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

A Publisher Correction to this article was published on 03 June 2019

This article has been updated

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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Fig. 1: MEGASTROKE study design.
Fig. 2: Association results of the transancestral GWAS meta-analysis and the prespecified ancestry-specific meta-analysis in European samples.
Fig. 3: Genetic overlap between stroke and related vascular traits at the 32 genome-wide-significant loci for stroke.
Fig. 4: Shared genetic contribution between stroke and related vascular traits.
Fig. 5: Connection between stroke risk genes and approved drugs for antithrombotic therapy.

Change history

  • 03 June 2019

    An amendment to this paper has been published and can be accessed via a link at the top of the paper.

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Writing and editing the manuscript: R.M., G.C., M.T., M.S., Y.O., S.D., and M.D. Study design/conception: R.M., M.D., S.D., B.M.P., G.J.F., J.W.J., J.I.R., J.G.W., M.F., H.I.Y., C.J., S. Seshadri, W.T.L., B.B.W., B.D.M., S.J.K., H.S.M., J.D., J.R., K.S., and O.M. Statistical analysis: A.-K.G., G.J.F., M.F., C.D.L., Y.O., E.L., B.R.S., R.M., M.S., M.T., A. Mishra, E.G.H., C.D.A., T.M.B., C. Carrera, I.C., W.-Y.L., S.L.P., K. Rannikmäe, K. Rice, S. Tiedt, J.C.C., A.D.J., P.I.W.d.B., S.W.v.d.L., P. Almgren, S. Gretarsdottir, and F.T. Sample/phenotype contribution: M.D., S.D., C.D.A., C. Cruchaga, I.C., H.I.H., J.W.J., N.S.R., A.S.B., A.C., A.S., A.S.H., A.P.R., A.L.D., A. Rolfs, A. Ruusalepp, A.G.L., A. Manichaikul, B.M.K., C.L.C., C.R., C.K., C. Tanislav, C. Tzourio, C.M.v.D., D.I.C., D.W., D.A.T., D.O.K., D.K.S., D.L., E.S.T., E.E.S., E.I., F.-C.H., G.P., H.A., H.H.H.A., H.S.M., I.E.C., J. Haessler, J. He, J. Hata, J.F.M., J.S.K., J.-M.L., J.D., J.W.C., J.R., J.J-C., J.A.J., K.S., K.M.R., K.L.K., K.L.W., L.J.L., L.A.L., M.A.N., M.A.I., M.d.H., M.R.I., M.J.O., M. Kanai, M. Kubo, M.W., M.M.S., N.J.W., N.K., O.R.B., P.F.M., P.T.E., P.K.M., P.E., P. Amouyel, P.v.d.H., Q.D., Q.Y., R.P.G., R.L.S., R.F.G., R.S., S.Y., S.K., S.T.E., S.B., S.A.L., S.J.K., S.R.H., S.W.-S., T.B.H., T.R., T.H.M., T.P., T.T., U.S., U.T., V.C., V.G., W.-M.C., V.N.S.T., X.J., B.M.P., J.I.R., J.G.W., O.M., C.J., J.C.H., S. Seshadri, T.A., G.B.B., R.D.B., A.H., N.L.S., R.L., C.M.L., T.N., P. M. Ridker, P. M. Rothwell, V.S., C.O.S., P.S., C.L.M.S., K.D.T., M. Civelek, D. Saleheen, D. Strbian, S. Sakaue, S. Gustafsson, S. Tiedt, S. Trompet, and I.F.-C.. Critical revision of article: R.M., M.D., S.D., B.M.P., C.J., J.I.R., O.M., S. Seshadri, G.J.F., J.W.J., W.T.L., C.D.A., D. Strbian, E.G.H., I.F.-C., S. Tiedt, C.L.M.S., C.O.S., C. Cruchaga, G.B.B., I.C., J.C.B., J. Hata, K. Rice, S.L.P., N.S.R., S.S.R., T.A., T.N., J.M.M.H., T.M.B., and V.S. Supervision: M.D., S.D., C.D.A., J.M.M.H, J.I.R., S. Seshadri, C.M.L., C.L.M.S., J.W.J., V.S., and J.C.B. GWAS analyses: R.M., G.C., M.T., S. Gretarsdottir, G.T., J. Hata, A.K.G., M. Chong, J.L.M.B., C. Carrera, A.H., G.J.F., and Y.K. Functional annotation: M.S., A. Mishra, R.M., G.C., M.T., L.R.-J., and A.K.G. Gene-based analysis: A. Mishra. Pathway analyses: A. Mishra, R.M., M. Chong, and K. Rice. Drug-target analysis: Y.O. Scoring method: M.S., R.M., S.D., and M.D. wGRS analysis: M.S. and R.M. LD-score regression analysis: R.M., M.S., and Y.K. Credible-SNP-set analysis: R.M., G.C., and M.S. Data for GWAS analysis, cross-phenotype analysis or QTL analysis: AFGen Consortium, Cohorts for Heart and AgingResearch in Genomic Epidemiology (CHARGE) Consortium, iGEN-BP Consortium, INVENT Consortium, STARNET, and Biobank Japan Cooperative Hospital Group. Consortia providing stroke data: COMPASS Consortium, EPIC-CVD Consortium, EPIC-InterAct Consortium, ISGC, METASTROKE Consortium, Neurology Working Group of the CHARGE Consortium, NINDS-SiGN, UK Young Lacunar DNA Study, and MEGASTROKE Consortium. The views expressed in this manuscript are those of the authors and do not necessarily represent the views of the National Heart, Lung, and Blood Institute or the National Institute of Neurological Disorders and Stroke. 

Corresponding authors

Correspondence to Stephanie Debette or Martin Dichgans.

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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.

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Supplementary information

Supplementary Text and Figures

Supplementary Figures 1–13, Supplementary Tables 1, 3–5, 8–10, 12, 14, 16 and 26, and Supplementary Note

Life Sciences Reporting Summary

Supplementary Table 2: Sample overview and genetic information of all studies

Given for each sample are the age distribution, gender distribution and risk factors distribution, if available. Further information on genotyping platform, technique, imputation parameters and QC parameters are given, if available.

Supplementary Table 6: Variance explained by the 32 lead SNPs. Shown are the lead SNPs of the 32 risk loci for stroke and the phenotypic variance explained as estimated by the method of So et al.

Variances are given for the Europeans-only and the East Asianonly meta-analysis. If a SNP was not available in the analysis, variance explained was set to zero.

Supplementary Table 7: Results from the Gene-based tests using VEGAS2

Data were analyzed for each ethnicity and a meta-analysis was calculated using Stouffer’s Z. Genome-wide results are displayed in bold (P < 2.02 x 10-6 for Bonferroni correction for the number of genes).

Supplementary Table 11: Results of the conditional analysis (GCTA-COJO) in the European sample

Shown are the 2-SNP or 3-SNP solutions for each lead SNP after conditioning on the lead SNP in Europeans. P-values of SNP2 and SNP3 were considered significant at P < 5 x10-8. SVS is omitted because there were no genome-wide significant signals to investigate.

Supplementary Table 13: Results from look-ups of the 32 genome-wide significant loci for stroke in published GWAS data from related phenotypes

Column D specifies the index SNPs of the non-stroke phenotype or SNPs in high LD with the index SNP (r 2 > 0.9) with the lowest Pvalue in the respective non-stroke phenotype. Index SNPs or proxy SNPs reaching P < 1.30 x 10-4 (0.05/32 loci/12 related vascular traits) in the respective related phenotype are shown. Index SNPs and proxy SNPs reaching genome-wide significance are marked by an asterisk in column G. Column F specifies the r 2 between the index SNP and the lead SNP in stroke.

Supplementary Table 15: MR-Egger regression and comparison with Inverse-Variance Weighted (IVW) estimates, for vascular wGRS showing a significant association with stroke risk

IVW estimates are derived from a fixed effects analysis using the GTX software (Online Methods); for the intercept of the MR-Egger analysis (Egger_intercept, Online Methods) we used a significance threshold of P < 0.05. Effect estimates are given per unit increase in the wGRS. CI: confidence interval; OR: odds ratio *The MR-Egger intercept estimate was nominally significant (P = 0.015) only for the association between the SBP wGRS and AS, and this was no longer the case after removing 6 of 37 SNPs that appeared as outliers on the leave-one-out plot (Online Methods), leading to causal estimates in broad agreement across regression techniques, with larger standard errors using the MR-Egger method as is typically the case (www.biorxiv.org/content/biorxiv/early/2017/07/05/159442.full.pdf and PMID: 26050253, 28527048). The causal estimates obtained by the weighted median approach (PMID: 27061298) are also in broad agreement with those from the IVW and the MR-Egger (beta ± s.e.: 0.032 ± 0.005, OR (95%CI): 1.03 (1.02-1.04), P = 9.48x10- 10).

Supplementary Table 17: Results of the epigwas analysis

Shown is the enrichment P-value of GWAS results in specific tissues. We used epigwas to calculate enrichment P-values for H3K4me1 (enhancers), H3K4me3 (promoters) and H3K9ac (active promoters).

Supplementary Table 18: Results of DEPICT pathway analysis

For each stroke subtype, SNPs with BF > 5 from the trans-ethnic meta-analysis were analyzed. Gene sets with a FDR < 0.05 were considered significant. Columns E-N show the Z-scores of the genes in the gene set.

Supplementary Table 19: Results from the Ingenuity Pathway Analysis

Shown are enrichment P-values for the corresponding Ingenuity canonical pathway and the proteins involved in the respective pathway. P-values are derived from Fisher’s exact test. FDR < 0.05 was considered significant and are displayed in bold. For The IPA Diseases and Bio Functions and for the IPA Tox Functions, Pvalues are given for the enrichment of specific function annotations.

Supplementary Table 20: Results from the VEGAS2 pathway analysis

Shown are pathways for each stroke subtype, the ethnicity-specific P-values and the meta-analysis P-value. Pathways with FDR < 0.05 were considered significant and are displayed in bold (CES only).

Supplementary Table 21: Results of the 95% credible set analysis

Results were obtained separately in European, East Asian, and African American ancestry samples. Shown is the number of SNPs in the 95% credible set (numerator) and the total number of SNPs in the analysis (denominator, r 2 > 0.1).

Supplementary Table 22: Detailed functional and biological information on SNPs at the 32 stroke risk loci

Shown are the lead SNPs and all proxy SNPs with r 2 > 0.8. We show information on nearby genes, the genomic consequence (intergenic, intronic, missense, regulatory), chromatin marks, eQTLs (GRASP_v2, GTEX_v6, BIOS, BLUEPRINT, STARNET, UCLA and HGVD), meQTLs (BLUEPRINT and ARIC) and pQTLs (KORA). We also give information whether this specific SNP is included in the 95% credible set analysis and the P-value of the Riviera-beta-analysis.

Supplementary Table 23: Relation of the lead and proxy SNPs (r 2 > 0.8) from 32 stroke risk loci with the best cis eQTL, meQTL and pQTL from various human bio-resources, grouped per tissue or cell type

Shown is the stroke subtype showing the most significant association; for meQTLs, CpG probe numbers are indicated in brackets after the gene name.

Supplementary Table 24: Biological candidate gene prioritization of 149 genes located in the 32 stroke associated risk loci

For each gene, we first list the biological score derived from 14 biological criteria and the overall score by including other biological information. All colored boxes have a value of 1; values of 0 signify no information or not satisfied criteria. For the genomic context, filled red boxes indicate that the criteria are satisfied. Filled blue boxes indicate significant QTL association (eQTL, geneexpression; meQTL, methylation; pQTL, protein). Filled yellow boxes indicate overlap with H3K4me3, H3K9ac and H3K4me1 peaks in cells types that showed significant enrichment in epigwas analysis. Filled green boxes indicate significantly enriched pathways. Filled purple boxes indicate overlap with drug target genes (ATC-C: Cardiovascular; ATC-B01: Antithrombotic).

Supplementary Table 25: Results of the drug class enrichment analysis

Shown is the number of genes falling into the respective Anatomical Therapeutic Chemical (ATC) drug class together with the respective statistics for genome-wide loci (BF > 6) and suggestive loci (BF > 5) both with and without the SH2B3 locus.

Supplementary Table 27: Information on the SNPs selected for the wGRS analysis

Given are the related vascular traits from which the respective wGRS were derived, the marker name (rs_id), the risk/other allele and the beta used as weight for the wGRS approach.

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Malik, R., Chauhan, G., Traylor, M. et al. Multiancestry genome-wide association study of 520,000 subjects identifies 32 loci associated with stroke and stroke subtypes. Nat Genet 50, 524–537 (2018). https://doi.org/10.1038/s41588-018-0058-3

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