An overview of statistical methods to detect and understand genotype-by-environment interaction and QTL-by-environment interaction

Rodrigues, Paulo Canas

doi:10.2478/bile-2018-0009

Cited by 16 publications

(8 citation statements)

References 62 publications

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“…A signifcant genotype by environment interaction for quantitative traits such as yield can reduce the usefulness of subsequent analyses, restrict the signifcance of inferences that would otherwise be valid, and severely restrict the possibility of choosing superior genotypes [10][11][12]. According to Rodrigues et al [13] and Rodrigues [14], genotype environment interaction is defned by the change in the genetic ranking of genotypes with respect to the environment; for example, a genotype that performs well in wellwatered conditions may perform poorly in dry conditions. Te ultimate objective of plant breeders in a crop improvement program is to develop genotypes that can be adapted to a wide variety of diverse environments [15].…”

Section: Introductionmentioning

confidence: 99%

AMMI and GGE Biplot Analyses for Mega Environment Identification and Selection of Some High-Yielding Cassava Genotypes for Multiple Environments

Daemo

Yohannes

Beyene

et al. 2023

International Journal of Agronomy

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Cassava (Manihot esculenta Crantz) is a staple food and generates income for smallholder farmers in southern Ethiopia. The performance of cassava genotypes varies in different growing environments; thus, the evaluation of genotypes tested in various environments plays an essential role in developing strategies to delineate environments, explore unstable genotypes in target environments, and identify stable genotypes for multiple environments. In this regard, there needs to be more information on the identification of mega-environments and stable genotypes with high yields for wide adaptation. Thus, this study aimed to identify mega-environment and high-yielding cassava genotypes for multiple environments using AMMI and GGE biplots. A total of 25 genotypes were evaluated in six environments using a RCBD during the 2020–2021 cropping season. The AMMI analysis of variances revealed that environments, genotypes, and genotype-environment interaction had a significant ( P ≤ 0.001 ) influence on cassava fresh storage root yield (t·ha−1), showing genetic variability among genotypes by changing environments. The genotype-by-environment interaction showed a 61.36% contribution to the total treatment SS variation, while the environment and genotype effects explained 28.16% and 10.48% of the total treatment SS, respectively. IPCA1 and IPCA2 accounted for 33.42% and 23.5% of the GE interactions SS, respectively. The GGE biplot showed that the six environments used in this study were delineated into three mega-environments, namely, the first (Tarcha and Disa), the second (Wara and Areka), and the third (Jimma and Bonbe). Those mega-environments could be helpful for genotype evaluation and effective breeding. The GGE biplot indicated that the vertex genotypes were G16, G17, and G25. They are regarded as specifically adapted genotypes since they are more responsive to environmental change. The GGE biplot also revealed that Tarcha was ideal, having the most discriminating and representative environment, while G10 was the ideal and the overall winning genotype for the current study. Moreover, the genotypes G10 and G14 were identified as being the most stable, with a higher fresh storage root yield than the grand mean. Thus, G10 and G14 were selected as superior genotypes that could be promoted to advanced yield trials to develop stable cultivars with better storage root yield of cassava.

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Section: Introductionmentioning

confidence: 99%

AMMI and GGE Biplot Analyses for Mega Environment Identification and Selection of Some High-Yielding Cassava Genotypes for Multiple Environments

Daemo

Yohannes

Beyene

et al. 2023

International Journal of Agronomy

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“…An overview of these techniques is presented in Malosetti et al. (2013), Rodrigues (2018), van Eeuwijk (1995), and van Eeuwijk et al. (2016), and their application in this paper is briefly presented below.…”

Section: Methodsmentioning

confidence: 99%

“…The nonlinear interactions between the yield components over developmental time feed into emerging properties of the crop growth model that in the end cause GEI in yield. When the yield component can be predicted from a selected set of markers (QTLs) or a full set of markers, like in genomic prediction (de los Campos et al, 2013;Heslot et al, 2015), yield performance for combinations of new genotypes in new environmental conditions could be predicted early on in the breeding cycle from marker profiles together with environmental inputs (Bustos-Korts et al, 2016, 2018Malosetti et al, 2016).…”

Section: Crop Sciencementioning

confidence: 99%

“…Here, the sensitivity of yield to the parameters was accomplished by applying multiple statistical methods for investigating two-way tables of genotype × environment means: factorial regression (van Eeuwijk et al, 1996), additive main effects and multiplicative interaction (AMMI) analysis (Gauch, 1988;Gollob, 1968;Mandel, 1969), and principal component analysis, or GGE (genotypic main effects and GEI) analysis (Yan & Kang, 2002). An overview of these techniques is presented in Malosetti et al (2013), Rodrigues (2018), van Eeuwijk (1995, and van Eeuwijk et al ( 2016), and their application in this paper is briefly presented below.…”

Section: Sensitivity Analysesmentioning

confidence: 99%

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An analysis of simulated yield data for pepper shows how genotype × environment interaction in yield can be understood in terms of yield components and their QTLs

et al. 2021

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Complex traits like yield are those in which phenotypic variation can be modeled as a linear function of a set of quantitative trait loci (QTLs) with environment dependency. This environment dependency can be observed at a phenotypic level as genotype × environment interaction (GEI) for yield itself and at an underlying genetic level as QTL × environment interaction (QEI). We show how GEI in yield may follow from pleiotropic QTLs for yield components that themselves are not environment dependent. We generated synthetic yield data via a crop growth model and analyzed these data by common statistical models for GEI and QEI. The QTLs for yield were pleiotropic with those for yield components. Such pleiotropy offers a path for improvement of yield under GEI. As a model system we used sweet pepper (Capsicum annuum L.) and developed an eco‐physiological model for yield with seven genotype‐specific inputs or yield components. Synthetic yields were simulated for a back cross population of 500 lines across a factorial combination of four major environmental drivers. The yield components were given a simple QTL basis and produced credible amounts and patterns of GEI for yield. The QEI for yield could be interpreted from the expression of QTLs for yield components and the interaction of these components with the environmental drivers. We see the generation of synthetic yield data via crop growth models followed by an analysis with statistical models for GEI and QEI to quantify the contribution of yield components to GEI as a helpful step in the development of yield prediction models for complex traits across environments that can also serve as a basis for decisions on selection strategies of complex traits.

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“…Modelos linear-bilineares (também chamados de multiplicativo) para o estudo de interações de dados de duas entradas foi estudado por Mandel (1961Mandel ( , 1969 ou a partir da matriz (SREG) de genótipo (G) mais GEI (GGE), e os padrões de resposta de genótipos e ambientes pode ser visualizados graficamente usando Biplots (GABRIEL, 1971;KEMPTON, 1984). O W-AMMIé uma generalização do modelo AMMI que permite ter em consideração a heterogeneidade da variância do erro (error variance) ao longo dos ambientes (RODRIGUES, 2012;RODRIGUES, et al, 2014).…”

Section: Figura 28 -O W-ggeunclassified

Comparação do GGE biplot-ponderado e AMMI- ponderado com outros modelos de interação genótipo x ambiente

Hongyu¹

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Comparison of weighted-GGE biplot and weighted-AMMI with other models of interaction genotype × environment Genotype × environment interaction (GEI) is an extremely important issue in plant breeding and production. The selection and recommendation of superior genotypes are hampered due to the occurrence of GEI and represents a major challenge for researchers. In this context, biplot analyzes have been increasingly used in analyzing agronomic data, in which data are represented by a table of two entries of means of GEI. However, the particularities in the biplot graphic hamper its interpretation, and could lead the researcher to errors. There are several models in the literature for DGE analysis (GEI data), among them, the most used are the AMMI model (Additive Main effects and Multiplicative Interaction Models) and GGE biplot (Genotype main effects + Genotype environment interaction). The AMMI model is a statistical method to understand the structure of interactions between genotypes and environments, combining the analysis of variance and principal component analysis, to adjust, respectively, the main effects (G and E) and the effects of GEI. The GGE Biplot groups genotype of additive effect with multiplicative effect of GEI, and submit these to the principal component analysis. There are two problems in using these models: i) can only be used to analyze MET data (multi-environments), which has a unique feature and ii) whose environments are heterogeneous. This paper aims to propose new W-GGE biplot models (Weighted Genotype main effects + Genotype environment interaction) and AMMI-weighted multi-environments for data analysis, and make a comparison between the existing models as AMMI and GGE biplot; mega-environment analysis; genotype evaluation, test environment within each mega-environment and understand the causes of GEI.

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An overview of statistical methods to detect and understand genotype-by-environment interaction and QTL-by-environment interaction

Cited by 16 publications

References 62 publications

AMMI and GGE Biplot Analyses for Mega Environment Identification and Selection of Some High-Yielding Cassava Genotypes for Multiple Environments

AMMI and GGE Biplot Analyses for Mega Environment Identification and Selection of Some High-Yielding Cassava Genotypes for Multiple Environments

An analysis of simulated yield data for pepper shows how genotype × environment interaction in yield can be understood in terms of yield components and their QTLs

Comparação do GGE biplot-ponderado e AMMI- ponderado com outros modelos de interação genótipo x ambiente

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