Genome-wide array data also allow very precise estimation of each individual's ancestry, which must be controlled for in any genetic association study (A. L. Price et al., 2006). Ancestry is often confounded with phenotypic differences. Even among southern and northern Europeans, for example, there are differences in allele frequencies for many variants. If one were to conduct a GWAS of height in a study sample composed of 50% Italians and 50% Danes without addressing the ethnic composition of the sample, one would obtain artifactually decreased p-values for a large number of SNPs. This is only because northern Europeans are taller on average than southern Europeans and anything that distinguishes the two groups – including genetic variants – will also be associated with height. The standard controls for ancestry include principal components computed on genome-wide variants, or mixed model association tests using a genetic kinship matrix to account for genetic relatedness between all individuals (H. M. Kang et al., 2010; A. L. Price et al., 2006). Unlike GWAS, candidate gene studies of a handful of SNPs do not allow such corrections, making allelic stratification a serious confound in the candidate gene approach.
