QTLbyenvironment

QTLbyenvironment, or QTL by environment interaction (QTLxE), describes a situation in which the effect of a quantitative trait locus depends on the environment in which the trait is measured. In other words, a given QTL may have a detectable effect in some environments but not in others, or its allelic effect may change direction across environments. QTLxE contributes to genotype-by-environment variation for complex traits such as grain yield, plant height, or quality traits and is a key consideration in translating QTL findings into breeding practice.

Detection and analysis typically rely on multi-environment trials in which phenotypes are collected for a set of genotypes across diverse environments, combined with genotypic data. Statistical models include main effects for QTL and environment plus QTLxE interaction terms. Approaches such as multi-environment QTL mapping, joint or mixed-model analyses, and extended interval-mapping methods are used. When environmental covariates are available, QTLxE can be analyzed with covariate-adjusted models to link QTL effects to environmental factors.

Applications include identifying QTL with stable effects across environments, as well as QTL whose effects are strong in specific environments. This information guides breeding strategies toward broad adaptation or targeted improvement. Stability metrics such as ecovalence, Finlay-Wilkinson regression, or Eberhart-Russell models are commonly referenced in interpretation.

Limitations involve higher experimental and computational demands, larger population sizes, and potential confounding with environmental covariates. Proper design and careful interpretation are required to distinguish true QTLxE from statistical artifacts. Examples have been reported in maize and rice for yield under drought, heat, or nutrient stress.