Rosenberger, Albert

[["dc.bibliographiccitation.artnumber","50"],["dc.bibliographiccitation.issue","1"],["dc.bibliographiccitation.journal","BMC Bioinformatics"],["dc.bibliographiccitation.volume","23"],["dc.contributor.author","Rosenberger, Albert"],["dc.contributor.author","Tozzi, Viola"],["dc.contributor.author","Bickeböller, Heike"],["dc.contributor.authorgroup","the INTEGRAL-ILCCO consortium"],["dc.date.accessioned","2022-04-01T10:03:06Z"],["dc.date.accessioned","2022-08-18T12:33:56Z"],["dc.date.available","2022-04-01T10:03:06Z"],["dc.date.available","2022-08-18T12:33:56Z"],["dc.date.issued","2022-01-24"],["dc.date.updated","2022-07-29T12:00:18Z"],["dc.description.abstract","Background\r\nImputation 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.\r\n\r\nResults\r\nApplying 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).\r\n\r\nConclusion\r\nWe recommend using Iam hiQ additional to other accuracy scores for variant filtering before stepping into the analysis of imputed GWAS data."],["dc.description.sponsorship","Open-Access-Publikationsfonds 2022"],["dc.identifier.citation","BMC Bioinformatics. 2022 Jan 24;23(1):50"],["dc.identifier.doi","10.1186/s12859-022-04568-3"],["dc.identifier.pii","4568"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/106083"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/112924"],["dc.language.iso","en"],["dc.notes.intern","DOI-Import GROB-530"],["dc.publisher","BioMed Central"],["dc.relation.eissn","1471-2105"],["dc.rights","CC BY 4.0"],["dc.rights.holder","The Author(s)"],["dc.rights.uri","https://creativecommons.org/licenses/by/4.0"],["dc.subject","GWAS"],["dc.subject","High-throughput genotyping"],["dc.subject","Genotype imputation"],["dc.subject","Accuracy measures"],["dc.title","Iam hiQ—a novel pair of accuracy indices for imputed genotypes"],["dc.type","journal_article"],["dc.type.internalPublication","yes"],["dc.type.subtype","original_ja"],["dspace.entity.type","Publication"]]

[["dc.bibliographiccitation.artnumber","316"],["dc.bibliographiccitation.issue","1"],["dc.bibliographiccitation.journal","BMC Bioinformatics"],["dc.bibliographiccitation.volume","23"],["dc.contributor.author","Thormann, Kolja A."],["dc.contributor.author","Tozzi, Viola"],["dc.contributor.author","Starke, Paula"],["dc.contributor.author","Bickeböller, Heike"],["dc.contributor.author","Baum, Marcus"],["dc.contributor.author","Rosenberger, Albert"],["dc.date.accessioned","2022-08-16T12:34:13Z"],["dc.date.available","2022-08-16T12:34:13Z"],["dc.date.issued","2022-08-04"],["dc.date.updated","2022-08-07T03:11:43Z"],["dc.description.abstract","Background\r\n ImputAccur is a software tool to measure genotype-imputation accuracy. Imputation of untyped markers is a standard approach in genome-wide association studies to close the gap between directly genotyped and other known DNA variants. However, high accuracy for imputed genotypes is fundamental. Several accuracy measures have been proposed, but unfortunately, they are implemented on different platforms, which is impractical.\r\n \r\n \r\n Results\r\n With ImputAccur, the accuracy measures info, Iam-hiQ and r2-based indices can be derived from standard output files of imputation software. Sample/probe and marker filtering is possible. This allows e.g. accurate marker filtering ahead of data analysis.\r\n \r\n \r\n Conclusions\r\n The source code (Python version 3.9.4), a standalone executive file, and example data for ImputAccur are freely available at \r\n https://gitlab.gwdg.de/kolja.thormann1/imputationquality.git\r\n \r\n ."],["dc.identifier.citation","BMC Bioinformatics. 2022 Aug 04;23(1):316"],["dc.identifier.doi","10.1186/s12859-022-04863-z"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/112733"],["dc.language.iso","en"],["dc.rights","CC BY 4.0"],["dc.rights.holder","The Author(s)"],["dc.subject","Imputation"],["dc.subject","Accuracy"],["dc.subject","GWAS"],["dc.subject","Marker selection"],["dc.subject","SNP"],["dc.subject","Quality control"],["dc.title","ImputAccur: fast and user-friendly calculation of genotype-imputation accuracy-measures"],["dc.type","journal_article"],["dc.type.internalPublication","yes"],["dc.type.subtype","original_ja"],["dc.type.version","published_version"],["dspace.entity.type","Publication"]]