This general concept of mapping in different populations was recently applied to refine the association between three variants in TCF7L2 and type 2 diabetes [8]; there, European samples and African samples were analyzed separately and the results (p-values) were compared. Our contribution here is to propose a more systematic approach for studying these correlated SNPs within a given r2 bin of interest, using the combined samples in a logistic regression framework that allows formal testing for heterogeneity of genetic effects across population groups. We then apply this method to a case-control cocaine-dependence dataset of European-Americans and African-Americans. In this application, the association signal we targeted is in the region of the CHRNA5-CHRNA3-CHRNB4 gene cluster. This gene cluster was originally reported to be associated with nicotine dependence [6], and has now shown evidence of association with cocaine dependence in European-Americans [9].
