A robust AMMI model for the analysis of genotype-by-environment data

Rodrigues, Paulo Canas; Monteiro, Andreia; Lourenço, Vanda

doi:10.1093/bioinformatics/btv533

Cited by 47 publications

(44 citation statements)

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“…A further alternative to the full interaction model is the additive main effects and multiplicative interaction (AMMI) model (Gollob, 1968;Mandel, 1969;Bradu and Gabriel, 1978;Gauch, 1988;Gauch, 1992;Paderewski et al, 2011;Hongyu et al, 2014;Rodrigues et al, 2016), which is more flexible than the Finlay and Wilkinson regression, because it can partition the interaction into = min ( − 1, − 1) terms. It combines analysis of variance (ANOVA) and…”

Section: Statistical Models Based On Regression and Singular Value Dementioning

confidence: 99%

“…Several data imputation techniques have been proposed to deal with the missing data, such as those of Arciniegas-Alarcón (2010 and Gauch and Zobel (1990). Rodrigues et al (2014) proposed a weighted AMMI algorithm where different weights are assigned to columns and/or rows and/or particular cells of the two-way data table, and Rodrigues et al (2016) proposed a robust version of the AMMI model that accounts for data contamination. Moreover, Josse et al (2014) and references therein discuss the use of the Bayesian AMMI model to study genotype-byenvironment data.…”

Section: Statistical Models Based On Regression and Singular Value Dementioning

confidence: 99%

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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: 99%

Section: Statistical Models Based On Regression and Singular Value Dementioning

confidence: 99%

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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“…Therefore, the use of robust methods has been advocated for inference in the linear and linear mixed model setups [6,25], as well as in ridge regression [1,15,26,27,45,52]. As a result of such considerations and the recent advances in computing power, it is not surprising that there has been a strong, renewed interest in exploring these techniques to robustify existing methods or develop new procedures robust to moderate deviations from model specifications [24,41].…”

Section: Introductionmentioning

confidence: 99%

Robust estimation of heritability and predictive accuracy in plant breeding: evaluation using simulation and empirical data

2020

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Background: Genomic prediction (GP) is used in animal and plant breeding to help identify the best genotypes for selection. One of the most important measures of the effectiveness and reliability of GP in plant breeding is predictive accuracy. An accurate estimate of this measure is thus central to GP. Moreover, regression models are the models of choice for analyzing field trial data in plant breeding. However, models that use the classical likelihood typically perform poorly, often resulting in biased parameter estimates, when their underlying assumptions are violated. This typically happens when data are contaminated with outliers. These biases often translate into inaccurate estimates of heritability and predictive accuracy, compromising the performance of GP. Since phenotypic data are susceptible to contamination, improving the methods for estimating heritability and predictive accuracy can enhance the performance of GP. Robust statistical methods provide an intuitively appealing and a theoretically well justified framework for overcoming some of the drawbacks of classical regression, most notably the departure from the normality assumption. We compare the performance of robust and classical approaches to two recently published methods for estimating heritability and predictive accuracy of GP using simulation of several plausible scenarios of random and block data contamination with outliers and commercial maize and rye breeding datasets. Results: The robust approach generally performed as good as or better than the classical approach in phenotypic data analysis and in estimating the predictive accuracy of heritability and genomic prediction under both the random and block contamination scenarios. Notably, it consistently outperformed the classical approach under the random contamination scenario. Analyses of the empirical maize and rye datasets further reinforce the stability and reliability of the robust approach in the presence of outliers or missing data. Conclusions: The proposed robust approach enhances the predictive accuracy of heritability and genomic prediction by minimizing the deleterious effects of outliers for a broad range of simulation scenarios and empirical breeding datasets. Accordingly, plant breeders should seriously consider regularly using the robust alongside the classical approach and increasing the number of replicates to three or more, to further enhance the accuracy of the robust approach.

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“…Assessing and modeling GEI is a key objective in evolutionary science (Gillespie and Turelli, 1989;Higginson and Reader, 2009;Ingleby et al, 2010), and in agricultural science to assist breeding in a context of global and local environmental changes (Falconer, 1952;Hammer et al, 2006). Multi-environment trials are the regular basis for the study of GEI, and the additive main effects and multiplicative interaction model is one of the most widely used analysis tools (Rodrigues et al, 2016). This has led to a widespread statistical conception of GEI, but there is a need to understand the origin of GEI when it is expressed at the level of a global performance (e.g., crop yield; Chapman, 2008).…”

Section: Introductionmentioning

confidence: 99%

Genotype-by-Environment Interactions Emerge from Simple Assemblages of Mathematical Functions in Ecological Models

et al. 2017

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A robust AMMI model for the analysis of genotype-by-environment data

Abstract: Supplementary data are available at Bioinformatics online.

Cited by 47 publications

References 41 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

Robust estimation of heritability and predictive accuracy in plant breeding: evaluation using simulation and empirical data

Genotype-by-Environment Interactions Emerge from Simple Assemblages of Mathematical Functions in Ecological Models

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