ArticleGenome biology2022
scGWAS: landscape of trait-cell type associations by integrating single-cell transcriptomics-wide and genome-wide association studies.
Article in Genome biology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers, 1 of them a synthesis that pooled it.
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Who cites it
19 citing papers in PubMed, 1 synthesis or guideline pooled it, 30 citations in OpenAlex.
- Integrating leiomyoma genetics, epigenomics, and single-cell transcriptomics reveals causal genetic variants, genes, and cell types.Nature communications · 2024Pooled it
- CausalCellInfer: Prioritizing Cell-Type-Specific Disease Associations From Genetically Predicted Transcriptomes in Biobank Cohorts.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- Integrating bulk and single-cell RNA sequencing with GWAS reveals regulatory networks underpinning complex traits in beef cattle.Journal of animal science and biotechnology · 2026Article
- Spatial GWAS Atlas: a knowledgebase for decoding the genetic architecture of complex traits in spatial resolution.Nucleic acids research · 2026Article
- Bidirectional Mendelian Randomization and Single‑Cell RNA Sequencing Reveal an NK Cell-Mediated Causal Link Between Endometriosis and Endometrial Cancer.International journal of women's health · 2026Article
- Integrating polygenic signals and single-cell multiomics identifies cell-type-specific regulomes critical for immune- and aging-related diseases.Nature aging · 2026Article
- Disentangling associations between complex traits and cell types with seismic.Nature communications · 2025Article
- Digital twins as global learning health and disease models for preventive and personalized medicine.Genome medicine · 2025Review
- MAAT: a new nonparametric Bayesian framework for incorporating multiple functional annotations in transcriptome-wide association studies.Genome biology · 2025Article
- Differential expression and co-expression reveal cell types relevant to genetic disorder phenotypes.Bioinformatics (Oxford, England) · 2024Article
- Gene-environment interactions within a precision environmental health framework.Cell genomics · 2024Review
- Prioritization of Kidney Cell Types Highlights Myofibroblast Cells in Regulating Human Blood Pressure.Kidney international reports · 2024Article
- CellSTAR: a comprehensive resource for single-cell transcriptomic annotation.Nucleic acids research · 2024Article
- Evaluating methods for integrating single-cell data and genetics to understand inflammatory disease complexity.Frontiers in immunology · 2024Article
- Dynamic regulatory elements in single-cell multimodal data implicate key immune cell states enriched for autoimmune disease heritability.Nature genetics · 2023Article
- Craniofacial developmental biology in the single-cell era.Development (Cambridge, England) · 2023Review
- Single-cell genomics meets human genetics.Nature reviews. Genetics · 2023Review
- Traumatic brain injury induces an adaptive immune response in the meningeal transcriptome that is amplified by aging.Frontiers in neuroscience · 2023Article
- scGWAS: landscape of trait-cell type associations by integrating single-cell transcriptomics-wide and genome-wide association studies.Genome biology · 2022Article
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Authors and funding
5 authors at 2 institutions in 1 country.
Funding
Abstract
backgroundThe rapid accumulation of single-cell RNA sequencing (scRNA-seq) data presents unique opportunities to decode the genetically mediated cell-type specificity in complex diseases. Here, we develop a new method, scGWAS, which effectively leverages scRNA-seq data to achieve two goals: (1) to infer the cell types in which the disease-associated genes manifest and (2) to construct cellular modules which imply disease-specific activation of different processes.
resultsscGWAS only utilizes the average gene expression for each cell type followed by virtual search processes to construct the null distributions of module scores, making it scalable to large scRNA-seq datasets. We demonstrated scGWAS in 40 genome-wide association studies (GWAS) datasets (average sample size N ≈ 154,000) using 18 scRNA-seq datasets from nine major human/mouse tissues (totaling 1.08 million cells) and identified 2533 trait and cell-type associations, each with significant modules for further investigation. The module genes were validated using disease or clinically annotated references from ClinVar, OMIM, and pLI variants.
conclusionsWe showed that the trait-cell type associations identified by scGWAS, while generally constrained to trait-tissue associations, could recapitulate many well-studied relationships and also reveal novel relationships, providing insights into the unsolved trait-tissue associations. Moreover, in each specific cell type, the associations with different traits were often mediated by different sets of risk genes, implying disease-specific activation of driving processes. In summary, scGWAS is a powerful tool for exploring the genetic basis of complex diseases at the cell type level using single-cell expression data.
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