Cluster and principal component analysis (PCA) in ashwagandha [Withania somnifera (L.) Dunal] for root traits

Ekka, Amita; Tirkey, Alice; Kujur, Nidhi

doi:10.22271/chemi.2021.v9.i1ap.11687

Cited by 5 publications

(3 citation statements)

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“…Multivariate analysis tools such as principal component analysis (PCA) and linear discriminant analysis have been extensively used in filed experiments (Ekka et al., 2021; Urun et al., 2021). These analyses were successfully employed to establish a relationship between traits but ranking of treatments based on single trait values remains a challenge.…”

Section: Introductionmentioning

confidence: 99%

Understanding of G × E interactions of yield attributes in soybean MAGIC population and characterization for charcoal rot resistance

Maranna,

Nataraj,

Kumawat

et al. 2024

Agronomy Journal

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Multi‐parent advanced generation inter‐cross (MAGIC) populations have been developed and utilized in crop improvement programs in several economically important crops. In the current study, soybean (Glycine max L.) MAGIC population was evaluated at four locations to identify stable and high‐yielding MAGIC RILs (recombinant inbred lines) and to understand the genotype and environmental interaction for grain yield and attributing traits. For different traits under study, pooled analysis of variance indicated the significant genotype × environmental interactions at p < 0.01. RILs such as G236, G455, G159, G493, and G488 found ideal for grain yield across the four environments. Through multi‐trait genotype‐ideotype distance index ideotype‐MAGIC RILs have been identified for each location. Significant positive correlation of grain yield with pods per plant, 100‐seed weight, plant height, number of nodes per plant, and branches per plant has been identified. Under field conditions, screening of F2:3 generation of MAGIC population revealed that only 59 progenies exhibited adult plant resistance for charcoal rot disease (caused by Macrophomina phaseolina). Although under controlled conditions parental genotypes EC 572136 and EC 572109 found to be partially resistant with least mean necrosis length, MAGIC RILs (F2:8) showed variable reaction. Thus, the study established diverse genetic resources useful for both mapping and varietal development program.

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Section: Introductionmentioning

confidence: 99%

Understanding of G × E interactions of yield attributes in soybean MAGIC population and characterization for charcoal rot resistance

Maranna,

Nataraj,

Kumawat

et al. 2024

Agronomy Journal

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“…Despite SMLR and CA's importance, one weakness is the collinearity often observed across a range of assessed traits leading to unfavorable results or bias if not handled correctly [15,28,29]. PCA and LDA have been thoroughly used for dimensionality minimization and visual convergence of a two-way table combining treatments and traits [30,31]. Even though all of the above analyses can provide a holistic view of the relationships between traits, treatments' classification depending on the trait data continues to be a challenge.…”

Section: Introductionmentioning

confidence: 99%

Identification of Wheat Ideotype under Multiple Abiotic Stresses and Complex Environmental Interplays by Multivariate Analysis Techniques

Al-Ashkar,

Sallam,

Ibrahim

et al. 2023

Plants

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Multiple abiotic stresses negatively impact wheat production all over the world. We need to increase productivity by 60% to provide food security to the world population of 9.6 billion by 2050; it is surely time to develop stress-tolerant genotypes with a thorough comprehension of the genetic basis and the plant’s capacity to tolerate these stresses and complex environmental reactions. To approach these goals, we used multivariate analysis techniques, the additive main effects and multiplicative interaction (AMMI) model for prediction, linear discriminant analysis (LDA) to enhance the reliability of the classification, multi-trait genotype-ideotype distance index (MGIDI) to detect the ideotype, and the weighted average of absolute scores (WAASB) index to recognize genotypes with stability that are highly productive. Six tolerance multi-indices were used to test twenty wheat genotypes grown under multiple abiotic stresses. The AMMI model showed varying differences with performance indices, which disagreed with the trait and genotype differences used. The G01, G12, G16, and G02 were selected as the appropriate and stable genotypes using the MGIDI with the six tolerance multi-indices. The biplot features the genotypes (G01, G03, G11, G16, G17, G18, and G20) that were most stable and had high tolerance across the environments. The pooled analyses (LDA, MGIDI, and WAASB) showed genotype G01 as the most stable candidate. The genotype (G01) is considered a novel genetic resource for improving productivity and stabilizing wheat programs under multiple abiotic stresses. Hence, these techniques, if used in an integrated manner, strongly support the plant breeders in multi-environment trials.

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“…Multivariate exploratory techniques such as Principal Component Analysis (PCA) and Linear Discriminant Analysis (LDA) have been extensively used for dimensionality reduction and visual approximation of a twoway table involving treatments and plant traits [14][15][16]. Although these approaches easily provide an overview of the relationships between traits, ranking the treatments based on trait values remains a challenge.…”

Section: Introductionmentioning

confidence: 99%

MGIDI: a powerful tool to analyze plant multivariate data

et al. 2022

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Background Commonly, several traits are assessed in agronomic experiments to better understand the factors under study. However, it is also common to see that even when several traits are available, researchers opt to follow the easiest way by applying univariate analyses and post-hoc tests for mean comparison for each trait, which arouses the hypothesis that the benefits of a multi-trait framework analysis may have not been fully exploited in this area. Results In this paper, we extended the theoretical foundations of the multi-trait genotype-ideotype distance index (MGIDI) to analyze multivariate data either in simple experiments (e.g., one-way layout with few treatments and traits) or complex experiments (e.g., with a factorial treatment structure). We proposed an optional weighting process that makes the ranking of treatments that stands out in traits with higher weights more likely. Its application is illustrated using (1) simulated data and (2) real data from a strawberry experiment that aims to select better factor combinations (namely, cultivar, transplant origin, and substrate mixture) based on the desired performance of 22 phenological, productive, physiological, and qualitative traits. Our results show that most of the strawberry traits are influenced by the cultivar, transplant origin, cultivation substrates, as well as by the interaction between cultivar and transplant origin. The MGIDI ranked the Albion cultivar originated from Imported transplants and the Camarosa cultivar originated from National transplants as the better factor combinations. The substrates with burned rice husk as the main component (70%) showed satisfactory physical proprieties, providing higher water use efficiency. The strengths and weakness view provided by the MGIDI revealed that looking for an ideal treatment should direct the efforts on increasing fruit production of Albion transplants from Imported origin. On the other hand, this treatment has strengths related to productive precocity, total soluble solids, and flesh firmness. Conclusions Overall, this study opens the door to the use of MGIDI beyond the plant breeding context, providing a unique, practical, robust, and easy-to-handle multi-trait-based framework to analyze multivariate data. There is an exciting possibility for this to open up new avenues of research, mainly because using the MGIDI in future studies will dramatically reduce the number of tables/figures needed, serving as a powerful tool to guide researchers toward better treatment recommendations.

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Cluster and principal component analysis (PCA) in ashwagandha [Withania somnifera (L.) Dunal] for root traits

Cited by 5 publications

References 0 publications

Understanding of G × E interactions of yield attributes in soybean MAGIC population and characterization for charcoal rot resistance

Understanding of G × E interactions of yield attributes in soybean MAGIC population and characterization for charcoal rot resistance

Identification of Wheat Ideotype under Multiple Abiotic Stresses and Complex Environmental Interplays by Multivariate Analysis Techniques

MGIDI: a powerful tool to analyze plant multivariate data

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