Whether driven by the general lack of success in finding single-gene contributions to complex disease, by increased knowledge about the potential involvement of specific biological interactions in complex disease, or by recent dramatic increases in computational power, a large number of approaches to detect locus × locus interactions were recently proposed and implemented. The six Genetic Analysis Workshop 14 (GAW14) papers summarized here each applied either existing or refined approaches with the goal of detecting gene × gene, or locus × locus, interactions in the GAW14 data. Five of six papers analyzed the simulated data; the other analyzed the Collaborative Study on the Genetics of Alcoholism data. The analytic strategies implemented for detecting interactions included multifactor dimensionality reduction, conditional linkage analysis, nonparametric linkage correlation, two-locus parametric linkage analysis, and a joint test of linkage and association. Overall, most of the groups found limited success in consistently detecting all of the simulated interactions due, in large part, to the nature of the generating model. Genet. Epidemiol. 29(Suppl. 1):S116–S119, 2005. © 2005 Wiley-Liss, Inc.