Insights from G×E interactions could have important policy implications for environmental health standards147, targeting of interventions148, and treatment selection149 (Box 2). For example, the Clean Air Act directs the U.S. Environmental Protection Agency to set standards to protect the most sensitive, including genetically susceptible individuals150, although it has been argued that public health interventions aimed at the whole population may be more effective151. As another example, suppose the joint effect of mutations in BRCA1/2 and radiotherapy in an individual were multiplicative; then even if the radiation effect in mutation carriers alone was not statistically significant or the joint effect was not significantly greater than additive, it would be misleading to conclude that radiotherapy was no more dangerous for carriers than for noncarriers, owing to their much higher baseline risk152. Since any statement about interaction is necessarily scale dependent (Box 1), it is essential that claims about the presence or absence of an interaction make clear whether it is a departure from an additive or multiplicative model on a scale of absolute or attributable risk, odds, underlying liability, or some other
