Bickeböller, Heike

[["dc.bibliographiccitation.firstpage","786"],["dc.bibliographiccitation.issue","7"],["dc.bibliographiccitation.journal","Genetic Epidemiology"],["dc.bibliographiccitation.volume","45"],["dc.contributor.author","Rosenberger, Albert"],["dc.contributor.author","Tozzi, Viola"],["dc.contributor.author","Baum, Marcus"],["dc.contributor.author","Thormann, Kolja"],["dc.contributor.author","Hung, Rayjean J."],["dc.contributor.author","Amos, Christopher I."],["dc.contributor.author","Bickeboeller, Heike"],["dc.date.accessioned","2021-10-04T08:01:50Z"],["dc.date.available","2021-10-04T08:01:50Z"],["dc.date.issued","2021"],["dc.identifier.isi","000694656700110"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/90193"],["dc.title","I'am 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","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"]]

[["dc.bibliographiccitation.firstpage","1716"],["dc.bibliographiccitation.issue","11"],["dc.bibliographiccitation.journal","European Journal of Human Genetics"],["dc.bibliographiccitation.lastpage","1723"],["dc.bibliographiccitation.volume","27"],["dc.contributor.author","Tozzi, Viola"],["dc.contributor.author","Rosenberger, Albert"],["dc.contributor.author","Kube, Dieter"],["dc.contributor.author","Bickeböller, Heike"],["dc.date.accessioned","2019-12-18T14:21:18Z"],["dc.date.available","2019-12-18T14:21:18Z"],["dc.date.issued","2019"],["dc.description.abstract","Genome-wide association studies have led in the past to the discovery of susceptibility genes for many diseases including cancer and inflammatory conditions. However, a number of these studies did not realise their full potential. A first critical step in developing such large-scale studies is the choice of genotyping array with respect to the study goal. Coverage is the central criterion for array evaluation. We distinguish between estimates of global coverage across the genome, coverage for each chromosome, coverage for selected pathways and the coverage for genes of interest. Here, we focus on inflammatory and immunological pathways and genes relevant for haematopoietic stem cell transplantation. We compared three arrays: the Infinium Global Screening Array-24 v1.0, the Infinium OncoArray-500 K BeadChip and the Infinium PsychArray-24 v1.2 BeadChip. We employed the European population from the 1000 Genomes Project as reference genome. Global coverage was found to range between 12.2 and 14.2% whereas coverage for a selected pathway ranged from 6.2 to 13.2% and gene coverage ranged from 0 to 54.1%. The Global Screening Array outperformed both other arrays in terms of global coverage, for most chromosomes, most considered pathways and most genes. When selecting suitable arrays for a new study, the coverage of pathways or genes of interest should be considered in addition to global coverage. Local coverage should be regarded when discussing association findings inconsistent across studies and can be useful in data analysis and decision making for additional genotyping."],["dc.identifier.doi","10.1038/s41431-019-0441-2"],["dc.identifier.pmid","31227809"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/62765"],["dc.language.iso","en"],["dc.relation.eissn","1476-5438"],["dc.relation.issn","1018-4813"],["dc.relation.issn","1476-5438"],["dc.title","Global, pathway and gene coverage of three Illumina arrays with respect to inflammatory and immune-related pathways"],["dc.type","journal_article"],["dc.type.internalPublication","yes"],["dspace.entity.type","Publication"]]