Most Gene x Environment (G×E) studies focused on polymorphic variants in metabolism genes affecting metabolic function of proteins that activate or detoxify exogenous and endogenous toxins. Examples include members of the cytochrome P-450 (CYP) superfamily of proteins, N-acetyltransferase 2 (NAT2), and glutathione S-transferases (GSTs) that are implicated in cancer, Parkinson disease (PD), and Alzheimer disease. For example, long before the first familial PD gene was identified, the ‘poor metabolizer’ enzymatic phenotype of the cytochrome P450 2D6 (CYP2D6) gene was the first PD candidate gene because the enzyme is active in the brain region linked to PD, metabolises relevant endogenous neural compounds, and inactivates neurotoxins known to cause Parkinsonism in animal models and humans. Many population studies have shown an increased risk of PD for CYP2D6 poor metabolizers compared with all other metabolizer types, and some PD studies that include pesticide exposures also observed G×Es for poor-metabolizer variants of CYP2D6.
Incorporating individual susceptibility in risk assessment has been a challenging endeavour as there is the problem of low statistical power when testing for G×E in studies designed to uncover main effects of variables. There is also the problem of the complexity of measuring environmental exposures and the difficulty in assigning temporality, especially in case-control studies.
Other problems include the limited range of genetic and/or environmental variation, the redundancy of metabolic pathways, the limited scope of minor biotransformation reactions, scale dependence in the definition of statistical interaction, and a lack of biological data on the health impact of many genetic variants.
Risk management implying priority setting and sound resource allocation should rely on risk characterisation, which in turn requires deep understanding of mechanisms of action of individual risk factors and relevant dose-response relationships. Most often, however, primary prevention aimed at eliminating exposure and hence also GxE remains the most pragmatic approach and perhaps the most effective one.
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