Using top PCs as covariates corrects for stratification in GWAS21–22 (EIGENSTRAT; see Web Resources). Like Structured Association, PCA will appropriately apply a greater correction to markers with large differences in allele frequency across ancestral populations. Unlike initial implementations of Structured Association, PCA is computationally tractable in large genome-wide data sets. Related approaches such as Multi-Dimensional Scaling (MDS) and Genetic Matching have also proven useful23–24 (PLINK; see Web Resources). When genome-wide data are not available (for example, in replication studies), Structured Association or PCA can infer genetic ancestry, and hence correct for stratification, using Ancestry-Informative Markers (AIMs)25. A common misconception is that AIMs should be used to infer genetic ancestry even when genome-wide data is available, but in fact the best ancestry estimates are obtained using a very large number of random markers.
