After a large-scale association study, the set of SNPs associated with disease will typically include correlated SNPs. There is a need for approaches that can help determine if a particular SNP among a correlated set is likely to be the biologically causative SNP and thus focus follow-up efforts in the laboratory on these most promising variants. One approach is to consider these correlated SNPs and systematically prioritize them according to known genomic and biological information such as locations of genes and cross-species conserved regions [20]. Here we have implemented a complementary method to prioritize SNPs that capitalizes on differences in LD patterns between different world population groups. We first identify the SNPs in strong LD with an associated SNP as measured by the correlation coefficient r2 in the initial population; when r2 is reduced in a second population, we then have the opportunity to refine the association and filter out some of the originally correlated variants.
