{"doi":"10.1093/bioinformatics/btab139","title":"CCmed: cross-condition mediation analysis for identifying replicable trans-associations mediated by cis-gene expression","abstract":"MOTIVATION: Trans-acting expression quantitative trait loci (eQTLs) collectively explain a substantial proportion of expression variation, yet are challenging to detect and replicate since their effects are often individually weak. A large proportion of genetic effects on distal genes are mediated through cis-gene expression. Cis-association (between SNP and cis-gene) and gene-gene correlation conditional on SNP genotype could establish trans-association (between SNP and trans-gene). Both cis-association and gene-gene conditional correlation have effects shared across relevant tissues and conditions, and trans-associations mediated by cis-gene expression also have effects shared across relevant conditions. RESULTS: We proposed a Cross-Condition Mediation analysis method (CCmed) for detecting cis-mediated trans-associations with replicable effects in relevant conditions/studies. CCmed integrates cis-association and gene-gene conditional correlation statistics from multiple tissues/studies. Motivated by the bimodal effect-sharing patterns of eQTLs, we proposed two variations of CCmed, CCmedmost and CCmedspec for detecting cross-tissue and tissue-specific trans-associations, respectively. We analyzed data of 13 brain tissues from the Genotype-Tissue Expression (GTEx) project, and identified trios with cis-mediated trans-associations across brain tissues, many of which showed evidence of trans-association in two replication studies. We also identified trans-genes associated with schizophrenia loci in at least two brain tissues. AVAILABILITY AND IMPLEMENTATION: CCmed software is available at http://github.com/kjgleason/CCmed. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.","journal":"Bioinformatics","year":2021,"id":203308,"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":5,"citer_count":0,"citers_with_citation_signal":0,"citers_with_endowment":0,"datacite_reuse_total":0,"is_dataset":false,"is_dataset_confidence":0.9517,"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":353303,"name":"Kevin J. Gleason","orcid":"0000-0002-3850-5093","position":1,"is_corresponding":false},{"id":104392,"name":"Jiebiao Wang","orcid":"0000-0003-3828-4400","position":2,"is_corresponding":false},{"id":28802,"name":"Jubao Duan","orcid":"0000-0002-7215-3220","position":3,"is_corresponding":false},{"id":104427,"name":"Xin He","orcid":"0000-0001-9011-5212","position":4,"is_corresponding":false},{"id":108967,"name":"Brandon L. Pierce","orcid":"0000-0002-7829-952X","position":5,"is_corresponding":false},{"id":226932,"name":"Lin Chen","orcid":"0000-0002-6645-5708","position":6,"is_corresponding":false},{"id":353304,"name":"Fan Yang","orcid":"0009-0005-9671-0991","position":0,"is_corresponding":true}],"reference_count":24,"raw_metadata":null,"created_at":"2026-07-18T23:51:14.206397Z","pmid":"33647928","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":[]}