In GWAS using large numbers of markers, multiple testing correction becomes an important issue, and a genome-wide significance threshold of p<5×10−7 has been proposed [16]. At the same time, adjustment for population stratification can decrease the necessary level of nominal significance even further. This can be illustrated, for example, by adopting the Genomic Control approach [19] where the factor λ by which the chi-squared statistic is inflated by confounding is first estimated from the null loci and correction is then applied by dividing the actual association chi-square statistic by λ. Figure 3 illustrates the effect that this procedure would have by showing, for each possible λ, the highest p-value that stays below 0.05 after correction. Two scenarios are presented: 1) tests with 1 degree of freedom (Allelic, Additive, Dominant and Receive) and 2) tests with 2 degrees of freedom (Genotypic). When λ = 1.5 (which would be common if patients and controls came from different European countries) (Table 2), the original p-value must be approximately three times lower than 0.05. For geographically distant samples, the necessary reduction may be by
