Capturing GEI Patterns for Quality Traits in Biparental Wheat Populations

Abstract

Genotype-by-environment interaction (GEI) is often a great challenge for breeders since it makes the selection of stable or superior genotypes more difficult. In order to reduce drawbacks caused by GEI and make the selection for wheat quality more effective, it is important to properly assess the effects of genotype, environment, and GEI on the trait of interest. In the present study, GEI patterns for the selected quality and mixograph traits were studied using the Additive Main Effects and Multiplicative Interaction (AMMI) model. Two biparental wheat populations consisting of 145 and 175 RILs were evaluated in six environments. The environment was the dominant source of variation for grain protein content (GPC), wet gluten content (WGC), and test weight (TW), accounting for approximately 40% to 85% of the total variation. The pattern was less consistent for mixograph traits for which the dominant source of variation has been shown to be trait and population-dependent. Overall, GEI has been shown to play a more important role for mixograph traits compared to other quality traits. Inspection of the AMMI2 biplot revealed some broadly adapted RILs, among which, MG124 is the most interesting, being the prevalent “winner” for GPC and WGC, but also the “winner” for non-correlated trait TW in environment SB10.

GGE-BIPLOT Analysis of Durum Wheat Yield Trials

“…On the other hand, the distinctive and nonrepresentative E6 helped determine specifically adapted genotypes. Therefore, a specifically adapted genotype to a particular environment could be conveniently described by employing this type of graphical representation (Plavsin et al, 2021). Furthermore, closer relationships between test environments showed that the same information could be obtained from fewer environments.…”

“…It could be possible because some genotypes were more sensitive to changes in the growing environment, while others would be stable in response to the environment. The 'which-won-where' biplot model may be associated with cross-over GEI, ME differentiation, specific adaptation (Gauch and Zobel, 1997;Yan et al, 2000;Yan and Tinker, 2006;Putto et al, 2008, Rao et al, 2011Nehe et al, 2019;Li and He, 2021;Plavsin et al, 2021). Based on the biplot analysis, test environments were divided into three MEs.…”

Evaluation of grain yield stability of tritipyrum as a novel cereal in comparison with triticale lines and bread wheat varieties through univariate and multivariate parametric methods

“…However, a few studies have been done on the cultivability and yield stability of these new crops worldwide [ 19 ]. Genotype-by-environment interaction (GEI) is often a great challenge for breeders since it makes the selection of stable or superior genotypes more difficult [ 20 ]. Accurate determination of yield stability of genotypes and quality traits is often difficult owing to the GEI [ 21 ].…”

Dissecting Genotype by Environment Interactions in Moroccan Wheat: An Advanced Biplot and Heatmap Analysis Unveiling Agronomic, Quality Traits, and Genotypic Stability for Tailored Breeding Strategies

“…Building on the previously discussed analyses, the meticulous examination of the interaction between genotypes and environments on various quality traits like protein content, gluten content, and baking strength across different locations and growing seasons reaffirms the complexity of G × E interactions. This has been a well-established notion in the agronomic literature, as highlighted by Johnson et al [ 51 ] and Plavšin et al [ 52 ]. The observed trends underscore the compelling influence of environmental factors overriding genotypic factors in specific contexts.…”