ArticlePLoS genetics2022
EPIC: Inferring relevant cell types for complex traits by integrating genome-wide association studies and single-cell RNA sequencing.
Article in PLoS genetics, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.
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Who cites it
15 citing papers in PubMed, 20 citations in OpenAlex.
- 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
- The gene expression landscape of disease genes.Genome biology · 2026Article
- Multi-omics analyses identify mannose phosphate isomerase-centered hypoxia-induced angiogenesis signature in colorectal cancer.Journal of translational medicine · 2025Article
- Benchmarking methods integrating GWAS and single-cell transcriptomic data for mapping trait-cell type associations.medRxiv : the preprint server for health sciences · 2025Article
- Connecting genomic results for psychiatric disorders to human brain cell types and regions reveals convergence with functional connectivity.Nature communications · 2025Article
- Aggregating dependent signals with heavy-tailed combination tests.Biometrika · 2025Article
- The goldmine of GWAS summary statistics: a systematic review of methods and tools.BioData mining · 2024Article
- Schizophrenia genomics: genetic complexity and functional insights.Nature reviews. Neuroscience · 2024Review
- Integration of human organoids single-cell transcriptomic profiles and human genetics repurposes critical cell type-specific drug targets for severe COVID-19.Cell proliferation · 2024Article
- Connecting genomic results for psychiatric disorders to human brain cell types and regions reveals convergence with functional connectivity.medRxiv : the preprint server for health sciences · 2024Article
- Evaluating methods for integrating single-cell data and genetics to understand inflammatory disease complexity.Frontiers in immunology · 2024Article
- Polygenic regression uncovers trait-relevant cellular contexts through pathway activation transformation of single-cell RNA sequencing data.Cell genomics · 2023Article
- A computational method for cell type-specific expression quantitative trait loci mapping using bulk RNA-seq data.Nature communications · 2023Article
- Multimodal analysis methods in predictive biomedicine.Computational and structural biotechnology journal · 2023Review
- A comprehensive comparison of multilocus association methods with summary statistics in genome-wide association studies.BMC bioinformatics · 2022Article
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Authors and funding
3 authors at 1 institution in 1 country.
Funding
Abstract
More than a decade of genome-wide association studies (GWASs) have identified genetic risk variants that are significantly associated with complex traits. Emerging evidence suggests that the function of trait-associated variants likely acts in a tissue- or cell-type-specific fashion. Yet, it remains challenging to prioritize trait-relevant tissues or cell types to elucidate disease etiology. Here, we present EPIC (cEll tyPe enrIChment), a statistical framework that relates large-scale GWAS summary statistics to cell-type-specific gene expression measurements from single-cell RNA sequencing (scRNA-seq). We derive powerful gene-level test statistics for common and rare variants, separately and jointly, and adopt generalized least squares to prioritize trait-relevant cell types while accounting for the correlation structures both within and between genes. Using enrichment of loci associated with four lipid traits in the liver and enrichment of loci associated with three neurological disorders in the brain as ground truths, we show that EPIC outperforms existing methods. We apply our framework to multiple scRNA-seq datasets from different platforms and identify cell types underlying type 2 diabetes and schizophrenia. The enrichment is replicated using independent GWAS and scRNA-seq datasets and further validated using PubMed search and existing bulk case-control testing results.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.