Cluster and Principal Component Analysis in Maize

Suryanarayana, L.; Sekhar, M. Reddi; Babu, D. Ratna; Ramana, A.V.; Rao, V. Srinivasa

doi:10.20546/ijcmas.2017.607.042

Cited by 8 publications

(8 citation statements)

References 4 publications

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“…This means that they maintained a uniform performance under diseased and disease-free conditions. This is also similar to reports from several other studies [22,23,24,25]. In the present study, genotype TGX 1993-4FN which was consistently the closest to the AMMI biplot origin, with the shortest vector projection on the GGE biplot, and with the lowest stability coefficients can be described as having the greatest stability.…”

Section: Discussionsupporting

confidence: 92%

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AMMI and GGE Analyses of Soyabean (Glycine max L. Merrill) Genotypes Infected and Uninfected with Cucumber mosaic virus

Salaudeen

Akinyemi

Wada

et al. 2019

AJRCS

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Soyabean is an important source of protein for millions of people in developing countries. However, infection by Cucumber mosaic virus (CMV) causes devastating losses. Cultivation of resistant varieties has been identified as the best management strategy in many crops. The present study was, therefore, conducted to identify soyabean genotypes with high stability for growth and seed weight under CMV and disease-free conditions. Thus, eight soyabean genotypes were evaluated as CMV-infected and uninfected, using completely randomised design replicated five times and set up in the screenhouse at the School of Agriculture and Agricultural Technology, Federal University of Minna, (lat.9°40֬ N;long 6°30֬ E at an altitude of 220 m.a.s.l), Nigeria in 2018. Soyabean seedlings were infected with the virus by sap transmission at 10 days after sowing (DAS). Additive Main Effects and Multiplicative (AMMI) analyses of the evaluated parameters for growth and seed weight of the test genotypes showed that environments’ effects -infected and uninfected- were significant (p<0.05). They accounted for 100% Genotype × Environment (G×E) interaction. Disease-free soyabean plants enhanced significantly higher growth and seed weight than the CMV-infected plants. The AMMI and Genotype main effects (G) plus Genotype×Environment (GGE) analyses showed that TGX 1993-4FN was the genotype with the greatest stability for leaf diameter, leaf length, number of leaves per plant, number of days to flowering and seed weight. It is recommended that, the soyabean genotype TGX 1993-4FN, can be exploited for breeding purposes and strategies that will prevent CMV infection in soyabean fields should so be adopted by farmers.

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Section: Discussionsupporting

confidence: 92%

“…Genotype × environment (G×E) interaction can be computed using Additive Main Effects and Multiplicative Interaction (AMMI) [21,22,23,24,25]. On the other hand, Genotype main effects (G) plus Genotype x Environment (GGE) interaction biplotsare a modification of the AMMI model [26].…”

Section: Introductionmentioning

confidence: 99%

AMMI and GGE Analyses of Soyabean (Glycine max L. Merrill) Genotypes Infected and Uninfected with Cucumber mosaic virus

Salaudeen

Akinyemi

Wada

et al. 2019

AJRCS

View full text Add to dashboard Cite

show abstract

“…Others recorded two PCs that contributed to 94.01% and 91.15% of total variation in root traits of 103 maize inbred lines evaluated under control and water-stressed conditions [53]. Suryanarayana et al [54] applied PCA and cluster analysis to data for 30 maize accessions in India. In their study the first five PCs had Eigen values greater than 1, which collectively explaining 85.31% of the variance.…”

Section: Discussionmentioning

confidence: 99%

Genetic Variability of Maize Hybrids and Populations and Interrelationships among Grain Yield and Its Related Traits under Drought and Low N Using Multivariate Analysis

Naggar

Shafik

Musa

et al. 2020

AJBGMB

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One of the best biometrical methods for estimating genetic diversity among germplasm collections is multivariate analysis; it is used to study their variability and genetic relatedness in order to increase their value in plant breeding programs. The objectives of the present study were to: (i) evaluate the magnitude of genetic diversity, based on phenotypic data, among 19 maize genotypes, under drought and/or low N stressed conditions in the field, using principle component analysis (PCA) and (ii) assess the interrelationships between maize grain yield and its related traits under such stressed conditions using genotype × trait (GT) biplot analysis. An experiment was conducted in two seasons using a split-split plot design with 3 replications, where 2 irrigation regimes (well-watered and water stressed at flowering) occupied the main plots, three N rates (high N, medium N and low N) occupied the sub plots and 19 maize genotypes occupied the sub-sub plots. The genotypes were evaluated for 19 agronomic traits. Analysis of variance was performed under each of the six environments. Significant differences (p≤0.01) were recorded among the maize genotypes for all studied traits under each environment. The best genotypes for each trait were identified. Results of the GT biplot indicated that high means of 100-kernel weight (100-KW), ears/plant (EPP), ear height, days to silking, days to anthesis, plant height, and chlorophyll concentration index (CCI) under water stress (WS), kernels/row (KPR), EPP, 100-KW and CCI under low N and KPR, EPP and 100-KW under WS combined with low N environment and low values of anthesis-silking interval (ASI) under the three stressed environments could be considered selection criteria for high grain yield under respective stressed environments and for drought and/or low N tolerance. It is recommended to select for high values of KPR, EPP and 100-KW and low value of ASI in order to increase grain yield under such stressed conditions.

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“…Others recorded two PCs that contributed to 94.01% and 91.15% of total variation in root traits of 103 maize inbred lines evaluated in control and water-stressed conditions [49]. Suryanarayana et al [50] applied PCA and cluster analysis to data for 30 maize accessions in India. In their study the first five PCs had eigenvalues greater than 1, which collectively explaining 85.31% of the variance.…”

Section: Dissimilarity Dendrogrammentioning

confidence: 99%

Genetic Diversity Based on Morphological Traits of 19 Maize Genotypes Using Principal Component Analysis and GT Biplot

Al-Naggar

Shafik

Musa

2020

ARRB

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Among the phenotypic, biochemical, and molecular methods employed in assessment of genetic diversity, the phenotypic method has proven efficient for the assessment, description and classification of germplasm collections to enhance their use in maize breeding. The objectives of the present study were: (i) to assess the extent of genetic diversity in a collection of Egyptian commercial maize hybrids and populations, through field evaluation under water and N stressed and non-stressed conditions, using morphological data based on Principle Component Analysis (PCA), (ii) to measure the genetic distance among these genotypes using UPGMA cluster analysis and (iii) to assess the relationship between grain yield and yield-related traits of maize genotypes using GT-biplot analysis. A two-year field experiment was conducted in a split-split plot design with 3 replications, where 2 irrigation regimes, three N rates and 19 maize genotypes occupied the main plots, sub plots and sub-sub plots, respectively. The germplasm was assessed for 21 agronomic traits. Highly significant differences (P ≤ 0.01) were observed among the maize hybrids and populations for all measured traits. Results of the GT biplot in the present study indicated that high values of 100-Kernel weight, ears/plant, kernels/plant, kernels/row, plant height, nitrogen use efficiency, nitrogen utilization efficiency, and grain nitrogen content and short ASI could be considered reliable secondary traits for improving grain yield under stressed and non-stressed conditions. The highest genetic distance was found between G9 (SC-2055) and each of G15 (American Early Dent), G18 (Midland) or G19 (Ried Type). The Agglomerative Hierarchical Clustering based on phenotypic data assigned the maize genotypes into five groups. The different groups obtained can be useful for deriving the inbred lines with diverse features and diversifying the heterotic pools.

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Cluster and Principal Component Analysis in Maize

Cited by 8 publications

References 4 publications

AMMI and GGE Analyses of Soyabean (Glycine max L. Merrill) Genotypes Infected and Uninfected with Cucumber mosaic virus

AMMI and GGE Analyses of Soyabean (Glycine max L. Merrill) Genotypes Infected and Uninfected with Cucumber mosaic virus

Genetic Variability of Maize Hybrids and Populations and Interrelationships among Grain Yield and Its Related Traits under Drought and Low N Using Multivariate Analysis

Genetic Diversity Based on Morphological Traits of 19 Maize Genotypes Using Principal Component Analysis and GT Biplot

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