ArticleNature communications2026
Leveraging cell-type specificity and similarity improves single-cell eQTL fine-mapping.
Article in Nature communications, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 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
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Who cites it
2 citing papers in PubMed.
- Leveraging cell-type specificity and similarity improves single-cell eQTL fine-mapping.Nature communications · 2026Article
- scTWAS: A powerful statistical framework for single-cell transcriptome-wide association studies.Research square · 2025Article
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Authors and funding
6 authors.
Funding
Abstract
Identifying cell-type-specific eQTL is important to understand the genetic regulation of gene expressions at the cell-type level and its relevance to complex traits. However, existing eQTL fine-mapping methods are limited in power and accuracy when cell types are analyzed separately. To improve eQTL mapping, we present CASE, a Bayesian framework to perform cell-type-specific and shared eQTL fine-mapping that simultaneously analyzes multiple cell types. CASE can effectively capture effect-sharing patterns across cell types while disentangling the confounding effects of linkage disequilibrium. We demonstrate that CASE outperforms the existing single-trait (SuSiE) and multi-trait (mvSuSiE) eQTL methods through comprehensive simulations. When applied to the OneK1K data, CASE identified more genetic regulations of gene expressions, better capturing cell type specificity and functionally enriched and disease-associated eQTL. The CASE framework for single-cell eQTL fine-mapping can be broadly applied to multi-tissue and multi-trait genetic studies.
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Registered trials
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