ArticleGenome biology2024
SURGE: uncovering context-specific genetic-regulation of gene expression from single-cell RNA sequencing using latent-factor models.
Article in Genome biology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
12 citing papers in PubMed, 11 citations in OpenAlex.
- Machine learning and statistical methods for molecular quantitative trait loci.Nature reviews. Genetics · 2026Review
- FastGxC: Fast and powerful context-specific eQTL mapping in bulk and single-cell data.Cell genomics · 2026Article
- eQTL analysis: A bridge from genome to mechanism.Genes & diseases · 2026Review
- Leveraging cell-type specificity and similarity improves single-cell eQTL fine-mapping.Nature communications · 2026Article
- Multidimensional mapping of stimulation-responsive regulatory elements and candidate causal variants in T cell activation.Science advances · 2026Article
- A unified framework for cell-type-specific eQTL prioritization by integrating bulk and scRNA-seq data.American journal of human genetics · 2025Article
- Cell type and dynamic state govern genetic regulation of gene expression in heterogeneous differentiating cultures.Cell genomics · 2024Article
- Genotype × environment interactions in gene regulation and complex traits.Nature genetics · 2024Review
- SURGE: uncovering context-specific genetic-regulation of gene expression from single-cell RNA sequencing using latent-factor models.Genome biology · 2024Article
- Mapping the landscape of lineage-specific dynamic regulation of gene expression using single-cell transcriptomics and application to genetics of complex disease.medRxiv : the preprint server for health sciences · 2023Article
- eQTL studies: from bulk tissues to single cells.Journal of genetics and genomics = Yi chuan xue bao · 2023Review
- eQTL Studies: from Bulk Tissues to Single Cells.ArXiv · 2023Article
Corrections and comments
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Authors and funding
8 authors at 4 institutions in 1 country.
Funding
Abstract
Genetic regulation of gene expression is a complex process, with genetic effects known to vary across cellular contexts such as cell types and environmental conditions. We developed SURGE, a method for unsupervised discovery of context-specific expression quantitative trait loci (eQTLs) from single-cell transcriptomic data. This allows discovery of the contexts or cell types modulating genetic regulation without prior knowledge. Applied to peripheral blood single-cell eQTL data, SURGE contexts capture continuous representations of distinct cell types and groupings of biologically related cell types. We demonstrate the disease-relevance of SURGE context-specific eQTLs using colocalization analysis and stratified LD-score regression.
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What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.