Any of the standard epidemiological designs to study main effects of genes or environmental factors — cohort, case-control, or hybrid designs such as nested case-control or case-cohort25-27 — can also be applied to the study of G×E interactions. The issues for choosing between the designs are similar for main effects and interactions — for example, control of confounding and other biases, temporal sequence of exposure and disease, data quality, ability to examine multiple endpoints, and efficiency to detect rare diseases or rare risk factors (Table 1). For simplicity, I treat G in this section as a single functional polymorphism, but it could comprise a risk-associated haplotype, several causal variants within a gene, or some risk index composed of multiple rare variants. The same analysis techniques could be applied in any case (e.g., multiple logistic regression) and the design considerations would be similar. The following non-traditional designs offer particular advantages for studying interactions.
