ArticleNature communications2024
Integrating single cell expression quantitative trait loci summary statistics to understand complex trait risk genes.
Article in Nature communications, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.
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Who cites it
14 citing papers in PubMed.
- Data-intensive immune network modelling for One Health.Briefings in bioinformatics · 2026Review
- Decoding Immune Regulation: From Genetic Variation to Mechanism Through Single-Cell Genomics.Immune network · 2026Review
- Single-Cell and Spatial Omics: Methods and Applications.MedComm · 2026Review
- Artificial intelligence in immunotherapy: revolutionizing diagnostic and therapeutic applications in cancer and autoimmune diseases.Clinical and experimental medicine · 2026Review
- Review
- Integrating axis quantitative trait loci looks beyond cell types and offers insights into brain-related traits.Nature communications · 2025Article
- Improved multiancestry fine-mapping identifies cis-regulatory variants underlying molecular traits and disease risk.Nature genetics · 2025Article
- Genomic and Transcriptomic Profiling of Amino Acid Compositions in Common Carp Fillets.Animals : an open access journal from MDPI · 2025Article
- An atlas of single-cell eQTLs dissects autoimmune disease genes and identifies novel drug classes for treatment.Cell genomics · 2025Article
- Immune Memory: A New Frontier in Treating Recurrent Inflammatory Skin Diseases.Clinical reviews in allergy & immunology · 2025Review
- Predicting gene expression from histone marks using chromatin deep learning models depends on histone mark function, regulatory distance and cellular states.Nucleic acids research · 2025Article
- scTWAS Atlas: an integrative knowledgebase of single-cell transcriptome-wide association studies.Nucleic acids research · 2025Article
- Achieving health equity in immune disease: leveraging big data and artificial intelligence in an evolving health system landscape.Frontiers in big data · 2025Review
- Novel susceptibility genes for varicose veins revealed by a cross-tissue transcriptome-wide association study.Science progressArticle
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Authors and funding
10 authors.
Funding
Abstract
Transcriptome-wide association study (TWAS) is a popular approach to dissect the functional consequence of disease associated non-coding variants. Most existing TWAS use bulk tissues and may not have the resolution to reveal cell-type specific target genes. Single-cell expression quantitative trait loci (sc-eQTL) datasets are emerging. The largest bulk- and sc-eQTL datasets are most conveniently available as summary statistics, but have not been broadly utilized in TWAS. Here, we present a new method EXPRESSO (EXpression PREdiction with Summary Statistics Only), to analyze sc-eQTL summary statistics, which also integrates 3D genomic data and epigenomic annotation to prioritize causal variants. EXPRESSO substantially improves existing methods. We apply EXPRESSO to analyze multi-ancestry GWAS datasets for 14 autoimmune diseases. EXPRESSO uniquely identifies 958 novel gene x trait associations, which is 26% more than the second-best method. Among them, 492 are unique to cell type level analysis and missed by TWAS using whole blood. We also develop a cell type aware drug repurposing pipeline, which leverages EXPRESSO results to identify drug compounds that can reverse disease gene expressions in relevant cell types. Our results point to multiple drugs with therapeutic potentials, including metformin for type 1 diabetes, and vitamin K for ulcerative colitis.
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