For complex-trait mapping in unrelated GAW18 individuals, Chen et al. [2013] reported that admixture mapping was underpowered, and Yorgov et al. [2013] found that simultaneously testing for admixture and association, while allowing for heterogeneous genetic effects, may improve the power to detect causative variants compared to association testing or admixture mapping alone. For association testing within admixed pedigree samples, Thornton et al. [2013] demonstrated that the EIGENSTRAT approach of incorporating the top 10 principal components in a linear regression model does not appropriately account for both population and pedigree structure in a sample. In contrast, the EMMAX method, a linear mixed-effects model approach that uses an empirical covariance matrix, was appropriately calibrated in this setting.
