We observed a large number of highly significant SNPs after imputation in a study comparing two healthy control groups genotyped on different platforms. Because both control groups are nested in the NHS and chosen using similar criteria, we expect no SNPs to significantly distinguish the two groups in the absence of measurement error, and we expect no differential population substructure. Thus, statistically significant SNPs are false positives, and must be due to genotyping or imputation error. Furthermore, because we see almost no inflation in Type I error among SNPs actually genotyped on both chips (Figure 1a), the false positives do not appear to result from genotyping error. Rather, the inflation in Type I error is seen among SNPs measured in one group and imputed in the other (and among SNPs imputed in both). In this setting, it would be detrimental to avoid imputation altogether since only about a quarter of the SNPs genotyped on each platform overlap, so that three-quarters of the SNPs on each chip would be unusable without any imputation. Thus, we need to understand the errors being introduced by imputation and attempt to control for them.
