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.

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Published in BMC Bioinformatics, v. 23, article no. 50.

© The Author(s) 2022

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Open Access funding enabled and organized by Projekt DEAL. The National Institutes of Health (7U19CA203654-02/ 397 114564-5111078 Integrative Analysis of Lung Cancer Etiology and Risk) supported this work. CARET is funded by the National Cancer Institute, National Institutes of Health through grants U01 CA063673, UM1 CA167462, R01 CA 111703, RO1 CA 151989, U01 CA167462 and funds from the Fred Hutchinson Cancer Research Center. Other individual funding for participating studies and members of INTEGRAL-ILCCO are listed elsewhere.

We acknowledge support by the Open Access Publication Funds of the Göttingen University.

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The data that support the findings of this study are available from ILCCO/INTEGRAL but restrictions apply to the availability of these data, which were used under license for the current study, and so are not publicly available. Data are however available from the authors upon reasonable request and with permission of ILCCO/INTEGRAL.

12859_2022_4568_MOESM1_ESM.docx (942 kB)
Additional file 1: A macro for SAS® 9.4 to calculate the measures IamHWE, Iamchance and hiQ for autosomal markers based on the dosagefile. Finally, tables and figures are given with markers and regions of low accuracy.

12859_2022_4568_MOESM2_ESM.pdf (460 kB)
Additional file 2: Other members (not co-authors) of the International Lung Cancer Consortium (ILCCO).