{"doi":"10.1101/2021.07.29.454316","title":"Modeling memory T cell states at single-cell resolution identifies <i>in vivo</i> state-dependence of eQTLs influencing disease","abstract":"Abstract Many non-coding genetic variants cause disease by modulating gene expression. However, identifying these expression quantitative trait loci (eQTLs) is complicated by gene-regulation differences between cell states. T cells, for example, have fluid, multifaceted functional states in vivo that cannot be modeled in eQTL studies that aggregate cells. Here, we modeled T cell states and eQTLs at single-cell resolution. Using &gt;500,000 resting memory T cells from 259 Peruvians, we found over one-third of the 6,511 cis -eQTLs had state-dependent effects. By integrating single-cell RNA and surface protein measurements, we defined continuous cell states that explained more eQTL variation than discrete states like CD4+ or CD8+ T cells and could have opposing effects on independent eQTL variants in a locus. Autoimmune variants were enriched in cell-state-dependent eQTLs, such as a rheumatoid-arthritis variant near ORMDL3 strongest in cytotoxic CD8+ T cells. These results argue that fine-grained cell state context is crucial to understanding disease-associated eQTLs.","journal":"bioRxiv (Cold Spring Harbor Laboratory)","year":2021,"id":216053,"datarank":0.26876392038420827,"base_score":1.791759469228055,"endowment":1.791759469228055,"self_citation_contribution":0.26876392038420827,"citation_network_contribution":0.0,"self_endowment_contribution":0.26876392038420827,"citer_contribution":0.0,"corpus_percentile":null,"corpus_rank":null,"citation_count":5,"citer_count":0,"citers_with_citation_signal":0,"citers_with_endowment":0,"datacite_reuse_total":0,"is_dataset":false,"is_dataset_confidence":0.9433,"is_data_producer":false,"deposit_databanks":null,"is_oa":true,"file_count":0,"downloads":0,"has_version_chain":false,"published_date":"2021-01-01","fair_score":null,"fair_percentile":null,"algorithm_id":"datarank_citation_only_1hop_v6","ranking_scope":"data_only","authors":[{"id":560264,"name":"Samira Asgari","orcid":"0000-0002-2347-8985","position":1,"is_corresponding":false},{"id":244836,"name":"Kazuyoshi Ishigaki","orcid":"0000-0003-2881-0657","position":2,"is_corresponding":false},{"id":244835,"name":"Tiffany Amariuta","orcid":"0000-0003-0121-1726","position":3,"is_corresponding":false},{"id":57807,"name":"Yang Luo","orcid":"0000-0001-7385-6166","position":4,"is_corresponding":false},{"id":552679,"name":"Jessica I. Beynor","orcid":"0000-0001-9430-0271","position":5,"is_corresponding":false},{"id":76151,"name":"Yuriy Baglaenko","orcid":"0000-0002-9020-6824","position":6,"is_corresponding":false},{"id":252291,"name":"Sara Suliman","orcid":"0000-0002-5154-576X","position":7,"is_corresponding":false},{"id":22002,"name":"Alkes L. Price","orcid":"0000-0002-2971-7975","position":8,"is_corresponding":false},{"id":376976,"name":"Leonid Lecca","orcid":"0000-0003-1363-3985","position":9,"is_corresponding":false},{"id":231936,"name":"Megan Murray","orcid":"0000-0003-0443-1986","position":10,"is_corresponding":false},{"id":312635,"name":"D. Branch Moody","orcid":"0000-0003-2306-3058","position":11,"is_corresponding":false},{"id":35259,"name":"Soumya Raychaudhuri","orcid":"0000-0002-1901-8265","position":12,"is_corresponding":false},{"id":291784,"name":"Aparna Nathan","orcid":"0000-0002-5975-2851","position":0,"is_corresponding":true}],"reference_count":70,"raw_metadata":{"citation_network_status":"fetched"},"created_at":"2026-07-18T23:53:00.192353Z","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":[]}