Identification of superior genotypes and soybean traits by multivariate analysis
            and selection index

Leite, Wallace de Sousa; Unêda-Trevisoli, Sandra Helena; Silva, Fabiana Mota da; Silva, Alysson Jalles da; Mauro, Antônio Orlando Di

doi:10.5935/1806-6690.20180056

Cited by 16 publications

(14 citation statements)

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“…Leite et al. () indicated that exploratory multivariate techniques can be applied to select genotypes by analyzing the agronomic traits altogether, increasing the chance of success of a breeding program.…”

Section: Discussionmentioning

confidence: 99%

“…In contrast, FAI‐BLUP priorates genotype selection based on the ideotype, in an attempt to obtain a set of desirable traits. Thus, the Mulamba and Mock () index is notable, based on the rank sum, provides balanced gains ensuring high total genetic gain in several crops (Leite et al., , ; Rezende et al., ). Dalbosco et al.…”

Section: Discussionmentioning

confidence: 99%

“…According to Zetouni, Henryon, Kargo, and Lassen (2017), reduced genetic gains may be obtained for specific traits when using selection indexes, however, this reduction may be balanced by the genetic gains in the set of traits. Leite et al (2018) indicated that exploratory multivariate techniques can be applied to select genotypes by analyzing the agronomic traits altogether, increasing the chance of success of a breeding program. Higher values of GY, OIL, PRT, PH, and TGW are desirable for soybean crop (Matei et al, 2017;Matei et al, 2018).…”

Section: Discussionmentioning

confidence: 99%

“…In contrast, FAI-BLUP priorates genotype selection based on the ideotype, in an attempt to obtain a set of desirable traits. Thus, the Mulamba and Mock (1978) index is notable, based on the rank sum, provides balanced gains ensuring high total genetic gain in several crops (Leite et al, 2016(Leite et al, , 2018Rezende et al, 2014). Dalbosco et al (2018) compared parametric and nonparametric indexes in passion fruit selection and observed superiority for Mulamba and Mock (1978) index.…”

Section: Discussionmentioning

confidence: 99%

“…Thus, selection based on the ideotype should increase the whole genotype performance. Multiple trait indexes have been applied to soybean crop for the simultaneous selection of several traits (Leite et al, 2016;Leite, Unêda-Trevisoli, Silva, Silva, & Mauro, 2018;Teixeira et al, 2017;Wiggins et al, 2019). de Siqueira Gesteira, Bruzi, Zito, Fronza, and Arantes (2018), used multitrait indexes to associate precocity, the best productivity performance, with high oil and protein content in grains.…”

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confidence: 99%

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Selection indexes based on linear‐bilinear models applied to soybean breeding

et al. 2020

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Recently developed selection indexes provide solutions for plant breeding, using linear‐bilinear models that consider factors as fixed or random. This work aimed to compare the multitrait selection indexes based on factor analysis and ideotype‐design (FAI‐BLUP), GGE biplot, and grain yield × trait index (GYT), and proposes the use of predicted genetic values together with the GYT index (best linear unbiased prediction used in grain yield*trait index, GYT‐BLUP). In addition, this work indicates the best index to select superior soybean [Glycine max (L.) Merr.] genotypes, closer to the ideotype. Data from 35 homozygous soybean lines and four checks, were obtained from trials conducted in six locations in the southern region of Brazil in the 2014/2015 crop season. The grain yield, yield components, morphological and grain composition were evaluated. Phenotypic data were used for GGE biplot and GYT analysis, using the software GGE biplot. Genetic values were predicted with mixed models considering genotype and location as random and fixed effects, respectively. Thus, genetic values were used in GYT‐BLUP and FAI‐BLUP indexes. These methods were compared by Spearman's rank correlation. Genetic gains obtained by indexes and traits were estimated. Soybean lines L1 and L22, and cultivars C3 and C4 were selected based on their performance for multiple traits, for indexes used. Thus, we suggest to combined FAI‐BLUP and GYT‐BLUP indexes. The GYT‐BLUP has a high importance for grain yield, which was related to all other traits. FAI‐BLUP gave similar weights for all traits. So, combining different approaches can provide better answers to breeders.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

mentioning

confidence: 99%

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Selection indexes based on linear‐bilinear models applied to soybean breeding

et al. 2020

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Comparative study of univariate and multivariate selection strategies based on an integrated approach applied to oilseed rape breeding

Oghan,

Bakhshi,

Rameeh

et al. 2023

Crop Science

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Choosing the right method to discover superior genotypes is always one of the major challenges that oilseed rape (Brassica napus L.) researchers deal with. This work aimed to compare the univariate and multivariate selection indices utilizing Variance‐based, AMMI (Additive Main effects and Multiplicative Interaction)‐based, BLUP (Best Linear Unbiased Prediction)‐based and GE/T (Genotype × Environment/Trait)‐based strategies to determine the best‐performing (regarding stability, yield, and agronomic traits) oilseed rape genotypes. Utilized indices were categorized into five general methods, including univariate performance selection index (UPSI: mean performance), univariate stability selection indices (USSIs: Var, Shukla, ASV, MASV, MASI, and WAASB), multivariate performance selection indices (MPSIs: GT, GYT, and FAI‐BLUP), univariate performance and stability selection indices (UPSSIs: CV, HMGV, RPGV, WAASBY, and GGE), and multivariate performance and stability selection index (MPSSI: MTSI). Data from 18 oilseed rape genotypes and two checks were obtained from trials conducted in eight locations in Iran during two consecutive crop years from 2018–2020. The grain yield and agronomic traits were evaluated. The significant genotype × environment interaction was found for grain yield and agronomic traits in the combined ANOVA. In terms of grain yield, the selection indices GGE, HMGV, and RPGV (6.51), GYT (6.43), GT (5.35), MTSI (4.95), FAI‐BLUP (4.64), and WAASBY (3.97) achieved better‐than‐average genetic gains. Using an optimized model of USSIs (O‐USSIs), genetic gain increased from ‐0.15 to 3.26 on average, which shows the efficiency of O‐USSIs in high‐yielding and stable genotypes selection. Based on spearman's ranking correlation, a positive relationship was found among MPSIs, MPSSI, UPSSIs, and UPSIs, but not for USSIs. Based on an integrated model of selection indices, genotypes G2, G10, G11, G14, and G20 were detected as superior genotypes with high‐yielding, high stability and desirable agronomic traits. It could be concluded that combining different approaches of genotype selection based on an integrated model can provide better answers to oilseed rape breeders.This article is protected by copyright. All rights reserved

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