- Genotype–environment interaction (G×E) occurs when different genotypes respond differently to environmental conditions, so the phenotype produced by a genotype depends partly on the environment in which it is expressed. In other words, the effect of a genotype is not necessarily the same in every environment. G×E is a fundamental concept in quantitative genetics, plant and animal breeding, ecology, evolutionary biology, and human genetics because it helps explain why genetic differences can produce different phenotypic outcomes under different environmental conditions.
- The phenotype of an individual can be viewed conceptually as the result of genetic effects, environmental effects, and their interaction. A simplified model can be written as: P=G+E+G×EP = G + E + G\times E
- where PP represents the phenotype, GG represents genetic effects, EE represents environmental effects, and G×EG\times E represents genotype–environment interaction. More complex statistical models can also include other effects such as dominance, epistasis, maternal effects, permanent environmental effects, and experimental or residual variation.
- A simple example illustrates G×E. Imagine two plant varieties grown under both high-water and drought conditions. Variety A may produce higher yields than Variety B when water is abundant, while Variety B may perform better under drought. The ranking of the varieties therefore changes across environments. This difference in response represents genotype–environment interaction. The genetic differences between the varieties are not expressed in exactly the same way under all environmental conditions.
- G×E can occur even when the ranking of genotypes does not change. For example, one genotype may perform better than another in every environment, but the difference between them may be large in one environment and small in another. This is sometimes described as a scale-type interaction, whereas a change in the ranking of genotypes is often called a crossover interaction. Crossover G×E is especially important for breeding because it can change which genotype is considered superior under different environmental conditions.
- G×E is closely related to phenotypic variation, because environmental conditions influence the observable expression of genetic differences. A trait may show substantial genetic variation in one environment but much less genetic variation in another. Consequently, the amount of observable phenotypic variation and the relative contribution of genetic effects can vary across environments.
- G×E also affects heritability. Heritability is a population- and environment-specific statistic describing the proportion of phenotypic variation associated with genetic differences under particular conditions. If environmental conditions change the magnitude of genetic differences among individuals, the estimated heritability of a trait may also change. A trait can therefore have high heritability in one environment and lower heritability in another without any change in the underlying DNA sequence of the individuals.
- The interaction between genotype and environment is particularly important for quantitative traits. Traits such as yield, growth rate, body weight, milk production, disease resistance, fertility, flowering time, and many physiological characteristics are influenced by many genes and environmental factors. Because these traits have complex genetic architectures, different combinations of genes and environments can produce different phenotypic outcomes.
- G×E can be illustrated using reaction norms. A reaction norm describes how the phenotype of a particular genotype changes across a range of environments. If reaction norms for different genotypes are parallel, there is little or no G×E. If their slopes differ, the genotypes respond differently to the environment. If the reaction norms cross, the ranking of genotypes changes across environments, indicating a crossover interaction.
- The magnitude of G×E can therefore be studied by comparing genotype performance across environments. Researchers may measure the same genotypes in different locations, seasons, temperatures, levels of nutrition, soil conditions, or management systems. These experiments allow researchers to determine whether genetic differences remain consistent across environments or whether environmental conditions modify the expression of genetic variation.
- G×E is closely connected to genetic correlation across environments. If the same trait is measured in two environments, the genetic correlation between performance in those environments can indicate how consistently genetic effects are expressed. A high positive genetic correlation suggests that genotypes tend to perform similarly across environments, whereas a lower correlation indicates stronger G×E and greater potential for genotype-specific environmental responses.
- For example, consider grain yield measured in two locations. If varieties that perform well in Location A also tend to perform well in Location B, the genetic correlation for yield across environments may be high. If varieties perform very differently in the two locations, the genetic correlation may be lower, indicating stronger environmental dependence of genetic performance.
- G×E is therefore related to genetic covariance and genetic correlation. Genetic covariance describes how genetic effects influencing traits or trait measurements vary together. When the same trait is measured across environments, genetic covariance between environments provides information about the consistency of genetic performance. This is particularly important when predicting selection response across multiple environments.
- Environmental correlation is related but distinct. Environmental correlation describes how environmental effects influencing two traits or measurements vary together, whereas G×E describes how genetic effects change in response to environmental conditions. Both concepts can contribute to observed phenotypic relationships, but they represent different components of variation.
- G×E is especially important in plant breeding. Crop varieties may perform differently depending on temperature, rainfall, soil fertility, water availability, disease pressure, planting date, and agricultural management. A variety that produces high yield under favorable conditions may not perform well under drought or poor soil conditions. Breeders therefore evaluate genotypes across multiple environments to identify varieties with either broad adaptation or specific adaptation.
- Genotype-by-environment interaction is equally important in animal breeding. Growth, milk production, reproductive performance, disease resistance, feed efficiency, and other traits can depend on housing systems, nutrition, climate, management, disease exposure, and production environments. A genotype selected for high performance in one production system may not provide the same genetic advantage in another.
- G×E has important consequences for genomic selection and genomic estimated breeding values (GEBVs). Genomic prediction models often assume that genetic effects are sufficiently consistent across the environments in which selection and production occur. When strong G×E exists, models that explicitly account for environmental conditions can improve prediction accuracy and help identify genotypes suited to particular environments.
- Modern genomic approaches can incorporate environmental information together with genotype data. Environmental variables may include temperature, rainfall, soil properties, altitude, management conditions, disease pressure, and other measurable factors. These approaches can help identify genetic variants whose effects depend on environmental conditions and can improve prediction of performance across complex environmental gradients.
- G×E can also affect genetic gain. If breeders select individuals in an environment that differs substantially from the target production environment, the selected genotypes may not perform optimally where they will ultimately be used. Strong G×E therefore makes the definition of the selection environment and target population of environments particularly important.
- Breeding programs may respond to G×E in several ways. One approach is to select genotypes with high average performance across many environments. Such genotypes are often described as having broad adaptation. Another approach is to develop or select genotypes specifically adapted to particular environmental conditions. Specific adaptation can be especially valuable when environmental differences are predictable and persistent.
- G×E can also influence the interpretation of breeding value. A breeding value is not necessarily a universal property of an individual that remains unchanged in every environment. When G×E is important, genetic merit may depend partly on the environment in which performance is evaluated. Breeding values can therefore be estimated for specific environments or modeled jointly across environments.
- G×E is also relevant to natural selection and evolutionary biology. If genotypes have different fitness in different environments, environmental variation can influence which genetic variants are favored by selection. A genotype that provides an advantage in one environment may provide little advantage or even a disadvantage in another. Spatial and temporal environmental variation can therefore maintain genetic diversity and contribute to complex patterns of adaptation.
- Environmental heterogeneity can create opportunities for local adaptation. Populations living in different environments may experience different selective pressures, leading to differences in allele frequencies and trait distributions. If genotypes perform best in the environments in which they evolved, this can produce genotype-by-environment interactions that contribute to population differentiation.
- G×E may also interact with genetic variation, mutation, recombination, gene flow, genetic drift, and natural selection. Environmental conditions determine which phenotypic differences are expressed and which differences affect fitness. Over generations, selection can alter allele frequencies, while gene flow can introduce genetic variants adapted to other environments. The resulting evolutionary dynamics can change both the genetic architecture of traits and the magnitude of G×E.
- G×E is particularly important under climate change. Changes in temperature, precipitation, extreme weather, seasonal patterns, and disease distributions can alter the environments in which organisms grow and reproduce. A genotype that is well adapted to current conditions may not remain optimal under future environments. Understanding G×E can therefore help predict which populations, crops, livestock breeds, and wild species are likely to be resilient to environmental change.
- In ecology, G×E helps explain why populations can respond differently to the same environmental change. Genetic differences within a population may cause some individuals to tolerate drought, heat, pathogens, or other stresses better than others. If these differences are heritable, natural selection can change the genetic composition of populations over time.
- G×E can also be related to phenotypic plasticity, which is the ability of a genotype to produce different phenotypes under different environmental conditions. Plasticity itself is not synonymous with G×E. Plasticity describes environmental sensitivity of a genotype, whereas G×E specifically concerns differences among genotypes in their environmental responses. G×E therefore arises when genotypes differ in their reaction norms.
- The distinction is important because all genotypes may change phenotype across environments without producing substantial G×E. If every genotype responds in essentially the same way, the environmental effect is largely common across genotypes. G×E occurs when the magnitude or direction of the response differs among genotypes.
- G×E can be estimated using several statistical approaches. Linear mixed models can include genotype, environment, and genotype-by-environment interaction terms. Random-effects models can estimate variance components associated with genetic effects and G×E. REML is commonly used to estimate these variance and covariance components. Multivariate models can treat performance in different environments as genetically correlated traits.
- Researchers may also use AMMI models, genotype stability analyses, reaction norms, factor-analytic models, and other multivariate approaches to investigate G×E. The appropriate method depends on the number of genotypes, environments, observations, experimental design, and research objective.
- The design of experiments is particularly important when estimating G×E. Replication across environments, balanced experimental designs, adequate sample sizes, and accurate environmental measurements help distinguish true genotype-by-environment effects from random variation. In field trials, differences in soil, moisture, temperature, and management can themselves introduce additional sources of environmental variation.
- G×E can also interact with epistasis, dominance, and other aspects of genetic architecture. The effect of an allele may depend on genetic background, and the effect of a genetic combination may change under different environmental conditions. Consequently, G×E can be part of a highly complex network of genetic and environmental interactions underlying quantitative traits.
- In human genetics, G×E is studied when genetic variants influence how individuals respond to environmental exposures. Examples include interactions involving diet, physical activity, smoking, pollutants, temperature, stress, and infectious agents. The same genetic variant may have different phenotypic consequences depending on environmental exposure. Such interactions are relevant to complex diseases and personalized approaches to prevention and treatment.
- However, identifying G×E in human populations can be challenging because environmental exposures are often difficult to measure accurately, genetic backgrounds vary, and many environmental factors are correlated with one another. Large datasets, appropriate statistical models, replication, and careful control of confounding are important when interpreting genotype-by-environment associations.
- A major implication of G×E is that genetic effects are often context dependent. A genetic variant should not always be assumed to have one fixed effect on a trait in every population and environment. Instead, its phenotypic effect may depend on environmental conditions, genetic background, developmental stage, and other factors.
- G×E also helps explain why results from one population or experiment may not always generalize to another. A genotype that is advantageous under one set of conditions may behave differently under another. This is one reason why genetic studies and breeding trials often benefit from replication across populations, locations, seasons, and environments.
- Overall, genotype–environment interaction describes differences among genotypes in their responses to environmental conditions. It is a central concept for understanding quantitative traits, phenotypic variation, genetic architecture, heritability, breeding value, genetic correlation, and natural selection. G×E explains why genetic effects can change across environments and why the best-performing genotype in one environment may not be the best in another.
- Understanding G×E is essential for predicting selection response, improving genetic gain, developing climate-resilient crops and livestock, studying adaptation, and interpreting complex-trait genetics. Together with phenotypic variation, environmental variation, genetic variation, genetic covariance, genetic correlation, phenotypic plasticity, and quantitative genetics, genotype–environment interaction provides a framework for understanding how genes and environments jointly shape biological traits across individuals, populations, and generations.
