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Meta-Analysis
. 2022 Nov;611(7934):115-123.
doi: 10.1038/s41586-022-05165-3. Epub 2022 Sep 30.

Stroke genetics informs drug discovery and risk prediction across ancestries

Aniket Mishra #  1 , Rainer Malik #  2 , Tsuyoshi Hachiya #  3 , Tuuli Jürgenson #  4   5 , Shinichi Namba #  6 , Daniel C Posner #  7 , Frederick K Kamanu  8   9 , Masaru Koido  10   11 , Quentin Le Grand  1 , Mingyang Shi  11 , Yunye He  11 , Marios K Georgakis  2   12   13 , Ilana Caro  1 , Kristi Krebs  4 , Yi-Ching Liaw  14   15 , Felix C Vaura  16   17 , Kuang Lin  18 , Bendik Slagsvold Winsvold  19   20   21 , Vinodh Srinivasasainagendra  22 , Livia Parodi  12   13 , Hee-Joon Bae  23 , Ganesh Chauhan  24 , Michael R Chong  25   26 , Liisa Tomppo  27 , Rufus Akinyemi  28   29 , Gennady V Roshchupkin  30   31 , Naomi Habib  32 , Yon Ho Jee  33 , Jesper Qvist Thomassen  34 , Vida Abedi  35   36 , Jara Cárcel-Márquez  37   38 , Marianne Nygaard  39   40 , Hampton L Leonard  41   42   43 , Chaojie Yang  44   45 , Ekaterina Yonova-Doing  46   47 , Maria J Knol  30 , Adam J Lewis  48 , Renae L Judy  49 , Tetsuro Ago  50 , Philippe Amouyel  51   52   53 , Nicole D Armstrong  54 , Mark K Bakker  55 , Traci M Bartz  56   57 , David A Bennett  58 , Joshua C Bis  56 , Constance Bordes  1 , Sigrid Børte  20   59   60 , Anael Cain  32 , Paul M Ridker  61   62 , Kelly Cho  7 , Zhengming Chen  18   63 , Carlos Cruchaga  64   65 , John W Cole  66   67 , Phil L de Jager  13   68 , Rafael de Cid  69 , Matthias Endres  70   71   72   73 , Leslie E Ferreira  74 , Mirjam I Geerlings  75 , Natalie C Gasca  57 , Vilmundur Gudnason  76   77 , Jun Hata  78 , Jing He  48 , Alicia K Heath  79 , Yuk-Lam Ho  7 , Aki S Havulinna  80   81 , Jemma C Hopewell  82 , Hyacinth I Hyacinth  83 , Michael Inouye  46   84   85   86   87 , Mina A Jacob  88 , Christina E Jeon  89 , Christina Jern  90   91 , Masahiro Kamouchi  92 , Keith L Keene  93 , Takanari Kitazono  50 , Steven J Kittner  67   94 , Takahiro Konuma  6 , Amit Kumar  24 , Paul Lacaze  95 , Lenore J Launer  96 , Keon-Joo Lee  97 , Kaido Lepik  4   98   99   100 , Jiang Li  35 , Liming Li  101 , Ani Manichaikul  44 , Hugh S Markus  102 , Nicholas A Marston  8   9 , Thomas Meitinger  103   104 , Braxton D Mitchell  105   106 , Felipe A Montellano  107   108 , Takayuki Morisaki  10 , Thomas H Mosley  109 , Mike A Nalls  41   42   43 , Børge G Nordestgaard  110   111 , Martin J O'Donnell  112 , Yukinori Okada  6   113   114   115   116   117 , N Charlotte Onland-Moret  75 , Bruce Ovbiagele  118 , Annette Peters  119   120   121 , Bruce M Psaty  56   122   123 , Stephen S Rich  44 , Jonathan Rosand  12   13   124 , Marc S Sabatine  8   9 , Ralph L Sacco  125   126 , Danish Saleheen  127 , Else Charlotte Sandset  128   129 , Veikko Salomaa  80 , Muralidharan Sargurupremraj  130 , Makoto Sasaki  3 , Claudia L Satizabal  130   131 , Carsten O Schmidt  132 , Atsushi Shimizu  3 , Nicholas L Smith  122   133   134 , Kelly L Sloane  135 , Yoichi Sutoh  3 , Yan V Sun  136   137 , Kozo Tanno  3 , Steffen Tiedt  2 , Turgut Tatlisumak  138 , Nuria P Torres-Aguila  37 , Hemant K Tiwari  22 , David-Alexandre Trégouët  1 , Stella Trompet  139   140 , Anil Man Tuladhar  88 , Anne Tybjærg-Hansen  34   111 , Marion van Vugt  141 , Riina Vibo  142 , Shefali S Verma  143 , Kerri L Wiggins  56 , Patrik Wennberg  144 , Daniel Woo  83 , Peter W F Wilson  136   145 , Huichun Xu  105 , Qiong Yang  131   146 , Kyungheon Yoon  147 ; COMPASS Consortium; INVENT Consortium; Dutch Parelsnoer Initiative (PSI) Cerebrovascular Disease Study Group; Estonian Biobank; PRECISE4Q Consortium; FinnGen Consortium; NINDS Stroke Genetics Network (SiGN); MEGASTROKE Consortium; SIREN Consortium; China Kadoorie Biobank Collaborative Group; VA Million Veteran Program; International Stroke Genetics Consortium (ISGC); Biobank Japan; CHARGE Consortium; GIGASTROKE Consortium; Iona Y Millwood  18   63 , Christian Gieger  148 , Toshiharu Ninomiya  78 , Hans J Grabe  149   150 , J Wouter Jukema  140   151   152 , Ina L Rissanen  75 , Daniel Strbian  27 , Young Jin Kim  147 , Pei-Hsin Chen  15 , Ernst Mayerhofer  12   13 , Joanna M M Howson  46   47 , Marguerite R Irvin  54 , Hieab Adams  153   154 , Sylvia Wassertheil-Smoller  155 , Kaare Christensen  39   40   156 , Mohammad A Ikram  30 , Tatjana Rundek  125   126 , Bradford B Worrall  157   158 , G Mark Lathrop  159 , Moeen Riaz  95 , Eleanor M Simonsick  160 , Janika Kõrv  142 , Paulo H C França  74 , Ramin Zand  161   162 , Kameshwar Prasad  24 , Ruth Frikke-Schmidt  34   111 , Frank-Erik de Leeuw  88 , Thomas Liman  71   75   163 , Karl Georg Haeusler  108 , Ynte M Ruigrok  55 , Peter Ulrich Heuschmann  107   164   165 , W T Longstreth  122   166 , Keum Ji Jung  18   167 , Lisa Bastarache  48 , Guillaume Paré  25   26   168   169 , Scott M Damrauer  170   171 , Daniel I Chasman  61   62 , Jerome I Rotter  172 , Christopher D Anderson  12   13   124   173 , John-Anker Zwart  19   20   59 , Teemu J Niiranen  16   17   174 , Myriam Fornage  175   176 , Yung-Po Liaw  15   177 , Sudha Seshadri  130   131   178 , Israel Fernández-Cadenas  37 , Robin G Walters  18   63 , Christian T Ruff  8   9 , Mayowa O Owolabi  28   179 , Jennifer E Huffman  7 , Lili Milani  4 , Yoichiro Kamatani  11 , Martin Dichgans  180   181   182 , Stephanie Debette  183   184
Collaborators, Affiliations
Meta-Analysis

Stroke genetics informs drug discovery and risk prediction across ancestries

Aniket Mishra et al. Nature. 2022 Nov.

Erratum in

  • Publisher Correction: Stroke genetics informs drug discovery and risk prediction across ancestries.
    Mishra A, Malik R, Hachiya T, Jürgenson T, Namba S, Posner DC, Kamanu FK, Koido M, Le Grand Q, Shi M, He Y, Georgakis MK, Caro I, Krebs K, Liaw YC, Vaura FC, Lin K, Winsvold BS, Srinivasasainagendra V, Parodi L, Bae HJ, Chauhan G, Chong MR, Tomppo L, Akinyemi R, Roshchupkin GV, Habib N, Jee YH, Thomassen JQ, Abedi V, Cárcel-Márquez J, Nygaard M, Leonard HL, Yang C, Yonova-Doing E, Knol MJ, Lewis AJ, Judy RL, Ago T, Amouyel P, Armstrong ND, Bakker MK, Bartz TM, Bennett DA, Bis JC, Bordes C, Børte S, Cain A, Ridker PM, Cho K, Chen Z, Cruchaga C, Cole JW, de Jager PL, de Cid R, Endres M, Ferreira LE, Geerlings MI, Gasca NC, Gudnason V, Hata J, He J, Heath AK, Ho YL, Havulinna AS, Hopewell JC, Hyacinth HI, Inouye M, Jacob MA, Jeon CE, Jern C, Kamouchi M, Keene KL, Kitazono T, Kittner SJ, Konuma T, Kumar A, Lacaze P, Launer LJ, Lee KJ, Lepik K, Li J, Li L, Manichaikul A, Markus HS, Marston NA, Meitinger T, Mitchell BD, Montellano FA, Morisaki T, Mosley TH, Nalls MA, Nordestgaard BG, O'Donnell MJ, Okada Y, Onland-Moret NC, Ovbiagele B, Peters A, Psaty BM, Rich SS, Rosand J, Sabatine MS, Sacco RL, Saleheen D, Sandset EC, Salomaa V, Sargurupremraj M, Sasaki M, Satizabal CL, Schmidt CO, Sh… See abstract for full author list ➔ Mishra A, et al. Nature. 2022 Dec;612(7938):E7. doi: 10.1038/s41586-022-05492-5. Nature. 2022. PMID: 36376532 Free PMC article. No abstract available.

Abstract

Previous genome-wide association studies (GWASs) of stroke - the second leading cause of death worldwide - were conducted predominantly in populations of European ancestry1,2. Here, in cross-ancestry GWAS meta-analyses of 110,182 patients who have had a stroke (five ancestries, 33% non-European) and 1,503,898 control individuals, we identify association signals for stroke and its subtypes at 89 (61 new) independent loci: 60 in primary inverse-variance-weighted analyses and 29 in secondary meta-regression and multitrait analyses. On the basis of internal cross-ancestry validation and an independent follow-up in 89,084 additional cases of stroke (30% non-European) and 1,013,843 control individuals, 87% of the primary stroke risk loci and 60% of the secondary stroke risk loci were replicated (P < 0.05). Effect sizes were highly correlated across ancestries. Cross-ancestry fine-mapping, in silico mutagenesis analysis3, and transcriptome-wide and proteome-wide association analyses revealed putative causal genes (such as SH3PXD2A and FURIN) and variants (such as at GRK5 and NOS3). Using a three-pronged approach4, we provide genetic evidence for putative drug effects, highlighting F11, KLKB1, PROC, GP1BA, LAMC2 and VCAM1 as possible targets, with drugs already under investigation for stroke for F11 and PROC. A polygenic score integrating cross-ancestry and ancestry-specific stroke GWASs with vascular-risk factor GWASs (integrative polygenic scores) strongly predicted ischaemic stroke in populations of European, East Asian and African ancestry5. Stroke genetic risk scores were predictive of ischaemic stroke independent of clinical risk factors in 52,600 clinical-trial participants with cardiometabolic disease. Our results provide insights to inform biology, reveal potential drug targets and derive genetic risk prediction tools across ancestries.

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Conflict of interest statement

C.D.A. has received sponsored research support from Bayer, and has consulted for ApoPharma; T. Konuma is an employee of JAPAN TOBACCO; M. E. reports grants from Bayer and fees paid to the Charité from Abbot, AstraZeneca, Bayer, Boehringer Ingelheim, BMS, Daiichi Sankyo, Amgen, GSK, Sanofi, Covidien, Novartis and Pfizer, all outside the submitted work; B.M.P. serves on the steering committee of the Yale Open Data Access Project funded by Johnson & Johnson; P.A. works with Fondation Alzheimer (nonprofit foundation) and Genoscreen (biotech company); H.L.L.’s participation in this project was part of a competitive contract awarded to Data Tecnica International by the National Institutes of Health to support open science research; M.A.N.’s participation in this project was part of a competitive contract awarded to Data Tecnica International by the National Institutes of Health to support open science research, he also currently serves on the scientific advisory board for Clover Therapeutics and is an advisor to Neuron23; N.A.M. declares institutional research grants to the TIMI Study Group at Brigham and Women’s Hospital from Amgen, Pfizer, Ionis, Novartis, AstraZeneca and NIH. The TIMI Study Group has received institutional research grant support through Brigham and Women’s Hospital from Abbott, Amgen, Anthos Therapeutics, ARCA Biopharma, AstraZeneca, Daiichi-Sankyo, Eisai, Intarcia, Ionis Pharmaceuticals, MedImmune, Merck, Novartis, Pfizer, Regeneron Pharmaceuticals, Roche, The Medicines Company, Zora Biosciences, Janssen Research and Development, Siemens Healthcare Diagnostics and Softcell Medical; F.K.K. declares that the TIMI Study Group has received institutional research grant support through Brigham and Women’s Hospital from Abbott, Amgen, Anthos Therapeutics, ARCA Biopharma, AstraZeneca, Daiichi-Sankyo, Eisai, Intarcia, Ionis Pharmaceuticals, MedImmune, Merck, Novartis, Pfizer, Regeneron Pharmaceuticals, Roche, The Medicines Company and Zora Biosciences; M.S.S. has consultancies with Althera, Amgen, Anthos Therapeutics, AstraZeneca, Beren Therapeutics, Bristol-Myers Squibb, DalCor, Dr. Reddy’s Laboratories, Fibrogen, IFM Therapeutics, Intarcia, Merck, Moderna, Novo Nordisk and Silence Therapeutics, and research grant support through Brigham and Women’s Hospital from Abbott, Amgen, Anthos Therapeutics, AstraZeneca, Bayer, Daiichi-Sankyo, Eisai, Intarcia, Ionis, Medicines Company, MedImmune, Merck, Novartis, Pfizer, Quark Pharmaceuticals; C.T.R. has consultancies with Anthos, Bayer, Bristol Myers Squibb, Boehringer Ingelheim, Daiichi Sankyo, Janssen and Pfizer, institutional research grant to the TIMI Study Group at Brigham and Women’s Hospital from Anthos, AstraZeneca, Boehringer Ingelheim, Daiichi Sankyo, Janssen, National Institutes of Health and Novartis, and consultancies with Anthos, Bayer, Bristol Myers Squibb, Boehringer Ingelheim, Daiichi Sankyo, Janssen and Pfizer. T.H. receives personal fees from Genome Analytics Japan; J.C.H. is supported by a personal fellowship from the British Heart Foundation (FS/14/55/30806), and acknowledges additional support from the Nuffield Department of Population Health (NDPH), University of Oxford, the British Heart Foundation Centre for Research Excellence, Oxford, and the Oxford Biomedical Research Centre. J.C.H. holds steering committee and Data and Safety Monitoring Board (DSMB) positions for various cardiovascular randomized controlled trials, and is a principal investigator/co-principal investigator of research grants from industry related to cardiovascular clinical trials and observational studies that are governed by University of Oxford contracts that protect personal independence. NDP.H also has a staff policy of not taking personal payments from industry (further details can be found online; https://www.ndph.ox.ac.uk/files/about/ndph-independence-of-research-policy-jun-20.pdf/@@download); S.S. has consultancies with Biogen; P.U.H. reports grants from German Ministry of Research and Education, during the conduct of the study, research grants from the German Ministry of Research and Education, European Union, Charité–Universitätsmedizin Berlin, Berlin Chamber of Physicians, German Parkinson Society, University Hospital Würzburg, Robert Koch Institute, German Heart Foundation, Federal Joint Committee (G-BA) within the Innovationfond, German Research Foundation, Bavarian State (ministry for science and the arts), German Cancer Aid, Charité–Universitätsmedizin Berlin (within Mondafis; supported by an unrestricted research grant to the Charité from Bayer), University Göttingen (within FIND-AF randomized; supported by an unrestricted research grant to the University Göttingen from Boehringer- Ingelheim), University Hospital Heidelberg (within RASUNOA-prime; supported by an unrestricted research grant to the University Hospital Heidelberg from Bayer, BMS, Boehringer-Ingelheim and Daiichi Sankyo), outside the submitted work; K.G.H. reports a study grant by Bayer, lecture fees/advisory board fees from Abbott, Alexion, AMARIN, AstraZeneca, Bayer, Biotronik, Boehringer Ingelheim, Bristol-Myers-Squibb, Daiichi Sankyo, Edwards Lifesciences, Medtronic, Pfizer, Premier Research, SUN Pharma and W. L. Gore & Associates; H.J.G. has received travel grants and speakers honoraria from Fresenius Medical Care, Neuraxpharm, Servier and Janssen Cilag as well as research funding from Fresenius Medical Care; J.M.M.H. is full time employee of Novo Nordisk; E.Y.-D. is full-time employee of Novo Nordisk. S. Damrauer receives research support from RenalytixAI and personal consulting fees from Calico Labs, outside the scope of the current research. H.B. reports grants from AstraZeneca, AstraZeneca Korea, Bayer Korea, Boehringer Ingelheim Korea, Boryung Pharmaceutical, Bristol Myers Squibb, Bristol Myers Squibb Korea, Chong Gun Dang Pharmaceutical, Daiichi Sankyo, Daiichi Sankyo Korea, Dong-A ST, Esai, Jeil Pharmaceutical, JLK, Korean Drug, SAMJIN Pharm., Servier Korea, Shinpoong Pharm., Shire International and Yuhan Corporation, and personal fees from Amgen Korea, Esai Korea, Otsuka Korea, Takeda Korea and Viatris Korea outside the submitted work. C.C. has received research support from GSK. The funders of the study had no role in the collection, analysis or interpretation of data, in the writing of the report or in the decision to submit the paper for publication. C.C. is a member of the advisory board of Vivid genetics; F.A.M. is supported by the German Research Foundation (Deutsche Forschungsgemeinschaft (DFG) within the UNION-CVD Clinician-Scientist Programme (project number 413657723) and has been previously supported by a MD/PhD Fellowship of the Interdisciplinary Center for Clinical Research, University Hospital Würzburg. A. Mishra, R.M., T.J., S.N., D.C.P., M. Koido, Q.L.G., M. Shi, Y.H., M.K.G., I.C., K.K., Yi-Ching Liaw, F.C.V., K. Lin., B.S.W., V. Srinivasasainagendra, L.P., G.C., M.R.C., L.T., R.A., G.V.R., N.H., Y.H.J., J.Q.T., V.A., J.C., M.N., C.Y., E.Y., M.J.K., A.J.L., R.L., T.A., N.D.A., M.K.B., T.M.B., D.A.B., J.C.B., C.B., S.B., A.C., P.M.R., K. Cho, Z.C., J.W.C., P.L.d., R.d.C., M.E., L.E.F., M.I.G., N.C.G., V.G., J. Hata, J. He., A.K.H., Y. Ho., A.S.H., H.I.H., M.I., M.A.J., C.E.J., C.J., M. Kamouchi, K.L.K., T. Kitazono, S.J.K., A.K., P.L., L.J.L., K. Lee, K. Lepik, J.L., L.L., A. Manichaikul, H.S.M., T. Meitinger, B.D.M., T. Morisaki, T.H.M., B.G.N., M.J.O., Y.O., N.C.O., B.O., A.P., S.S.R., J.R., M.S.S., R.L.S., D. Saleheen, E.C.S., V. Salomaa, M. Sargurupremraj, M. Sasaki, C.L.S., C.O.S., A.S., N.L.S., K.S., Y.S., Y.V.S., K.T., S. Tiedt, T.T., N.P.T., H.K.T., D.T., S. Trompet, A.M.T., A.T., M.v.V., R.V., S.S.V., K.L.W., P.W., D.W., P.W.W., H.X., Q.Y., K.Y., I.Y.M., C.G., T.N., J.W.J., I.L.R., D. Strbian, Y.J.K., P.C., E.M., M.R.I., H.A., S.W., K. Christensen, M.A.I., T.R., B.B.W., G.M.L., M.R., E.M.S., J.K., P.H.F., R.Z., K.P., R.F., F.d.L., T.L., Y.M.R., W.T.L., K.J.J., L.B., G.P., D.I.C., J.I.R., J.Z., T.J.N., M.F., Yung-Po Liaw, I.F., R.G.W., M.O.O., J.E.H., L.M., Y.K., M.D. and S. Debette declare no competing interests.

Figures

Fig. 1
Fig. 1. Identifying genetic variants that influence stroke risk.
Ideogram showing 89 genome-wide significant stroke-risk loci. The shapes correspond to ancestry: circles, cross-ancestry (CROSS-ANC); diamonds, Europeans (EUR); triangles, East Asians (EAS); squares, African Americans (AFR) or South Asians (SAS). Colours correspond to stroke types: green, AS; red, AIS; light blue, SVS; dark blue, CES; purple, LAS. The nearest genes to lead variants are displayed. Loci are characterized as follows, on the basis of replication results (Methods): bold with asterisk, high confidence; bold without asterisk, intermediate confidence; not bold, low confidence; underlined, loci identified in secondary MR-MEGA and MTAG analyses. Black and grey font indicate new and known loci, respectively. The numbers at the top indicate the chromosome.
Fig. 2
Fig. 2. Effect-size comparison across ancestry groups of lead variants identified in stroke GWASs and cross-ancestry fine-mapping.
a, Plots showing the Pearson’s correlation coefficient (r) between the effect sizes (β) of the 60 stroke-risk alleles on AS significant after multiple-testing correction (P < 0.017) in Europeans and East Asians (left; r (95% CI) = 0.66 (0.47–0.79), P = 1 × 10−7); Europeans and African Americans (middle; r (95% CI) = 0.55 (0.33–0.71), P = 2 × 10−5); and East Asians and African Americans (right; r (95% CI) = 0.74 (0.58–0.85), P = 8 × 10−10). n = 60 independent stroke-risk variants  from the IVW meta-analyses were used to compute Pearson’s correlation coefficients (r) of the effect sizes between ancestries. The nearest gene is reported for SNPs showing a difference in effect size (β, absolute value) of >0.05 between a pair of ancestries. The dots represent the effect-size (β) estimates and the bars represent the 95% CI of the estimates. Two-sided P values of the deviation of Pearson’s correlation coefficient from zero are reported. Colour corresponds to genome-wide significant association (P < 5 × 10−8) in individual ancestries: purple, European only (±cross-ancestry); green, East Asian only (±cross-ancestry); yellow, African American only (±cross-ancestry); blue, both ancestries (±cross-ancestry); red, cross-ancestry only; grey, not genome-wide significant in two plotted ancestries and in cross-ancestry. b, Locus plots of variants at SH3PXD2A in five ancestries. Fine-mapped variants are shown only in European and East Asian individuals (insufficient power for other ancestries). Variants are coloured on the basis of their linkage disequilibrium with the cross-ancestry lead variant (rs4918058), shown by the purple diamonds. In the fine-mapping plots, variants in the SuSiE 95% credible sets (CS) are shown. Shared variants between credible sets of European and East Asian participants are indicated by black circles. The red vertical lines represent the position of the lead variants in European (rs55983834) and East Asian (rs4918058) participants. The grey dashed horizontal lines represent P = 5 × 10−8. The linkage disequilibrium of each ancestry was derived from the 1000 Genomes Project.
Fig. 3
Fig. 3. Genomics-driven drug discovery.
Overlap enrichment analysis using GREP (top). Middle, integrating MR results using cis- and trans-pQTLs as instrumental variables with data from drug databases. Bottom, negative correlation tests between compound-regulated gene expression profiles and genetically determined case–control gene expression profiles using Trans-Phar.
Fig. 4
Fig. 4. Risk prediction in a population and trial setting.
a–d, The association of iPGS with ischaemic stroke (AIS) in European (Estonian Biobank) (a), East Asian (BioBank Japan) (b), African American (Million Veteran Program) (c) and European participants in clinical trials (d). Compared with the middle decile (45–55%) of the population as a reference group, the risk of high-iPGS groups with varying percentile thresholds was estimated using a Cox proportional hazards model for European and African American individuals and logistic regression models for East Asian individuals with adjustments for age, sex and the top five genetic principal components. e, Kaplan–Meier event rates for ischaemic stroke in European participants in five clinical trials (Methods) by tertile of GRS at 3 years (the GRS uses effect estimates of the cross-ancestry AS GWAS as weights) showing higher GRS increases risk of ischaemic stroke (Ptrend = 1.4 × 10−4). The two-sided Ptrendvalue was computed using Cox regression. Int., intermediate.
Extended Data Fig. 1
Extended Data Fig. 1. GIGASTROKE study workflow.
Study workflow and rationale. EUR: European; EAS: East-Asian; AFR: African; HIS: Hispanic; SAS: South Asian; AS: any stroke; AIS: any ischaemic stroke; LAS: large artery stroke; CES: cardioembolic stroke; SVS: small vessel stroke; GWAS: genome-wide association study; IVW: inverse-variance weighted; MR-MEGA: meta-regression of multi-ethnic genetic association; COJO:conditional and joint analysis; VEGAS2:versatile gene-based association study 2; MTAG: multi-trait analysis of GWAS; TWAS: Transcriptome-wide association study; coloc: Colocalisation Test; PWAS: Proteome-wide association studies; pQTL-MR: protein quantitative trait loci Mendelian Randomization; SuSiE: sum of single effects model; MENTR: Mutation Effect prediction on Non-coding RNA TRanscription; PIP: posterior probability; FDR: false discovery rate; LDSC-COV: covariate-adjusted LD score regression; MR-Egger: Mendelian randomization-Egger; GREP: genome for REPositioning drugs; ATC: Anatomical Therapeutic Chemical; P+T: pruning and thresholding; PRScs: polygenic risk score under continuous shrinkage; BBJ: Biobank Japan; TIMI: thrombolysis in myocardial infarction; MVP: Million Veteran Program; SIREN: Stroke Investigative Research and Educational Network.
Extended Data Fig. 2
Extended Data Fig. 2. Graphical representation of replication results.
Shown are effect sizes and 95% confidence intervals for the 48 replicated IVW loci for (A) cross-ancestry discovery and replication association results for any stroke and (B) cross-ancestry discovery and replication association results for any ischaemic stroke. OR = odds ratio; 95% CI = 95% confidence interval.
Extended Data Fig. 3
Extended Data Fig. 3. Increase in power with increase in population diversity.
The scatter plot shows the number of loci identified with incremental increase in sample size and population diversity. The diagonal line reflects the increase in number of genome-wide significant loci with increasing sample size of European ancestry only. The increase in number of loci departs from this line when adding non-European ancestry samples; EUR: European; EAS: East Asian; AFR: African; HIS: Hispanic; SAS: South Asian; BBJ: Biobank Japan; CKB: China Kadoorie Biobank. Strat0_EUR: N = 22,634 stroke cases (European population-based longitudinal cohorts); Strat1_Strat0&EUR: N = 26,253 stroke cases, adding Spanish and Estonian samples; Strat2_Strat1&EUR: N = 32,980 stroke cases, adding German and Dutch samples; Strat3_Strat2&EUR: N = 42,783, adding large European biobanks; Strat4_Strat3&EUR: N = 73,652 stroke cases, adding MEGASTROKE European; Strat5_Strat4&EAS: N = 91,303 stroke cases, adding Japanese BBJ; Strat6_Strat5&EAS: N = 99,661 stroke cases, adding Chinese CKB; Strat7_Strat6&EAS: N = 101,065 stroke cases, adding other East Asian samples; Strat8_Strat7&AFR: N = 105,026 stroke cases, adding African ancestry samples; Strat9_Strat8&HIS: N = 106,542 stroke cases, adding South American ancestry samples; Strat10_Strat9&SAS, N = 110,182 stroke cases, adding South Asian ancestry samples.
Extended Data Fig. 4
Extended Data Fig. 4. Association of stroke risk variants with vascular risk traits.
We report only associations for which the stroke lead variant of a proxy in very high LD (r2 > 0.9) showed genome-wide significant association with the vascular risk trait in a prior GWAS. Colours represent the Z-scores of association of stroke risk increasing alleles with the trait.
Extended Data Fig. 5
Extended Data Fig. 5. Genetic correlations and Mendelian randomization causal estimates of 12 vascular risk factors and disease traits with stroke (any and stroke subtypes), in European ancestry participants.
Larger squares correspond to more significant P-values, with genetic correlations or Mendelian randomization (MR) causal estimates (expressed in Z-scores) significantly different from zero at a P < 0.05 shown as a full-sized square. Genetic correlations or causal estimates that are significant after multiple testing correction (P < 4.17 × 10−3) are marked with an asterisk. Two-sided P-values were calculated using LD score regression (LDSC) for genetic correlations and inverse variance weighted analysis for MR.
Extended Data Fig. 6
Extended Data Fig. 6. Genetic correlations and Mendelian randomization causal estimates of 10 vascular risk factors and disease traits with stroke (any and stroke subtypes), in East Asian ancestry participants.
Larger squares correspond to more significant P-values, with genetic correlations or Mendelian randomization (MR) causal estimates significantly different from zero at a P < 0.05 shown as a full-sized square. Genetic correlations or causal estimates (expressed in Z-scores) that are significant after multiple testing correction (P < 5 × 10−3) are marked with an asterisk. Two-sided P-values were calculated using LD score regression (LDSC) for genetic correlations and inverse variance weighted analysis for MR. CPD: cigarettes per day.
Extended Data Fig. 7
Extended Data Fig. 7. Transcriptome-wide association study of stroke in multiple tissues.
Heatmap of the transcriptome-wide association studies (TWAS) of stroke (any stroke and stroke subtypes) showing transcriptome-wide significant associations with supporting evidence from colocalization; Coloured squares are TWAS significant associations based on two-sided p-values after multiple testing correction (p < 2.0 × 10−6); * Conditionally significant (p < 0.05) and COLOC PP4 ≥ 0.75; Genes are presented on the x-axis, those underlined in blue are in a stroke GWAS locus, those underlined in purple are not within a genome-wide significant stroke risk locus (Methods); Tissue types are on the y-axis (blue: cross-tissue weights; pink: arterial; orange: heart; green: brain).
Extended Data Fig. 8
Extended Data Fig. 8. Single-nucleus gene expression/enrichment analysis and proteome-wide association study (PWAS) of stroke in brain tissue.
(A) Single-nucleus gene expression data of TWAS-COLOC genes in dorsolateral prefrontal cortex (ROS-MAP study); Dot plot of the mean expression level in expressing cells (colour) and percent of expressing cells (circle size) of selected genes across different cell types; (B) Proteome-wide association study (PWAS) of stroke in brain tissue; Box plot showing effect estimates (odds ratio) for associations of pQTL of ICA1L in the ROS-MAP (N = 376 independent samples) and Banner (N = 152 independent samples) studies with any stroke (AS) and any ischaemic stroke (AIS), identified in PWAS after multiple testing correction. Odds ratios ± 95% CIs are shown. Dashed line indicates an odds ratio of 1. Two-sided p-values were computed using the TWAS-COLOC approach. (C) Cell-type enrichment in human and mouse single cell RNA-seq databases using STEAP; the UpSet plot displays the number of significant enrichment results, by stroke subtype (horizontally; 2 for CES, 5 for AIS, 6 for AS, and 12 for LAS) and by cell subtype (vertically; 2 cell-types show significant enrichment in LAS, AIS, and AS, 2 cell-types in AIS and AS, and 1 cell-type in LAS and AS, while 9 cell-types show significant enrichment in LAS only, 2 in CES only and 1 in AS and AIS respectively); details are displayed in Supplementary Table 29. AS: any stroke; AIS: any ischaemic stroke; LAS: large artery stroke; CES: cardioembolic stroke; VLMC: vascular and leptomeningeal cells, OPC: oligodendrocyte progenitor cells, SMC: smooth muscle cells; VSMCA: vascular smooth muscle cells, arterial.
Extended Data Fig. 9
Extended Data Fig. 9. Drug target pQTL PheWAS.
PheWAS in Estonian biobank for rs2289252, a cis-pQTL of F11. Each triangle in the plot represents one Phecode and the direction of the triangle represents direction of effect. Two-sided P-values were calculated using logistic regression to test association between the pQTL and Phecodes (p = 3.45 × 10−6 for phenome-wide significance).
Extended Data Fig. 10
Extended Data Fig. 10. Derivation and evaluation of integrative polygenic score models for Europeans and East Asians.
(A) With summary statistics of 22 GWAS (10 GIGASTROKE and 12 on vascular risk factors) and linkage disequilibrium reference data of 1000 Genomes Europeans (n = 503) and East Asians (n = 504), we computed 37 candidate PGS models using P+T, LDpred, and PRScs algorithms. For each GWAS, the best PGS model was selected based on the maximal area under the curve (AUC) values in the training dataset of Europeans (any ischaemic stroke [AIS] case-control data, Ncases/Ncontrols = 1,003/8,997) and East Asians (AIS case-control data, Ncases/Ncontrols = 577/9,232). Out of 22 selected PGS models derived from the 22 GWAS, 11 and 7 were significantly associated with AIS in the European and East Asian training dataset respectively (Bonferroni-corrected P < 0.05). (B) The significant PGS models were used as the variables for elastic-net logistic regression and the weights for the variables were trained using the model training dataset. The European iPGS model consisting of 1,213,574 variants and an East-Asian iPGS model consisting of 6,010,730 variants were constructed by combining the 11 and 7 significant PGS models using the elastic-net derived weights respectively. The European and East Asian iPGS models were evaluated in the European (a European prospective cohort data with 102,099 participants including 1,128 incident IS cases) and East-Asian (AIS case-control data, Ncases/Ncontrol = 1,470/40,459) model evaluation dataset (Methods); AS indicates any stroke; AIS, any ischaemic stroke; LAS, large artery stroke; SVS, small vessel stroke; CES, cardioembolic stroke; AF, atrial fibrillation; CAD, coronary artery disease; T2D, type 2 diabetes; SBP, systolic blood pressure; DBP, diastolic blood pressure; TC, total cholesterol; LDL-C, low-density lipoprotein cholesterol; HDL-C, high-density lipoprotein cholesterol; TG, triglyceride; BMI, body mass index; AUC indicates area under the curve; EUR, European; EAS, East Asian; GWAS, genome-wide association study; LD, linkage disequilibrium; PGS, polygenic score.
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