Implicating genes, pleiotropy, and sexual dimorphism at blood lipid loci through multi-ancestry meta-analysis

Stavroula Kanoni, William Harvey Research Institute, Barts and the London School of Medicine and Dentistry, Queen Mary University of London, Charterhouse Square, London, EC1M 6BQ, UK.
Sarah E. Graham, Department of Internal Medicine, Division of Cardiology, University of Michigan, Ann Arbor, MI, 48109, USA.
Yuxuan Wang, Department of Biostatistics, Boston University School of Public Health, 801 Massachusetts Ave, Boston, MA, 02118, USA.
Ida Surakka, Department of Internal Medicine, Division of Cardiology, University of Michigan, Ann Arbor, MI, 48109, USA.
Shweta Ramdas, Department of Genetics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA.
Xiang Zhu, Department of Statistics, The Pennsylvania State University, University Park, PA, USA.
Shoa L. Clarke, VA Palo Alto Health Care Systems, Palo Alto, CA, USA.
Konain Fatima Bhatti, William Harvey Research Institute, Barts and the London School of Medicine and Dentistry, Queen Mary University of London, Charterhouse Square, London, EC1M 6BQ, UK.
Sailaja Vedantam, Boston Children's Hospital, EndocrinologyBoston, MA, 02115, USA.
Thomas W. Winkler, Department of Genetic Epidemiology, University of Regensburg, Regensburg, Germany.
Adam E. Locke, McDonnell Genome Institute and Department of Medicine, Washington University, St. Louis, MO, 63108, USA.
Eirini Marouli, William Harvey Research Institute, Barts and the London School of Medicine and Dentistry, Queen Mary University of London, Charterhouse Square, London, EC1M 6BQ, UK.
Greg J. Zajac, Department of Biostatistics, Center for Statistical Genetics, University of Michigan, Ann Arbor, MI, USA.
Kuan-Han H. Wu, Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI, 48109, USA.
Ioanna Ntalla, Clinical Pharmacology, William Harvey Research Institute, Barts and The London School of Medicine and Dentistry, Queen Mary University of London, London, EC1M 6BQ, UK.
Qin Hui, Department of Epidemiology, Emory University Rollins School of Public Health, Atlanta, GA, USA.
Derek Klarin, VA Palo Alto Health Care Systems, Palo Alto, CA, USA.
Austin T. Hilliard, VA Palo Alto Health Care Systems, Palo Alto, CA, USA.
Zeyuan Wang, Department of Epidemiology, Emory University Rollins School of Public Health, Atlanta, GA, USA.
Chao Xue, Department of Internal Medicine, Division of Cardiology, University of Michigan, Ann Arbor, MI, 48109, USA.
Gudmar Thorleifsson, deCODE Genetics/Amgen, Inc. Sturlugata 8, Reykjavik, 102, Iceland.
Anna Helgadottir, deCODE Genetics/Amgen, Inc. Sturlugata 8, Reykjavik, 102, Iceland.
Daniel F. Gudbjartsson, deCODE Genetics/Amgen, Inc. Sturlugata 8, Reykjavik, 102, Iceland.
Hilma Holm, deCODE Genetics/Amgen, Inc. Sturlugata 8, Reykjavik, 102, Iceland.
Isleifur Olafsson, Department of Clinical Biochemistry, Landspitali - National University Hospital of Iceland, Hringbraut, Reykjavik, 101, Iceland.
Mi Yeong Hwang, Division of Genome Science, Department of Precision Medicine, National Institute of Health, Chungcheongbuk-Do, South Korea.
Sohee Han, Division of Genome Science, Department of Precision Medicine, National Institute of Health, Chungcheongbuk-Do, South Korea.
Masato Akiyama, Laboratory for Statistical Analysis, RIKEN Center for Integrative Medical Sciences, Yokohama, Japan.
Saori Sakaue, Laboratory for Statistical Analysis, RIKEN Center for Integrative Medical Sciences, Yokohama, Japan.
Chikashi Terao, Laboratory for Statistical and Translational Genetics, RIKEN Center for Integrative Medical Sciences, Yokohama, Japan.
Masahiro Kanai, Program in Medical and Population Genetics, Broad Institute of Harvard and MIT, Cambridge, MA, USA.
Wei Zhou, Program in Medical and Population Genetics, Broad Institute of Harvard and MIT, Cambridge, MA, USA.

Document Type

Journal Article

Publication Date

12-27-2022

Journal

Genome biology

Volume

23

Issue

1

DOI

10.1186/s13059-022-02837-1

Keywords

Cholesterol; GWAS; Genetics; Genome-wide association study; Lipids

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.

Department

Medicine

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