Overall, the LDLR and 6PGD loci, together with many other anecdotal examples, suggest that genotype imputation can improve the power of genomewide association analyses. Nevertheless, accurately estimating the impact of genotype imputation on the power of a genomewide association studies is more challenging. We have tried to accurately quantify this potential power gain in two ways: first, by generating and analyzing simulated datasets; and second, by analyzing datasets that combine genomewide genotype data and large scale surveys of gene expression. The second approach is especially attractive because true positive associations between genetic variants and transcript levels are easy to identify (they often map to the locus encoding the transcript). Both approaches suggest that genotype imputation can increase the power of gene-mapping studies, particularly when the associated variants have frequencies <10-20%. When we imputed genotypes and then reanalyzed the gene expression data of Dixon et al. (28) we mapped, on average, 10% more genomewide association peaks to the locus surrounding each transcript than before imputation (Liang , Cookson and Abecasis, unpublished data).
