{"doi":"10.1101/2020.10.27.356766","title":"<i>BrainGENIE</i> : The Brain Gene Expression and Network Imputation Engine","abstract":"Abstract In vivo experimental analysis of human brain tissue poses substantial challenges and ethical concerns. We developed a novel method called the Brain Gene Expression and Network Imputation Engine ( BrainGENIE ) that uses peripheral-blood transcriptomes to predict brain-tissue-specific gene-expression levels. BrainGENIE reliably predicted brain-tissue-specific expression levels for 1,733 – 11,569 genes (false-discovery rate-adjusted p &lt;0.05), including many transcripts that cannot be predicted reliably by a transcriptome imputation method such as PrediXcan . We tested the generalizability of BrainGENIE in external within-individual data from ex vivo peripheral blood and postmortem brain samples from the Religious Orders Study and Memory and Aging Project, wherein we validated 39% of predicted gene expression levels as concordant with observed expression levels in dorsolateral prefrontal cortex and 23% in caudate. BrainGENIE recapitulated diagnosis-related gene expression changes in brain better than direct correlations from blood and predictions from PrediXcan. BrainGENIE complements and, in some ways, outperforms existing transcriptome-imputation tools, providing biologically meaningful predictions and opening new research avenues.","journal":"bioRxiv (Cold Spring Harbor Laboratory)","year":2020,"id":131472,"datarank":0.0,"base_score":0.0,"endowment":0.0,"self_citation_contribution":0.0,"citation_network_contribution":0.0,"self_endowment_contribution":0.0,"citer_contribution":0.0,"corpus_percentile":null,"corpus_rank":null,"citation_count":0,"citer_count":0,"citers_with_citation_signal":0,"citers_with_endowment":0,"datacite_reuse_total":0,"is_dataset":false,"is_dataset_confidence":0.9439,"is_data_producer":false,"deposit_databanks":null,"is_oa":true,"file_count":0,"downloads":0,"has_version_chain":false,"published_date":"2020-01-01","fair_score":null,"fair_percentile":null,"algorithm_id":"datarank_citation_only_1hop_v6","ranking_scope":"data_only","authors":[{"id":585480,"name":"Thomas P. Quinn","orcid":"0000-0003-0286-6329","position":1,"is_corresponding":false},{"id":21513,"name":"Chunling Zhang","orcid":"0000-0003-2752-1028","position":2,"is_corresponding":false},{"id":586265,"name":"Gentry C. Hearn","orcid":null,"position":3,"is_corresponding":false},{"id":585481,"name":"Samuel Chen","orcid":"0009-0000-9380-7011","position":4,"is_corresponding":false},{"id":96018,"name":"Sek Won Kong","orcid":"0000-0003-4877-7567","position":5,"is_corresponding":false},{"id":78861,"name":"Murray J. Cairns","orcid":"0000-0003-2490-2538","position":6,"is_corresponding":false},{"id":411595,"name":"Ming T. Tsuang","orcid":"0000-0002-0076-5340","position":7,"is_corresponding":false},{"id":109307,"name":"Stephen V. Faraone","orcid":"0000-0002-9217-3982","position":8,"is_corresponding":false},{"id":1060,"name":"Stephen J. Glatt","orcid":"0000-0002-0360-7567","position":9,"is_corresponding":false},{"id":542544,"name":"Jonathan Hess","orcid":"0000-0001-8406-632X","position":0,"is_corresponding":true}],"reference_count":60,"raw_metadata":null,"created_at":"2026-07-18T23:16:03.875886Z","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":[]}