Genetic Diversity Based on Cluster and Principal Component Analyses for Agro-morphological Traits of Wheat Germplasm

Solomon Mengistu; Mekonnen Asefa

doi:doi:10.11648/j.ijgg.20221003.13

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Genetic Diversity Based on Cluster and Principal Component Analyses for Agro-morphological Traits of Wheat Germplasm

Solomon Mengistu, Mekonnen Asefa

Published in International Journal of Genetics and Genomics (Volume 10, Issue 3)

Received: 26 July 2022 Accepted: 24 August 2022 Published: 31 August 2022

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Abstract

Understanding of the genetic variability in germplasm is crucial for successful choosing varsities in breeding programs. An investigation was conducted to evaluate the genetic variability by cluster and principal component analysis for yield and its nine contributing traits in twenty seven wheat accessions that obtained from Ethiopian Biodiversity Institute and two standard checks namely: - Hidase and Denda were planted at Melkasa Agricultural Research Farm 2017, in main rainy season. Out of different techniques available for assessing the genetic diversity, principal component and cluster analysis are the most important and widely used methods The agro-morphological characters used for analysis were days to 50% heading, plant height, number of tillers per plant, days to 75% maturity, spike length, number of spikelets per spike, number of grain per spike, grain size and yield per plot. Principal component analysis categorized the entire accessions into nine important components explaining nearly 100% of genetic variation. Cluster analysis similarly, categorized the examined characters into four groups. In cluster IV, two accessions namely AC-231520 and AC-222300 have achieved higher rates of yield and yield attributes traits and may be reflected as potential to advance breeding programs. The outcome of the current research could be utilized in designing and implementation of forthcoming breeding programs in wheat.

Published in	International Journal of Genetics and Genomics (Volume 10, Issue 3)
DOI	10.11648/j.ijgg.20221003.13
Page(s)	79-84
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), 2024. Published by Science Publishing Group

Keywords

Characters, Germplasm, Principal Component and Cluster Analysis

References

[1]	CSA. 2021. Agricultural sample survey: Report on area and production of major crops (Private peasant holdings, Meher Season). Volume I Statistical Bulletins 590, Addis Ababa, Ethiopia.
[2]	Zegeye F, Alamirew B, Tolossa D. 2020. Analysis of wheat yield gap and variability in Ethiopia. Int J AgricEconon. 5 (4): 89-98.
[3]	Hundie B, Verkuiji H, Mawangi W, Tanner DG. 2000. Adaptation of improved wheat technologies in adaba and dodolaworedas of the Bale highlands of Ethiopia. Addis Ababa, Ethiopia: CIMMYT/EARO.
[4]	Tahmasebi, G., J. Heydarnezhadian and A. P. Aboughadareh. 2013. Evaluation of yield and yield components in some of promising wheat lines. Inter. J. Agric. Crop Sci., 5 (20): 2379-2384.
[5]	Mary, S. S. and A. Gopalan. 2006. Dissection of genetic attributes yield traits of fodder cowpea in F3 and F4. J. Appl. Sci. Res., 2 (6): 805-808.
[6]	Baenziger, P., I. Dweikat, K. Gill, K. Eskridge, T. Berke, M. Shah and A. Mahmood. 2011. Understanding grain yield: It is a journey, not a destination. Czech. J. Genet. Plant Breed. 47 (Special Issue): S77-S88.
[7]	Devesh, P., Moitra, P. K., Shukla, R. S. and Pandey, S. (2019) Genetic Diversity and Principal Component Analyses for Yield, Components and Quality Traits of Advanced Lines of Wheat. Journal of Pharmacognosy and Phytochemistry, 8, 4834-4839.
[8]	Beheshtizadeh, H., Rezaie, A., Rezaie, A. and Ghandi, A. (2013) Principal Component Analysis and Determination of the Selection Criteria in Bread Wheat (Triticumaestivum L.) Genotypes. The International Journal of Agriculture and Crop Sciences, 5, 2024-2027.
[9]	Lysenko, A. A. (2011) Comparative Productivity of Pea Varieties of Various Morphotypes and the Creation of a New Selection Material on Their Basis. Zernograd, 23.
[10]	Brown-Guedira, G. L., Thompson, J. A., Nelson, R. L. and Warburton, M. L. (2000) Evaluation of Genetic Diversity of Soybean Introductions and North American Ancestors Using RAPD and SSR Markers. Crop Science, 40, 815-823. https://doi.org/10.2135/cropsci2000.403815x
[11]	Mujaju, C. and Chakuya, E. (2008) Morphological Variation of Sorghum Landrace Accessions On-Farm in Semi-Arid Areas of Zimbabwe. International Journal of Botany, 4, 376-382. https://doi.org/10.3923/ijb.2008.376.382
[12]	Mustafa, H. S., Farooq, J., Ejaz-ul-Hasan, Bibi, T and Mahmood, M. (2015) Cluster and Principle Component Analyses of Maize Accessions under Normal and Water Stress Conditions. Journal of Agricultural Sciences, 60, 33-48. https://doi.org/10.2298/JAS1501033M
[13]	L evesque, R. (2007). SPSS. Statistical package for the social sciences, version 16.0. SPSS Programming and data management. A guide for SPSS and SAS users, fourth edition, SPSS inc., Chicago III.
[14]	Drezner G, Dvojkovic K, Horvat D, Novoselovic D, Lalic A. 2007. Environmental impacts on wheat agronomics quality traits. Cereal Research 35, 357–360.
[15]	Leilah, A. A. and Al-Khateeb, SA. (2005). Statistical analysis of wheat yield under drought conditions. Journal of Arid Environments, 61 (3): 483-496.
[16]	Deng, S., X. Wu, Y. Wu, R. Zhou, H. Wang, J. Jia and S. Liu. 2011. Characterization and precise mapping of a QTL increasing spike number with pleiotropic effects in wheat. Theor. Appl. Genet., 122 (2): 281-289.
[17]	Hailegiorgis, D., Mesfin, M, and Genet, T. (2011) Genetic Divergence Analysis on Some Bread Wheat Genotypes Grown in Ethiopia. Journal of Central European Agriculture, 12, 344-352. https://doi.org/10.5513/JCEA01/12.2.922
[18]	Chekalin, N. M., Tishchenko, V. N. and Batashova, M. E. (2008) Selection and Genetics of Individual Cultures. FOP Govorov, S. V., Poltava, 368 p.
[19]	Degewione, A. and Alamerew, S. (2013) Genetic Diversity in Bread Wheat (TriticumaestivumL.) Genotypes. Pakistan Journal of Biological Sciences, 16, 1330-1335. https://doi.org/10.3923/pjbs.2013.1330.1335
[20]	Akter, A., Hasan, M. J., Paul, A. K., Mutlib, M. M. and Hossain, M. K. (2009) Selection of Parent for Improvement of Restorer Line in Rice (Oryza sativa L.). SAARC Journal of Agriculture, 7, 43-50.
[21]	Ajmal, S. U., Minhas, N. M., Hamdani, A., Shakir, A., Zubair, M., Ahmad, Z. 2013. Multivariate analysis of genetic divergence in wheat (Triticumaestivum L) germplasm. Pak. J. Bot., 45 (5): 1643-1648.
[22]	Ali, Y., Atta, B. M., Akhter, J., Monneveux, P., Lateef, Z. 2008. Genetic variability, association and diversity studies in wheat (Triticumaestivum L.) germplasm. Pak. J. Bot., 40 (5): 2087-2097.

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APA Style

Solomon Mengistu, Mekonnen Asefa. (2022). Genetic Diversity Based on Cluster and Principal Component Analyses for Agro-morphological Traits of Wheat Germplasm. International Journal of Genetics and Genomics, 10(3), 79-84. https://doi.org/10.11648/j.ijgg.20221003.13

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ACS Style

Solomon Mengistu; Mekonnen Asefa. Genetic Diversity Based on Cluster and Principal Component Analyses for Agro-morphological Traits of Wheat Germplasm. Int. J. Genet. Genomics 2022, 10(3), 79-84. doi: 10.11648/j.ijgg.20221003.13

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AMA Style

Solomon Mengistu, Mekonnen Asefa. Genetic Diversity Based on Cluster and Principal Component Analyses for Agro-morphological Traits of Wheat Germplasm. Int J Genet Genomics. 2022;10(3):79-84. doi: 10.11648/j.ijgg.20221003.13

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@article{10.11648/j.ijgg.20221003.13,
  author = {Solomon Mengistu and Mekonnen Asefa},
  title = {Genetic Diversity Based on Cluster and Principal Component Analyses for Agro-morphological Traits of Wheat Germplasm},
  journal = {International Journal of Genetics and Genomics},
  volume = {10},
  number = {3},
  pages = {79-84},
  doi = {10.11648/j.ijgg.20221003.13},
  url = {https://doi.org/10.11648/j.ijgg.20221003.13},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ijgg.20221003.13},
  abstract = {Understanding of the genetic variability in germplasm is crucial for successful choosing varsities in breeding programs. An investigation was conducted to evaluate the genetic variability by cluster and principal component analysis for yield and its nine contributing traits in twenty seven wheat accessions that obtained from Ethiopian Biodiversity Institute and two standard checks namely: - Hidase and Denda were planted at Melkasa Agricultural Research Farm 2017, in main rainy season. Out of different techniques available for assessing the genetic diversity, principal component and cluster analysis are the most important and widely used methods The agro-morphological characters used for analysis were days to 50% heading, plant height, number of tillers per plant, days to 75% maturity, spike length, number of spikelets per spike, number of grain per spike, grain size and yield per plot. Principal component analysis categorized the entire accessions into nine important components explaining nearly 100% of genetic variation. Cluster analysis similarly, categorized the examined characters into four groups. In cluster IV, two accessions namely AC-231520 and AC-222300 have achieved higher rates of yield and yield attributes traits and may be reflected as potential to advance breeding programs. The outcome of the current research could be utilized in designing and implementation of forthcoming breeding programs in wheat.},
 year = {2022}
}

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TY  - JOUR
T1  - Genetic Diversity Based on Cluster and Principal Component Analyses for Agro-morphological Traits of Wheat Germplasm
AU  - Solomon Mengistu
AU  - Mekonnen Asefa
Y1  - 2022/08/31
PY  - 2022
N1  - https://doi.org/10.11648/j.ijgg.20221003.13
DO  - 10.11648/j.ijgg.20221003.13
T2  - International Journal of Genetics and Genomics
JF  - International Journal of Genetics and Genomics
JO  - International Journal of Genetics and Genomics
SP  - 79
EP  - 84
PB  - Science Publishing Group
SN  - 2376-7359
UR  - https://doi.org/10.11648/j.ijgg.20221003.13
AB  - Understanding of the genetic variability in germplasm is crucial for successful choosing varsities in breeding programs. An investigation was conducted to evaluate the genetic variability by cluster and principal component analysis for yield and its nine contributing traits in twenty seven wheat accessions that obtained from Ethiopian Biodiversity Institute and two standard checks namely: - Hidase and Denda were planted at Melkasa Agricultural Research Farm 2017, in main rainy season. Out of different techniques available for assessing the genetic diversity, principal component and cluster analysis are the most important and widely used methods The agro-morphological characters used for analysis were days to 50% heading, plant height, number of tillers per plant, days to 75% maturity, spike length, number of spikelets per spike, number of grain per spike, grain size and yield per plot. Principal component analysis categorized the entire accessions into nine important components explaining nearly 100% of genetic variation. Cluster analysis similarly, categorized the examined characters into four groups. In cluster IV, two accessions namely AC-231520 and AC-222300 have achieved higher rates of yield and yield attributes traits and may be reflected as potential to advance breeding programs. The outcome of the current research could be utilized in designing and implementation of forthcoming breeding programs in wheat.
VL  - 10
IS  - 3
ER  -

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Author Information

Solomon Mengistu

Crop and Horticulture Directorate, Ethiopian Biodiversity Institute, Addis Ababa, Ethiopia
Mekonnen Asefa

Crop and Horticulture Directorate, Ethiopian Biodiversity Institute, Addis Ababa, Ethiopia

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Plain Text BibTeX RIS

APA Style

Solomon Mengistu, Mekonnen Asefa. (2022). Genetic Diversity Based on Cluster and Principal Component Analyses for Agro-morphological Traits of Wheat Germplasm. International Journal of Genetics and Genomics, 10(3), 79-84. https://doi.org/10.11648/j.ijgg.20221003.13

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ACS Style

Solomon Mengistu; Mekonnen Asefa. Genetic Diversity Based on Cluster and Principal Component Analyses for Agro-morphological Traits of Wheat Germplasm. Int. J. Genet. Genomics 2022, 10(3), 79-84. doi: 10.11648/j.ijgg.20221003.13

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AMA Style

Solomon Mengistu, Mekonnen Asefa. Genetic Diversity Based on Cluster and Principal Component Analyses for Agro-morphological Traits of Wheat Germplasm. Int J Genet Genomics. 2022;10(3):79-84. doi: 10.11648/j.ijgg.20221003.13

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@article{10.11648/j.ijgg.20221003.13,
  author = {Solomon Mengistu and Mekonnen Asefa},
  title = {Genetic Diversity Based on Cluster and Principal Component Analyses for Agro-morphological Traits of Wheat Germplasm},
  journal = {International Journal of Genetics and Genomics},
  volume = {10},
  number = {3},
  pages = {79-84},
  doi = {10.11648/j.ijgg.20221003.13},
  url = {https://doi.org/10.11648/j.ijgg.20221003.13},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ijgg.20221003.13},
  abstract = {Understanding of the genetic variability in germplasm is crucial for successful choosing varsities in breeding programs. An investigation was conducted to evaluate the genetic variability by cluster and principal component analysis for yield and its nine contributing traits in twenty seven wheat accessions that obtained from Ethiopian Biodiversity Institute and two standard checks namely: - Hidase and Denda were planted at Melkasa Agricultural Research Farm 2017, in main rainy season. Out of different techniques available for assessing the genetic diversity, principal component and cluster analysis are the most important and widely used methods The agro-morphological characters used for analysis were days to 50% heading, plant height, number of tillers per plant, days to 75% maturity, spike length, number of spikelets per spike, number of grain per spike, grain size and yield per plot. Principal component analysis categorized the entire accessions into nine important components explaining nearly 100% of genetic variation. Cluster analysis similarly, categorized the examined characters into four groups. In cluster IV, two accessions namely AC-231520 and AC-222300 have achieved higher rates of yield and yield attributes traits and may be reflected as potential to advance breeding programs. The outcome of the current research could be utilized in designing and implementation of forthcoming breeding programs in wheat.},
 year = {2022}
}

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TY  - JOUR
T1  - Genetic Diversity Based on Cluster and Principal Component Analyses for Agro-morphological Traits of Wheat Germplasm
AU  - Solomon Mengistu
AU  - Mekonnen Asefa
Y1  - 2022/08/31
PY  - 2022
N1  - https://doi.org/10.11648/j.ijgg.20221003.13
DO  - 10.11648/j.ijgg.20221003.13
T2  - International Journal of Genetics and Genomics
JF  - International Journal of Genetics and Genomics
JO  - International Journal of Genetics and Genomics
SP  - 79
EP  - 84
PB  - Science Publishing Group
SN  - 2376-7359
UR  - https://doi.org/10.11648/j.ijgg.20221003.13
AB  - Understanding of the genetic variability in germplasm is crucial for successful choosing varsities in breeding programs. An investigation was conducted to evaluate the genetic variability by cluster and principal component analysis for yield and its nine contributing traits in twenty seven wheat accessions that obtained from Ethiopian Biodiversity Institute and two standard checks namely: - Hidase and Denda were planted at Melkasa Agricultural Research Farm 2017, in main rainy season. Out of different techniques available for assessing the genetic diversity, principal component and cluster analysis are the most important and widely used methods The agro-morphological characters used for analysis were days to 50% heading, plant height, number of tillers per plant, days to 75% maturity, spike length, number of spikelets per spike, number of grain per spike, grain size and yield per plot. Principal component analysis categorized the entire accessions into nine important components explaining nearly 100% of genetic variation. Cluster analysis similarly, categorized the examined characters into four groups. In cluster IV, two accessions namely AC-231520 and AC-222300 have achieved higher rates of yield and yield attributes traits and may be reflected as potential to advance breeding programs. The outcome of the current research could be utilized in designing and implementation of forthcoming breeding programs in wheat.
VL  - 10
IS  - 3
ER  -

Copy | Download