Principal Component Analysis in Inbreds of Maize (Zea mays L.)

Sandeep, S.

doi:10.18782/2320-7051.5762

Cited by 5 publications

(8 citation statements)

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“…The average intra and inter-cluster Euclidean 2 distance were estimated based on Ward's minimum variance and are presented in the Table 3. Similar results of clustering were reported by Mehrnaz et al, (2014), Hafiz et al, (2015), Muhammad et al, (2015) and Sandeep et al, (2015).…”

Section: Resultssupporting

confidence: 90%

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Principal Component and Cluster Analysis in Inbred Lines of Maize (Zea mays L.)

Mounika¹,

Ahamed²,

Umar³

2018

Int.J.Curr.Microbiol.App.Sci

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Section: Resultssupporting

confidence: 90%

“…Two dimensional scatter diagram was shown in Figures 1, and the genotypes numbered 41 and 36 i.e., CDM-306 and CDM-320 scattered away from other genotypes.These results were in accordance with those ofJinju et al, (2009),Muhammad et al, (2012),Sandeep et al, (2015),Avinash and Mishra (2016) andShrestha (2016) in maize.…”

supporting

confidence: 93%

Principal Component and Cluster Analysis in Inbred Lines of Maize (Zea mays L.)

Mounika¹,

Ahamed²,

Umar³

2018

Int.J.Curr.Microbiol.App.Sci

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“…2). The results are in accordance with the findings of Sandeep et al (2015); Avinash and Mishra (2016) and Shrestha (2016).…”

Section: Correlation Path and Principal Component Analysissupporting

confidence: 92%

Genetic Divergence Among Maize (Zea Mays L.) Inbred Lines Using Morphometric Markers

Awasthi¹,

Lata²,

Guleria³

et al. 2021

IJAR

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Genetic divergence study is imperative in breeding programmes in order to select distant parents. Forty maize inbred lines were assessed using morphometric markers to identify the potential inbred lines for yield and its component traits. The inbred lines were raised in ÃŽÂ±-RBD design replicated twice during kharif 2019. The morphological diversity analysis was done as per the standard statistical procedures using Mahalanobis D2-statistic. Analysis of variance revealed significant mean sum of squares due to genotypes for all the traits suggesting ample amount of genetic variability present among them. Moderate PCV, GCV and high heritability coupled with moderate genetic advance was observed for grain yield per plant, plant and cob placement height. Grain yield per plant exhibited significant positive correlation with all the traits except days to 50 per cent pollen shed and silking. Path analysis revealed high direct effects of days to 50 per cent pollen shed, harvest index, grains per row and 100 grain-weight on grain yield per plant therefore these morphometric traits would be the best selection indices to select high yielding genotypes. D2-statistics grouped 40 maize genotypes into three clusters. Cluster III has the highest cluster mean values for all the traits.Seven genotypes viz., 4186-4-05-1-1, LM-14, BAJIM-08-27, BIOM 10, LQPM24ÃƒÂ—1114-5, 9180-2, LDH 5(Vivek-21-1) were superior over the best check on the basis of mean performance and disease resistance and were diverse therefore, could be used in maize improvement program.

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“…In maize research's, PCA was applied to evaluate the relationship of climatic indices and yields variables (Meyer et al, 1991), in classi cation of Italian maize germplasm (Brandolini and Brandolini, 2001), for predicting biomass and grain yields (Shukla et al, 2004), to assess a atoxin contamination (Yao et al, 2011), to evaluated yield contributing variables (Bharathiveeramani and Prakash, 2012) and for an automatic system to kernel inspection (Valiente-González et al, 2014). PCA was also applied to characterize maize hybrids for water shortage (Guimarães et al, 2014), to evaluated sixty inbreds lines (Sandeep et al, 2017), to characterize grain yield and other variables in different maize hybrids grown under heat and drought stress (Ali et al, 2015), to predicting owering time, yield, and kernel dimensions by analyzing aerial images (Wu et al, 2019), to evaluated plant nutrient traits in baby maize (Magudeeswari et al, 2019) and to characterize fty-six Algerian maize populations (Belalia et al, 2019) and twenty-six sweet maize genotypes (Hemavathy, 2020).…”

Section: Introductionmentioning

confidence: 99%

“…In the owering stage, the rst two components explained 85.08% of the total variance (PC1 = 60.05% and PC2 = 25.03%) and, in the grain swelling stage the rst two components explained 98.52% of the total variance (PC1 = 91.48% and PC2 = 7.04%) Ali et al (2015). evaluated sixteen variables in twelve F1 single cross-maize hybrids and four crop growing seasons and found that the rst four components had eigenvalues greater than 1, with variance of 43.5% and 24.4%, respectively, for PC1 and PC2 Sandeep et al (2017). evaluated twelve variables in sixty inbreds lines and found that the rst three components presented 82.41% of the total variance, with 58.36%, 16.11% and 7.94% for PC1, PC2 and PC3.In twelve baby corn genotypes,Magudeeswari et al (2019) veri ed positive and negative correlations among six traits in the range of |0.003 ≤ r ≤ 0.848| and the absolute r mean was 0.44.…”

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confidence: 99%

Precision of Principal Component Analysis in a Larger Phenotypic Data Sets of Maize

Filho

Toebe

2022

Preprint

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In maize, research with multiple variables has been carried out and the principal component analysis (PCA) has been used in order to reduce the data dimensionality. The objective of this work was to study the impact of observations number and correlation matrix coefficients (r) magnitude in the eigenvalue estimates precision of the PCA, with simulated and real larger phenotypic data sets of maize. Seven data files were simulated, formed by twelve variables and 6340 observations. The first data file was simulated with 66 r of the correlation matrix equal to 0.35. The remaining six files were simulated with 66 r equal to 0.45, 0.55, 0.65, 0.75, 0.85 and 0.95. Real data file with twelve variables of 6340 maize plants with r in the interval |0.01 ≤ r ≤ 0.99| was used. For the eight cases, PCA were performed in 3000 resamples with replacement, for sample sizes of 12 to 1000 observations. Insufficient sampling generates inaccurate and biased principal components (PC1 and PC2) eigenvalues estimates, and samples with a high observations number allow reliable PCA. The precision of the PC1 and PC2 eigenvalues estimates increases with the highest observations number. The precision of the PC1 eigenvalues estimates increases with larger r magnitudes, and the PC2 eigenvalues decreases.

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Principal Component Analysis in Inbreds of Maize (Zea mays L.)

Cited by 5 publications

References 0 publications

Principal Component and Cluster Analysis in Inbred Lines of Maize (Zea mays L.)

Principal Component and Cluster Analysis in Inbred Lines of Maize (Zea mays L.)

Genetic Divergence Among Maize (Zea Mays L.) Inbred Lines Using Morphometric Markers

Precision of Principal Component Analysis in a Larger Phenotypic Data Sets of Maize

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