{"doi":"10.1038/s41588-022-01187-9","title":"Identifying disease-critical cell types and cellular processes by integrating single-cell RNA-sequencing and human genetics","abstract":"Genome-wide association studies provide a powerful means of identifying loci and genes contributing to disease, but in many cases, the related cell types/states through which genes confer disease risk remain unknown. Deciphering such relationships is important for identifying pathogenic processes and developing therapeutics. In the present study, we introduce sc-linker, a framework for integrating single-cell RNA-sequencing, epigenomic SNP-to-gene maps and genome-wide association study summary statistics to infer the underlying cell types and processes by which genetic variants influence disease. The inferred disease enrichments recapitulated known biology and highlighted notable cell-disease relationships, including γ-aminobutyric acid-ergic neurons in major depressive disorder, a disease-dependent M-cell program in ulcerative colitis and a disease-specific complement cascade process in multiple sclerosis. In autoimmune disease, both healthy and disease-dependent immune cell-type programs were associated, whereas only disease-dependent epithelial cell programs were prominent, suggesting a role in disease response rather than initiation. Our framework provides a powerful approach for identifying the cell types and cellular processes by which genetic variants influence disease.","journal":"Nature Genetics","year":2022,"id":11910,"datarank":0.8195747707538417,"base_score":5.4638318050256105,"endowment":5.4638318050256105,"self_citation_contribution":0.8195747707538417,"citation_network_contribution":0.0,"self_endowment_contribution":0.8195747707538417,"citer_contribution":0.0,"corpus_percentile":null,"corpus_rank":null,"citation_count":235,"citer_count":0,"citers_with_citation_signal":0,"citers_with_endowment":0,"datacite_reuse_total":0,"is_dataset":false,"is_dataset_confidence":0.0488,"is_oa":true,"file_count":0,"downloads":0,"has_version_chain":false,"published_date":"2022-09-29","fair_score":null,"fair_percentile":null,"algorithm_id":"datarank_citation_only_1hop_v6","ranking_scope":"data_only","authors":[{"id":6116,"name":"Kushal K. Dey","orcid":"0000-0002-3520-2345","position":1,"is_corresponding":false},{"id":3188,"name":"Daniel T. Montoro","orcid":"0000-0002-6222-2149","position":2,"is_corresponding":false},{"id":49072,"name":"Rahul Mohan","orcid":"0000-0002-5503-1686","position":3,"is_corresponding":false},{"id":95472,"name":"Steven Gazal","orcid":"0000-0003-4510-5730","position":4,"is_corresponding":false},{"id":3038,"name":"Jesse M. Engreitz","orcid":"0000-0002-5754-1719","position":5,"is_corresponding":false},{"id":5342,"name":"Preben Bo Mortensen","orcid":"0000-0002-5230-9865","position":6,"is_corresponding":false},{"id":22002,"name":"Alkes L. Price","orcid":"0000-0002-2971-7975","position":7,"is_corresponding":false},{"id":29633,"name":"Prisca Liberali","orcid":"0000-0003-0695-6081","position":8,"is_corresponding":false},{"id":43378,"name":"Karthik A. Jagadeesh","orcid":"0000-0002-0957-812X","position":0,"is_corresponding":true}],"reference_count":107,"raw_metadata":{"citation_network_status":"fetched"},"created_at":"2026-03-01T18:20:47.508186Z","pmid":null,"pmcid":null,"fwci":null,"citation_percentile":null,"influential_citations":0,"oa_status":null,"license":null,"views":0,"total_file_size_bytes":0,"version_count":0,"fair_f":null,"fair_a":null,"fair_i":null,"fair_r":null,"fair_zscore":null,"fair_rationale":null,"fair_model":null,"fair_agent_version":null,"fair_fulltext_source":null,"fair_has_llm":null,"fair_computed_at":null,"clinical_trials":[],"software_tools":[],"db_accessions":[],"linked_datasets":[],"topics":[]}