{"database":"biostudies-literature","file_versions":[],"scores":null,"additional":{"submitter":["Kanoni S"],"funding":["Economic and Social Research Council","BLRD VA","Intramural NIH HHS","Cancer Research UK","NICHD NIH HHS","British Heart Foundation","NIA NIH HHS","NIH/NHLBI","NHLBI NIH HHS","NNF Center for Basic Metabolic Research","Public Health Agency","CSRD VA","NIDDK NIH HHS","Medical Research Council","NHGRI NIH HHS","National Institute for Health Research (NIHR)","Wellcome Trust","Novo Nordisk Fonden"],"pagination":["268"],"full_dataset_link":["https://www.ebi.ac.uk/biostudies/studies/S-EPMC9793579"],"repository":["biostudies-literature"],"omics_type":["Unknown"],"volume":["23(1)"],"pubmed_abstract":["<h4>Background</h4>Genetic variants within nearly 1000 loci are known to contribute to modulation of blood lipid levels. However, the biological pathways underlying these associations are frequently unknown, limiting understanding of these findings and hindering downstream translational efforts such as drug target discovery.<h4>Results</h4>To expand our understanding of the underlying biological pathways and mechanisms controlling blood lipid levels, we leverage a large multi-ancestry meta-analysis (N = 1,654,960) of blood lipids to prioritize putative causal genes for 2286 lipid associations using six gene prediction approaches. Using phenome-wide association (PheWAS) scans, we identify relationships of genetically predicted lipid levels to other diseases and conditions. We confirm known "],"journal":["Genome biology"],"pubmed_title":["Implicating genes, pleiotropy, and sexual dimorphism at blood lipid loci through multi-ancestry meta-analysis."],"pmcid":["PMC9793579"],"funding_grant_id":["R01 HD030880","MR/S011676/1","NNF13OC0005785","MC_UU_00011","Z01 HG200362","ES/L008459/1","CBMR - 008","I01 BX004821","MR/R024227/1","P30 DK020572","NNF14OC0010513","T32 HG000040","G0601463","R01 HL109946","Hansen Group","MC_UU_00007/10","P30 AG010161","U01 HG011723","R01 DK075787","FS/14/66/3129","U54 HG006938","BRC-1215-2001","R01 DK072193","NNF19OC0055250","201543/B/16/Z","C18281/A19169","R01 AG015819","STL/5569/19","ES/S007253/1","221854/Z/20/Z","MC_PC_20026","CL-2021-25-004","R03 HL154284","R01 HL153805","R01 DK093757","Grarup Group","NF-SI-0611-10170","R01HL127564","P30 AG072975","217065/Z/19/Z","202802/Z/16/Z","MC_UU_00019/1","R01 HL142711","U01 HG011172","RG/14/5/30893","MC_UU_00011/1","R01 HL127564","IK2 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are known to contribute to modulation of blood lipid levels. However, the biological pathways underlying these associations are frequently unknown, limiting understanding of these findings and hindering downstream translational efforts such as drug target discovery.<h4>Results</h4>To expand our understanding of the underlying biological pathways and mechanisms controlling blood lipid levels, we leverage a large multi-ancestry meta-analysis (N = 1,654,960) of blood lipids to prioritize putative causal genes for 2286 lipid associations using six gene prediction approaches. Using phenome-wide association (PheWAS) scans, we identify relationships of genetically predicted lipid levels to other diseases and conditions. We confirm known ","dates":{"release":"2022-01-01T00:00:00Z","publication":"2022 Dec","modification":"2026-05-31T02:11:56.214Z","creation":"2025-04-04T21:00:59.311Z"},"accession":"S-EPMC9793579","cross_references":{"pubmed":["36575460"],"doi":["10.1186/s13059-022-02837-1"]}}