Our approach uses logistic regression, which is a classical tool for genetic association studies. We chose the 2-df test as our primary association test simply to allow for potential differences in populations up front. The key point is that this framework then allows for formal testing of heterogeneity that can be used specifically to filter across the correlated, associated variants. Logistic regression can test for heterogeneity of SNPs regardless of their correlations with each other, and has been used in the literature to analyze uncorrelated SNPs, in distinct genes, to confirm agreement of association results across datasets. However, the extension of analysis to highly correlated SNPs in the r2 bin, as well as to populations having differing LD structure, not only allows filtering of correlated, significant signals (an important goal), but also can help prevent "false negative" findings of apparent non-consistency when a locus does indeed have consistent biological effect across populations. For example, suppose a study in one population detects association at a single genotyped SNP but ignores correlates, so that only the same SNP is tested using logistic
