Gene–environment interaction
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. For example, sunlight exposure has a stronger influence on skin cancer risk in fair-skinned humans than in individuals with darker skin.
These interactions are of particular interest to genetic epidemiologists for predicting disease rates and methods of prevention with respect to public health. The term is also used amongst developmental psychobiologists to better understand individual and evolutionary development.
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.
Statistical analysis of the genetic and environmental differences contributing to the phenotype would have to be used to confirm these as gene–environment interactions. In developmental genetics, a causal interaction is enough to confirm gene–environment interactions.
History of the definition
The history of defining gene–environment interaction dates back to the 1930s and remains a topic of debate today. The first instance of debate occurred between Ronald Fisher and Lancelot Hogben.Fisher sought to eliminate interaction from statistical studies as it was a phenomenon that could be removed using a variation in scale. Hogben believed that the interaction should be investigated instead of eliminated as it provided information on the causation of certain elements of development.
A similar argument faced multiple scientists in the 1970s. Arthur Jensen published the study "How much can we boost IQ and scholastic achievement?", which amongst much criticism also faced contention by scientists Richard Lewontin and David Layzer. Lewontin and Layzer argued that in order to conclude causal mechanisms, the gene–environment interaction could not be ignored in the context of the study while Jensen defended that interaction was purely a statistical phenomenon and not related to development.
Around the same time, Kenneth J. Rothman supported the use of a statistical definition for interaction while researchers Kupper and Hogan believed the definition and existence of interaction was dependent on the model being used.
The most recent criticisms were spurred by Moffitt and Caspi's studies on 5-HTTLPR and stress and its influence on depression. In contrast to previous debates, Moffitt and Caspi were now using the statistical analysis to prove that interaction existed and could be used to uncover the mechanisms of a vulnerability trait. Contention came from Zammit, Owen and Lewis who reiterated the concerns of Fisher in that the statistical effect was not related to the developmental process and would not be replicable with a difference of scale.
Definitions
There are two different conceptions of gene–environment interaction today. Tabery has labeled them biometric and developmental interaction, while Sesardic uses the terms statistical and commonsense interaction.The biometric 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. 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.
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.
Epidemiological models of GxE
In epidemiology, the following models can be used to group the different interactions between gene and environment.Model A describes a genotype that increases the level of expression of a risk factor but does not cause the disease itself. For example, the PKU gene results in higher levels of phenylalanine than normal which in turn causes mental retardation.
The risk factor in Model B in contrast has a direct effect on disease susceptibility which is amplified by the genetic susceptibility. Model C depicts the inverse, where the genetic susceptibility directly effects disease while the risk factor amplifies this effect. In each independent situation, the factor directly effecting the disease can cause disease by itself.
Model D differs as neither factor in this situation can effect disease risk, however, when both genetic susceptibility and risk factor are present the risk is increased. For example, the G6PD deficiency gene when combined with fava bean consumption results in hemolytic anemia. This disease does not arise in individuals that eat fava beans and lack G6PD deficiency nor in G6PD-deficient people who do not eat fava beans.
Lastly, Model E depicts a scenario where the environmental risk factor and genetic susceptibility can individually both influence disease risk. When combined, however, the effect on disease risk differs.
The models are limited by the fact that the variables are binary and so do not consider polygenic or continuous scale variable scenarios.
Methods of analysis
Traditional genetic designs
Adoption studies
Adoption studies have been used to investigate how similar individuals that have been adopted are to their biological parents with whom they did not share the same environment with. Additionally, adopted individuals are compared to their adoptive family due to the difference in genes but shared environment. For example, an adoption study showed that Swedish men with disadvantaged adoptive environments and a genetic predisposition were more likely to abuse alcohol.Twin studies
Using monozygotic twins, the effects of different environments on identical genotypes could be observed. Later studies leverage biometrical modelling techniques to include the comparisons of dizygotic twins to ultimately determine the different levels of gene expression in different environments.Family studies
Family-based research focuses on the comparison of low-risk controls to high risk children to determine the environmental effect on subjects with different levels of genetic risk. For example, a Danish study on high-risk children with mothers who had schizophrenia depicted that children without a stable caregiver were associated with an increased risk of schizophrenia.Molecular analyses
Interaction with single genes
The often used method to detect gene–environment interactions is by studying the effect a single gene variation has with respect to a particular environment. Single nucleotide polymorphisms are compared with single binary exposure factors to determine any effects.Candidate studies such as these require strong biological hypotheses which are currently difficult to select given the little understanding of biological mechanisms that lead to higher risk.
These studies are also often difficult to replicate commonly due to small sample sizes which typically results in disputed results.
The polygenic nature of complex phenotypes suggests single candidate studies could be ineffective in determining the various smaller scale effects from the large number of influencing gene variants.
Interaction with multiple genes
Since the same environmental factor could interact with multiple genes, a polygenic approach can be taken to analyze GxE interactions. A polygenic score is generated using the alleles associated with a trait and their respective weights based on effect and examined in combination with environmental exposure. Though this method of research is still early, it is consistent with psychiatric disorders. As a result of the overlap of endophenotypes amongst disorders this suggests that the outcomes of gene–environment interactions are applicable across various diagnoses.Genome-wide association studies and genome wide interaction studies
A genome wide interaction scan approach examines the interaction between the environment and a large number of independent SNP's. An effective approach to this all-encompassing study occurs in two-steps where the genome is first filtered using gene-level tests and pathway based gene set analyses. The second step uses the SNP's with G–E association and tests for interaction.The differential susceptibility hypothesis has been reaffirmed through genome wide approaches.