Admixed populations, such as African Americans, are an important resource for identifying mutations contributing to human disease. Until recently, disease mapping in such populations has used so called admixture linkage disequilibrium" to map loci for diseases whose incidence varies markedly between populations. These analysis methods typically require unlinked markers spaced at wide intervals, while data now becoming available provide genotype information at much higher densities. High density data provides exquisite information about admixture, and association information, but presents methodological challenges. We have developed and implemented an approach to utilize such data to probabilistically infer admixture segments, and to perform disease mapping. The approach uses the HapMap data as a framework, and employs a fully Bayesian methodology, providing a natural weighting of both broad-scale admixture linkage disequilibrium and fine-scale association information. The software is currently being applied to data from a prostate cancer association study examining African American cases and controls. Future population genetics based directions are briefly discussed.