Figure 1

Summary of the general steps to implement the MDR method (adapted from Ritchie et al.[9]) In step one, the data are divided into a training set and an independent testing set for cross-validation. In step two, a set of n factors is then selected from the pool of all factors. In step three, the n factors and their possible multifactor cells are represented in n-dimensional space. In step four, each multifactor cell in the n-dimensional space is labelled as high risk if the ratio of affected individuals to unaffected individuals exceeds a threshold of one, and low risk if the threshold is not exceeded. In steps five and six, the model with the best misclassification error is selected and the prediction error of the model is estimated using the independent test data. Steps one through to six are repeated for each possible cross-validation interval. Bars represent hypothetical distributions of cases (left) and controls (right) with each multifactor combination. Dark-shaded cells represent high-risk genotype combinations, whereas light-shaded cells represent low-risk genotype combinations. White cells represent genotype combinations for which no data were observed.