Dramatic advances in genotyping technology have established a need for fast, flexible analysis methods for genetic association studies. Common complex diseases, such as Parkinson's disease or multiple sclerosis, are thought to involve an interplay of multiple genes working either independently or together to influence disease risk. Also, multiple underlying traits, each its own genetic basis may be defined together as a single disease. These effects - trait heterogeneity, locus heterogeneity, and gene-gene interactions (epistasis) - contribute to the complex architecture of common genetic diseases. Association Rule Discovery (ARD) searches for frequent itemsets to identify rule-based patterns in large scale data. In this study, we apply Apriori (an ARD algorithm) to simulated genetic data with varying degrees of complexity. Apriori using information difference to prior as a rule measure shows good power to detect functional effects in simulated cases of simple trait heterogeneity, trait heterogeneity and epistasis, and moderate power in cases of trait heterogeneity and locus heterogeneity. Also, we illustrate that bootstrapping the rule induction process does not considerably improve the power to detect these effects. These results show that ARD is a framework with sufficient flexibility to characterize complex genetic effects.