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Implicating genes, pleiotropy, and sexual dimorphism at blood lipid loci through multi-ancestry meta-analysis.
Kanoni, Stavroula; Graham, Sarah E; Wang, Yuxuan; Surakka, Ida; Ramdas, Shweta; Zhu, Xiang; Clarke, Shoa L; Bhatti, Konain Fatima; Vedantam, Sailaja; Winkler, Thomas W; Locke, Adam E; Marouli, Eirini; Zajac, Greg J M; Wu, Kuan-Han H; Ntalla, Ioanna; Hui, Qin; Klarin, Derek; Hilliard, Austin T; Wang, Zeyuan; Xue, Chao; Thorleifsson, Gudmar; Helgadottir, Anna; Gudbjartsson, Daniel F; Holm, Hilma; Olafsson, Isleifur; Hwang, Mi Yeong; Han, Sohee; Akiyama, Masato; Sakaue, Saori; Terao, Chikashi; Kanai, Masahiro; Zhou, Wei; Brumpton, Ben M; Rasheed, Humaira; Havulinna, Aki S; Veturi, Yogasudha; Pacheco, Jennifer Allen; Rosenthal, Elisabeth A; Lingren, Todd; Feng, QiPing; Kullo, Iftikhar J; Narita, Akira; Takayama, Jun; Martin, Hilary C; Hunt, Karen A; Trivedi, Bhavi; Haessler, Jeffrey; Giulianini, Franco; Bradford, Yuki; Miller, Jason E.
Afiliação
  • Kanoni S; William Harvey Research Institute, Barts and the London School of Medicine and Dentistry, Queen Mary University of London, Charterhouse Square, London, EC1M 6BQ, UK.
  • Graham SE; Department of Internal Medicine, Division of Cardiology, University of Michigan, Ann Arbor, MI, 48109, USA.
  • Wang Y; Department of Biostatistics, Boston University School of Public Health, 801 Massachusetts Ave, Boston, MA, 02118, USA.
  • Surakka I; Department of Internal Medicine, Division of Cardiology, University of Michigan, Ann Arbor, MI, 48109, USA.
  • Ramdas S; Department of Genetics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA.
  • Zhu X; Department of Statistics, The Pennsylvania State University, University Park, PA, USA.
  • Clarke SL; Huck Institutes of the Life Sciences, The Pennsylvania State University, University Park, PA, USA.
  • Bhatti KF; VA Palo Alto Health Care Systems, Palo Alto, CA, USA.
  • Vedantam S; Department of Statistics, Stanford University, Stanford, CA, USA.
  • Winkler TW; VA Palo Alto Health Care Systems, Palo Alto, CA, USA.
  • Locke AE; Department of Medicine, Division of Cardiovascular Medicine, Stanford University School of Medicine, Stanford, CA, 94305, USA.
  • Marouli E; William Harvey Research Institute, Barts and the London School of Medicine and Dentistry, Queen Mary University of London, Charterhouse Square, London, EC1M 6BQ, UK.
  • Zajac GJM; Boston Children's Hospital, EndocrinologyBoston, MA, 02115, USA.
  • Wu KH; Program in Medical and Population Genetics, Broad Institute of Harvard and MIT, Cambridge, MA, USA.
  • Ntalla I; Department of Genetic Epidemiology, University of Regensburg, Regensburg, Germany.
  • Hui Q; McDonnell Genome Institute and Department of Medicine, Washington University, St. Louis, MO, 63108, USA.
  • Klarin D; William Harvey Research Institute, Barts and the London School of Medicine and Dentistry, Queen Mary University of London, Charterhouse Square, London, EC1M 6BQ, UK.
  • Hilliard AT; Department of Biostatistics, Center for Statistical Genetics, University of Michigan, Ann Arbor, MI, USA.
  • Wang Z; Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI, 48109, USA.
  • Xue C; Clinical Pharmacology, William Harvey Research Institute, Barts and The London School of Medicine and Dentistry, Queen Mary University of London, London, EC1M 6BQ, UK.
  • Thorleifsson G; Department of Epidemiology, Emory University Rollins School of Public Health, Atlanta, GA, USA.
  • Helgadottir A; Atlanta VA Health Care System, Decatur, GA, USA.
  • Gudbjartsson DF; VA Palo Alto Health Care Systems, Palo Alto, CA, USA.
  • Holm H; Department of Surgery, Stanford University School of Medicine, Stanford, CA, USA.
  • Olafsson I; VA Palo Alto Health Care Systems, Palo Alto, CA, USA.
  • Hwang MY; Department of Epidemiology, Emory University Rollins School of Public Health, Atlanta, GA, USA.
  • Han S; Atlanta VA Health Care System, Decatur, GA, USA.
  • Akiyama M; Department of Internal Medicine, Division of Cardiology, University of Michigan, Ann Arbor, MI, 48109, USA.
  • Sakaue S; deCODE Genetics/Amgen, Inc. Sturlugata 8, Reykjavik, 102, Iceland.
  • Terao C; deCODE Genetics/Amgen, Inc. Sturlugata 8, Reykjavik, 102, Iceland.
  • Kanai M; deCODE Genetics/Amgen, Inc. Sturlugata 8, Reykjavik, 102, Iceland.
  • Zhou W; School of Engineering and Natural Sciences, University of Iceland, Sæmundargötu 2, Reykjavik, 102, Iceland.
  • Brumpton BM; deCODE Genetics/Amgen, Inc. Sturlugata 8, Reykjavik, 102, Iceland.
  • Rasheed H; Department of Clinical Biochemistry, Landspitali - National University Hospital of Iceland, Hringbraut, Reykjavik, 101, Iceland.
  • Havulinna AS; Division of Genome Science, Department of Precision Medicine, National Institute of Health, Chungcheongbuk-Do, South Korea.
  • Veturi Y; Division of Genome Science, Department of Precision Medicine, National Institute of Health, Chungcheongbuk-Do, South Korea.
  • Pacheco JA; Laboratory for Statistical Analysis, RIKEN Center for Integrative Medical Sciences, Yokohama, Japan.
  • Rosenthal EA; Department of Ophthalmology, Graduate School of Medical Sciences, Kyushu University, Fukuoka, Japan.
  • Lingren T; Laboratory for Statistical Analysis, RIKEN Center for Integrative Medical Sciences, Yokohama, Japan.
  • Feng Q; Department of Statistical Genetics, Osaka University Graduate School of Medicine, Osaka, Japan.
  • Kullo IJ; Department of Allergy and Rheumatology, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan.
  • Narita A; Laboratory for Statistical and Translational Genetics, RIKEN Center for Integrative Medical Sciences, Yokohama, Japan.
  • Takayama J; Program in Medical and Population Genetics, Broad Institute of Harvard and MIT, Cambridge, MA, USA.
  • Martin HC; Laboratory for Statistical Analysis, RIKEN Center for Integrative Medical Sciences, Yokohama, Japan.
  • Hunt KA; Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA.
  • Trivedi B; Program in Medical and Population Genetics, Broad Institute of Harvard and MIT, Cambridge, MA, USA.
  • Haessler J; Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI, 48109, USA.
  • Giulianini F; Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, MA, USA.
  • Bradford Y; K.G. Jebsen Center for Genetic Epidemiology, Department of Public Health and Nursing, NTNU, Norwegian University of Science and Technology, Trondheim, Norway.
  • Miller JE; MRC Integrative Epidemiology Unit (IEU), Bristol Medical School, University of Bristol, Oakfield House, Oakfield Grove, Bristol, BS8 2BN, UK.
Genome Biol ; 23(1): 268, 2022 12 27.
Article em En | MEDLINE | ID: mdl-36575460
ABSTRACT

BACKGROUND:

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.

RESULTS:

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 pleiotropic associations with cardiovascular phenotypes and determine novel associations, notably with cholelithiasis risk. We perform sex-stratified GWAS meta-analysis of lipid levels and show that 3-5% of autosomal lipid-associated loci demonstrate sex-biased effects. Finally, we report 21 novel lipid loci identified on the X chromosome. Many of the sex-biased autosomal and X chromosome lipid loci show pleiotropic associations with sex hormones, emphasizing the role of hormone regulation in lipid metabolism.

CONCLUSIONS:

Taken together, our findings provide insights into the biological mechanisms through which associated variants lead to altered lipid levels and potentially cardiovascular disease risk.
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Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Predisposição Genética para Doença / Estudo de Associação Genômica Ampla Tipo de estudo: Prognostic_studies / Systematic_reviews Limite: Humans Idioma: En Revista: Genome Biol Ano de publicação: 2022 Tipo de documento: Article

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Predisposição Genética para Doença / Estudo de Associação Genômica Ampla Tipo de estudo: Prognostic_studies / Systematic_reviews Limite: Humans Idioma: En Revista: Genome Biol Ano de publicação: 2022 Tipo de documento: Article