{"doi":"10.1101/2024.11.11.623049","title":"scPrediXcan integrates advances in deep learning and single-cell data into a powerful cell-type–specific transcriptome-wide association study framework","abstract":"Transcriptome-wide association studies (TWAS) help identify disease causing genes, but often fail to pinpoint disease mechanisms at the cellular level because of the limited sample sizes and sparsity of cell-type-specific expression data. Here we propose scPrediXcan which integrates state-of-the-art deep learning approaches that predict epigenetic features from DNA sequences with the canonical TWAS framework. Our prediction approach, ctPred, predicts cell-type-specific expression with high accuracy and captures complex gene regulatory grammar that linear models overlook. Applied to type 2 diabetes and systemic lupus erythematosus, scPrediXcan outperformed the canonical TWAS framework by identifying more candidate causal genes, explaining more genome-wide association studies (GWAS) loci, and providing insights into the cellular specificity of TWAS hits. Overall, our results demonstrate that scPrediXcan represents a significant advance, promising to deepen our understanding of the cellular mechanisms underlying complex diseases.","journal":"bioRxiv (Cold Spring Harbor Laboratory)","year":2024,"id":492378,"datarank":0.10397207708399181,"base_score":0.6931471805599453,"endowment":0.6931471805599453,"self_citation_contribution":0.10397207708399181,"citation_network_contribution":0.0,"self_endowment_contribution":0.10397207708399181,"citer_contribution":0.0,"corpus_percentile":null,"corpus_rank":null,"citation_count":1,"citer_count":0,"citers_with_citation_signal":0,"citers_with_endowment":0,"datacite_reuse_total":0,"is_dataset":false,"is_dataset_confidence":0.9479,"is_data_producer":false,"deposit_databanks":null,"is_oa":true,"file_count":0,"downloads":0,"has_version_chain":false,"published_date":"2024-01-01","fair_score":null,"fair_percentile":null,"algorithm_id":"datarank_citation_only_1hop_v6","ranking_scope":"data_only","authors":[{"id":1339071,"name":"Temidayo Adeluwa","orcid":null,"position":1,"is_corresponding":false},{"id":454901,"name":"Lisha Zhu","orcid":"0000-0002-3573-5512","position":2,"is_corresponding":false},{"id":1339072,"name":"Sofia Salazar","orcid":null,"position":3,"is_corresponding":false},{"id":1338601,"name":"Sarah Sumner","orcid":"0000-0001-5763-4416","position":4,"is_corresponding":false},{"id":379200,"name":"Hyunki Kim","orcid":"0000-0003-2292-5584","position":5,"is_corresponding":false},{"id":822526,"name":"Saideep Gona","orcid":"0000-0002-7469-2607","position":6,"is_corresponding":false},{"id":1128780,"name":"Festus M. Nyasimi","orcid":"0000-0001-5062-758X","position":7,"is_corresponding":false},{"id":269959,"name":"Rohit Kulkarni","orcid":"0000-0001-5029-6119","position":8,"is_corresponding":false},{"id":12165,"name":"Joseph E. Powell","orcid":"0000-0002-5070-4124","position":9,"is_corresponding":false},{"id":64667,"name":"Ravi Madduri","orcid":"0000-0003-2130-2887","position":10,"is_corresponding":false},{"id":230575,"name":"Boxiang Liu","orcid":"0000-0002-2595-4463","position":11,"is_corresponding":false},{"id":256389,"name":"Mengjie Chen","orcid":"0000-0003-1579-087X","position":12,"is_corresponding":false},{"id":29405,"name":"Hae Kyung Im","orcid":"0000-0003-0333-5685","position":13,"is_corresponding":false},{"id":810454,"name":"Yichao Zhou","orcid":"0009-0003-8632-446X","position":0,"is_corresponding":true}],"reference_count":0,"raw_metadata":{"citation_network_status":"fetched"},"created_at":"2026-07-19T02:08:56.210382Z","pmid":"39605417","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":[]}