Research Articles
Vol. 12 No. 3 (2025)
Decoding genetic diversity and biochemical relationships in southern Kerala’s traditional Mango varieties using comprehensive marker analysis
Farming Systems Research Station, Sadanandapuram, Kottarakkara, Kerala Agricultural University, Kollam 691 531 Kerala, India
Farming Systems Research Station, Sadanandapuram, Kottarakkara, Kerala Agricultural University, Kollam 691 531 Kerala, India
Division of Plant Physiology, Indian Agricultural Research Institute, New Delhi 110 012, India
Farming Systems Research Station, Sadanandapuram, Kottarakkara, Kerala Agricultural University, Kollam 691 531 Kerala, India
Farming Systems Research Station, Sadanandapuram, Kottarakkara, Kerala Agricultural University, Kollam 691 531 Kerala, India
Abstract
This study explored the genetic diversity and relatedness of 34 mango accessions using 34 SSR markers, which were highly polymorphic and informative. The number of alleles per locus ranged from 2 to 7, with an average of 2.85, comparable to previous studies. The average PIC value was 0.502, underscoring the markers' suitability for genetic diversity assessment. The heterozygosity value averaged 0.473, indicating substantial genetic variability among the accessions. Unique fingerprints for five accessions were identified using seven SSR loci, demonstrating the markers' effectiveness in generating distinct genetic profiles. Additionally, specific SSR markers were significantly associated with biochemical traits, including glutamate synthase, catalase and peroxidase activities, highlighting their potential in trait-linked marker-assisted selection (MAS). Minimal Marker software indicated that 12 marker sets could distinguish all accessions, enhancing genotyping efficiency. Genome-wide association analysis found significant correlations between biochemical traits and specific SSR markers, with glutamate synthase, catalase and peroxidase activities associated with multiple SSR markers. These findings highlight the potential of SSR markers in identifying genomic regions linked to critical biochemical traits, aiding marker-assisted selection and breeding for stress tolerance and fruit quality. Genetic structure analysis revealed seven distinct clusters among the accessions, supported by UPGMA and FCA analyses, indicating moderate genetic differentiation. Jaccard pairwise similarity coefficients showed modest genetic diversity, consistent with previous studies. The UPGMA dendrogram categorized the accessions into seven major clusters, providing insights into their genetic relationships. This research offers valuable information for the genetic identification, characterization and conservation of mango accessions, supporting germplasm management, breeding programs and phylogenetic studies. These findings provide a foundation for marker-assisted breeding programs targeting stress tolerance and fruit quality traits in mango. The study also supports conservation efforts for Kerala’s traditional mango germplasm by identifying genetically unique accessions.
References
- 1. Mukherjee SK. Origin of mango (Mangifera indica). Econ Bot. 1972;26(3):26-33. https://doi.org/10.1007/BF02861039
- 2. Ferwerda FP, Wit F. Outlines of perennial crop breeding in the tropics. Misc Pap. 1969;2(1):56-62.
- 3. Degani C, Cohen M, El-Batsri R, Gazit S. PGI isozyme diversity and its genetic control in mango. HortScience. 2019;27(3):252. https://doi.org/10.21273/hortsci.27.3.252
- 4. Lakshminarayana S. Mango. In: Nagy S, Shaw PE, editors. Tropical and subtropical fruits. AVI Publ. 1980;1(2):184-97.
- 5. Karihaloo JL, Dwivedi YK, Archak S, Gaikwad AB. Analysis of genetic diversity of Indian mango cultivars using RAPD markers. J Hortic Sci Biotechnol. 2003;78(3):106-12. https://doi.org/10.1080/14620316.2003.11511619
- 6. Viruel MA, Escribano P, Barbieri M, Ferri M, Hormaza JI. Fingerprinting, embryo type and geographic differentiation in mango (Mangifera indica L., Anacardiaceae) with microsatellites. Mol Breed. 2005;15(4):383-93. https://doi.org/10.1007/s11032-004-7982-x
- 7. Schnell RJ, Olano CT, Quintanilla WE, Meerow AW. Isolation and characterization of 15 microsatellite loci from mango (Mangifera indica L.) and cross-species amplification in closely related taxa. Mol Ecol Notes. 2005;5(3):625-7. https://doi.org/10.1111/j.1471-8286.2005.01018.x
- 8. Ravishankar KV, Bommisetty P, Bajpai A, Srivastava N, Mani BH, Vasugi C, et al. Genetic diversity and population structure analysis of mango (Mangifera indica) cultivars assessed by microsatellite markers. Trees. 2015;29(3):775-83. https://doi.org/10.1007/s00468-015-1155-x
- 9. Singh R, Iquebal MA, Mishra CN, Jaiswal S, Kumar D, Raghav N, et al. Development of model web-server for crop variety identification using throughput SNP genotyping data. Sci Rep. 2019. https://doi.org/10.1038/s41598-019-41204-2
- 10. Kumari R, Wankhede DP, Bajpai A, Maurya A, Prasad K, Gautam D, et al. Genome wide identification and characterization of microsatellite markers in black pepper (Piper nigrum): A valuable resource for boosting genomics applications. PLoS ONE. 2019;3(2):156-63. https://doi.org/10.1371/journal.pone.0226002
- 11. Hadwan MH. Simple spectrophotometric assay for measuring catalase activity in biological tissues. BMC Biochem. 2018;19(1):190-8. https://doi.org/10.1186/s12858-018-0097-5
- 12. Senthilkumar M, Amaresan N, Sankaranarayanan A. Estimation of glutamine synthetase (GS), glutamate synthase (GOGAT), and glucose dehydrogenase. Mol Ecol Notes. 2021;4(1):65-76. https://doi.org/10.1007/978-1-0716-1080-0_7
- 13. Oberbacher MF, Vines HM. Spectrophotometric assay of ascorbic acid oxidase. Nature. 1963;197(4873):186-93. https://doi.org/10.1038/1971203a0
- 14. Maehly AC, Chance B. The assay of catalases and peroxidases. Methods Biochem Anal. 1954;3(2):224-32. https://doi.org/10.1002/9780470110171.ch14
- 15. Beauchamp C, Fridovich I. Superoxide dismutase: Improved assays and an assay applicable to acrylamide gels. Anal Biochem. 1971;44(1):320-7. https://doi.org/10.1016/0003-2697(71)90370-8
- 16. Amiryousefi A, Hyvönen J, Poczai P. iMEC: Online marker efficiency calculator. Appl Plant Sci. 2018;6(6):118-25. https://doi.org/10.1002/aps3.1159
- 17. Peakall R, Smouse PE. GenALEx 6.5: Genetic analysis in Excel. Population genetic software for teaching and research—an update. Bioinformatics. 2012;28(19):123-35. https://doi.org/10.1093/bioinformatics/bts460
- 18. Botstein D, White RL, Skolnick M, Davis RW. Construction of a genetic linkage map in man using restriction fragment length polymorphisms. Am J Hum Genet. 1980;32(3):167-75.
- 19. Rohlf FJ. NTSYS-pc: numerical taxonomy and multivariate analysis system. Appl Biostat. 1998;2(1):187-94.
- 20. Fujii H, Ogata T, Shimada T, Endo T, Iketani H, Shimizu T, et al. Minimal marker: An algorithm and computer program for the identification of minimal sets of discriminating DNA markers for efficient variety identification. J Bioinform Comput Biol. 2013;11(2):166-72. https://doi.org/10.1142/S0219720012500229
- 21. Yang X, Xu Y, Shah T, Li H, Han Z, Li J, et al. Comparison of SSRs and SNPs in assessment of genetic relatedness in maize. Genetica. 2011;139(8):1045-54. https://doi.org/10.1007/s10709-011-9600-9
- 22. Geleta LF, Labuschagne MT, Viljoen CD. Genetic variability in pepper (Capsicum annuum L.) estimated by morphological data and amplified fragment length polymorphism markers. Biodivers Conserv. 2005;14(10):2361-75. https://doi.org/10.1007/s10531-004-1575-z
- 23. Archak S, Karihaloo JL, Jain A. RAPD markers reveal narrowing genetic base of Indian cashew germplasm. BMC Plant Biol. 2003;3:10. https://doi.org/10.1186/1471-2229-3-10
- 24. Moreira PA, Ferreira ME. Genetic diversity analysis of Mangifera indica L. accessions using microsatellite markers. Trop Genet. 2020;1(1):47-55. https://doi.org/10.1007/s11295-020-01457-4
- 25. Yamanaka S, Hasegawa K, Ishimaru M. Analysis of genetic diversity and structure in Mangifera indica using SSR markers. Plant Genet Resour. 2021;19(2):83-90. https://doi.org/10.1017/S1479262120000391
- 26. Lalremruata J, Lalhruaitluanga H, Shivanna MB, Murthy HN, Paek KY. Assessment of genetic diversity in mango (Mangifera indica L.) cultivars of North East India using microsatellite markers. Biotechnol Biotechnol Equip. 2019;33(1):754-60. https://doi.org/10.1080/13102818.2019.1613202
- 27. Warburton ML, Reif JC, Frisch M, Bohn M, Bedoya C, Xia XC, et al. Genetic diversity in CIMMYT non-temperate maize germplasm: Landraces, open pollinated varieties, and inbred lines. Crop Sci. 2008;48(2):617-24. https://doi.org/10.2135/cropsci2007.02.0081
- 28. Gaikwad AB, Karihaloo JL. Genetic diversity in Indian mango landraces as revealed by RAPD markers. J Hortic Sci Biotechnol. 2007;82(6):929-33. https://doi.org/10.1080/14620316.2007.11512310
- 29. Evans J, Szymczyk M, Brummer EC, Nipper R. Characterizing population structure and genetic diversity in US alfalfa germplasm using SSR markers. Mol Breed. 2012;30(2):479-88. https://doi.org/10.1007/s11032-011-9641-9
- 30. Huang H, Zhang R, Dong L, Han Q, Zhou Z, Liu Z, et al. DNA fingerprinting of mango (Mangifera indica L.) cultivars using SSR markers. Acta Hortic. 2014;1024:113-9. https://doi.org/10.17660/ActaHortic.2014.1024.14
- 31. Yamanaka S, Hosaka F, Matsumura M, Onoue-Makishi Y, Nashima K, Urasaki N, et al. Genetic diversity and relatedness of mango cultivars assessed by SSR markers. Breeding Sci. 2019;69(2):231-9. https://doi.org/10.1270/jsbbs.18204
- 32. Kumar M, Yadav V, Tuteja N, Johri AK. Antioxidant enzyme activities in maize plants colonized with Piriformospora indica. Microbio. 2009;155(3):52-63. https://doi.org/10.1099/mic.0.019869-0
- 33. Kumar S, Chagné D, Bink MCAM, Volz RK, Whitworth C, Carlisle C. Genomic selection for fruit quality traits in apple (Malus domestica Borkh.). PLoS ONE. 2012;7(5):308-17. https://doi.org/10.1371/journal.pone.0036674
- 34. Kostick SA, Bernardo R, Luby JJ. Genomewide selection for fruit quality traits in apple: breeding insights gained from prediction and postdiction. Hortic Res. 2023;10(6):122-9. https://doi.org/10.1093/hr/uhad088
- 35. Sharma P, Jha AB, Dubey RS. Oxidative stress and antioxidative defense systems in plants growing under abiotic stresses. Handbook Plant Crop Stress. 2016;2(1):144-55. https://doi.org/10.1201/9781351104609-7
- 36. Sallam M, Ghazy A, Al-Doss A, Al-Ashkar I. Combining genetic and phenotypic analyses for detecting bread wheat genotypes of drought tolerance through multivariate analysis techniques. Life. 2024;14(2):166-73. https://doi.org/10.3390/life14020183
- 37. Varalakshmi S, Sahoo S, Singh NK, Pareek N, Garkoti P, Senthilkumar V, et al. Marker–trait association for protein content among maize wild accessions and coix using SSR markers. Agron. 2023;13(8):27-36. https://doi.org/10.3390/agronomy13082138
- 38. Bajpai A, Kumari R, Wankhede DP, Maurya A, Prasad K, Gautam D, et al. Genome wide identification and characterization of microsatellite markers in black pepper (Piper nigrum): a valuable resource for boosting genomics applications. PLoS ONE. 2019;3(2):144-55. https://doi.org/10.1371/journal.pone.0226002
- 39. Jombart T, Devillard S, Balloux F. Discriminant analysis of principal components: a new method for the analysis of genetically structured populations. BMC Genet. 2010;2(1):11-23. https://doi.org/10.1186/1471-2156-11-94
- 40. Laikre L, Schwartz MK, Waples RS, Ryman N. Compromising genetic diversity in the wild: unmonitored large-scale release of plants and animals. Trends Ecol Evol. 2010;25(9):82-91. https://doi.org/10.1016/j.tree.2010.06.013
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