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@ARTICLE{Nasr:285635,
      author       = {Nasr, Mohammed Kamal and König, Eva and Fuchsberger,
                      Christian and Ghasemi, Sahar and Völker, Uwe and Völzke,
                      Henry and Grabe, Hans J and Teumer, Alexander},
      title        = {{R}emoving array-specific batch effects in {GWAS}
                      mega-analyses by applying a two-step imputation workflow.},
      journal      = {Bioinformatics advances},
      volume       = {6},
      number       = {1},
      issn         = {2635-0041},
      address      = {Oxford},
      publisher    = {Oxford University Press},
      reportid     = {DZNE-2026-00274},
      pages        = {vbaf317},
      year         = {2026},
      abstract     = {Combining 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.},
      cin          = {AG Grabe},
      ddc          = {004},
      cid          = {I:(DE-2719)5000001},
      pnm          = {353 - Clinical and Health Care Research (POF4-353)},
      pid          = {G:(DE-HGF)POF4-353},
      typ          = {PUB:(DE-HGF)16},
      pubmed       = {pmid:41808772},
      pmc          = {pmc:PMC12970591},
      doi          = {10.1093/bioadv/vbaf317},
      url          = {https://pub.dzne.de/record/285635},
}