Statistical methods to study adaptability and stability of wheat genotypes

Bornhofen, Elesandro; Benin, Giovani; Storck, Lindolfo; Woyann, Leomar Guilherme; Duarte, Thiago Franco; Stoco, Matheus Giovane; Marchioro, Volmir Sérgio

doi:10.1590/1678-4499.557

Cited by 46 publications

(39 citation statements)

References 26 publications

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“…Due to various factors that influence the determination of which genotype should be selected for specific growing conditions, it is necessary to employ and adapt biometric models to obtain accurate and reliable estimates that minimize distortions imposed by biases intrinsic to phenotypic measurements (Bornhofen et al, 2017). Phenotypic expression of seed yield is more than 80% due to growing environment effects.…”

Section: Introductionmentioning

confidence: 99%

Research Article Phenotypic and predicted genetic approaches for genotype ranking of wheat seed yield in Brazil

Szareski¹,

Carvalho²,

Kehl³

et al. 2018

Genet. Mol. Res.

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This work was aimed at determining stability and adaptability through Additive Main Effects and Multiplicative Interaction (AMMI) and Genotype Main Effects and Genotype Environment Interaction (GGE) methodologies, as well as to estimate and predict Restricted Maximum Likelihood/Best Linear Unbiased Prediction (REML/BLUP) parameters and employ them in multivariate models using wheat genotypes grown in the major wheat regions of Brazil. The trials were conducted during the 2017growing seasnon across 12 regions of Brazil, with nine wheat genotypes, arranged in three replicates. When there were significant G x E interactions, the AMMI and GGE methods were applied. The scores were represented in biplot graphs through multivariate methodology of the principal components. REML/BLUP estimates and predictions were employed in the GGE multivariate method to obtain inferences based on genetic effects, which was denominated predicted genetic GGE approach. The predicted genetic approach was superior to a phenotypic comparison to explain the effects of genotypes x ©FUNPEC-RP www.funpecrp.com.br Genetics and Molecular Research 17 (3): gmr18026 V.J. Szareski et al 2 environments interaction for wheat seed yield in Brazil. Specific adaptability for seed yield was established through phenotypic and genetic predicted approaches for genotypes BRS 331 and Marfimin the environment Itapeva, SP, as well as the genotype FPS Certerotoin the environment Cascavel. PR, and BRS 327 in the environment Cruz Alta, RS. The use of multivariate biometric methodologies along with the new predicted genetic approach enables reliable positioning of wheat genotypes for seed production across the main wheat regions of Brazil.

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Section: Introductionmentioning

confidence: 99%

Research Article Phenotypic and predicted genetic approaches for genotype ranking of wheat seed yield in Brazil

Szareski¹,

Carvalho²,

Kehl³

et al. 2018

Genet. Mol. Res.

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“…Users who research the associations between stability indexes (e.g., Woyann et al, 2018; Bornhofen et al, 2017; Freiria et al, 2018; Shahbazi, 2019) often find difficulties in computing the set of statistics and binding them into a “ ready-to-read ” file. provides an efficient solution for doing that.…”

Section: The Packagementioning

confidence: 99%

“…Although useful, these packages do not offer options to perform a complete analysis of MET data, i.e., to provide tools for all steps of the analysis (check, manipulation, analysis, and visualization of data). For example, requires as input data a two-way table containing genotype by environment means with genotypes in rows and environments in columns, but doesn’t provide any function to create quickly such table from data that often is in a “ long ” format in R. In addition, several studies often compare different stability methods (e.g., Woyann et al, 2018; Scapim et al, 2010; Bornhofen et al, 2017; Freiria et al, 2018; Shahbazi, 2019; Teodoro et al, 2019). This requires a range of different packages to be used, making it the coding tedious and difficult to follow.…”

Section: Introductionmentioning

confidence: 99%

metan: an R package for multi-environment trial analysis

Olivoto

Lúcio

2020

Preprint

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10 1. Multi-environment trials (MET) are crucial steps in plant breeding programs that aim 11 increasing crop productivity to ensure global food security. The analysis of MET data 12 requires the combination of several approaches including data manipulation, visualization, 13 and modeling. As new methods are proposed, analyzing MET data correctly and 14 completely remains a challenge, often intractable with existing tools. 15 2. Here we describe the metan R package, a collection of functions that implement a 16 workflow-based approach to (a) check, manipulate and summarise typical MET data; (b) 17 analyze individual environments using both fixed and mixed-effect models; (c) compute 18 parametric and non-parametric stability statistics; (c) implement biometrical models 19 widely used in MET analysis; and (d) plot typical MET data quickly.203. In this paper, we present a summary of the functions implemented in metan and how 21 they integrate into a workflow to explore and analyze MET data. We guide the user 22 along a gentle learning curve and show how adding only a few commands or options at 23 a time, powerfull analyzes can be implemented. 24 4. metan offers a flexible, intuitive, and richly documented working environment with tools 25 that will facilitate the implementation of a complete analysis of MET data sets. 26 Key-words: AMMI, biometry, genotype-environment interaction, GGE biplot, multi-27 environment trials, R software, stability, statistics 28 29In 50 years the world average of cereal yields has increased by 64%, from 1.68 to 30 2.76 t ha −1 . In the same period, the total production of cereals has raised from 1.305 × 10 9 to 31 3.6 × 10 9 t, an increase of 175%, while the cultivated area increased by only 7.9% in the same 32 period (FAOSTAT, 2019). These unparallel increases have been possible due to the improved 33 cultivation techniques in combination with superior cultivars. For maize, for example, 50% 34 of the increase in yield was due to breeding (Duvick, 2005). Plant breeding programs have 35 been developing new cultivars for adaptation to new locations, management practices, or 36 growing conditions, in a clear and crucial example of exploitation of genotype-vs-environment 37 interaction (GEI). 38The breeders' desire to modeling the GEI appropriately has led to the development of 39 the so-called stability analyses, which includes ANOVA-based methods (Yates ) and some methods that combines 43 different statistical techniques, such as the Additive Main Effect and Multiplicative Interaction, 44 AMMI, (Gauch, 2013), and Genotype plus Genotype-vs-Environment interaction, GGE, (Yan 45 & Kang, 2003). Then, it is no surprise that scientific production related to multi-environment 46 trial analysis has been growing fast in the last decades. A bibliometric survey in the SCOPUS 47 database revealed that in the last half-century (1969-2019) 6590 documents were published 48 in 902 sources (Journals, books, etc.) by 19.351 authors. In this period, the number of 49 publications has been increased on av...

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“…A frequent question of the breeder is about which method to choose. Studies have showed that some methods traditionally used for evaluating phenotypic stability are not complementary, because they are based on similar concepts of stability (Bornhofen et al 2017). Rono et al (2016) suggested that this choice can be made according to the profile and the characteristics of the data set to be analyzed.…”

Section: Introductionmentioning

confidence: 99%

Genotype by environment interaction and adaptability of photoperiod-sensitive biomass sorghum hybrids

et al. 2019

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Biomass sorghum is an alternative feedstock to cogenerate energy and produce second-generation ethanol. The aim of this study was to evaluate the Genotype by Environment Interaction (GEI) in biomass sorghum and to identify the hybrids that associate high adaptability and stability using the Toler nonlinear regression, the Genotypes plus Genotype by Environment (GGE) biplot, and the Annicchiarico recommendation index. Thirty-three experimental photoperiod-sensitive single-cross hybrids and three checks were evaluated in relation to the traits: flowering time, plant height, moisture content, green mass yield, and dry mass yield. It PLANT BREEDING -Article was observed that the effects of hybrids, environment, and GEI were expressive. The GEI was predominantly complex for the traits related to the biomass yield. The Toler, GGE biplot and Annicchiarico methods show complementarity. The experimental hybrids 1, 8, 22, 31 and 33 are promising because of associating stability and lower recommendation risk. The hybrids 1 and 8 present broad adaptability, while the hybrids 22, 31 and 33 exhibit specific adaptability to high quality environments.

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Statistical methods to study adaptability and stability of wheat genotypes

Abstract: The data analyzed in this study showed that the AMMI1 and GGE biplot methods are equivalent to rank genotypes for stability and adaptability.

Cited by 46 publications

References 26 publications

Research Article Phenotypic and predicted genetic approaches for genotype ranking of wheat seed yield in Brazil

Research Article Phenotypic and predicted genetic approaches for genotype ranking of wheat seed yield in Brazil

metan: an R package for multi-environment trial analysis

Genotype by environment interaction and adaptability of photoperiod-sensitive biomass sorghum hybrids

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