Nature Genetics· 2026Q1
Efficient and accurate mixed model association tool for single-cell eQTL analysis
- 24citations
- Q1SCImago
- 2026year
Short summary
SAIGE-QTL, a new tool, directly maps single-cell eQTLs without pseudobulk aggregation, improving power and enabling rare variant testing.
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Key points
- SAIGE-QTL directly maps eQTLs from single-cell RNA sequencing data, avoiding pseudobulk aggregation.
- The tool is designed to be robust and scalable for large single-cell datasets.
- SAIGE-QTL enables testing of rare genetic variants through set-based tests.
- Evaluations demonstrate improved power for eQTL mapping over existing methods.
AI-generated from the title and abstract; the full text is not read.
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.
The authors' abstract, as published at the source. Nature Genetics, 2026 · DOI ↗
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Field: Genetics (Biochemistry, Genetics and Molecular Biology)
GeneticsBiochemistry, Genetics and Molecular Biology