Multi-trait genomic selection indexes applied to identification of superior genotypes

Silva, Lidiane Aparecida; Peixoto, Marco Antônio; Peixoto, Leonardo de Azevedo; Romero, Juan Vicente; Bhering, Leonardo Lopes

doi:10.1590/1678-4499.20200381

Cited by 3 publications

(1 citation statement)

References 31 publications

(36 reference statements)

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“…In the context of multiple selection, linear indexes can be used [ 17 ]. One fragility of linear selection indexes is the collinearity often observed in the set of assessed traits, which can bias the coefficients of multiple regression, and thus erode selection gains [ 18 ]. To overcome this fragility, the multi-trait genotype-ideotype distance index (MGIDI) has been proposed [ 19 ].…”

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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Section: Introductionmentioning

confidence: 99%

MGIDI: a powerful tool to analyze plant multivariate data

et al. 2022

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Balancing genomic selection efforts for allogamous plant breeding programs

Resende

2024

J. Crop Sci. Biotechnol.

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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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Multi-trait genomic selection indexes applied to identification of superior genotypes

Cited by 3 publications

References 31 publications

MGIDI: a powerful tool to analyze plant multivariate data

MGIDI: a powerful tool to analyze plant multivariate data

Balancing genomic selection efforts for allogamous plant breeding programs

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

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