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Meta-Analysis
. 2018 Apr;50(4):524-537.
doi: 10.1038/s41588-018-0058-3. Epub 2018 Mar 12.

Multiancestry genome-wide association study of 520,000 subjects identifies 32 loci associated with stroke and stroke subtypes

Rainer Malik #  1 Ganesh Chauhan #  2 Matthew Traylor #  3 Muralidharan Sargurupremraj #  4   5 Yukinori Okada #  6   7   8 Aniket Mishra  4   5 Loes Rutten-Jacobs  3 Anne-Katrin Giese  9 Sander W van der Laan  10 Solveig Gretarsdottir  11 Christopher D Anderson  12   13   14 Michael Chong  15 Hieab H H Adams  16   17 Tetsuro Ago  18 Peter Almgren  19 Philippe Amouyel  20   21 Hakan Ay  13   22 Traci M Bartz  23 Oscar R Benavente  24 Steve Bevan  25 Giorgio B Boncoraglio  26 Robert D Brown Jr  27 Adam S Butterworth  28   29 Caty Carrera  30   31 Cara L Carty  32   33 Daniel I Chasman  34   35 Wei-Min Chen  36 John W Cole  37 Adolfo Correa  38 Ioana Cotlarciuc  39 Carlos Cruchaga  40   41 John Danesh  28   42   43   44 Paul I W de Bakker  45   46 Anita L DeStefano  47   48 Marcel den Hoed  49 Qing Duan  50 Stefan T Engelter  51   52 Guido J Falcone  53   54 Rebecca F Gottesman  55 Raji P Grewal  56 Vilmundur Gudnason  57   58 Stefan Gustafsson  59 Jeffrey Haessler  60 Tamara B Harris  61 Ahamad Hassan  62 Aki S Havulinna  63   64 Susan R Heckbert  65 Elizabeth G Holliday  66   67 George Howard  68 Fang-Chi Hsu  69 Hyacinth I Hyacinth  70 M Arfan Ikram  16 Erik Ingelsson  71   59 Marguerite R Irvin  72 Xueqiu Jian  73 Jordi Jiménez-Conde  74 Julie A Johnson  75   76 J Wouter Jukema  77 Masahiro Kanai  6   7   78 Keith L Keene  79   80 Brett M Kissela  81 Dawn O Kleindorfer  81 Charles Kooperberg  60 Michiaki Kubo  82 Leslie A Lange  83 Carl D Langefeld  84 Claudia Langenberg  85 Lenore J Launer  86 Jin-Moo Lee  87 Robin Lemmens  88   89 Didier Leys  90 Cathryn M Lewis  91   92 Wei-Yu Lin  28   93 Arne G Lindgren  94   95 Erik Lorentzen  96 Patrik K Magnusson  97 Jane Maguire  98 Ani Manichaikul  36 Patrick F McArdle  99 James F Meschia  100 Braxton D Mitchell  99   101 Thomas H Mosley  102   103 Michael A Nalls  104   105 Toshiharu Ninomiya  106 Martin J O'Donnell  15   107 Bruce M Psaty  108   65   109   110 Sara L Pulit  45   111 Kristiina Rannikmäe  112   113 Alexander P Reiner  65   114 Kathryn M Rexrode  115 Kenneth Rice  116 Stephen S Rich  36 Paul M Ridker  34   35 Natalia S Rost  9   13 Peter M Rothwell  117 Jerome I Rotter  118   119 Tatjana Rundek  120 Ralph L Sacco  120 Saori Sakaue  7   121 Michele M Sale  122 Veikko Salomaa  63 Bishwa R Sapkota  123 Reinhold Schmidt  124 Carsten O Schmidt  125 Ulf Schminke  126 Pankaj Sharma  39 Agnieszka Slowik  127 Cathie L M Sudlow  112   113 Christian Tanislav  128 Turgut Tatlisumak  129   130 Kent D Taylor  118   119 Vincent N S Thijs  131   132 Gudmar Thorleifsson  11 Unnur Thorsteinsdottir  11 Steffen Tiedt  1 Stella Trompet  133 Christophe Tzourio  5   134   135 Cornelia M van Duijn  136   137 Matthew Walters  138 Nicholas J Wareham  85 Sylvia Wassertheil-Smoller  139 James G Wilson  140 Kerri L Wiggins  108 Qiong Yang  47 Salim Yusuf  15 AFGen ConsortiumCohorts for Heart and Aging Research in Genomic Epidemiology (CHARGE) ConsortiumInternational Genomics of Blood Pressure (iGEN-BP) ConsortiumINVENT ConsortiumSTARNETJoshua C Bis  108 Tomi Pastinen  141 Arno Ruusalepp  142   143   144 Eric E Schadt  145 Simon Koplev  145 Johan L M Björkegren  145   146   147   144 Veronica Codoni  148   149 Mete Civelek  122   150 Nicholas L Smith  65   151   152 David A Trégouët  148   149 Ingrid E Christophersen  54   153   154 Carolina Roselli  54 Steven A Lubitz  54   153 Patrick T Ellinor  54   153 E Shyong Tai  155 Jaspal S Kooner  156 Norihiro Kato  157 Jiang He  158 Pim van der Harst  159 Paul Elliott  160 John C Chambers  161   162 Fumihiko Takeuchi  157 Andrew D Johnson  48   163 BioBank Japan Cooperative Hospital GroupCOMPASS ConsortiumEPIC-CVD ConsortiumEPIC-InterAct ConsortiumInternational Stroke Genetics Consortium (ISGC)METASTROKE ConsortiumNeurology Working Group of the CHARGE ConsortiumNINDS Stroke Genetics Network (SiGN)UK Young Lacunar DNA StudyMEGASTROKE ConsortiumDharambir K Sanghera  123   164   165 Olle Melander  19 Christina Jern  166 Daniel Strbian  167   168 Israel Fernandez-Cadenas  30   31 W T Longstreth Jr  65   169 Arndt Rolfs  170 Jun Hata  106 Daniel Woo  81 Jonathan Rosand  12   13   14 Guillaume Pare  15 Jemma C Hopewell  171 Danish Saleheen  172 Kari Stefansson #  11   58 Bradford B Worrall #  173 Steven J Kittner #  37 Sudha Seshadri #  48   174 Myriam Fornage #  73   175 Hugh S Markus #  3 Joanna M M Howson #  28 Yoichiro Kamatani #  6   176 Stephanie Debette #  177   178 Martin Dichgans #  179   180   181
Collaborators, Affiliations
Meta-Analysis

Multiancestry genome-wide association study of 520,000 subjects identifies 32 loci associated with stroke and stroke subtypes

Rainer Malik et al. Nat Genet. 2018 Apr.

Erratum in

  • Publisher Correction: Multiancestry genome-wide association study of 520,000 subjects identifies 32 loci associated with stroke and stroke subtypes.
    Malik R, Chauhan G, Traylor M, Sargurupremraj M, Okada Y, Mishra A, Rutten-Jacobs L, Giese AK, van der Laan SW, Gretarsdottir S, Anderson CD, Chong M, Adams HHH, Ago T, Almgren P, Amouyel P, Ay H, Bartz TM, Benavente OR, Bevan S, Boncoraglio GB, Brown RD Jr, Butterworth AS, Carrera C, Carty CL, Chasman DI, Chen WM, Cole JW, Correa A, Cotlarciuc I, Cruchaga C, Danesh J, de Bakker PIW, DeStefano AL, den Hoed M, Duan Q, Engelter ST, Falcone GJ, Gottesman RF, Grewal RP, Gudnason V, Gustafsson S, Haessler J, Harris TB, Hassan A, Havulinna AS, Heckbert SR, Holliday EG, Howard G, Hsu FC, Hyacinth HI, Ikram MA, Ingelsson E, Irvin MR, Jian X, Jiménez-Conde J, Johnson JA, Jukema JW, Kanai M, Keene KL, Kissela BM, Kleindorfer DO, Kooperberg C, Kubo M, Lange LA, Langefeld CD, Langenberg C, Launer LJ, Lee JM, Lemmens R, Leys D, Lewis CM, Lin WY, Lindgren AG, Lorentzen E, Magnusson PK, Maguire J, Manichaikul A, McArdle PF, Meschia JF, Mitchell BD, Mosley TH, Nalls MA, Ninomiya T, O'Donnell MJ, Psaty BM, Pulit SL, Rannikmäe K, Reiner AP, Rexrode KM, Rice K, Rich SS, Ridker PM, Rost NS, Rothwell PM, Rotter JI, Rundek T, Sacco RL, Sakaue S, Sale MM, Salomaa V, Sapkota BR, Schmidt R, Schmidt CO, Sc… See abstract for full author list ➔ Malik R, et al. Nat Genet. 2019 Jul;51(7):1192-1193. doi: 10.1038/s41588-019-0449-0. Nat Genet. 2019. PMID: 31160810

Abstract

Stroke has multiple etiologies, but the underlying genes and pathways are largely unknown. We conducted a multiancestry genome-wide-association meta-analysis in 521,612 individuals (67,162 cases and 454,450 controls) and discovered 22 new stroke risk loci, bringing the total to 32. We further found shared genetic variation with related vascular traits, including blood pressure, cardiac traits, and venous thromboembolism, at individual loci (n = 18), and using genetic risk scores and linkage-disequilibrium-score regression. Several loci exhibited distinct association and pleiotropy patterns for etiological stroke subtypes. Eleven new susceptibility loci indicate mechanisms not previously implicated in stroke pathophysiology, with prioritization of risk variants and genes accomplished through bioinformatics analyses using extensive functional datasets. Stroke risk loci were significantly enriched in drug targets for antithrombotic therapy.

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Conflict of interest statement

Competing interests

S. Gretarsdottir, G.T., U.T., and K.S. are all employees of deCODE Genetics/Amgen, Inc. M.A.N. is an employee of Data Tecnica International. P.T.E. is the PI on a grant from Bayer HealthCare to the Broad Institute, focused on the genetics and therapeutics of atrial fibrillation. S.A.L. receives sponsored research support from Bayer HealthCare, Biotronik, and Boehringer Ingelheim, and has consulted for St. Jude Medical and Quest Diagnostics. E.I. is a scientific advisor for Precision Wellness, Cellink and Olink Proteomics for work unrelated to the present project. 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. The remaining authors have no disclosures.

Figures

Fig. 1
Fig. 1. MEGASTROKE study design
Variants were retained that passed central quality control (QC) criteria (Methods). The numbers of cases and controls are listed for each ancestry group. HRC, Haplotype Reference Consortium; imp, measure of imputation quality (Methods); FE, fixed effects; EUR, European ancestry; AFR, African ancestry; EAS, East Asian ancestry; SAS, South Asian ancestry; ASN, mixed Asian ancestry; LAT, Latin American ancestry; Phet, heterogeneity P value; PPhet, posterior probability of heterogeneity. *The ASN and LAT ancestries were composed of a single study and hence did not require ancestry-specific meta-analysis.
Fig. 2
Fig. 2. Association results of the transancestral GWAS meta-analysis and the prespecified ancestry-specific meta-analysis in European samples
Shown are novel (red) and known (black) genetic loci associated with any stroke or stroke subtypes. Top, Manhattan plot from the MANTRA transancestral GWAS meta-analysis for any stroke. The dotted line marks the threshold of statistical significance (log10(BF) >6.0).
Fig. 3
Fig. 3. Genetic overlap between stroke and related vascular traits at the 32 genome-wide-significant loci for stroke
a, Association results from the look-ups in published GWAS data for related vascular traits. Symbol sizes reflect P values for association with the related trait. b, Venn diagram. Loci reaching genome-wide significance for association with stroke subtypes are marked with a dagger symbol (for CES), underlined (for LAS), or marked with an asterisk (for SVS). Novel loci are in bold. SH3PXD2A, WNT2B, PDE3A, and OBFC1 have previously been associated with AF (SH3PXD2A), or diastolic (WNT2B and PDE3A), or systolic (OBFC1) BP, but the respective lead SNPs were in low LD (r2 <0.1 in the 1000G cosmopolitan panel) with variants associated with stroke in the current GWAS. MRI, magnetic resonance imaging; IMT, intima-media thickness; LDL, low-density lipoprotein; HDL, high-density lipoprotein. The lead variant for TBX3 is not included in the original datasets for BP traits (SBP and DBP). Results are based on a perfect proxy SNP (rs35432, r2 = 1 in the European 1000G phase 3 reference).
Fig. 4
Fig. 4. Shared genetic contribution between stroke and related vascular traits
Contributions determined by weighted genetic risk scores (wGRS, top) and LD-score regression analysis (bottom). Effect sizes and significance levels are represented by color and symbol size. β, wGRS effect size; R(g), genetic correlation. DBP, diastolic blood pressure; SBP, systolic blood pressure; MAP, mean arterial pressure; PP, pulse pressure; HTN, hypertension, TGL, triglyceride level. Sample sizes for related vascular traits are displayed in Supplementary Table 12. NS, nonsignificant.
Fig. 5
Fig. 5. Connection between stroke risk genes and approved drugs for antithrombotic therapy
Shown are the connections among lead SNPs at stroke risk loci, biological stroke risk genes, and individual targeted drugs. Lead SNPs reaching suggestive evidence for association (MANTRA transancestral meta-analysis log10(BF) >5) are shown in gray.

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