In the quest for discovering disease susceptibility genes, the reality of gene-gene and gene-environment interactions creates difficult challenges for many current statistical approaches. In an attempt to overcome limitations with current disease gene detection methods, the multifactor dimensionality reduction (MDR) approach was previously developed. In brief, MDR is a method that reduces the dimensionality of multilocus information to identify polymorphisms associated with an increased risk of disease. This approach takes multilocus genotypes and develops a model for defining disease risk by pooling high-risk genotype combinations into one group and low-risk combinations into another. Cross-validation and permutation testing are used to identify optimal models. While this approach was initially developed for studies of complex disease, it is also directly applicable to pharmacogenomic studies where the outcome variable is drug treatment response/nonresponse or toxicity/no toxicity. MDR is a nonparametric and model-free approach that has been shown to have reasonable power to detect epistasis in both theoretical and empirical studies. This computational technology is described in detail in this review, and its application in pharmacogenomic studies is demonstrated.