al., 2007] program based on the simulated HapMap, or (c) tests using imputed allele counts for all the markers in the simulated HapMap. Results are summarized in Table IV. The first row in the table shows the significance thresholds used for each analysis (since approaches (b) and (c) both increase the total number of tests, note that the P-value threshold increases slightly when multi-marker tests are used and increases further when imputation is used). Subsequent rows summarize power for markers of different allele frequencies. In populations with strong LD, it is clear that for common susceptibility alleles the single marker tests provide high power and that imputation or multi-marker analyses provide only small gains in power. However, for rarer alleles (such as those with frequencies <5%), imputation can provide dramatic increases in power. For instance, power increased from 24.4 to 56.2% when the disease allele frequency was 2.5% and imputation was used in the panel with CEU-like LD. As large genome scans and meta-analyses that are well-powered to evaluate rarer variants with modest effects are completed, we believe that imputation will become an increasingly important primary analysis and there are now examples of confirmed disease susceptibility loci that would have
