A design combining both related and unrelated controls, named the case-combined-control design, was recently proposed to increase the power for detecting gene–environment (G×E) interaction. Under a conditional analytic approach, the case-combined-control design appeared to be more efficient and feasible than a classical case–control study for detecting interaction involving rare events.Methods
We now propose an unconditional analytic strategy to further increase the power for detecting gene–environment (G×E) interactions. This strategy allows the estimation of G×E interaction and exposure (E) main effects under certain assumptions (e.g. no correlation in E between siblings and the same exposure frequency in both control groups). Only the genetic (G) main effect cannot be estimated because it is biased.Results
Using simulations, we show that unconditional logistic regression analysis is often more efficient than conditional analysis for detecting G×E interaction, particularly for a rare gene and strong effects. The unconditional analysis is also at least as efficient as the conditional analysis when the gene is common and the main and joint effects of E and G are small.Conclusions
Under the required assumptions, the unconditional analysis retains more information than does the conditional analysis for which only discordant case–control pairs are informative leading to more precise estimates of the odds ratios.