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Current IssueEditorial BoardOnline First ArticlesArchive

volume 47 issue 2 (april 2013) : 116-123

HIERARCHICAL CLUSTERING OF INDIAN WHEAT VARIETIES USING MORPHOLOGICAL DIVERSITY ASSESSMENT

R
Rekha Malik*
H
Hemani Sharma
A
Ajay Verma
S
Sushila Kundu
I
Indu Sharma
R
Ravish Chatrath
1Directorate of Wheat Research, Karnal-132 001, India

  • Submitted|

  • First Online |

  • doi

Cite article:- Malik* Rekha, Sharma Hemani, Verma Ajay, Kundu Sushila, Sharma Indu, Chatrath Ravish (2025). HIERARCHICAL CLUSTERING OF INDIAN WHEAT VARIETIES USING MORPHOLOGICAL DIVERSITY ASSESSMENT. Indian Journal of Agricultural Research. 47(2): 116-123. doi: .

ABSTRACT

Morphological data for 36 descriptors of 258 bread wheat varieties was used to identify traits actually contributing for genetic variability using SAS software program.  Total 20 morphological traits were found to be significant in expression of diversity in a selected set. These identified descriptors were used to study genetic diversity expression for hierarchical cluster analysis on the standardized quantitative data, using Ward’s minimum variance method with an R2 (squared multiple correlation) of 0.70 for grouping the accessions as per the PROC-CLUSTER program in SAS. The cluster tree revealed twenty seven genetically similar groups for 258 wheat genotypes. These groups will be used to develop mini core set of bread wheat varieties that will represent the morphological diversity available in Indian bread wheat and may be valuable for biochemical and molecular interpretations of diverse alleles present in Indian wheat for morphogenetic traits.

REFERENCES

  1. Amirul Islam, F.M.; Basford, K.E.; Redden R.J. and Beebe S. (2006). Preliminary evaluation of the common bean core collection at CIAT. Plant Gen. Res. Newsl., 145: 29-37. 
  2. Brown, A.H.D.; Grace, J.P. and Speer S.S. (1987). Designation of a “core” collection of perennial Glycine. Soybean. Genet. Newsl., 14: 59-70.
  3. El- Deeb, A.A. and Mohamed, N.A. (1999). Factor and cluster analysis for some quantitative characters in sesame (Sesamum indicum L.). The Annual Conference ISSR, Cairo University, 4–6 December,Vol. 34, Part (II).
  4. Erskine, W. and Muehlbauer, F.J. (1991). Allozyme and morphological variability, outcrossing rate and core collection formation in lentil germplasm. Theor. Appl. Genet., 83: 119-125.
  5. Frankel, O. H. and Brown, A. H. D. (1984). Plant genetic resources today: a critical appraisal. In: Holden J. H. W., and Williams J. T. (eds), Crop Genetic Resources: Conservation and Evaluation, George Allen & Unwin Ltd., London, pp. 249-257.
  6. Franco, J.; Crossa, J.; Warburton, M.L. and Taba, S. (2006). Sampling strategies for conserving maize diversity when forming core subsets using genetic markers. Crop Sci., 46: 854-864.
  7. Hamon, S.; Noirot, M. and Antony, F. (1995). Developing a coffee core collection using the principal components score strategy with quantitative data, in: Hodgkin T., Brown A.H.D., van Hintum T.J.L., Morales E.A.V. (Eds.), Core Collection of Plant Genetic Resources, IPGRI-Wiley & Sons, Chichester, UK, pp. 117-126.
  8. Hodgkin, T.; Brown, A.H.D.; van Hintum, Th.J.L. and Morales, E.A.V. (eds) (1995a). Core Collections of Plant Genetic Resources, John-Wiley & Sons, Chichester, UK.
  9. Huaman, Z.; Aguilar, C. and Ortiz, R. (1999). Selection a Peruvian sweet potato core collection on the basis of morphological, eco-geographical, and disease and pest reaction data. Theor. Appl. Genet., 98: 840-844.
  10. Jaynes, D.B.; Kaspar, T.C.; Colvin, T.S. and James, D.E. (2003). Cluster analysis of spatio temporal corn yield pattern in an Iowa field. Agron. J., 95 (3): 574-586.
  11. Knupffer, H. and van Hintum, Th.J.L. (1995). The barley core collection: an international effort. In: Hodkin, T.; Brown, A.H.D.;van Hintum, Th.J.L. and Morales, E.A.V. (eds), Core Collection of Plant Genetic Resources, International Plant Genetic Resources Institute (IPGRI). John Wiley and Sons, New York, USA, pp. 171-178.
  12. Kundu, S.; Shoran, J.; Mishra, B. and Gupta, R.K. (2006). Indian wheat varieties at a glance. Directorate of wheat Research, Karnal – 132001, Indian Res. Bulletin No., 21: 447.
  13. Leilah, A.A. and Al-Khateeb, S.A. (2005). Statistical analysis of wheat yield under drought conditions. J. Arid Environ., 61: 483-496.
  14. Mahajan, R.K.; Bisht, I.S.; Agrawal, R.C. and Rana, R.S. (1996). Studies on south Asian okra collection: a methodology for establishing a representative core set using characterization data. Genet. Resour. Crop Evol., 43: 244-255.
  15. Maqbool, R.; Sajjad, M.; Khaliq, I.; Rehman, A.; Khan, A.S. and Khan, S.H. (2010). Morphological diversity and trait association in Bread Wheat (Triticum aestivum). American-Eursian J. Agric. & Sci., 8(2): 216-224.
  16. Moukoumbi, Y.D.; Sie, M.; Vodouhe, R.; Dri, B.N.; Toulou, B.; Ogunbayo, S.A. and Ahanchede, A. (2011). Assessing phenotypic diversity of interspecific rice varieties using agro-morphological characterization. J. Plant Breeding Crop Sci., 3(5): 74-86.
  17. Ibrahim, O.M.; Mohamed, M.H.; Tawfik, M.M. and Badr, E.A. (2011). Genetic diversity assessment of barley (Hordeum vulgare L.) genotypes using cluster analysis. Int. J. Acad. Res., 3: 81-85.
  18. Peters, J.P. and Martinelli, J.A. (1989). Hierarchical cluster analysis as a tool to manage variation in germplasm collections. Theor. Appl. Genet., 78: 42-48.
  19. SAS. (1989). SAS/STAT user’s guide version 6, 4th ed. SAS Institute, Cary, N.C.
  20. Stalker, H.T. (1980). Utilization of wild species for crop improvement. Adv. Agron., 33: 111-147.
  21. Upadhyaya, H.D.; Bramel, P.J. and Singh, S. (2001). Development of a chick pea core subset using geographic distribution and quantitative traits. Crop Sci., 41: 206-210.
  22. Upadhyaya, H.D.; Gowda, C. L. L.; Reddy, K. N. and Singh, S. (2009a). Augmenting the Pearl Millet Core-Collection for Enhancing Germplasm Utilization in Crop Improvement. Crop Sci., 49: 573-580.
  23. Ward, J. (1963). Hierarchical grouping to optimize an objective function. J. Am. Stat. Assoc., 38: 236-244.
  24. Zarkti, H.; Ouabbou, H.; Hilali, A. and Udupa, S.M. (2010). Detection of genetic diversity in Moroccan durum wheat accessions using agro-morphological traits and microsatellite markers. Afr. J. Agri. Res., 5(14): 1837-1844.
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    volume 47 issue 2 (april 2013) : 116-123

    HIERARCHICAL CLUSTERING OF INDIAN WHEAT VARIETIES USING MORPHOLOGICAL DIVERSITY ASSESSMENT

    R
    Rekha Malik*
    H
    Hemani Sharma
    A
    Ajay Verma
    S
    Sushila Kundu
    I
    Indu Sharma
    R
    Ravish Chatrath
    1Directorate of Wheat Research, Karnal-132 001, India

    • Submitted|

    • First Online |

    • doi

    Cite article:- Malik* Rekha, Sharma Hemani, Verma Ajay, Kundu Sushila, Sharma Indu, Chatrath Ravish (2025). HIERARCHICAL CLUSTERING OF INDIAN WHEAT VARIETIES USING MORPHOLOGICAL DIVERSITY ASSESSMENT. Indian Journal of Agricultural Research. 47(2): 116-123. doi: .

    ABSTRACT

    Morphological data for 36 descriptors of 258 bread wheat varieties was used to identify traits actually contributing for genetic variability using SAS software program.  Total 20 morphological traits were found to be significant in expression of diversity in a selected set. These identified descriptors were used to study genetic diversity expression for hierarchical cluster analysis on the standardized quantitative data, using Ward’s minimum variance method with an R2 (squared multiple correlation) of 0.70 for grouping the accessions as per the PROC-CLUSTER program in SAS. The cluster tree revealed twenty seven genetically similar groups for 258 wheat genotypes. These groups will be used to develop mini core set of bread wheat varieties that will represent the morphological diversity available in Indian bread wheat and may be valuable for biochemical and molecular interpretations of diverse alleles present in Indian wheat for morphogenetic traits.

    REFERENCES

    1. Amirul Islam, F.M.; Basford, K.E.; Redden R.J. and Beebe S. (2006). Preliminary evaluation of the common bean core collection at CIAT. Plant Gen. Res. Newsl., 145: 29-37. 
    2. Brown, A.H.D.; Grace, J.P. and Speer S.S. (1987). Designation of a “core” collection of perennial Glycine. Soybean. Genet. Newsl., 14: 59-70.
    3. El- Deeb, A.A. and Mohamed, N.A. (1999). Factor and cluster analysis for some quantitative characters in sesame (Sesamum indicum L.). The Annual Conference ISSR, Cairo University, 4–6 December,Vol. 34, Part (II).
    4. Erskine, W. and Muehlbauer, F.J. (1991). Allozyme and morphological variability, outcrossing rate and core collection formation in lentil germplasm. Theor. Appl. Genet., 83: 119-125.
    5. Frankel, O. H. and Brown, A. H. D. (1984). Plant genetic resources today: a critical appraisal. In: Holden J. H. W., and Williams J. T. (eds), Crop Genetic Resources: Conservation and Evaluation, George Allen & Unwin Ltd., London, pp. 249-257.
    6. Franco, J.; Crossa, J.; Warburton, M.L. and Taba, S. (2006). Sampling strategies for conserving maize diversity when forming core subsets using genetic markers. Crop Sci., 46: 854-864.
    7. Hamon, S.; Noirot, M. and Antony, F. (1995). Developing a coffee core collection using the principal components score strategy with quantitative data, in: Hodgkin T., Brown A.H.D., van Hintum T.J.L., Morales E.A.V. (Eds.), Core Collection of Plant Genetic Resources, IPGRI-Wiley & Sons, Chichester, UK, pp. 117-126.
    8. Hodgkin, T.; Brown, A.H.D.; van Hintum, Th.J.L. and Morales, E.A.V. (eds) (1995a). Core Collections of Plant Genetic Resources, John-Wiley & Sons, Chichester, UK.
    9. Huaman, Z.; Aguilar, C. and Ortiz, R. (1999). Selection a Peruvian sweet potato core collection on the basis of morphological, eco-geographical, and disease and pest reaction data. Theor. Appl. Genet., 98: 840-844.
    10. Jaynes, D.B.; Kaspar, T.C.; Colvin, T.S. and James, D.E. (2003). Cluster analysis of spatio temporal corn yield pattern in an Iowa field. Agron. J., 95 (3): 574-586.
    11. Knupffer, H. and van Hintum, Th.J.L. (1995). The barley core collection: an international effort. In: Hodkin, T.; Brown, A.H.D.;van Hintum, Th.J.L. and Morales, E.A.V. (eds), Core Collection of Plant Genetic Resources, International Plant Genetic Resources Institute (IPGRI). John Wiley and Sons, New York, USA, pp. 171-178.
    12. Kundu, S.; Shoran, J.; Mishra, B. and Gupta, R.K. (2006). Indian wheat varieties at a glance. Directorate of wheat Research, Karnal – 132001, Indian Res. Bulletin No., 21: 447.
    13. Leilah, A.A. and Al-Khateeb, S.A. (2005). Statistical analysis of wheat yield under drought conditions. J. Arid Environ., 61: 483-496.
    14. Mahajan, R.K.; Bisht, I.S.; Agrawal, R.C. and Rana, R.S. (1996). Studies on south Asian okra collection: a methodology for establishing a representative core set using characterization data. Genet. Resour. Crop Evol., 43: 244-255.
    15. Maqbool, R.; Sajjad, M.; Khaliq, I.; Rehman, A.; Khan, A.S. and Khan, S.H. (2010). Morphological diversity and trait association in Bread Wheat (Triticum aestivum). American-Eursian J. Agric. & Sci., 8(2): 216-224.
    16. Moukoumbi, Y.D.; Sie, M.; Vodouhe, R.; Dri, B.N.; Toulou, B.; Ogunbayo, S.A. and Ahanchede, A. (2011). Assessing phenotypic diversity of interspecific rice varieties using agro-morphological characterization. J. Plant Breeding Crop Sci., 3(5): 74-86.
    17. Ibrahim, O.M.; Mohamed, M.H.; Tawfik, M.M. and Badr, E.A. (2011). Genetic diversity assessment of barley (Hordeum vulgare L.) genotypes using cluster analysis. Int. J. Acad. Res., 3: 81-85.
    18. Peters, J.P. and Martinelli, J.A. (1989). Hierarchical cluster analysis as a tool to manage variation in germplasm collections. Theor. Appl. Genet., 78: 42-48.
    19. SAS. (1989). SAS/STAT user’s guide version 6, 4th ed. SAS Institute, Cary, N.C.
    20. Stalker, H.T. (1980). Utilization of wild species for crop improvement. Adv. Agron., 33: 111-147.
    21. Upadhyaya, H.D.; Bramel, P.J. and Singh, S. (2001). Development of a chick pea core subset using geographic distribution and quantitative traits. Crop Sci., 41: 206-210.
    22. Upadhyaya, H.D.; Gowda, C. L. L.; Reddy, K. N. and Singh, S. (2009a). Augmenting the Pearl Millet Core-Collection for Enhancing Germplasm Utilization in Crop Improvement. Crop Sci., 49: 573-580.
    23. Ward, J. (1963). Hierarchical grouping to optimize an objective function. J. Am. Stat. Assoc., 38: 236-244.
    24. Zarkti, H.; Ouabbou, H.; Hilali, A. and Udupa, S.M. (2010). Detection of genetic diversity in Moroccan durum wheat accessions using agro-morphological traits and microsatellite markers. Afr. J. Agri. Res., 5(14): 1837-1844.
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