Nature Communications

Nat Commun. 9: 5141

GWAS and colocalization analyses implicate carotid intima-media thickness and carotid plaque loci in cardiovascular outcomes

Nora Franceschini¹, http://orcid.org/0000-0003-2786-1225Claudia Giambartolomei², Paul S. de Vries³, Chris Finan⁴, Joshua C. Bis⁵, http://orcid.org/0000-0001-6718-3559Rachael P. Huntley⁴, http://orcid.org/0000-0002-9791-0064Ruth C. Lovering⁴, http://orcid.org/0000-0002-7919-8528Salman M. Tajuddin⁶, http://orcid.org/0000-0003-0292-5421Thomas W. Winkler⁷, Misa Graff¹, Maryam Kavousi⁸, Caroline Dale⁹, Albert V. Smith¹⁰¹¹, Edith Hofer¹²¹³, Elisabeth M. van Leeuwen⁸, http://orcid.org/0000-0001-5047-4077Ilja M. Nolte¹⁴, Lingyi Lu¹⁵, http://orcid.org/0000-0002-4059-1779Markus Scholz¹⁶¹⁷, Muralidharan Sargurupremraj¹⁸, Niina Pitkänen¹⁹, Oscar Franzén²⁰²¹, http://orcid.org/0000-0002-6361-5059Peter K. Joshi²², Raymond Noordam²³, Riccardo E. Marioni²⁴²⁵, Shih-Jen Hwang²⁶²⁷, Solomon K. Musani²⁸, Ulf Schminke²⁹, Walter Palmas³⁰, http://orcid.org/0000-0001-5037-4834Aaron Isaacs⁸³¹, http://orcid.org/0000-0002-9501-600XAdolfo Correa²⁸, Alan B. Zonderman⁶, Albert Hofman⁸³², http://orcid.org/0000-0002-8309-094XAlexander Teumer³³³⁴, Amanda J. Cox³⁵³⁶, André G. Uitterlinden⁸³⁷, http://orcid.org/0000-0003-2079-4779Andrew Wong³⁸, Andries J. Smit³⁹, http://orcid.org/0000-0002-0106-1150Anne B. Newman⁴⁰, Annie Britton⁴¹, Arno Ruusalepp²¹⁴²⁴³, http://orcid.org/0000-0002-4360-8003Bengt Sennblad⁴⁴⁴⁵, Bo Hedblad⁴⁶, Bogdan Pasaniuc²⁴⁷, Brenda W. Penninx⁴⁸, Carl D. Langefeld¹⁵, Christina L. Wassel⁴⁹, Christophe Tzourio¹⁸, Cristiano Fava⁴⁶⁵⁰, http://orcid.org/0000-0002-2766-8882Damiano Baldassarre⁵¹⁵², Daniel H. O’Leary⁵³, Daniel Teupser¹⁷⁵⁴, http://orcid.org/0000-0001-7386-2857Diana Kuh³⁸, Elena Tremoli⁵²⁵⁵, Elmo Mannarino⁵⁶, Enzo Grossi⁵⁷, Eric Boerwinkle³⁵⁸, http://orcid.org/0000-0002-7892-8808Eric E. Schadt²⁰²¹, Erik Ingelsson⁵⁹⁶⁰⁶¹, Fabrizio Veglia⁵², http://orcid.org/0000-0001-9435-9441Fernando Rivadeneira⁸³⁷, Frank Beutner⁶², Ganesh Chauhan¹⁸⁶³, Gerardo Heiss¹, Harold Snieder¹⁴, Harry Campbell²², Henry Völzke³³³⁴, Hugh S. Markus⁶⁴, Ian J. Deary²⁴⁶⁵, http://orcid.org/0000-0002-3246-8359J. Wouter Jukema⁶⁶, Jacqueline de Graaf⁶⁷, Jacqueline Price²², Janne Pott¹⁶¹⁷, Jemma C. Hopewell⁶⁸, Jingjing Liang⁶⁹, Joachim Thiery¹⁷⁷⁰, Jorgen Engmann⁴, Karl Gertow⁴⁴, http://orcid.org/0000-0002-3071-7278Kenneth Rice⁷¹, Kent D. Taylor⁷², http://orcid.org/0000-0002-6397-7009Klodian Dhana⁷³, Lambertus A. L. M. Kiemeney⁷⁴, Lars Lind⁷⁵, Laura M. Raffield⁷⁶, Lenore J. Launer⁶, Lesca M. Holdt¹⁷⁵⁴, Marcus Dörr³⁴⁷⁷, http://orcid.org/0000-0002-0654-387XMartin Dichgans⁷⁸⁷⁹, http://orcid.org/0000-0001-6624-8621Matthew Traylor⁶⁴, Matthias Sitzer⁸⁰, Meena Kumari⁴¹⁸¹, http://orcid.org/0000-0002-4699-5627Mika Kivimaki⁴¹, Mike A. Nalls⁸²⁸³, Olle Melander⁴⁶, Olli Raitakari¹⁹⁸⁴, Oscar H. Franco⁸⁸⁵, Oscar L. Rueda-Ochoa⁸⁸⁶, http://orcid.org/0000-0002-4640-6239Panos Roussos²⁰⁸⁷⁸⁸, Peter H. Whincup⁸⁹, http://orcid.org/0000-0001-9088-234XPhilippe Amouyel⁹⁰⁹¹⁹², Philippe Giral⁹³, Pramod Anugu²⁸, Quenna Wong⁹⁴, Rainer Malik⁷⁸, Rainer Rauramaa⁹⁵⁹⁶, http://orcid.org/0000-0003-1924-1202Ralph Burkhardt¹⁷⁹⁷⁹⁸, Rebecca Hardy³⁸, Reinhold Schmidt¹², Renée de Mutsert⁹⁹, http://orcid.org/0000-0001-7240-4563Richard W. Morris¹⁰⁰, Rona J. Strawbridge⁴⁴¹⁰¹, S. Goya Wannamethee¹⁰², http://orcid.org/0000-0002-2452-1500Sara Hägg¹⁰³, Sonia Shah⁴, Stela McLachlan²², Stella Trompet²³⁶⁶, Sudha Seshadri¹⁰⁴, Sudhir Kurl¹⁰⁵, Susan R. Heckbert⁵¹⁰⁶, Susan Ring¹⁰⁷¹⁰⁸, Tamara B. Harris⁶, Terho Lehtimäki¹⁰⁹¹¹⁰, Tessel E. Galesloot⁷⁴, Tina Shah⁴, Ulf de Faire¹¹¹¹¹², Vincent Plagnol¹¹³, Wayne D. Rosamond¹, Wendy Post¹¹⁴, Xiaofeng Zhu⁶⁹, Xiaoling Zhang²⁷¹¹⁵, Xiuqing Guo⁷²¹¹⁶, Yasaman Saba¹¹⁷, MEGASTROKE Consortium

Yukinori Okada¹³³¹³⁴¹³⁵, Aniket Mishra¹³⁶¹³⁷, Loes Rutten-Jacobs¹³⁸, Anne-Katrin Giese¹³⁹¹⁴⁰, Sander W. van der Laan¹⁴¹, Solveig Gretarsdottir¹⁴², Christopher D. Anderson¹⁴⁰¹⁴³¹⁴⁴, Michael Chong¹⁴⁵, Hieab H. H. Adams⁸, Tetsuro Ago¹⁴⁶, Peter Almgren¹⁴⁷, Philippe Amouyel¹⁴⁸¹⁴⁹, Hakan Ay¹⁴⁰¹⁵⁰, Traci M. Bartz⁵⁷¹, Oscar R. Benavente¹⁵¹, Steve Bevan¹⁵², Giorgio B. Boncoraglio¹⁵³, Robert D. Brown Jr.¹⁵⁴, Adam S. Butterworth¹⁵⁵¹⁵⁶, Caty Carrera¹⁵⁷¹⁵⁸, Cara L. Carty¹⁵⁹¹⁶⁰, Daniel I. Chasman¹³²¹⁶¹, Wei-Min Chen¹⁶², John W. Cole¹⁶³, Ioana Cotlarciuc¹⁶⁴, Carlos Cruchaga¹⁶⁵¹⁶⁶, John Danesh¹⁵⁵¹⁵⁶¹⁶⁷, Paul I. W. de Bakker¹⁶⁸¹⁶⁹, Anita L. DeStefano²⁷¹⁷⁰, Marcel den Hoed¹⁷¹, Qing Duan¹⁷², Stefan T. Engelter¹⁷³¹⁷⁴, Guido J. Falcone¹⁴⁴¹⁷⁵, Rebecca F. Gottesman¹⁷⁶, Raji P. Grewal¹⁷⁷, Stefan Gustafsson⁶⁰, Jeffrey Haessler¹⁷⁸, Tamara B. Harris⁶, Ahamad Hassan¹⁷⁹, Aki S. Havulinna¹⁸⁰¹⁸¹, Elizabeth G. Holliday¹⁸²¹⁸³, George Howard¹⁸⁴, Fang-Chi Hsu¹⁵, Hyacinth I. Hyacinth¹⁸⁵, M. Arfan Ikram⁸, Marguerite R. Irvin¹⁸⁶, Xueqiu Jian¹⁸⁷, Jordi Jiménez-Conde¹⁸⁸, Julie A. Johnson¹⁸⁹¹⁹⁰, J. Wouter Jukema⁶⁶, Masahiro Kanai², Keith L. Keene¹⁹¹¹⁹², Brett M. Kissela¹⁹³, Dawn O. Kleindorfer¹⁹³, Charles Kooperberg¹⁷⁸, Michiaki Kubo¹⁹⁴, Leslie Lange¹⁹⁵, Carl D. Langefeld¹⁹⁶, Claudia Langenberg¹⁷², Jin-Moo Lee¹⁹⁷, Robin Lemmens¹⁹⁸¹⁹⁹, Didier Leys²⁰⁰, Cathryn M. Lewis²⁰¹²⁰², Wei-Yu Lin²⁰³²⁰⁴, Arne G. Lindgren²⁰⁵²⁰⁶, Erik Lorentzen²⁰⁷, Patrik K. Magnusson¹⁰³, Jane Maguire²⁰⁸, Ani Manichaikul¹⁶², Patrick F. McArdle²⁰⁹, James F. Meschia²¹⁰, Thomas H. Mosley²¹¹²¹², Toshiharu Ninomiya²¹³, Martin J. O’Donnell¹⁴⁵²¹⁴, Sara L. Pulit²¹⁵, Kristiina Rannikmäe²¹⁶, Alexander P. Reiner¹⁷⁸²¹⁷, Kathryn M. Rexrode²¹⁸, Stephen S. Rich¹⁶², Paul M. Ridker¹³²¹⁶¹, Natalia S. Rost¹³⁹¹⁴⁰, Peter M. Rothwell²¹⁹, Tatjana Rundek²²⁰, Ralph L. Sacco²²⁰, Saori Sakaue³²²¹, Michele M. Sale¹⁶², Veikko Salomaa¹⁸⁰, Bishwa R. Sapkota²²², Reinhold Schmidt²²³, Carsten O. Schmidt²²⁴, Ulf Schminke²¹⁷, Pankaj Sharma¹⁶⁴, Agnieszka Slowik²²⁵, Cathie L. M. Sudlow²²⁶, Christian Tanislav²²⁷, Turgut Tatlisumak²²⁸²²⁹, Vincent N. S. Thijs²³⁰²³¹, Gudmar Thorleifsson¹⁴², Unnur Thorsteinsdottir¹⁴², Steffen Tiedt⁷⁸, Stella Trompet²³, Matthew Walters²³², Nicholas J. Wareham¹⁷², Sylvia Wassertheil-Smoller²³³, Kerri L. Wiggins⁵, Qiong Yang¹⁷⁰, Salim Yusuf¹⁴⁵, Tomi Pastinen²³⁴, Arno Ruusalepp²¹⁴²⁴³, Eric E. Schadt²⁰, Simon Koplev²⁰, Veronica Codoni²³⁵²³⁶, Mete Civelek¹⁶²²³⁷, Nick Smith⁵¹⁰⁶²³⁸, David A. Trégouët²³⁵²³⁶, Ingrid E. Christophersen¹⁴⁴²³⁹²⁴⁰, Carolina Roselli¹⁴⁴, Steven A. Lubitz¹⁴⁴²³⁹, Patrick T. Ellinor¹⁴⁴²³⁹, E. Shyong Tai²⁴¹, Jaspal S. Kooner²⁴², Norihiro Kato²⁴³, Jiang He²⁴⁴, Pim van der Harst²⁴⁵, Paul Elliott²⁴⁶, John C. Chambers¹¹⁸²⁴⁷, Fumihiko Takeuchi²⁴³, Andrew D. Johnson²⁷, Dharambir K. Sanghera²²²²⁴⁸²⁴⁹, Olle Melander⁴⁶, Christina Jern²⁵⁰, Daniel Strbian²⁵¹²⁵², Israel Fernandez-Cadenas¹⁵⁷¹⁵⁸, W. T. Longstreth Jr⁵²⁵³, Arndt Rolfs¹⁴⁷, Jun Hata²¹³, Daniel Woo¹⁹³, Jonathan Rosand¹⁴⁰¹⁴³¹⁴⁴, Guillaume Pare¹⁴⁵, Danish Saleheen²⁵⁴, Kari Stefansson¹⁴²²⁵⁵, Bradford B. Worrall²⁵⁶, Steven J. Kittner¹⁶³, Joanna M. M. Howson¹⁵⁵, Yoichiro Kamatani¹³³²⁵⁷

, Abbas Dehghan⁸¹¹⁸, Adrie Seldenrijk¹¹⁹, Alanna C. Morrison³, Anders Hamsten⁴⁴, Bruce M. Psaty¹⁰⁶¹²⁰, Cornelia M. van Duijn⁸⁶⁸, http://orcid.org/0000-0002-6793-2262Deborah A. Lawlor¹⁰⁷¹⁰⁸, Dennis O. Mook-Kanamori⁹⁹¹²¹, Donald W. Bowden¹²², Helena Schmidt¹¹⁷, http://orcid.org/0000-0001-5751-9178James F. Wilson²²¹²³, James G. Wilson¹²⁴, Jerome I. Rotter⁷²¹¹⁶, http://orcid.org/0000-0002-9812-6642Joanna M. Wardlaw²⁴¹²⁵, John Deanfield⁴, Julian Halcox¹²⁶, http://orcid.org/0000-0002-7200-5455Leo-Pekka Lyytikäinen¹⁰⁹¹¹⁰, Markus Loeffler¹⁶¹⁷, Michele K. Evans⁶, Stéphanie Debette¹⁸, Steve E. Humphries¹²⁷, Uwe Völker³⁴¹²⁸, http://orcid.org/0000-0001-5696-0084Vilmundur Gudnason¹⁰¹¹, Aroon D. Hingorani⁴, Johan L. M. Björkegren

johan.bjorkegren@mssm.edu

²⁰²¹⁴²¹²⁹, Juan P. Casas⁹, Christopher J. O’Donnell

Christopher.ODonnell@va.gov

¹³⁰¹³¹¹³²

1. 0000 0001 1034 1720grid.410711.2Department of Epidemiology, University of North Carolina, Chapel Hill, NC 27516 USA

2. 0000 0000 9632 6718grid.19006.3eDepartment of Pathology and Laboratory Medicine, University of California (UCLA), Los Angeles, Los Angeles, CA 90095 USA

3. 0000 0000 9206 2401grid.267308.8Human Genetics Center, Department of Epidemiology, Human Genetics, and Environmental Sciences, School of Public Health, The University of Texas Health Science Center at Houston, Houston, TX 77030 USA

4. 0000000121901201grid.83440.3bInstitute of Cardiovascular Science, University College London, London, WC1 6BT UK

5. 0000000122986657grid.34477.33Cardiovascular Health Research Unit, Department of Medicine, University of Washington, Seattle, WA 98101 USA

6. 0000 0000 9372 4913grid.419475.aLaboratory of Epidemiology and Population Sciences, National Institute on Aging, National Institutes of Health, Baltimore, MD 20892 USA

7. 0000 0001 2190 5763grid.7727.5Department of Genetic Epidemiology, University of Regensburg, Regensburg, 93053 Germany

8. 000000040459992Xgrid.5645.2Department of Epidemiology, Erasmus Medical Center, Rotterdam, 3015 The Netherlands

9. 0000000121901201grid.83440.3bInstitute of Health Informatics, University College London, London, WC1E 6BT UK

10. 0000 0000 9458 5898grid.420802.cIcelandic Heart Association, Kopavogur, IS-201 Iceland

11. 0000 0004 0640 0021grid.14013.37University of Iceland, Reykjavik, 101 Iceland

12. 0000 0000 8988 2476grid.11598.34Clinical Division of Neurogeriatrics, Department of Neurology, Medical University of Graz, Graz, 8036 Austria

13. 0000 0000 8988 2476grid.11598.34Institute for Medical Informatics, Statistics and Documentation, Medical University of Graz, Graz, 8036 Austria

14. 0000 0000 9558 4598grid.4494.dDepartment of Epidemiology, University of Groningen, University Medical Center Groningen, Groningen, 3015 The Netherlands

15. 0000 0001 2185 3318grid.241167.7Department of Biostatistical Sciences, Wake Forest University School of Medicine, Winston-Salem, NC 27157 USA

16. 0000 0001 2230 9752grid.9647.cInstitute for Medical Informatics, Statistics and Epidemiology, , University of Leipzig, Leipzig, 04107 Germany

17. 0000 0001 2230 9752grid.9647.cLIFE Research Center for Civilization Diseases, University of Leipzig, Leipzig, 04107 Germany

18. Univ. Bordeaux, Inserm, Bordeaux Population Health Research Center, UMR 1219, CHU Bordeaux, F-33000 Bordeaux, France

19. 0000 0001 2097 1371grid.1374.1Research Centre of Applied and Preventive Cardiovascular Medicine, University of Turku, Turku, 20520 Finland

20. 0000 0001 0670 2351grid.59734.3cDepartment of Genetics and Genomic Sciences, The Icahn Institute for Genomics and Multiscale Biology Icahn School of Medicine at Mount Sinai, New York, NY 10029 USA

21. grid.433458.dClinical Gene Networks AB, Stockholm, 104 62 Sweden

22. 0000 0004 1936 7988grid.4305.2Usher Institute of Population Health Sciences and Informatics, University of Edinburgh, Edinburgh, EH8 9AG UK

23. 0000000089452978grid.10419.3dDepartment of Internal Medicine, Section of Gerontology and Geriatrics, Leiden University Medical Center, Leiden, 2300 RC The Netherlands

24. 0000 0004 1936 7988grid.4305.2Centre for Cognitive Ageing and Cognitive Epidemiology, University of Edinburgh, Edinburgh, EH8 9JZ UK

25. 0000 0004 1936 7988grid.4305.2Medical Genetics Section, Centre for Genomic and Experimental Medicine, Institute of Genetics and Molecular Medicine, University of Edinburgh, Edinburgh, EH4 2XU UK

26. 0000 0001 2293 4638grid.279885.9Population Sciences Branch, Division of Intramural Research, NHLBI, NIH, Framingham, MA 01702-5827 USA

27. 0000 0001 2293 4638grid.279885.9National Heart, Lung and Blood Institute’s Intramural Research Program, Framingham Heart Study, Framingham, MA 01702-5827 USA

28. 0000 0004 1937 0407grid.410721.1Department of Medicine, University of Mississippi Medical Center, Jackson, MS 39216 USA

29. grid.5603.0Department of Neurology, University Medicine Greifswald, Greifswald, 17475 Germany

30. 0000000419368729grid.21729.3fDepartment of Medicine, Columbia University, New York, NY 10032 USA

31. 0000 0001 0481 6099grid.5012.6Department of Biochemistry, Maastricht Centre for Systems Biology (MaCSBio), CARIM School for Cardiovascular Diseases, Maastricht University, Maastricht, 6229 The Netherlands

32. 000000041936754Xgrid.38142.3cDepartment of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA 02115 USA

33. grid.5603.0Institute for Community Medicine, University Medicine Greifswald, Greifswald, 17475 Germany

34. 0000 0004 5937 5237grid.452396.fDZHK (German Center for Cardiovascular Research), partner site Greifswald, Greifswald, 17475 Germany

35. 0000 0001 2185 3318grid.241167.7Center for Diabetes Research, Wake Forest School of Medicine, Winston-Salem, NC 25157 USA

36. 0000 0004 0437 5432grid.1022.1Menzies Health Institute Queensland, Griffith University, Southport, QLD 4222 Australia

37. 000000040459992Xgrid.5645.2Department of Internal Medicine, Erasmus Medical Center, University Medical Center Rotterdam, Rotterdam, 3015 The Netherlands

38. 0000 0004 0427 2580grid.268922.5MRC Unit for Lifelong Health and Ageing at UCL, London, WC1E 6BT UK

39. 0000 0000 9558 4598grid.4494.dDepartment of Medicine, University of Groningen, University Medical Center Groningen, Groningen, 2300 The Netherlands

40. 0000 0004 1936 9000grid.21925.3dDepartment of Epidemiology, and School of Medicine, Division of Geriatric Medicine, University of Pittsburgh, Pittsburgh, PA 15213 USA

41. 0000000121901201grid.83440.3bDepartment of Epidemiology and Public Health, University College London, London, WC1E 6BT UK

42. 0000 0001 0943 7661grid.10939.32Department of Pathophysiology, Institute of Biomedicine and Translation Medicine, University of Tartu, Biomeedikum, Tartu, 51010 Estonia

43. 0000 0001 0585 7044grid.412269.aDepartment of Cardiac Surgery, Tartu University Hospital, Tartu, 51010 Estonia

44. 0000 0004 1937 0626grid.4714.6Cardiovascular Medicine Unit, Department of Medicine Solna, Karolinska Institutet, Stockholm, 17177 Sweden

45. 0000 0004 1936 9457grid.8993.bDepartment of Cell and Molecular Biology, National Bioinformatics Infrastructure Sweden, Science for Life Laboratory, Uppsala University, Uppsala, 75108 Sweden

46. 0000 0001 0930 2361grid.4514.4Department of Clinical Sciences in Malmö, Lund University, Malmö, SE-205 02 Sweden

47. 0000 0000 9632 6718grid.19006.3eDepartment of Human Genetics, University of California (UCLA), Los Angeles, CA 90095 USA

48. 0000 0004 0435 165Xgrid.16872.3aDepartment of Psychiatry, EMGO Institute for Health and Care Research and Neuroscience Campus Amsterdam, VU University Medical Center, Amsterdam, 1081 HL The Netherlands

49. grid.422785.eApplied Sciences, Premier, Inc., Charlotte, NC 28277 USA

50. 0000 0004 1763 1124grid.5611.3Department of Medicine, University of Verona, Verona, 37134 Italy

51. 0000 0004 1757 2822grid.4708.bDepartment of Medical Biotechnology and Translational Medicine, Università di Milano, Milan, 20133 Italy

52. 0000 0004 1760 1750grid.418230.cCentro Cardiologico Monzino, IRCCS, Milan, 20138 Italy

53. 0000 0000 8934 4045grid.67033.31St. Elizabeth’s Medical Center, Tufts University School of Medicine, Boston, MA 02135 USA

54. 0000 0004 0477 2585grid.411095.8Institute of Laboratory Medicine, University Hospital Munich, LMU Munich, 80539 Germany

55. 0000 0004 1757 2822grid.4708.bDipartimento di Scienze Farmacologiche e Biomolecolari, Università di Milano, Milan, 20133 Italy

56. 0000 0004 1757 3630grid.9027.cDepartment of Clinical and Experimental Medicine, Internal Medicine, Angiology and Arteriosclerosis Diseases, University of Perugia, Perugia, 06123 Italy

57. 0000 0004 1781 8749grid.418324.8Centro Diagnostico Italiano, Milan, 20147 Italy

58. 0000 0001 2160 926Xgrid.39382.33Human Genome Sequencing Center, Baylor College of Medicine, Houston, TX 77030-3411 USA

59. 0000000419368956grid.168010.eDepartment of Medicine, Division of Cardiovascular Medicine, Stanford University School of Medicine, Stanford, CA 94309 USA

60. 0000 0004 1936 9457grid.8993.bDepartment of Medical Sciences, Molecular Epidemiology, Uppsala University, Uppsala, 75185 Sweden

61. 0000000419368956grid.168010.eStanford Cardiovascular Institute, Stanford University, Stanford, CA G1120 USA

62. 0000 0001 2230 9752grid.9647.cHeart Center Leipzig, Leipzig, 04103 Germany

63. 0000 0001 0482 5067grid.34980.36Centre for Brain Research, Indian Institute of Science, Bangalore, 560012 India

64. 0000000121885934grid.5335.0Stroke Research Group, Department of Clinical Neurosciences, University of Cambridge, Cambridge, CB2 0QQ UK

65. 0000 0004 1936 7988grid.4305.2Department of Psychology, University of Edinburgh, Edinburgh, EH8 9JZ UK

66. 0000000089452978grid.10419.3dDepartment of Cardiology, Leiden University Medical Center, Leiden, 2300 RC The Netherlands

67. 0000 0004 0444 9382grid.10417.33Department of Internal Medicine, Radboud University Medical Center, Nijmegen, 6525 GA The Netherlands

68. 0000 0004 1936 8948grid.4991.5Clinical Trial Service Unit and Epidemiological Studies Unit, Nuffield Department of Population Health, University of Oxford, Oxford, OX3 7LF UK

69. 0000 0001 2164 3847grid.67105.35Department of Population and Quantitative Health Sciences, School of Medicine, Case Western Reserve University, Cleveland, OH 44106 USA

70. 0000 0001 2230 9752grid.9647.cInstitute for Laboratory Medicine, University of Leipzig, Leipzig, 04109 Germany

71. 0000000122986657grid.34477.33Department of Biostatistics, University of Washington, Seattle, WA 98105 USA

72. 0000 0001 0157 6501grid.239844.0Institute for Translational Genomics and Population Sciences, Los Angeles Biomedical Research Institute at Harbor-UCLA Medical Center, Torrance, CA 90502 USA

73. 0000 0001 0705 3621grid.240684.cDepartment of Internal Medicine, Rush University Medical Center, Chicago, IL 60612 USA

74. 0000 0004 0444 9382grid.10417.33Radboud Institute for Health Sciences, Radboud University Medical Center, Nijmegen, GA 6525 The Netherlands

75. 0000 0004 1936 9457grid.8993.bDepartment of Medical Sciences, Cardiovascular Epidemiology, Uppsala University, Uppsala, 751 05 Sweden

76. 0000 0001 1034 1720grid.410711.2Department of Genetics, University of North Carolina, Chapel Hill, NC 27516 USA

77. grid.5603.0Department of Internal Medicine B, University Medicine Greifswald, Greifswald, 17475 Germany

78. Institute for Stroke and Dementia Research (ISD), University Hospital, Ludwig-Maximilians-University (LMU), Munich, 80539 Germany

79. grid.452617.3Munich Cluster for Systems Neurology (SyNergy), Munich, 81377 Germany

80. 0000 0004 1936 9721grid.7839.5Department of Neurology, Center for Neurology and Neurosurgery, Johann Wolfgang Goethe-University, Frankfurt am Main, 60323 Germany

81. 0000 0001 0942 6946grid.8356.8Institute for Social and Economic Research, Essex University, Colchester, CO4 3SQ UK

82. 0000 0001 2297 5165grid.94365.3dLaboratory of Neurogenetics, National Institute on Aging, National Institutes of Health, Bethesda, MD 20892 USA

83. Data Tecnica International, Glen Echo, MD 20812 USA

84. 0000 0004 0628 215Xgrid.410552.7Department of Clinical Physiology and Nuclear Medicine, Turku University Hospital, Turku, 20521 Finland

85. 0000 0001 0726 5157grid.5734.5Institute of Social and Preventive Medicine (ISPM), University of Bern, Bern, 3012 Switzerland

86. 0000 0001 2105 7207grid.411595.dElectrocardiography Research Group, School of Medicine, Universidad Industrial de Santander, Bucaramanga, Santander 680003 Colombia

87. 0000 0001 0670 2351grid.59734.3cDepartment of Psychiatry and Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, New York, NY 10029 USA

88. 0000 0004 0420 1184grid.274295.fMental Illness Research Education and Clinical Center (MIRECC), James J. Peters VA Medical Center, Bronx, New York, NY 10468 USA

89. 0000 0001 2161 2573grid.4464.2Population Health Research Institute, St George’s, University of London, London, SW17 0RE UK

90. Inserm U1167, F-59000 Lille, France

91. 0000 0001 2159 9858grid.8970.6Institut Pasteur de Lille, U1167, F-59000 Lille, France

92. 0000 0001 2186 1211grid.4461.7Université de Lille, U1167 - RID-AGE & Centre Hospitalier Universitaire de Lille, U1167, F-59000 Lille, France

93. Sorbonne Université, Cardiovascular Prevention Unit, Pitié Salpétrière Hospital, Paris, 75013 France

94. 0000000122986657grid.34477.33Collaborative Health Studies Coordinating Center, Department of Biostatistics, University of Washington, Seattle, WA 98195 USA

95. grid.419013.eFoundation for Research in Health Exercise and Nutrition, Kuopio Research Institute of Exercise Medicine, Kuopio, 70100 Finland

96. 0000 0004 0628 207Xgrid.410705.7Department of Clinical Physiology and Nuclear Medicine, Kuopio University Hospital, Kuopio, 70210 Finland

97. 0000 0001 2230 9752grid.9647.cInstitute of Laboratory Medicine, University of Leipzig, Leipzig, 04109 Germany

98. 0000 0000 9194 7179grid.411941.8Institute of Clinical Chemistry and Laboratory Medicine, University Hospital Regensburg, Regensburg, 93053 Germany

99. 0000000089452978grid.10419.3dDepartment of Clinical Epidemiology, Leiden University Medical Center, Leiden, 2333 The Netherlands

100. 0000 0004 1936 7603grid.5337.2Department of Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, BS8 1QU UK

101. 0000 0001 2193 314Xgrid.8756.cMental Health and Wellbeing, Institute of Health and Wellbeing, University of Glasgow, Glasgow, G12 0XH UK

102. 0000000121901201grid.83440.3bDepartment of Primary Care & Population Health, University College London, London, WC1E 6BT UK

103. 0000 0004 1937 0626grid.4714.6Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, SE-171 77 Sweden

104. 0000 0004 0367 5222grid.475010.7Department of Neurology, Boston University School of Medicine, Boston, MA 02118 USA

105. 0000 0001 0726 2490grid.9668.1Institute of Public Health and Clinical Nutrition, University of Eastern Finland, Kuopio Campus, Kuopio, FI-70210 Finland

106. 0000 0004 0615 7519grid.488833.cKaiser Permanente Washington Health Research Institute, Seattle, WA 98101 USA

107. 0000 0004 1936 7603grid.5337.2Population Health Science, Bristol Medical School, University of Bristol, Bristol, BS8 1QU UK

108. 0000 0004 1936 7603grid.5337.2MRC Integrative Epidemiology Unit at the University of Bristol, Bristol, BS8 1TH UK

109. Department of Clinical Chemistry, Fimlab Laboratories, Tampere, 33014 Finland

110. 0000 0001 2314 6254grid.5509.9Department of Clinical Chemistry, University of Tampere School of Medicine, Tampere, 33014 Finland

111. 0000 0004 1937 0626grid.4714.6Division of Cardiovascular Epidemiology, Institute of Environmental Medicine, Karolinska Institutet, Stockholm, S-171 77 Sweden

112. 0000 0000 9241 5705grid.24381.3cDepartment of Cardiology, Karolinska University Hospital, Stockholm, S-171 77 Sweden

113. 0000000121901201grid.83440.3bGenetics Institute, University College London, London, WC1E 6BT UK

114. 0000 0001 2171 9311grid.21107.35Departments of Medicine and Epidemiology, Johns Hopkins University, Baltimore, MD 21205 USA

115. 0000 0004 1936 7558grid.189504.1Section of Biomedical Genetics, School of Medicine, Boston University, Boston, MA 02215 USA

116. Department of Pediatrics, Los Angeles Biomedical Research Institute at Harbor-UCLA Medical Center, Torrance, CA 90502 USA

117. 0000 0000 8988 2476grid.11598.34Institute of Molecular Biology and Biochemistry, Centre for Molecular Medicine, Medical University of Graz, Graz, 8010 Austria

118. 0000 0001 2113 8111grid.7445.2Department of Epidemiology & Biostatistics, Imperial College London, London, SW7 2AZ UK

119. GGZ inGeest and Amsterdam Public Health Research Institute, Department of Psychiatry, Amsterdam University Medical Center, Amsterdam, 1081 HV The Netherlands

120. 0000000122986657grid.34477.33Cardiovascular Health Research Unit and Departments of Medicine, Epidemiology, and Health Services, University of Washington, Seattle, WA 98195 USA

121. 0000000089452978grid.10419.3dDepartment of Public Health and Primary Care, Leiden University Medical Center, Leiden, 2333 ZA The Netherlands

122. 0000 0001 2185 3318grid.241167.7Center for Human Genomics, Wake Forest University School of Medicine, Winston-Salem, NC 27157 USA

123. MRC Human Genetics Unit, Institute of Genetics and Molecular Medicine, University of Edinburgh, Western General Hospital, Edinburgh, EH4 2XU UK

124. 0000 0004 1937 0407grid.410721.1Department of Physiology and Biophysics, University of Mississippi Medical Center, Jackson, MS 39216 USA

125. 0000 0004 1936 7988grid.4305.2Centre for Clinical Brain Sciences, and UK Dementia Research Institute at the University of Edinburgh, Edinburgh, EH16 4SB UK

126. 0000 0001 0658 8800grid.4827.9Swansea University Medical School, Swansea, SA2 8PP UK

127. 0000000121901201grid.83440.3bCentre for Cardiovascular Genetics, Institute Cardiovascular Science, University College London, London, WC1E 6BT UK

128. grid.5603.0Interfaculty Institute for Genetics and Functional Genomics, University Medicine Greifswald, Greifswald, 17475 Germany

129. Integrated Cardio Metabolic Centre, Department of Medicine, Karolinska Institutet, Karolinska Universitetssjukhuset, Huddinge, SE-141 57 Sweden

130. Intramural Administration Management Branch, National Heart, Lung, and Blood Institute, NIH, Bethesda, MD 20892 USA

131. Cardiology Section, Boston Veteran’s Administration Healthcare, Boston, MA 02130 USA

132. 000000041936754Xgrid.38142.3cHarvard Medical School, Boston, MA 02115 USA

133. 0000000094465255grid.7597.cLaboratory for Statistical Analysis, RIKEN Center for Integrative Medical Sciences, Yokohama, 230-0045 Japan

134. 0000 0004 0373 3971grid.136593.bDepartment of Statistical Genetics, Osaka University Graduate School of Medicine, Osaka, 565-0871 Japan

135. 0000 0004 0373 3971grid.136593.bLaboratory of Statistical Immunology, Immunology Frontier Research Center (WPI-IFReC), Osaka University, Suita, 565-0871 Japan

136. INSERM U1219 Bordeaux Population Health Research Center, Bordeaux, F-33000 France

137. 0000 0001 2106 639Xgrid.412041.2University of Bordeaux, Bordeaux, F-33000 France

138. 0000000121885934grid.5335.0Stroke Research Group, Division of Clinical Neurosciences, University of Cambridge, Cambridge, CB2 1TN UK

139. 000000041936754Xgrid.38142.3cDepartment of Neurology, Massachusetts General Hospital, Harvard Medical School, Boston, MA 02114 USA

140. 0000 0004 0386 9924grid.32224.35J. Philip Kistler Stroke Research Center, Department of Neurology, MGH, Boston, MA 02215 USA

141. 0000000090126352grid.7692.aLaboratory of Experimental Cardiology, Division of Heart and Lungs, University Medical Center Utrecht, Utrecht, 3584 CX Netherlands

142. deCODE genetics/AMGEN inc, Reykjavik, 101 Iceland

143. 0000 0004 0386 9924grid.32224.35Center for Genomic Medicine, Massachusetts General Hospital (MGH), Boston, MA 02114 USA

144. grid.66859.34Program in Medical and Population Genetics, Broad Institute, Cambridge, MA 02142 USA

145. 0000 0004 1936 8227grid.25073.33Population Health Research Institute, McMaster University, Hamilton, L8L 2X2 Canada

146. 0000 0001 2242 4849grid.177174.3Department of Medicine and Clinical Science, Graduate School of Medical Sciences, Kyushu University, Fukuoka, 819-0935 Japan

147. Albrecht Kossel Institute, University Clinic of Rostock, Rostock, 18147 Germany

148. 0000 0001 2159 9858grid.8970.6INSERM U1167, Institut Pasteur de Lille, Lille, F-59000 France

149. 0000 0004 0471 8845grid.410463.4Department of Public Health, Lille University Hospital, Lille, F-59000 France

150. Department of Radiology, Massachusetts General Hospital, Harvard Medical School, AA Martinos Center for Biomedical Imaging, Boston, MA 02129 USA

151. 0000 0001 2288 9830grid.17091.3eDivision of Neurology, Faculty of Medicine, Brain Research Center, University of British Columbia, Vancouver, 170-637 Canada

152. 0000 0004 0420 4262grid.36511.30School of Life Science, University of Lincoln, Lincoln, LN6 7TS UK

153. 0000 0001 0707 5492grid.417894.7Department of Cerebrovascular Diseases, Fondazione IRCCS Istituto Neurologico Carlo Besta, Milano, 20133 Italy

154. 0000 0004 0459 167Xgrid.66875.3aDepartment of Neurology, Mayo Clinic Rochester, Rochester, MN 55905 USA

155. 0000000121885934grid.5335.0MRC/BHF Cardiovascular Epidemiology Unit, Department of Public Health and Primary Care, University of Cambridge, Cambridge, CB2 1TN UK

156. 0000000121885934grid.5335.0The National Institute for Health Research Blood and Transplant Research Unit in Donor Health and Genomics, University of Cambridge, Cambridge, CB2 1TN UK

157. 0000 0001 0675 8654grid.411083.fNeurovascular Research Laboratory, Vall d’Hebron Institut of Research, Neurology and Medicine Departments-Universitat Autònoma de Barcelona, Vall d’Hebrón Hospital, Barcelona, 08193 Spain

158. Stroke Pharmacogenomics and Genetics, Fundacio Docència i Recerca MutuaTerrassa, Terrassa, 08222 Spain

159. 0000 0004 0482 1586grid.239560.bChildren’s Research Institute, Children’s National Medical Center, Washington, DC 20052 USA

160. 0000 0004 1936 9510grid.253615.6Center for Translational Science, George Washington University, Washington, DC 20052 USA

161. 0000 0004 0378 8294grid.62560.37Division of Preventive Medicine, Brigham and Women’s Hospital, Boston, MA 02115 USA

162. 0000 0000 9136 933Xgrid.27755.32Department of Public Health Sciences, Center for Public Health Genomics, University of Virginia School of Medicine, Charlottesville, VA 22904-4259 USA

163. 0000 0001 2175 4264grid.411024.2Department of Neurology, University of Maryland School of Medicine and Baltimore VAMC, Baltimore, MD 21201 USA

164. 0000 0001 2188 881Xgrid.4970.aInstitute of Cardiovascular Research, Royal Holloway University of London, Egham, TW20 OEX UK

165. 0000 0001 2355 7002grid.4367.6Department of Psychiatry,The Hope Center Program on Protein Aggregation and Neurodegeneration (HPAN), Washington University, School of Medicine, St. Louis, MO 98195 USA

166. 0000 0001 2355 7002grid.4367.6Department of Developmental Biology, Washington University School of Medicine, St. Louis, MO 98195 USA

167. 0000 0004 0606 5382grid.10306.34Wellcome Trust Sanger Institute, Hinxton, CB10 1SA UK

168. 0000000090126352grid.7692.aDepartment of Medical Genetics, University Medical Center Utrecht, Utrecht, 3584 CX The Netherlands

169. 0000000090126352grid.7692.aDepartment of Epidemiology, Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht, 3584 CX The Netherlands

170. 0000 0004 1936 7558grid.189504.1Boston University School of Public Health, Boston, MA 02118 USA

171. 0000 0004 1936 9457grid.8993.bDepartment of Immunology, Genetics and Pathology and Science for Life Laboratory, Uppsala University, Uppsala, 751 05 Sweden

172. 0000 0004 0369 9638grid.470900.aMRC Epidemiology Unit, University of Cambridge School of Clinical Medicine, Institute of Metabolic Science, Cambridge Biomedical Campus, Cambridge, CB2 0SL UK

173. grid.410567.1Department of Neurology and Stroke Center, Basel University Hospital, Basel, 4031 Switzerland

174. 0000 0004 0617 9945grid.459496.3Neurorehabilitation Unit, University and University Center for Medicine of Aging and Rehabilitation Basel, Felix Platter Hospital, Basel, 4055 Switzerland

175. 0000000419368710grid.47100.32Department of Neurology, Yale University School of Medicine, New Haven, CT 06510 USA

176. 0000 0001 2171 9311grid.21107.35Department of Neurology, Johns Hopkins University School of Medicine, Baltimore, MD 21205 USA

177. Neuroscience Institute, SF Medical Center, Trenton, NJ 08629 USA

178. 0000 0001 2180 1622grid.270240.3Division of Public Health Sciences, Fred Hutchinson Cancer Research Center, Seattle, WA 98109-1024 USA

179. 0000 0001 0097 2705grid.418161.bDepartment of Neurology, Leeds General Infirmary, Leeds Teaching Hospitals NHS Trust, Leeds, LS1 3EX UK

180. 0000 0001 1013 0499grid.14758.3fNational Institute for Health and Welfare, Helsinki, FI-00271 Finland

181. 0000 0004 0409 5350grid.452494.aFIMM - Institute for Molecular Medicine Finland, Helsinki, FI-00271 Finland

182. grid.413648.cPublic Health Stream, Hunter Medical Research Institute, New Lambton, NSW 2305 Australia

183. 0000 0000 8831 109Xgrid.266842.cFaculty of Health and Medicine, University of Newcastle, Newcastle, 2308 Australia

184. 0000000106344187grid.265892.2School of Public Health, University of Alabama at Birmingham, Birmingham, AL 35487 USA

185. 0000 0001 0941 6502grid.189967.8Aflac Cancer and Blood Disorder Center, Department of Pediatrics, Emory University School of Medicine, Atlanta, GA 30322 USA

186. 0000000106344187grid.265892.2Epidemiology, School of Public Health, University of Alabama at Birmingham, Birmingham, 35487 USA

187. 0000 0000 9206 2401grid.267308.8Brown Foundation Institute of Molecular Medicine, University of Texas Health Science Center at Houston, Houston, TX 77030 USA

188. grid.7080.fNeurovascular Research Group (NEUVAS), Neurology Department, Institut Hospital del Mar d’Investigació Mèdica, Universitat Autònoma de Barcelona, Barcelona, 08193 Spain

189. 0000 0004 1936 8091grid.15276.37Department of Pharmacotherapy and Translational Research and Center for Pharmacogenomics, University of Florida, College of Pharmacy, Gainesville, FL 32611 USA

190. 0000 0004 1936 8091grid.15276.37Division of Cardiovascular Medicine, College of Medicine, University of Florida, Gainesville, FL 32611 USA

191. 0000 0001 2191 0423grid.255364.3Department of Biology, East Carolina University, Greenville, NC 27858 USA

192. 0000 0001 2191 0423grid.255364.3Center for Health Disparities, East Carolina University, Greenville, NC 27858 USA

193. 0000 0001 2179 9593grid.24827.3bUniversity of Cincinnati College of Medicine, Cincinnati, OH 45220 USA

194. RIKEN Center for Integrative Medical Sciences, Yokohama, 230-0045 Japan

195. 0000000107903411grid.241116.1University of Colorado, Denver, CO 80203 USA

196. 0000 0001 2185 3318grid.241167.7Center for Public Health Genomics and Department of Biostatistical Sciences, Wake Forest School of Medicine, Winston-Salem, NC 27157 USA

197. 0000 0001 2355 7002grid.4367.6Department of Neurology, Radiology, and Biomedical Engineering, Washington University School of Medicine, St. Louis, MO 98195 USA

198. 0000 0001 0668 7884grid.5596.fDepartment of Neurosciences, Experimental Neurology, KU Leuven – University of Leuven, Leuven, 3000 Belgium

199. 0000 0004 0626 3338grid.410569.fVIB Center for Brain & Disease Research, University Hospitals Leuven, Department of Neurology, Leuven, 3000 Belgium

200. University of Lille, INSERM U1171, CHU Lille, Lille, F-59000 France

201. 0000 0001 2322 6764grid.13097.3cDepartment of Medical and Molecular Genetics, King’s College London, London, WC2R 2LS UK

202. 0000 0001 2322 6764grid.13097.3cSGDP Centre, Institute of Psychiatry, Psychology & Neuroscience, King’s College London, London, WC2R 2LS UK

203. 0000000121885934grid.5335.0Cardiovascular Epidemiology Unit, Department Public Health & Primary Care, University of Cambridge, Cambridge, CB1 8RN UK

204. 0000 0001 0462 7212grid.1006.7Northern Institute for Cancer Research, Paul O’Gorman Building, Newcastle University, Newcastle, NE2 4AD UK

205. 0000 0001 0930 2361grid.4514.4Department of Clinical Sciences Lund, Neurology, Lund University, Lund, 221 00 Sweden

206. 0000 0004 0623 9987grid.411843.bDepartment of Neurology and Rehabilitation Medicine, Skåne University Hospital, Lund, 222 29 Sweden

207. 0000 0000 9919 9582grid.8761.8Bioinformatics Core Facility, University of Gothenburg, Gothenburg, 405 30 Sweden

208. 0000 0004 1936 7611grid.117476.2University of Technology Sydney, Faculty of Health, Ultimo, NSW 2007 Australia

209. 0000 0001 2175 4264grid.411024.2Department of Medicine, University of Maryland School of Medicine, Baltimore, MD 21201 USA

210. 0000 0004 0443 9942grid.417467.7Department of Neurology, Mayo Clinic, Jacksonville, FL 32224 USA

211. 0000 0004 1937 0407grid.410721.1Division of Geriatrics, School of Medicine, University of Mississippi Medical Center, Jackson, MS 39216 USA

212. 0000 0004 1937 0407grid.410721.1Memory Impairment and Neurodegenerative Dementia Center, University of Mississippi Medical Center, Jackson, FL 39216 USA

213. 0000 0001 2242 4849grid.177174.3Department of Epidemiology and Public Health, Graduate School of Medical Sciences, Kyushu University, Fukuoka, 819-0395 Japan

214. 0000 0004 0488 0789grid.6142.1Clinical Research Facility, Department of Medicine, NUI Galway, Galway, H91 TK33 Ireland

215. 0000000090126352grid.7692.aDepartment of Neurology, Brain Center Rudolf Magnus, University Medical Center Utrecht, Utrecht, 3584 The Netherlands

216. 0000 0004 1936 7988grid.4305.2Centre for Clinical Brain Sciences, University of Edinburgh, Edinburgh, EH4 2XU UK

217. grid.5603.0Department of Neurology, University Medicine Greifswald, Greifswald, 17489 Germany

218. 0000 0004 0378 8294grid.62560.37Department of Medicine, Brigham and Women’s Hospital, Boston, MA 02115 USA

219. 0000 0004 1936 8948grid.4991.5Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, OX3 9DU UK

220. 0000 0004 1936 8606grid.26790.3aDepartment of Neurology, Miller School of Medicine, University of Miami, Miami, FL 33136 USA

221. 0000 0001 2151 536Xgrid.26999.3dDepartment of Allergy and Rheumatology, Graduate School of Medicine, the University of Tokyo, Tokyo, 13-8654 Japan

222. 0000 0001 2179 3618grid.266902.9Department of Pediatrics, College of Medicine, University of Oklahoma Health Sciences Center, Oklahoma City, OK 73104 USA

223. 0000 0000 8988 2476grid.11598.34Department of Neurology, Medical University of Graz, Graz, 8036 Austria

224. grid.5603.0University Medicine Greifswald, Institute for Community Medicine, SHIP-KEF, Greifswald, 17489 Germany

225. 0000 0001 2162 9631grid.5522.0Department of Neurology, Jagiellonian University, Krakow, 31-007 Poland

226. 0000 0004 1936 7988grid.4305.2University of Edinburgh, Edinburgh, EH8 9JZ UK

227. 0000 0001 2165 8627grid.8664.cDepartment of Neurology, Justus Liebig University, Giessen, 35390 Germany

228. 0000 0000 9919 9582grid.8761.8Department of Clinical Neurosciences/Neurology, Institute of Neuroscience and Physiology, Sahlgrenska Academy at University of Gothenburg, Gothenburg, SE-405 Sweden

229. 000000009445082Xgrid.1649.aSahlgrenska University Hospital, Gothenburg, SE-405 Sweden

230. 0000 0004 0606 5526grid.418025.aStroke Division, Florey Institute of Neuroscience and Mental Health, Heidelberg, VIC 3084 Australia

231. grid.410678.cAustin Health, Department of Neurology, Heidelberg, Victoria 3084 Australia

232. 0000 0001 2193 314Xgrid.8756.cSchool of Medicine, Dentistry and Nursing at the University of Glasgow, Glasgow, G12 8QQ UK

233. 0000000121791997grid.251993.5Department of Epidemiology and Population Health, Albert Einstein College of Medicine, Bronx, NY 10461 USA

234. 0000 0004 1936 8649grid.14709.3bDepartment of Human Genetics, McGill University, Montreal, H3A 0G4 Canada

235. Sorbonne Universités, UPMC Univ. Paris 06, INSERM, UMR_S 1166, Team Genomics & Pathophysiology of Cardiovascular Diseases, Paris, 75006 France

236. grid.477396.8ICAN, Institute for Cardiometabolism and Nutrition, Paris, 75013 France

237. 0000 0000 9136 933Xgrid.27755.32Department of Biomedical Engineering, University of Virginia, Charlottesville, VA 22904-4259 USA

238. grid.484325.cSeattle Epidemiologic Research and Information Center, VA Office of Research and Development, Seattle, WA 98108 USA

239. 0000 0004 0386 9924grid.32224.35Cardiovascular Research Center, Massachusetts General Hospital, Boston, MA 02114 USA

240. 0000 0004 0389 7802grid.459157.bDepartment of Medical Research, Bærum Hospital, Vestre Viken Hospital Trust, Rud, 3004 Norway

241. 0000 0001 2180 6431grid.4280.eSaw Swee Hock School of Public Health, National University of Singapore and National University Health System, Singapore, 119077 Singapore

242. 0000 0001 2113 8111grid.7445.2National Heart and Lung Institute, Imperial College London, London, SW7 2AZ UK

243. 0000 0004 0489 0290grid.45203.30Department of Gene Diagnostics and Therapeutics, Research Institute, National Center for Global Health and Medicine, Tokyo, 162-8655 Japan

244. 0000 0001 2217 8588grid.265219.bDepartment of Epidemiology, Tulane University School of Public Health and Tropical Medicine, New Orleans, LA 70112 USA

245. Department of Cardiology, University Medical Center Groningen, University of Groningen, Groningen, 9700 RB Netherlands

246. Department of Epidemiology and Biostatistics, Imperial College London, MRC-PHE Centre for Environment and Health, School of Public Health, London, W2 1PG UK

247. grid.412922.eDepartment of Cardiology, Ealing Hospital NHS Trust, Southall, HA1 3UJ UK

248. 0000 0001 2179 3618grid.266902.9Department of Pharmaceutical Sciences, College of Pharmacy, University of Oklahoma Health Sciences Center, Oklahoma City, OK 73104 USA

249. Oklahoma Center for Neuroscience, Oklahoma City, OK 73104 USA

250. 0000 0000 9919 9582grid.8761.8Department of Pathology and Genetics, Institute of Biomedicine, The Sahlgrenska Academy at University of Gothenburg, Gothenburg, SE-405 Sweden

251. 0000 0000 9950 5666grid.15485.3dDepartment of Neurology, Helsinki University Hospital, Helsinki, FI-00029 Finland

252. 0000 0004 0410 2071grid.7737.4Clinical Neurosciences, Neurology, University of Helsinki, Helsinki, FI-00029 Finland

253. 0000000122986657grid.34477.33Department of Neurology, University of Washington, Seattle, WA 98195 USA

254. 0000 0004 1936 8972grid.25879.31Department of Genetics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104 USA

255. 0000 0004 0640 0021grid.14013.37Faculty of Medicine, University of Iceland, Reykjavik, 201 Iceland

256. 0000 0000 9136 933Xgrid.27755.32Departments of Neurology and Public Health Sciences, University of Virginia School of Medicine, Charlottesville, VA 22908 USA

257. 0000 0004 0372 2033grid.258799.8Center for Genomic Medicine, Kyoto University Graduate School of Medicine, Kyoto, 606-8501 Japan

© The Author(s) 2018

DOI: 10.1038/s41467-018-07340-5

Published online: 3 December 2018

Abstract

Carotid artery intima media thickness (cIMT) and carotid plaque are measures of subclinical atherosclerosis associated with ischemic stroke and coronary heart disease (CHD). Here, we undertake meta-analyses of genome-wide association studies (GWAS) in 71,128 individuals for cIMT, and 48,434 individuals for carotid plaque traits. We identify eight novel susceptibility loci for cIMT, one independent association at the previously-identified PINX1 locus, and one novel locus for carotid plaque. Colocalization analysis with nearby vascular expression quantitative loci (cis-eQTLs) derived from arterial wall and metabolic tissues obtained from patients with CHD identifies candidate genes at two potentially additional loci, ADAMTS9 and LOXL4. LD score regression reveals significant genetic correlations between cIMT and plaque traits, and both cIMT and plaque with CHD, any stroke subtype and ischemic stroke. Our study provides insights into genes and tissue-specific regulatory mechanisms linking atherosclerosis both to its functional genomic origins and its clinical consequences in humans.

Abstract

Carotid intima-media thickness (cIMT) and plaque are associated with subclinical atherosclerosis and coronary heart disease (CHD). Here, the authors identify and prioritize genetic loci for cIMT and plaque by GWAS and colocalization approaches and further demonstrate genetic correlation with CHD and stroke.

Introduction

Atherosclerosis is characterized by an accumulation of lipid-rich and inflammatory deposits (plaques) in the sub-intimal space of medium and large arteries. Plaque enlargement leads to blood flow limitation, organ ischemia, and/or tissue necrosis. Plaque rupture can lead to abrupt vascular occlusion, which underlies clinical cardiovascular events, including myocardial infarction and ischemic stroke. Coronary heart disease (CHD) accounts for one in seven deaths, and stroke accounts for one in 20 deaths in the US¹. Because atherosclerosis has a long pre-clinical phase, early detection of atherosclerosis using non-invasive methods may help identify individuals at risk for atherosclerotic clinical events², and provides an opportunity for prevention. Subclinical atherosclerosis can be detected by B-mode ultrasound measurement of common carotid artery intima-media thickness (cIMT) or carotid plaques¹.

Subclinical and clinical atherosclerosis has known genetic components³. Genome-wide association studies (GWAS) of subclinical atherosclerosis have previously identified three loci significantly associated with cIMT at ZHX2, APOC1, and PINX1, and two loci associated with common carotid artery plaque at PIK3CG and EDNRA⁴. An exome-wide-association study identified significant associations of the APOE ε2 allele with cIMT and coronary artery calcification⁵. The APOE single nucleotide polymorphism (SNP) rs7412 is in linkage disequilibrium (LD) with the APOC1 variant, thus representing the same signal. Additional GWAS-identified associations were reported for carotid plaque at the 9p21 and SFXN2 loci⁶, and for cIMT at the CFDP1-TMEM170A locus⁷. However, these prior studies were of limited sample size and genomic coverage, and failed to investigate the etiological role that subclinical atherosclerosis may have on atherosclerotic clinical events.

Herein, we perform a large meta-analysis of GWAS of subclinical atherosclerosis by analyzing 1000 Genomes imputed genotype data obtained from collaborations between the Cohorts for Heart and Aging Research in Genomic Epidemiology (CHARGE) consortium⁸ and the University College London-Edinburgh-Bristol (UCLEB) consortium⁹. One of the greatest challenges in the translation of GWAS findings to biological understanding is related to the limited access to RNA expression data from disease-relevant tissues. Consequently, we sought to reliably identify the tissue-specific gene regulatory functions responsible for the GWAS signals by prioritizing candidate genes for established and novel loci of cIMT and carotid plaque using statistical methods for colocalization¹⁰. These methods integrate identified loci with expression quantitative loci (eQTLs) inferred from cardiovascular disease-relevant genetics of RNA expression, the Stockholm-Tartu Atherosclerosis Reverse Network Engineering Task (STARNET) study, where arterial wall and metabolic-related RNA samples were collected from up to 600 patients with CHD¹¹. We also evaluate the relationships of cIMT and carotid plaque with clinically apparent CHD and stroke using summary data from two large consortia. In summary, our study sequentially assesses the genetic epidemiology and tissue-specific patterns of gene regulation involved in the formation of subclinical atherosclerosis traits across cardiovascular disease-related tissues.

Results

Study description

The study design is shown in Fig. 1. We undertook meta-analysis of GWAS in individuals of European ancestry for cIMT (up to 71,128 participants from 31 studies) and carotid plaque (up to 48,434 participants from 17 studies; 21,540 with defined carotid plaque) (Supplementary Table 1). cIMT and plaque were evaluated using high-resolution B-mode ultrasonography and reading protocols as previously reported⁴. Carotid plaque was defined by atherosclerotic thickening of the common carotid artery wall or the proxy measure of luminal stenosis greater than 25% (Supplementary Table 2). Each cohort performed association analyses using standardized protocols (Methods) for variants imputed based on the 1000 Genomes Project (1000G) phase 1 v3 reference. Extensive quality control (QC) was applied to data, and there was little evidence for population stratification in any of the studies for either trait (Supplementary Table 3). The study-specific results were combined using fixed-effect meta-analyses, given the low heterogeneity across studies (0% heterogeneity)¹².

Fig. 1View larger versionFig. 1. Overall study design. a GWAS meta-analyses of cIMT and carotid plaque for gene discovery. b Local and genome-wide shared genetic basis using gene expression and clinical outcomes GWAS data

GWAS meta-analyses of cIMT and carotid plaque

For cIMT, 11 loci had at least one SNP association that reached the genome-wide association threshold (p < 5 × 10⁻⁸), of which eight were newly described and three have been previously reported (Table 1). The closest genes for the eight loci were: 1q32.2 intergenic (rs201648240), ATP6AP1L (rs224904), AIG1 (rs6907215), PIK3CG (rs13225723), MCPH1 (rs2912063), SGK223 (rs11785239), VTI1 (rs1196033), and CBFA2T3 (rs844396). For three loci previously reported, the closest genes were ZHX2 (rs148147734), PINX1 (rs200482500), and APOE (rs7412).

See full tableTable 1. Loci significantly associated with cIMT and plaque GWAS

The PIK3CG is a newly described locus for cIMT, but has been previously reported in a GWAS of carotid plaque⁴. The two signals on chromosome 8 near MCPH1 (rs2912063) and SGK223 (rs11785239) were confirmed to be independent through conditional analysis (Supplementary Table 4). At the PINX1 locus, the lowest association p-value variant (rs200482500) was not in LD with the previously reported associated variant in the region (rs6601530, r² = 0.0, Table 1), thus representing an independent signal at this locus. Two additional loci for cIMT had an SNP that reached suggestive evidence for association (p < 1.0 × 10⁻⁷) including an SNP nearby APOB (rs515135) and an intronic low frequency variant at ATG4B (rs139302128, minor allele frequency [MAF] = 0.03) (Supplementary Table 5).

The GWAS meta-analysis for carotid plaque identified five loci, of which one has not been previously described (nearby gene LDLR) (Table 1). At four known loci associated with carotid plaque (nearby genes EDNRA, PIK3CG, CFDP1-TMEM170A, and at the 9p21 region), the most significantly associated variants were in LD with the previously reported SNPs (Table 1)⁴,⁶,⁷, indicating that these SNPs mark the same association at each locus. Two suggestive loci (p < 10⁻⁷) were also identified nearby the genes TMCO5B and STEAP2-AS1 (Supplementary Table 5). Conditional analyses confirmed the presence of a single independent signal at each locus. Manhattan and QQ plots from the meta-analysis of cIMT and carotid plaque are shown in Supplementary Figure 1 and regional plots in Supplementary Figure 2. Forest Plots for all loci are shown in Supplementary Figure 3.

Regulatory annotations of GWAS SNPs for cIMT/carotid plaque

To better define potentially causal variants within the identified genetic risk loci, we jointly analyzed the GWAS data with functional genomic information such as annotations on active transcription sites or open chromatin regions (i.e., performed a fine-mapping functional genome-wide association analysis using fGWAS¹³). Only variants in the PINX1 region were found to have a high probability that its association with cIMT is driven by SNPs that fall within transcription sites in adipose-derived mesenchymal stem cells at a DNaseI-hypersensitive site (Supplementary Figure 4), a finding that provides a down-stream mechanistic explanation for the cIMT signal in the PINX1 locus.

To further explore the regulatory functions of variants in the identified loci for cIMT and carotid plaque, we investigated whether the identified lead SNPs were also eQTLs using vascular RNAseq data from GTEx (aorta, coronary and tibial arteries, heart atrial appendage, and heart left ventricle) and from the coronary artery disease cohort of STARNET (i.e., from the atherosclerotic-lesion-free internal mammary artery [MAM] and atherosclerotic aortic root [AOR]). Lead SNP associated with cIMT and carotid plaque (rs13225723) in the PIK3CG locus was found to be vascular-specific eQTLs for CCDC71L and PRKAR2B in GTEx aorta as well as in STARNET AOR and MAM tissues (Table 2, Fig. 2), suggesting that the genetic regulation of these two genes are responsible for risk variation in cIMT and carotid plaque development in this locus.

See full tableTable 2. Gene expression results for significant SNPs in GTEx and STARNET tissues

Fig. 2View larger versionFig. 2. Pairwise colocalization results for genes identified for cIMT and carotid plaque GWAS meta-analysis with STARNET expression datasets. Red indicates a high posterior probability of colocalization and blue a high probability of no colocalization of the same SNP with tissue eQTLs

Colocalization analysis of GWAS data and STARNET eQTLs

To identify further candidate genes in tissues affected by atherosclerosis that had strong evidence of sharing the same variant for cIMT and carotid plaque as found in our GWAS, we conducted pairwise colocalization analysis of these genetic variants with cis-eQTLs in the STARNET study¹⁰.

The pairwise colocalization analysis is based on coloc, a Bayesian statistical methodology that tests pairwise colocalization of SNPs in GWAS with eQTLs and, in this fashion, generates posterior probabilities for each locus weighting the evidence for competing hypothesis of either no colocalization or sharing of a distinct SNP at each locus¹⁰. We used summary statistics from all SNPs within a 200-kb window around each gene covered by the eQTL datasets (N = 18,705, see Methods), and analyzed each eQTL-GWAS dataset pair (Supplementary Table 6). A posterior probability of ≥75% was considered strong evidence of the tissue-specific eQTL-GWAS pair influencing both the expression and GWAS trait at a particular region. Results for this analysis are shown in Table 3 and Supplementary Figure 5. The strongest evidence for an effect on gene expression within the regions identified in our standard GWAS meta-analysis was for the CCDC71L and PRKAR2B genes at the previously described chromosome 7 cIMT locus (PIK3CG in Table 2, Fig. 2). These genes showed evidence of colocalization for both cIMT and carotid plaque in AOR and MAM tissues (Table 3, Fig. 3). CCDC71L had the highest probability (>95%) for colocalization for cIMT, and MAM and AOR tissue eQTLs, and for carotid plaque, and MAM and AOR tissue eQTLs. We found a low probability of colocalization of the SNP with the PIK3CG gene expression (<1%).

See full tableTable 3. Colocalization of cIMT and plaque with eQTLs in tissues from patients with CHD in STARNET tissues for genes/tissues combinations that have more than 75% probability to share the same associated variant

Fig. 3View larger versionFig. 3. Association results at the CCDC71L locus (chromosome 7), showing a high posterior probability of a shared variant for cIMT and carotid plaque in AOR and MAM eQTLs. −log10(p) SNP association p-values for cIMT (plot A) and carotid plaque (plot B), and eQTL in AOR (plot C) and eQTL in SF (plot D). Association results in SF tissue have a low probability of a shared signal with cIMT and carotid plaque, possibly indicating a different mechanism in this tissue. eQTLs in MAM are identical to AOR and not shown. The p-values were calculated by fitting a linear regression model with cIMT or plaque as dependent variable and imputed SNPs as independent variables. Each dot is an SNP and the color indicates linkage disequilibrium (r²) with the best hit (in purple)

The eQTL associations at two additional loci (ADAMTS9, LOXL4) in MAM or AOR showed evidence of colocalization with cIMT or carotid plaque, although GWAS association p-values at these loci did not meet the genome-wide significance threshold (Table 3, Supplementary Figure 5). Albeit with weaker magnitudes, the expression of these two genes were also associated with the top colocalizing SNPs as detected in RNAseq data in GTEx aorta (rs17676309, chr3:64730121, ADAMTS9, p = 0.0003 and rs55917128, chr10:100023359, LOXL4, p = 0.0005).

Colocalization of CHD and stroke GWAS and STARNET eQTLs

We next assessed if the four genes (CCDC71L, PRKAR2B, ADAMTS9, LOXL4) identified through colocalization of cIMT/carotid plaque with tissue-specific eQTLs also showed evidence for colocalization with CHD and stroke traits (Supplementary Data 1 and Supplementary Figure 6). We used GWAS summary data for CHD (CARDIoGRAMPlusC4D), and stroke subtypes (MEGASTROKE) and AOR and MAM STARNET tissue eQTLs for these analyses. CCDC71L and PRKAR2B had suggestive evidence of sharing the same variant with large vessel disease stroke in both AOR and MAM tissues (probability of colocalization ≥20%, Supplementary Data 1). In contrast, there was strong evidence (≥75%) to reject a shared variant for CHD and eQTLs at this locus, thus suggesting there is atherosclerotic outcome specificity at vascular level for this locus (Supplementary Figure 5). Three of these genes, CCDC71L, PRKAR2B, and ADAMTS9, showed evidence for shared genetic influences of cIMT or carotid plaque on CHD/stroke outcomes when testing the joint association using moloc, a multiple-trait extension of coloc¹⁴ (Supplementary Table 7). We also highlight the expression of KIAA1462 gene in MAM, carotid plaque/cIMT, and CHD, which were positively correlated (Supplementary Figure 7). This gene has suggestive evidence of pairwise colocalization with carotid plaque (67% of probability of shared variant between carotid plaque and eQTL in MAM), as well as a high probability of shared variant between MAM eQTL expression of this gene, GWAS carotid plaque or cIMT, and CHD traits (Supplementary Table 7). We note, however, that the GWAS signal for outcomes across the datasets did not reach genome-wide significance and larger sample sizes may be needed to strengthen the evidence for involvement in disease outcomes.

Genetic correlations of cIMT/carotid plaque and clinical outcomes

To provide etiological insights into the role of measures of subclinical atherosclerosis and major atherosclerotic disease outcomes such as CHD and ischemic stroke, we quantified the genetic correlation using cross-trait LD score regression, a method that estimates genetic correlation across different traits using summary level data¹⁵. We used summary statistics between cIMT/carotid plaque with CHD and stroke meta-analysis of GWAS. Both cIMT and carotid plaque had positive significant genetic correlations with CHD (all p < 0.05 after adjusting for multiple testing), though the magnitude of the correlation was twice as strong for carotid plaque (0.52) as for cIMT (0.20) (Table 4). There was also evidence for genetic correlations between cIMT with any stroke and ischemic stroke subtype.

See full tableTable 4. Genetic correlation between CHD and stroke traits with cIMT and plaque, and cIMT with plaque using LD score and meta-GWAS

Pathway analysis and druggability

Gene Ontology (GO) analyses of genes identified in the loci for cIMT and carotid plaque according to our meta-analysis of GWAS (Table 1 and Supplementary Table 5) and in the colocalization analyses (Table 3, Supplementary Table 7) showed that cIMT genes are enriched in lipoprotein-related terms and cholesterol efflux, whereas carotid plaque genes are enriched in terms associated with fibroblast apoptosis (Supplementary Figure 8). Analysis of the cIMT genes using a GO Slim additionally identified several of the genes that were associated with terms describing cardiovascular development, cell adhesion, and immune processes, processes already considered relevant to atherosclerosis. Specifically, there is corroborating evidence from GO that CCDC71L, PRKAR2B, and TWIST1 are associated with cIMT/carotid plaque as they are involved in lipid metabolism, with similar support that ADAMTS9, CDH13, and KIAA1462 are associated with cIMT or carotid plaque risk as they are all involved in cell adhesion and, together with TWIST1, in cardiovascular system development (Supplementary Data 2).

From the loci associated with cIMT and carotid plaque, we identified seven genes (ATG4B, ALPL, LDLR, APOB, EDNRA, APOE, and ADAMTS9) whose encoded proteins are targets at various stages of the drug development process (Supplementary Tables 8 and 9). ADAMTS9 gene encodes a protein likely to be druggable¹⁶. ATG4B, ALPL, and LDLR are proteins being targeted by compounds in pre-clinical phase (tier 2), while APOB and EDNRA are proteins targeted by drugs in clinical phase or licensed (tier 1). APOB is the target of an approved FDA drug for treatment of familial hypercholesterolemia. EDNRA gene encodes for endothelin A receptor, against which several antagonists have been developed for the treatment of pulmonary arterial hypertension or which are in advanced clinical phase development for non-small cell lung cancer and diabetic nephropathy.

Discussion

We provide results of a large meta-analysis of GWAS of subclinical atherosclerosis and we integrate our results with tissue-specific gene expression data using eQTLs from both the early (MAM) and late advanced (AOR) atherosclerotic arterial wall from the STARNET study to enable reliable discovery of genes with biological evidence of an increased probability for conferring inherited risk of atherosclerosis development. Our discovery approach using GWAS meta-analyses identified 16 loci significantly associated with either cIMT or carotid plaque, of which nine are novel.

The integration of GWAS and tissue-specific cis-eQTLs for the joint analyses of tissue-specific eQTLs from CHD patients identified two potentially additional loci colocalizing with cIMT or carotid plaque: chr3:63561280-65833136 (ADAMTS9), chr10:99017729-101017321 (LOXL4). ADAMTS9 is a metalloproteinase involved in thrombosis and angiogenesis and has been associated with cardiometabolic traits (waist-to-hip ratio, waist circumference, and type 2 diabetes) in GWAS, and with coronary artery calcification in a gene-by-smoking interaction GWAS¹⁷,¹⁸. LOXL4 encodes a lysyl oxidase involved in crosslinks of collagen and elastin in the extracellular matrix. This family of proteins are involved in the development of elastic vessels and mechanical strength of the vessel wall, and their inhibition was associated with the development of abdominal aortic aneurysms and more severe atherosclerosis in experimental models¹⁹.

Some loci identified in our meta-analysis of GWAS include genes in known pathways for atherosclerosis, including LDLR, which is related to lipid pathways and CHD, and identified for associations with carotid plaque in our study. For most of the loci, however, the underlying gene implicated in signals are unknown. Our colocalization approach found both CCDC71L and PRKAR2B as the most likely genes at the chromosome 7 locus, where PIK3CG was previously the suggested gene. This finding is in agreement with a targeted sequencing study of subclinical atherosclerosis¹⁵. An additional SNP (rs342286) at this locus has been associated with platelets volume and reactivity, and cardiovascular traits. However, rs342286 is not in LD with our most significant SNP and it is not associated with cIMT or carotid plaque in our studies (p = 0.49 and 0.01, respectively). Of interest, the variant we identified in this study showed evidence for colocalization with cIMT/carotid plaque and large vessel disease stroke but not CHD, therefore showing tissue and outcome-specificity. CCDC71L has unknown function. PRKAR2B codes for one of the several regulatory subunits of cAMP-dependent protein kinase and its expression is ubiquitous. In vitro studies have shown that adenosine-induced apoptosis of arterial smooth muscle cells involves a cAMP-dependent pathway²⁰.

Measures of cIMT and carotid plaque reflect vascular pathophysiologic and atherosclerosis processes, respectively, with carotid plaque more strongly reflecting atherosclerotic clinical events. An important contribution of this study is the supporting evidence for overall genetic correlations of CHD and stroke (any cause and ischemic stroke) with subclinical atherosclerosis traits, estimated using LD score methods. Further highlighting the potential biological relevance of our findings, the genetic correlations estimates for CHD were stronger for carotid plaque than for cIMT. However, cIMT and carotid plaque GWAS were correlated, and the genetic correlations estimates with stroke were similar for cIMT or carotid plaque, and not significant for carotid plaque. The colocalization analyses provided additional insights in the relationships between subclinical atherosclerosis, clinical outcomes, and tissue-specific regulation at specific genomic regions. For example, our suggestive top gene association in multi-trait colocalization for KIAA1462 included MAM eQTLs, carotid plaque, and CHD, supporting the shared genetic effects at this locus of atherosclerosis in carotid and coronary arteries. KIAA1462 has been previously reported in the same locus identified by GWAS for CHD²¹. This gene encodes a protein involved in cell–cell junctions in endothelial cells²², which was recently shown to be involved in pathologic angiogenic process in in vitro and in vivo experimental models²³. These findings suggest that there may be important differences in vascular bed regulation at distinctive regions for atherosclerotic cardiovascular and stroke outcomes that may help to identify genes and specific targets for CHD or stroke prevention and treatment.

Additional studies in diverse and large samples across the multiple datasets are needed to explore these results further. As more summary statistics become available for other clinical end-points beyond stroke and CHD (both in terms of larger sample size and richer genome coverage), and as further refinements in clinical phenotypes emerge (e.g. from CHD to acute coronary syndrome sub-components), strategies to integrate this knowledge using methods such as moloc¹⁰ and eCAVIAR²⁴ will continue to be essential for harnessing genome-wide findings in the drug-discovery process.

In summary, our study is a large GWAS meta-analysis of cIMT and carotid plaque. Through a sequential approach of discovery and colocalization studies, we provide deeper insights into disease causal genes of subclinical cIMT and carotid plaque formation. We confirmed three loci and identified nine novel loci in the meta-analyses of cIMT and carotid plaque. Additionally, we provide strong evidence for the role of three novel genes from our integrative analysis of GWAS and eQTL data. Moreover, the identified correlations with CHD and stroke highlight novel biological pathways that merit further assessments as novel targets for drug development.

Methods

Ethics statement

All human research was approved by the relevant institutional review boards for each study, and conducted according to the Declaration of Helsinki. All participants provided written informed consent.

Populations and phenotypes

The discovery GWAS in this study consists of a collaboration between the CHARGE ⁸ and the UCLEB consortia⁹, for genetic studies of cIMT and carotid plaque among individuals of European ancestry (Supplementary Note 1). All studies followed standardized protocols for phenotype ascertainment and statistical analyses. The descriptive characteristics of participating studies are shown in Supplementary Table 1.

cIMT and carotid plaque measures were evaluated using high-resolution B-mode ultrasonography and reading protocols as previously reported⁴. We used data from the baseline examination or the first examination in which carotid ultrasonography was obtained. cIMT was defined by the mean of the maximum of several common carotid artery measurements, measured at the far wall or the near wall. For most studies, this was an average of multiple measurements from both the left and right arteries. We also examined a carotid plaque phenotype, defined by atherosclerotic thickening of the carotid artery wall or the proxy measure of luminal stenosis greater than 25% (Supplementary Table 2).

Genotyping, imputation, and study-level quality control

Genotyping arrays and QC pre-imputation are shown in Supplementary Table 3. Each GWAS study conducted genome-wide imputation using a Phase 1 integrated (March 2012 release) reference panel from the 1000G Consortium using IMPUTE2²⁵ or MaCH/minimac²⁶, and used Human Reference Genome Build 37. Sample QC was performed with exclusions based on call rates, extreme heterozygosity, sex discordance, cryptic relatedness, and outlying ethnicity. SNP QC excluded variants based on call rates across samples and extreme deviation from Hardy–Weinberg equilibrium (Supplementary Table 3). Non-autosomal SNPs were excluded from imputation and association analysis.

Pre-meta-analysis GWAS study-level QC was performed using EasyQC software²⁷. This QC excluded markers absent in the 1000G reference panel; non A/C/G/T/D/I markers; duplicate markers with low call rate; monomorphic SNPs and those with missing values in alleles, allele frequency, and beta estimates; SNPs with large effect estimates or standard error (SE) ≥10; and SNPs with allele frequency difference >0.3 compared to 1000G reference panel. There was a total of 9,574,088 SNPs for the cIMT meta-analysis and 8,578,107 SNPs for the carotid plaque meta-analysis.

Statistical analyses

Within each study, we used linear and logistic regression to model cIMT and carotid plaque, respectively, and an additive genetic model (SNP dosage) adjusted for age, sex, and up to 10 principal components. We combined summary estimates from each study and each trait using an inverse variance weighted meta-analysis. Additional filters were applied during meta-analyses including imputation quality (MACH r² < 0.3 and IMPUTE info <0.4), a minor allele frequency (MAF) <0.01, and SNPs that were not present in at least four studies. The genome-wide significance threshold was considered at p < 5.0 × 10⁻⁸.

To assess the evidence for independent associations at each locus attaining genome-wide significance, we performed conditional analysis in a 1-Mb genomic interval flanking the lead SNP using GCTA²⁸. This approach uses summary meta-analysis statistics and a LD matrix from an ancestry-matched sample to perform approximate conditional SNP association analysis. The estimated LD matrix was based on 9713 unrelated individuals of European ancestry from the ARIC study, which was genotyped using an Affymetrix 6.0 array and imputed to the 1000G panel using IMPUTE2²⁵.

Gene expression analysis using GTEx

GTEx Analysis V6 (dbGaP Accession phs000424.v6.p1) eQTL results were downloaded from GTEx portal for 44 tissues, and then mapped to SNPs listed in Table 1. We used a false discovery rate (FDR) of ≤0.05.

Colocalization analyses using eQTLs

We integrated our GWAS results with cis-eQTL data using a Bayesian method (coloc)¹⁰. This method evaluates whether the GWAS and eQTL associations best fit a model in which the associations are due to a single shared variant (summarized by the posterior probability). We used gene expression datasets from multiple tissues from patients with CHD of the STARNET study, including blood, MAM, AOR, subcutaneous fat (SF), visceral fat (VAF), skeletal muscle (SKLM), and liver (LIV) obtained from 600 patients during open heart surgery¹¹. Pairwise colocalization was tested between these expression disease tissue datasets and GWAS results from our cIMT/carotid plaque GWAS meta-analysis. We used GWAS and eQTL summary statistics of SNPs within a 200-kb window around each gene covered by the eQTL datasets. A posterior probability of colocalization ≥0.75 was considered a strong evidence for a causal gene. Next, we reported the gene(s) in the STARNET datasets that had the strongest evidence of sharing the same variant with cIMT or carotid plaque genome-wide. In an alternative analysis, we also tested loci with an SNP that reached a threshold of significant or suggestive genome-wide significance for cIMT or carotid plaque (reported in Table 1, Supplementary Table 5). For each region 200kb around the SNP with the lowest association p-value, we report the gene with the highest probability of being responsible for the GWAS signal (Supplementary Table 6).

Pairwise colocalization for these genes was also tested for publicly available GWAS for CHD case-controls (CARDIoGRAMPlusC4D) and stroke case-controls (MEGASTROKE consortium). The MEGASTROKE dataset uses genotypes imputed to the 1000G phase I haplotype panel. The European ancestry sample used to generate these results consisted of 40,585 stroke cases and 406,111 controls from 15 cohorts and two consortia: the METASTROKE and CHARGE consortia²⁹. The phenotypes used in this analysis were any stroke (n = 39,067 cases, total n = 442,142), ischemic stroke (IS, n = 32,686 cases, total n = 423,266), and etiologic stroke subtypes:cardioembolic stroke (CE, n = 6,820 cases, total n = 314,368), large vessel disease (n = 4,113, total n = 202,263), and small vessel disease (SVD, n = 4,975, total n = 242,250). To explore multi-trait colocalizations, we used moloc¹⁴ with prior probabilities of 10⁻⁴ for GWAS/GWAS/eQTL, 10⁻⁶ for GWAS+eQTL/GWAS or GWAS+GWAS/eQTL, and 10⁻⁷ for colocalization of all three association signals.

Functional annotation and epigenetic enrichment analyses

From the Epigenome Roadmap Project³⁰,³¹, we obtained regulatory information using broad classes of chromatin states (n = 127 tissues) capturing promoter-associated, transcription-associated, active intergenic, and large-scale repressed and repeat-associated states. From ENCODE³², we obtained chromatin states, uniformly processed transcription factor (TF) Chip assays and DNaseI Hypersensitivity sites (DHS) for nine cells lines. From FANTOM5³³, we used information from expression of enhancers in each tissue (n = 112), and enhancers that are positively differentially expressed against any other tissue (n = 110).

We used fGWAS¹³ to identify genomic annotations that are enriched within the cIMT results and to select the variants with support for a functional role based on the most informative annotations. We only considered cIMT for these analyses because of the small number of identified loci for carotid plaque. We first estimated the enrichment parameters for each annotation individually and identified the set of annotations with significant marginal associations. We then applied 10-fold cross-validation likelihood and forward selection to identify the set of annotations that significantly improve the model fit, and reverse selection of each annotation included in the model, as suggested in the fGWAS workflow. We reported the model with the highest cross-validation likelihood and SNPs that have regional posterior probability of association (PPA) >0.9 and directly overlap the genomic annotations considered.

Overall genetic correlation analysis

Genetic correlation between cIMT/carotid plaque, CHD, and stroke traits were calculated using LD score regression approach LD-score, which uses GWAS summary statistics and is not affected by sample overlap. This method relies on the fact that the χ² association statistic for a given SNP includes the effects of all SNPs that are in LD with it and it calculates genetic correlation by partitioning the SNP heritabilities¹⁵. Genetic correlations between stroke traits (IS, CE, large vessel disease, and SVD) and cIMT and carotid plaque were calculated using software available at http://github.com/bulik/ldsc with GWAS summary statistics for our cIMT/carotid plaque GWAS, CARDIOGRAMPlusC4D data, and stroke GWAS. We used the LD-scores¹⁵, which are based on the 1000 Genomes European population and estimated within 1-cM windows. Based on ten tests performed (two subclinical traits and five outcomes), we set the significance threshold to p = 0.005.

PATHWAY ANALYSES. Methods for GO Slim: The Ensembl identifiers of all protein-coding genes identified as in LD with the 12 variants for cIMT and 15 variants for carotid plaque (including variants from main and suggestive signals, Table 1 and Supplementary Table 5), and five genes for which there is strong evidence of colocalization (Table 3), were mapped to UniProt accession numbers, using the UniProt ID mapping service (http://www.uniprot.org/uploadlists/). A GO Slim analysis was performed on this list using QuickGO (www.ebi.ac.uk/QuickGO) and the Generic GO Slim. The GO terms used in the final slim analysis were further refined by adding/removing GO terms to provide more detailed information about the processes covered.

Methods for GO term enrichment analysis: The VLAD gene list analysis and visualization tool (http://proto.informatics.jax.org/prototypes/vlad/) was used to perform a GO term enrichment analysis on the same UniProt accessions as listed for the GO Slim. The background annotation set was obtained from the goa_human.gaf file (dated 21 November 2017, downloaded from ftp://ftp.ebi.ac.uk/pub/databases/GO/goa/HUMAN/) and the ontology data was obtained from the go-basic.obo file provided in the VLAD tool (analysis run 28 November 2017).

The LD block around top SNPs associated with cIMT and carotid plaque was constructed using LD information from the 1000 Genomes panel, as previously outlined in Finan et al.¹⁶. Briefly, the boundaries of the LD region were defined as the positions of the variants furthest upstream and downstream of a GWAS SNP with an r² value of ≥0.5 and within a 1-Mbp flank on either side of the GWAS variant. Associated variants that were not present in the 1000 Genomes panel that were not in LD with any other variants were given a nominal flank of 2.5 kbp on either side of the association. Gene annotations using Ensembl version 79 were then overlapped to the LD region.

Druggable genes

We examined the druggability status for the nearest coding genes identified in our GWAS analysis on cIMT and carotid plaque, including significant (novel and replicated) and suggestive ones, as well as genes identified through colocalization analysis. The druggable gene set was calculated using the previously described criteria: novel targets of first-in-class drugs licensed since 2005; the targets of drugs currently in late phase clinical development; pre-clinical phase small molecules with protein binding measurements reported in the ChEMBL database; and genes encoding secreted or plasma membrane proteins that are targets of monoclonal antibodies and other bio-therapeutics¹⁶. We defined three tiers of druggable gene sets based on their drug development. In Tier 1, 1427 genes were targets of approved small molecules and biotherapeutic drugs and clinical-phase drug candidates. Tier 2 comprised 682 genes encoding targets with known bioactive drug-like small molecule binding partners and those with significant sequence similarity to approved drug targets. Tier 3 contained 2370 genes encoding secreted or extracellular proteins, proteins with more distant similarity to approved drug targets, and druggable genes not included in Tier 1 or 2 such as GPCRs, nuclear hormone receptors, ion channels, kinases, and phosphodiesterases.

URLs

For GTEx, see http://gtexportal.org/. For Coloc, see https://cran.r-project.org/web/packages/coloc/coloc.pdf. For, Moloc, see https://github.com/clagiamba/moloc/blob/master/man/moloc-package.Rd. For CARDIoGRAMPlusC4D, see www.cardiogramplusc4d.org/. For LD scores, www.broadinstitute.org/~bulik/eur_ldscores/. For UniProt ID, www.uniprot.org/uploadlists/. For QuickGO, www.ebi.ac.uk/QuickGO. For VLAD tool, see http://proto.informatics.jax.org/prototypes/vlad/.

Supplementary Material

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Footnotes

A full list of consortium members can be found at the end of the article.

Publisher’s note: Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

These authors contributed equally: Nora Franceschini, Claudia Giambartolomei.

Electronic supplementary material

Supplementary Information accompanies this paper at 10.1038/s41467-018-07340-5.

Acknowledgements

The work was supported by the following grants: National Institute of Health grants: R21HL123677, R21-HL140385, DK104806-01A1, R01-MD012765-01A1 (NF), National Institutes of Health awards R01HG009120, R01HG006399, U01CA194393, T32NS048004 (CG), the American Heart Association Grant #17POST33350042 (PV), the British Heart Foundation (RG/13/5/30112) and the National Institute for Health Research University College London Hospitals Biomedical Research Centre (RCL and RPH), the British Heart Foundation FS/14/55/30806 (JCH), the German Federal Ministry of Education and Research (BMBF) in the context of the e:Med program (e:AtheroSysMed), the DFG as part of the CRC 1123 (B3), and the FP7/2007-2103 European Union project CVgenes@target (grant agreement number Health-F2-2013-601456). We thank Li-Ming Gan for assistance with the STARNET study and Jon White for assistance with UCLEB analyses. Additional acknowledgements are included in Supplementary Note 2.

Author contributions

N.F., C.G., J.C.B., M.K., C.D., M.S., S.K.M., U.S., W.P., A.B.Z., A.H., A.T., A.G.U., A.B.N., B.W.P., C.D.L., E.T., F.R., H.V., I.J.D., L.J.L., M.D., O.R., O.H.F., R.S., R.M., T.B.H., T.L., U.F., W.P., A.D., A.S., A.H., C.M.D., D.A.L., D.O.M.-K., D.W.B., H.S., J.F.W., J.G.W., J.I.R., J.M.W., M.L., M.K.E., S.E.H., U.V., V.G., A.D.H., J.P.C., and C.J.O. contributed to study concept and design. C.D., M.S., S.K.M., U.S., W.P., A.I., A.C., A.B.Z., A.T., A.G.U., A.W., A.J.S., A.B., A.R., B.H., B.W.P., C.F., D.B., D.H.O., D.T., D.K., E.T., E.M., E.G., E.B., E.E.S., E.I., F.R., F.B., G.H., H.C., H.V., H.S.M., I.J.D., J.W.J., J.G., J.P., J.T., J.E., K.D.T., L.K., L.L., L.J.L., L.H., M.D., M.S., M.K., M.K., M.A.N., O.M., O.R., P.H.W., P.G., P.A., R.R., R.B., R.H., R.S., R.M., R.W.M.,. S.G.W., S.M.L., S.T., S.K., S.R.H., S.R., T.B.H., T.L., T.G., T.S., U.F., V.P., W.R., W.P., X.Z., A.S., A.H., B.M.P., C.M.D., D.A.L., D.O.M.-K., D.W.B., H.S., J.F.W., J.G.W., J.I.R., J.C.H., J.M.W., J.D., J.H., M.K.E., S.E.H., U.V., V.G., A.D.H., J.L.M.B., J.P.C., and C.J.O. contributed to acquisition of genotyping or phenotypic data. N.F., C.G., C.F., J.C.B., R.P.H., R.C.L., S.M.T., T.W.W., M.G., M.K., C.D., A.V.S., E.H., E.M.L., I.M.N., L.L., M.S., M.S., N.P., O.F., P.K.J., R.N., R.E.M., S.-J.H., S.K.M., U.S., A.I., A.T., K.R., A.J.C., B.S., C.D.L., C.W., F.V., G.C., H.S., J.P., J.L., K.G., L.M.R., M.T., M.A.N., O.M., P.R., P.A., Q.W., R.J.S., S.H., S.S., S.M.L., T.S., X.Z., X.Z., X.G., Y.S., and L.-.P.L. contributed to statistical analysis and interpretation of the data. N.F., C.G., P.S.V., J.C.B., M.K., S.K.M., A.D.H., and J.P.C. contributed to drafting of the manuscript. All authors contributed to the critical revision of the manuscript.

Data availability

All relevant summary statistics data that support the findings of this study have been deposit in the database of Genotypes and Phenotypes (dbGaP) under the CHARGE acquisition number (https://www.ncbi.nlm.nih.gov/projects/gap/cgi-bin/study.cgi?study_id=phs000930.v6.p1; accession phs000930.v6.p1). GWAS data for most US studies are already available in dbGAP.

Notes

Competing interests

C.F. received a fee for speaking at a course by Springer Healthcare/Malesci. E.I. is a scientific advisor for Precision Wellness, Cellink and Olink Proteomics for work unrelated to the present project. M.A.N.’s participation in this project was supported by a consulting contract between Data Tecnica International and the National Institute on Aging, NIH, Bethesda, MD, USA. M.A.N. also consults for Illumina Inc., the Michael J. Fox Foundation, and University of California Healthcare. B.M.P. serves on the DSMB of a clinical trial funded by Zoll LifeCor and on the Steering Committee of the Yale Open Data Access Project funded by Johnson & Johnson. D.A.L. has received support from Roche Diagnostics and Medtronic for biomarker research unrelated to this paper. J.P.C. has received funding from GSK regarding methodological work around electronic health records, and -omics for drug discovery. All remaining authors declare no competing interests.

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Fig. 1.

Overall study design. a GWAS meta-analyses of cIMT and carotid plaque for gene discovery. b Local and genome-wide shared genetic basis using gene expression and clinical outcomes GWAS data

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Table 1.

Loci significantly associated with cIMT and plaque GWAS

SNP | Chr:position | Nearest coding gene | Alleles (effect/other) | Effect allele freq. | Beta (SE) | p | N
Newly identified loci for cIMT
rs201648240 | 1:208953176-indel | LINC01717 | −/AA | 0.83 | −0.0062 (0.0011) | 4 × 10⁻⁹ | 54,752
rs224904 | 5:81637916 | ATP6AP1L | C/G | 0.95 | −0.0088 (0.0016) | 5 × 10⁻⁸ | 68,962
rs6907215 | 6:143608968 | AIG1 | T/C | 0.60 | −0.0040 (0.0007) | 5 × 10⁻⁸ | 64,586
rs13225723 | 7:106416467 | PIK3CG | A/G | 0.22 | 0.0052 (0.0009) | 3 × 10⁻⁹ | 68,070
rs2912063 | 8:6486033 | MCPH1 | A/G | 0.71 | 0.0045 (0.0008) | 9 × 10⁻⁹ | 67,401
rs11785239 | 8:8205010 | SGK223 | T/C | 0.65 | −0.0043 (0.0008) | 9 × 10⁻⁹ | 67,107
rs11196033 | 10:114410998 | VTI1A | A/C | 0.48 | 0.0042 (0.0008) | 4 × 10⁻⁸ | 57,995
rs844396 | 16:88966667 | CBFA2T3 | T/C | 0.30 | −0.0051 (0.0009) | 6 × 10⁻⁹ | 50,377
Newly identified loci for plaque
rs200495339 | 19:11189298-indel | LDLR | −/G | 0.11 | −0.1023 (0.0179) | 1  × 10⁻⁸ | 36,569
Known loci for cIMT
rs148147734a | 8:123401537-indel | ZHX2 | −/G | 0.54 | 0.0050 (0.0007) | 3  × 10⁻¹¹ | 58,141
rs200482500a | 8:10606223-indel | PINX1 | −/GTACC | 0.52 | 0.0056 (0.0008) | 7 × 10⁻¹² | 58,141
rs7412a | 19:45412079 | APOE | T/C | 0.08 | −0.0119 (0.0015) | 1 × 10⁻¹⁴ | 44,607
Known loci for plaque
rs11413744b | 4:148395284-indel | EDNRA | −/T | 0.86 | −0.1586 (0.0253) | 4 × 10⁻¹⁰ | 39,577
rs17477177b | 7:106411858 | PIK3CG | T/C | 0.79 | −0.1305 (0.0197) | 4 × 10⁻¹¹ | 47,863
rs9632884b | 9:22072301 | 9p21 | C/G | 0.48 | 0.1127 (0.0163) | 5 × 10⁻¹² | 45,943
rs113309773b | 16:75432686-indel | CFDP1- TMEM170A | −/C | 0.46 | −0.1259 (0.0194) | 9 × 10⁻¹¹ | 37,104

p = p-values of association from linear regression analysis, N = total number in meta-analyses

aPublished cIMT SNP in LD with our most significant SNP: rs11781551 (r² = 0.95 with rs148147734), rs6601530 (r² = 0 with rs200482500), and rs445925 (r² = 0.60 with rs7412)

bPublished plaque SNP in LD with our most significant SNP: rs1878406 (r² = 0.98 with rs11413744), rs17398575 (r² = 0.8 with rs17477177), rs9644862 (r² = 0.79 with rs9632884), and rs4888378 (r² = 0.94 with rs113309773)

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Table 2.

Gene expression results for significant SNPs in GTEx and STARNET tissues

SNP
eQTLa (Gene, p) GTEx
eQTLa (Gene, p) STARNET tissues

AORb
HEART (ATR/VEN)c
AOR
MAM

rs201648240
CAMK1G, 0.0094
AL031316.1, 0.0040

CD34,0.00532
TRAF3IP3, 0.0097

rs6907215

AL023584.1, 0.005384704 (VEN)
ENSG00000217648, 0.00046
ENSG00000217648, 0.8 × 10⁻⁵

rs13225723
AC005050.1, 1 × 10⁻¹⁰
ENSG00000177820.5, 7.0 × 10⁻⁵
CCDC71L, 5 × 10⁻⁶
PRKAR2B, 4 × 10⁻⁸
PIK3CG, 10 × 10⁻³

CCDC71L, 6 × 10⁻³⁶
PRKAR2B, 7 × 10⁻⁷
SYPL1, 0.0043
CCDC71L, 3 × 10⁻³³
PRKAR2B, 6 × 10⁻⁸
NAMPT, 6 × 10⁻⁶

rs2912063
MCPH1, 0.0041
ENSG00000271743.1, 0.0093 (VEN)
MCPH1-AS1, 0.0020

rs11785239

AC022784.1, 0.0078 (VEN)
ERI1, 0.0069
PPP1R3B, 0.0036

rs844396
ENSG00000141012.8, 0.003
AC092384.2, 0.001
CBFA2T3, 1 × 10⁻⁷
ZNF469, 0.004 (ATR)
AC092384.3, 5 × 10⁻⁶ (ATR)
AC092384.1, 0.002 (ATR)
CBFA2T3, 0.0004 (ATR)
ZNF469, 0.002 (VEN)
AC138028.4, 0.001 (VEN)
ENSG00000224888.3, 0.009(VEN)
PIEZO1, 0.0004 (VEN)
GALNS, 0.004 (VEN)
RPL13, 0.0024
ZNF276, 0.0070
TRAPPC2L, 0.0091
TRAPPC2L, 0.0040
ZNF276, 0.0059

rs200495339

ENSG00000267105.1, 0.0005 (VEN)

rs148147734
DERL1, 0.0082

rs200482500
AF131215.6, 0.005
AF131215.5, 0.001
AF131215.5, 0.002 (ATR)
AF131215.6, 0.003 (VEN)
AF131215.5, 0.004 (VEN)

rs7412
ENSG00000267163.1, 0.007

rs11413744
PRMT9, 0.004

rs17477177
ENSG00000267052.1, 6 × 10⁻¹¹
ENSG00000177820.5, 5 × 10⁻⁶
CCDC71L, 4 × 10⁻⁷
PRKAR2B, 2 × 10⁻⁸
BCAP29, 0.002 (ATR)
CCDC71L, 2 × 10⁻³⁷
PRKAR2B, 6 × 10⁻⁷
SYPL1, 0.0091
CCDC71L,1 × 10⁻³³
PRKAR2B, 2 × 10⁻⁸
NAMPT, 1 × 10⁻⁵

rs9632884

DMRTA1, 0.007 (ATR)
CDKN2B, 2 × 10⁻³
CDKN2B, 2 × 10⁻³

rs113309773
BCAR1, 6 × 10⁻¹¹
ENSG00000261783.1, 2 × 10⁻¹⁶
GABARAPL2, 0.004
ENSG00000261783.1, 1 × 10⁻⁵ (ATR)
ENSG00000166822.8, 0.005 (ATR)
ENSG00000261783.1, 0.0003 (VEN)
ZFP1, 4 × 10⁻⁴
AC009078.2, 0.002
BCAR1, 3 × 10⁻¹²
CFDP1, 0.002
TMEM170A, 0.009

p = p-values of association from linear regression analysis

aThe lead SNP from GWAS is considered an eQTL if the cis-association has a nominal p-value of association <0.01. Multiple but not all lead SNPs reach genome-wide significance (p < 10⁻⁴).

bThis includes aorta (AOR)

cThis includes heart atrial (ATR) and heart left ventricle (VEN)

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Fig. 2.

Pairwise colocalization results for genes identified for cIMT and carotid plaque GWAS meta-analysis with STARNET expression datasets. Red indicates a high posterior probability of colocalization and blue a high probability of no colocalization of the same SNP with tissue eQTLs

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Table 3.

Colocalization of cIMT and plaque with eQTLs in tissues from patients with CHD in STARNET tissues for genes/tissues combinations that have more than 75% probability to share the same associated variant

Region (chr:start-stop)
Trait
Gene
SNP with best joint probability
p, BETA (SE), Tissue posterior probability (PPA)a
Direction of effect GWAS/eQTL

cIMT /plaque GWAS
AOR eQTL
MAM eQTL

chr3:63561280-65833136
cIMT
ADAMTS9
rs17676309 (T/C)
2 × 10⁻⁶,
-0.0035 (0.0007)
2 × 10⁻²⁵,
−0.65 (0.06)
PPA=0.93
1 × 10⁻²³,
−0.61 (0.06)
PPA=0.89
−/−

chr10:99017729-101017321
cIMT
LOXL4
rs55917128 (T/C)
5 × 10⁻⁷,
0.0037 (0.0007)
6 × 10⁻⁸,
0.33 (0.06)
PPA=0.79

+/+

chr7:105299372-107743409
cIMT
CCDC71L
PRKAR2B
rs12705390 (A/G)
5 × 10⁻⁹,
0.0049 (0.0008)
2 × 10⁻³⁷,
0.81 (0.06)
PPA=0.97
6 × 10⁻⁷,
0.34 (0.07)
PPA=0.93
1 × 10⁻³³,
0.755 (0.06)
PPA=0.97
2 × 10⁻⁸,
0.368 (0.06)
PPA=0.96
+/+
+/+

Plaque
CCDC71L
PRKAR2B
rs12705390 (A/G)
4 × 10⁻⁸,
0.12 (0.022)
2 × 10⁻³⁷,
0.80 (0.06)
PPA=0.97
6 × 10⁻⁷,
0.33 (0.07)
PPA=0.93
1 × 10⁻³³,
0.75 (0.06)
PPA=0.97
2 × 10⁻⁸,
0.37 (0.06)
PPA=0.96
+/+
+/+

PPA posterior probability of sharing same SNP higher than 75%, cIMT common carotid artery intima-media thickness, AOR aorta, MAM mammary artery

aThis signal reaches genome-wide significance in cIMT/plaque, and reaches a high probability of being mediated by the genes in AOR and MAM

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Fig. 3.

Association results at the CCDC71L locus (chromosome 7), showing a high posterior probability of a shared variant for cIMT and carotid plaque in AOR and MAM eQTLs. −log10(p) SNP association p-values for cIMT (plot A) and carotid plaque (plot B), and eQTL in AOR (plot C) and eQTL in SF (plot D). Association results in SF tissue have a low probability of a shared signal with cIMT and carotid plaque, possibly indicating a different mechanism in this tissue. eQTLs in MAM are identical to AOR and not shown. The p-values were calculated by fitting a linear regression model with cIMT or plaque as dependent variable and imputed SNPs as independent variables. Each dot is an SNP and the color indicates linkage disequilibrium (r²) with the best hit (in purple)

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Table 4.

Genetic correlation between CHD and stroke traits with cIMT and plaque, and cIMT with plaque using LD score and meta-GWAS

Cardiovascular disease trait | Subclinical atherosclerosis trait | Genetic correlation | SE | z | p
CHDa | cIMT | 0.20 | 0.05 | 4.1114 | 4 × 10⁻⁵
Any stroke | cIMT | 0.30 | 0.07 | 4.2301 | 2.3 × 10⁻⁵
Ischemic strokeb | cIMT | 0.31 | 0.07 | 4.646 | 3.4 × 10⁻⁶
Cardio-embolic strokeb | cIMT | 0.10 | 0.09 | 1.0729 | 0.28
Small vessel disease strokeb | cIMT | 0.33 | 0.18 | 1.8728 | 0.06
CHDa | Carotid plaque | 0.52 | 0.08 | 6.4263 | 1.3 × 10⁻¹⁰
Any strokeb | Carotid plaque | 0.28 | 0.10 | 2.7097 | 0.007
Ischemic strokeb | Carotid plaque | 0.27 | 0.10 | 2.6578 | 0.008
Cardio-embolic strokeb | Carotid plaque | 0.06 | 0.14 | 0.4684 | 0.64
Small vessel disease strokeb | Carotid plaque | −0.03 | 0.24 | −0.1344 | 0.89
Plaque | cIMT | 0.40 | 0.10 | 3.9667 | 7.3 × 10⁻⁵

aCARDIoGRAMPlusC4D

bMEGASTROKE consortium. Unable to estimate the genetic correlations with large vessel disease

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