{"doi":"10.1101/2021.01.25.21250099","title":"Trans-ethnic eQTL meta-analysis of human brain reveals regulatory architecture and candidate causal variants for brain-related traits","abstract":"<jats:title>Abstract</jats:title>\n                <jats:p>While large-scale genome-wide association studies (GWAS) have identified hundreds of loci associated with neuropsychiatric and neurodegenerative traits, identifying the variants, genes and molecular mechanisms underlying these traits remains challenging. Integrating GWAS results with expression quantitative trait loci (eQTLs) and identifying shared genetic architecture has been widely adopted to nominate genes and candidate causal variants. However, this integrative approach is often limited by the sample size, the statistical power of the eQTL dataset, and the strong linkage disequilibrium between variants. Here we developed the multivariate multiple QTL (mmQTL) approach and applied it to perform a large-scale trans-ethnic eQTL meta-analysis to increase power and fine-mapping resolution. Importantly, this method also increases power to identify conditional eQTL’s that are enriched for cell type specific regulatory effects. Analysis of 3,188 RNA-seq samples from 2,029 donors, including 444 non-European individuals, yields an effective sample size of 2,974, which is substantially larger than previous brain eQTL efforts. Joint statistical fine-mapping of eQTL and GWAS identified 301 variant-trait pairs for 23 brain-related traits driven by 189 unique candidate causal variants for 179 unique genes. This integrative analysis identifies novel disease genes and elucidates potential regulatory mechanisms for genes underlying schizophrenia, bipolar disorder and Alzheimer’s disease.</jats:p>","journal":null,"year":null,"id":603219,"datarank":0.32958368660043297,"base_score":2.1972245773362196,"endowment":2.1972245773362196,"self_citation_contribution":0.32958368660043297,"citation_network_contribution":0.0,"self_endowment_contribution":0.32958368660043297,"citer_contribution":0.0,"corpus_percentile":null,"corpus_rank":null,"citation_count":8,"citer_count":0,"citers_with_citation_signal":0,"citers_with_endowment":0,"datacite_reuse_total":0,"is_dataset":false,"is_dataset_confidence":null,"is_data_producer":false,"deposit_databanks":null,"is_oa":false,"file_count":0,"downloads":0,"has_version_chain":false,"published_date":null,"fair_score":null,"fair_percentile":null,"algorithm_id":"datarank_citation_only_1hop_v6","ranking_scope":"data_only","authors":[{"id":284407,"name":"Jaroslav Bendl","orcid":"0000-0001-9989-2720","position":1,"is_corresponding":false},{"id":89986,"name":"Roman Kosoy","orcid":"0000-0002-3080-7900","position":2,"is_corresponding":false},{"id":284413,"name":"John F. Fullard","orcid":"0000-0001-9874-2907","position":3,"is_corresponding":false},{"id":1202,"name":"Gabriel E. Hoffman","orcid":"0000-0002-0957-0224","position":4,"is_corresponding":false},{"id":1203,"name":"Panos Roussos","orcid":"0000-0002-4640-6239","position":5,"is_corresponding":false},{"id":21511,"name":"Biao Zeng","orcid":"0000-0003-4900-2288","position":0,"is_corresponding":false}],"reference_count":0,"raw_metadata":{"has_enrichment":true,"resolved":true,"title":"Trans-ethnic eQTL meta-analysis of human brain reveals regulatory architecture and candidate causal variants for brain-related traits","abstract":"<jats:title>Abstract</jats:title>\n                <jats:p>While large-scale genome-wide association studies (GWAS) have identified hundreds of loci associated with neuropsychiatric and neurodegenerative traits, identifying the variants, genes and molecular mechanisms underlying these traits remains challenging. Integrating GWAS results with expression quantitative trait loci (eQTLs) and identifying shared genetic architecture has been widely adopted to nominate genes and candidate causal variants. However, this integrative approach is often limited by the sample size, the statistical power of the eQTL dataset, and the strong linkage disequilibrium between variants. Here we developed the multivariate multiple QTL (mmQTL) approach and applied it to perform a large-scale trans-ethnic eQTL meta-analysis to increase power and fine-mapping resolution. Importantly, this method also increases power to identify conditional eQTL’s that are enriched for cell type specific regulatory effects. Analysis of 3,188 RNA-seq samples from 2,029 donors, including 444 non-European individuals, yields an effective sample size of 2,974, which is substantially larger than previous brain eQTL efforts. Joint statistical fine-mapping of eQTL and GWAS identified 301 variant-trait pairs for 23 brain-related traits driven by 189 unique candidate causal variants for 179 unique genes. This integrative analysis identifies novel disease genes and elucidates potential regulatory mechanisms for genes underlying schizophrenia, bipolar disorder and Alzheimer’s disease.</jats:p>","is_dataset_classified":null,"base_score":0.0,"endowment":0.0,"datacite_reuse_total":0,"file_count":0,"downloads":0,"views":0,"has_version_chain":false,"is_dataset":false,"is_oa":false,"pmid":"23304386","pmcid":null,"openalex_id":null,"authors":[],"funders":[{"funder_name":"National Institutes of Health","grant_id":"1R01MH109897-01","title":"Integrated Multiscale Networks in Schizophrenia"},{"funder_name":"National Institutes of Health","grant_id":"1S10OD026880-01","title":"Big Omics Data Engine 2 Supercomputer"},{"funder_name":"National Institutes of Health","grant_id":"5R01MH125246-05","title":"Multiethnic genomic epigenomic and transcriptomic fine-mapping and functional validation analysis of schizophrenia and bipolar disorder risk loci"},{"funder_name":"National Institutes of Health","grant_id":"1S10OD018522-01","title":"Transforming Genomics with 5 PB Big Omics Data Engine Cray CS300-AC Supercomputer"},{"funder_name":"National Institutes of Health","grant_id":"5U01MH116442-03","title":"The 3D genome in transcriptional regulation across the postnatal life span, with implications for schizophrenia and bipolar disorder"},{"funder_name":"National Institutes of Health","grant_id":"5R01MH109677-02","title":"Risk genetic variants and cis regulation of gene expression in Bipolar Disorder"},{"funder_name":"National Institutes of Health","grant_id":"3R01AG050986-04S1","title":"Higher Order Chromatin and Genetic Risk for Alzheimer's Disease"},{"funder_name":"National Institutes of Health","grant_id":"5R01AG067025-04","title":"Understanding the molecular mechanisms that contribute to neuropsychiatric symptoms in Alzheimer Disease"},{"funder_name":"National Institutes of Health","grant_id":"5R01AG065582-04","title":"Understanding the protective and neuroinflammatory role of human brain immune cells in Alzheimer Disease"}],"total_grants":9,"fwci":null,"citation_percentile":null,"influential_citations":0,"citation_trend":[],"oa_status":"green","license":"cc-by-nc-nd","oa_locations":[{"url":"https://www.medrxiv.org/content/medrxiv/early/2021/01/30/2021.01.25.21250099.full.pdf","host_type":"repository"},{"url":"https://syndication.highwire.org/content/doi/10.1101/2021.01.25.21250099","host_type":"publisher"},{"url":"https://doi.org/10.21203/rs.3.rs-150239/v1","host_type":""},{"url":"https://www.researchsquare.com/article/rs-150239/v1.pdf?c=1631866960000","host_type":""},{"url":"https://doi.org/10.1101/2021.01.25.21250099","host_type":""},{"url":"https://dx.doi.org/10.21203/rs.3.rs-150239/v1","host_type":""}],"fields_of_study":["0301 basic medicine","0303 health sciences","03 medical and health sciences"],"mesh_terms":[],"keywords":[],"sdg_mappings":[],"linked_datasets":[],"clinical_trials":[],"software_tools":[],"database_accessions":[],"source":"live","citation_network_status":"fetched"},"created_at":"2026-07-29T21:23:54.041432Z","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":[]}