ArticleCell genomics2026
Transcriptome-wide association studies at cell-state level using single-cell eQTL data.
Article in Cell genomics, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed.
- Article
- A pseudotime-dependent TWAS framework identifies disease genes along cell developmental paths.HGG advances · 2026Article
- Regulatory network topology and the genetic architecture of gene expression.Cell genomics · 2026Article
- Decoding Immune Regulation: From Genetic Variation to Mechanism Through Single-Cell Genomics.Immune network · 2026Review
- Integrative single-cell eQTL and multi-omics analyses reveal AIM1 and ANXA1 as immune-related hub genes and potential therapeutic targets in head and neck cancer.Frontiers in oncology · 2026Article
- Integrative genomic analysis reveals causal relationships between breast mammary tissue gene expression and breast cancer risk using multi-method Mendelian randomization.Discover oncology · 2025Article
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6 authors.
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Abstract
Transcriptome-wide association studies (TWASs) are widely used to prioritize genes for diseases. Current methods test gene-disease associations at the bulk tissue or cell-type-specific pseudobulk level, which do not account for the heterogeneity within cell types. We present TWiST, a statistical method for TWAS at cell-state resolution using single-cell expression quantitative trait locus (eQTL) data. Our method uses pseudotime to represent cell states and models the effect of gene expression on the trait as a continuous pseudotemporal curve. Therefore, it allows flexible hypothesis testing of global, dynamic, and nonlinear associations. Through simulation studies and real data analysis, we demonstrated that TWiST leads to significantly improved power compared to pseudobulk methods. Application to the OneK1K study identified hundreds of genes with dynamic effects on autoimmune diseases along the trajectory of immune cell differentiation. TWiST presents great promise to understand disease genetics using single-cell studies.
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