In Method 2, we used imputation quality to filter SNPs before performing any association tests. This approach improved the results and does not require genotyping any additional controls. It reduces the number of SNPs available for analysis, but still allows the use of more SNPs than just those actually genotyped on both platforms. However, in our example of SNPs genotyped on Illumina and imputed on Affy, even after filtering to SNPs imputed with R2 > 0.99 (allowing us to retain only 30% of SNPs), we are left with 57 SNPs with highly significant p-values out of 112,249 remaining SNPs. So if this method is used, researchers should be prepared to sift through many false positives in a second stage analysis to find any true associations. Furthermore, this method will tend to reduce power to detect SNPs in regions with low linkage disequilibrium. Beecham et al. (2010) demonstrated this problem by pooling two case-control GWA studies for Alzheimer disease which had been genotyped on different chips, and testing for associations in the APOE gene, which is known to be strongly associated
