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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.
Cell Genomics
|May 19, 2026
Summary
FastGxC efficiently maps context-specific expression quantitative trait loci (eQTLs), crucial for understanding complex diseases. This new method is significantly faster and more powerful, accelerating genetic discovery.
Area of Science:
- Genomics
- Systems Biology
- Computational Biology
Background:
- Context-specific expression quantitative trait loci (eQTLs) are vital for understanding genetic contributions to complex diseases.
- Existing methods for characterizing and interpreting eQTLs are computationally intensive and limited in scope.
Purpose of the Study:
- To introduce FastGxC, a novel computational method for efficient mapping of context-specific eQTLs.
- To leverage correlation structures in multi-tissue bulk and single-cell RNA sequencing data for improved eQTL analysis.
Main Methods:
- FastGxC utilizes correlation structures from multi-tissue bulk and single-cell RNA sequencing data.
- The method was validated through simulations and applied to large-scale human datasets (698 multi-tissue, 1,218 PBMC single-cell).
Main Results:
- FastGxC demonstrated a 9-fold increase in power and a 10^6-fold speedup compared to existing methods.
- Generated comprehensive tissue- and cell-type-specific eQTL maps with significant enrichment in open chromatin regions.
- Improved precision in identifying trait contexts and expanded candidate causal genes by up to 25%.
Conclusions:
- FastGxC offers a powerful and efficient framework for mapping context-specific eQTLs.
- The method enhances the understanding of gene regulatory mechanisms underlying complex traits.
- Accelerates the discovery of genetic variants influencing gene expression in specific biological contexts.
