Principal components analysis was conducted using the Eigensoft package (22,23) to identify population groups of the GWAS subjects. After pruning the GWAS SNPs for linkage disequilibrium (R2) greater than 80%, 145,472 SNPs that were common to the GWAS dataset and HapMap panel were analyzed to characterize the underlying genetic architecture of the samples. The first principal components score distinguished AAs and EAs; these groups were subsequently analyzed separately. Quality control steps were performed using PLINK software (24). We removed individuals with discordant sex information, an overall call rate of less than 98%, and a heterozygosity rate (excluding sex chromosomes) outside three standard deviations of the mean of all individuals.
