I am hiQ—a novel pair of accuracy indices for imputed genotypes
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I am hiQ—a novel pair of accuracy indices for imputed genotypes. / The INTEGRAL-ILCCO Consortium.
In: BMC Bioinformatics, Vol. 23, No. 1, 50, 2022.Research output: Contribution to journal › Journal article › Research › peer-review
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TY - JOUR
T1 - I am hiQ—a novel pair of accuracy indices for imputed genotypes
AU - Rosenberger, Albert
AU - Tozzi, Viola
AU - Bickeböller, Heike
AU - Hung, Rayjean J.
AU - Christiani, David C.
AU - Caporaso, Neil E.
AU - Liu, Geoffrey
AU - Bojesen, Stig E.
AU - Le Marchand, Loic
AU - Albanes, Demetrios
AU - Aldrich, Melinda C.
AU - Tardon, Adonina
AU - Fernández-Tardón, Guillermo
AU - Rennert, Gad
AU - Field, John K.
AU - Davies, Mike
AU - Liloglou, Triantafillos
AU - Kiemeney, Lambertus A.
AU - Lazarus, Philip
AU - Haugen, Aage
AU - Zienolddiny, Shanbeh
AU - Lam, Stephen
AU - Schabath, Matthew B.
AU - Andrew, Angeline S.
AU - Duell, Eric J.
AU - Arnold, Susanne M.
AU - Brunnström, Hans
AU - Melander, Olle
AU - Goodman, Gary E.
AU - Chen, Chu
AU - Doherty, Jennifer A.
AU - Teare, Marion Dawn
AU - Cox, Angela
AU - Woll, Penella J.
AU - Risch, Angela
AU - Muley, Thomas R.
AU - Johansson, Mikael
AU - Brennan, Paul
AU - Landi, Maria Teresa
AU - Shete, Sanjay S.
AU - Amos, Christopher I.
AU - The INTEGRAL-ILCCO Consortium
N1 - Funding Information: 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 [, ]. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. Funding Information: We acknowledge support by the Open Access Publication Funds of the G?ttingen University. Funding Information: We acknowledge support by the Open Access Publication Funds of the Göttingen University.
PY - 2022
Y1 - 2022
N2 - 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.
AB - 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.
KW - Accuracy measures
KW - Genotype imputation
KW - GWAS
KW - High-throughput genotyping
U2 - 10.1186/s12859-022-04568-3
DO - 10.1186/s12859-022-04568-3
M3 - Journal article
C2 - 35073846
AN - SCOPUS:85123801091
VL - 23
JO - B M C Bioinformatics
JF - B M C Bioinformatics
SN - 1471-2105
IS - 1
M1 - 50
ER -
ID: 327691190