A polygenic model has been proposed to explain the bulk of the genetic component of breast cancer aetiology and this is probably to include both main effects and interactions between multiple loci. However, the power to detect the interactions using traditional analytical methods is very limited for most studies. Multifactor dimensionality reduction (MDR) has been suggested to have increased power to detect interactions and is increasing being used in published studies. We applied MDR to a two-stage case-control breast cancer study conducted in Spain and Finland. In the stage 1 Spanish study of 864 cases and 845 controls, we evaluated interaction between 474 single-nucleotide polymorphisms in 120 cancer-related genes, subdivided into 34 genetic pathways and found evidence of a four-way interaction between genes in the FatiGO-defined B-cell receptor-signalling pathway (P < 0.006). However, this result was not replicated in the stage 2 Finnish study of 580 cases and 920 controls (P = 0.99). A number of technical issues in applying MDR to case-control data were identified and discussed. One of these is that the estimated sign test P-value can vary substantially at random, which raises doubts about its reliability. More generally, the present study serves as an important caution in the interpretation of results from single studies of gene-gene or gene-environment interaction in complex diseases. Just as for genetic main effects, the replication of positive findings in additional independent series is essential.