Genome-wide association study of intracranial aneurysms identifies 17 risk loci and genetic overlap with clinical risk factors

Abstract

Rupture of an intracranial aneurysm leads to subarachnoid hemorrhage, a severe type of stroke. To discover new risk loci and the genetic architecture of intracranial aneurysms, we performed a cross-ancestry, genome-wide association study in 10,754 cases and 306,882 controls of European and East Asian ancestry. We discovered 17 risk loci, 11 of which are new. We reveal a polygenic architecture and explain over half of the disease heritability. We show a high genetic correlation between ruptured and unruptured intracranial aneurysms. We also find a suggestive role for endothelial cells by using gene mapping and heritability enrichment. Drug-target enrichment shows pleiotropy between intracranial aneurysms and antiepileptic and sex hormone drugs, providing insights into intracranial aneurysm pathophysiology. Finally, genetic risks for smoking and high blood pressure, the two main clinical risk factors, play important roles in intracranial aneurysm risk, and drive most of the genetic correlation between intracranial aneurysms and other cerebrovascular traits.

Access options

Rent or Buy article

Get time limited or full article access on ReadCube.

from$8.99

All prices are NET prices.

Fig. 1: GWAS meta-analysis association results.
Fig. 2: Heritability and functional enrichment analyses.
Fig. 3: Cross-trait analyses.

Data availability

Summary statistics for stages 1 and 2 GWAS meta-analyses, the SAH-only and uIA-only GWAS, and a meta-analysis consisting of just East Asian samples, including effective sample size per SNP, can be accessed through Figshare (https://doi.org/10.6084/m9.figshare.11303372) and through the Cerebrovascular Disease Knowledge Portal (http://www.cerebrovascularportal.org). Detailed information on access to publicly available data is given in the Nature Research Reporting Summary.

References

  1. 1.

    Vlak, M. H., Algra, A., Brandenburg, R. & Rinkel, G. J. Prevalence of unruptured intracranial aneurysms, with emphasis on sex, age, comorbidity, country, and time period: a systematic review and meta-analysis. Lancet Neurol. 10, 626–636 (2011).

    Article  Google Scholar 

  2. 2.

    Nieuwkamp, D. J. et al. Changes in case fatality of aneurysmal subarachnoid haemorrhage over time, according to age, sex, and region: a meta-analysis. Lancet Neurol. 8, 635–642 (2009).

    Article  Google Scholar 

  3. 3.

    Korja, M. et al. Genetic epidemiology of spontaneous subarachnoid hemorrhage: Nordic Twin Study. Stroke 41, 2458–2462 (2010).

    Article  Google Scholar 

  4. 4.

    Kurki, M. I. et al. High risk population isolate reveals low frequency variants predisposing to intracranial aneurysms. PLoS Genet. 10, e1004134 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  5. 5.

    Yasuno, K. et al. Common variant near the endothelin receptor type A (EDNRA) gene is associated with intracranial aneurysm risk. Proc. Natl Acad. Sci. USA 108, 19707–19712 (2011).

    CAS  Article  PubMed  Google Scholar 

  6. 6.

    Yan, J. et al. Genetic study of intracranial aneurysms. Stroke 46, 620–626 (2015).

    CAS  Article  Google Scholar 

  7. 7.

    Santiago-Sim, T. et al. THSD1 (Thrombospondin Type 1 Domain Containing Protein 1) mutation in the pathogenesis of intracranial aneurysm and subarachnoid hemorrhage. Stroke 47, 3005–3013 (2016).

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  8. 8.

    Bourcier, R. et al. Rare coding variants in ANGPTL6 are associated with familial forms of intracranial aneurysm. Am. J. Hum. Genet. 102, 133–141 (2018).

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  9. 9.

    Lorenzo-Betancor, O. et al. PCNT point mutations and familial intracranial aneurysms. Neurology 91, e2170–e2181 (2018).

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  10. 10.

    Zhou, S. et al. RNF213 is associated with intracranial aneurysms in the French-Canadian population. Am. J. Hum. Genet. 99, 1072–1085 (2016).

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  11. 11.

    Hussain, I., Duffis, E. J., Gandhi, C. D. & Prestigiacomo, C. J. Genome-wide association studies of intracranial aneurysms: an update. Stroke 44, 2670–2675 (2013).

    Article  PubMed  Google Scholar 

  12. 12.

    Foroud, T. et al. Genome-wide association study of intracranial aneurysms confirms role of Anril and SOX17 in disease risk. Stroke 43, 2846–2852 (2012).

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  13. 13.

    Yasuno, K. et al. Genome-wide association study of intracranial aneurysm identifies three new risk loci. Nat. Genet. 42, 420–425 (2010).

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  14. 14.

    Zhou, W. et al. Efficiently controlling for case–control imbalance and sample relatedness in large-scale genetic association studies. Nat. Genet. 50, 1335–1341 (2018).

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  15. 15.

    Yang, J. et al. Conditional and joint multiple-SNP analysis of GWAS summary statistics identifies additional variants influencing complex traits. Nat. Genet. 44, 369–375 (2012).

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  16. 16.

    Zhu, Z. H. et al. Causal associations between risk factors and common diseases inferred from GWAS summary data. Nat. Commun. 9, 224 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  17. 17.

    Tobacco Genetics Consortium. Genome-wide meta-analyses identify multiple loci associated with smoking behavior. Nat. Genet. 42, 441–447 (2010).

    Article  CAS  Google Scholar 

  18. 18.

    Evangelou, E. et al. Genetic analysis of over 1 million people identifies 535 new loci associated with blood pressure traits. Nat. Genet. 50, 1412–1425 (2018).

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  19. 19.

    Lee, S. et al. Deficiency of endothelium-specific transcription factor Sox17 induces intracranial aneurysm. Circulation 131, 995–1005 (2015).

    CAS  Article  PubMed  Google Scholar 

  20. 20.

    Laarman, M. D. et al. Chromatin conformation links putative enhancers in intracranial aneurysm-associated regions to potential candidate genes. J. Am. Heart Assoc. 8, e011201 (2019).

    Article  PubMed  PubMed Central  Google Scholar 

  21. 21.

    Giri, A. et al. Trans-ethnic association study of blood pressure determinants in over 750,000 individuals. Nat. Genet. 51, 51–62 (2019).

    CAS  Article  PubMed  Google Scholar 

  22. 22.

    Kichaev, G. et al. Leveraging polygenic functional enrichment to improve GWAS power. Am. J. Hum. Genet. 104, 65–75 (2019).

    CAS  Article  PubMed  Google Scholar 

  23. 23.

    Takeuchi, F. et al. Interethnic analyses of blood pressure loci in populations of East Asian and European descent. Nat. Commun. 9, 5052 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  24. 24.

    Hoffmann, T. J. et al. Genome-wide association analyses using electronic health records identify new loci influencing blood pressure variation. Nat. Genet. 49, 54–64 (2017).

    CAS  Article  PubMed  Google Scholar 

  25. 25.

    Huang, L. et al. A missense variant in FGD6 confers increased risk of polypoidal choroidal vasculopathy. Nat. Genet. 48, 640–647 (2016).

    CAS  Article  PubMed  Google Scholar 

  26. 26.

    Romanoski, C. E. et al. Systems genetics analysis of gene-by-environment interactions in human cells. Am. J. Hum. Genet. 86, 399–410 (2010).

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  27. 27.

    Haasdijk, R. A. et al. THSD1 preserves vascular integrity and protects against intraplaque haemorrhaging in ApoE−/− mice. Cardiovasc. Res. 110, 129–139 (2016).

    CAS  Article  PubMed  Google Scholar 

  28. 28.

    Camacho Leal Mdel, P. et al. p130Cas/BCAR1 scaffold protein in tissue homeostasis and pathogenesis. Gene 562, 1–7 (2015).

    Article  CAS  PubMed  Google Scholar 

  29. 29.

    Nedeljkovic, I. et al. Understanding the role of the chromosome 15q25.1 in COPD through epigenetics and transcriptomics. Eur. J. Hum. Genet. 26, 709–722 (2018).

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  30. 30.

    David, S. P. et al. Genome-wide meta-analyses of smoking behaviors in African Americans. Transl. Psychiatry 2, e119 (2012).

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  31. 31.

    Liu, M. et al. Association studies of up to 1.2 million individuals yield new insights into the genetic etiology of tobacco and alcohol use. Nat. Genet. 51, 237–244 (2019).

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  32. 32.

    Lutz, S. M. et al. A genome-wide association study identifies risk loci for spirometric measures among smokers of European and African ancestry. BMC Genet. 16, 138 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  33. 33.

    Bulik-Sullivan, B. K. et al. LD score regression distinguishes confounding from polygenicity in genome-wide association studies. Nat. Genet. 47, 291–295 (2015).

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  34. 34.

    Speed, D. & Balding, D. J. SumHer better estimates the SNP heritability of complex traits from summary statistics. Nat. Genet. 51, 277–284 (2019).

    CAS  Article  Google Scholar 

  35. 35.

    Watanabe, K. et al. A global overview of pleiotropy and genetic architecture in complex traits. Nat. Genet. 51, 1339–1348 (2019).

    CAS  Article  Google Scholar 

  36. 36.

    Skene, N. G. et al. Genetic identification of brain cell types underlying schizophrenia. Nat. Genet. 50, 825–833 (2018).

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  37. 37.

    He, L. et al. Single-cell RNA sequencing of mouse brain and lung vascular and vessel-associated cell types. Sci. Data 5, 180160 (2018).

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  38. 38.

    Backes, D., Rinkel, G. J., Laban, K. G., Algra, A. & Vergouwen, M. D. Patient- and aneurysm-specific risk factors for intracranial aneurysm growth: a systematic review and meta-analysis. Stroke 47, 951–957 (2016).

    Article  PubMed  PubMed Central  Google Scholar 

  39. 39.

    Muller, T. B., Vik, A., Romundstad, P. R. & Sandvei, M. S. Risk factors for unruptured intracranial aneurysms and subarachnoid hemorrhage in a prospective population-based study. Stroke 50, 2952–2955 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  40. 40.

    Algra, A. M., Klijn, C. J., Helmerhorst, F. M., Algra, A. & Rinkel, G. J. Female risk factors for subarachnoid hemorrhage: a systematic review. Neurology 79, 1230–1236 (2012).

    Article  PubMed  Google Scholar 

  41. 41.

    Zheng, J. et al. LD Hub: a centralized database and web interface to perform LD score regression that maximizes the potential of summary level GWAS data for SNP heritability and genetic correlation analysis. Bioinformatics 33, 272–279 (2017).

    CAS  Article  PubMed  Google Scholar 

  42. 42.

    Malik, R. 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).

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  43. 43.

    Weinsheimer, S. et al. Genome-wide association study of sporadic brain arteriovenous malformations. J. Neurol. Neurosurg. Psychiatry 87, 916–923 (2016).

    Article  PubMed  PubMed Central  Google Scholar 

  44. 44.

    Debette, S. et al. Common variation in PHACTR1 is associated with susceptibility to cervical artery dissection. Nat. Genet. 47, 78–83 (2015).

    CAS  Article  PubMed  Google Scholar 

  45. 45.

    Jones, G. T. et al. Meta-analysis of genome-wide association studies for abdominal aortic aneurysm identifies four new disease-specific risk loci. Circ. Res. 120, 341–353 (2017).

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  46. 46.

    Hankey, G. J. Stroke. Lancet 389, 641–654 (2017).

    Article  PubMed  PubMed Central  Google Scholar 

  47. 47.

    An, S. J., Kim, T. J. & Yoon, B. W. Epidemiology, risk factors, and clinical features of intracerebral hemorrhage: an update. J. Stroke 19, 3–10 (2017).

    Article  PubMed  PubMed Central  Google Scholar 

  48. 48.

    Gaspar, H. A. & Breen, G. Drug enrichment and discovery from schizophrenia genome-wide association results: an analysis and visualisation approach. Sci. Rep. 7, 12460 (2017).

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  49. 49.

    Rogawski, M. A. & Loscher, W. The neurobiology of antiepileptic drugs. Nat. Rev. Neurosci. 5, 553–564 (2004).

    CAS  Article  PubMed  Google Scholar 

  50. 50.

    Lindbohm, J. V., Kaprio, J., Jousilahti, P., Salomaa, V. & Korja, M. Sex, smoking, and risk for subarachnoid hemorrhage. Stroke 47, 1975–1981 (2016).

    Article  PubMed  Google Scholar 

  51. 51.

    Vlak, M. H., Rinkel, G. J., Greebe, P. & Algra, A. Risk of rupture of an intracranial aneurysm based on patient characteristics: a case–control study. Stroke 44, 1256–1259 (2013).

    Article  PubMed  Google Scholar 

  52. 52.

    Juvela, S., Poussa, K. & Porras, M. Factors affecting formation and growth of intracranial aneurysms: a long-term follow-up study. Stroke 32, 485–491 (2001).

    CAS  Article  PubMed  Google Scholar 

  53. 53.

    Kobeissi, E., Hibino, M., Pan, H. & Aune, D. Blood pressure, hypertension and the risk of abdominal aortic aneurysms: a systematic review and meta-analysis of cohort studies. Eur. J. Epidemiol. 34, 547–555 (2019).

    Article  PubMed  PubMed Central  Google Scholar 

  54. 54.

    Cheng, J. et al. Ion channels and vascular diseases. Arterioscler. Thromb. Vasc. Biol. 39, e146–e156 (2019).

    CAS  PubMed  Google Scholar 

  55. 55.

    Bulley, S. et al. Arterial smooth muscle cell PKD2 (TRPP1) channels regulate systemic blood pressure. eLife 7, e42628 (2018).

    Article  PubMed  PubMed Central  Google Scholar 

  56. 56.

    Perrone, R. D., Malek, A. M. & Watnick, T. Vascular complications in autosomal dominant polycystic kidney disease. Nat. Rev. Nephrol. 11, 589–598 (2015).

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  57. 57.

    Willer, C. J., Li, Y. & Abecasis, G. R. METAL: fast and efficient meta-analysis of genomewide association scans. Bioinformatics 26, 2190–2191 (2010).

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  58. 58.

    Hormozdiari, F. et al. Colocalization of GWAS and eQTL signals detects target genes. Am. J. Hum. Genet. 99, 1245–1260 (2016).

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  59. 59.

    Gusev, A. et al. Integrative approaches for large-scale transcriptome-wide association studies. Nat. Genet. 48, 245–252 (2016).

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  60. 60.

    Zhu, Z. et al. Integration of summary data from GWAS and eQTL studies predicts complex trait gene targets. Nat. Genet. 48, 481–487 (2016).

    CAS  Article  PubMed  Google Scholar 

  61. 61.

    Finucane, H. K. et al. Partitioning heritability by functional annotation using genome-wide association summary statistics. Nat. Genet. 47, 1228–1235 (2015).

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  62. 62.

    Iotchkova, V. et al. GARFIELD classifies disease-relevant genomic features through integration of functional annotations with association signals. Nat. Genet. 51, 343–353 (2019).

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  63. 63.

    Woo, D. et al. Meta-analysis of genome-wide association studies identifies 1q22 as a susceptibility locus for intracerebral hemorrhage. Am. J. Hum. Genet. 94, 511–521 (2014).

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  64. 64.

    Brown, B. C., Asian Genetic Epidemiology Network-Type 2 Diabetes Consortium, Ye, C. J., Price, A. L. & Zaitlen, N. Transethnic genetic-correlation estimates from summary statistics. Am. J. Hum. Genet. 99, 76–88 (2016).

  65. 65.

    Mootha, V. K. et al. PGC-1alpha-responsive genes involved in oxidative phosphorylation are coordinately downregulated in human diabetes. Nat. Genet. 34, 267–273 (2003).

    CAS  Article  Google Scholar 

  66. 66.

    Subramanian, A. et al. Gene set enrichment analysis: a knowledge-based approach for interpreting genome-wide expression profiles. Proc. Natl Acad. Sci. USA 102, 15545–15550 (2005).

    CAS  Article  PubMed  PubMed Central  Google Scholar 

Download references

Acknowledgements

This research has been conducted using the UK Biobank Resource under application no. 2532. We thank R. McLaughlin for the advice on population-based heritability analysis. We thank M. Gunel and K. Yasuno for their help with genotyping DNA samples of the Utrecht 1, Finland and @neurIST cohorts. We thank the staff and participants of all CADISP centers for their important contributions. We acknowledge the contribution of participants, project staff, and the China National Center for Disease Control and Prevention (CDC) and its regional offices to the CKB. China’s National Health Insurance provided electronic linkage to all hospital treatments. We thank K. Jebsen for genotyping quality control and imputation of the HUNT Study. For providing clinical information and biological samples collected during the @neurIST project, we thank J. Macho, T. Dóczi, J. Byrne, P. Summers, R. Risselada, M. Sturkenboom, U. Patel, S. Coley, A. Waterworth, D. Rüfenacht, C. Proust and F. Cambien. We acknowledge the support from the Netherlands Cardiovascular Research Initiative: an initiative with support of the Dutch Heart Foundation, CVON2015-08 ERASE. This project has received funding from the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation program (grant no. 852173). This project has received funding from the ERC under the European Union’s Horizon 2020 research and innovation program (grant no. 772376—EScORIAL). The BBJ project was supported by the Ministry of Education, Culture, Sports, Sciences and Technology of the Japanese government, and the Japan Agency for Medical Research and Development (19km0605001). The CADISP study has been supported by INSERM, Lille 2 University, Institut Pasteur de Lille and Lille University Hospital, and received funding from the European Regional Development Fund (FEDER funds) and Région Nord-Pas-de-Calais in the framework of Contrat de Projets Etat-Region 2007–2013 Région Nord-Pas-de-Calais (grant no. 09120030), Centre National de Génotypage, the Emil Aaltonen Foundation, the Paavo Ilmari Ahvenainen Foundation, the Helsinki University Central Hospital Research Fund, the Helsinki University Medical Foundation, the Päivikki and Sakari Sohlberg Foundation, the Aarne Koskelo Foundation, the Maire Taponen Foundation, the Aarne and Aili Turunen Foundation, the Lilly Foundation, the Alfred Kordelin Foundation, the Finnish Medical Foundation, the Orion Farmos Research Foundation, the Maud Kuistila Foundation, the Finnish Brain Foundation, the Biomedicum Helsinki Foundation, Projet Hospitalier de Recherche Clinique Régional, Fondation de France, Génopôle de Lille, Adrinord, the Basel Stroke Funds, and the Käthe-Zingg-Schwichtenberg-Fonds of the Swiss Academy of Medical Sciences and the Swiss Heart Foundation. S.D. received funding from the French National Funding Agency (ANR), and the ERC under the European Union’s Horizon 2020 research and innovation program (grant no. 640643). J.P. was supported by a Jagiellonian University Medical College (grant no. K/ZDS/001456). CKB was supported as follows: baseline survey and first re-survey: Hong Kong Kadoorie Charitable Foundation; long-term follow-up: UK Wellcome Trust (grant nos. 202922/Z/16/Z, 104085/Z/14/Z, 088158/Z/09/Z), National Natural Science Foundation of China (grant nos. 81390540, 81390541, 81390544) and National Key Research and Development Program of China (grant nos. 2016YFC 0900500, 0900501, 0900504, 1303904). DNA extraction and genotyping: GlaxoSmithKline, UK Medical Research Council (grant nos. MC_PC_13049, MC-PC-14135). Core funding to the Clinical Trial Service Unit and Epidemiological Studies Unit at Oxford University was provided by the British Heart Foundation, UK Medical Research Council and Cancer Research UK. S.Z. and G.A.R. received funding from the Canadian Institutes of Health Research (CIHR). This project has received funding from the European Union’s Horizon 2020 research and innovation program (no. 666881), SVDs@target (to M.D.) and CoSTREAM (Common Mechanisms and Pathways in Stroke and Alzheimer’s Disease; grant no. 667375, to M.D.); the DFG (Deutsche Forschungsgemeinschaft) as part of the Munich Cluster for Systems Neurology (EXC 2145 SyNergy—ID 390857198) and the CRC 1123 (B3, to M.D.); the Corona Foundation (to M.D.); the Fondation Leducq (Transatlantic Network of Excellence on the Pathogenesis of Small Vessel Disease of the Brain, to M.D.); the e:Med program (e:AtheroSysMed, to M.D.); and the FP7/2007-2103 European Union project CVgenes@target (grant no. Health-F2-2013-601456, to M.D.). K.R. is funded by the Health Data Research UK (HDRUK) fellowship MR/S004130/1. C.L.M.S. was funded by the UK Biobank, HDRUK and Scottish Funding Council. I.C.H. received funding from the Alzheimer Research UK and Dunhill Medical Trust Foundation. J.P.B. and D.W. were supported by National Institutes of Health (NIH) funding. D.J.W. and V.S.A. received funding support from the Stroke Association. D.J.W. and H.H. received funding for genotyping from the NIHR University College London Hospitals Biomedical Research Center. The Nord-Trøndelag Health Study (HUNT Study) is a collaboration by the HUNT Research Center, Faculty of Medicine at the Norwegian University of Science and Technology (NTNU), the Norwegian Institute of Public Health and the Nord-Trøndelag County Council. The genotyping was financed by the NIH, University of Michigan, the Norwegian Research Council and Central Norway Regional Health Authority and the Faculty of Medicine and Health Sciences, NTNU. P.B. and C.M.F. were supported by EU commission FP6—IST – 027703 @neurIST-Integrated biomedical informatics for the management of cerebral aneurysms. P.B., S.M., S.H., S.S., J.D. and O.M. were supported by the grant (no. MRD 2014/261) from the Swiss SystemsX.ch initiative and evaluated by the Swiss National Science Foundation (AneuX project).

Author information

Affiliations

Authors

Consortia

Contributions

J.H.V. and Y.M.R. contributed equally to this study. M.K.B., Y.M.R. and J.H.V. wrote the manuscript. Y.M.R. and J.H.V. supervised the project. I.C.H., S.D., B.B.W., J.P., A.S., E.I.G.-P., M.N., J.E.J., M.v.U.Z.F., A.L., J.P.B., D.J.W., D.W., R.R., P.B., Y.K., J.H.V. and Y.M.R. designed the study. M.K.B., R.A.A.v.d.S. and W.v.R. performed the association analyses and scripts. M.K.B., R.A.A.v.d.S. and W.v.R. performed the functional analyses and scripts. S.M., R.B., C.M.F., S.H., S.S., J.D., O.M. and P.B. prepared the phenotypes. R.A.A.v.d.S. and K.R.v.E. provided technical assistance. M.K.B., Y.M.R., G.J.E.R., J.H.V., L.H.v.d.B., P.B., S.M., E.I.G.-P., M.N., J.P., A.S., J.E.E., M.v.U.Z.F., A.L., G.A.R., S.Z., N.U.K., R.M., K.R., C.L.M.S., D.J.W., I.C.H., H.H., V.S.A., J.P.B., D.W., R.R., R.B., C.D., O.N., J.-C.G., E.S., F.E., H.D. and W.M.M.V. contributed the phenotypes and genotypes. Y.K., M.K., M.A., C.T., K.M. (BBJ), R.G.W., K.L., L.L., I.Y.M., Z.C. (CKB), B.S.W., S.B., M.B.J., B.M.B., M.S.S., C.J.W., K.H., J.-A.Z. (HUNT), M.J.B., G.T.J. (AAA), H.K., J.G.Z., C.J.M.K., N.U.K. (AVM), D.W. (ICH), R.M., M.D. (IS), S.D., T.T., M.S. and P.A. (cervical artery dissection) summarized the statistical contributions. J.R.I.C. and G.B. performed drug-target and MAGMA pathway enrichment analyses. D.J.W. critiqued the output for important intellectual content.

Corresponding authors

Correspondence to Mark K. Bakker or Ynte M. Ruigrok.

Ethics declarations

Competing interests

When this study was conducted, C.L.M.S. was chief scientist for the UK Biobank study.

Additional information

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

Supplementary information

Supplementary Information

Supplementary Note and Figs. 1–3

Reporting Summary

Supplementary Tables

Supplementary Tables 1–22

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Bakker, M.K., van der Spek, R.A.A., van Rheenen, W. et al. Genome-wide association study of intracranial aneurysms identifies 17 risk loci and genetic overlap with clinical risk factors. Nat Genet (2020). https://doi.org/10.1038/s41588-020-00725-7

Download citation

Search

Nature Briefing

Sign up for the Nature Briefing newsletter — what matters in science, free to your inbox daily.

Get the most important science stories of the day, free in your inbox. Sign up for Nature Briefing