ArticleResearch square2025
scTWAS: A powerful statistical framework for single-cell transcriptome-wide association studies.
Article in Research square, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
Transcriptome-wide association studies (TWAS) have successfully identified genes associated with complex traits and diseases, but most rely on bulk transcriptome data, overlooking cell-type-specific contexts. Population-scale single-cell RNA sequencing data now enable such analyses, but present unique challenges due to strong noises, technical variations, and high sparsity. Here, we propose scTWAS, a statistical method to conduct cell-type-specific TWAS using single-cell data. Leveraging a latent-variable model and moment-based estimation to address the challenges of single-cell data, scTWAS consistently improves the prediction of genetically regulated gene expression across cell types in both blood and brain tissues. Compared to existing methods, scTWAS identified substantially more gene-trait associations across 29 hematological traits and three immune-related diseases in immune cell types. An application to Alzheimer's disease also revealed cell-subtype-specific associations, including
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