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image of Phenomorphological Characterization and Genetic Diversity Assessment of Grain and Vegetable Soybean (Glycine max. (L.) Merrill) Lines for Breeding Advancements

Abstract

Introduction

Vegetable soybean is emerging as a valuable crop due to its nutritional and economic benefits. However, its genetic and phenotypic diversity remains less explored compared to grain-type soybeans. This study aimed to evaluate the breeding potential of vegetable soybeans through a comparative analysis of grain- and vegetable-type genotypes.

Methods

Ten soybean genotypes (six vegetable-type and four grain-type) were characterized using phenotypic, reproductive, and genetic trait evaluations. Observations included growth stages, pod traits, and yield-related characteristics. Statistical analyses such as ANOVA, GCV, heritability estimates, Principal Component Analysis (PCA), hierarchical and Tocher’s clustering, and Simple Sequence Repeat (SSR) marker analysis were conducted to assess trait variability and genetic diversity.

Results

Significant genotype-specific variation was observed. EC892880 matured fastest (R1-R7 in 37-57 days), while RKS-18 took 112 days. EC892882 exhibited the longest pod length (5.63 cm), and EC892880 had the highest number of pods per cluster (6.93). High genetic control was noted for days to 50% flowering (GCV: 27.42%, heritability: 99.89%) and test weight (GCV: 25.38%, heritability: 99.74%). PCA revealed that days to pod setting and maturity (R7) were the primary contributors to phenotypic variation, with PC1 and PC2 accounting for 84.5% of the total variance. Tocher’s analysis showed the highest genetic divergence (D2 = 28,292.49) between Clusters II and III. Among 45 SSR markers, 41 were amplified but showed no polymorphism.

Discussion

The results highlight substantial phenotypic diversity among genotypes, especially in maturity duration and yield-related traits, with some traits under strong genetic control. However, the lack of SSR polymorphism suggests limited molecular diversity, indicating the need for more robust genomic tools. Vegetable-type soybeans showed high intra-group similarity, which may limit genetic gain unless broader diversity is introduced.

Conclusion

This study identifies key traits and diverse genotypes suitable for targeted breeding in vegetable soybeans. The findings emphasize the potential of phenotypic selection and highlight the urgent need for enhanced genomic marker development to facilitate molecular breeding efforts in vegetable soybean improvement.

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2025-06-10
2025-09-29
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References

  1. Singh G. Plant systematics: An integrated approach. CRC Press 2019 10.1201/9780429289521
    [Google Scholar]
  2. Hymowitz T. Shurtleff W.R. Debunking soybean Myths and legends in the historical and popular literature. Crop Sci. 2005 45 2 473 476 10.2135/cropsci2005.0473
    [Google Scholar]
  3. Kim M.Y. Van K. Kang Y.J. Kim K.H. Lee S.H. Tracing soybean domestication history: From nucleotide to genome. Breed. Sci. 2012 61 5 445 452 10.1270/jsbbs.61.445 23136484
    [Google Scholar]
  4. Wen Z. Ding Y. Zhao T. Gai J. Genetic diversity and peculiarity of annual wild soybean (G. soja Sieb. et Zucc.) from various eco-regions in China. Theor. Appl. Genet. 2009 119 2 371 381 10.1007/s00122‑009‑1045‑y 19449177
    [Google Scholar]
  5. Smýkal P. Coyne C.J. Ambrose M.J. Legume crops phylogeny and genetic diversity for science and breeding. Crit. Rev. Plant Sci. 2015 34 1-3 43 104 10.1080/07352689.2014.897904
    [Google Scholar]
  6. Hymowitz T. Collins F.I. Panczner J. Walker W.M. Relationship between the content of oil, protein, and sugar in soybean seed 1. Agron. J. 1972 64 5 613 616 10.2134/agronj1972.00021962006400050019x
    [Google Scholar]
  7. Gizlice Z. Carter T.E. Burton J.W. Genetic diversity in North American Soybean: I. Multivariate analysis of founding stock and relation to coefficient of parentage. Crop Sci. 1993 33 3 614 620 10.2135/cropsci1993.0011183X003300030038x
    [Google Scholar]
  8. Kofsky J. Zhang H. Song B.H. The untapped genetic reservoir: The past, current, and future applications of the wild soybean (Glycine soja). Front Plant Sci 2018 9 949 10.3389/fpls.2018.00949 30038633
    [Google Scholar]
  9. Nair R.M. Boddepalli V.N. Yan M.R. Global status of vegetable soybean. Plants 2023 12 3 609 10.3390/plants12030609 36771696
    [Google Scholar]
  10. Agarwal D.K. Billore S.D. Sharma A.N. Dupare B.U. Srivastava S.K. Soybean: Introduction, improvement, and utilization in India—Problems and prospects. Agric. Res. 2013 2 4 293 300 10.1007/s40003‑013‑0088‑0
    [Google Scholar]
  11. Sahoo S. Singha C. Govind A. Moghimi A. Review of climate-resilient agriculture for ensuring food security: Sustainability opportunities and challenges of India. Environmen Sustain Indicator 2025 25 100544 10.1016/j.indic.2024.100544
    [Google Scholar]
  12. Bhattacharyya R. Ghosh B. Mishra P. Soil Degradation in India: Challenges and Potential Solutions. Sustainability (Basel) 2015 7 4 3528 3570 10.3390/su7043528
    [Google Scholar]
  13. Das U. Chandramouli L. Uttarkar A. Kumar J. Niranjan V. Discovery of natural compounds as novel FMS-like tyrosine kinase-3 (FLT3) therapeutic inhibitors for the treatment of acute myeloid leukemia: An in-silico approach. Aspect Molecular Medicine 2025 5 100058 10.1016/j.amolm.2024.100058
    [Google Scholar]
  14. Keatinge J.D.H. Easdown W.J. Yang R.Y. Chadha M.L. Shanmugasundaram S. Overcoming chronic malnutrition in a future warming world: The key importance of mungbean and vegetable soybean. Euphytica 2011 180 1 129 141 10.1007/s10681‑011‑0401‑6
    [Google Scholar]
  15. Kebede E. Contribution, utilization, and improvement of legumes-driven biological nitrogen fixation in agricultural systems. Front. Sustain. Food Syst. 2021 5 767998 10.3389/fsufs.2021.767998
    [Google Scholar]
  16. Ohyama T. Tewari K. Ishikawa S. Role of nitrogen on growth and seed yield of soybean and a new fertilization technique to promote nitrogen fixation and seed yield. In:Kasai M, Ed Soybean - The Basis of Yield, Biomass and Productivity. InTechOpen. Kasai M. 2017 10.5772/66743
    [Google Scholar]
  17. Qin P. Wang T. Luo Y. A review on plant-based proteins from soybean: Health benefits and soy product development. J Agriculture Food Research 2022 7 100265 10.1016/j.jafr.2021.100265
    [Google Scholar]
  18. Messina M. Shearer G. Petersen K. Soybean oil lowers circulating cholesterol levels and coronary heart disease risk, and has no effect on markers of inflammation and oxidation. Nutrition 2021 89 111343 10.1016/j.nut.2021.111343 34171740
    [Google Scholar]
  19. Agyenim-Boateng K.G. Zhang S. Zhang S. The nutritional composition of the vegetable soybean (maodou) and its potential in combatting malnutrition. Front. Nutr. 2023 9 1034115 10.3389/fnut.2022.1034115 36687682
    [Google Scholar]
  20. Shanmugasundaram S. Yan M.R. Vegetable soybean. In: Guriqbal Singh GS, Ed. The soybean: Botany, production and uses. Guriqbal Singh G.S. Wallingford CABI 2010 427 460 10.1079/9781845936440.0427
    [Google Scholar]
  21. Satyavathi C.T. Karamakar P. Bharadwaj C. Tiwari S. Ancestral analysis of soybean varieties: An overview. Indian J. Genet. Plant Breed. 2001 63 87 88
    [Google Scholar]
  22. Young G. Mebrahtu T. Johnson J. Acceptability of green soybeans as a vegetable entity. Plant Foods Hum. Nutr. 2000 55 4 323 333 10.1023/A:1008164925103 11086875
    [Google Scholar]
  23. Wang L. Guan R. Zhangxiong L. Chang R. Qiu L. Genetic diversity of chinese cultivated soybean revealed by SSR markers. Crop Sci. 2006 46 3 1032 1038 10.2135/cropsci2005.0051
    [Google Scholar]
  24. Mahoussi K.A.D. Eric E.A. Symphorien A. Vegetable soybean, edamame: Research, production, utilization and analysis of its adoption in Sub-Saharan Africa. J. Hortic. For. 2020 12 1 1 12 10.5897/JHF2019.0604
    [Google Scholar]
  25. Saxena K.B. Kumar R.V. Sultana R. Quality nutrition through pigeonpea: A review. Health (Irvine Calif.) 2010 2 11 1335 1344 10.4236/health.2010.211199
    [Google Scholar]
  26. Das U. Banerjee S. Sarkar M. Bibliometric analysis of circular RNA cancer vaccines and their emerging impact. Vacunas 2025 26 2 500391 10.1016/j.vacun.2025.500391
    [Google Scholar]
  27. McFarlane I. O’Connor E.A. World soybean trade: Growth and sustainability. Modern Economy 2014 5 5 580 588 10.4236/me.2014.55054
    [Google Scholar]
  28. Stevenson J.R. Villoria N. Byerlee D. Kelley T. Maredia M. Green Revolution research saved an estimated 18 to 27 million hectares from being brought into agricultural production. Proc. Natl. Acad. Sci. USA 2013 110 21 8363 8368 10.1073/pnas.1208065110 23671086
    [Google Scholar]
  29. Das U. Chanda T. Kumar J. Peter A. Discovery of Natural MCL1 Inhibitors using Pharmacophore modelling. QSAR, Docking, ADMET, Molecular Dynamics, and DFT Analysis 2024
    [Google Scholar]
  30. Simanungkalit R.D.M. Roughley R.J. Hastuti R.D. Indrasumunar A. Pratiwi E. Inoculation of soybean with selected strains of Bradyrhizobium japonicum can increase yield on acid soils in Indonesia. Soil Biol. Biochem. 1996 28 2 257 259 10.1016/0038‑0717(95)00135‑2
    [Google Scholar]
  31. Nadeem M.A. Nawaz M.A. Shahid M.Q. DNA molecular markers in plant breeding: current status and recent advancements in genomic selection and genome editing. Biotechnol. Biotechnol. Equip. 2018 32 2 261 285 10.1080/13102818.2017.1400401
    [Google Scholar]
  32. Powell W. Machray G.C. Provan J. Polymorphism revealed by simple sequence repeats. Trends Plant Sci. 1996 1 7 215 222 10.1016/1360‑1385(96)86898‑1
    [Google Scholar]
  33. Tautz D. Hypervariability of simple sequences as a general source for polymorphic DNA markers. Nucleic Acids Res. 1989 17 16 6463 6471 10.1093/nar/17.16.6463 2780284
    [Google Scholar]
  34. Das U. Uttarkar A. Kumar J. Niranjan V. In silico exploration natural compounds for the discovery of novel DNMT3A inhibitors as potential therapeutic agents for acute myeloid leukemia. In Silico Re-search Biomedicine 2025 2025 1 100006 10.1016/j.insi.2025.100006
    [Google Scholar]
  35. Dirlewanger E. Cosson P. Tavaud M. Development of microsatellite markers in peach [Prunus persica (L.) Batsch] and their use in genetic diversity analysis in peach and sweet cherry (Prunus avium L.). Theor. Appl. Genet. 2002 105 1 127 138 10.1007/s00122‑002‑0867‑7 12582570
    [Google Scholar]
  36. Shirasawa K. Ishii K. Kim C. Development of capsicum EST–SSR markers for species identification and in silico mapping onto the tomato genome sequence. Mol. Breed. 2013 31 1 101 110 10.1007/s11032‑012‑9774‑z 23316112
    [Google Scholar]
  37. Ward J.H. Hierarchical grouping to optimize an objective function. J. Am. Stat. Assoc. 1963 58 301 236 244 10.1080/01621459.1963.10500845
    [Google Scholar]
  38. Wang T. Analysis of variance components for genetic markers with unphased genotypes. Front. Genet. 2016 7 123 10.3389/fgene.2016.00123 27468297
    [Google Scholar]
  39. Field-Fote E.E. Importance and significance: Synonyms sometimes but not specifically in statistics. J. Neurol. Phys. Ther. 2019 43 4 195 196 10.1097/NPT.0000000000000294 31517748
    [Google Scholar]
  40. Panse V.G. Sukhatme P.V. Statistical methods for agricultural workers. Indian Coun Agricult Res 1954 361 1954
    [Google Scholar]
  41. Burton G.W. Quantitative inheritance in Pearl Millet (Pennisetum glaucum)1. Agron. J. 1951 43 9 409 417 10.2134/agronj1951.00021962004300090001x
    [Google Scholar]
  42. Lush J.L. Animal Breeding Plans. USA Lowa State College Press 1945
    [Google Scholar]
  43. Vetter T.R. Descriptive statistics: Reporting the answers to the 5 basic questions of who, what, why, when, where, and a sixth, so what? Anesth. Analg. 2017 125 5 1797 1802 10.1213/ANE.0000000000002471 28891910
    [Google Scholar]
  44. SHK M Standard Deviation Lovric M. International Ency-clopedia of Statistical Science. Heidelberg Springer 2011 1378 1379
    [Google Scholar]
  45. Burton G.W. DeVane E.H. Estimating heritability in Tall Fescue (Festuca Arundinacea) from replicated clonal material. Agron. J. 1953 45 10 478 481 10.2134/agronj1953.00021962004500100005x
    [Google Scholar]
  46. Robinson H. Comstock R.E. Harvey P. Estimates of heritability and the degree of dominance in corn. Agron. J. 1949 41 8 353 359 10.2134/agronj1949.00021962004100080005x
    [Google Scholar]
  47. Hotelling H. Analysis of a complex of statistical variables into principal components. J. Educ. Psychol. 1933 24 6 417 441 10.1037/h0071325
    [Google Scholar]
  48. Pearson K. On lines and planes of closest fit to systems of points in space. Lond. Edinb. Dublin Philos. Mag. J. Sci. 1901 2 11 559 572 10.1080/14786440109462720
    [Google Scholar]
  49. Das U. Emerging trends and research landscape of the tumor microenvironment in head-and-neck cancer: A comprehensive bibliometric analysis. Cancer Plus 2025 7 1 64 10.36922/CP025060008
    [Google Scholar]
  50. Jackson J.E. Estimation of models with variable coefficients. Polit. Anal. 1991 3 27 49 10.1093/pan/3.1.27
    [Google Scholar]
  51. Jolliffe I.T. Principal component analysis for special types of data. Cham Springer 2002
    [Google Scholar]
  52. Johnson H.W. Robinson H. Comstock R. Estimates of genetic and environmental variability in soybeans. Agron. J. 1955 47 7 314 318 10.2134/agronj1955.00021962004700070009x
    [Google Scholar]
  53. Md. Mia M, Akter N, Golam Mostofa Md, Farhad I. Analyses of genetic variability, character association, heritability and genetic advance of tossa jute (Corchorus olitorius) genotypes for morphology and stem anatomy. Am J Biosci 2020 8 4 99 110 10.11648/j.ajbio.20200804.12
    [Google Scholar]
  54. Mahalanobis P. Proceedings of the national institute of sciences of india. Gen Distance Stat 1936; 2 49 55
    [Google Scholar]
  55. Rao C. Advanced statistical methods in biometric research. Ameri-can J Biolog Anthropol 1952 12 2 268 270 10.1002/ajpa.1330120224
    [Google Scholar]
  56. Porebski S. Bailey L.G. Baum B.R. Modification of a CTAB DNA extraction protocol for plants containing high polysaccharide and polyphenol components. Plant Mol. Biol. Report. 1997 15 1 8 15 10.1007/BF02772108
    [Google Scholar]
  57. Thompson J.D. Higgins D.G. Gibson T.J. CLUSTAL W: Improving the sensitivity of progressive multiple sequence alignment through sequence weighting, position-specific gap penalties and weight matrix choice. Nucleic Acids Res. 1994 22 22 4673 4680 10.1093/nar/22.22.4673 7984417
    [Google Scholar]
  58. Rozen S. Skaletsky H. Primer3 on the WWW for general users and for biologist programmers. In:Bioinformatics Methods and Protocols. New Jersey Humana Press 1999 365 386 10.1385/1‑59259‑192‑2:365
    [Google Scholar]
  59. Khanande A.S. Jadhav P.V. Kale P.B. Genetic diversity in vegetable and grain type soybean genotypes identified using morphological descriptor and EST-SSR markers. Vegetos 2016 29 3 158 10.5958/2229‑4473.2016.00085.9
    [Google Scholar]
  60. Zhang L. Kyei-Boahen S. Growth and yield of vegetable soybean (Edamame) in Mississippi. Horttechnology 2007 17 1 26 31 10.21273/HORTTECH.17.1.26
    [Google Scholar]
  61. Shilpashree N. Devi S.N. Manjunathagowda D.C. Morphological characterization, variability and Diversity among vegetable soybean (Glycine max L.). Genotypes. Plants 2021 10 4 671 10.3390/plants10040671 33807322
    [Google Scholar]
  62. Nzaranyimana T. Determining the effects of sulfur fertility levels on edamame soybean [Glycine max (L) Merrill] protein components USA: Illinois State University 2017
    [Google Scholar]
  63. Williams M.M. Phenomorphological characterization of vegetable soybean germplasm lines for commercial production. Crop Sci. 2015 55 3 1274 1279 10.2135/cropsci2014.10.0690
    [Google Scholar]
  64. Karyawati A.S. Puspitaningrum E.S.V. Correlation and path analysis for agronomic traits contributing to yield in 30 genotypes of soybean. Biodiversitas (Surak.) 2021 22 3 10.13057/biodiv/d220309
    [Google Scholar]
  65. Bhandari H. Bhanu A.N. Srivastava K. Assessment of genetic diversity in crop plants-an overview. Adv. Plants Agric. Res. 2017 7 279 286
    [Google Scholar]
  66. Fisher R.A. Inverse Probability. Math. Proc. Camb. Philos. Soc. 1930 26 4 528 535 10.1017/S0305004100016297
    [Google Scholar]
  67. Das U. Banerjee S. Sarkar M. Circular RNA vaccines: Pioneering the next-gen cancer immunotherapy. Cancer Pathog Thera 2024 1 8 10.1016/j.cpt.2024.11.003
    [Google Scholar]
  68. Rao C.R. Multivariate analysis: An indispensable statistical aid in applied research. Indian J Statist 1960 22 4 317 338
    [Google Scholar]
  69. Singh C. Modern techniques of raising field crops. 1983 Available from: https://www.amazon.com/Modern-Techniques-Raising-Field-Crops/dp/9389688493 [
    [Google Scholar]
  70. Kumar S.P.J. Susmita C. Sripathy K.V. Molecular characterization and genetic diversity studies of Indian soybean (Glycine max (L.) Merr.) cultivars using SSR markers. Mol. Biol. Rep. 2022 49 3 2129 2140 10.1007/s11033‑021‑07030‑4 34894334
    [Google Scholar]
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