We propose an approach that can help refine association signals and distinguish among these correlated variants. The overall idea is to follow up initial GWA signals in a population sample having a different population history from the initial "discovery" sample. The first step is to obtain genotypes in both population samples at the discovered associated SNP and at correlated SNPs in its r2 bin. The second step is to perform formal heterogeneity testing to highlight variants that evidence similar genetic effect in both populations. To aid interpretation, an additional step would be to evaluate the power to filter out a SNP by the heterogeneity test under the assumption of a range of SNP allele frequencies in the second population. We propose specific ways to accomplish these steps in the methods below. This overall approach, which we call cross-population contrast mapping, works on the hypothesis that important biological mechanisms underlying disease are shared in common across human populations, although differences in allele frequencies at risk loci can lead to differences in prevalence in the different populations. When a risk allele has
