of the case-only design and unbiasedness of a case-control design. By combining case-control and case-only analysis, Li and Conti [2009] developed a Bayes model averaging approach to obtain a single estimate of the interaction effect. Through simulation, their Bayes model averaging approach was shown to be more powerful and robust to violations of independence than traditional approaches. Although complex disease is likely to be more complicated than can be defined by simple two-way interaction models, the development of powerful tools that incorporate the joint effects of genes and environment is an important step toward understanding disease outcomes.
