Analyzing genotype-by-environment interaction using curvilinear regression

Pereira, Dulce G.; Rodrigues, Paulo Canas; Mejza, Iwona; Mejza, Stanisław; Mexia, João T.

doi:10.1590/s0103-90162012000600003

Cited by 4 publications

(3 citation statements)

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“…In this way the phenotypic responses per block are regressed across environments, resulting in × regressions, where is the number of blocks. Other studies and generalisations related to Gusmão's approach were presented by Pereira et al (2007Pereira et al ( , 2012b.…”

Section: Statistical Models Based On Regression and Singular Value Dementioning

confidence: 97%

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

Rodrigues

2018

Biometrical Letters

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SummaryGenotype-by-environment interaction (GEI) is frequently encountered in multi-environment trials, and represents differential responses of genotypes across environments. With the development of molecular markers and mapping techniques, researchers can go one step further and analyse the whole genome to detect specific locations of genes which influence a quantitative trait such as yield. Such a location is called a quantitative trait locus (QTL), and when these QTLs have different expression across environments we talk about QTL-by-environment interaction (QEI), which is the basis of GEI. Good understanding of these interactions enables researchers to select better genotypes across different environmental conditions, and consequently to improve crops in developed and developing countries. In this paper we present an overview of statistical methods and models commonly used to detect and to understand GEI and QEI, ranging from the simple joint regression model to complex eco-physiological genotype-to-phenotype simulation models.

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Section: Statistical Models Based On Regression and Singular Value Dementioning

confidence: 97%

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

Rodrigues

2018

Biometrical Letters

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“…The occurrence of GEIs in multi-environment wheat yield trials is common in Brazil (Silva et al, 2011) and other countries (Sabaghnia et al, 2012;Graybosch et al, 2012;Tsenov and Atanasova, 2013;Cormier et al, 2013;Malik et al, 2013;Roostaei et al, 2014). In similar cases where the GEI is present in the joint analysis, the characterization of genotypes using adaptability and stability analyses is indicated (Yan et al, 2007;Miranda et al, 2009;Araújo et al, 2012;Pereira et al, 2012;Colombari Filho et al, 2013).…”

Section: Joint Analysis Of Variance By Genotype Classmentioning

confidence: 99%

Strategy for grouping wheat genotypes according to environmental responses in multi-location trials

Storck¹,

Benin²,

Marchioro³

et al. 2016

Aust J Crop Sci

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A better understanding of genotype x environment interactions (GEIs) can improve the selection and recommendation of wheat genotypes that are widely adapted and stable in different environments. The objective of this study was to evaluate a strategy to perform a joint analysis in wheat yield trials with genotypes classified according to their interactions with the environment. Measurements of the adaptability and stability estimates were used in the proposed strategy. We analyzed grain yield data from 367 wheat genotypes in 348 yield trials classified into 58 groups. In each group, 25 genotypes at different numbers of locations were evaluated using data from 2010, 2011 and 2012. Joint analysis of variance was performed for each group to determine the genotype contribution to the GEI. The genotypes were grouped into two classes: genotypes that interacted with the environment and genotypes with no interaction with the environment. An additional joint analysis of variance was performed for each class. Grouping genotypes into different classes provided valuable information concerning the dynamics of the GEI, which could enable plant breeders to maximize the efficiency of selection and offer recommendations for stable and widely adapted genotypes. The highest selective accuracy for the genotype effects was obtained for the joint analysis when the GEI was insignificant. The joint analysis of trials for wheat genotypes that interacted with the environment presented the highest selective accuracy for the GEI effect and a reduced selective accuracy for the genotype effect.

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“…The genotype x environment interaction (GxE) and the quantitative trait locus (QTL) x environment interaction (QxE) are common phenomena in multienvironmental trials (METs), and they represent a major challenge for breeders who intend to develop more adapted genotypes to different environmental conditions. The modelling strategies that have been used to understand GxE and QxE are based on fixed effect models, such as regression techniques (Rodrigues et al, 2011;Pereira et al, 2012aPereira et al, , 2012b, as well as on singular-value decomposition techniques (SVD) (Gauch Jr., 1992;Paderewski et al, 2011;Paderewski & Rodrigues, 2014), and on mixed effects models (Alimi et al, 2012).…”

Section: Introductionmentioning

confidence: 99%

A weighted AMMI algorithm for nonreplicated data

Assis

Dias

Rodrigues

2018

Pesq. agropec. bras.

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The objective of this work was to propose a weighting scheme for the additive main effects and multiplicative interactions (AMMI) model, as well as to assess the usefulness of this W-AMMI model in the study of genotype x environment interaction (GxE) and quantitative trait locus x environment interaction (QxE) for nonreplicated data. Data from the 'Harrington' x TR306 barley (Hordeum vulgare) mapping population, with 141 genotypes evaluated in 25 environments, were used to compare the results from the AMMI model with those of two proposed versions of the W-AMMI model: equal weights per row and equal weights per column. The proposed W-AMMI columns algorithm is viable to analyze data with heterogeneous variance, when there are no replicates available. The use of the AMMI and W-AMMI models, in the indicated cases, improves QTL detection, besides providing a sound interpretation of GxE and a better understanding of QxE, which allows obtaining valuable information on increasing productivities in different environments.

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Analyzing genotype-by-environment interaction using curvilinear regression

Cited by 4 publications

References 14 publications

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

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

Strategy for grouping wheat genotypes according to environmental responses in multi-location trials

A weighted AMMI algorithm for nonreplicated data

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