Application of principal component analysis for rice f5 families characterization and evaluation

Tejaswini, K. L. Y.; Manukonda, Srinivas; Kumar, B. N. V. S. R. Ravi; Rao, P. Vivekananda; M, Lal Ahamed; Raju, S. Krishnam

doi:10.31783/elsr.2018.417284

Cited by 12 publications

(10 citation statements)

References 1 publication

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“…In the fifth principal component, number of spikelets/panicle, flag leaf width, number of filled grains/panicle and biological yield is the greatest contributor to the observed variation. Similar kind of results were found by [35,36,37]. Based on PCA most of the important yield and yield attributing traits were present in PC5 (Table 10) and from first three PCs it was cleared that the days to Days to 50% flowering, Plant height and panicle length is high.…”

Section: Principle Component Analysissupporting

confidence: 80%

Characterization of the Genetic Variability in Rice (Oryza sativa L.) and its Assessment Using Principal Component Analysis (PCA)

Naik

Chetariya²,

Akkenapally³

2022

IJECC

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Present investigation was conducted to estimate genetic parameters such as Genotypic Coefficient of Variation (GCV), Phenotypic Coefficient of Variation (PCV), heritability and Genetic Advance (GA) along with correlations, path coefficients, Genetic divergence and Principal Component Analysis (PCA) from data collected on 21 rice genotypes. The results revealed highly significant mean squares due to genotypes for all traits studied, indicating the existence of sufficient variation among the genotypes and therefore an ample scope for effective selection. High PCV, GCV, heritability and genetic advance as per cent of mean was observed for 1000-grain weight, seed yield per plant, and harvest index, indicating the effectiveness of direct selection for improvement of these traits. Thousand grain weight and harvest index had recorded positive and significant association with seed yield per plant. The results on path analysis also revealed high and positive direct effect for harvest index followed by flag leaf width, days to 50% flowering, days to maturity, plant height, panicle length, number of filled grains/panicle, number of spikelets/panicle and hence, these traits were identified as the most effective selection criteria for improvement of seed yield per plant in rice genotypes. The results on divergence analysis revealed that the genotypes were grouped into five clusters. Cluster I constituted maximum number of (17) genotypes. Maximum differences among the genotypes within the same cluster (intra-cluster) were shown by cluster I (18.33) followed by cluster II, III, IV, V showed zero intra -cluster distance. Cluster III and V (37.41) showed maximum inter cluster distance, suggesting that the genotypes constituted in these clusters may be used as parents for future hybridization programme. Principal Component Analysis (PCA) was utilized to estimate the relative contribution of various traits for total variability. Five components were found to possess eigen value more than 1.00. The PC1, PC2, PC3, PC4 and PC5 contributed 31.2, 14.5, 12.6, 10.5 and 10.1 per cent of variability. Together, they accounted for 78.9% of the variability of the genotypes used in the study has revealed the traits contributing for the variation.

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Section: Principle Component Analysissupporting

confidence: 80%

Characterization of the Genetic Variability in Rice (Oryza sativa L.) and its Assessment Using Principal Component Analysis (PCA)

Naik

Chetariya²,

Akkenapally³

2022

IJECC

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“…Findings of PCA (Table 6) showed that first five PCs (Fig 1) recorded eigen values greater than one and contributed to 75.32 per cent of variability among the 32 mutant families in M 3 generation. In the previous studies Riaz et al (2018), Tejaswini et al (2018) found that first five PCs were explained the maximum variance of the total set. The PC 1 explained 23.87 per cent of total variation which was elucidated by all the quantitative traits with positive eigen vector whereas, panicle weight, panicle length and plant height were higher contributing traits to the total variation.…”

Section: Principal Component Analysismentioning

confidence: 87%

Multivariate Analysis in Rice (Oryza sativa L.) Mutant Families from Anna (R) 4 Cultivar

Sushmitharaj

Arunachalam

Vanniarajan

et al. 2020

IJARe

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Background: The drought is one of the major limiting factors in rice production especially in rainfed environment. The rice production loss in rainfed has ranged from 17 to 40 percent during severe drought seasons. The rice cultivar Anna (R) 4 is having best adoptive characteristics to suits direct seeded rice under rainfed cultivation. But it has less market preference due its undesirable grain characters at consumer level. Hence, induced mutagenesis approach resorted to correct the undesirable traits and improve this variety to suits present needs. The mutant lines are having desirable changes and shows more similarity with Anna (R) 4 were identified by multivariate analyses. Methods: The present investigation was carried out during rabi 2018-19 to access the genetic relatedness of the 32 mutant families derived from Anna (R) 4 rice cultivar through electron beam, gamma rays and recurrent EMS mutagenesis. Data on twelve morphological traits were analysed for genetic divergence, principal component analysis and UPGMA clustering. Result: The mutant families were grouped in eight clusters with maximum and minimum diversity (D2) values of 457.21 and 10.90 respectively. The principal component analysis revealed that 75.32 per cent of total variability was explained by first five PCs and traits contributed to the divergence among the mutant families was discussed. The cluster tree obtained from UPGMA clustering based on the dissimilarity among the mutants was narrated.

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“…2 and Fig. 3) Evaluation of rice genotypes through PCA 3D scatter diagram was also carried out by Tejaswini et al (2018) and Ibrahim et al (2021).…”

Section: Sujan Acharjee Et Almentioning

confidence: 99%

“…According to Tejaswini et al (2018), the genotypes identified on the extremely positive side of both the X and Y axis were considered to be better genotypes against the contributing traits of PC 1 and PC 2, respectively, however, in the 3D (three dimensional) scattered diagram, it was considered an additional vertical Z-axis and in that case, the genotypes plotted higher along the Z-axis will also be considered as better performers against the contributing traits of PC 3. Under, irrigated condition, PC1 was loaded with high coefficient values ( irrespective of direction) of most of the grain yield-related traits, thus the genotypes reside towards the higher value or right side along with the PCA score I axis (X-axis) of the 3D scattered diagram ( Fig 2 .) may be considered as promising landraces as far as grain yield is concerned.…”

Section: Sujan Acharjee Et Almentioning

confidence: 99%

Screening of rice landraces for potential drought tolerance through comparative studies of genetic variability and principal component analysis

2021

EJPB

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To identify stable yielding rice landraces with optimum drought tolerance ability, the present comparative study of genetic variability and principal component analysis was carried out based on different morphological traits of 60 landraces of northeast India belonging to upland rice. The sensitivity of the landraces under drought stress conditions was confirmed by the first-order statistics of genetic variation as well as Principal component analysis (PCA) since comparatively more numbers (6) of principal components were found under drought stress over the irrigated conditions with 74.12 per cent of cumulative variability. Characters viz., leaf rolling index, root: culm ratio, the number of spikelets per panicle, the number of filled grains per panicle, 100 seed weight and grain yield per plant were identified as suitable selection indexes for landraces under drought stress conditions. Based upon the eigenvalue and scattered diagram analysis under PCA, the landraces viz., Garomalati, Chikanswari Kabar, Turkey, Tarkol, Maimi Taukha and Madoop were found to be diverse and promising genotypes under drought stress. Hence, these landraces may be utilised as potential parents in the future breeding programme.

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Application of principal component analysis for rice f5 families characterization and evaluation

Cited by 12 publications

References 1 publication

Characterization of the Genetic Variability in Rice (Oryza sativa L.) and its Assessment Using Principal Component Analysis (PCA)

Characterization of the Genetic Variability in Rice (Oryza sativa L.) and its Assessment Using Principal Component Analysis (PCA)

Multivariate Analysis in Rice (Oryza sativa L.) Mutant Families from Anna (R) 4 Cultivar

Screening of rice landraces for potential drought tolerance through comparative studies of genetic variability and principal component analysis

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