%0 Journal Article
%A Korshevniuk, Maryna
%A Westra, Harm-Jan
%A Oelen, Roy
%A van der Wijst, Monique G P
%A Franke, Lude
%A Bonder, Marc Jan
%T Optimized summary-statistic-based single-cell eQTL meta-analysis.
%J Scientific reports
%V 15
%N 1
%@ 2045-2322
%C [London]
%I Springer Nature
%M DKFZ-2025-01625
%P 28407
%D 2025
%X The identification of expression quantitative trait loci (eQTLs) holds great potential to improve the interpretation of disease-associated genetic variation. As many such disease-associated variants act in a context-, tissue- or even cell-type-specific manner, single-cell RNA-sequencing (scRNA-seq) data is uniquely suitable for identifying the specific cell type or context in which these genetic variants act. However, due to the limited sample sizes in single-cell studies, discovery of cell-type-specific eQTLs is now limited. To improve power to detect such eQTLs, large-scale joint analyses are needed. These are however, complicated by privacy constraints due to sharing of genotype data and the measurement and technical variety across different scRNA-seq datasets as a result of differences in mRNA capture efficiency, experimental protocols, and sequencing strategies. A solution to these issues is a federated weighted meta-analysis (WMA) approach in which summary statistics are integrated using dataset-specific weights. Here, we compare different strategies and provide best practice recommendations for eQTL WMA across scRNA-seq datasets.
%K Quantitative Trait Loci
%K Single-Cell Analysis: methods
%K Humans
%K Sequence Analysis, RNA
%K Weighted meta-analysis (Other)
%K eQTL (Other)
%K scRNA-seq (Other)
%F PUB:(DE-HGF)16
%9 Journal Article
%$ pmid:40759673
%R 10.1038/s41598-025-08808-3
%U https://inrepo02.dkfz.de/record/303376