In the search for genetic high-order interactions parametric approaches have severe limitations when there are too many independent variables in relation to the number of observed outcome events. The Multifactor Dimensionality Reduction method, MDR (Ritchie et al. 2001) has achieved a great popularity in case-control genetic association screening. It tackles the dimensionality problem of interaction detection by reducing the dimension to one, after pooling multi-locus genotypes into two groups of risk: high and low. Martin et al. (2003) combined the MDR method with the genotype-Pedigree Disequilibrium Test (MDR-PDT) to allow identification of single-locus effects or joint effects of multiple loci in nuclear families.

When analyzing gene-gene interactions, adjustment for confounding variables and for main effects is usually required and parametric methods might be more flexible. We propose a novel multifactor dimensionality reduction screening strategy for genetic interaction association analysis that encompasses both family-based and population-based designs. It has its foundation in the flexible Model-Based Multifactor Dimensionality Reduction Method, MB-MDR, of Calle et al. (2008). The approach is validated via simulation studies and evaluated on a real-life dataset.