<abstract xmlns="http://www.w3.org/1999/xhtml"><p>Genotype-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.</p></abstract>

Genotype-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.

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

Biometrical Letters

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

Genotype-by-environment interaction (GEI) is frequently encountered in multi-environment trials, and represents differential responses of genotypes across...

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

Published Online: Dec 14, 2018

Page range: 123 - 138

DOI: https://doi.org/10.2478/bile-2018-0009

Keywords
Genotype-by-environment interactions, QTL-by-environment interactions, QTL detection, Additive main effects and multiplicative interaction model, Eco-physiological genotype-to-phenotype simulation models

© 2018 Paulo C. Rodrigues, published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

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

Published Online: Dec 14, 2018

Page range: 123 - 138

DOI: https://doi.org/10.2478/bile-2018-0009

KeywordsGenotype-by-environment interactions, QTL-by-environment interactions, QTL detection, Additive main effects and multiplicative interaction model, Eco-physiological genotype-to-phenotype simulation models

© 2018 Paulo C. Rodrigues, published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

Keywords
Genotype-by-environment interactions, QTL-by-environment interactions, QTL detection, Additive main effects and multiplicative interaction model, Eco-physiological genotype-to-phenotype simulation models