increased problems with multiple testing when large-scale genotypic information is crossed with many potential environmental variables of interest. However, one area that has received little scrutiny is the statistical methods that are routinely applied to test for gene-environment interaction. The most common way to assess interaction effects is by using a cross-product term representing the product of the genetic (G) and environmental (E) variables, within the context of a multiple regression model. A significant nonzero coefficient for the interaction term is interpreted as evidence of statistical interaction. There are two standard ways of coding a genetic polymorphism for this purpose: either as a binary variable (0, 1) or a three category variable, coded as 0, 1, 2. A binary coding implies a particular genetic model, for example, with 0 corresponding to 0 or 1 copies of a selected risk allele and 1 corresponding to two copies of that risk allele (recessive model). Similarly, binary coding can be employed to model genetic dominance, with 0 indicating 0 copies of the risk allele and 1 indicating one or two copies of the risk allele. Often, however, we do not know the true functional model associated with a given marker of interest. Accordingly,
