page_title: Gene–environment interaction
section_title: Examples
hierarchical_section_title: Gene–environment interaction / Examples
caption_attribution_description: English: Mean Bristle Number by Degrees Centigrade
context_page_description: Gene–environment interaction is when two different genotypes respond to environmental variation in different ways. A norm of reaction is a graph that shows the relationship between genes and environmental factors when phenotypic differences are continuous. They can help illustrate GxE interactions. When the norm of reaction is not parallel, as shown in the figure below, there is a gene by environment interaction. This indicates that each genotype responds to environmental variation in a different way. Environmental variation can be physical, chemical, biological, behavior patterns or life events.
  Gene–environment interactions are studied to gain a better understanding of various phenomena. In genetic epidemiology, gene–environment interactions are useful for understanding some diseases. Sometimes, sensitivity to environmental risk factors for a disease are inherited rather than the disease itself being inherited. Individuals with different genotypes are affected differently by exposure to the same environmental factors, and thus gene–environment interactions can result in different disease phenotypes.
context_section_description: In Drosophila: A classic example of gene–environment interaction was performed on Drosophila by Gupta and Lewontin in 1981. In their experiment they demonstrated that the mean bristle number on Drosophila could vary with changing temperatures. As seen in the graph to the right, different genotypes reacted differently to the changing environment. Each line represents a given genotype, and the slope of the line reflects the changing phenotype (bristle number) with changing temperature. Some individuals had an increase in bristle number with increasing temperature while others had a sharp decrease in bristle number with increasing temperature. This showed that the norms of reaction were not parallel for these flies, proving that gene–environment interactions exist.
  In plants: One very interesting approach about genotype by environment interaction strategies is its use in the selection of sugarcane cultivars adapted to different environments. In this article, they analyzed twenty sugarcane genotypes grown in eight different locations over two crop cycles to identify mega-environments related to higher cane yield, measured in tons of cane per hectare (TCH) and percentage of sucrose (Pol% cane) using biplot multivariate GEI models. The authors then created a novel strategy to study both yield variables in a two-way coupled strategy even though the results showed a mean negative correlation. Through coinertia analysis, it was possible to determine the best-fitted genotypes for both yield variables in all environments. The use of these novel strategies like coinertia in GEI, proved to be a great complement analysis to AMMI and GGE, especially when the yield improvement implies multiple yield variables. Seven genetically distinct yarrow plants were collected and three cuttings taken from each plant. One cutting of each genotype was planted at low, medium, and high elevations, respectively. When the plants matured, no one genotype grew best at all altitudes, and at each altitude the seven genotypes fared differently. For example, one genotype grew the tallest at the medium elevation but attained only middling height at the other two elevations. The best growers at low and high elevation grew poorly at medium elevation. The medium altitude produced the worst overall results, but still yielded one tall and two medium-tall samples. Altitude had an effect on each genotype, but not to the same degree nor in the same way. A sorghum bi-parental population was repeatedly grown in seven diverse geographic locations across years. A group of genotypes requires similar growing degree-day (GDD) to flower across all environments, while another group of genotypes need less GDD in certain environments, but higher GDD in different environments to flower. The complex flowering time patterns is attributed to the interaction of major flowering time genes (Ma₁, Ma₆, FT, ELF3) and an explicit environmental factor, photothermal time (PTT) capturing the interaction between temperature and photoperiod.
  Phenylketonuria (PKU) is a human genetic condition caused by mutations to a gene coding for a particular liver enzyme. In the absence of this enzyme, an amino acid known as phenylalanine does not get converted into the next amino acid in a biochemical pathway, and therefore too much phenylalanine passes into the blood and other tissues. This disturbs brain development leading to mental retardation and other problems. PKU affects approximately 1 out of every 15,000 infants in the U.S. However, most affected infants do not grow up impaired because of a standard screening program used in the U.S. and other industrialized societies. Newborns found to have high levels of phenylalanine in their blood can be put on a special, phenylalanine-free diet. If they are put on this diet right away and stay on it, these children avoid the severe effects of PKU. This example shows that a change in environment (lowering Phenylalanine consumption) can affect the phenotype of a particular trait, demonstrating a gene–environment interaction.
  A single nucleotide polymorphism rs1
caption: Mean bristle number by °C
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