Editors' ChoiceGenetic Epidemiology

Tuning In to Gene-Environment Synergy

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Science Translational Medicine  02 Nov 2011:
Vol. 3, Issue 107, pp. 107ec176
DOI: 10.1126/scitranslmed.3003370

Those who believe that the human genome is trying to tell us more about disease risk are looking to the field of information theory for help in decoding its messages. That field, based on the mathematics of encoding and communicating information, has helped Fan and colleagues to flesh out statistical methods for determining what gene-gene and gene-environment interactions tell us about complex diseases, such as cancer.

To date, computational limitations have impeded statistical testing of genome-wide interactions between two or more genetic or environmental variables. Moreover, existing statistical methods have not been able to properly evaluate rare combinations of factors. Fan et al. aimed to measure how certain combinations of variables could reduce the uncertainty in predicting disease status; in other words, they looked for sets of genes and environmental factors that best explained disease status. The researchers first tested the hypothesis that gene mutations and smoking synergistically raise the risk of bladder cancer. For example, would someone with two high-risk DNA-repair genotypes have a greater risk of disease than the sum of risks for individuals who have only one or the other high-risk genotype? And do the same two genotypes have different effects in smokers (who rapidly accumulate DNA damage) than in nonsmokers? Their analysis confirmed a known interaction between genotypes in a DNA repair gene (XPD), and knowledge of smoking history did not improve prediction of disease.

This approach promises to be more powerful than existing methods for detecting interactions among two or more genetic and environmental risk factors. The synthesis of risk factors will help us to understand the mechanisms of complex diseases and to better predict our risk for developing these diseases. We may also be able to prevent disease and identify treatment targets using the complete underlying message hidden within the interactions between our genes and the environment.

R. Fan et al., Entropy-based information gain approaches to detect and to characterize gene-gene and gene-environment interactions/correlations of complex diseases. Genet. Epidemiol. 35, 706–721 (2011). [Abstract]

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