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 |
Genetic Diversity, Genotypes, Principal Component Analysis, Sunflower, Ethiopia
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APA Style
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
ACS 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
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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Download@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}
}
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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