Gene–environment interaction

Gene–environment interaction (or genotype–environment interaction or G×E) 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.[1] 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.

This norm of reaction shows lines that are not parallel indicating a gene by environment interaction. Each genotype is responding to environmental variation in a different way.

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. For example, sunlight exposure has a stronger influence on skin cancer risk in fair-skinned humans than in individuals with darker skin.[2]

Nature versus nurture debates assume that variation in a trait is primarily due to either genetic differences or environmental differences. However, the current scientific opinion holds that neither genetic differences nor environmental differences are solely responsible for producing phenotypic variation, and that virtually all traits are influenced by both genetic and environmental differences.[3][4][5] Statistical analysis of the genetic and environmental differences contributing to the phenotype would have to be used to confirm these as gene-environment interactions.


There are two different conceptions of gene–environment interaction. Tabery[6] has labeled them biometric and developmental interaction, while Sesardic[7] uses the terms statistical and commonsense interaction.

The biometric (or statistical) conception has its origins in research programs that seek to measure the relative proportions of genetic and environmental contributions to phenotypic variation within populations. Biometric gene–environment interaction has particular currency in population genetics and behavioral genetics.[6] Any interaction results in the breakdown of the additivity of the main effects of heredity and environment, but whether such interaction is present in particular settings is an empirical question. Biometric interaction is relevant in the context of research on individual differences rather than in the context of the development of a particular organism.[8]

Developmental gene–environment interaction is a concept more commonly used by developmental geneticists and developmental psychobiologists. Developmental interaction is not seen merely as a statistical phenomenon. Whether statistical interaction is present or not, developmental interaction is in any case manifested in the causal interaction of genes and environments in producing an individual's phenotype.[8]


Mean Bristle Number by °C
  1. 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.[9]
  2. In plants: 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.[10]
  3. 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.[11] This example shows that a change in environment (lowering Phenylalanine consumption) can affect the phenotype of a particular trait, demonstrating a gene-environment interaction.
  4. A single nucleotide polymorphism rs1800566 in NAD(P)H Quinone Dehydrogenase 1 (NQO1) alters the risk of asthma and general lung injury upon interaction with NOx pollutants, in individuals with this mutation. [12] [13]
  5. A functional polymorphism in the monoamine oxidase A (MAOA) gene promoter can moderate the association between early life trauma and increased risk for violence and antisocial behavior. Low MAOA activity is a significant risk factor for aggressive and antisocial behavior in adults who report victimization as children. Persons who were abused as children but have a genotype conferring high levels of MAOA expression are less likely to develop symptoms of antisocial behavior.[14] These findings must be interpreted with caution, however, because gene association studies on complex traits are notorious for being very difficult to confirm.[15]
  6. In Drosophila Eggs:
    Egg Development Time by Temperature
    Contrary to the aforementioned examples, length of egg development in drosophila as a function of temperature demonstrates the lack of gene-environment interactions. The attached graph shows parallel reaction norms for a variety of individual drosophila flies, showing that there is not a gene-environment interaction present between the two variables. In other words, each genotype responds similarly to the changing environment producing similar phenotypes. For all individual genotypes, average egg development time decreases with increasing temperature. The environment is influencing each of the genotypes in the same predictable manner.[9]

Medical significance

See also


  1. Krebs, J.R. (April 2, 2012). An Introduction to Behavioural Ecology. Oxford: Wiley-Blackwell. ISBN 1405114169.
  2. Green A; Trichopoulos D (2002). Skin cancer. In Textbook of Cancer Epidemiology (eds Adami, H., Hunter, D. & Trichopoulos, D.) pp. 281–300. Oxford: Oxford University Press.
  3. Ridley, M. (2003) Nature via Nurture: Genes, Experience, & What Makes Us Human. Harper Collins. ISBN 0-00-200663-4
  4. Rutter, Michael. (2006) Genes and Behavior: Nature-Nurture Interplay Explained Oxford, UK: Blackwell Publishers
  5. Cuhna, Flavio and James J. Heckman Investing in Our Young People, in A. J. Reynolds, A. Rolnick, M. M. Englund, & J. Temple, eds., Cost-effective Early Childhood Programs in the First Decade: A Human Capital Integration, Chapter 18, pp. 381-414, 2010, New York: Cambridge University Press
  6. 1 2 Tabery, J (2007). "Biometric and developmental gene-environment interactions: Looking back, moving forward". Development and Psychopathology. 19: 961–976. doi:10.1017/s0954579407000478.
  7. Sesardic, N. (2005). Making sense of heritability. Cambridge: Cambridge University Press, p. 48.
  8. 1 2 Tabery, James and Griffiths, Paul E. (2010). Historical and Philosophical Perspectives on Behavioral Genetics and Developmental Science", in Hood, Halpern, Greenberg, and Lerner (Eds.), Handbook of Developmental Science, Behavior, and Genetics. Wiley-Blackwell, pp. 41-60.
  9. 1 2 Anand P. Gupta; R. C. Lewontin (1982). "A Study of Reaction Norms in Natural Populations of Drosophila pseudoobscura". Evolution. 36 (5): 934–948. doi:10.2307/2408073.
  10. Clausen J, Keck D, Hiesey WM (1948). "Experimental studies on the nature of species. III. Environmental responses of climatic races of Achillea, Carnegie Inst Washington Publ 581": 1–129.
  11. AAAS publication on Behavioral Genetics
  12. Castro-Giner, F., Künzli, N., Jacquemin, B., Forsberg, B., de Cid, R., Sunyer, J., Jarvis, D., Briggs, D., Vienneau, D., Norback, D., González, J. R. (2009). Traffic-related air pollution, oxidative stress genes, and asthma (ECHRS). Environmental health perspectives, 1919-1924.
  13. Basharat, Z., Messaoudi, A., Ruba, S., Yasmin, A. (2016). NQO1 rs1800566 polymorph is more prone to NOx induced lung injury: Endorsing deleterious functionality through informatics approach. Gene.591(1):14-20.
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  17. Khoury MJ, Davis R, Gwinn M, Lindegren ML, Yoon P (2005). "Do we need genomic research for the prevention of common diseases with environmental causes?". Am J Epidemiol. 161 (9): 799–805. doi:10.1093/aje/kwi113. PMID 15840611.
  18. Eichelbaum, Michel; Ingelman-Sundberg, Magnus; Evans, William (2006). "Pharmacogenomics and Individualized Drug Therapy". Annual Review of Medicine. 57: 119–137. doi:10.1146/
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