{"doi":"10.1101/2024.10.15.24315557","title":"Genome-wide association study for circulating metabolic traits in 619,372 individuals","abstract":"<jats:title>Abstract</jats:title>\n                <jats:p>\n                  Interpreting genetic associations with complex traits can be greatly improved by detailed understanding of the molecular consequences of these variants. However, although genome-wide association studies (GWAS) for common complex diseases routinely profile 1M+ individuals, studies of molecular phenotypes have lagged behind. We performed a GWAS meta-analysis for 249 circulating metabolic traits in the Estonian Biobank and the UK Biobank in up to 619,372 individuals, identifying 88,604 significant locus-metabolite associations and 8,774 independent lead variants, including 987 lead variants with a minor allele frequency less than 1%. We demonstrate how common and low-frequency associations converge on shared genes and pathways, bridging the gap between rare-variant burden testing and common-variant GWAS. We used Mendelian randomisation (MR) to explore putative causal links between metabolic traits, coronary artery disease and type 2 diabetes (T2D). Surprisingly, up to 85% of the tested metabolite-disease pairs had statistically significant genome-wide MR estimates, likely reflecting complex indirect effects driven by horisontal pleiotropy. To avoid these pleiotropic effects, we used\n                  <jats:italic>cis</jats:italic>\n                  -MR to test the phenotypic impact of inhibiting specific drug targets. We found that although plasma levels of branched-chain amino acids (BCAAs) have been associated with T2D in both observational and genome-wide MR studies, inhibiting the BCAA catabolism pathway to lower BCAA levels is unlikely to reduce T2D risk. Our publicly available results provide a valuable novel resource for GWAS interpretation and drug target prioritisation.\n                </jats:p>","journal":null,"year":null,"id":618943,"datarank":0.4335557636844247,"base_score":2.8903717578961645,"endowment":2.8903717578961645,"self_citation_contribution":0.4335557636844247,"citation_network_contribution":0.0,"self_endowment_contribution":0.4335557636844247,"citer_contribution":0.0,"corpus_percentile":null,"corpus_rank":null,"citation_count":17,"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":614300,"name":"Jaanika Kronberg","orcid":"0000-0003-2362-656X","position":1,"is_corresponding":false},{"id":1335414,"name":"Adriaan van der Graaf","orcid":"0000-0002-8898-8484","position":2,"is_corresponding":false},{"id":1597037,"name":"Mihkel Jesse","orcid":null,"position":3,"is_corresponding":false},{"id":289151,"name":"Erik Abner","orcid":"0000-0002-6529-3161","position":4,"is_corresponding":false},{"id":108546,"name":"Urmo Võsa","orcid":"0000-0003-3476-1652","position":5,"is_corresponding":false},{"id":1175121,"name":"Ida Rahu","orcid":"0000-0001-5497-5522","position":6,"is_corresponding":false},{"id":577290,"name":"Nele Taba","orcid":"0000-0003-1953-2819","position":7,"is_corresponding":false},{"id":1597038,"name":"Anastassia Kolde","orcid":"0009-0002-6963-7053","position":8,"is_corresponding":false},{"id":1597039,"name":"Dzvenymyra Yarish","orcid":null,"position":9,"is_corresponding":false},{"id":21697,"name":"Krista Fischer","orcid":"0000-0002-3521-0599","position":11,"is_corresponding":false},{"id":5243,"name":"Zoltán Kutalik","orcid":"0000-0001-8285-7523","position":12,"is_corresponding":false},{"id":1054,"name":"Tõnu Esko","orcid":null,"position":13,"is_corresponding":false},{"id":550870,"name":"Kaur Alasoo","orcid":"0000-0002-1761-8881","position":14,"is_corresponding":false},{"id":251616,"name":"Priit Palta","orcid":"0000-0001-9320-7008","position":15,"is_corresponding":false},{"id":1175120,"name":"Ralf Tambets","orcid":"0009-0001-4245-265X","position":0,"is_corresponding":false}],"reference_count":0,"raw_metadata":{"has_enrichment":true,"resolved":true,"title":"Genome-wide association study for circulating metabolic traits in 619,372 individuals","abstract":"<jats:title>Abstract</jats:title>\n                <jats:p>\n                  Interpreting genetic associations with complex traits can be greatly improved by detailed understanding of the molecular consequences of these variants. However, although genome-wide association studies (GWAS) for common complex diseases routinely profile 1M+ individuals, studies of molecular phenotypes have lagged behind. We performed a GWAS meta-analysis for 249 circulating metabolic traits in the Estonian Biobank and the UK Biobank in up to 619,372 individuals, identifying 88,604 significant locus-metabolite associations and 8,774 independent lead variants, including 987 lead variants with a minor allele frequency less than 1%. We demonstrate how common and low-frequency associations converge on shared genes and pathways, bridging the gap between rare-variant burden testing and common-variant GWAS. We used Mendelian randomisation (MR) to explore putative causal links between metabolic traits, coronary artery disease and type 2 diabetes (T2D). Surprisingly, up to 85% of the tested metabolite-disease pairs had statistically significant genome-wide MR estimates, likely reflecting complex indirect effects driven by horisontal pleiotropy. To avoid these pleiotropic effects, we used\n                  <jats:italic>cis</jats:italic>\n                  -MR to test the phenotypic impact of inhibiting specific drug targets. We found that although plasma levels of branched-chain amino acids (BCAAs) have been associated with T2D in both observational and genome-wide MR studies, inhibiting the BCAA catabolism pathway to lower BCAA levels is unlikely to reduce T2D risk. Our publicly available results provide a valuable novel resource for GWAS interpretation and drug target prioritisation.\n                </jats:p>","is_dataset_classified":null,"base_score":2.8903717578961645,"endowment":2.8903717578961645,"datacite_reuse_total":0,"file_count":0,"downloads":0,"views":0,"has_version_chain":false,"is_dataset":false,"is_oa":false,"pmid":"40297438","pmcid":null,"openalex_id":"https://openalex.org/W4403453208","authors":[],"funders":[{"funder_name":"NIGMS NIH HHS","grant_id":"R01 GM129325","title":null}],"total_grants":1,"fwci":null,"citation_percentile":null,"influential_citations":0,"citation_trend":[{"year":2024,"count":2},{"year":2025,"count":10},{"year":2026,"count":5}],"oa_status":"green","license":"cc-by","oa_locations":[{"url":"https://www.medrxiv.org/content/medrxiv/early/2024/10/16/2024.10.15.24315557.full.pdf","host_type":"repository"},{"url":"https://www.medrxiv.org/content/medrxiv/early/2024/10/16/2024.10.15.24315557.full.pdf","host_type":"repository"},{"url":"https://syndication.highwire.org/content/doi/10.1101/2024.10.15.24315557","host_type":"publisher"},{"url":"https://doi.org/10.1101/2024.10.15.24315557","host_type":"repository"},{"url":"https://pubmed.ncbi.nlm.nih.gov/40297438","host_type":"repository"},{"url":"https://www.ncbi.nlm.nih.gov/pmc/articles/12036396","host_type":"repository"}],"fields_of_study":["Genetic Associations and Epidemiology","Bioinformatics and Genomic Networks","Epigenetics and DNA Methylation"],"mesh_terms":[],"keywords":["Association (psychology)","Genome-wide association study","Computational biology","Biology","Genome","Genetics","Psychology","Single-nucleotide polymorphism","Gene","Genotype"],"sdg_mappings":[],"linked_datasets":[],"clinical_trials":[],"software_tools":[],"database_accessions":[],"source":"live","citation_network_status":"fetched"},"created_at":"2026-08-03T05:36:57.969661Z","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":[]}