Admixture mapping is a disease-mapping strategy to identify disease susceptibility variants in an admixed population that is a result of mating between 2 historically separated populations differing in allele frequencies and disease prevalence. With the increasing availability of high-density genotyping data generated in genome-wide association studies, it is of interest to investigate how to apply admixture mapping in the context of the genome-wide association studies and how to adjust for admixture in association tests. In this study, we first evaluated 3 different local ancestry inference methods, LAMP, LAMP-LD, and MULTIMIX. Then we applied admixture mapping analysis based on estimated local ancestry. Finally, we performed association tests with adjustment for local ancestry.