Efficient and accurate mixed model association tool for single-cell eQTL analysis
Abstract
Understanding the genetic basis of gene expression can help us understand the molecular underpinnings of human traits and disease. Expression quantitative trait locus (eQTL) mapping can help in studying this relationship but have been shown to be very cell-type specific, motivating the use of single-cell RNA sequencing and single-cell eQTLs to obtain a more granular view of genetic regulation. Current methods for single-cell eQTL mapping either rely on the "pseudobulk" approach and traditional pipelines for bulk transcriptomics or do not scale well to large datasets. Here, we propose SAIGE-QTL, a robust and scalable tool that can directly map eQTLs using single-cell profiles without needing aggregation at the pseudobulk level. Additionally, SAIGE-QTL allows for testing the effects of less frequent/rare genetic variation through set-based tests, which is traditionally excluded from eQTL mapping studies. We evaluate the performance of SAIGE-QTL on both real and simulated data and demonstrate the improved power for eQTL mapping over existing pipelines.
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Authors: Wei Zhou, Anna S. E. Cuomo, Linfeng Hu, Angli Xue, Masahiro Kanai, Yijia Christiana Liu, Taeyeop Kang, Youngdae Kim, Alex Rodriguez, Ravi Madduri, Bram L. Gorissen, Grant Chau, Anne Senabouth, M. Grace Gordon, Richard Perez, Pooja Kathail, Min Cheol Kim, Chun Jimmie Ye, Chirag Krishna, Ramnik J. Xavier, Daniel G. MacArthur, Joseph E. Powell, Mark J. Daly, Benjamin M. Neale
Institutions: Harvard University, University of Helsinki, University of California, San Francisco, UNSW Sydney, Broad Institute, Massachusetts General Hospital, Murdoch Children's Research Institute, Garvan Institute of Medical Research, Argonne National Laboratory, Ulsan National Institute of Science and Technology, Institute for Molecular Medicine Finland, Stanley Center for Psychiatric Research, Center for Computational and Integrative Biology, Massachusetts General Hospital