TY  - JOUR
AU  - Korshevniuk, Maryna
AU  - Westra, Harm-Jan
AU  - Oelen, Roy
AU  - van der Wijst, Monique G P
AU  - Franke, Lude
AU  - Bonder, Marc Jan
TI  - Optimized summary-statistic-based single-cell eQTL meta-analysis.
JO  - Scientific reports
VL  - 15
IS  - 1
SN  - 2045-2322
CY  - [London]
PB  - Springer Nature
M1  - DKFZ-2025-01625
SP  - 28407
PY  - 2025
AB  - 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.
KW  - Quantitative Trait Loci
KW  - Single-Cell Analysis: methods
KW  - Humans
KW  - Sequence Analysis, RNA
KW  - Weighted meta-analysis (Other)
KW  - eQTL (Other)
KW  - scRNA-seq (Other)
LB  - PUB:(DE-HGF)16
C6  - pmid:40759673
DO  - DOI:10.1038/s41598-025-08808-3
UR  - https://inrepo02.dkfz.de/record/303376
ER  -