negative significant correlation with UHML. NBP showed highly significant and positive correlation with ELG. SI recorded highly significant and positive correlations with UHML, UI, ELG and LI. Molecular diversity analysis Knowledge of the genetic dissimilarity among cotton germplasm lines/varieties is the foremost important step in any crop improvement program. Fifty-four cotton germplasm lines collected from All India Coordinated Cotton Improvement Trails of cotton from different parts of India were included and profiled for DNA polymorphism using 44 SSR markers sourced from the cotton marker database. Out of 44 SSR markers, 12 were polymorphic and amplified a total of 24 alleles. Polymorphic information content (PIC) of 12 markers ranged from 0.23 to 0.50 with an average PIC value of 0.44 indicating the potentialities of these markers for assessing molecular diversity. The highest PIC value was exhibited by the markers, DPL0068 (0.50), NAU2443 (0.50), and BNL3569 (0.50) followed by NAU3588 (0.48), MGHES0026 (0.47) and BNL3090 (0.46). The banding pattern of BNL3569 for 54 genotypes studied in the present investigation is presented in Fig. 3 . High PIC markers can efficiently distinguish the genotypes and are said to be more informative. Hence, these markers may be used for diversity studies, gene mapping-related studies, etc. The lowest PIC value was exhibited by BNL0569 (0.23) indicating less discriminatory power of this marker in distinguishing the genotypes. PIC provides a more accurate assessment of diversity and also signifies the discriminatory power of a locus, as it takes considers the number of expressed alleles and the relative frequencies of each allele. In the present study out of 12 markers screened, three markers were found to be highly informative with > 0.5 PIC. Thus, based on the PIC values, the markers used in the present study showed appreciable levels of polymorphism in the cotton genotypes. The average PIC value, in the present study (0.44) is more than the previous studies of Kumbhalkar et al. (2018) who reported an average PIC value of 0.21 and 0.35 for fiber length and fiber strength respectively, whereas higher average PIC value was reported by Abdellatif et al. (2012) with an average PIC value of 0.86. The effective number of alleles ranged from 2.00 (DPL0068) to 1.30 (BNL0569) with a mean of 1.75 and the Shannon index ranged from 0.70 (DPL0068) to 0.39 (BNL0569) with a mean of 0.61. The primers DPL0068, NAU2443, and BNL3569 possess high PIC values indicating their usefulness for discriminating germplasm lines, the effective number of alleles, and Shannon index values. SSR markers being codominant in inheritance are known to detect heterozygosity. Heterozygosity is a measure of genetic variation within a population. Estimated mean heterozygosity among the genotypes was found to be higher compared to those observed by Rakshit et al. (2010) and Tyagi et al. (2014). The higher heterozygosity for few markers might be due to the amplification of similar sequences in different genomic regions mainly due to duplications during evolution of tetraploid cotton. Cotton being a complex allotetraploid, extensive heterogeneity and inherent residual heterozygosity is bound to exist (Zhang et al. 2013). Cryptic genetic variations at DNA level also contribute to higher heterozygosity (Rakshit et al. 2011) apart from limited self pollinations and rare pollen contamination. The gene diversity is the probability of two randomly chosen alleles being different from a population. The gene diversity ranged from 0.02 (NAU2691) to 0.64 (TMB2295). The mean gene diversity value of 0.28 was observed among 48 tetraploid cultivars. Table 3 Characteristics of the amplification products with polymorphic SSR primer S.No. Marker PIC *na *ne *I 1. BNL0569 0.23 2.00 1.30 0.39 2. MGHES0026 0.47 2.00 1.78 0.63 3. BNL1604 0.45 2.00 1.80 0.64 4. BNL3569 0.50 2.00 1.97 0.69 5. BNL3410 0.42 2.00 1.67 0.59 6. BNL3090 0.46 2.00 1.67 0.59 7. MGHES006 0.44 2.00 1.79 0.63 8. NAU3467 0.44 2.00 1.60 0.56 9. NAU3588 0.48 2.00 1.82 0.64 10. NAU2443 0.50 2.00 1.96 0.68 11. TMB1268 0.36 2.00 1.60 0.56 12. DPL0068 0.50 2.00 2.00 0.70 Minimum 0.23 1.30 0.39 Maximum 0.50 2.00 0.70 Mean 0.44 2.00 1.75 0.61 * na = Observed number of alleles * ne = Effective number of alleles [Kimura and Crow (1964)] * I = Shannon's Information index [Lewontin (1972)] Test of significance of mean phenotypic values of different alleles of the polymorphic markers related to various fibre quality traits using student’s t-test revealed the alleles of the marker BNL0569 showing significant differences in between them for MIC and STR as reported by Yu et al. (2013) (Fig. 4 ). Similarly, the alleles of the marker BNL1604 showed significant differences in between them for the trait UHML as reported by Darmanov et al. (2022). Classification of cotton genotypes for fiber quality traits using cluster analysis Cluster analysis and construction of a dendrogram for 54 genotypes were done by neighbor-joining method based on dissimilarity matrix using Software Darwin 6.0 and the results are presented in Table 4 . The 54genotypes were grouped into five major clusters. Cluster I comprising of 5 genotypes, further divided into two sub-clusters (IA, IB) (Fig. 5 ). Subcluster IA comprised of 2 genotypes, whereas Cluster IB comprises of 3 genotypes. Cluster II also comprises of 13 genotypes, further divided into two sub clusters (IIA, IIB). Sub cluster IIA comprised of 6 genotypes, whereas Cluster IIB comprises of 7 genotypes. Cluster III comprises of 14 genotypes, further divided into two sub clusters (IIIA, IIIB). Sub cluster IIIA comprised of 5 genotypes, whereas Cluster IIIB comprises of 9 genotypes. Cluster IV had of 11 genotypes, further divided into two sub-clusters (IVA, IVB). Sub-cluster IVA comprises 4 genotypes whereas sub-cluster IVB comprises of genotypes. Cluster V had 11 genotypes, further divided into two sub-clusters (VA, VB). Sub-cluster VA comprises gene genotypes and sub-cluster VB comprises 10 genotypes. Table 4 Grouping of genotypes into clusters based on molecular diversity S. No. Cluster Name of the sub-cluster Number of genotypes Genotypes 1. I IA 02 GP 66 and GP 2. IB 03 GP 65, GP 106 and GP 109. 2. II IIA 06 GP 11, GP 20, GP 28, GP 32, GP 37 and GP 45. IIB 07 GP 5, GP 6, GP 19, GP 46, GP 49, GP 52 and LHDP 1. 3. III IIIA 05 GP 90, GP 92, GP 95, GP 139 and NDLH 2005-4. IIIB 09 GP 99, GP 101, GP 105, GP 123, GP 129, GP 130, GP 131, GP 140 and GP 144. 4. IV IVA 04 GP 42, GP 56, GP 61 and NDLH 1938. IVB 07 GP 55, GP 73, GP 74, GP 76, GP 81, GP 86 and GP 88. 5. V VA 01 GP 121. VB 10 GP 9, GP 145, GP 148, GP 151, GP 152, GP 155, GP 157, GP 158, GP 159 and NDLH 2051-1. Principal Coordinate Analysis The principal coordinate analysis demonstrated that the 54 cotton genotypes can be separated distinctly from each other (Fig. 6 ). A two-dimensional scatter plot involving all the genotypes shown that, the first three principal coordinates explained 22.46%, 16.91% and 10.00% of the total variation of 63.21% as shown in Table 5 . Table 5 Principal coordinate analysis for various fiber quality and yield-related traits in cotton Axis Eigen Value Inertia (%) 1. 0.0724 22.46 2. 0.0545 16.91 3. 0.0322 10.00 4. 0.0272 8.45 5. 0.0173 5.39 Declarations Ethical Approval : Not applicable Competing interests : All the authors hereby declare that there is no competing interests. Authors' contributions : Rani Chapara: Designed and executed the experiment and written the manuscript of the article. K.V. Siva Reddy: Helped in execution of the experiment in field. M. Sudha Rani: Overall supervised the experiment and guided in writing of the script of the article. K. Sudhamani: Analyzed the data using SPSS software. A.D.G. Diana Grace: Helped in crop pest management during the crop tenure N. Venkata Lakshmi: Helped in crop agronomy management during the crop tenure. B. Sreekanth: Helped in crop nutrition management B. Sree Lakshmi: Overall execution of the experiment and crop disease management in cotton crop V. Roja: Scientist (Agricultural Biotechnology): Helped in molecular markers analysis. Funding: All the authors acknowledge the Director of Research of Acharya N.G. Ranga Agricultural University and Associate Director Research, RARS, Lam, Guntur for allocation of resources for the project execution. Availability of data and materials: The planting material and laboratory facility was from RARS, Lam, Guntur. References Abdellatif, K. F., Khidr, Y. A., El-Mansy, Y. M., El-Lawendey, M. M., & Soliman, Y. A. (2012). Molecular diversity of Egyptian cotton (Gossypium barbadense L.) and its relation to varietal development. 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Plant systematics and evolution, 301:327-336. Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-3327039","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":231704994,"identity":"06c5932a-44a4-4f8d-8b34-c66a1178f3e3","order_by":0,"name":"Rani Chapara","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA/klEQVRIiWNgGAWjYJCCA4wNYJrxAZKgBFFamA2I1sIA1cJGQBkUmLefPXjg5w67PP7Zh59V/NxRm88/I4Hxww8GizxcWmTO5CUc7D2TXCxxLs3sZu+Z45YzbiQwS/YwSBTj0iLBkGNwmLGNObHhDIPZDd62YwYMNxIYpIESiQ24tPC/AWmpT5x/hv1b4V+gFnmgLb/xapEA23I4ccMZHjNm3rYaA4MbCWz4bZF4Y3Cwt+14seEZnmJp2bYDBoZnHrZZ9hjgc1iO8YefbdV5cmfYN35821ZnIHc8+fCNHxV1OLXAQAKUPswAiSYD3ErRtdQRVjoKRsEoGAUjDgAAmHxZkMc4TqkAAAAASUVORK5CYII=","orcid":"","institution":"Acharya N. G. Ranga Agricultural University","correspondingAuthor":true,"submittingAuthor":false,"prefix":"","firstName":"Rani","middleName":"","lastName":"Chapara","suffix":""},{"id":231704995,"identity":"3a51197f-0905-4e6d-850c-6b6275fe6c76","order_by":1,"name":"K. V. Siva Reddy","email":"","orcid":"","institution":"Acharya N. G. Ranga Agricultural University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"K.","middleName":"V. Siva","lastName":"Reddy","suffix":""},{"id":231704996,"identity":"1750fab2-1114-4183-83fa-c307d834bb23","order_by":2,"name":"M. Sudha Rani","email":"","orcid":"","institution":"Acharya N. G. Ranga Agricultural University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"M.","middleName":"Sudha","lastName":"Rani","suffix":""},{"id":231704997,"identity":"b8680251-6759-423b-876d-9fa59cc0fd06","order_by":3,"name":"K. Sudhamani","email":"","orcid":"","institution":"Acharya N. G. Ranga Agricultural University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"K.","middleName":"","lastName":"Sudhamani","suffix":""},{"id":231704998,"identity":"6fe57cb1-2355-44cb-a9fc-74f45710bb14","order_by":4,"name":"A. D.G. Diana Grace","email":"","orcid":"","institution":"Acharya N. G. Ranga Agricultural University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"A.","middleName":"D.G. Diana","lastName":"Grace","suffix":""},{"id":231704999,"identity":"6d033b92-8187-49ca-bd8f-9f8f73823bd5","order_by":5,"name":"N. Venkata Lakshmi","email":"","orcid":"","institution":"Acharya N. G. Ranga Agricultural University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"N.","middleName":"Venkata","lastName":"Lakshmi","suffix":""},{"id":231705000,"identity":"1a523933-4131-4c8a-9eb4-edef26640607","order_by":6,"name":"B. Sreekanth","email":"","orcid":"","institution":"Acharya N. G. Ranga Agricultural University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"B.","middleName":"","lastName":"Sreekanth","suffix":""},{"id":231705001,"identity":"736827a8-b7a8-448d-84c2-ce98be680806","order_by":7,"name":"B. Sree Lakshmi","email":"","orcid":"","institution":"Acharya N. G. Ranga Agricultural University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"B.","middleName":"Sree","lastName":"Lakshmi","suffix":""},{"id":231705002,"identity":"cd92ef22-80c9-4289-ae30-99f017e30926","order_by":8,"name":"V. Roja","email":"","orcid":"","institution":"Acharya N. G. Ranga Agricultural University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"V.","middleName":"","lastName":"Roja","suffix":""}],"badges":[],"createdAt":"2023-09-05 09:29:11","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-3327039/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-3327039/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":43013563,"identity":"a3355693-f8f4-405e-84a2-21b01118929b","added_by":"auto","created_at":"2023-09-12 14:57:03","extension":"jpeg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":1650846,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eFrequency distribution of yield-related and fibre quality traits among the cotton genotypes\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"floatimage1.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-3327039/v1/80727b9cbcd77a7071db6ee2.jpeg"},{"id":43013564,"identity":"5f0425ca-9340-4c39-ad24-20ad898a6883","added_by":"auto","created_at":"2023-09-12 14:57:03","extension":"jpeg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":1740614,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eCorrelation analysis for various fiber quality and yield-related traits\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003ePH-Plant height (cm), NM-No. of Monopodia, NS-No. of Sympodia, NBP-No. of Bolls / Plant, BW-Boll weight (g), SI- Seed Index (g), GOT- Ginning Out Turn, UHML- Upper Half Mean Length (inches), UI- Fibre Uniformity (%), MIC-Micronaire, STR- Fibre Strength (g/tex), ELG- Elongation (or Fibre Elasticity (%)), LI-Lint Index.\u003c/p\u003e","description":"","filename":"floatimage2.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-3327039/v1/bcaa816c01a551f7bc2c5b27.jpeg"},{"id":43013562,"identity":"2f94a991-3eae-493a-9de6-8eb101e09a74","added_by":"auto","created_at":"2023-09-12 14:57:03","extension":"jpeg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":38904,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eAmplification pattern of BNL3569 among 54 cotton genotypes\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"floatimage3.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-3327039/v1/b4002b02be478114a4e9c065.jpeg"},{"id":43014742,"identity":"e20da26f-3f9a-481b-b4f9-021ef5c6b843","added_by":"auto","created_at":"2023-09-12 15:05:03","extension":"jpeg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":409476,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eTest of significance of mean phenotypic values of different alleles of the markers related to fibre quality traits \u003c/strong\u003eEach box represents the mean values of the genotypes having the respective allele. * Indicates P\u0026lt;0.05, ** indicates P\u0026lt;0.01, according to student’s t-test.\u003c/p\u003e","description":"","filename":"floatimage4.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-3327039/v1/73043b92eda3d464fee44c56.jpeg"},{"id":43014741,"identity":"7cd568c6-ea88-461c-9969-10196ac1c3b2","added_by":"auto","created_at":"2023-09-12 15:05:03","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":70014,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eCluster analysis using DARWIN\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"floatimage5.png","url":"https://assets-eu.researchsquare.com/files/rs-3327039/v1/72090da8548f561ad94f0bc0.png"},{"id":43015109,"identity":"43c46c49-1a1a-4c07-aad2-1b624b793e4d","added_by":"auto","created_at":"2023-09-12 15:13:03","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":63282,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003ePCOA using DARWIN\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"floatimage6.png","url":"https://assets-eu.researchsquare.com/files/rs-3327039/v1/05c47765dc466be4cf588c84.png"},{"id":62803020,"identity":"f8e8c3b9-ca56-49f3-80d3-727465bd0fe3","added_by":"auto","created_at":"2024-08-19 16:44:38","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":4769706,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-3327039/v1/7831ea65-522d-4ca1-afb1-bbe15da9a11a.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Molecular genetic diversity in relation to the quantitative and qualitative (fiber quality) traits in upland cotton (Gossypium hirsutum L.)","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eCotton is the most important fiber crops and the second most important oilseed crops in the world (Yu and Kohel, 2001). China, India, the United States, Pakistan, and Brazil are the top five cotton-producing nations, together accounting for around two-thirds of global cotton production (Townsend, 2020). Cotton contains around 50 species, which are distributed in tropics and subtropical regions. Among these species, upland cotton (\u003cem\u003eGossypium hirsutum L\u003c/em\u003e.) is the world's major fibre producing crop, grown in over 80 countries. \u003cem\u003eG. hirsutum\u003c/em\u003e currently accounts for 95% of global cotton production (Chen et al., 2007). On the other hand, this domesticated upland cotton has narrow genetic base (Tyagi et al., 2014).\u003c/p\u003e \u003cp\u003eGenetic diversity of cotton plays an important role in sustainable development and food security, as it allows the cultivation of crops in the presence of various biotic and abiotic stresses (Begna and Begna, 2021). It is also important for the selection of parents that can be used in plant breeding programs (Sun et al., 2019). Characterizing genetic diversity and degree of association between and within varieties is the first step toward developing germplasm and crop cultivars (Swarup et al., 2021). Successful crop improvement depends on genetic variability that arises from genetic diversity and lack of genetic diversity may limit breeding progress and gain from selection (Govindaraj et al., 2015).\u003c/p\u003e \u003cp\u003eMolecular markers have been used to measure genetic diversity and relationship within species and between their wild relatives in cotton (Abdellatif et al., 2012; Santhy et al., 2019; Kushanov et al., 2021). Among all molecular markers, Simple Sequence Repeats (SSRs) have been involved in many genetic diversity studies (Zhao et al., 2015; Shi et al., 2023). The present study was aimed to analyze the genetic diversity among the fifty-four tetraploid cotton genotypes using DNA-based markers (SSRs) in order to study the cotton varietal development and evolution.\u003c/p\u003e"},{"header":"2. Materials and Methods","content":"\u003cp\u003e \u003cb\u003ePlant material\u003c/b\u003e \u003c/p\u003e \u003cp\u003eIn the present study, a set of 50 tetraploid cotton advanced breeding lines (ABLs) were included. In addition, 4 known local check varieties for various fiber quality traits have also included (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eList of cotton genotypes included in the study and their pedigree\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eS.No.\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGenotype\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePedigree\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSource\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGP-2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSCS 1002\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eAICRP on cotton trials\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGP-5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCNH-19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eAICRP on cotton trials\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGP-6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eDSC-54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eAICRP on cotton trials\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGP-9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eARBH 1352\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eAICRP on cotton trials\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGP-11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eARBC 1301 OT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eAICRP on cotton trials\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGP-19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eL 1008 X L 788-1-2-1-2-1-1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eAICRP on cotton trials\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGP-20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eTSH 0533-1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eAICRP on cotton trials\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGP-28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eGISV 298\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eAICRP on cotton trials\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGP-32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCSH 3622\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eAICRP on cotton trials\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGP-37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSHC 374\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eAICRP on cotton trials\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGP-42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCCH 16\u0026thinsp;\u0026minus;\u0026thinsp;5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eAICRP on cotton trials\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGP-45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCSH 820\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eAICRP on cotton trials\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGP-46\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eGSHV 164\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eAICRP on cotton trials\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGP-49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eRS 2514\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eAICRP on cotton trials\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGP-52\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eL 762\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eAICRP on cotton trials\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGP-55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNA 606\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eAICRP on cotton trials\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGP-56\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePBH 174\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eAICRP on cotton trials\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGP-61\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eH1519\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eAICRP on cotton trials\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGP-65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCSH 820\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eAICRP on cotton trials\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGP-66\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCSH 3088\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eAICRP on cotton trials\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGP-73\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eL 801\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eAICRP on cotton trials\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGP-75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePBH 174\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eAICRP on cotton trials\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGP-76\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eGBHV 185\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eAICRP on cotton trials\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGP-81\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eLH 2298 OT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eAICRP on cotton trials\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGP-86\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eF 2383\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eAICRP on cotton trials\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGP-88\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eARBC 1401\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eAICRP on cotton trials\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGP-90\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eGSHV-01/1338\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eAICRP on cotton trials\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGP-92\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eGSHV 160\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eAICRP on cotton trials\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGP-95\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eL 1801\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eAICRP on cotton trials\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGP-99\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eDSC 1402 OT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eAICRP on cotton trials\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGP-101\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eRAHC 1011\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eAICRP on cotton trials\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGP-105\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCSH \u0026minus;\u0026thinsp;73\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eAICRP on cotton trials\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGP-106\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCCH 13\u0026thinsp;\u0026minus;\u0026thinsp;3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eAICRP on cotton trials\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGP-109\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCCH 19\u0026thinsp;\u0026minus;\u0026thinsp;4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eAICRP on cotton trials\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGP-121\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eRAH 1076\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eAICRP on cotton trials\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGP-123\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eGTHV 18117\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eAICRP on cotton trials\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGP-129\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eBS 8\u0026ndash;19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eAICRP on cotton trials\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGP-130\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eRHCHD 1312\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eAICRP on cotton trials\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGP-131\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSuraj (C)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eAICRP on cotton trials\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGP-139\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNDLH 1938\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eAICRP on cotton trials\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGP-140\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eRAH 1076\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eAICRP on cotton trials\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGP-144\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSHS 234\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eAICRP on cotton trials\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGP-145\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eBS 7\u0026ndash;19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eAICRP on cotton trials\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGP-148\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCCH 19\u0026thinsp;\u0026minus;\u0026thinsp;2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eAICRP on cotton trials\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGP-151\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eTSH 383\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eAICRP on cotton trials\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e46\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGP-152\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eGTHV 15134\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eAICRP on cotton trials\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e47\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGP-155\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCPD 1902\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eAICRP on cotton trials\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e48\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGP-157\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eRB 615\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eAICRP on cotton trials\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGP-158\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eRAT 1047\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eAICRP on cotton trials\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGP-159\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eF 2383\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eAICRP on cotton trials\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e51\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLHDP 1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eLocal Check\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eStation trials, RARS, Lam\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e52\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNDLH 2005-4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCheck\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eAICRP on cotton\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e53\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNDLH 1938\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCheck\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eAICRP on cotton\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNDLH 2051-1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCheck\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eAICRP on cotton\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003ePhenotyping for various fiber quality and yield related traits\u003c/b\u003e \u003c/p\u003e \u003cp\u003eThe 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 conditioned at 21\u0026thinsp;\u0026plusmn;\u0026thinsp;1\u0026deg;C and 65\u0026thinsp;\u0026plusmn;\u0026thinsp;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., GOT- Ginning Out Turn, UHML- Upper Half Mean Length (inches), UI- Fibre Uniformity (%), MIC-Micronaire, STR- Fibre Strength (g/tex), ELG- Elongation (or Fibre Elasticity, %), LI-Lint Index. Besides fiber quality traits, yield related traits such as PH-Plant height (cm), NM-No. of Monopodia, NS-No. of Sympodia, NBP-No. of Bolls / Plant, BW-Boll weight (g) and SI- Seed Index (g) were also evaluated.\u003cdiv class=\"BlockQuote\"\u003e\u003cp\u003e\u003cb\u003eDNA extraction and genotyping\u003c/b\u003e\u003c/p\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eGenomic DNA was isolated from leaf samples using CTAB method (Murray and Thompson, 1980) and the quantification along with relative purity were estimated with nanodrop (ND-1000, Thermo Scientific, Nanodrop Technologies, U.S.A). The final concentration of each DNA sample was adjusted to 50 ng/\u0026micro;l and 44 SSR primers linked to fiber quality traits were used for the molecular profiling. The genomic DNA has been amplified in a polymerase chain reaction (PCR) (Eppendorf, Hamburg, Germany). The PCR was performed in a final volume of 10 \u0026micro;l, containing 2 \u0026micro;l of 50 ng template DNA, 10X PCR buffer (1 \u0026micro;l), 25mM MgCl2 (1 \u0026micro;l), 2.5 mM dNTP (0.5 \u0026micro;l), 0.5 \u0026micro;l of each 10 pmol primer and 0.2 \u0026micro;l of 5U/ \u0026micro;l of Taq-DNA polymerase. The temperature profile for DNA amplification was 5 min at 94\u0026deg;C for initial denaturation, 30 s at 94\u0026deg;C for denaturation, 45s at 55\u0026ndash;61\u0026deg;C for primer annealing (vary as per primer annealing temperature), and 60s at 72\u0026deg;C for primer extension for 35 cycles. The PCR reaction was completed with 5 min incubation at 72\u0026deg;C for a final extension. Finally, the PCR products were run on 2.5% agarose gel in 0.5X Tris-Borate EDTA (TBE) buffer for gel electrophoresis, and the amplicons were visualized under a gel documentation system (iBright1500, invitrogen, US).\u003c/p\u003e \u003cp\u003e \u003cb\u003eStatistical analysis\u003c/b\u003e \u003c/p\u003e \u003cp\u003eThe amplified products for marker analysis were scored visually based on the presence (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e) or absence (0) of the band for each marker. Each marker fragment was treated as a unit character and only unambiguous bands were scored. The total number of bands and the number of polymorphic bands were calculated as well as the polymorphic information content (PIC) which was calculated according to Anderson et al. (1993) using the following simplified formula:\u003c/p\u003e \u003cp\u003e\u003cimg src=\"data:image/png;base64,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\"\u003e\u003cbr\u003e\u003c/p\u003e\u003cp\u003ewhere \u003cem\u003ei\u003c/em\u003e\u0026thinsp;=\u0026thinsp;1 to n and P\u003csub\u003e\u003cem\u003eij\u003c/em\u003e\u003c/sub\u003e is the frequency of the j\u003csup\u003eth\u003c/sup\u003e allele for the i\u003csup\u003eth\u003c/sup\u003e band scored for a particular marker.\u003c/p\u003e \u003cp\u003eGenetic distances were estimated among the genotypes using simple matching coefficients by bootstrapping 1000 times and all the genotypes were clustered using a neighbor-joining method based on a dissimilarity matrix obtained using Darwin 6.0 (Perrier and Jacquemond, 2006) software. Principal coordinate analysis was performed to highlight the resolving power and the first three components were used to represent the genetic variability among the genotypes. PCOORDA was performed using the dissimilarity matrix constructed using Darwin version 6.0. The phenotypic data recorded was subjected to various statistical analyses.\u003c/p\u003e"},{"header":"3. Results and Discussion","content":"\u003cp\u003e \u003cb\u003ePhenotyping of fiber quality and yield related traits\u003c/b\u003e \u003c/p\u003e \u003cp\u003eThe population mean values of most of the traits were lower or no-par with the average trait values of local check varieties (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Wide data range has been recorded for all the fiber quality and yield-related traits. The frequency distribution for all the fiber quality and yield-related traits was depicted in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. All the recorded traits except PH and NBP seem to follow normal distribution, with skewness values lying in between \u0026minus;\u0026thinsp;1 and +\u0026thinsp;1 and kurtosis values between \u0026minus;\u0026thinsp;3 and +\u0026thinsp;3. This indicates that polygenic inheritance accounts for the majority of fiber quality and yield-related traits, which might be controlled by both major and minor genes.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eMean performance of various cotton genotypes for various fiber quality and yield-related traits\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\"\u0026plusmn;\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eTrait\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"4\" nameend=\"c5\" namest=\"c2\"\u003e \u003cp\u003eCheck varieties\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003eGenotypes\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLHDP 1\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNDLH 2005-4\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNDLH 1938\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eNDLH 2051-1\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eRange\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eMean\u0026thinsp;\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e\u0026plusmn;\u003c/span\u003e\u0026thinsp;SE\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003ePH\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e115.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e148.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e110.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e140.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e91.3\u0026ndash;197\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c7\"\u003e \u003cp\u003e117.86\u0026thinsp;\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e\u0026plusmn;\u003c/span\u003e\u0026thinsp;2.38\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eNM\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1\u0026ndash;3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c7\"\u003e \u003cp\u003e1.53\u0026thinsp;\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e\u0026plusmn;\u003c/span\u003e\u0026thinsp;0.09\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eNS\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e18.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e21.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e15.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e10-24.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c7\"\u003e \u003cp\u003e16.53\u0026thinsp;\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e\u0026plusmn;\u003c/span\u003e\u0026thinsp;0.43\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eNBP\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e53.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e43.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e56\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e47.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e22\u0026ndash;116\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c7\"\u003e \u003cp\u003e44.78\u0026thinsp;\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e\u0026plusmn;\u003c/span\u003e\u0026thinsp;2.1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eBW\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2.4\u0026ndash;4.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c7\"\u003e \u003cp\u003e3.41\u0026thinsp;\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e\u0026plusmn;\u003c/span\u003e\u0026thinsp;0.06\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSI\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e14.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e9.3\u0026ndash;17.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c7\"\u003e \u003cp\u003e13.08\u0026thinsp;\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e\u0026plusmn;\u003c/span\u003e\u0026thinsp;0.28\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eGOT\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e35.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e34.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e36.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e33.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e28.6\u0026ndash;39.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c7\"\u003e \u003cp\u003e34.94\u0026thinsp;\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e\u0026plusmn;\u003c/span\u003e\u0026thinsp;0.4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eUHML\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e28.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e30.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e27.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e27.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e25.4\u0026ndash;30.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c7\"\u003e \u003cp\u003e28.26\u0026thinsp;\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e\u0026plusmn;\u003c/span\u003e\u0026thinsp;0.2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eUI\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e82\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e82\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e82\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e82\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e81\u0026ndash;84\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c7\"\u003e \u003cp\u003e82.67\u0026thinsp;\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e\u0026plusmn;\u003c/span\u003e\u0026thinsp;0.11\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMIC\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2.9\u0026ndash;5.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c7\"\u003e \u003cp\u003e4.19\u0026thinsp;\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e\u0026plusmn;\u003c/span\u003e\u0026thinsp;0.07\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSTR\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e29.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e27.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e28.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e27.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e24.3\u0026ndash;32.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c7\"\u003e \u003cp\u003e28.47\u0026thinsp;\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e\u0026plusmn;\u003c/span\u003e\u0026thinsp;0.22\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eELG\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e6.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e5.3\u0026ndash;7.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c7\"\u003e \u003cp\u003e6.32\u0026thinsp;\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e\u0026plusmn;\u003c/span\u003e\u0026thinsp;0.06\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eLI\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3.3\u0026ndash;6.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c7\"\u003e \u003cp\u003e4.54\u0026thinsp;\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e\u0026plusmn;\u003c/span\u003e\u0026thinsp;0.09\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003ePH-Plant height (cm), NM-No. of Monopodia, NS-No. of Sympodia, NBP-No. of Bolls / Plant, BW-Boll weight (g), SI- Seed Index (g), GOT- Ginning Out Turn, UHML- Upper Half Mean Length (inches), UI- Fibre Uniformity (%), MIC-Micronaire, STR- Fibre Strength (g/tex), ELG- Elongation (or Fibre Elasticity (%)), LI-Lint Index.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003eCharacter associations\u003c/b\u003e \u003c/p\u003e \u003cp\u003eUnderstanding the relationships between fiber quality and yield-related traits can help researchers to make informed decisions about which traits to prioritize in their breeding programs. For instance, a positive correlation among fiber quality and yield suggests that plants with greater fiber quality are more likely to have higher yields. On the other hand, if negative correlations are found among these variables, it indicates trade-offs among fiber quality and yield-related traits, and that efforts to improve one of these traits may negatively impact the others. Correlation analysis for fiber quality and yield-related traits in F\u003csub\u003e5:6\u003c/sub\u003e generation was represented in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e.\u003c/p\u003e \u003cp\u003eAmong the yield-related traits, significant positive correlation was recorded between PH and NBP (0.292). Among the fiber quality traits, GOT showed significant negative correlations with UHML (-0.535) and UI (-0.485). Similar results were observed by Nikhil et. al., (2018). UHML recorded highly significant and positive correlations with UI (0.921), STR (0.41) and ELG (0.391). UI recorded significant positive correlations with STR (0.429) and ELG (0.415). STR recorded positive significant correlation with ELG (0.342) and ELG showed positive correlation with LI (0.46). Interestingly, inter correlations among fiber quality and yield traits has been recorded. PH showed positive correlation with ELG. NM recorded positive correlation with GOT and negative significant correlation with UHML. NBP showed highly significant and positive correlation with ELG. SI recorded highly significant and positive correlations with UHML, UI, ELG and LI.\u003c/p\u003e \u003cp\u003e \u003cb\u003eMolecular diversity analysis\u003c/b\u003e \u003c/p\u003e \u003cp\u003eKnowledge of the genetic dissimilarity among cotton germplasm lines/varieties is the foremost important step in any crop improvement program. Fifty-four cotton germplasm lines collected from All India Coordinated Cotton Improvement Trails of cotton from different parts of India were included and profiled for DNA polymorphism using 44 SSR markers sourced from the cotton marker database. Out of 44 SSR markers, 12 were polymorphic and amplified a total of 24 alleles. Polymorphic information content (PIC) of 12 markers ranged from 0.23 to 0.50 with an average PIC value of 0.44 indicating the potentialities of these markers for assessing molecular diversity. The highest PIC value was exhibited by the markers, DPL0068 (0.50), NAU2443 (0.50), and BNL3569 (0.50) followed by NAU3588 (0.48), MGHES0026 (0.47) and BNL3090 (0.46). The banding pattern of BNL3569 for 54 genotypes studied in the present investigation is presented in Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e. High PIC markers can efficiently distinguish the genotypes and are said to be more informative. Hence, these markers may be used for diversity studies, gene mapping-related studies, etc. The lowest PIC value was exhibited by BNL0569 (0.23) indicating less discriminatory power of this marker in distinguishing the genotypes. PIC provides a more accurate assessment of diversity and also signifies the discriminatory power of a locus, as it takes considers the number of expressed alleles and the relative frequencies of each allele. In the present study out of 12 markers screened, three markers were found to be highly informative with \u0026gt;\u0026thinsp;0.5 PIC. Thus, based on the PIC values, the markers used in the present study showed appreciable levels of polymorphism in the cotton genotypes. The average PIC value, in the present study (0.44) is more than the previous studies of Kumbhalkar et al. (2018) who reported an average PIC value of 0.21 and 0.35 for fiber length and fiber strength respectively, whereas higher average PIC value was reported by Abdellatif et al. (2012) with an average PIC value of 0.86. The effective number of alleles ranged from 2.00 (DPL0068) to 1.30 (BNL0569) with a mean of 1.75 and the Shannon index ranged from 0.70 (DPL0068) to 0.39 (BNL0569) with a mean of 0.61. The primers DPL0068, NAU2443, and BNL3569 possess high PIC values indicating their usefulness for discriminating germplasm lines, the effective number of alleles, and Shannon index values. SSR markers being codominant in inheritance are known to detect heterozygosity. Heterozygosity is a measure of genetic variation within a population. Estimated mean heterozygosity among the genotypes was found to be higher compared to those observed by Rakshit et al. (2010) and Tyagi et al. (2014). The higher heterozygosity for few markers might be due to the amplification of similar sequences in different genomic regions mainly due to duplications during evolution of tetraploid cotton.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eCotton being a complex allotetraploid, extensive heterogeneity and inherent residual heterozygosity is bound to exist (Zhang et al. 2013). Cryptic genetic variations at DNA level also contribute to higher heterozygosity (Rakshit et al. 2011) apart from limited self pollinations and rare pollen contamination. The gene diversity is the probability of two randomly chosen alleles being different from a population. The gene diversity ranged from 0.02 (NAU2691) to 0.64 (TMB2295). The mean gene diversity value of 0.28 was observed among 48 tetraploid cultivars.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eCharacteristics of the amplification products with polymorphic SSR primer\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eS.No.\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMarker\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePIC\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e*na\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e*ne\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e*I\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBNL0569\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.39\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMGHES0026\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.47\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.78\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.63\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBNL1604\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.80\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.64\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e4.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBNL3569\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.97\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.69\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e5.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBNL3410\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.59\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e6.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBNL3090\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.46\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.59\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e7.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMGHES006\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.79\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.63\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e8.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNAU3467\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.56\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e9.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNAU3588\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.48\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.82\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.64\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e10.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNAU2443\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.96\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.68\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e11.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTMB1268\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.56\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e12.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDPL0068\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.70\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eMinimum\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e0.23\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e1.30\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e0.39\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eMaximum\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e0.50\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e2.00\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e0.70\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eMean\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e0.44\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e2.00\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e1.75\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e0.61\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e* na\u0026thinsp;=\u0026thinsp;Observed number of alleles\u003c/p\u003e \u003cp\u003e* ne\u0026thinsp;=\u0026thinsp;Effective number of alleles [Kimura and Crow (1964)]\u003c/p\u003e \u003cp\u003e* I\u0026thinsp;=\u0026thinsp;Shannon's Information index [Lewontin (1972)]\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eTest of significance of mean phenotypic values of different alleles of the polymorphic markers related to various fibre quality traits using student\u0026rsquo;s t-test revealed the alleles of the marker BNL0569 showing significant differences in between them for MIC and STR as reported by Yu et al. (2013) (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). Similarly, the alleles of the marker BNL1604 showed significant differences in between them for the trait UHML as reported by Darmanov et al. (2022).\u003c/p\u003e \u003cp\u003e \u003cb\u003eClassification of cotton genotypes for fiber quality traits using cluster analysis\u003c/b\u003e \u003c/p\u003e \u003cp\u003eCluster analysis and construction of a dendrogram for 54 genotypes were done by neighbor-joining method based on dissimilarity matrix using Software Darwin 6.0 and the results are presented in Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e. The 54genotypes were grouped into five major clusters. Cluster I comprising of 5 genotypes, further divided into two sub-clusters (IA, IB) (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e). Subcluster IA comprised of 2 genotypes, whereas Cluster IB comprises of 3 genotypes. Cluster II also comprises of 13 genotypes, further divided into two sub clusters (IIA, IIB). Sub cluster IIA comprised of 6 genotypes, whereas Cluster IIB comprises of 7 genotypes. Cluster III comprises of 14 genotypes, further divided into two sub clusters (IIIA, IIIB). Sub cluster IIIA comprised of 5 genotypes, whereas Cluster IIIB comprises of 9 genotypes. Cluster IV had of 11 genotypes, further divided into two sub-clusters (IVA, IVB). Sub-cluster IVA comprises 4 genotypes whereas sub-cluster IVB comprises of genotypes. Cluster V had 11 genotypes, further divided into two sub-clusters (VA, VB). Sub-cluster VA comprises gene genotypes and sub-cluster VB comprises 10 genotypes.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eGrouping of genotypes into clusters based on molecular diversity\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eS. No.\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCluster\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eName of the sub-cluster\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNumber of genotypes\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eGenotypes\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e1.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eIA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eGP 66 and GP 2.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eIB\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eGP 65, GP 106 and GP 109.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e2.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eII\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eIIA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eGP 11, GP 20, GP 28, GP 32, GP 37 and GP 45.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eIIB\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eGP 5, GP 6, GP 19, GP 46, GP 49, GP 52 and LHDP 1.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e3.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eIII\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eIIIA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eGP 90, GP 92, GP 95, GP 139 and NDLH 2005-4.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eIIIB\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eGP 99, GP 101, GP 105, GP 123, GP 129, GP 130, GP 131, GP 140 and GP 144.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e4.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eIV\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eIVA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eGP 42, GP 56, GP 61 and NDLH 1938.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eIVB\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eGP 55, GP 73, GP 74, GP 76, GP 81, GP 86 and GP 88.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e5.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eV\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eVA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eGP 121.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eVB\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eGP 9, GP 145, GP 148, GP 151, GP 152, GP 155, GP 157, GP 158, GP 159 and NDLH 2051-1.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003ePrincipal Coordinate Analysis\u003c/b\u003e \u003c/p\u003e \u003cp\u003eThe principal coordinate analysis demonstrated that the 54 cotton genotypes can be separated distinctly from each other (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e). A two-dimensional scatter plot involving all the genotypes shown that, the first three principal coordinates explained 22.46%, 16.91% and 10.00% of the total variation of 63.21% as shown in Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003ePrincipal coordinate analysis for various fiber quality and yield-related traits in cotton\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAxis\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEigen Value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eInertia (%)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.0724\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e22.46\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.0545\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e16.91\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.0322\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e10.00\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e4.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.0272\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e8.45\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e5.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.0173\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e5.39\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthical Approval\u003c/strong\u003e: Not applicable\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e: All the authors hereby declare that there is no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors' contributions\u003c/strong\u003e:\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eRani Chapara: Designed and executed the experiment and written the manuscript of the article.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eK.V. Siva Reddy: Helped in execution of the experiment in field.\u003c/p\u003e\n\u003cp\u003eM. Sudha Rani: Overall supervised the experiment and guided in writing of the script of the article.\u003c/p\u003e\n\u003cp\u003eK. Sudhamani: Analyzed the data using SPSS software.\u003c/p\u003e\n\u003cp\u003eA.D.G. Diana Grace: Helped in crop pest management during the crop tenure\u003c/p\u003e\n\u003cp\u003eN. Venkata Lakshmi: Helped in crop agronomy management during the crop tenure.\u003c/p\u003e\n\u003cp\u003eB. Sreekanth: Helped in crop nutrition management\u003c/p\u003e\n\u003cp\u003eB. Sree Lakshmi: Overall execution of the experiment and crop disease management in cotton crop\u003c/p\u003e\n\u003cp\u003eV. Roja: Scientist (Agricultural Biotechnology): \u0026nbsp;Helped in molecular markers analysis.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding: \u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll the authors acknowledge the Director of Research of Acharya N.G. Ranga Agricultural University and Associate Director Research, RARS, Lam, Guntur for allocation of resources for the project execution.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials:\u0026nbsp;\u003c/strong\u003eThe planting material and laboratory facility was from RARS, Lam, Guntur.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eAbdellatif, K. F., Khidr, Y. A., El-Mansy, Y. M., El-Lawendey, M. M., \u0026amp; Soliman, Y. A. (2012). Molecular diversity of Egyptian cotton (Gossypium barbadense L.) and its relation to varietal development. Journal of Crop Science and Biotechnology. 15:93-99.\u003c/li\u003e\n\u003cli\u003eAnderson, A., Churchill, G. A., Autrique, J. E., Tanksley, S. D., \u0026amp; Sorrells, M. E. (1993). Optimizing parental selection for genetic linkage maps. Genome. 36(1):181-186.\u003c/li\u003e\n\u003cli\u003eBegna, T., \u0026amp; Begna, T. (2021). Role and economic importance of crop genetic diversity in food security. International Journal of Agricultural Science and Food Technology. 7(1):164-169.\u003c/li\u003e\n\u003cli\u003eBeura, K., \u0026amp; Rakshit, A. (2011). Effect of Bt cotton on nutrient dynamics under varied soil type. Italian Journal of Agronomy. 6(4):e35-e35.\u003c/li\u003e\n\u003cli\u003eChen, Z. J., Scheffler, B. E., Dennis, E., Triplett, B. A., Zhang, T., Guo, W \u0026amp; Paterson, A. H. (2007). Toward sequencing cotton (Gossypium) genomes. Plant physiology. 145(4):1303-1310.\u003c/li\u003e\n\u003cli\u003eDarmanov, M. M., Makamov, A. K., Ayubov, M. S., Khusenov, N. N., Buriev, Z. T., Shermatov, S. E., ... \u0026amp; Abdurakhmonov, I. Y. (2022). Development of superior fibre quality upland cotton cultivar series \u0026lsquo;Ravnaq\u0026rsquo;using marker-assisted selection. Frontiers in Plant Science. 13:906472.\u003c/li\u003e\n\u003cli\u003eGovindaraj, M., Vetriventhan, M., \u0026amp; Srinivasan, M. (2015). Importance of genetic diversity assessment in crop plants and its recent advances: an overview of its analytical perspectives. Genetics research international, 2015.\u003c/li\u003e\n\u003cli\u003eKimura M, Crow JF (1964). The number of alleles that can be maintained in a finite population. Genetics, 49: 725-738.\u003c/li\u003e\n\u003cli\u003eKumbhalkar, H. B., Gawande, V. L., Gahukar, S. J., Waghmare, V. N., Mawle, S. R., \u0026amp; Ingle, K. P. (2018). Molecular Profiling of Cotton Genotypes for Fibre Properties Using Diagnostic Set of Microsatellite (SSR) Markers. Current Journal of Applied Science and Technology, 29(3): 1-16.\u003c/li\u003e\n\u003cli\u003eKushanov, F. N., Turaev, O. S., Ernazarova, D. K., Gapparov, B. M., Oripova, B. B., Kudratova, M. K., Abdurakhmonov, I. Y. (2021). Genetic diversity, QTL mapping, and marker-assisted selection technology in cotton (Gossypium spp.). Frontiers in plant science, 12:779386.\u003c/li\u003e\n\u003cli\u003eLewontin RC (1972). Testing the theory of natural selection. Nature, 236: 181-182.\u003c/li\u003e\n\u003cli\u003eMurray, M. G., \u0026amp; Thompson, W. (1980). Rapid isolation of high molecular weight plant DNA. Nucleic acids research, 8(19): 4321-4326.\u003c/li\u003e\n\u003cli\u003eNikhil, P. G., Nidagundi, J. M., \u0026amp; Hugar, A. (2018). Correlation and path analysis studies of yield and fibre quality traits in cotton (Gossypium hirsutum L.). Journal of Pharmacognosy and Phytochemistry, 7(5): 2596-2599.\u003c/li\u003e\n\u003cli\u003ePerrier, X., \u0026amp; Jacquemoud-Collet, J. P. (2006). DARwin software: Dissimilarity analysis and representation for windows. Website http://darwin. cirad. fr/darwin [accessed 1 March 2013].\u003c/li\u003e\n\u003cli\u003eRakshit, A., Rakshit, S., Singh, J., Chopra, S. K., Balyan, H. S., Gupta, P. K., \u0026amp; Bhat, S. R. (2010). Association of AFLP and SSR markers with agronomic and fibre quality traits in Gossypium hirsutum L. Journal of genetics, 89: 155-162.\u003c/li\u003e\n\u003cli\u003eSanthy, V., Meshram, M., Santosh, H. B., \u0026amp; Kranthi, K. R. (2019). Molecular diversity analysis and DNA fingerprinting of cotton varieties of India. Indian journal of genetics and plant breeding, 79(04):719\u0026ndash;725. \u003c/li\u003e\n\u003cli\u003eShi, J., Zhang, Y., Wang, N., Xu, Q., Huo, F., Liu, X., \u0026amp; Yan, G. (2023). Genetic diversity and population structure analysis of upland cotton (Gossypium hirsutum L.) germplasm in China based on SSR markers. Genetic Resources and Crop Evolution: 1-12.\u003c/li\u003e\n\u003cli\u003eSun, Z., Wang, X., Liu, Z., Gu, Q., Zhang, Y., Li, Z. \u0026amp; Ma, Z. (2019). Evaluation of the genetic diversity of fibre quality traits in upland cotton (Gossypium hirsutum L.) inferred from phenotypic variations. Journal of Cotton Research, 2:1-8.\u003c/li\u003e\n\u003cli\u003eSwarup, S., Cargill, E. J., Crosby, K., Flagel, L., Kniskern, J., \u0026amp; Glenn, K. C. (2021). Genetic diversity is indispensable for plant breeding to improve crops. Crop Science, 61(2):839-852. \u003c/li\u003e\n\u003cli\u003eTownsend, T. (2020). World natural fibre production and employment. In Handbook of natural fibres (pp. 15-36). Woodhead Publishing.\u003c/li\u003e\n\u003cli\u003eTyagi, P., Gore, M. A., Bowman, D. T., Campbell, B. T., Udall, J. A., \u0026amp; Kuraparthy, V. (2014). Genetic diversity and population structure in the US Upland cotton (Gossypium hirsutum L.). Theoretical and Applied Genetics, 127: 283-295.\u003c/li\u003e\n\u003cli\u003eYu, J., Zhang, K., Li, S., Yu, S., Zhai, H., Wu, M. \u0026amp; Zhang, J. (2013). Mapping quantitative trait loci for lint yield and fiber quality across environments in a Gossypium hirsutum \u0026times; Gossypium barbadense backcross inbred line population. Theoretical and Applied Genetics, 126:275-287.\u003c/li\u003e\n\u003cli\u003eYu, Z. H., \u0026amp; Kohel, R. J. (2001). Cotton genome research in the United States. Genetic improvement of Cotton. Science Publishers, inc., enfield, NH, 103-121.\u003c/li\u003e\n\u003cli\u003eZhang, Y. C., Kuang, M., Yang, W. H., Xu, H. X., Zhou, D. Y., Wang, Y. Q. \u0026amp; Wang, F. (2013). Construction of a primary DNA fingerprint database for cotton cultivars. Genetics and Molecular Research, 12(2):1897-1906.\u003c/li\u003e\n\u003cli\u003eZhao, Y., Wang, H., Chen, W., Li, Y., Gong, H., Sang, X. \u0026amp; Zeng, F. (2015). Genetic diversity and population structure of elite cotton (Gossypium hirsutum L.) germplasm revealed by SSR markers. Plant systematics and evolution, 301:327-336.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Cotton, Cluster analysis, Genetic diversity, Molecular diversity, SSR markers, PCOORDA","lastPublishedDoi":"10.21203/rs.3.rs-3327039/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-3327039/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eAn attempt has been made to assess molecular genetic diversity at Regional Agricultural Research Station, Lam, Guntur using fifty-four tetraploid cotton (\u003cem\u003eGossypium hirsutum\u003c/em\u003e L.) genotypes with forty-four SSR markers related to various fibre quality traits. A total number of 24 alleles, with an average of 1.75 effective alleles per locus, were generated by these markers. The polymorphism information content (PIC) value ranged from 0.23 to 0.50 with a mean of 0.44 indicating lesser variation for various fibre quality traits within the investigated material. Using principal coordinate analysis (PCOORDA), cotton genotypes were separated by the first three principal coordinates (PC1, PC2, and PC3) accounting for 11.5, 8.6, and 7.2% of the total genetic variance, respectively. The SSR markers revealed a genetic similarity of 63.21 among the varieties studied.\u003c/p\u003e","manuscriptTitle":"Molecular genetic diversity in relation to the quantitative and qualitative (fiber quality) traits in upland cotton (Gossypium hirsutum L.)","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2023-09-12 14:56:59","doi":"10.21203/rs.3.rs-3327039/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"b0f8e0c3-066d-4c3f-851b-806fcb745451","owner":[],"postedDate":"September 12th, 2023","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2024-08-19T16:27:56+00:00","versionOfRecord":[],"versionCreatedAt":"2023-09-12 14:56:59","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-3327039","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-3327039","identity":"rs-3327039","version":["v1"]},"buildId":"-HB7Z8yhvgn0wM9Nzuekk","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}