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Background Imputation of untyped markers is a standard tool in genome-wide association studies to close the gap between directly genotyped and other known DNA variants. However, high accuracy with which genotypes are imputed is fundamental. Several accuracy measures have been proposed and some are implemented in imputation software, unfortunately diversely across platforms. In the present paper, we introduce  Iam   hiQ , an independent pair of accuracy measures that can be applied to dosage files, the output of all imputation software.  Iam  ( imputation accuracy measure ) quantifies the average amount of individual-specific versus population-specific genotype information in a linear manner.  hiQ (heterogeneity in quantities of dosages)  addresses the inter-individual heterogeneity between dosages of a marker across the sample at hand. Results Applying both measures to a large case–control sample of the International Lung Cancer Consortium (ILCCO), comprising 27,065 individuals, we found meaningful thresholds for  Iam  and  hiQ  suitable to classify markers of poor accuracy. We demonstrate how Manhattan-like plots and moving averages of  Iam  and  hiQ  can be useful to identify regions enriched with less accurate imputed markers, whereas these regions would by missed when applying the accuracy measure  info  (implemented in IMPUTE2). Conclusion We recommend using  Iam hiQ  additional to other accuracy scores for variant filtering before stepping into the analysis of imputed GWAS data.

Iam hiQ—a novel pair of accuracy indices for imputed genotypes