Assessment of genetic diversity in aromatic rice (<i>Oryza sativa</i> L.) germplasm using PCA and cluster analysis

Shoba, D.; Vijayan, Roshni; Robin, S.; Manivannan, N.; Iyanar, K.; Arunachalam, P.; Nadarajan, N.; Pillai, M. Arumugam; Geetha, S.

doi:10.5958/0975-928x.2019.00140.6

Cited by 28 publications

(27 citation statements)

References 3 publications

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“…PCA reveals the patterns and eliminates the redundancy in datasets, as variation occurs commonly in plants for yield and yield-related traits [13]. e primary benefit of PCA is to quantify the significance of each dimension for relating the variability of a dataset [14]. Mahalanobis D 2 statistics is an effective tool in quantifying the degree of genetic divergence at the genotypic level based on generalized distance [15].…”

Section: Introductionmentioning

confidence: 99%

Multivariate Analysis of Phenotypic Diversity of Rice (Oryza sativa L.) Landraces from Lamjung and Tanahun Districts, Nepal

Dhakal

Pokhrel

Sharma

2020

International Journal of Agronomy

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The magnitude and nature of genetic divergence play a vital role in the selection of the desirable landraces for its utilization in the breeding program. A study was carried out with 30 rice landraces at the Institute of Agriculture and Animal Science, Lamjung Campus, during June–November 2018 to determine relation among individuals, estimate the relative contribution of various traits of rice using principal component analysis, and identify the potential parents for hybridization using Mahalanobis distance (D2). The principal component analysis revealed that five among the thirteen principal components were significant (eigenvalue >1) and contributed to 29.96%, 20.26%, 13.56%, 11.68%, and 9.22% of the total variance, respectively. PC1 included the traits that were related mostly to the yield, yield attributing, and grain characteristics. Landraces from Anadi group, Jetho Budo, Jarneli, and Rato Masino performed well in PC1 while landraces such as Mansara, Pakhe Sali, and Aanga performed well in PC2. The landraces were grouped into six clusters where 12 landraces were grouped into cluster I. Cluster analysis showed maximum and minimum intracluster distance in cluster VI (D2 = 35.77) and cluster I (D2 = 18.59), respectively. The maximum intercluster distance was obtained between clusters V and VI (D2 = 40.18) followed by clusters III and VI (D2 = 36.17) and clusters IV and VI (D2 = 35.74). Cluster III showed the highest mean value for grain width, flag leaf breadth, yield, and minimum mean value for plant height while mean values of total grain per panicle, filled grain percentage, and thousand-grain weight were maximum in cluster IV. Mean values of effective tiller and kernel width were found maximum in clusters V and VI, respectively. Landraces from clusters V and VI or clusters III and VI or clusters IV and VI can be used in the hybridization program to develop the superior hybrids by exploiting heterosis in segregating generation.

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Section: Introductionmentioning

confidence: 99%

Multivariate Analysis of Phenotypic Diversity of Rice (Oryza sativa L.) Landraces from Lamjung and Tanahun Districts, Nepal

Dhakal

Pokhrel

Sharma

2020

International Journal of Agronomy

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“…Principal component analysis (PCA) analyses data consisting of several intercorrelated quantitative dependent variables as observations reported by Mahendran et al, 2015. The primary benefit of PCA is to quantify the significance of each dimension for relating the variability of a dataset (Shoba et al, 2019). Principal Component Analysis (PCA) could be used to reveal patterns and eliminate redundancy in data sets and variations routinely occur in crop species for yield and grain quality reported by Maji and Shaibu, 2012.…”

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Genetic Diversity Analysis for Yield Associated and Quality Traits in Promising Rice Varieties of Tamil Nadu

Naik¹,

Pillai²,

Saravanan³

2021

IJBSM

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An experiment was conducted with 55 rice varieties to assess the genetic diversity by using Mahalanobis D2 Statistical and characterization of genotypes using principal component analysis. All genotypes exhibited a wide and significant variation for 19 traits, by cluster analysis grouped into ten clusters. The maximum genotypes were included in Cluster 6 (16) followed by cluster 4 (10), cluster 3 (8), cluster 2 (7), cluster 5 (5), cluster 8 (4), cluster 1 (2), with 29.09, 18.18, 14.54, 12.72, 9.09, 7.27 and 3.63 proportion respectively, the rest of three clusters had one genotype each. Maximum cluster distance obtained between cluster×constituted by single entry (Pusa Basmati) showed highest inter cluster distance from cluster V (20727.37), VII (18414.79), I (17228.89) and cluster III (17010.24) are having very high inter cluster distance and also by cluster IX from cluster VIII (8852.36), VI (7559.67), I (7444.68) and cluster VII (6666.83) followed by cluster VI from cluster V (6225.95). The lowest inter cluster distance was observed between cluster II and cluster IV III and VI followed by between cluster I and cluster VIII, XI, II, VI and cluster IV. The intra cluster D2 values ranged from Zero (Cluster VII, IX, X) to 2233.91 (Cluster VIII). Contribution of amylose content was highest towards genetic divergence (23.43%) by taking 348 times ranked first followed by days to 50% flowering (23.37%) by 347 times, single plant yield (23.3%) by 346 times. The PCA analysis showed that first eight principal components accounted for about 85.4%.

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“…2). Shobha et al 2019 [10] reported the highest variability in PC1 with eigen value more than 1.0 in 67 aromatic rice genotypes. Patel et al studied genetic diversity study in finger millet with the help of principal component analysis and found three principal components 73.40 per cent of total variation.…”

Section: Results and Discussion Principal Component Analysismentioning

confidence: 95%

Principal component analysis in finger millet (Eleusine coracana L.) genotypes for diversity studies

Ladumor¹,

Patil²,

Patel³

et al. 2021

Int. J. Chem. Stud.

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Assessment of genetic diversity in aromatic rice (Oryza sativa L.) germplasm using PCA and cluster analysis

Cited by 28 publications

References 3 publications

Multivariate Analysis of Phenotypic Diversity of Rice (Oryza sativa L.) Landraces from Lamjung and Tanahun Districts, Nepal

Multivariate Analysis of Phenotypic Diversity of Rice (Oryza sativa L.) Landraces from Lamjung and Tanahun Districts, Nepal

Genetic Diversity Analysis for Yield Associated and Quality Traits in Promising Rice Varieties of Tamil Nadu

Principal component analysis in finger millet (Eleusine coracana L.) genotypes for diversity studies

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