Studies targeting high-risk families will not be suitable for this purpose. Alternatively, association studies, which are based on a comparison of a large number of affected and unaffected individuals from the unrelated general population, can provide sufficient power to distinguish slight variations in cancer risks. Since association studies use just the same approach as used in conventional epidemiological studies, the boundary between genetic and conventional epidemiology is now becoming less clear. For rare germline mutations with high penetrance, study design and analysis are straightforward since the mutation at a single gene is the only variable influencing predisposition to cancer. For polymorphism with high prevalence, however, the risk of cancer is the result of a complex combination of polygenic and environmental factors, hence special attention is needed to disentangling them.
Interaction is a model-dependent concept.
For example, gene-environment interactions exist if the joint effect of genetic factors and the environmental exposure differs from the product of the risks of the individual factors on a multiplicative model and of the sum of the background disease rate and the excess rates for the environmental exposure and for the genetic factors on an additive model. In most previous studies, interaction is defined on according to a multiplicative model, mainly because of its convenient statistical properties. The definition of interaction can be applied to a combination of any risk factors. To date, however, most epidemiological studies could not deal with interaction sufficiently because of the limited sample size, relative to the number of risk factor variables to be considered. In the usual analysis, priority is set on the evaluation of the dose-response relationship assuming no interaction.