Genetic diversity assessment of extant cotton varieties based on Principal Component Analysis (PCA) and cluster analysis of enlisted DUS traits
Morphological characterization of 47 tetraploid cotton varieties cultivated in different zones of India was carried out over two seasons. The lay out followed randomized block Design and evaluation was done using 36 DUS descriptors in two replications. The visual characters showed uniform expression...
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| Format: | Article |
| Language: | English |
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Indian Society of Plant Breeders
2020-06-01
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| Series: | Electronic Journal of Plant Breeding |
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| _version_ | 1811190129445830656 |
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| author | V. Santhy, K. Rathinavel, M. Saravanan, Mithila Meshram and C. Priyadharshini |
| author_facet | V. Santhy, K. Rathinavel, M. Saravanan, Mithila Meshram and C. Priyadharshini |
| author_sort | V. Santhy, K. Rathinavel, M. Saravanan, Mithila Meshram and C. Priyadharshini |
| collection | DOAJ |
| description | Morphological characterization of 47 tetraploid cotton varieties cultivated in different zones of India was carried out over two seasons. The lay out followed randomized block Design and evaluation was done using 36 DUS descriptors in two replications. The visual characters showed uniform expression within the variety for two consecutive years indicating that they were uniform and stable in expression. Eleven out of 37 traits were monomorphic among the varieties. The remaining 26 characters were used for Principal Component Analysis to find the contribution of traits towards total variability. The PCA identified a total of 10 Components with Eigen values more than 1 contributing to a cumulative 77.74 % variability. The first component (PC1) exhibited maximum variability and highly correlated with traits such as leaf shape and petal spot which are also included in the grouping characters of DUS test guideline. The scatter diagram drawn using first two principle components with highest variability as well as the hierarchical cluster analysis performed using all the ten components distinctly classified genotypes in a consistent manner. The grouping of genotypes was attributed to relatively high contribution from few characters or variables which had high positive loadings, distributed among first two components rather than small contribution from each character. |
| first_indexed | 2024-04-11T14:46:06Z |
| format | Article |
| id | doaj.art-99db74529cf64eafa96d1eeddf02aaf8 |
| institution | Directory Open Access Journal |
| issn | 0975-928X |
| language | English |
| last_indexed | 2024-04-11T14:46:06Z |
| publishDate | 2020-06-01 |
| publisher | Indian Society of Plant Breeders |
| record_format | Article |
| series | Electronic Journal of Plant Breeding |
| spelling | doaj.art-99db74529cf64eafa96d1eeddf02aaf82022-12-22T04:17:38ZengIndian Society of Plant BreedersElectronic Journal of Plant Breeding0975-928X2020-06-0111243043810.37992/2020.1102.075Genetic diversity assessment of extant cotton varieties based on Principal Component Analysis (PCA) and cluster analysis of enlisted DUS traitsV. Santhy, K. Rathinavel, M. Saravanan, Mithila Meshram and C. PriyadharshiniMorphological characterization of 47 tetraploid cotton varieties cultivated in different zones of India was carried out over two seasons. The lay out followed randomized block Design and evaluation was done using 36 DUS descriptors in two replications. The visual characters showed uniform expression within the variety for two consecutive years indicating that they were uniform and stable in expression. Eleven out of 37 traits were monomorphic among the varieties. The remaining 26 characters were used for Principal Component Analysis to find the contribution of traits towards total variability. The PCA identified a total of 10 Components with Eigen values more than 1 contributing to a cumulative 77.74 % variability. The first component (PC1) exhibited maximum variability and highly correlated with traits such as leaf shape and petal spot which are also included in the grouping characters of DUS test guideline. The scatter diagram drawn using first two principle components with highest variability as well as the hierarchical cluster analysis performed using all the ten components distinctly classified genotypes in a consistent manner. The grouping of genotypes was attributed to relatively high contribution from few characters or variables which had high positive loadings, distributed among first two components rather than small contribution from each character.cotton varietiesvariety protectionprincipal component analysisgenetic diversity |
| spellingShingle | V. Santhy, K. Rathinavel, M. Saravanan, Mithila Meshram and C. Priyadharshini Genetic diversity assessment of extant cotton varieties based on Principal Component Analysis (PCA) and cluster analysis of enlisted DUS traits Electronic Journal of Plant Breeding cotton varieties variety protection principal component analysis genetic diversity |
| title | Genetic diversity assessment of extant cotton varieties based on Principal Component Analysis (PCA) and cluster analysis of enlisted DUS traits |
| title_full | Genetic diversity assessment of extant cotton varieties based on Principal Component Analysis (PCA) and cluster analysis of enlisted DUS traits |
| title_fullStr | Genetic diversity assessment of extant cotton varieties based on Principal Component Analysis (PCA) and cluster analysis of enlisted DUS traits |
| title_full_unstemmed | Genetic diversity assessment of extant cotton varieties based on Principal Component Analysis (PCA) and cluster analysis of enlisted DUS traits |
| title_short | Genetic diversity assessment of extant cotton varieties based on Principal Component Analysis (PCA) and cluster analysis of enlisted DUS traits |
| title_sort | genetic diversity assessment of extant cotton varieties based on principal component analysis pca and cluster analysis of enlisted dus traits |
| topic | cotton varieties variety protection principal component analysis genetic diversity |
| work_keys_str_mv | AT vsanthykrathinavelmsaravananmithilameshramandcpriyadharshini geneticdiversityassessmentofextantcottonvarietiesbasedonprincipalcomponentanalysispcaandclusteranalysisofenlisteddustraits |