Background: Gene-gene and gene-environment interactions are known to contribute significantly to variation of complex phenotypes in model organisms. However, their identification in human associations studies remains challenging for myriad reasons. In the case of gene-gene interactions, the large number of potential interacting pairs presents computational, multiple hypothesis correction, and other statistical power issues. In the case of gene-environment interactions, the lack of consistently measured environmental covariates in most disease studies precludes searching for interactions and creates difficulties for replicating studies. Results: In this work, we develop a new statistical approach to address these issues that leverages genetic ancestry (θ) in admixed populations. We applied our method to gene expression and methylation data from African American and Latino admixed individuals, identifying nine interactions that were significant at a threshold of . We replicate two of these interactions and show that a third has previously been identified in a genetic interaction screen for rheumatoid arthritis. Conclusion: We show that genetic ancestry can be a useful proxy for unknown and unmeasured environmental exposures with which it is correlated