Journal Article

DKFZ-2025-01625

http://join2-wiki.gsi.de/foswiki/pub/Main/Artwork/join2_logo100x88.png Optimized summary-statistic-based single-cell eQTL meta-analysis.

Korshevniuk, M. ; Westra, H.-J. ; Oelen, R. ; van der Wijst, M. G. P. ; Franke, L. ; Bonder, M. J.DKFZ* ; Consortium, s.-e. (Collaboration Author) ; Alquicira-Hernández, J. (Contributor) ; Kaptijn, D. (Contributor) ; Korshevniuk, M. (Contributor) ; Lee, J. T. H. (Contributor) ; Michielsen, L. (Contributor) ; Neavin, D. (Contributor) ; Oelen, R. (Contributor) ; Ripoll-Cladellas, A. (Contributor) ; Vochterloo, M. (Contributor) ; Ando, Y. (Contributor) ; Bayaraa, O. (Contributor) ; van Blokland, I. (Contributor) ; Dieng, M. M. (Contributor) ; Gordon, M. G. (Contributor) ; Groot, H. E. (Contributor) ; van der Harst, P. (Contributor) ; Hon, C.-C. (Contributor) ; Idaghdour, Y. (Contributor) ; Manikanda, V. (Contributor) ; Moody, J. (Contributor) ; Nawijn, M. C. (Contributor) ; Okada, Y. (Contributor) ; Stegle, O. (Contributor)DKFZ* ; Park, W.-Y. (Contributor) ; Rajagopalan, D. (Contributor) ; Shahin, T. (Contributor) ; Shin, J. W. (Contributor) ; Trynka, G. (Contributor) ; Westra, H.-J. (Contributor) ; Yazar, S. (Contributor) ; Ye, J. (Contributor) ; Hemberg, M. (Contributor) ; Mahfouz, A. (Contributor) ; Melé, M. (Contributor) ; Powell, J. E. (Contributor) ; Franke, L. (Contributor) ; van der Wijst, M. G. P. (Contributor) ; Bonder, M. J. (Contributor)

2025
Springer Nature [London]

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Please use a persistent id in citations: doi:10.1038/s41598-025-08808-3

Abstract: 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.

Keyword(s): Quantitative Trait Loci (MeSH) ; Single-Cell Analysis: methods (MeSH) ; Humans (MeSH) ; Sequence Analysis, RNA (MeSH) ; Weighted meta-analysis ; eQTL ; scRNA-seq

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Contributing Institute(s):

1. B260 Bioinformatik der Genomik und Systemgenetik (B260)

Research Program(s):

1. 312 - Funktionelle und strukturelle Genomforschung (POF4-312) (POF4-312)

Appears in the scientific report 2025

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