ArticleCell genomics2025
An atlas of single-cell eQTLs dissects autoimmune disease genes and identifies novel drug classes for treatment.
Article in Cell genomics, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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Who cites it
9 citing papers in PubMed.
- Artificial intelligence in biomarker discovery for diseases: diagnostic and therapeutic prospects.Signal transduction and targeted therapy · 2026Review
- Translating genome-wide association studies at multiple scales: Drug target prioritization, cellular architectures, and organ imaging.Cell genomics · 2026Review
- Decoding Immune Regulation: From Genetic Variation to Mechanism Through Single-Cell Genomics.Immune network · 2026Review
- Single-cell eQTL-based Mendelian randomization identifies immune cell subtype-specific regulators of epigenetic aging and prioritizes candidate therapeutic targets.Biogerontology · 2026Review
- ceQTL: a co-expression QTL model to detect a variant that affects transcription factor binding and its target regulation.Briefings in bioinformatics · 2026Article
- Causal cross-trait mapping at single-cell resolution identifies shared immunogenetic drivers of migraine and Meniere's disease.The journal of headache and pain · 2026Article
- Article
- Integrating axis quantitative trait loci looks beyond cell types and offers insights into brain-related traits.Nature communications · 2025Article
- Integrative multi-omics QTL colocalization maps regulatory architecture in aging human brain.medRxiv : the preprint server for health sciences · 2025Article
Corrections and comments
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Authors and funding
13 authors.
Funding
Abstract
Most variants identified from genome-wide association studies (GWASs) are non-coding and regulate gene expression. However, many risk loci fail to colocalize with expression quantitative trait loci (eQTLs), potentially due to limited GWAS and eQTL analysis power or cellular heterogeneity. Population-scale single-cell RNA-sequencing (scRNA-seq) datasets are emerging, enabling mapping of eQTLs in different cell types (sc-eQTLs). Compared to eQTL data from bulk tissues (bk-eQTLs), sc-eQTL datasets are smaller. We propose a joint model of bk-eQTLs as a weighted sum of sc-eQTLs (JOBS) from constituent cell types to improve power. Applying JOBS to One1K1K and eQTLGen data, we identify 586% more eQTLs, matching the power of 4× the sample sizes of OneK1K. Integrating sc-eQTLs with GWAS data creates an atlas for 14 immune-mediated disorders, colocalizing 29.9% or 32.2% more loci than using sc-eQTL or bk-eQTL alone. Extending JOBS, we develop a drug-repurposing pipeline and identify novel drugs validated by real-world data.
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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.