Current disease association studies are routinely conducted on a genome-wide scale, testing hundreds of thousands or millions of genetic markers. Besides detecting marginal associations of individual markers with the disease, it is also of interest to identify gene–gene and gene–environment interactions, which confer susceptibility to the disease risk. The astronomical number of possible combinations of markers and environmental factors, however, makes interaction mapping a daunting task both computationally and statistically. In this paper, we review and discuss a set of Bayesian partition methods developed recently for mapping single-nucleotide polymorphisms in case-control studies, their extension to quantitative traits, and further generalization to multiple traits. We use simulation and real data sets to demonstrate the performance of these methods, and we compare them with some existing interaction mapping algorithms. With the recent advance in high-throughput sequencing technologies, genome-wide measurements of epigenetic factor enrichment, structural variations, and transcription activities become available at the individual level. The tsunami of data creates more challenges for gene–gene interaction mapping, but at the same time provides new opportunities that, if utilized properly through sophisticated statistical means, can improve the power of mapping interactions at the genome scale.