Optimized summary-statistic-based single-cell eQTL meta-analysis
Maryna Korshevniuk (University Medical Center Groningen)
Harm Jan Westra (University Medical Center Groningen, Oncode Institute)
Roy Oelen (University Medical Center Groningen)
Monique G.P. van der Wijst (University Medical Center Groningen, Oncode Institute)
Lude Franke (Oncode Institute, University Medical Center Groningen)
Marc Jan Bonder (University Medical Center Groningen, Oncode Institute, European Molecular Biology Laboratory, German Cancer Research Center)
Marc Jan Bonder (European Molecular Biology Laboratory, University Medical Center Groningen, German Cancer Research Center)
L.C.M. Michielsen (Leiden University Medical Center, TU Delft - Electrical Engineering, Mathematics and Computer Science)
Ahmed Mahfouz (Leiden University Medical Center, TU Delft - Electrical Engineering, Mathematics and Computer Science)
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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.
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