Optimized summary-statistic-based single-cell eQTL meta-analysis
Abstract
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.
Article Details
Authors (38)
José Alquicira-Hernández
Daniel Kaptijn
Maryna Korshevniuk
Jimmy Tsz Hang Lee
Lieke Michielsen
Drew Neavin
Roy Oelen
Aida Ripoll-Cladellas
Martijn Vochterloo
Yoshinari Ando
RIKEN Center for Integrative Medical Sciences
Odmaa Bayaraa
Irene van Blokland
Mame M. Dieng
M. Grace Gordon
Hilde E. Groot
Pim van der Harst
Chung-Chau Hon
Youssef Idaghdour
Vinu Manikanda
Jonathan Moody
RIKEN Center for Integrative Medical Sciences
Martijn C. Nawijn
Yukinori Okada
Oliver Stegle
Woong-Yang Park
Deepa Rajagopalan
Tala Shahin
Jay W. Shin
RIKEN Center for Integrative Medical Sciences
Gosia Trynka
Harm-Jan Westra
Seyhan Yazar
Jimmie Ye
Martin Hemberg
Broad Institute of Harvard and MIT, Cambridge, MA, USA.
Ahmed Mahfouz
Marta Melé
Joseph E. Powell
Lude Franke
Monique G. P. van der Wijst
Marc Jan Bonder