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Evaluation of Sunflower Genotypes Using Principal Component Analysis

Received: 29 January 2022     Accepted: 21 February 2022     Published: 25 February 2022
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Abstract

Evaluation of genetic resources using morphological, physiological and biochemical data is important for effective breeding program. Principal component analysis is one of the multivariate technique used genetic resources evaluation using bi plot diagrams. The present study was conducted to evaluate sunflower genotypes for genetic diversity using multivariate analysis particularly Principal component analysis. The study was conducted during 2017/18 at central highlands of Ethiopia using 25 sunflower genotypes. The genotypes were planted using lattice design with two replication in the main season at Holetta and Adadi. The data for fifteen quantitative traits; ray floret number, leaf number, petiole length, seed yield per plant, number of seed per plant, seed yield per hectare, oil yield, oil content, head diameter, stem diameter, plant height, days to flowering, days to maturity, seed filling percentage and hundred seed weight were collected and principal component analysis was done using SAS 9.3. Eigen value greater than one was observed for the first five principal components. The first five principal components extracted showed 84.72% of total variation. The first and the second principal components contributed more than half of the total variation. The first principal component attributes 31.9% of total variation whereas, the second, the third, the fourth and the fifth principal components contributes, 22.72%, 12.25%, 10.11%, and 7.75% respectively. Different traits contribute chiefly to different principal components. Among all traits studied days to maturity and seed filling percentage contributed to the variation in three principal components out of the total principal components. The results from this study showed that there is considerable variation for the traits studied in sunflower genotypes suggesting that there is an opportunity for genetic improvement through selection directly from genotypes and or their parents.

Published in International Journal of Genetics and Genomics (Volume 10, Issue 1)
DOI 10.11648/j.ijgg.20221001.15
Page(s) 32-36
Creative Commons

This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.

Copyright

Copyright © The Author(s), 2022. Published by Science Publishing Group

Keywords

Genetic Diversity, Genotypes, Principal Component Analysis, Sunflower, Ethiopia

References
[1] Maia Filho, FF, RF Pereira, IF Alves, SN Cavalcante, EF Mesquita and TC Suassuna. 2013. Growth and phytomass of sunflower variety ‘Embrapa 122/V-2000’ fertilized with cattle manure in two soils. Agropecu. Scientific Semiarid. 9: 67-75.
[2] Nobre, R. G., H. R. Gheyi, F. A. L. Soares, and J. F. Cardoso. 2011. Sunflower production under saline stress and nitrogen fertilization. Rev. Bras. Soil Science. 35: 929-937.
[3] Dutta, Anand, and Sudhir K. Dutta., 2003. "Vitamin E and its role in the prevention of atherosclerosis and Carcinogenesis: a review." Journal of the American College of Nutrition 22 (4): 258-268.
[4] Dong, G., Liu, G., Li, K. 2007. Studying genetic diversity in the coregermplasm of confectionary sunflower (Helianthus annuus L.) in China based on AFLP and morphological analysis. Russian Journal of Genetics 43 (6): 627-635.
[5] Mohammadi SA, Prasanna BM. 2003. Analysis of genetic diversity in crop plants: Salient statistical tools and consideration. Crop Science 43, 1235-1248.
[6] Venujayakanth, B., Dudhat A S., Swaminathan B., and Anurag ML. 2017. Assessing Crop Genetic Diversity Using Principle Component Analysis: A Review. Trends in Biosciences 10 (2): 523-528.
[7] Nazir, A., Farooq, J., Mahmood, A., Shahid, M., & Riaz, M. 2013. Estimation of genetic diversity for CLCuV, earliness and fiber quality traits using various statistical procedures in different crosses of Gossypiumhirsutum L., 43 (4).
[8] Chahal, G. and Gosal, S. S. (2002) Principles and Procedures of Plant Breeding: Biotechnological and Conventional Approaches. Narosa Publishing House, New Delhi, Vol. 21, 64-89.
[9] Kline, P., 2014. An easy guide to factor analysis. London: Routledge.
[10] Kholghi, M., Bernousi, I., Darvishzadeh, R. and Pirzad, A. 2011. Correlation and path-coefficient analysis of seed yield and yield related trait in Iranian confectionery sunflower populations. African Journal of Biotechnology, 10 (61): 13058-13063.
[11] Masvodza DR, Gasura E, Zifodya N, Sibanda P & Chisikaurayi B (2015). Genetic diversity analysis of local and foreign sunflower germplasm (Helianthus annuus) for the national breeding program: Zimbabwe. J Cereals Oilseeds 6 (1): 1-7.
[12] Ghaffari M (2004). Use of principal component analysis method for selection of superior three ways crosses hybrids in sunflower. Seed and Plant 19 (4): 513-527.
[13] Ghaffoor A & Arshad M (2008). Multivariate analysis for quantitative traits to determine genetic diversity of black gram (Vigna mungo L. Hepper) germplasm. Pak J Bot 40 (6): 2307-2313.
[14] Naila Gandahi, Aftab Ahmed Mahar, Abdul Wahid Baloch, Sarfraz Ahmed Ansari, Tauqeer Ahmad Yasir, Munaiza Baloch, Liaquat Ali Bhutto, Asad Mari and Tanweer Fatah Abro (2017). Assessment of genetic diversity for quantitative traits in sunflower germplasm. Pure and Applied Biology. Vol. 6, Issue 1, pp 261-266.
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    Mohammed Abu. (2022). Evaluation of Sunflower Genotypes Using Principal Component Analysis. International Journal of Genetics and Genomics, 10(1), 32-36. https://doi.org/10.11648/j.ijgg.20221001.15

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    Mohammed Abu. Evaluation of Sunflower Genotypes Using Principal Component Analysis. Int. J. Genet. Genomics 2022, 10(1), 32-36. doi: 10.11648/j.ijgg.20221001.15

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    AMA Style

    Mohammed Abu. Evaluation of Sunflower Genotypes Using Principal Component Analysis. Int J Genet Genomics. 2022;10(1):32-36. doi: 10.11648/j.ijgg.20221001.15

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  • @article{10.11648/j.ijgg.20221001.15,
      author = {Mohammed Abu},
      title = {Evaluation of Sunflower Genotypes Using Principal Component Analysis},
      journal = {International Journal of Genetics and Genomics},
      volume = {10},
      number = {1},
      pages = {32-36},
      doi = {10.11648/j.ijgg.20221001.15},
      url = {https://doi.org/10.11648/j.ijgg.20221001.15},
      eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ijgg.20221001.15},
      abstract = {Evaluation of genetic resources using morphological, physiological and biochemical data is important for effective breeding program. Principal component analysis is one of the multivariate technique used genetic resources evaluation using bi plot diagrams. The present study was conducted to evaluate sunflower genotypes for genetic diversity using multivariate analysis particularly Principal component analysis. The study was conducted during 2017/18 at central highlands of Ethiopia using 25 sunflower genotypes. The genotypes were planted using lattice design with two replication in the main season at Holetta and Adadi. The data for fifteen quantitative traits; ray floret number, leaf number, petiole length, seed yield per plant, number of seed per plant, seed yield per hectare, oil yield, oil content, head diameter, stem diameter, plant height, days to flowering, days to maturity, seed filling percentage and hundred seed weight were collected and principal component analysis was done using SAS 9.3. Eigen value greater than one was observed for the first five principal components. The first five principal components extracted showed 84.72% of total variation. The first and the second principal components contributed more than half of the total variation. The first principal component attributes 31.9% of total variation whereas, the second, the third, the fourth and the fifth principal components contributes, 22.72%, 12.25%, 10.11%, and 7.75% respectively. Different traits contribute chiefly to different principal components. Among all traits studied days to maturity and seed filling percentage contributed to the variation in three principal components out of the total principal components. The results from this study showed that there is considerable variation for the traits studied in sunflower genotypes suggesting that there is an opportunity for genetic improvement through selection directly from genotypes and or their parents.},
     year = {2022}
    }
    

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  • TY  - JOUR
    T1  - Evaluation of Sunflower Genotypes Using Principal Component Analysis
    AU  - Mohammed Abu
    Y1  - 2022/02/25
    PY  - 2022
    N1  - https://doi.org/10.11648/j.ijgg.20221001.15
    DO  - 10.11648/j.ijgg.20221001.15
    T2  - International Journal of Genetics and Genomics
    JF  - International Journal of Genetics and Genomics
    JO  - International Journal of Genetics and Genomics
    SP  - 32
    EP  - 36
    PB  - Science Publishing Group
    SN  - 2376-7359
    UR  - https://doi.org/10.11648/j.ijgg.20221001.15
    AB  - Evaluation of genetic resources using morphological, physiological and biochemical data is important for effective breeding program. Principal component analysis is one of the multivariate technique used genetic resources evaluation using bi plot diagrams. The present study was conducted to evaluate sunflower genotypes for genetic diversity using multivariate analysis particularly Principal component analysis. The study was conducted during 2017/18 at central highlands of Ethiopia using 25 sunflower genotypes. The genotypes were planted using lattice design with two replication in the main season at Holetta and Adadi. The data for fifteen quantitative traits; ray floret number, leaf number, petiole length, seed yield per plant, number of seed per plant, seed yield per hectare, oil yield, oil content, head diameter, stem diameter, plant height, days to flowering, days to maturity, seed filling percentage and hundred seed weight were collected and principal component analysis was done using SAS 9.3. Eigen value greater than one was observed for the first five principal components. The first five principal components extracted showed 84.72% of total variation. The first and the second principal components contributed more than half of the total variation. The first principal component attributes 31.9% of total variation whereas, the second, the third, the fourth and the fifth principal components contributes, 22.72%, 12.25%, 10.11%, and 7.75% respectively. Different traits contribute chiefly to different principal components. Among all traits studied days to maturity and seed filling percentage contributed to the variation in three principal components out of the total principal components. The results from this study showed that there is considerable variation for the traits studied in sunflower genotypes suggesting that there is an opportunity for genetic improvement through selection directly from genotypes and or their parents.
    VL  - 10
    IS  - 1
    ER  - 

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Author Information
  • Ethiopian Institute of Agricultural Research, Holetta Research Centre, Addis Ababa, Ethiopia

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