000285635 001__ 285635
000285635 005__ 20260313131516.0
000285635 0247_ $$2doi$$a10.1093/bioadv/vbaf317
000285635 0247_ $$2pmid$$apmid:41808772
000285635 0247_ $$2pmc$$apmc:PMC12970591
000285635 037__ $$aDZNE-2026-00274
000285635 041__ $$aEnglish
000285635 082__ $$a004
000285635 1001_ $$aNasr, Mohammed Kamal$$b0
000285635 245__ $$aRemoving array-specific batch effects in GWAS mega-analyses by applying a two-step imputation workflow.
000285635 260__ $$aOxford$$bOxford University Press$$c2026
000285635 3367_ $$2DRIVER$$aarticle
000285635 3367_ $$2DataCite$$aOutput Types/Journal article
000285635 3367_ $$0PUB:(DE-HGF)16$$2PUB:(DE-HGF)$$aJournal Article$$bjournal$$mjournal$$s1773404024_16831
000285635 3367_ $$2BibTeX$$aARTICLE
000285635 3367_ $$2ORCID$$aJOURNAL_ARTICLE
000285635 3367_ $$00$$2EndNote$$aJournal Article
000285635 520__ $$aCombining genetic data from different genotyping arrays (mega-analysis) increases statistical power but introduces array-specific batch effects that may bias results. This project developed a two-step genotype imputation workflow addressing this bias in studies using multiple genotyping platforms.Genotype data of 10 647 individuals generated using five different arrays were included. The two-step method involved creating intermediate array-type specific panels, which were then imputed against the 1000 Genomes reference panel. Batch effects were assessed using genetic principal component analysis of the combined imputed dataset. Performance was evaluated by comparing imputation quality and allele frequency differences between the two-step and the conventional array-specific imputation. Additionally, concordance with a whole-genome-sequenced subgroup was examined. Genome-wide association analysis on goiter risk and thyroid gland volume was conducted to compare outcomes between both imputation approaches.The workflow eliminated array-driven batch effect from the first 20 PCs and showed high correlation with the conventional approach for allele frequencies (r 2 > 0.99). GWAS using the two-step imputation confirmed known associations on thyroid traits and revealed novel loci for thyroid volume (TG, PAX8, IGFBP5, NRG1), and goiter (XKR6), the latter not significant in the conventional imputation.The study provides a workflow for high-quality imputation results without batch effects, fostering genetic analysis involving multiple genotyping arrays.
000285635 536__ $$0G:(DE-HGF)POF4-353$$a353 - Clinical and Health Care Research (POF4-353)$$cPOF4-353$$fPOF IV$$x0
000285635 588__ $$aDataset connected to CrossRef, PubMed, , Journals: pub.dzne.de
000285635 7001_ $$00000-0002-2079-3525$$aKönig, Eva$$b1
000285635 7001_ $$00000-0002-5918-8947$$aFuchsberger, Christian$$b2
000285635 7001_ $$aGhasemi, Sahar$$b3
000285635 7001_ $$00000-0002-5689-3448$$aVölker, Uwe$$b4
000285635 7001_ $$00000-0001-7003-399X$$aVölzke, Henry$$b5
000285635 7001_ $$0P:(DE-2719)2811781$$aGrabe, Hans J$$b6$$udzne
000285635 7001_ $$00000-0002-8309-094X$$aTeumer, Alexander$$b7
000285635 773__ $$0PERI:(DE-600)3076075-6$$a10.1093/bioadv/vbaf317$$gVol. 6, no. 1, p. vbaf317$$n1$$pvbaf317$$tBioinformatics advances$$v6$$x2635-0041$$y2026
000285635 8564_ $$uhttps://pub.dzne.de/record/285635/files/DZNE-2026-00274.pdf$$yRestricted
000285635 8564_ $$uhttps://pub.dzne.de/record/285635/files/DZNE-2026-00274.pdf?subformat=pdfa$$xpdfa$$yRestricted
000285635 9101_ $$0I:(DE-588)1065079516$$6P:(DE-2719)2811781$$aDeutsches Zentrum für Neurodegenerative Erkrankungen$$b6$$kDZNE
000285635 9131_ $$0G:(DE-HGF)POF4-353$$1G:(DE-HGF)POF4-350$$2G:(DE-HGF)POF4-300$$3G:(DE-HGF)POF4$$4G:(DE-HGF)POF$$aDE-HGF$$bGesundheit$$lNeurodegenerative Diseases$$vClinical and Health Care Research$$x0
000285635 915__ $$0StatID:(DE-HGF)0200$$2StatID$$aDBCoverage$$bSCOPUS$$d2025-11-12
000285635 915__ $$0StatID:(DE-HGF)0300$$2StatID$$aDBCoverage$$bMedline$$d2025-11-12
000285635 915__ $$0StatID:(DE-HGF)0320$$2StatID$$aDBCoverage$$bPubMed Central$$d2025-11-12
000285635 915__ $$0StatID:(DE-HGF)0501$$2StatID$$aDBCoverage$$bDOAJ Seal$$d2024-01-31T16:07:00Z
000285635 915__ $$0StatID:(DE-HGF)0500$$2StatID$$aDBCoverage$$bDOAJ$$d2024-01-31T16:07:00Z
000285635 915__ $$0StatID:(DE-HGF)0030$$2StatID$$aPeer Review$$bDOAJ : Peer review$$d2024-01-31T16:07:00Z
000285635 915__ $$0LIC:(DE-HGF)CCBYNV$$2V:(DE-HGF)$$aCreative Commons Attribution CC BY (No Version)$$bDOAJ$$d2024-01-31T16:07:00Z
000285635 915__ $$0StatID:(DE-HGF)0199$$2StatID$$aDBCoverage$$bClarivate Analytics Master Journal List$$d2025-11-12
000285635 915__ $$0StatID:(DE-HGF)1190$$2StatID$$aDBCoverage$$bBiological Abstracts$$d2025-11-12
000285635 915__ $$0StatID:(DE-HGF)0112$$2StatID$$aWoS$$bEmerging Sources Citation Index$$d2025-11-12
000285635 915__ $$0StatID:(DE-HGF)0150$$2StatID$$aDBCoverage$$bWeb of Science Core Collection$$d2025-11-12
000285635 915__ $$0StatID:(DE-HGF)1050$$2StatID$$aDBCoverage$$bBIOSIS Previews$$d2025-11-12
000285635 915__ $$0StatID:(DE-HGF)0561$$2StatID$$aArticle Processing Charges$$d2025-11-12
000285635 915__ $$0StatID:(DE-HGF)0700$$2StatID$$aFees$$d2025-11-12
000285635 9201_ $$0I:(DE-2719)5000001$$kAG Grabe$$lBiomarkers of Dementia in the General Population$$x0
000285635 980__ $$ajournal
000285635 980__ $$aEDITORS
000285635 980__ $$aVDBINPRINT
000285635 980__ $$aI:(DE-2719)5000001
000285635 980__ $$aUNRESTRICTED