TY  - JOUR
AU  - Nasr, Mohammed Kamal
AU  - König, Eva
AU  - Fuchsberger, Christian
AU  - Ghasemi, Sahar
AU  - Völker, Uwe
AU  - Völzke, Henry
AU  - Grabe, Hans J
AU  - Teumer, Alexander
TI  - Removing array-specific batch effects in GWAS mega-analyses by applying a two-step imputation workflow.
JO  - Bioinformatics advances
VL  - 6
IS  - 1
SN  - 2635-0041
CY  - Oxford
PB  - Oxford University Press
M1  - DZNE-2026-00274
SP  - vbaf317
PY  - 2026
AB  - 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.
LB  - PUB:(DE-HGF)16
C6  - pmid:41808772
C2  - pmc:PMC12970591
DO  - DOI:10.1093/bioadv/vbaf317
UR  - https://pub.dzne.de/record/285635
ER  -