Genotype-by-Environment Interactions

Abstract

The penultimate success of a plant breeding programme depends on its ability to provide farmers with genotypes/clones with guaranteed superior performance (phenotype) in terms of yield and/or quality across a range of environments. While there can be clones that do well across a wide range of conditions (widely adapted genotypes), there are also clones that perform well exclusively under a restricted set of environments (specifically adapted genotypes). As in widely adapted genotypes, specific adaptation of genotypes is also closely related to the phenomenon of genotype-by-environment interaction. Information about phenotypic stability and adaptability assessed through GE interaction studies is prime for the selection of crop varieties/clones. Since phenotypic performance of a genotype is not necessarily the same under diverse agro-ecological conditions, the concept of stability has been defined and assessed in several ways and several biometrical methods including univariate and multivariate analyses (Lin et al. 1986; Becker and Leon 1988; Crossa 1990). The most widely used is the regression method, based on regressing the mean value of each genotype on the environmental index or marginal means of environments (Romagosa and Fox 1993). A good method to measure stability was proposed by Finlay and Wilkinson (1963) and was later improved by Eberhart and Russell (1966). They were followed by AMMI model (Gauch and Zobel 1996) and GGE biplot (Yan and Kang 2003). All these merely tried to group genotypes and environments and do not use other information than the two-way table of means. Further, factorial regression was introduced as an approach to explicitly utilize genotypic and environmental covariates for describing and explaining GE interactions. Finally, QTL modelling was put forth as a natural extension of factorial regression, where marker information is translated into genetic predictors. Tests for regression coefficients corresponding to these genetic predictors are tests for main effect QTL expression and QTL by environment interaction (QEI). QTL models for which QEI depends on environmental covariables form an interesting model class for predicting GEI, for new genotypes and new environments. QTL technology has not been efficient for predicting complex traits affected by a large number of loci. Recent delineation of high-density markers has been useful to predict genomic breeding values, thus increasing the precision of genetic value prediction over that achieved with the traditional use of pedigree information (Crossa 2012). Genomic data also allow assessing chromosome regions through marker effects and studying the pattern of covariability of marker effects across differential environmental conditions. For realistic modelling of genotypic differences across multiple environments, sophisticated mixed models are necessary to allow for heterogeneity of genetic variances and correlations across environments. Models like (a) additive model, (b) regression on the mean model, (c) additive main effects and multiplicative interactions model, (d) factorial regression models, (e) mixed models for genetic variances and covariances and (f) modelling main effect QTLs and QTL-by-environment interaction are some of the strategies being highlighted for the study of GE interactions (Malosetti et al. 2013).