Related Experiment Video
Updated: May 21, 2026
Multiplexed Single Cell mRNA Sequencing Analysis of Mouse Embryonic Cells
Published on: January 7, 2020
FastGxC: Fast and powerful context-specific eQTL mapping in bulk and single-cell data
Lena Krockenberger1, Andrew Lu2, Mike Thompson3
1Bioinformatics Interdepartmental Graduate Program, University of California, Los Angeles, Los Angeles, CA 90095, USA; Department of Pathology and Laboratory Medicine, University of California, Los Angeles, Los Angeles, CA 90095, USA.
Abstract:
Context-specific expression quantitative trait loci (eQTLs) mediate genetic risk for complex diseases, but current methods limit their characterization and interpretation. We introduce FastGxC, a method for efficiently mapping context-specific eQTLs by leveraging correlation structure in multi-tissue bulk and single-cell RNA sequencing studies. In simulations, FastGxC is nine times more powerful and 106 times faster than existing approaches, reducing computation time from years to minutes. We applied FastGxC to bulk multi-tissue (n = 698) and peripheral blood mononuclear cell (PBMC) single-cell RNA sequencing datasets (n = 1,218), generating comprehensive tissue- and cell-type-specific eQTL maps. These eQTLs showed 4-fold enrichment in context-matched open chromatin and were twice as enriched as standard context-specific eQTLs. FastGxC improved precision in identifying relevant trait contexts by 3-fold and expanded candidate causal genes by 25% in cell types and by 6% in tissues. FastGxC provides a powerful framework for mapping context-specific eQTLs, advancing our understanding of gene regulatory mechanisms underlying complex traits.
