1
Common variation in PHACTR1 is associated with susceptibility to
cervical artery dissection
Supplementary Appendix
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List of contents
Supplementary Note CADISP co-investigator list International Stroke Genetics Consortium Members Acknowledgments Abbreviations Sample selection Genotyping platforms and quality control filters Imputation Screening for latent population substructure and association analysis Meta-analysis strategy Candidate gene selection Functional annotation
Supplementary Figures Supplementary Figure 1: CADISP inclusion criteria Supplementary Figure 2: Patient recruiting centers for the CeAD GWAS Supplementary Figure 3A and B: Patient selection Supplementary Figure 4: Principal component analysis plots for selecting control samples Supplementary Figure 5: QQ plots for CeAD GWAS Supplementary Figure 6: Manhattan plots for CeAD GWAS Supplementary Figure 7: Forest plots for associations of CeAD with rs9349379 (PHACTR1), rs6820391 (LNX1), and rs12402265 (FGGY)
Supplementary Tables Supplementary Table 1: Genotyping platforms Supplementary Table 2: GWAS associations with cervical artery dissection (CeAD) at a p-value < 10-4 Supplementary Table 3: Association with non-CeAD IS of SNPs yielding the most significant associations with CeAD Supplementary Table 4: Recruiting centers for follow-up CeAD cases and controls Supplementary Table 5: Association results for the top SNPs selected for follow-up Supplementary Table 6: Evidence for null hypothesis H0 according to Bayesian approach Supplementary Table 7: Probability of false discovery for SNPs in the 6 genetic loci selected for follow-up according to Bayesian approach Supplementary Table 8: Associations of top SNPs from CeAD GWAS stratified on sex Supplementary Table 9: Associations of top genotyped SNPs from CeAD GWAS according to the presence or absence of migraine Supplementary Table 10: Associations of top genotyped SNPs from CeAD GWAS according to presence or absence of recent cervical trauma Supplementary Table 11: Associations of top SNPs from CeAD GWAS with age of onset of CeAD Supplementary Table 12: Associations of top genotyped SNPs from CeAD GWAS according to presence or absence of cerebral ischemia Supplementary Table 13: SNP – CeAD associations reaching p<10-5 in GWAS of carotid dissection or GWAS of vertebral dissection Supplementary Table 14: Association of CeAD with SNPs previously found to be associated with CeAD in published candidate gene studies Supplementary Table 15: Association of CeAD with SNPs in COL3A1 Supplementary Table 16: Association of CeAD with SNPs associated with intracranial aneurysms in published GWAS
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Supplementary Table 17: Association of CeAD with SNPs associated with aortic aneurysms and dissections in published GWAS Supplementary Table 18: Associations of known susceptibility SNPs for myocardial infarction with CeAD Supplementary Table 19: Association of CeAD with SNPs associated with other subtypes of ischemic stroke in published GWAS Supplementary Table 20: Associations of known susceptibility SNPs for migraine with CeAD Supplementary Table 21: Cis-eQTL associations with SNPs in the top 6 risk loci selected for follow-up Supplementary Table 22: Power estimates to detect an association in the follow-up sample
Supplementary References
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Supplementary Note
CADISP (Cervical Artery Dissections and Ischemic Stroke Patients) Co-investigators
Robin Lemmens, MD, PhD, Department of Neurology, Leuven University Hospital, and Vesalius
Research Center, VIB, Leuven, Belgium; Massimo Pandolfo, MD PhD, Department of Neurology,
Erasme Hospital, Free University of Brussels and Laboratory of Experimental Neurology, ULB,
Brussels, Belgium; Marie Bodenant, MD, Department of Neurology, Lille University Hospital –
EA1046, France; Fabien Louillet, MD and Jean-Louis Mas, MD PhD, Department of Neurology, Sainte-
Anne University Hospital, Paris, France; Sandrine Deltour, MD, Sara Leder, MD, Anne Léger, MD,
Department of Neurology, Pitié-Salpêtrière University Hospital, Paris, France; Sandrine Canaple, MD
and Olivier Godefroy, MD PhD, Department of Neurology, Amiens University Hospital, France;
Maurice Giroud, MD PhD, and Agnès Jacquin, MD, Department of Neurology, Dijon University
Hospital, France; Thierry Moulin, MD PhD and Fabrice Vuillier, MD, Department of Neurology,
Besançon University Hospital, France; Christophe Tzourio, MD PhD, Inserm U897, University of
Bordeaux, France; Michael Dos Santos, MD, Department of Neurology, Klinikum Ludwigshafen,
Germany; Rainer Malik, MD PhD, Department of Neurology, University Hospital of Munich, Germany;
Ingrid Hausser, PhD, Department of Dermatology, Heidelberg University Hospital, Germany;
Constanze Thomas-Feles, MD, and Ralf Weber, MD, Department of Rehabilitation Schmieder-Klinik,
Heidelberg, Germany; Caspar Grond-Ginsbach, PhD, and Werner Hacke, MD PhD, Department of
Neurology, Heidelberg University Hospital, Germany; Alessia Giossi, MD, Irene Volonghi, MD, Paolo
Costa, MD, Elisabetta del Zotto, MD PhD, Andrea Morotti , MD, and Loris Poli, MD, Department of
Clinical and Experimental Sciences, Neurology Clinic, Brescia University Hospital, Italy; Maria Lorenza
Muiesan, MD, Massimo Salvetti, MD, Enrico Agabiti Rosei, MD, Department of Clinical and
Experimental Sciences, Clinica Medica, Brescia University Hospital, Italy; Silvia Lanfranconi, MD and
Pierluigi Baron, MD PhD, Department of Neurology, IRCCS Foundation Ca’Granda Hospital Policlinic
Hospital, University of Milan, Italy; Carlo Ferrarese, MD PhD, and Emanuela Susani, MD, University of
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Milano Bicocca, San Gerardo Hospital, Monza, Italy; Giacomo Giacalone, MD, Milan Scientific
Institute San Raffaele University Hospital, Italy; Stefano Paolucci, MD PhD, Department of
Rehabilitation, Santa Lucia Hospital, Rome, Italy; Raffaele Palmirotta, MD, Fiorella Guadagni, MD,
Department of Laboratory Medicine & Advanced Biotechnologies, IRCCS San Raffaele Pisana, Rome,
Italy; Maurizio Paciaroni, MD PhD, Stroke Unit and Division of Cardiovascular Medicine, University of
Perugia Santa Maria della Misericordia Hospital, Sant'Andrea delle Fratte, Perugia, Italy; Elena
Ballabio, MD, Eugenio A. Parati, MD, Department of Cerebrovascular Diseases, and Emilio Ciusani,
PhD, Laboratory of Clinical Investigation, Fondazione IRCCS Istituto Neurologico Carlo Besta, Milano,
Italy; Felix Fluri, MD, Florian Hatz, MD, Dominique Gisler, MD and Margareth Amort, MD,
Department of Neurology, Basel University Hospital, Switzerland; Steve Bevan, PhD, Tom James, BSc,
Stroke and Dementia Research Centre, St George’s University of London, UK; Sandra Olsson, PhD,
Lukas Holmegaard, MD, Institute of Neuroscience and Physiology, the Sahlgrenska Academy at
University of Gothenburg, Gothenburg, Sweden; Ayse Altintas, MD PhD, Department of Neurology,
University Hospital of Istanbul, Turkey; Juan José Martin, MD, Department of Neurology, University
Hospital Sanatorio Allende, Cordoba, Argentina; Steven Kittner, MD MPH, Braxton Mitchell, PhD,
Colin Stine, PhD, Jeff O’Connell, PhD, and Nicole Dueker, PhD, Maryland Stroke Center, Department
of Neurology, University of Maryland School of Medicine, Baltimore, Maryland, USA; Peter J.
Koudstaal, MD PhD, Lonneke M.L. de Lau MD PhD, Department of Neurology, Erasmus MC University
Medical Center, Rotterdam, the Netherlands; Albert Hofman MD PhD, Benjamin F Verhaaren, MD
MSc, Department of Epidemiology, Erasmus MC, Rotterdam, Andre G Uitterlinden PhD, Department
of Internal Medicine, Erasmus MC; Joan Montaner, MD PhD, Maite Mendioroz, MD PhD, Laboratorio
Neurovascular, Institut de Recerca, Hospital Vall d'Hebron, Barcelona, Spain; Sunaina Yadav, MSc,
and Muhammad Saleem Khan, BSc, Imperial College Cerebrovascular Research Unit (ICCRU), Imperial
College London, UK; Michael Wilder, MD, University of Utah, USA; Ewoud van Dijk, MD PhD, Noortje
Maaijwee, MD, Loes Rutten-Jacobs, MSc, Donders Institute for Brain, Cognition and Behaviour,
Centre for Neuroscience, Department of Neurology, Radboud University Nijmegen Medical Centre,
Nature Genetics: doi:10.1038/ng.3154
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Nijmegen, The Netherlands; Jamie Kramer, BA, Departments of Neurology and Public Health
Sciences University of Virginia, Charlottesville, Virginia, USA; Shaneela Malik, MD, Henry Ford
Hospital, Detroit, Michigan, USA, Thomas G Brott, MD, Department of Neurology, Mayo Clinic,
Jacksonville, Florida, USA; Robert D Brown Jr, MD, Department of Neurology, Mayo Clinic, Rochester,
Minnesota, USA; Andrew Singleton, PhD, Molecular Genetics Section, Laboratory of Neurogenetics,
National Institute on Aging, NIH; John Hardy, PhD, Department of Molecular Neuroscience Institute
of Neurology, University College London, London, United Kingdom; Stephen S Rich, PhD, Department
of Public Health Sciences and the Center for Public Health Genomics, University of Virginia,
Charlottesville, Virginia, USA; Christian Tanislav, MD PhD, Department of Neurology, University
Hospital of Giessen, Germany; Jan Jungehülsing, Charité Universitätsmedizin Berlin, Department of
Neurology, Berlin, Germany.
International Stroke Genetics Consortium Members among authors of this manuscript
(www.strokegenetics.org)
Stéphanie Debette, Ganesh Chauhan, Alessandro Pezzini, Vincent Thijs, Hugh S Markus, Martin
Dichgans, Andrew M Southerland, Anna Bersano, John Cole, Jennifer J Majersik, Pankaj Sharma,
Israel Fernandez-Cadenas, Katarina Jood, Michael A Nalls, Christina Jern, Yu-Ching Cheng, Giorgio B
Boncoraglio, James F Meschia, Arndt Rolfs, Bradford B Worrall, Turgut Tatlisumak
Acknowledgments
The authors are grateful for the contribution and assistance of Marja Metso, RN (collection of data
and technical assistance), Department of Neurology, Helsinki University Central Hospital, Helsinki,
Finland; Laurence Bellengier, MS (data monitoring and technical assistance), Sabrina Schilling, MS
(data monitoring and technical assistance), Pr. Christian Libersa, MD, PhD (supervision of personnel),
Dr. Dominique Deplanque, MD, PhD (supervision of personnel), Centre d’Investigation Clinique,
University Hospital of Lille, France; Dr. Nathalie Fievet, PhD (technical assistance), Inserm U744,
Nature Genetics: doi:10.1038/ng.3154
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Pasteur Institute, Lille, France; Dr. Jean-Christophe Corvol, MD, PhD (supervision of personnel), Sylvie
Montel, MS (technical assistance) and Christine Rémy, MS (technical assistance), Centre
d’Investigation Clinique, Pitié-Salpêtrière University Hospital, Paris, France; Dr. Ana Lopes Da Cruz,
PhD (technical assistance), Laboratory of Experimental Neurology, ULB, Brussels, Belgium; Annet
Tiemessen, MS (technical assistance), Stroke team, University Hospital Basel, Switzerland; Dr. Marie-
Luise Arnold, PhD (collection of data and technical assistance), Department of Neurology, Heidelberg
University Hospital, Germany; Dr. Anja Schirmacher, PhD (technical assistance), Department of
Neurology, University of Muenster, Germany; Ingrid Eriksson (technical assistance), Institute of
Neuroscience and Physiology, the Sahlgrenska Academy at University of Gothenburg; Dr. Anne
Boland, PhD (technical assistance), Centre National de Génotypage, Evry, France; Dr. Carole Proust,
PhD (technical assistance), INSERM UMR S937, University Paris VI Pierre et Marie Curie, France. The
Neurovascular Research Laboratory of Hospital Vall d'Hebron takes part in the Spanish Stroke
Genetics Consortium (www.genestroke.com) and in the Cooperative Neurovascular Research
RENEVAS (RD06/0026/0010). The authors are grateful to the International Stroke Genetics
Consortium (www.strokegenetics.org), through which several recruiting centers were identified, for
its continued support.
The Vobarno Study is supported in part by grants from the European Community Network of
Excellence (InGenious HyperCare, 2006-2010); the Italian University and Research Ministry, Regione
Lombardia and the Fondazione della Comunità Bresciana Onlus.
The MONA-LISA Study was made possible by an unrestricted grant from Pfizer and by a grant from
the ANR (ANR-05-PNRA-018).
The Utah cervical artery dissection collection is supported by Award Number UL1TR000105-05 from
the National Center for Research Resources by the Public Health Services (University Hospital, Salt
Lake City, Utah).
Nature Genetics: doi:10.1038/ng.3154
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GEOS was supported by the National Institutes of Health Genes, Environment and Health Initiative
(GEI) Grant U01 HG004436, as part of the GENEVA consortium under GEI, with additional support
provided by the Mid-Atlantic Nutrition and Obesity Research Center (P30 DK072488), and the Office
of Research and Development, Medical Research Service, and the Baltimore Geriatrics Research,
Education, and Clinical Center of the Department of Veterans Affairs; genotyping services for GEOS
were provided by the Johns Hopkins University Center for Inherited Disease Research (CIDR) and
funded by GEI grant U01HG004438-01, assistance with data cleaning was provided by the GENEVA
Coordinating Center (U01 HG 004446; PI Bruce S Weir); GEOS study recruitment and assembly of
datasets were supported by a Cooperative Agreement with the Division of Adult and Community
Health, Centers for Disease Control and by grants from the National Institute of Neurological
Disorders and Stroke (NINDS) and the NIH Office of Research on Women's Health (R01 NS45012, U01
NS069208-01).
The Besta Cerebrovascular Diseases Registry (CEDIR) was supported by the Italian Ministry of Health,
years 2007–2010 (Annual Research Funding; Grant Numbers: RC 2007/LR6, RC 2008/LR6; RC
2009/LR8; RC 2010/LR8).
The ISGS/SWISS study was supported in part by the Intramural Research Program of the National
Institute on Aging, NIH project Z01 AG-000954-06; ISGS/SWISS used samples and clinical data from the
NIH-NINDS Human Genetics Resource Center DNA and Cell Line Repository
(http://ccr.coriell.org/ninds), human subjects protocol numbers 2003-081 and 2004-147; ISGS/SWISS
used stroke-free participants from the Baltimore Longitudinal Study of Aging (BLSA) as controls with
the permission of Dr. Luigi Ferrucci; the inclusion of BLSA samples was supported in part by the
Intramural Research Program of the National Institute on Aging, NIH project Z01 AG-000015-50,
human subjects protocol number 2003-078; the ISGS study was funded by NIH-NINDS Grant R01 NS-
42733 (J. F. Meschia, P.I.); the SWISS study was funded by NIH-NINDS Grant R01 NS-39987 (J. F.
Meschia, P.I.); the ISGS/SWISS study utilized the high-performance computational capabilities of the
Biowulf Linux cluster at the NIH (http://biowulf.nih.gov).
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The Virginia cervical artery dissection collection was supported by the NIH/NHGRI "Genomics and
Randomized Trials Network (GARNET)" HG005160 grant.
The GWA database of the Rotterdam Study was funded through the Netherlands Organization of
Scientific Research NWO (nr. 175.010.2005.011); the Rotterdam Study is supported by the Erasmus
Medical Center and Erasmus University, Rotterdam; the Netherlands organization for scientific
research (NWO), the Netherlands Organization for the Health Research and Development (ZonMw),
the Research Institute for Diseases in the Elderly (RIDE), the Ministry of Education, Culture and
Science, the Ministry for Health, Welfare and Sports, the European Commission (DG XII), and the
Municipality of Rotterdam; further financial support was obtained from the Netherlands Heart
Foundation (Nederlandse Hartstichting) 2009B102.
The Spanish cervical artery dissection collection has received funding from Spanish government
(PI10/01212 and CP12/03298) and the European Union's Seventh Framework Programme (FP7/2007-
2013) under grant agreements #201024 and #202213 (European Stroke Network).
The Gothenburg cervical artery dissection collection was supported by the Swedish Research Council
(K2011-65X-14605-09-6) and the Swedish Heart-Lung Foundation (20100256).
The Muenster cervical artery dissection collection was in part supported by Competence Net Stroke,
Germany (BMBF), by the “Innovative Medizinische Forschung” (IMF) of the Medical Faculty of the
University of Münster, by the Neuromedical Foundation, Münster and by the Leibniz Institute of
Atherosclerosis Research.
The Nijmegen cervical artery dissection collection was supported by the “Dutch Epilepsy Fund” (grant
10-18).
The Sifap study (Stroke In Young Fabry Patients, www.sifap.eu; ClinicalTrials.gov: NCT00414583) has
been supported partially by an unrestricted scientific grant from Shire Human Genetic Therapies; the
sponsors of the study had no role in the study design, data collection, data analysis, interpretation,
writing of the manuscript, or the decision to submit the manuscript for publication.
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The KORA research platform (Collaborative Health Research in the Region of Augsburg) was initiated
and financed by the Helmholtz Center Munich, German Research Center for Environmental Health,
which is funded by the German Federal Ministry of Education and Research and by the State of
Bavaria.
French samples included in CADISP-2 were supported by an ERCA grant from the French Ministry of
Health.
The BRAINS study is funded by grants from the Henry Smith Charity and the British Council (UKIERI).
Abbreviations
BRAINS: Bio-Repository of DNA in Stroke CeAD: Cervical Artery Dissection CADISP: Cervical Artery Dissection and Ischemic Stroke Patients CEDIR: The Besta Cerebrovascular Diseases Registry CI: Confidence Interval CNG: Centre National de Genotypage GEOS: Genetics of Early Onset Stroke GWAS: Genome Wide Association Study IS: Ischemic Stroke ISGS/SWISS: Ischemic Stroke Genetics Study / Siblings with Ischemic Stroke Study KORA: Kooperative Gesundheitsforschung in der Region Augsburg LD: Linkage Disequilibrium LNX1: Ligand of Numb, protein X1 LRP1: low density lipoprotein receptor-related protein 1 MAF: Minor Allele Frequency MONA-LISA: MOnitoring NAtionaL du rISque Artériel MTHFR: Methylenetetrahydrofolate Reductase OR: Odds Ratio PC: Principal Component PHACTR1: Phosphatase and Actin Regulator 1 QC: Quality Control SD: Standard Deviation SNP: Single Nucleotide Polymorphism UVA: University of Virginia vEDS: Vascular Ehlers-Danlos syndrome WTCCC2: Wellcome Trust Case Control Consortium 2
Sample selection
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Patient populations
Cervical Artery Dissection (CeAD) patients included in GWAS
Consecutive patients evaluated in a department of neurology specialized in stroke care with a diagnosis of
CeAD were included in the CADISP study between 2004 and 2009, the structure and methods of which have
been described in detail previously.1 Patients were recruited both prospectively and retrospectively.
Retrospective patients are participants who had a qualifying event before the beginning of the study in
each center and were identified through local registries of CeAD patients. The vast majority of patients had
a qualifying event between 1999 and 2009 (<4% had a qualifying event before 1999). A total of 1,111 CeAD
patients, who satisfied CADISP inclusion criteria (Supplementary Fig. 1) after careful data monitoring, were
recruited in 18 centers from 7 European countries (Supplementary Fig. 2 and 3A). Detailed information on
putative risk factors, clinical and radiological characteristics and short-term outcome were collected using a
standardized questionnaire.1-3 We excluded 55 patients due to either unavailability of geographically matched
healthy controls, genetically confirmed diagnosis of vascular Ehlers-Danlos syndrome (vEDS), or non-
European origin. Of the remaining 1,056 CeAD patients, 955 patients with good quality DNA available were
genotyped at the Centre National de Génotypage, Evry, France (CNG, www.cng.fr); genotyping was successful
in 952 patients. Of these, a total of 942 CeAD patients who met genotyping quality control criteria were
available for the present analysis (CADISP-1, see flow diagram in Supplementary Fig. 3A).
To increase the sample size for genetic analyses, an independent sample of 501 CeAD patients of
European origin was recruited between 2008 and 2010, in some CADISP centers and additional 12
centers in 7 European countries and the United States (Supplementary Fig. 2), applying the same
inclusion criteria as for the CADISP study (Supplementary Fig. 1). Of these, 451 patients who were
successfully genotyped and met quality control criteria were available for the present analysis (CADISP-
2, Supplementary Fig. 3B). Of note, several CeAD patients included in CADISP-2 were drawn from
already existing DNA databases of ischemic stroke patients (CEDIR,4 Erasmus MC stroke cohort,5
Barcelona stroke genetics study6) most of which were identified through the International Stroke
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Genetics Consortium (www.strokegenetics.org). Therefore, by design CeAD patients in CADISP-2 have a
slightly higher rate of ischemic stroke.
In total 1,393 CeAD patients of European origin were available for the present analysis.
Non-CeAD ischemic stroke (IS) patients
Non-CeAD IS patients were recruited for a specificity analysis in the same centers as CADISP-1 CeAD
patients (Supplementary Fig. 2). These were patients with a diagnosis of IS, in whom CeAD had been
formally ruled out according to CADISP inclusion criteria (Supplementary Fig. 1). Non-CeAD IS patients
were frequency-matched on age (by 5-year intervals) and sex on CeAD patients. A total of 658 non-
CeAD IS patients were included. We excluded 19 patients due to unavailability of geographically
matched healthy controls, or due to non-European origin; of the remaining 639 non-CeAD IS patients,
613 individuals had good quality DNA available and were genotyped at the CNG. Of these, a total of 583
non-CeAD IS patients who were successfully genotyped and met genotyping quality control criteria
were available for the present analysis (Supplementary Fig. 3A). The breakdown of TOAST subtypes
was:7 large artery atherosclerosis, N=74 (12.7%), cardioembolism, N=216 (37.1%), small vessel disease,
N=37 (6.4%), other etiologies, N=18 (3.1%), undetermined etiology, N=238 (40.8%).
Control populations
The majority of controls (N=14,203, of which 74 Finns and 14,129 non-Finnish Europeans) were
selected from an anonymized control genotype database at the CNG, in order to match cases for ethnic
background, based on principal component analysis (PCA, Supplementary Fig. 4). European reference
samples from the genotype repository at the CNG were also analyzed simultaneously to provide
improved geographical resolution. EIGENSOFT software (version 3.0) 8 was used to perform principal
component analysis (PCA) in these samples.
Additional Finnish controls were recruited within the CADISP study, both from the general population
and among spouses and unrelated friends of CADISP patients, within the Helsinki area. A total of 234
individuals were eligible for genotyping at the CNG. Of these, 213 individuals who were genotyped
successfully and met quality control criteria were available for the present analysis.
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Thus, 14,416 controls were available in total.
Follow-up samples
We sought to replicate our strongest association signals in independent cohorts. Additional DNA
samples from CeAD patients of European origin were collected in 2010-2011 following CADISP
inclusion criteria (Supplementary Fig. 1), through departments of neurology specialized in stroke
care in Germany, the Netherlands, Sweden, Italy, France, Switzerland and the USA (Supplementary
Table 4). Many of the follow-up samples were identified through the International Stroke Genetics
Consortium (www.strokegenetics.org). Some of these (85 CeAD patients, 998 controls) had already
been genotyped as part of genomewide association studies of ischemic stroke: GEOS,9
ISGS/SWISS,10,11 and WTCCC2 (Munich center)12 for cases; KORA,13 ISGS/SWISS,10 and Rotterdam
Study for controls.14 In addition, DNA samples from 238 CeAD patients and 1,488 controls were
genotyped genome-wide or on a custom chip (Supplementary Note). Finally, DNA samples from 327
additional CeAD patients and 97 controls, recruited in the same centers as other patients included in
the discovery or follow-up analyses, were genotyped for the top 5 genotyped loci and the top
imputed locus (1-2 SNP(s) per locus). Of these 650 CeAD patients, 55 were excluded due to
unavailability of information on the dissection site, leaving us with 595 CeAD patients for analysis.
Most of these were also drawn from already existing DNA databases of ischemic stroke patients
(SIFAP,15 SAHLSIS, Gothenburg,16 Nijmegen,17 Barcelona stroke genetics study6) Some of the controls
for the follow-up study were drawn from existing population-based studies geographically close to the
centers recruiting CeAD patients (Vobarno-Study,18 MONA-LISA Strasbourg Study,19 Rotterdam
Study20,21) (Supplementary Table 4). Finally, a case-control dataset of 64 CeAD patients and 65
controls were recruited specifically for this project in Moscow, Russia. In total, 659 CeAD patients
and 2,648 geographically matched controls were available for analyses.
Genotyping and quality control (QC) filters
Genotyping and QC for discovery GWAS
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Genotyping methods are detailed in Supplementary Table 1. Almost all DNA samples were genotyped
at the Centre National de Génotypage, Evry, France (CNG, www.cng.fr). Genotypes of 942 CeAD
patients for CADISP-1 and 583 non-CeAD IS patients were obtained by Human610-Quad BeadChip
(Illumina, San Diego, USA); genotypes of 451 CeAD patients for CADISP-2 and 213 Finnish controls were
obtained by Human660W-Quad BeadChip (Illumina). In CADISP-2, a subset of 26 samples had already
been genotyped on a genome-wide chip (also Human660W-Quad BeadChip, Illumina) at University
College London, UK (BRAINS, www.BrainsGenetics.com).22,23
Illumina BeadStudio® was used for genotype calling. Sample quality control filters were set to
exclude individuals with a call rate <0.95 (<0.96 for BRAINS samples), individuals showing
discrepancies between genetically inferred sex and given clinical data (X heterozygosity <0.10 but
given sex is female, and X heterozygosity >0.20 but given sex is male), and duplicates. Estimation of
IBD status was performed using PLINK (version 1.07),24 to identify potential cryptic relatedness.
When strongly related subjects were identified (full siblings or parent-offspring relationship), one of
them only was selected for the analysis. Finally, non-European individuals determined by principal
component analysis with HapMap2 samples (CEU, CHB, JPT, and YRI) were removed. These processes
removed 10 and 23 individuals from CADISP-1 and CADISP-2, respectively.
In addition, the top genotyped SNPs were also genotyped using Sequenom technology in a random
subset of 423 samples: the genotype consistency rate with Illumina genomewide genotypes was
>99%.
Genotyping and QC for follow-up studies
Genotyping methods are detailed in Supplementary Table 1.
Some participants included in the follow-up study had already been genotyped as part of other
genomewide association studies, either of ischemic stroke, or in a population-based setting (N=85
cases and 998 controls): GEOS9 at the Johns Hopkins Center for Inherited Disease Research (CIDR),
USA, on an Illumina Human Omni1 Quad® chip (N=32/68); ISGS/SWISS,10 at the National Institute on
Aging, Bethesda, Maryland, USA, on an Illumina 610 and 660 for cases and Illumina HumanHap
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550Kv1 or 550Kv3 for controls (N=10/51); WTCCC212 at the Wellcome Trust Sanger Institute (WTSI),
on the Illumina Human660W-Quad® chip (for N=43 CeAD cases); at the Helmholtz Center on the
Illumina Human550k® platform (for N=479 KORA controls); at the Human Genotyping Facility,
Genetic Laboratory Department of Internal Medicine, Erasmus MC, Rotterdam, The Netherlands on
an Illumina Human 550K Chip® (for N=400 controls from the Rotterdam Study). For these, QC filters
are detailed in the corresponding manuscripts.9,10,12 Briefly, QC filters included a SNP call rate <95%,
discordance between self-reported sex and sex determined from X chromosome heterozygosity,
minor allele frequency <0.01, Hardy-Weinberg equilibrium p<1×10−4 in controls and p<1×10−7 in cases
for ISGS, p<1×10−20 for WTCCC2 and p<10-6 for GEOS; evidence of cryptic relatedness was examined
using pairwise identical by descent (IBD) estimates in PLINK (samples were excluded if they shared
greater than a 0.125 proportion of alleles in ISGS,10 and for IBD>5% in WTCCC212). In ISGS,
multidimensional scaling analyses were performed to verify European ancestry and individuals
having estimated principal component vector 1 and 2 values greater than 3 standard deviations from
the combined CEU/TSI means for each vector were excluded as outliers.10
In addition, DNA samples from 206 CeAD patients were genotyped at the CNG on an Illumina Human
660W-Quad BeadChip®. These patients were also genotyped by Sequenom technology on a custom
chip for the top imputed SNPs. Moreover, 1,488 controls were genotyped by Sequenom technology
for the top SNPs. The SNP call rate was >98% for all SNPs selected for follow-up. All SNPs were in
Hardy-Weinberg equilibrium in controls.
Finally, to extend the size of the follow-up studies, DNA samples from 304 additional CeAD patients
and 97 controls, recruited in the same centers as other patients included in the discovery or follow-
up analyses, were genotyped for the top 5 genotyped loci and the top imputed locus (1-2 SNP(s) per
locus) using KASPAR© technology.
Of these 648 CeAD patients in total, 55 were excluded due to unavailability of information on the
dissection site, leaving us with 593 CeAD patients in the follow-up studies for analysis.
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At a final stage, we genotyped DNA of additional 64 CeAD patients and 65 controls for the top 5
genotyped loci (6 SNPs) using KASPAR© technology.
Imputation
Discovery GWAS
Imputation analysis in the discovery GWAS was performed for regional association plots of significant
loci, and for evaluating association statistics of previously published loci. By using filtered genotyped
SNPs, genotype imputation was performed by using MACH®
(http://genome.sph.umich.edu/wiki/MaCH) and Minimac®
(http://genome.sph.umich.edu/wiki/Minimac) softwares, using the HapMap2 CEU release 22
reference panel following standard procedures. Since some of the published SNPs were not included
in the HapMap2 reference panel, we additionally performed genotype imputation on the 1000G1008
panel for Europeans from the 1000 genomes project (www.1000genomes.org). Imputed genotypic
dosage data with good imputation quality (R-square >0.3) and minor allele frequency >0.01 were
used for association analysis.
In addition, the top imputed SNPs were also genotyped using Sequenom technology in a random
subset of 1,663 samples. The correlation coefficient between imputed dosage and Sequenom
validation genotype by was estimated, and SNPs with an r2 < 0.5 were excluded from further
analyses.
Follow-up studies
In order to analyze the top imputed SNPs from the discovery GWAS in follow-up samples from GEOS
(Maryland, USA), ISGS/SWISS (Florida, USA) and Munich-Augsburg (Bavaria, Germany), imputation
was performed on the HapMap Phase 2 and the 1000G1008 panels using MACH® and Minimac® for
GEOS cases and controls (Illumina Human Omni1 Quad Chip®) ISGS/SWISS cases and controls
(Illumina Human 660W-Quad BeadChip®), Munich-WTCCC2 cases (Illumina Human 610K BeadChip®)
and KORA controls (Illumina Human 550K Chip®) (Supplementary Table 1). In order to analyze the
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top imputed SNPs from the discovery GWAS in follow-up samples from the Netherlands, imputation
was performed on the HapMap Phase 2 and the 1000G1008 panels using MACH® and Minimac® for
Rotterdam Study controls (Illumina Human 550K Chip®); best guessed genotypes based on imputed
dosage were used (2 if imputed dosage >1.5, 1 if 0.5 < imputed dosage <= 1.5 and 0 if imputed
dosage <= 0.5), except for rs9409407 and rs2163474, for which observed genotypes were available in
the Rotterdam Study sample. For SNPs imputed on both HapMap2 and 1000G1008, the imputation
yielding the largest R-square was chosen.
Screening for latent population substructure and association analysis
Genome wide association analysis was done separately for each sampling stage (CADISP-1 or CADISP-
2). CADISP-1 and CADISP-2 samples were screened for latent population substructure, including
cryptic relatedness, using principal component analysis (PCA) as implemented in the EIGENSOFT
software (version 3.0).8,25 Within CADISP-1, Finnish samples made up a distinct cluster from the other
CADISP-1 subjects (Supplementary Figure 4), therefore Finnish samples were separated from
CADISP-1 for analyses (i.e. the latter were performed separately for CADISP-1 Finnish and CADISP-1
non-Finnish). Non-CeAD IS samples (collected at the same time and from the same institutions as
CADISP-1) were also divided into Finnish and non-Finnish subgroups. PCA was done again, using only
non-Finnish samples for the analysis. The first ten principal components and sex were included as
covariates in the logistic regression for CADISP-1 non-Finnish and CADISP-2 analyses. We included
principal components that represent known highly variable polymorphisms within European
populations (LCT gene on chromosome 2, common inversion on chromosome 8, and HLA) and we did
not observe any additional improvement of the genomic inflation factor beyond the 10 first principal
components. For CADISP-1 Finnish analyses, only the first principal component found within this
group was used as a covariate, as no additional improvement of the genomic inflation factor was
observed by including additional principal components.
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We studied the quantile-quantile (Q-Q) plot (Supplementary Figure 5) to ensure that the p-value
distributions in each of the samples conformed to a null distribution at all but the extreme tail, and
we calculated the genomic inflation factor lambda, which measures over-dispersion of test-statistics
from association tests, indicating population stratification.26 Genomic control was applied to correct
for residual inflation for each analysis (CADISP-1 Finnish, CADISP-1 non-Finnish and CADISP-2).
Association analysis was done similarly for imputation genotypes by using the mach2dat® software
(http://genome.sph.umich.edu/wiki/Mach2dat:_Association_with_MACH_output). Imputed
genotypic dosage was used as an explanation variable. Genomic control was also applied, by using
the same lambda value as for the GWAS with genotyped SNPs.
Meta-analysis strategy
For the meta-analysis of GWAS we performed a fixed effects inverse-variance weighted meta-
analysis technique using METAL® (utilizing the “standard error” option), after applying genomic
control within each sub-study (CADISP-1 Finnish, CADISP-1 non-Finnish, CADISP-2). Inverse-variance
weighted meta-analysis consists of weighing beta estimates by their inverse variance and obtaining a
combined estimate by summing the weighted betas and dividing them by the summed weights.
Hence, for imputed SNPs, results imputed with low certainty were down-weighted because the low
informativity of imputation leads to a large variance.
For the follow-up analysis, as we did not have genome-wide data for all samples, follow-up case-
control samples were analyzed separately by inclusion country for the top SNPs and results were
then meta-analyzed, using a fixed-effects inverse-variance weighted meta-analysis. Given the small
sample size for some analyses, we filtered out results with absolute effect estimates (beta) > 4 before
meta-analysis (of note, none of the results for the top 6 genotyped SNPs were filtered out by this
method).
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Candidate gene selection
As some of our top loci had previously been reported to be associated with myocardial infarction and
with migraine, we examined whether other susceptibility loci for these diseases discovered through a
genome-wide approach are also associated with CeAD (Supplementary Table 15 and 17). Since CeAD
is a major cause for ischemic stroke in young adults, we evaluated whether published susceptibility
SNPs for ischemic stroke of other etiologies are also associated with CeAD (Supplementary Table 16).
We examined associations with candidate SNPs previously reported to be significantly associated
with CeAD. We systematically reviewed published genetic association studies of CeAD using the same
search criteria as previously published,27 extending the search until march 13, 2012. We identified 18
candidate gene based genetic association studies (2 additional studies have been published since the
systematic review.28,29) Of these, 5 have reported significant associations with 3 different candidate
genes: ICAM-1 (rs5498),30 COL3A1 (3’ UTR 2-bp deletion),31 and MTHFR (rs1801133).32-34 Two of these
(rs5498 and rs1801133 were genotyped in our samples (Supplementary Table 11), while genotypes
for the 3’ UTR 2-bp deletion in COL3A1 were not available (and no proxy known). Of note, our sample
partially overlapped with two of the previously published CeAD candidate gene studies, however,
given the much larger sample size of the present analysis compared to the previous publications (N=
106 CeAD patients / 187 controls,32 and N= 45 CeAD patients / 50 controls31) this is unlikely to have
influenced our results.
We also examined associations with SNPs in collagen type III alpha 1 (COL3A1), as COL3A1 harbors
the causal mutations for vascular Ehlers-Danlos syndrome, a rare monogenic connective tissue
disorder known to cause CeAD. We included all available SNPs from the 1000 genome imputation
located in COL3A1 or within 100kb of the gene start or end, yielding 608 SNPs (Supplementary Table
12).
We tested associations of CeAD with SNPs previously found to be associated with intracranial
aneurysms in published GWAS, as an increased prevalence of intracranial aneurysms has been found
in CeAD patients, suggesting common pathways.35 GWAS on intracranial aneurysms in European
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populations were identified through the online catalogue of GWAS
(http://www.genome.gov/gwastudies, queried on August 30, 2013). These included 9 SNPs mapping
7 loci, summarized in Supplementary Table 13.
We also examined whether published susceptibility SNPs for thoracic aortic aneurysms and
dissection or abdominal aortic aneurysms are associated with CeAD (Supplementary Table 14).
Functional annotation
Functional annotation of the SNPs yielding the most significant associations in the GWAS meta-
analysis (SNPs selected for follow-up and all genotyped SNPs with p<10-4, Supplementary Table 2
and 5) was first undertaken using the SNAP program (http://www.broad.mit.edu/mpg/snap/) and
the Pupasuite program (http://pupasuite.bioinfo.cipf.es/).36 We looked for SNPs that were intragenic
non-synonymous coding SNPs, splice-site variants, transcription factor binding sites or corresponded
to a miRNA target or sequence.
We also performed an expression quantitative trait locus (eQTL) analysis:
Proxy SNPs in linkage disequilibrium (r2>0.5 in 1000GpIv3) were identified. For rs9349379 a less
stringent LD threshold was used (r2>0.1 in 1000GpIv3), as there are no SNPs in strong linkage
disequilibrium with this SNP. This is also true on the most recent 1000G European reference panel
(Phase I v3). Index SNPs and proxies were a collected database of expression SNP (eSNP) results. The
collected eSNP results met criteria for statistical thresholds for association with gene transcript levels
as described in the original papers.
Blood cell related eQTL studies included fresh lymphocytes,37 fresh leukocytes,38 leukocyte samples
in individuals with Celiac disease,39 whole blood samples,40-50 lymphoblastoid cell lines (LCL) derived
from asthmatic children,51,52 HapMap LCL from 3 populations,53 a separate study on HapMap CEU
LCL,54 additional LCL population samples,55-59 CD19+ B cells,60 primary PHA-stimulated T cells,55,58
CD4+ T cells,61 peripheral blood monocytes,60,62,63 and CD14+ monocytes before and after stimulation
with LPS or interferon-gamma,64 CD11+ dendritic cells before and after Mycobacterium tuberculosis
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infection,65 and a separate study of dendritic cells.66 Micro-RNA QTLs67 and DNase-I QTLs68 were also
queried for LCL.
Non-blood cell tissue eQTLs searched included omental and subcutaneous adipose,40,48,57,69
stomach,69 endometrial carcinomas,70 ER+ and ER- breast cancer tumor cells,71 brain cortex,62,72,73
gliomas,74 pre-frontal cortex,75-77 parietal lobe,78 frontal cortex,77,79 temporal cortex,73,77,79
hippocampus,77 thalamus,77 pons,79 cerebellum,73,77-79 3 additional large studies of brain regions
including prefrontal cortex, visual cortex and cerebellum, respectively,80 liver, 69,81,8283,84 osteoblasts,85
intestine,86 skeletal muscle,87 breast tissue (normal and cancer),88,89 lung,48,90,91 skin,48,57,92 primary
fibroblasts,55,58 sputum,93 and heart tissue from left ventricles,48 and left and right atria.94 Micro-RNA
QTLs were also queried for gluteal and abdominal adipose.95
Additional eQTL data was integrated from online sources including ScanDB, the Broad Institute GTex
browser, and the Prichard Lab (eqtl.uchicago.edu). Cerebellum, parietal lobe and liver eQTL data was
downloaded from ScanDB and cis-eQTLs were limited to those with P<1.0x10-6 and trans-eQTLs with
P<5.00x10-8. The top 1000 eQTL results were downloaded from the GTex Browser at the Broad
Institute for 9 tissues on 11/26/2013: thyroid, leg skin (sun exposed), tibial nerve, tibial artery,
skeletal muscle, lung, heart (left ventricle), whole blood, and subcutaneous adipose.48 All GTex
results had associations with P<8.40x10-7.
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Supplementary Figures Supplementary Figure 1: CADISP inclusion criteria
CeAD patients Non-CeAD IS patients
Inclusion Criteria
Typical radiological aspect of dissection* in a cervical artery (carotid, vertebral)
Recent ischemic stroke No signs of CeAD on ultrasound and angiography (MR or CT
or conventional), performed < 7 days after the stroke
Exclusion criteria
Purely intracranial dissection Iatrogenic dissection after endovascular procedure Age <18 years at inclusion Monogenic disorder known to cause CeAD (e.g. vascular
Ehlers-Danlos syndrome) , for genetic analyses Non-European origin, for genetic analyses
Possible IS with normal cerebral imaging CeAD cannot be ruled out (e.g. persistent arterial occlusion
without mural hematoma) Endovascular or surgical procedure on coronary, cervical or
cerebral arteries <48h Cardiopathies with very high embolic risk † Arterial vasospasm after subarachnoid hemorrhage Auto-immune disease possibly explaining IS Monogenic disease explaining IS ‡ Age < 18 years at inclusion Non-European origin, for genetic analyses
CeAD: Cervical Artery Dissection; IS: Ischemic Stroke; MR: Magnetic Resonance; CT: Computed Tomography; * Mural hematoma, pseudoaneurysm, long tapering stenosis, intimal flap, double lumen, or occlusion > 2 cm above the carotid bifurcation revealing a pseudoaneurysm or a long tapering stenosis after recanalization; † Mechanical prosthetic valves, mitral stenosis with atrial fibrillation, intracardiac tumor, infectious endocarditis, myocardial infarction < 4 months; ‡ e.g. CADASIL, Fabry disease, MELAS, homocystinuria, sickle cell disease
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Supplementary Figure 2: Patient recruiting centers for the CeAD GWAS
CADISP-1: in red, CADISP-1 recruiting centers included in the genetic analyses: Belgium, Brussels and Leuven; Finland, Helsinki; France, Lille, Paris, Amiens, Dijon, Besançon; Germany, Heidelberg, Ludwigshafen, Munich; Italy,Brescia, Perugia, Milan, Monza, Rome; Switzerland, Basel; UK, London. In blue, CADISP recruiting centers that were excluded from the genetic analyses: Turkey, Istanbul; Argentina, Cordoba.
CADISP-2: recruiting centers collaborating with the CADISP consortium for the genetics project, i.e. France, Nantes, Paris; Germany, Münster; Italy, Milan; Netherlands, Rotterdam; Spain, Barcelona; Switzerland, Lausanne; UK, London; USA, Salt Lake City - UT, Charlottesville – VA
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Supplementary Figure 3A: Selection of CADISP-1 CeAD patients and non-CeAD IS patients
In N=19 (CeAD: N=3; non-CeAD IS: N=16) genome-wide genotyping failed
Patients fulfilling CADISP clinical inclusion criteria after data monitoring, N=1750 (CeAD: N=1111; IS: N=658)
Patients eligible for the CADISP-genetics study, N=1695 (CeAD: N=1056; non-CeAD IS: N=639)
N=18 (CeAD: N=15; non-CeAD IS: N=3) patients from Turkey and Argentina were not included in the CADISP-genetics study, due to unavailability of controls from same region
Patients included in the CADISP-genetics study, N=1568 (CeAD: N=955; non-CeAD IS: N=613)
N=1 (CeAD: N=1) patient with genetically confirmed vascular Ehlers-Danlos syndrome excluded
For N=127 (CeAD: N=101; non-CeAD IS: N=26) patients good quality DNA was not available
N=36 (CeAD: N=20; non-CeAD IS: N=16) patients of non-European origin excluded from genetic analyses
Patients included in the present study, N=1525 (CeAD: N=942; non-CeAD IS: N=583) -Finnish: N=332 (CeAD: N=170; non-CeAD IS: N=162) -non-Finnish: N=1193 (CeAD: N=772; non-CeAD IS: N=421)
-Belgium: N=43 (CeAD: N=33; non-CeAD IS: N=10) -France: N=520 (CeAD: N=299; non-CeAD IS: N=221) -Germany: N=243 (CeAD: N=205; non-CeAD IS: N=38) -Italy: N=220 (CeAD: N=122; non-CeAD IS: N=98) -Switzerland: N=136 (CeAD: N=82; non-CeAD IS: N=54) -UK: N=31 (CeAD: N=31)
Patients successfully genotyped, N=1549 (CeAD: N=952; non-CeAD IS: 597)
N=24 (CeAD: N=10; non-CeAD IS: N=14) failed quality control (call rate < 95%, sex inconsistency between reported gender and sex on genetic analysis, cryptic relatedness, and PCA outliers)
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Supplementary Figure 3B: Selection of CADISP-2 CeAD patients
Patients included in the replication study, N=501 CeAD patients
Patients eligible for the CADISP-genetics study, N=475 CeAD patients
N=26 CeAD patients already successfully genotyped on genome-wide chip as part of independent stroke GWAS
Patients included in the CADISP-genetics study, N=455 CeAD patients
For N=20 CeAD patients good quality DNA was not available
N=451 CeAD patients included in the present study, -Belgium: N=20 -France: N=16 -Germany: N=198 -Italy: N=33 -Netherlands: N=16 -Spain: N=9 -Switzerland: N=46 -UK: N=19 -USA: N=94 (Utah, N=26; Virginia, N=68)
Patients successfully genotyped on Illumina 660K, N=444 CeAD patients
In N=11 CeAD patients genome-wide genotyping failed
N=19 CeAD patients failed quality control (outliers on principal component analysis, sex inconsistencies between reported gender and sex on genetic analysis)
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Supplementary Figure 4: Principal component analysis plots for selecting control samples Principal component scores of CADISP samples (cases in red and controls in blue) are plotted on European reference samples (in grey) from genotype repository data of the Centre National de Genotypage (CNG), Evry, France. The population labels on the plots are derived from this reference database: 1,0 Belgium; 2, Croatia; 3, Czech Republic; 4, France; 5, Germany; 6, Greece; 7, Hungary; 8, Ireland; 9, Italy; 10, Norway; 11, Poland; 12, Romania; 13, Russia; 14, Slovakia; 15, Spain; 16, Sweden; 17, UK. In the CADISP-1 plot there is a distinct case / control cluster at the right that consists of Finnish samples, which were therefore analyzed separately from the other samples. Non-European individuals had already been excluded by using eigenvectors 1 and 2 (data not shown).
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Principal component scores of CADISP samples (cases in red and controls in blue) are plotted on European reference samples (in grey) from genotype repository data of the Centre National de Genotypage (CNG), Evry, France. The population labels on the plots are derived from this reference database: 1,0 Belgium; 2, Croatia; 3, Czech Republic; 4, France; 5, Germany; 6, Greece; 7, Hungary; 8, Ireland; 9, Italy; 10, Norway; 11, Poland; 12, Romania; 13, Russia; 14, Slovakia; 15, Spain; 16, Sweden; 17, UK. In the CADISP-1 plot there is a distinct case / control cluster at the right that consists of Finnish samples, which were therefore analyzed separately from the other samples. Non-European individuals had already been excluded by using eigenvectors 1 and 2 (data not shown).
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Principal component scores of CADISP samples (cases in red and controls in blue) are plotted on European reference samples (in grey) from genotype repository data of the Centre National de Genotypage (CNG), Evry, France. The population labels on the plots are derived from this reference database: 1,0 Belgium; 2, Croatia; 3, Czech Republic; 4, France; 5, Germany; 6, Greece; 7, Hungary; 8, Ireland; 9, Italy; 10, Norway; 11, Poland; 12, Romania; 13, Russia; 14, Slovakia; 15, Spain; 16, Sweden; 17, UK. In the CADISP-1 plot there is a distinct case / control cluster at the right that consists of Finnish samples, which were therefore analyzed separately from the other samples. Non-European individuals had already been excluded by using eigenvectors 1 and 2 (data not shown).
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Principal component scores of CADISP samples (cases in red and controls in blue) are plotted on European reference samples (in grey) from genotype repository data of the Centre National de Genotypage (CNG), Evry, France. The population labels on the plots are derived from this reference database: 1,0 Belgium; 2, Croatia; 3, Czech Republic; 4, France; 5, Germany; 6, Greece; 7, Hungary; 8, Ireland; 9, Italy; 10, Norway; 11, Poland; 12, Romania; 13, Russia; 14, Slovakia; 15, Spain; 16, Sweden; 17, UK. In the CADISP-1 plot there is a distinct case / control cluster at the right that consists of Finnish samples, which were therefore analyzed separately from the other samples. Non-European individuals had already been excluded by using eigenvectors 1 and 2 (data not shown).
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Principal component scores of CADISP samples (cases in red and controls in blue) are plotted on European reference samples (in grey) from genotype repository data of the Centre National de Genotypage (CNG), Evry, France. The population labels on the plots are derived from this reference database: 1,0 Belgium; 2, Croatia; 3, Czech Republic; 4, France; 5, Germany; 6, Greece; 7, Hungary; 8, Ireland; 9, Italy; 10, Norway; 11, Poland; 12, Romania; 13, Russia; 14, Slovakia; 15, Spain; 16, Sweden; 17, UK. In the CADISP-1 plot there is a distinct case / control cluster at the right that consists of Finnish samples, which were therefore analyzed separately from the other samples. Non-European individuals had already been excluded by using eigenvectors 1 and 2 (data not shown).
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Supplementary Figure 5: Quantile– quantile plot showing observed versus expected p-values for CeAD
QQ-plot from each GWAS study; red line shows the distribution under the null hypothesis; p-values before genomic control are in grey; p-values corrected by genomic control are in blue
-log10expected P
λ = 1.062 -log10expected P
λ = 1.039
-lo
g 10o
bse
rved
P
-lo
g 10o
bse
rved
P
-lo
g 10o
bse
rved
P
-log10expected P
λ = 1.062
CADISP-1 non-Finnish
CADISP-1 Finnish
CADISP-2
-log10expected P
λ = 1.034
Nominal Genomic control
Nominal Genomic control
Nominal Genomic control
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QQ-plot for the GWAS meta-analysis; red line shows the distribution under the null hypothesis; p-values before genomic control are in grey; p-values corrected by genomic control are in blue
GWAS meta-analysis -l
og 1
0o
bse
rved
P
-log10expected P
λ = 1.032
Nominal Genomic control
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Supplementary Figure 6: Genome-wide signal intensity plot showing individual probability values against their genomic position for CeAD (meta-analysis of
CADISP-1 Finnish, CADISP-1 non-Finnish, CADISP-2)
Within each chromosome (x-axis), results from the genome-wide association analysis are plotted from left to right starting at the p-terminal end. Red line
indicates preset threshold for genome-wide significance, p<5x10-8
PHACTR1
LRP1
LNX1 FGGY
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Supplementary Figure 7: Genome-wide signal intensity plot showing individual probability values against their genomic position for CeAD
CADISP-1 Finnish
CADISP-1 Finnish
CADISP-1 non-Finnish
CADISP-1 Finnish
-lo
g 10P
Chromosome
Chromosome
-lo
g 10P
CADISP-2
Chromosome
-lo
g 10P
Within each chromosome (x-axis), results from the genome-wide association analysis are plotted from left to right starting at the p-terminal end. Genomic control has already been applied to these results. Red line indicates preset threshold for genome-wide significance, p<5x10-8
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Supplementary Figure 8: Forest plots for associations of CeAD with rs9349379 (PHACTR1), rs11172113 (LRP1), and rs6820391 (LNX1) Forest plot for top hits (rs9349379, rs11172113, and rs6820391). Individual discovery GWAS and follow-up samples, as well as combined discovery GWAS
samples, follow-up samples and discovery GWAS + follow-up samples are plotted against individual effect sizes (odds ratios). Size of boxes is inversely
proportional to variance. Horizontal lines are 95% confidence intervals.
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Supplementary Table 1: Genotyping platforms
Illumina Human 550K Chip®
Illumina Human 610K BeadChip®
Illumina Human 660W-Quad BeadChip®
Illumina Human Omni1 Quad Chip®
Sequenom® Custom Chip*
Kaspar® Technology
Total
CeAD patients Discovery GWAS
942 CADISP-1 non Finnish and
CADISP-1 Finnish
451 CADISP-2
1,393
CeAD patients Follow-up study
259† SIFAP (Germany), Brescia (Italy) ,
Nijmegen (Netherlands), Gothenburg (Sweden), Basel-Lausanne (Switzerland), UVA (Virginia, USA), ISGS/SWISS (Virginia and Florida, USA),
Munich-WTCCC2 (Germany)
32 GEOS (Maryland, USA)
368 SIFAP (Germany),
Heidelberg (Germany), Brescia (Italy), Gothenburg
(Sweden), Basel (Switzerland), Barcelona (Spain), UVA (Virginia, USA), Moscow (Russia)
659
Non-CeAD IS patients 583 CADISP-1 non Finnish and
CADISP-1 Finnish
583
Controls for CeAD GWAS (Discovery)
13,358 1,058 14,416
Controls for Follow-up study
879 KORA
(Germany), Rotterdam
(Netherlands)
51 ISGS/SWISS (Virginia, USA)
68 GEOS (Maryland, USA)
1,488 MONA-LISA Strasbourg
(France), Münster (Germany), KORA
(Germany), Gothenburg (Sweden), Vobarno-
Brescia (Italy), Nijmegen (Netherlands)
162 Barcelona (Spain)
ISGS/SWISS (Virginia, USA), Moscow (Russia)
2,648
*Two custom chips were designed (Sequenom®) including the 19 top genotyped and 21 top imputed loci from the discovery GWAS;
†Of these, 43 samples from Munich
were genotyped at the Wellcome Trust Sanger Institute (WTSI) and 10 samples from ISGS/SWISS (9 ISGS and 1 SWISS) were genotyped at the National Institute on Aging, Bethesda, Maryland, USA, the remaining samples were gentoyped at the Centre National de Génotypage (CNG); UVA: University of Virginia; WTCCC2: Wellcome Trust Case Control Consortium 2; ISGS/SWISS: Ischemic Stroke Genetics Study / Siblings with Ischemic Stroke Study; GEOS: Genetics of Early Onset Stroke, MONA-LISA: MOnitoring NAtionaL du rISque Artériel, KORA: Kooperative Gesundheitsforschung in der Region Augsburg For follow-up analyses of the top 19 genotyped SNPs in samples from Germany (SIFAP and KORA), Italy , the Netherlands, Sweden and Switzerland, we used genotypes obtained with the Illumina Human 660W-Quad BeadChip® for cases and with the Sequenom® custom chip for controls, except for Rotterdam (Netherlands) controls in whom genotypes obtained with the Illumina Human 550K Chip® were used; for follow-up analyses of the top 21 imputed SNPs in samples from Germany (SIFAP and KORA), Italy , the Netherlands, Sweden and Switzerland, we used genotypes obtained with the Sequenom® custom chip for both cases and controls, except for Rotterdam (Netherlands) controls, in whom best guessed genotypes based on HapMap2 and 1000G1008 imputation were used. The UVA-ISGS/SWISS sample includes
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Supplementary Tables
Supplementary Table 2: GWAS associations with CeAD at a p-value < 10-4 (genotyped SNPs)
SNP CHR POS Nearest gene Location Effect /
Alternative alleles
CADISP-1 non-Finnish CADISP-1 Finnish CADISP-2 Meta analysis
Effect allele frq OR P
Effect allele frq OR P
Effect allele frq OR P OR (95% CI) P value
case control case control case control
rs9349379 6 13011943 PHACTR1 INTRONIC G/A 0.340 0.395 0.76 4.75E-06 0.409 0.455 0.84 2.08E-01 0.327 0.398 0.73 1.07E-05 0.75 (0.69-0.82) 4.46E-10
rs11172113 12 55813550 LRP1 INTRONIC C/T 0.331 0.383 0.77 9.16E-06 0.356 0.386 0.90 4.49E-01 0.342 0.398 0.77 3.73E-04 0.78 (0.71-0.85) 4.22E-08
rs6741522 2 185544143 ZNF804A INTERGENIC A/G 0.178 0.141 1.33 1.25E-04 0.127 0.098 1.32 2.22E-01 0.170 0.132 1.38 1.42E-03 1.34 (1.19-1.50) 5.65E-07
rs1466535 12 55820737 LRP1 INTRONIC A/G 0.271 0.321 0.75 5.57E-06 0.300 0.284 1.11 4.65E-01 0.296 0.343 0.80 2.99E-03 0.80 (0.73-0.88) 2.07E-06
rs12402265 1 59463190 FGGY INTERGENIC A/G 0.301 0.262 1.21 2.00E-03 0.318 0.281 1.16 3.07E-01 0.334 0.284 1.28 6.86E-04 1.23 (1.13-1.35) 6.12E-06
rs6820391 4 54109453 LNX1 INTRONIC A/C 0.328 0.288 1.21 1.69E-03 0.321 0.288 1.15 3.36E-01 0.333 0.281 1.26 7.71E-04 1.23 (1.12-1.35) 6.35E-06
rs9910373 17 35561768 CASC3 INTRONIC G/A 0.042 0.024 2.03 1.65E-06 0.012 0.020 0.66 4.81E-01 0.031 0.023 1.35 1.60E-01 1.68 (1.33-2.13) 1.44E-05
rs1436565 4 97014009 PDHA2 INTERGENIC A/C 0.309 0.268 1.21 1.38E-03 0.327 0.311 1.12 4.27E-01 0.312 0.250 1.26 2.31E-03 1.22 (1.12-1.34) 1.59E-05
rs1107366 3 127386855 ALDH1L1 UPSTREAM G/A 0.515 0.473 1.17 5.10E-03 0.509 0.441 1.26 1.03E-01 0.532 0.478 1.22 1.96E-03 1.21 (1.11-1.31) 1.61E-05
rs13184907 5 103704240 RAB9P1 INTERGENIC G/A 0.275 0.236 1.29 6.93E-05 0.259 0.271 0.97 8.73E-01 0.259 0.223 1.21 1.27E-02 1.24 (1.12-1.36) 1.75E-05
rs9874508 3 127385107 ALDH1L1 UPSTREAM A/G 0.515 0.473 1.17 5.54E-03 0.509 0.441 1.26 1.03E-01 0.531 0.477 1.22 2.20E-03 1.20 (1.11-1.31) 1.94E-05
rs9398148 6 97170276 FHL5 SYNONYMOUS_CODING G/A 0.398 0.352 1.20 1.48E-03 0.318 0.287 1.17 2.94E-01 0.391 0.347 1.21 4.87E-03 1.21 (1.11-1.32) 2.11E-05
rs7130937 11 122558531 CLMP INTRONIC A/G 0.038 0.025 1.64 1.14E-03 0.041 0.026 1.86 1.07E-01 0.041 0.027 1.49 1.43E-02 1.63 (1.30-2.04) 2.10E-05
rs10217064 8 123479292 MRPS36P3 INTERGENIC C/T 0.510 0.480 1.21 6.08E-04 0.465 0.439 1.06 6.75E-01 0.517 0.468 1.23 4.46E-03 1.20 (1.10-1.30) 2.14E-05
rs3094575 6 29623781 OR2I1P INTERGENIC T/C 0.276 0.261 1.28 3.58E-04 0.221 0.167 1.45 3.45E-02 0.254 0.249 1.15 7.98E-02 1.25 (1.13-1.39) 2.22E-05
rs11258437 10 13609811 BEND7 INTRONIC C/T 0.422 0.443 0.90 5.05E-02 0.312 0.396 0.65 4.11E-03 0.386 0.447 0.77 5.76E-04 0.83 (0.76-0.90) 2.32E-05
rs2976414 8 24921034 NEFL INTERGENIC T/C 0.368 0.398 0.81 3.69E-04 0.338 0.366 0.88 3.95E-01 0.387 0.436 0.82 1.61E-02 0.83 (0.76-0.90) 2.33E-05
rs12758643 1 59442506 FGGY INTERGENIC T/C 0.300 0.258 1.23 8.76E-04 0.315 0.290 1.09 5.53E-01 0.318 0.274 1.24 4.93E-03 1.22 (1.11-1.33) 2.49E-05
rs12215208 6 12958280 PHACTR1 INTRONIC T/C 0.409 0.450 0.83 1.32E-03 0.509 0.530 0.87 3.21E-01 0.396 0.448 0.82 6.06E-03 0.83 (0.76-0.91) 2.52E-05
rs3094576 6 29624221 OR2I1P UPSTREAM,INTERGENIC A/C 0.161 0.159 1.38 1.08e-04 0.109 0.071 1.64 4.62e-02 0.136 0.138 1.17 1.60e-01 1.32 (1.16-1.50) 2.607E-05
rs2145309 10 105440115 SH3PXD2A INTRONIC A/G 0.271 0.308 0.82 1.92e-03 0.344 0.416 0.78 8.41e-02 0.274 0.317 0.82 1.41e-02 0.82 (0.74-0.90) 2.664E-05
rs12770479 10 13577210 BEND7 INTRONIC A/G 0.343 0.364 0.89 3.93e-02 0.262 0.347 0.59 1.25e-03 0.324 0.378 0.79 2.06e-03 0.83 (0.76-0.90) 3.038E-05
rs7086483 10 17556772 ST8SIA6 INTERGENIC A/C 0.203 0.231 0.81 2.00e-03 0.224 0.263 0.83 2.51e-01 0.208 0.247 0.79 6.17e-03 0.80 (0.72-0.89) 3.045E-05
rs4351426 8 123450673 MRPS36P3 INTERGENIC T/C 0.514 0.483 1.21 5.79e-04 0.465 0.453 1.05 7.19e-01 0.518 0.468 1.21 7.07e-03 1.19 (1.10-1.30) 3.229E-05
rs12548629 8 104270577 BAALC INTRONIC T/C 0.259 0.289 0.81 8.45e-04 0.272 0.301 0.89 4.50e-01 0.252 0.291 0.81 9.12e-03 0.81 (0.74-0.90) 3.263E-05
rs1483940 3 177639966 TBL1XR1 INTERGENIC A/G 0.144 0.112 1.30 1.26e-03 0.168 0.125 1.45 5.78e-02 0.142 0.113 1.25 3.21e-02 1.29 (1.15-1.46) 3.283E-05
rs9329342 10 13572654 BEND7 INTRONIC T/C 0.343 0.363 0.89 4.25e-02 0.262 0.347 0.59 1.24e-03 0.324 0.378 0.79 2.10e-03 0.83 (0.76-0.90) 3.382E-05
rs10842327 12 24372728 SOX5 INTERGENIC G/A 0.163 0.176 0.89 1.21e-01 0.141 0.209 0.66 3.12e-02 0.133 0.190 0.65 2.30e-05 0.78 (0.70-0.88) 3.465E-05
rs6772736 3 77368734 ROBO2 INTRONIC A/G 0.340 0.380 0.79 3.53e-05 0.397 0.446 0.82 1.56e-01 0.374 0.401 0.91 2.18e-01 0.83 (0.76-0.91) 3.633E-05
rs7910407 10 13603822 BEND7 INTRONIC C/T 0.246 0.269 0.87 2.51e-02 0.206 0.306 0.52 2.19e-04 0.241 0.284 0.81 1.26e-02 0.81 (0.74-0.90) 3.958E-05
rs13042529 20 17646510 BANF2 INTRONIC A/G 0.144 0.119 1.27 3.56e-03 0.179 0.167 1.13 4.99e-01 0.160 0.128 1.36 1.85e-03 1.29 (1.14-1.45) 3.896E-05
rs17345277 5 103794893 RAB9P1 INTERGENIC C/T 0.320 0.284 1.16 1.53e-02 0.318 0.240 1.61 2.92e-03 0.327 0.284 1.22 1.01e-02 1.21 (1.11-1.33) 3.994E-05
rs779714 3 7484984 GRM7 INTRONIC C/T 0.143 0.167 0.76 4.42e-04 0.135 0.141 0.87 4.91e-01 0.134 0.158 0.78 2.07e-02 0.77 (0.69-0.87) 4.053E-05
rs12752853 1 59441293 FGGY INTERGENIC T/C 0.299 0.257 1.22 1.16e-03 0.315 0.293 1.10 5.31e-01 0.316 0.271 1.23 7.09e-03 1.21 (1.11-1.33) 4.126E-05
rs12733512 1 59419566 FGGY INTERGENIC T/C 0.278 0.244 1.19 4.66e-03 0.315 0.276 1.21 2.00e-01 0.296 0.251 1.25 4.34e-03 1.22 (1.11-1.34) 4.246E-05
rs4452797 8 123450200 MRPS36P3 INTERGENIC G/A 0.514 0.484 1.20 7.72e-04 0.465 0.453 1.05 7.19e-01 0.518 0.468 1.21 7.26e-03 1.19 (1.10-1.30) 4.303E-05
rs12133893 1 59415019 FGGY INTERGENIC C/A 0.301 0.267 1.19 4.58e-03 0.335 0.288 1.25 1.38e-01 0.320 0.278 1.23 6.53e-03 1.21 (1.10-1.33) 4.499E-05
rs10903372 10 1161734 WDR37 INTRONIC T/C 0.174 0.144 1.19 1.72e-02 0.183 0.136 1.48 4.30e-02 0.188 0.144 1.32 2.70e-03 1.26 (1.13-1.41) 4.75E-05
rs2734335 6 32001923 C2 UPSTREAM G/A 0.478 0.463 1.26 2.09e-04 0.591 0.601 0.99 9.40e-01 0.520 0.489 1.22 1.72e-02 1.22 (1.11-1.34) 4.868E-05
rs12819896 12 29426982 ERGIC2 UPSTREAM G/A 0.196 0.163 1.32 9.55e-05 0.156 0.126 1.25 2.74e-01 0.166 0.154 1.15 1.40e-01 1.26 (1.13-1.40) 5.14E-05
rs7790530 7 70912121 CALN1 INTRONIC T/C 0.268 0.295 0.85 7.48e-03 0.268 0.331 0.73 3.77e-02 0.254 0.297 0.81 1.06e-02 0.82 (0.75-0.90) 5.222E-05
rs13436709 5 145294113 SH3RF2 UPSTREAM A/G 0.289 0.249 1.27 1.19e-04 0.232 0.213 1.12 4.80e-01 0.265 0.238 1.15 7.65e-02 1.22 (1.11-1.34) 5.27E-05
rs11112465 12 104241728 APPL2 INTERGENIC G/A 0.246 0.201 1.33 1.51e-05 0.191 0.134 1.42 6.34e-02 0.222 0.210 1.05 5.49e-01 1.23 (1.11-1.36) 5.403E-05
rs878017 10 13606210 BEND7 INTRONIC G/A 0.422 0.439 0.91 8.91e-02 0.312 0.390 0.65 4.11e-03 0.386 0.442 0.78 6.42e-04 0.84 (0.77-0.91) 5.597E-05
rs4748376 10 17517513 ST8SIA6 INTRONIC G/A 0.175 0.203 0.79 9.11e-04 0.259 0.256 1.05 7.68e-01 0.179 0.218 0.75 1.97e-03 0.80 (0.72-0.89) 5.403E-05
rs1012168 15 34239950 LOC751603 INTERGENIC A/C 0.208 0.177 1.35 2.67e-05 0.091 0.087 1.07 7.88e-01 0.183 0.167 1.14 1.63e-01 1.26 (1.12-1.4) 5.715E-05
rs6443345 3 177655465 TBL1XR1 INTERGENIC G/A 0.148 0.115 1.29 1.42e-03 0.165 0.124 1.43 6.84e-02 0.144 0.117 1.23 4.43e-02 1.28 (1.14-1.45) 5.682E-05
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rs4566655 4 7219899 SORCS2 INTERGENIC G/T 0.153 0.185 0.77 7.21e-04 0.083 0.099 0.77 2.88e-01 0.153 0.180 0.81 3.41e-02 0.79 (0.7-0.88) 6.328E-05
rs6056281 20 8883312 PLCB1 INTERGENIC A/C 0.335 0.290 1.22 7.76e-04 0.303 0.229 1.46 2.43e-02 0.314 0.291 1.13 1.14e-01 1.20 (1.1-1.32) 6.337E-05
rs10501995 11 101575571 YAP1 INTRONIC G/A 0.106 0.075 1.32 2.92e-03 0.071 0.056 1.12 7.03e-01 0.111 0.080 1.4 3.81e-03 1.33 (1.16-1.54) 6.428E-05
rs10807323 6 12903017 PHACTR1 INTRONIC A/G 0.383 0.421 0.83 1.01e-03 0.503 0.516 1.10 4.96e-01 0.377 0.422 0.84 1.48e-02 0.84 (0.77-0.91) 6.384E-05
rs2169127 10 60741905 FAM13C INTRONIC T/C 0.314 0.338 0.87 2.19e-02 0.265 0.329 0.72 3.54e-02 0.289 0.328 0.79 3.14e-03 0.83 (0.76-0.91) 6.578E-05
rs2239512 19 40523489 CD22 INTRONIC T/C 0.098 0.131 0.73 6.41e-04 0.094 0.126 0.76 2.39e-01 0.096 0.126 0.79 4.57e-02 0.75 (0.65-0.86) 6.693E-05
rs1294433 6 6690162 LOC652960 INTERGENIC G/A 0.497 0.460 1.21 6.02e-04 0.441 0.372 1.32 4.83e-02 0.482 0.460 1.12 1.12e-01 1.19 (1.09-1.29) 6.653E-05
rs7129480 11 12334342 MICALCL INTRONIC T/C 0.103 0.094 1.10 2.94e-01 0.127 0.080 1.71 1.68e-02 0.144 0.098 1.52 4.91e-05 1.31 (1.15-1.5) 6.956E-05
rs13163497 5 52214765 ITGA1 INTRONIC A/G 0.105 0.125 0.84 4.72e-02 0.150 0.179 0.78 2.06e-01 0.080 0.129 0.60 8.82e-05 0.75 (0.66-0.87) 6.897E-05
rs1493967 11 12335460 MICALCL INTRONIC T/C 0.103 0.094 1.10 2.97e-01 0.127 0.080 1.71 1.68e-02 0.144 0.098 1.52 4.91e-05 1.31 (1.15-1.5) 7.108E-05
rs4658145 1 90308995 ZNF326 INTERGENIC T/C 0.159 0.133 1.29 1.16e-03 0.103 0.070 1.56 7.88e-02 0.148 0.130 1.22 5.00e-02 1.28 (1.13-1.44) 7.043E-05
rs7225825 17 11184890 DNAH9 INTRONIC A/G 0.139 0.103 1.41 2.91e-05 0.091 0.131 0.71 1.31e-01 0.131 0.107 1.28 2.40e-02 1.29 (1.14-1.47) 7.035E-05
rs13294 1 148751611 ECM1 NON_SYNONYMOUS_CODING A/G 0.364 0.405 0.83 1.14e-03 0.374 0.376 0.95 7.47e-01 0.369 0.413 0.83 9.07e-03 0.84 (0.77-0.91) 7.15E-05
rs1490762 5 23862990 PRDM9 INTERGENIC G/A 0.445 0.404 1.21 8.32e-04 0.524 0.444 1.35 2.86e-02 0.455 0.430 1.12 1.20e-01 1.19 (1.09-1.29) 7.141E-05
rs4529013 4 87682837 PTPN13 INTERGENIC A/G 0.369 0.346 1.22 5.46e-04 0.353 0.284 1.36 3.65e-02 0.364 0.349 1.11 1.49e-01 1.19 (1.09-1.3) 7.383E-05
rs10783388 12 49467966 ATF1 INTRONIC T/C 0.409 0.354 1.22 4.89e-04 0.312 0.336 0.93 6.49e-01 0.393 0.336 1.22 7.40e-03 1.19 (1.09-1.3) 7.342E-05
rs7673698 4 87625590 MAPK10 INTERGENIC C/T 0.380 0.358 1.21 7.70e-04 0.356 0.293 1.32 5.92e-02 0.376 0.357 1.13 9.83e-02 1.19 (1.09-1.3) 7.544E-05
rs1894116 11 101575849 YAP1 INTRONIC G/A 0.106 0.075 1.31 3.03e-03 0.071 0.056 1.12 7.03e-01 0.111 0.081 1.39 4.60e-03 1.33 (1.15-1.53) 7.715E-05
rs11586795 1 39004386 RRAGC INTERGENIC A/G 0.131 0.105 1.24 9.45e-03 0.129 0.131 1.08 6.99e-01 0.135 0.094 1.43 7.47e-04 1.29 (1.14-1.46) 7.886E-05
rs12814930 12 29325607 FAR2 INTRONIC A/C 0.201 0.167 1.32 7.90e-05 0.156 0.128 1.22 3.30e-01 0.166 0.156 1.13 2.00e-01 1.25 (1.12-1.39) 7.88E-05
rs11205277 1 148159496 SF3B4 UPSTREAM G/A 0.483 0.448 1.18 3.34e-03 0.394 0.326 1.26 1.03e-01 0.475 0.441 1.18 2.18e-02 1.19 (1.09-1.29) 8.093E-05
rs11947775 4 87626542 MAPK10 INTERGENIC C/T 0.380 0.359 1.21 9.93e-04 0.355 0.294 1.31 6.63e-02 0.378 0.358 1.14 8.13e-02 1.19 (1.09-1.3) 7.978E-05
rs1464028 4 54155581 LNX1 UPSTREAM A/C 0.313 0.278 1.20 2.78e-03 0.291 0.254 1.23 1.82e-01 0.296 0.267 1.20 1.82e-02 1.2 (1.1-1.32) 8.315E-05
rs1509954 10 64260356 EGR2 INTERGENIC C/T 0.080 0.056 1.51 8.87e-05 0.053 0.021 2.20 4.15e-02 0.057 0.047 1.13 4.39e-01 1.41 (1.19-1.68) 8.402E-05
rs4865540 5 52220025 ITGA1 INTRONIC A/C 0.155 0.182 0.86 5.46e-02 0.179 0.215 0.81 2.24e-01 0.128 0.185 0.66 1.03e-04 0.79 (0.7-0.89) 8.489E-05
rs16889917 4 13854427 BOD1L INTERGENIC C/T 0.197 0.160 1.26 9.81e-04 0.150 0.099 1.62 2.03e-02 0.184 0.164 1.15 1.27e-01 1.25 (1.12-1.39) 8.621E-05
rs11580100 1 240893955 PLD5 INTERGENIC C/T 0.364 0.393 0.85 6.89e-03 0.403 0.420 0.95 7.06e-01 0.345 0.396 0.79 1.37e-03 0.84 (0.77-0.92) 8.809E-05
rs7562882 2 157557872 CDK7PS INTERGENIC C/T 0.269 0.307 0.77 2.66e-05 0.321 0.368 0.82 1.64e-01 0.315 0.323 0.94 3.84e-01 0.83 (0.76-0.91) 8.908E-05
rs2296625 10 853964 LARP4B INTRONIC C/T 0.178 0.159 1.15 5.94e-02 0.238 0.164 1.62 8.64e-03 0.207 0.163 1.31 2.60e-03 1.24 (1.11-1.39) 9.218E-05
rs1831474 10 20076086 PLXDC2 INTERGENIC A/G 0.427 0.386 1.16 9.13e-03 0.482 0.448 1.16 2.73e-01 0.462 0.418 1.23 3.61e-03 1.19 (1.09-1.29) 9.355E-05
Chr: Chromosome; Effect allele frq: frequency of effect allele; OR: odds ratio for effect allele; P: p-values obtained as described in the text. Alleles and chromosomal positions were identified on the basis of the plus strand of the National Center for Biotechnology Information (NCBI) build 36. The Nearest gene names are based on the Human Gene Organization (HUGO) Gene Nomenclature System.
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Supplementary Table 3: Association with non-CeAD IS of SNPs yielding the most significant associations with CeAD (N= 583 cases / 14,416 controls)
* Our power to detect an association with non-CeAD IS, at p<0.01 (Bonferroni correction for 5 loci) and OR>1.25, was 56% for SNPs with an EAF = 0.13 and 87% for SNPs with an EAF = 0.40 (http://pngu.mgh.harvard.edu/~purcell/gpc/); EA: Effect Allele; EAF: Effect Allele Frequency; FDR: False Discovery Rate. Alleles and chromosomal positions were identified on the basis of the plus strand of the National Center for Biotechnology Information (NCBI) build 36.
SNP Chr Position EA EAF Gene OR 95%CI P value* P value (FDR)
rs12402265 1 59463190 A 0.27 FGGY 0.98 0.85-1.14 0.82 0.88
rs6741522 2 185544143 A 0.13 ZNF804A 1.06 0.88-1.28 0.52 0.88
rs6820391 4 54109453 A 0.29 LNX1 0.95 0.83-1.10 0.53 0.88
rs9349379 6 13011943 G 0.40 PHACTR1 0.98 0.85-1.11 0.72 0.88
rs11172113 12 55813550 C 0.39 LRP1 0.96 0.84-1.10 0.57 0.88
rs1466535 12 55820737 A 0.32 LRP1 1.01 0.88-1.16 0.88 0.88
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Supplementary Table 4: Recruiting centers and matching algorithm for follow-up CeAD cases and controls
Follow-up CeAD cases * Follow-up controls *
Country Center / Study City(ies) N Country Center / Study City(ies) N
USA University of Maryland (GEOS) Baltimore (MD) 32 USA University of Maryland (GEOS) Baltimore (MD) 68
USA University of Virginia and Mayo Clinic Florida (ISGS/SWISS)
Charlottesville (VA) and Jacksonville (FL) 32II USA
University of Virginia and Mayo Clinic Florida (ISGS/SWISS)
Charlottesville (VA) and Jacksonville (FL) 51
USA University of Virginia Charlottesville (VA) 48II USA
University of Virginia and Mayo Clinic Florida (ISGS/SWISS)
Charlottesville (VA) and Jacksonville (FL) 75
Germany Munich University Hospital (WTCCC2)
Munich 43 Germany KORA Study Augsburg 479
Germany SIFAP Study (German centers) and Heidelberg University Hospital
Germany Cities participating in SIFAP but not in CADISP; Heidelberg
161 Germany KORA Study and Münster University Hospital
Augsburg and Münster 876
Italy Brescia University Hospital Brescia 121 Italy Vobarno Study, Brescia University Hospital
Vobarno, near Brescia 200
Netherlands Radboud University Nijmegen Medical Center/the FUTURE study
Nijmegen 20 Netherlands Radboud University Nijmegen Medical Center and Rotterdam Study †
Nijmegen (N=36) and Rotterdam (N=400) 436
Russia Research Center of Neurology RAMS, Moscow
Moscow 64 Russia Research Center of Neurology RAMS, Moscow
Moscow 65
Spain Hospital Vall d'Hebron Barcelona 17 Spain Hospital Vall d'Hebron Barcelona 22
Sweden The Sahlgrenska Academy at Gothenburg University (SAHLSIS)
Gothenburg 71 Sweden The Sahlgrenska Academy at Gothenburg University (SAHLSIS)
Gothenburg 67
Switzerland Basel and Lausanne University Hospitals
Basel, Lausanne 50 France MONA-LISA Study, Strasbourg Strasbourg ‡ 309
TOTAL 659 2,648
* Only individuals of European origin were included; † 400 individuals were randomly drawn from the Rotterdam Study database (Original Rotterdam Study, total N=5,974); ‡ Strasbourg is very close to the Swiss border and is located 140km and 340km away from Basel and Lausanne; historically these regions are closely related; § Altenburg, Bayreuth, Berlin, Bremen, Celle, Chemnitz, Dresden, Düsseldorf, Frankfurt, Giessen, Greifswald, Hamburg, Jena, Kiel, Leipzig, Marburg, Mülhausen, Thüringen, Neuköln, Regensburg, Rostock, Ulm; II these were analyzed separately because the first set of cases and controls (N=32/51) was genotyped genome-wide on an Illumina Human 660W-Quad BeadChip®, whereas the second set of cases and controls (N=48/75) was genotyped for the top loci using Kaspar© technology; UVA: University of Virginia; WTCCC2: Wellcome Trust Case Control Consortium 2; SAHLSIS: the Sahlgenska Academy Study on Ischemic Stroke; ISGS/SWISS: Ischemic Stroke Genetics Study / Siblings with Ischemic Stroke Study; GEOS: Genetics of Early Onset Stroke, MONA-LISA: MOnitoring NAtionaL du rISque Artériel, KORA: Kooperative Gesundheitsforschung in der Region Augsburg
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Supplementary Table 5: Association results for the top SNPs selected for follow-up
SNP Chr Position (build36)
Position (build37)
Imputed R2† Nearest
Gene Location EA EAF*
GWAS meta-analysis
(N=1,393/14,416)
Follow-up meta-analyis
(N=659/2,648)
GWAS + Follow-up meta-analyis
(N=2,052/17,064)
OR 95%CI P-value OR 95%CI P-value OR 95%CI P-value P(het)
rs12402265§ 1 59463190 59690602 0 n.a. FGGY INTERGENIC A 0.27 1.23 1.13-1.35 6.12x10-6 1.21 1.04-1.40 0.012 1.23 1.14-1.33 2.30x10-7 0.83
rs6741522 II 2 185544143 185836148 0 n.a. ZNF804A INTERGENIC A 0.13 1.34 1.19-1.50 5.65x10-7 1.09 0.91-1.31 0.36 1.26 1.15-1.39 2.29x10-6 0.06
rs6761601‡ 2 185570497 185862252 HapMap2 0.99 ZNF804A INTERGENIC G 0.19 1.27 1.14-1.41 8.21x10-6 1.04 0.87-1.23 0.67 1.20 1.10-1.31 5.35x10-5 0.05
rs6820391 4 54109453 54414696 0 n.a. LNX1 INTRONIC A 0.29 1.23 1.12-1.35 6.35x10-6 1.28 1.10-1.47 9.28x10-4 1.24 1.15-1.34 2.36x10-8 0.68
rs12215208‡§ 6 12958280 12850294 0 n.a. PHACTR1 INTRONIC T 0.46 0.83 0.76-0.91 2.52x10-5 0.84 0.73-0.97 0.015 0.83 0.77-0.9 1.17x10-6 0.91
rs9349379 II 6 13011943 12903957 0 n.a. PHACTR1 INTRONIC G 0.40 0.75 0.69-0.82 4.46x10-10 0.81 0.71-0.94 3.91x10-3 0.77 0.72-0.83 1.00x10-11 0.36
rs11172113 II 12 55813550 57527283 0 n.a. LRP1 INTRONIC C 0.39 0.78 0.71-0.85 4.22x10-8 0.93 0.81-1.07 0.34 0.82 0.76-0.89 3.03x10-7 0.03
rs1466535§ II 12 55820737 57534470 0 n.a. LRP1 INTRONIC A 0.32 0.80 0.73-0.88 2.07x10-6 0.92 0.80-1.07 0.30 0.83 0.77-0.90 4.94x10-6 0.10
rs75453177‡ II 18 66672732 1000G 0.94 CCDC102B INTRONIC G 0.03 1.81 1.52-2.16 4.11x10-11 1.41 0.89-2.25 0.15 1.76 1.49-2.07 2.31x10-11 0.33
rs2163474‡ II 18 66673592 1000G 0.97 CCDC102B INTRONIC A 0.03 1.78 1.50-2.11 3.86x10-11 1.24 0.81-1.9 0.32 1.69 1.44-1.98 7.65x10-11 0.13
SNPs in bold had an improved p-value after combining the discovery and follow-up sample; EA: Effect Allele; P het: p-value for heterogeneity between GWAS (discovery) and follow-up; * EAF: Effect Allele (Minor Allele) Frequency in GWAS controls; † R2 representing imputation informativity; ‡ N=512/2045 (rs2163474) and 507/2034 (rs75453177) in the follow-up meta-analysis; § These SNPs were imputed in the Maryland follow-up sample (on 1000G for rs1466535, and rs12215208, with an R-Square of 0.95, and 0.90 respectively, on HapMap2 for rs12402265 with an R-Square of 1; II pairwise linkage disequilibrium between SNPs, on 1000GpIv3, is: r2=0.67 for rs6741522 and rs6761601 ; r2=0.39 for rs12215208 and rs9349379; r2=0.60 for rs11172113 and rs1466535; r2=0.88 for rs75453177 and rs2163474. Alleles and chromosomal positions were identified on the basis of the plus strand of the National Center for Biotechnology Information (NCBI) build 36 and build 37.
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Supplementary Table 6: Evidence for null hypothesis H0 according to Bayesian approach
SNP Chr Position Nearest gene Frequentist P-value ABF
GWAS Follow-up Combined GWAS Follow-up Combined
rs12402265 1 59690602 FGGY 6.12x10-6 1.17x10-2 2.30x10-7 2.28x10-4 0.18 1.10x10-5
rs6741522 2 185836148 ZNF804A 5.65x10-7 0.36 2.29x10-6 4.27x10-5 1.71 1.36x10-4
rs6761601 2 185862252 ZNF804A 8.21x10-6 0.67 5.35x10-5 4.45x10-4 2.36 2.43x10-3
rs6820391 4 54414696 LNX1 6.35x10-6 9.28x10-4 2.36x10-8 5.71x10-4 2.30x10-2 3.25x10-6
rs12215208 6 12850294 PHACTR1 2.52x10-5 1.50x10-2 1.17x10-6 1.82x10-3 0.22 1.15x10-4
rs9349379 6 12903957 PHACTR1 4.46x10-10 3.91x10-3 1.00x10-11 6.51 x10-9 0.07 1.70x10-10
rs11172113 12 57527283 LRP1 4.22x10-8 0.34 3.03x10-7 4.01x10-6 2.06 2.37x10-5
rs1466535 12 57534470 LRP1 2.07x10-6 0.30 4.94x10-6 1.35x10-4 1.82 2.90x10-4
rs75453177 18 66672732 CCDC102B 4.11x10-11 0.15 2.31x10-11 2.46x10-8 0.84 1.09x10-8
rs2163474 18 66673592 CCDC102B 3.86x10-11 0.32 7.65x10-11 2.07x10-8 1.09 2.66x10-8
ABF: Asymptotic Bayes Factor
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Supplementary Table 7: Probability of false discovery for SNPs in the 6 genetic loci selected for follow-up according to Bayesian approach
SNP
BFDP
GWAS Follow-up Combined
1-1/1M 1-2/1M 1-1/500K 1-2/500K 1-1/100K 1-2/100K 1-1/40 1-2/40 1-1/6 1-2/6 1-1/1M 1-2/1M 1-1/500K 1-2/500K 1-1/100K 1-2/100K
rs12402265 1.00 0.99 0.99 0.98 0.96 0.92 0.87 0.77 0.47 0.26 0.92 0.85 0.85 0.73 0.52 0.35
rs6741522 0.98 0.96 0.96 0.91 0.81 0.68 0.99 0.97 0.90 0.77 0.99 0.99 0.99 0.97 0.93 0.87
rs6761601 1.00 1.00 1.00 0.99 0.98 0.96 0.99 0.98 0.92 0.83 1.00 1.00 1.00 1.00 1.00 0.99
rs6820391 1.00 1.00 1.00 0.99 0.98 0.97 0.47 0.30 0.10 4.39x10-2 0.76 0.62 0.62 0.45 0.25 0.14
rs12215208 1.00 1.00 1.00 1.00 0.99 0.99 0.90 0.81 0.53 0.31 0.99 0.98 0.98 0.97 0.92 0.85
rs9349379 6.46x10-3 3.24x10-3 3.24x10-3 1.62x10-3 6.50x10-4 3.25x10-4 0.74 0.59 0.27 0.13 1.70x10-4 8.51x10-5 8.51x10-5 4.25x10-5 1.70x10-5 8.51x10-6
rs11172113 0.80 0.67 0.67 0.50 0.29 0.17 0.99 0.98 0.91 0.80 0.96 0.92 0.92 0.86 0.70 0.54
rs1466535 0.99 0.99 0.99 0.97 0.93 0.87 0.99 0.97 0.90 0.78 1.00 0.99 0.99 0.99 0.97 0.94
rs75453177 2.40x10-2 1.22x10-2 1.22x10-2 6.12x10-3 2.46x10-3 1.23x10-3 0.97 0.94 0.81 0.63 1.08x10-2 5.44x10-3 5.44x10-3 2.73x10-3 1.09x10-3 5.46x10-4
rs2163474 2.03x10-2 1.02x10-2 1.02x10-2 5.15x10-3 2.07x10-3 1.03x10-3 0.98 0.95 0.84 0.68 2.59x10-2 1.31x10-2 1.31x10-2 6.61x10-3 2.65x10-3 1.33x10-3
BFDP: Bayesian False Discovery Probability (values of the title row under BFDP reflect prior probabilities of the null hypothesis H0)
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Supplementary Table 8: Associations of top genotyped SNPs from CeAD GWAS stratified on sex
Men
(808 cases / 14,416 controls)
Women (585 cases / 14,416 controls)
SNP Chr Position EA Gene OR CI p OR CI p p interaction *
rs12402265 1 59463190 A FGGY 1.26 1.11-1.42 2.02e-04 1.22 1.06-1.40 5.83e-03 0.88
rs6741522 2 185544143 A ZNF804A 1.35 1.16-1.56 1.06e-04 1.33 1.11-1.58 1.49e-03 0.93
rs6820391 4 54109453 A LNX1 1.18 1.05-1.34 5.59e-03 1.29 1.13-1.48 1.95e-04 0.32
rs9349379 6 13011943 G PHACTR1 0.78 0.69-0.88 3.33e-05 0.71 0.62-0.82 1.36e-06 0.32
rs11172113 12 55813550 C LRP1 0.77 0.69-0.87 2.31e-05 0.79 0.69-0.90 5.21e-04 0.72
rs1466535 12 55820737 A LRP1 0.78 0.69-0.88 7.96e-05 0.82 0.71-0.94 5.91e-03 0.47
* p-value for interaction with sex. Alleles and chromosomal positions were identified on the basis of the plus strand of the National Center for Biotechnology Information (NCBI) build 36.
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Supplementary Table 9: Associations of top genotyped SNPs from CeAD GWAS according to the presence or absence of migraine
CeAD with migraine
(338 cases / 9259 controls)
CeAD without migraine (587 cases / 9259 controls)
SNP Chr Position EA Gene OR CI p OR CI p pheterogeneity*
rs12402265 1 59463190 A FGGY 1.24 1.04-1.48 0.0143 1.18 1.02-1.35 0.0232 0.36
rs6741522 2 185544143 A ZNF804A 1.50 1.21-1.86 2.15x10-4 1.28 1.08-1.52 4.49x10-3 0.26
rs6820391 4 54109453 A LNX1 1.13 0.95-1.35 0.173 1.22 1.07-1.40 3.81x10-3 0.31
rs9349379 6 13011943 G PHACTR1 0.79 0.66-0.94 6.83x10-3 0.77 0.67-0.88 1.12x10-4 0.51
rs11172113 12 55813550 C LRP1 0.73 0.61-0.87 3.79x10-4 0.81 0.71-0.93 2.14x10-3 0.39
rs1466535 12 55820737 A LRP1 0.72 0.59-0.86 4.59x10-4 0.84 0.73-0.97 0.0195 0.096
* Information on migraine history (using International Headache Society criteria98) was obtained in CADISP-1 CeAD patients using a standardized protocol.3 and was available in 925 patients: 36.5% of CeAD patients reported a history of migraine prior to the dissection; † pheterogeneity = p-value of association with migraine in a case-only analysis (i.e. among patients with CeAD), used as a surrogate test of interaction, as information on migraine was not available in controls (see methods section in main manuscript); Alleles and chromosomal positions were identified on the basis of the plus strand of the National Center for Biotechnology Information (NCBI) build 36.
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Supplementary Table 10: Associations of top genotyped SNPs from CeAD GWAS according to presence or absence of recent cervical trauma
Presence of
recent cervical trauma (364 cases / 9259 controls)
*
Absence of recent cervical trauma
(570 cases / 9259 controls) *
SNP Chr Position EA Gene OR CI p OR CI p pheterogeneity†
rs12402265 1 59463190 A FGGY 1.17 0.99-1.39 0.0635 1.23 1.07-1.42 4.09e-03 0.56
rs6741522 2 185544143 A ZNF804A 1.54 1.26-1.89 2.53e-05 1.22 1.02-1.45 0.0324 0.094
rs6820391 4 54109453 A LNX1 1.21 1.03-1.43 0.0214 1.18 1.03-1.36 0.0168 0.59
rs9349379 6 13011943 G PHACTR1 0.77 0.65-0.90 1.54e-03 0.78 0.68-0.90 3.99e-04 0.46
rs11172113 12 55813550 C LRP1 0.78 0.66-0.92 2.65e-03 0.79 0.69-0.91 7.23e-04 0.95
rs1466535 12 55820737 A LRP1 0.75 0.63-0.89 1.28e-03 0.84 0.73-0.97 0.0191 0.22
* Information on recent cervical trauma (i.e. in the preceding month) was obtained in CADISP-1 CeAD patients using a standardized protocol.3 and was available in 934 patients: 39.0% of CeAD patients reported a cervical trauma in the previous month (considered minor. i.e. not leading to a medical visit or hospitalization. in 88%); † pheterogeneity = p-value of association with recent cervical trauma in a case-only analysis (i.e. among patients with CeAD) used as a surrogate test of interaction. as information on recent cervical trauma was not available in controls (see methods section in main manuscript). Alleles and chromosomal positions were identified on the basis of the plus strand of the National Center for Biotechnology Information (NCBI) build 36.
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Supplementary Table 11: Association between age of onset of CeAD and SNPs yielding the most significant associations with CeAD
SNP Chr Position CeAD
Risk Allele* Gene beta SE P value
P value (FDR)
rs12402265 1 59463190 A FGGY -0.278 0.413 0.50 0.93
rs6741522 2 185544143 A ZNF804A -0.098 0.507 0.85 0.93
rs6820391 4 54109453 A LNX1 0.036 0.393 0.93 0.93
rs9349379† 6 13011943 A PHACTR1 -1.091 0.397 0.006 0.036
rs11172113 12 55813550 T LRP1 -0.399 0.400 0.32 0.93
rs1466535 12 55820737 G LRP1 -0.175 0.425 0.68 0.93
Chr: Chromosome; OR: odds ratio; CI: Confidence interval; p-values are from case-only association analysis for age of onset; SE: standard error. Alleles and chromosomal positions were identified on the basis of the plus strand of the National Center for Biotechnology Information (NCBI) build 36; *effects on age of onset are shown for the allele associated with an increased risk of CeAD; † Mean age of onset of CeAD ± standard deviation was 43.98±10.14 years for rs9349379-AA carriers, 43.87±10.00 years for rs9349379-AG carriers, and 47.43±10.27 years for rs9349379-GG carriers
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Supplementary Table 12: Associations of top genotyped SNPs from CeAD GWAS according to presence or absence of cerebral ischemia
With cerebral ischemia (1.038 cases / 14.416 controls)
Without cerebral ischemia (294 cases / 14.416 controls)
SNP Chr Position EA Gene OR CI p OR CI p pheterogeneity*
rs12402265 1 59463190 A FGGY 1.24 1.12-1.37 5.05e-05 1.23 1.02-1.47 0.027 0.92
rs6741522 2 185544143 A ZNF804A 1.28 1.12-1.46 1.92e-04 1.48 1.19-1.85 5.39e-04 0.32
rs6820391 4 54109453 A LNX1 1.26 1.14-1.40 5.29e-06 1.08 0.90-1.3 0.39 0.12
rs9349379 6 13011943 G PHACTR1 0.79 0.71-0.87 2.49e-06 0.64 0.54-0.78 3.12e-06 0.031
rs11172113 12 55813550 C LRP1 0.81 0.73-0.89 2.61e-05 0.72 0.6-0.87 5.05e-04 0.25
rs1466535 12 55820737 A LRP1 0.84 0.76-0.94 1.31e-03 0.72 0.59-0.87 7.40e-04 0.067
Alleles and chromosomal positions were identified on the basis of the plus strand of the National Center for Biotechnology Information (NCBI) build 36; cerebral ischemia corresponds to ischemic stroke or transient ischemic attack (including transient monocular blindness); * pheterogeneity = p-value of association with presence of cerebral ischemia, in a case-only analysis used as a surrogate test of heterogeneity (see methods section)
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Supplementary Table 13: SNP – CeAD associations reaching p<10-5 in GWAS of carotid artery dissection or GWAS of vertebral artery dissection
SNP Chr Position Gene A1 A2 β(CeAD) SE(CeAD) P(CeAD) β(Car) SE(Car) P(Car) β(Ver) SE(Ver) P(Ver) P(het)
rs6056281 20 8935312 PLCB1 A C 0.1855 0.0464 6.34x10-5
0.2761 0.0567 1.13x10-6
0.0460 0.0774 0.55 0.02
rs13042529 20 17698510 BANF2 A G 0.2514 0.0611 3.90x10-5
0.3549 0.0736 1.44x10-6
0.0102 0.1061 0.92 5.73x10-3
rs6772736 3 77286044 ROBO2 A G -0.185 0.0448 3.63x10-5
-0.2701 0.0564 1.69x10-6
-0.0566 0.0720 0.43 0.03
rs8109263 19 20797644 ZNF626 T G 0.1981 0.0679 3.50x10-3
0.3763 0.0793 2.06x10-6
-0.0881 0.1243 0.48 6.80x10-4
rs11205277 1 149892872 SF3B4 A G -0.1700 0.0431 8.09x10-5
-0.2512 0.0534 2.53x10-6
-0.0377 0.0710 0.60 2.81x10-3
rs7285609 22 40358148 GRAP2 T C 0.1595 0.0439 2.80x10-4
0.2511 0.0549 4.87x10-6
-0.0217 0.0711 0.76 1.95x10-3
rs1540661 6 150929203 PLEKHG1 T C 0.2380 0.0752 1.55x10-3
0.0734 0.0986 0.46 0.5213 0.1132 4.12x10-6
3.91x10-3
rs17421651 6 150924288 PLEKHG1 T C -0.2629 0.0734 3.44x10-4
-0.1173 0.0955 0.22 -0.5042 0.1119 6.65x10-6
0.01
rs16916113 11 91247119 FAT3 T C 0.2982 0.1011 3.17x10-3
0.0572 0.1396 0.68 0.6430 0.1430 6.94x10-6
1.76x10-3
rs2600306 3 2663757 CNTN4 T C 0.1429 0.0437 1.08x10-3
0.0466 0.0549 0.40 0.3171 0.0714 8.95x10-6
4.95x10-4
CeAD: results for all CeAD together; Car: results for carotid artery dissection only; Ver: results for vertebral artery dissection only; p (het): p-value for heterogeneity according to dissection site
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Supplementary Table 14: Association of CeAD with SNPs previously found to be associated with CeAD in published candidate gene studies
SNP Chr Position Gene Published Risk Allele
OR 95%CI P value P value (FDR)
Author. year
rs1801133 (C677T ) 1p36.3 11778965 MTHFR T 1.06 0.97-1.16 0.17 0.34 Pezzini et al. Stroke 2002 33 Pezzini et al. Stroke 2007 32
Arauz et al. Cerebrovasc Dis 2007
rs5498 (E469K) 19p13.3-p13.2 10256683 ICAM1 G 0.99 0.91-1.08 0.88 0.88 Longoni et al. Neurology 2006 30
Chr: Chromosome; OR: odds ratio; CI: Confidence interval; p-values are from GWAS (after genomic control in analysis adjusted for principal components). Alleles and chromosomal positions were identified on the basis of the plus strand of the National Center for Biotechnology Information (NCBI) build 36.
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Supplementary Table 15: Association of CeAD with SNPs in COL3A1 (chromosome 2)
SNP position (build 37) EA OR CI p (raw) p (FDR) DistanceCOL3A1
rs62181702 189813323 a 0.85 0.75-0.96 0.00664 0.155 25775
rs41272817 189854746 a 0.57 0.38-0.86 0.007278 0.155 wg
rs56278801 189774361 t 1.16 1.04-1.3 0.009686 0.155 64737
rs17357408 189781621 t 1.16 1.04-1.3 0.009889 0.155 57477
rs56311677 189769689 t 0.86 0.77-0.97 0.009999 0.155 69409
rs57079725 189769275 a 0.86 0.77-0.97 0.01018 0.155 69823
rs11681095 189772877 t 1.16 1.03-1.29 0.01123 0.155 66221
rs59972417 189773925 a 1.16 1.03-1.29 0.01123 0.155 65173
rs61375046 189778132 t 1.16 1.03-1.29 0.01147 0.155 60966
rs17357492 189782744 a 1.15 1.03-1.29 0.01157 0.155 56354
rs11686968 189782047 t 1.15 1.03-1.29 0.01181 0.155 57051
rs17357358 189781347 t 1.15 1.03-1.29 0.01181 0.155 57751
rs17357387 189781380 a 0.87 0.77-0.97 0.01181 0.155 57718
rs59963454 189780817 a 1.15 1.03-1.29 0.01181 0.155 58281
rs60849913 189780498 t 1.15 1.03-1.29 0.01181 0.155 58600
rs6738043 189770767 a 1.15 1.03-1.29 0.01184 0.155 68331
rs6734328 189786480 a 0.87 0.77-0.97 0.01194 0.155 52618
chr2:189788260 189788260 c 0.87 0.77-0.97 0.01198 0.155 50838
rs56036327 189789424 a 0.87 0.77-0.97 0.01198 0.155 49674
rs10168216 189871573 a 1.48 1.09-2.01 0.01213 0.155 wg
rs55915126 189779148 t 0.87 0.77-0.97 0.01209 0.155 59950
rs10194475 189871623 a 0.68 0.5-0.92 0.01222 0.155 wg
rs13429708 189783043 a 1.15 1.03-1.29 0.01215 0.155 56055
rs11692997 189782139 t 0.87 0.77-0.97 0.01219 0.155 56959
rs61090110 189779464 a 0.87 0.77-0.97 0.01237 0.155 59634
rs59151031 189774304 a 0.87 0.78-0.97 0.01248 0.155 64794
rs7424790 189902190 a 1.15 1.03-1.28 0.01468 0.155 5550
rs41272839 189859908 t 0.66 0.47-0.92 0.01471 0.155 wg
rs7421040 189905601 t 0.87 0.78-0.97 0.01512 0.155 28129
chr2:189856625 189856625 t 0.66 0.47-0.92 0.01505 0.155 wg
rs7423808 189891500 t 0.87 0.78-0.97 0.01524 0.155 14028
rs13008197 189901060 t 1.14 1.03-1.28 0.01536 0.155 23588
rs4667259 189900093 t 1.14 1.03-1.28 0.01536 0.155 22621
rs10194304 189871627 t 0.69 0.51-0.93 0.01531 0.155 wg
rs7606293 189890454 a 1.15 1.03-1.28 0.01547 0.155 12982
chr2:189853696 189853696 a 1.52 1.08-2.12 0.0154 0.155 wg
rs6434310 189889823 a 0.87 0.78-0.97 0.01588 0.155 12351
rs13401377 189883973 t 0.87 0.77-0.97 0.01598 0.155 6501
chr2:189850103 189850103 t 0.66 0.47-0.93 0.01592 0.155 wg
rs12105778 189809650 t 0.87 0.78-0.97 0.01601 0.155 29448
rs6746794 189782576 t 1.15 1.03-1.29 0.01613 0.155 56522
rs12693526 189901295 a 1.14 1.03-1.28 0.01639 0.155 23823
rs3765160 189868381 a 0.71 0.54-0.94 0.0164 0.155 wg
rs13306257 189867144 a 1.48 1.07-2.04 0.01646 0.155 wg
rs3765161 189868410 t 0.71 0.54-0.94 0.01657 0.155 wg
rs12105340 189865657 t 0.68 0.49-0.93 0.01663 0.155 wg
rs6434311 189889886 a 0.87 0.78-0.98 0.01674 0.155 12414
rs11902058 189862808 a 1.48 1.07-2.04 0.01665 0.155 wg
rs28736387 189862890 a 0.68 0.49-0.93 0.01665 0.155 wg
rs57298826 189778029 a 0.82 0.7-0.96 0.01676 0.155 61069
rs58948599 189778030 a 0.82 0.7-0.96 0.01676 0.155 61068
rs41265581 189844304 a 0.66 0.47-0.93 0.01705 0.155 wg
rs13306267 189860630 a 1.48 1.07-2.04 0.01742 0.155 wg
rs13306269 189860792 t 1.48 1.07-2.04 0.01742 0.155 wg
rs12105286 189860144 t 0.68 0.49-0.93 0.01745 0.155 wg
rs2271679 189859073 a 0.68 0.49-0.93 0.01749 0.155 wg
rs4308073 189796924 a 0.86 0.77-0.97 0.01744 0.155 42174
rs7424137 189883644 a 0.87 0.77-0.98 0.01771 0.155 6172
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rs7425499 189891639 a 0.88 0.78-0.98 0.01806 0.155 14167
rs55972740 189865225 t 0.68 0.49-0.94 0.01866 0.155 wg
rs56276188 189790528 t 0.77 0.62-0.96 0.01891 0.155 48570
rs2271680 189862352 a 0.68 0.5-0.94 0.01919 0.155 wg
rs13306268 189860657 a 1.47 1.06-2.02 0.01975 0.155 wg
rs16830842 189797627 c 0.81 0.68-0.97 0.01977 0.155 41471
rs13393368 189901119 a 0.87 0.77-0.98 0.01991 0.155 23647
rs7576108 189852115 a 0.69 0.5-0.95 0.02119 0.155 wg
rs10497695 189850694 t 1.46 1.06-2.01 0.02133 0.155 wg
chr2:189778601 189778601 a 0.87 0.78-0.98 0.02141 0.155 60497
rs16830840 189796272 a 0.81 0.68-0.97 0.02222 0.155 42826
rs10194682 189906743 a 1.14 1.02-1.27 0.02274 0.155 29271
rs3736488 189856086 a 1.48 1.05-2.08 0.02596 0.155 wg
rs12693528 189977464 t 1.13 1.01-1.27 0.02961 0.155 20297
rs10194770 189906814 c 0.87 0.77-0.99 0.02963 0.155 29342
rs7557945 189975790 t 1.13 1.01-1.26 0.03021 0.155 98318
rs7568732 189975415 a 1.13 1.01-1.26 0.03021 0.155 97943
rs11690723 189974104 c 1.13 1.01-1.26 0.03065 0.155 96632
rs10181597 189954227 t 0.89 0.79-0.99 0.03135 0.155 76755
rs12622083 189935693 a 1.13 1.01-1.26 0.03214 0.155 58221
rs7588568 189805077 a 0.9 0.81-0.99 0.03204 0.155 34021
rs7420511 189948267 t 1.13 1.01-1.25 0.03234 0.155 70795
rs62181700 189805784 a 1.11 1.01-1.23 0.0322 0.155 33314
chr2:189809112 189809112 t 0.81 0.66-0.98 0.03226 0.155 29986
rs6434318 189941473 a 1.12 1.01-1.25 0.03276 0.155 64001
rs10178886 189947848 t 0.89 0.8-0.99 0.03295 0.155 70376
rs7426169 189945414 a 1.13 1.01-1.25 0.03295 0.155 67942
rs6414121 189925313 c 1.12 1.01-1.25 0.03325 0.155 47841
rs6434316 189930854 a 1.12 1.01-1.25 0.03325 0.155 53382
rs6434317 189936687 t 0.89 0.8-0.99 0.03325 0.155 59215
rs7421525 189939718 a 1.12 1.01-1.25 0.03325 0.155 62246
rs1028158 189752643 a 1.11 1.01-1.22 0.03314 0.155 86455
rs11686253 189911568 a 1.12 1.01-1.25 0.03349 0.155 34096
chr2:189803682 189803682 t 1.51 1.03-2.21 0.0333 0.155 35416
chr2:189754104 189754104 t 1.56 1.03-2.36 0.0337 0.155 84994
rs6434321 189942556 t 1.12 1.01-1.25 0.03395 0.155 65084
rs10178611 189923401 t 0.89 0.8-0.99 0.03463 0.155 45929
rs12989558 189923434 a 0.89 0.8-0.99 0.03463 0.155 45962
rs6434323 189958348 a 0.89 0.79-0.99 0.03464 0.155 80876
rs58220378 189854349 t 1.71 1.04-2.81 0.03474 0.155 wg
rs7602447 189919517 t 0.89 0.8-0.99 0.03526 0.155 42045
rs7423973 189916513 t 0.89 0.8-0.99 0.03699 0.155 39041
chr2:189955346 189955346 t 1.14 1.01-1.29 0.03726 0.155 77874
rs6749305 189819684 a 0.82 0.68-0.99 0.03734 0.155 19414
rs6434315 189924514 t 0.89 0.8-0.99 0.03774 0.155 47042
rs16830961 189837112 a 1.22 1.01-1.46 0.03754 0.155 1986
rs6738371 189918787 t 1.12 1.01-1.25 0.0381 0.155 41315
chr2:189769630 189769630 t 1.1 1.01-1.21 0.03819 0.155 69468
rs4667253 189799682 t 1.1 1-1.2 0.03836 0.155 39416
rs12693527 189913273 t 1.12 1.01-1.25 0.03882 0.155 35801
rs7425297 189968219 c 0.88 0.78-0.99 0.03887 0.155 90747
rs7425294 189968175 a 0.88 0.78-0.99 0.03916 0.155 90703
chr2:189757127 189757127 t 1.25 1.01-1.54 0.04209 0.155 81971
chr2:189757269 189757269 a 0.8 0.65-0.99 0.04209 0.155 81829
chr2:189809243 189809243 a 0.81 0.65-0.99 0.04283 0.155 29855
rs13421823 189938960 t 0.88 0.78-1 0.0437 0.155 61488
rs12616391 189854348 a 0.52 0.27-0.98 0.0437 0.155 wg
rs10931393 189953960 a 0.88 0.78-1 0.04403 0.155 76488
chr2:189762524 189762524 a 1.24 1.01-1.54 0.04361 0.155 76574
chr2:189755909 189755909 a 1.25 1.01-1.54 0.04366 0.155 83189
rs28763879 189870376 t 1.61 1.01-2.55 0.04441 0.155 wg
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chr2:189763472 189763472 c 1.24 1.01-1.54 0.04393 0.155 75626
rs11675562 189764237 t 1.24 1.01-1.54 0.04393 0.155 74861
rs7425309 189968347 a 0.88 0.77-1 0.04567 0.155 90875
chr2:189791390 189791390 a 0.8 0.65-1 0.04536 0.155 47708
rs2351416 189769956 t 0.91 0.83-1 0.04586 0.155 69142
chr2:189956761 189956761 t 1.14 1-1.29 0.04606 0.155 79289
chr2:189962922 189962922 a 1.13 1-1.28 0.04607 0.155 85450
rs7425292 189968352 c 1.14 1-1.3 0.04656 0.155 90880
chr2:189790137 189790137 a 1.24 1-1.53 0.04631 0.155 48961
chr2:189789458 189789458 t 1.24 1-1.53 0.04762 0.155 49640
chr2:189961255 189961255 a 0.88 0.78-1 0.04909 0.155 83783
rs57547130 189912371 t 1.13 1-1.27 0.04959 0.155 34899
rs58289599 189964389 t 0.88 0.77-1 0.04957 0.155 86917
rs61410138 189964395 t 1.14 1-1.3 0.04957 0.155 86923
rs7424585 189941784 a 0.89 0.79-1 0.04969 0.155 64312
Results from 1000 genome imputation analysis. All SNPs located in COL3A1 and within 100kb of the start and end of the gene were tested. Only SNPs with raw p-value < 0.05 are shown (out of a total of 608 SNPs imputed on the 1000 genome); FDR: False Discovery Rate; wg: within gene; EA: effect allele; * distance to gene start or gene end. Alleles and chromosomal positions were identified on the basis of the plus strand of the National Center for Biotechnology Information (NCBI) build 37.
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Supplementary Table 16: Association of CeAD with SNPs associated with intracranial aneurysms (IA) in published GWAS
SNP Chr Position Gene IA
Risk Allele OR 95%CI P value
P value (FDR)
Author. year
rs700651 2q33.1 198339959 BOLL, PLCL1 G 0.95 0.87-1.04 0.29 0.46 Bilguvar et al. Nat Genet 2008 99
rs6842241 4q31.22 148400819 EDNRA C 1.05 0.93-1.19 0.41 0.46 Low et al. Hum Mol Genet 2012 100
rs10958409 8q11.23 55489644 SOX17 A 0.92 0.82-1.03 0.16 0.46 Bilguvar et al. Nat Genet 2008 99
rs1504749 8q11.23 55473264 SOX17 C 0.94 0.85-1.05 0.25 0.46 Yasuno et al. Nat Genet 2010 101
rs9298506 8q11.23 55600077 SOX17 A 0.93 0.84-1.04 0.19 0.46 Bilguvar et al. Nat Genet 2008 99 Yasuno et al. Nat Genet 2010 101
rs1333040 9p21.3 22073404 CDKN2A, CDKN2B T 1.09 1.00-1.19 0.047 0.38 Bilguvar et al. Nat Genet 2008 99 Yasuno et al. Nat Genet 2010 101
rs12413409 10q24.32 104709086 CNNM2 G 0.97 0.84-1.12 0.66 0.66 Yasuno et al. Nat Genet 2010 101
rs9315204 13q13.1 32591837 STARD13 T 1.04 0.94-1.15 0.41 0.47 Yasuno et al. Nat Genet 2010 101
rs11661542 18q11.2 18477693 RBBP8 C 0.96 0.88-1.05 0.37 0.47 Yasuno et al. Nat Genet 2010 101
Chr: Chromosome; FDR: False Discovery Rate; OR: odds ratio; CI: Confidence interval; p-values are from GWAS (after genomic control. in analysis adjusted for principal components); Alleles and chromosomal positions were identified on the basis of the plus strand of the National Center for Biotechnology Information (NCBI) build 36.
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Supplementary Table 17: Association of CeAD with SNPs associated with aortic aneurysms (AA) and dissection in published GWAS
Phenotype SNP Chr Position Gene AA
Risk Allele OR 95%CI P value Author, year
Thoracic AA and dissections rs10519177 15 46544486 FBN1 G 1.00 0.91-1.10 1.00 LeMaire et al. Nat Genet 2011 102
Thoracic AA and dissections rs4774517 15 46546582 FBN1 A 1.00 0.91-1.10 0.98 LeMaire et al. Nat Genet 2011 102
Thoracic AA and dissections rs755251 15 46599311 FBN1 G 1.00 0.90-1.10 0.92 LeMaire et al. Nat Genet 2011 102
Thoracic AA and dissections rs1036477 15 46702217 FBN1 G 1.05 0.92-1.20 0.46 LeMaire et al. Nat Genet 2011 102
Thoracic AA and dissections rs2118181 15 46703175 FBN1 G 1.05 0.92-1.20 0.50 LeMaire et al. Nat Genet 2011 102
Abdominal AA rs1466535 12 55820737 LRP1 C 1.25 1.37-1.14 2.07x10-6 Bown, et al., Am J Hum Genet 2011 103
Chr: Chromosome; OR: odds ratio; CI: Confidence interval; p-values are from GWAS (after genomic control. in analysis adjusted for principal components). Alleles and chromosomal positions were identified on the basis of the plus strand of the National Center for Biotechnology Information (NCBI) build 36.
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Supplementary Table 18: Associations of known susceptibility SNPs for myocardial infarction with CeAD
SNP Chr Pos Gene MI risk allele
OR (95% CI) P value P value (FDR)
Rsq Reference
rs11206510 1 55496039 PCSK9 T 1.10 (0.99-1.23) 0.09 0.51 -- Kathiresan et al. Nat Genet 2009 104
rs17114036 1 56962821 PPAP2B A 0.90 (0.78-1.04) 0.14 0.63 0.992 Schunkert et al. Nat Genet 2011 105
rs599839 1 109822166 PSRC1 A 1.00 (0.90-1.11) 0.96 0.98 0.953 Samani et al. N Engl J Med 2007 106
rs4845625 1 154422067 IL6R T 1.01 (0.92-1.10) 0.88 0.97 0.983 The CARDIoGRAMplusC4D Consortium 107
rs17465637 1 222823529 MIA3 C 1.22 (1.05-1.42) 0.01 0.085 0.422 Samani et al. N Engl J Med 2007 106
rs515135 2 21286057 APOB C 1.01 (0.91-1.13) 0.83 0.97 0.970 The CARDIoGRAMplusC4D Consortium 107
rs6544713 2 44073881 ABCG5-ABCG8 T 1.11 (1.01-1.21) 0.02 0.15 -- The CARDIoGRAMplusC4D Consortium 107
rs1561198 2 85809989 VAMP5-VAMP8-GGCX T 0.96 (0.89-1.05) 0.39 0.82 0.998 The CARDIoGRAMplusC4D Consortium 107
rs2252641 2 145801461 ZEB2-AC074093.1 C 1.05 (0.96-1.14) 0.28 0.71 -- The CARDIoGRAMplusC4D Consortium 107
rs6725887 2 203745885 WDR12 C 1.01 (0.89-1.14) 0.89 0.97 -- Kathiresan et al. Nat Genet 2009 104
rs9818870 3 138122122 MRAS T 0.98 (0.87-1.11) 0.77 0.97 0.966 Erdmann et al. Nat Genet 2009 108
rs1878406 4 148393664 EDNRA T 0.93 (0.82-1.05) 0.26 0.71 0.981 The CARDIoGRAMplusC4D Consortium 107
rs7692387 4 156635309 GUCY1A3 G 1.02 (0.91-1.13) 0.75 0.97 -- The CARDIoGRAMplusC4D Consortium 107
rs273909 5 131667353 SLC22A4-SLC22A5 G 1.05 (0.91-1.20) 0.52 0.95 0.921 The CARDIoGRAMplusC4D Consortium 107
rs6903956 6 11774583 C6orf105 A 1.01 (0.93-1.11) 0.76 0.97 -- Wang, F. et al. Nat Genet 2011 109
rs9349379 6 12903957 PHACTR1 G 0.75 (0.69-0.82) 4.46x10-10
2.27x10-8
-- Kathiresan et al. Nat Genet 2009 104
rs12526453 6 12927544 PHACTR1 C 0.88 (0.81-0.97) 0.006 0.061 0.983 Kathiresan et al. Nat Genet 2009 104
rs17609940 6 35034800 ANKS1A G 1.05 (0.94-1.18) 0.36 0.80 0.997 Schunkert et al. Nat Genet 2011 105
rs10947789 6 39174922 KCNK5 T 1.04 (0.95-1.15) 0.40 0.82 0.987 The CARDIoGRAMplusC4D Consortium 107
rs12190287 6 134214525 TCF21 C 1.01 (0.90-1.12) 0.90 0.97 0.636 Schunkert et al. Nat Genet 2011 105
rs2048327 6 160863532 SLC22A3-LPAL2-LPA C 0.95 (0.87-1.04) 0.23 0.71 -- The CARDIoGRAMplusC4D Consortium 107
rs3798220 6 160961137 SLC22A3-LPAL2-LPA C 1.04 (0.72-1.50) 0.83 0.97 0.903 Tregouet et al. Nat Genet 2009 110
rs4252120 6 161143608 PLG T 1.02 (0.93-1.12) 0.72 0.97 0.996 The CARDIoGRAMplusC4D Consortium
107
rs2023938 7 19036775 HDAC9 C 0.80 (0.69-0.93) 0,0037 0.047 -- The CARDIoGRAMplusC4D Consortium 107
rs10953541 7 107244545 BCAP29 C 0.99 (0.90-1.09) 0.85 0.97 -- C4D Genetics Consortium. Nat Genet 2011 111
rs11556924 7 129663496 ZC3HC1 C 1.00 (0.90-1.10) 0.93 0.97 0.703 Schunkert et al. Nat Genet 2011 105
rs264 8 19813180 LPL G 1.00 (0.89-1.13) 0.99 0.99 -- The CARDIoGRAMplusC4D Consortium 107
rs2954029 8 126490972 TRIB1 A 1.03 (0.94-1.12) 0.52 0.95 0.992 The CARDIoGRAMplusC4D Consortium 107
rs3217992 9 22003223 CDKN2B-AS1 T 1.15 (1.06-1.26) 0.001 0.025 -- The CARDIoGRAMplusC4D Consortium 107
rs1333049‡ 9 22125503 CDKN2B-AS1 C 1.08 (0.99-1.19) 0.10 0.51 0.808 Samani et al. N Engl J Med 2007
106
rs579459 9 136154168 ABO C 0.99 (0.90-1.10) 0.91 0.97 0.968 Schunkert et al. Nat Genet 2011 105
rs2505083 10 30335122 KIAA1462 C 1.02 (0.93-1.11) 0.69 0.97 -- C4D Genetics Consortium. Nat Genet 2011 111
rs2047009 10 44539913 CXCL12 G 0.92 (0.84-1.00) 0.04 0.25 -- The CARDIoGRAMplusC4D Consortium 107
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rs501120§ 10 44753867 CXCL12 T 1.09 (0.96-1.23) 0.20 0.68 0.984 Samani et al. N Engl J Med 2007
106
rs1412444 10 91002927 LIPA T 1.05 (0.96-1.15) 0.32 0.78 -- C4D Genetics Consortium. Nat Genet 2011 111
rs12413409 10 104719096 CYP17A1-CNNM2-NT5C2 G 0.97 (0.84-1.12) 0.66 0.97 -- Schunkert et al. Nat Genet 2011 105
rs974819 11 103660567 PDGFD T 0.93 (0.85-1.03) 0.15 0.63 -- C4D Genetics Consortium. Nat Genet 2011 111
rs964184 11 116648917 ZNF259-APOA5-APOA1 G 0.93 (0.82-1.06) 0.28 0.71 0.963 Schunkert et al. Nat Genet 2011 105
rs3184504 12 111884608 SH2B3 T 1.06 (0.98-1.16) 0.16 0.63 -- Gudbjartsson et al. Nat Genet 2009 112
rs9319428 13 28973621 FLT1 A 1.06 (0.96-1.16) 0.25 0.71 -- The CARDIoGRAMplusC4D Consortium 107
rs4773144a 13 110960712 COL4A1-COL4A2 G NA NA NA 0.106 Schunkert et al. Nat Genet 2011
105
rs9515203II 13 111049623 COL4A1-COL4A2 T 1.06 (0.94-1.18) 0.34 0.79 0.716 The CARDIoGRAMplusC4D Consortium
107
rs2895811 14 100133942 HHIPL1 C 0.98 (0.90-1.07) 0.62 0.97 -- Schunkert et al. Nat Genet 2011 105
rs1994016 15 79080234 ADAMTS7 C 0.96 (0.86-1.08) 0.54 0.95 0.572 Reilly et al. Lancet 2011 113
rs17514846 15 91416550 FURIN-FES A 0.99 (0.91-1.08) 0.84 0.97 -- The CARDIoGRAMplusC4D Consortium 107
rs216172 17 2126504 SMG6 C 0.94 (0.86-1.03) 0.18 0.66 0.996 Schunkert et al. Nat Genet 2011 105
rs12936587 17 17543722 RAI1-PEMT-RASD1 G 0.99 (0.91-1.08) 0.85 0.97 0.970 Schunkert et al. Nat Genet 2011 105
rs46522 17 46988597 UBE2Z T 1.03 (0.95-1.13) 0.45 0.88 0.987 Schunkert et al. Nat Genet 2011 105
rs1122608 19 11163601 LDLR G 1.02 (0.93-1.13) 0.67 0.97 0.982 Kathiresan et al. Nat Genet 2009 104
rs2075650† 19 45395619 ApoE-ApoC1 G 1.01 (0.89-1.15) 0.84 0.97 -- The CARDIoGRAMplusC4D Consortium
107
rs445925 19 45415640 ApoE-ApoC1 G 1.05 (0.89-1.23) 0.58 0.97 0.803 The CARDIoGRAMplusC4D Consortium 107
rs9982601 21 35599128 SLC5A3-MRPS6-KCNE2 T 0.80 (0.69-0.93) 0.0036 0.047 0.819 Kathiresan et al. Nat Genet 2009 104
Chr: Chromosome; CI: Confidence interval; FDR: False Discovery Rate; MI: Myocardial Infarction; OR: odds ratio; *Rsq values (R-square for imputation quality) are shown if the results are from imputation analysis (1000G1008); † LD with rs445925 is r2=0.024; ‡ LD with rs3217992 is r2=0.37; § LD with rs2047009 is r2=0.065; II LD with rs4773144 is r2=0.005; Association with this SNP was not analyzed due to very low R-square for imputation quality (Rsq=0.11). Alleles and chromosomal positions were identified on the basis of the plus strand of the National Center for Biotechnology Information (NCBI) build 37.
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Supplementary Table 19: Association of CeAD with SNPs associated with other subtypes of ischemic stroke (IS) in published GWAS
SNP Chr Position Gene IS Risk Allele IS subtype OR 95%CI P value P value (FDR) Author. year
rs2200733 4q25 111929618 PITX2 A Cardioembolic 0.99 0.88-1.13 0.918 0.92 Gretarsdottir et al. Ann Neurol 2008 114 Lemmens et al. Stroke 2010 115
Bellenguez et al. Nat Genet 2012 12 rs1906599 4q25 111932135 PITX2 A Cardioembolic 0.98 0.89-1.09 0.714 0.82
rs11984041 7p21.1 18998460 HDAC9 A Large artery
atherosclerosis 0.80 0.68-0.93 0.00350 0.028 Bellenguez et al. Nat Genet 2012 12
rs2383207 9p21.3 22105959 CDKN2A. CDKN2B G Large artery
atherosclerosis 1.10 1.01-1.20 0.0313 0.08 Gschwendtner et al. Ann Neurol 2009 116
rs556621 6p21.1 44702137 SUPT3H. CDC5L A Large artery
atherosclerosis 0.92 0.84-1.01 0.076 0.15 Holliday et al.. Nat Genet 2012 117
rs11833579 12p13.33 645460 NINJ2 G All and non-
cardioembolic 1.07 0.96-1.19 0.211 0.34 Ikram et al. NEJM 2009 118
rs7193343 16q22.3 71586661 ZFHX3 A Cardioembolic 0.94 0.83-1.05 0.264 0.35
Gudbjartsson et al. Nat Genet 2009 119
rs12932445 16q22.3 71627389 ZFHX3 G Cardioembolic 0.88 0.78-0.99 0.0295 0.08
Alleles and chromosomal positions were identified on the basis of the plus strand of the National Center for Biotechnology Information (NCBI) build 36.
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Supplementary Table 20: Associations of known susceptibility SNPs for migraine with CeAD
SNP * Chr Position Gene
Migraine risk allele
OR (95%CI) P value P value (FDR)
Reference
rs2274316 1q22 154712866 MEF2D C 0.98 (0.89-1.07) 0.59 0.81 Anttila, Nat Genet 2013 120
rs3790455 † 1q22 154722925 MEF2D C 1.03 (0.94-1.12) 0.57 0.81 Freilinger, Nat Genet 2012 121
rs1050316 † 1q22 154701327 MEF2D G 1.03 (0.94-1.13) 0.53 0.81 Freilinger, Nat Genet 2012 121
rs2651899 1p36.32 3073572 PRDM16 C 1.08 (0.99-1.17) 0.09 0.37 Chasman, Nat Genet 2011
122; Anttila, Nat Genet 2013
120
rs10915437 1p36.32 4082866 AJAP1 A 1.00 (0.92-1.10) 0.93 0.93 Anttila, Nat Genet 2013 120
rs12134493 1p13.2 115479469 TSPAN2 A 1.03 (0.91-1.18) 0.62 0.81 Anttila, Nat Genet 2013 120
rs7577262 2q37.1 234483608 TRPM8 G 0.93 (0.81-1.06) 0.27 0.66 Anttila, Nat Genet 2013 120
rs10166942 ‡ 2q37.1 234489832 TRPM8 T 0.92 (0.83-1.02) 0.13 0.37 Chasman, Nat Genet 2011 122
rs6790925 3p24 30455089 TGFBR2 T 1.07 (0.98-1.17) 0.11 0.37 Anttila, Nat Genet 2013 120
rs7640543 § 3p24 30437407 TGFBR2 A 1.05 (0.96-1.15) 0.31 0.66 Freilinger, Nat Genet 2012 121
rs9349379 6p24.1-p23 13011943 PHACTR1 A 1.32 (1.16-1.47) 4.46x10-10
7.58x10-9
Freilinger, Nat Genet 2012
121; Anttila, Nat Genet 2013
120
rs13208321 6q16.1 96967075 FHL5 A 1.18 (1.07-1.31) 6.80x10-4
4.00x10-3
Anttila, Nat Genet 2013 120
rs4379368 7p14.1 40432725 c7orf10 T 0.99 (0.87-1.14) 0.91 0.93 Anttila, Nat Genet 2013 120
rs10504861 8q21.3 89617048 MMP16 C 1.01 (0.90-1.12) 0.90 0.93 Anttila, Nat Genet 2013 120
rs1835740 8q22.1 98236089 MTDH, PGCP A 1.02 (0.92-1.13) 0.68 0.83 Anttila, Nat Genet 2010 123
rs6478241 9q33 118292450 ASTN2 A 1.04 (0.95-1.13) 0.41 0.77 Freilinger, Nat Genet 2012
121; Anttila, Nat Genet 2013
120
rs11172113 12q13.3 55813550 LRP1 T 1.28 (1.17-1.40) 4.22x10-8
3.59x10-7
Chasman, Nat Genet 2011
122; Anttila, Nat Genet 2013
120
Chr: Chromosome; CI: Confidence interval; FDR: False Discovery Rate; OR: odds ratio; Alleles and chromosomal positions were identified on the basis of the plus strand of the National Center for Biotechnology Information (NCBI) build 36; † LD with rs2274316 is r2=1 for rs3790455 and r2=0.90 for rs1050316; ‡ LD with rs7577262 is r2=0.65; § LD with rs7577262 is r2=0.72
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Supplementary Table 21: Cis- eQTL associations with SNPs in the top 6 risk loci selected for follow-up
eQTL SNPid eQTL SNP p (CeAD GWAS)
Index SNP r^2 Tissue (PubMed ID) eQTL.p Chr B36 pos ArrayID Transcript Allele Modeled
Probe Chr Probe Location
rs11172113 4,22E-08 rs11172113 Index Whole blood (24013639) 4,44E-53 12 55813550 1660397 STAT6 C 12 55775592
rs11172113 4,22E-08 rs11172113 Index Whole blood(21829388) 3,60E-13 12 55813550 1660397 STAT6;NAB2
rs11172113 4,22E-08 rs11172113 Index Omental adipose (21602305) 1,55E-10 12 55813550 1,002E+10 LRP1
rs11172113 4,22E-08 rs11172113 Index CD14+ monocytes (IFNg stimulated) (24604202)
2,31E-09 12 57527283 1660397 STAT6
rs11172113 4,22E-08 rs11172113 Index CD14+ monocytes (untreated) (24604202)
1,52E-08 12 57527283 1660397 STAT6
rs1385526 4,61E-08 rs1466535 0,994 Whole blood (24013639) 4,47E-178 12 55819016 1660397 STAT6 C 12 55775592
rs4759277 4,61E-08 rs1466535 0,936 Whole blood (24013639) 2,76E-177 12 55819957 1660397 STAT6 A 12 55775592
rs1466535 2,07E-06 rs1466535 Index Whole blood (24013639) 6,06E-176 12 55820737 1660397 STAT6 A 12 55775592
rs1466535 2,07E-06 rs1466535 Index Whole blood(21829388) 3,70E-55 12 55820737 1660397 STAT6;NAB2
rs1466535 2,07E-06 rs1466535 Index Monocytes (20502693) 5,16E-48 12 55820737 STAT6
rs4759277 4,61E-08 rs1466535 0,936 Monocytes (20502693) 5,15E-46 12 55819957 STAT6
rs4367982 4,61E-08 rs1466535 0,936 CD14+ monocytes (untreated) (24604202)
3,70E-20 12 57531632 1660397 STAT6
rs4759277 4,61E-08 rs1466535 0,936 CD14+ monocytes (untreated) (24604202)
4,20E-20 12 57533690 1660397 STAT6
rs4367982 4,61E-08 rs1466535 0,936 CD14+ monocytes (IFNg stimulated) (24604202)
5,07E-20 12 57531632 1660397 STAT6
rs4759277 4,61E-08 rs1466535 0,936 CD14+ monocytes (IFNg stimulated) (24604202)
6,06E-20 12 57533690 1660397 STAT6
rs1385526 4,61E-08 rs1466535 0,994 Skin (22941192) 2,84E-16 12 55819016 ILMN_1763198 STAT6
rs1385526 4,61E-08 rs1466535 0,994 Subc adipose (22941192) 7,49E-16 12 55819016 ILMN_1763198 STAT6
rs1466535 2,07E-06 rs1466535 Index Skin (22941192) 3,53E-15 12 55820737 ILMN_1763198 STAT6
rs1385526 4,61E-08 rs1466535 0,994 LCL (22941192) 3,65E-15 12 55819016 ILMN_1763198 STAT6
rs4759277 4,61E-08 rs1466535 0,936 Whole blood (22692066) 6,07E-15 12 55819957 ILMN_1763198 STAT6
rs1466535 2,07E-06 rs1466535 Index Subc adipose (22941192) 1,64E-14 12 55820737 ILMN_1763198 STAT6
rs1466535 2,07E-06 rs1466535 Index LCL (22941192) 9,29E-14 12 55820737 ILMN_1763198 STAT6
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rs4759277 4,61E-08 rs1466535 0,936 Skin (22941192) 2,34E-13 12 55819957 ILMN_1763198 STAT6
rs4759277 4,61E-08 rs1466535 0,936 Subc adipose (22941192) 9,31E-13 12 55819957 ILMN_1763198 STAT6
rs4759277 4,61E-08 rs1466535 0,936 LCL (22941192) 1,09E-12 12 55819957 ILMN_1763198 STAT6
rs4759277 4,61E-08 rs1466535 0,936 Whole blood (24013639) 2,28E-12 12 55819957 1110494 TMEM194A A 12 55736187
rs1385526 4,61E-08 rs1466535 0,994 Whole blood (24013639) 2,35E-12 12 55819016 1110494 TMEM194A C 12 55736187
rs1466535 2,07E-06 rs1466535 Index Whole blood (24013639) 3,39E-12 12 55820737 1110494 TMEM194A A 12 55736187
rs4367982 4,61E-08 rs1466535 0,936 CD14+ monocytes (2h LPS stimulated) (24604202)
1,51E-11 12 57531632 1660397 STAT6
rs4759277 4,61E-08 rs1466535 0,936 CD14+ monocytes (2h LPS stimulated) (24604202)
1,63E-11 12 57533690 1660397 STAT6
rs1466535 2,07E-06 rs1466535 Index Whole blood (22692066) 2,55E-11 12 55820737 ILMN_1763198 STAT6
rs4367982 4,61E-08 rs1466535 0,936 CD14+ monocytes (24h LPS stimulated) (24604202)
4,06E-10 12 57531632 1660397 STAT6
rs4759277 4,61E-08 rs1466535 0,936 CD14+ monocytes (24h LPS stimulated) (24604202)
4,08E-10 12 57533690 1660397 STAT6
rs9463110 7,20E-05 rs9349379 0,211 Cerebellum (23622250) 1,42E-09 6 12890588 1,002E+10 AF085859
rs4361612 3,16E-03 rs9349379 0,152 Whole blood (24013639) 5,42E-12 6 13144628 3170139 AL008729.1-1,PHACTR1 G 6 13393053
rs9473427 4,34E-03 rs9349379 0,148 Whole blood (24013639) 4,92E-13 6 13186630 3170139 AL008729.1-1,PHACTR1 C 6 13393053
rs11961962 3,34E-03 rs9349379 0,145 Whole blood (24013639) 2,35E-12 6 13154215 3170139 AL008729.1-1,PHACTR1 A 6 13393053
rs13192747 3,42E-03 rs9349379 0,145 Whole blood (24013639) 2,68E-12 6 13153124 3170139 AL008729.1-1,PHACTR1 T 6 13393053
rs13209107 3,68E-03 rs9349379 0,145 Whole blood (24013639) 2,68E-12 6 13153057 3170139 AL008729.1-1,PHACTR1 G 6 13393053
rs4373361 3,16E-03 rs9349379 0,145 Whole blood (24013639) 7,78E-12 6 13146291 3170139 AL008729.1-1,PHACTR1 G 6 13393053
rs4379290 3,35E-03 rs9349379 0,145 Whole blood (24013639) 2,68E-12 6 13153001 3170139 AL008729.1-1,PHACTR1 G 6 13393053
rs7762827 5,67E-03 rs9349379 0,145 Whole blood (24013639) 1,89E-12 6 13162718 3170139 AL008729.1-1,PHACTR1 G 6 13393053
rs9296592 5,00E-03 rs9349379 0,145 Whole blood (24013639) 1,66E-12 6 13163392 3170139 AL008729.1-1,PHACTR1 C 6 13393053
rs13190774 3,33E-03 rs9349379 0,139 Whole blood (24013639) 3,99E-14 6 13149644 3170139 AL008729.1-1,PHACTR1 T 6 13393053
rs13205551 3,26E-03 rs9349379 0,139 Whole blood (24013639) 5,51E-12 6 13149156 3170139 AL008729.1-1,PHACTR1 G 6 13393053
rs13205960 3,26E-03 rs9349379 0,139 Whole blood (24013639) 4,50E-12 6 13149520 3170139 AL008729.1-1,PHACTR1 C 6 13393053
rs13219842 3,26E-03 rs9349379 0,139 Whole blood (24013639) 5,54E-12 6 13149488 3170139 AL008729.1-1,PHACTR1 A 6 13393053
rs7738252 4,13E-03 rs9349379 0,138 Whole blood (24013639) 1,71E-12 6 13168554 3170139 AL008729.1-1,PHACTR1 G 6 13393053
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rs7750023 3,46E-03 rs9349379 0,136 Whole blood (24013639) 2,87E-11 6 13157856 3170139 AL008729.1-1,PHACTR1 G 6 13393053
rs7750099 2,01E-03 rs9349379 0,136 Whole blood (24013639) 2,60E-11 6 13160171 3170139 AL008729.1-1,PHACTR1 C 6 13393053
rs7775103 1,94E-03 rs9349379 0,136 Whole blood (24013639) 2,46E-11 6 13160221 3170139 AL008729.1-1,PHACTR1 C 6 13393053
rs9473285 2,06E-03 rs9349379 0,136 Whole blood (24013639) 2,67E-11 6 13157333 3170139 AL008729.1-1,PHACTR1 C 6 13393053
rs1937768 1,81E-04 rs9349379 0,113 Whole blood (24013639) 8,68E-17 6 13227857 3170139 AL008729.1-1,PHACTR1 G 6 13393053
rs17375327 2,37E-04 rs9349379 0,107 Whole blood (24013639) 7,61E-17 6 13227389 3170139 AL008729.1-1,PHACTR1 G 6 13393053
rs7744129 2,07E-03 rs9349379 0,101 Whole blood (24013639) 3,28E-16 6 13226325 3170139 AL008729.1-1,PHACTR1 A 6 13393053
rs7769033 1,96E-03 rs9349379 0,101 Whole blood (24013639) 3,34E-16 6 13226229 3170139 AL008729.1-1,PHACTR1 C 6 13393053
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Supplementary Table 22: Power estimates to detect an association in the follow-up sample
Power
Gene SNP At p < 0.05 At p < 0.005 * At p < 0.017 †
All CeAD (N=659)
PHACTR1 rs9349379 0.99 0.96
PHACTR1 ‡ rs12215208 0.82 0.51
LRP1 rs11172113 0.97 0.87
LRP1 rs1466535 0.93 0.73
ZNF804A rs6741522 0.94 0.75
ZNF804A ‡ rs6761601 0.85 0.58
FGGY rs12402265 0.85 0.57
LNX1 rs6820391 0.88 0.62
CCDC102B ‡ rs2163474 0.91 0.82
CCDC102B ‡ rs75453177 0.93 0.85
Carotid dissection (N=372)
LRP1 rs11172113 0.97 0.83 0.92
LRP1 rs1466535 0.97 0.85 0.93
LNX1 rs6820391 0.69 0.36 0.53
Power calculations were performed using Quanto,124 and based on a disease prevalence of 0.001 for all CeAD and of 0.0006 for carotid dissection, and using the odds ratios obtained in the discovery sample (i.e. assuming no Winner’s curse phenomenon); *Bonferroni correction for 10 SNPs; † Bonferroni correction for 3 SNPs (loci with dissection site heterogeneity); ‡ N=593 (rs12215208 and rs6761601), N=512 (rs2163474) and 507 (rs75453177)
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Supplementary References 1. Debette, S. et al. CADISP-genetics: an International project searching for genetic risk factors
of cervical artery dissections. Int J Stroke 4, 224-30 (2009). 2. Debette, S. et al. Association of vascular risk factors with cervical artery dissection and
ischemic stroke in young adults. Circulation 123, 1537-44 (2011). 3. Debette, S. et al. Differential features of carotid and vertebral artery dissections: the CADISP
study. Neurology 77, 1174-81 (2011). 4. Cheng, Y.C. et al. Are myocardial infarction--associated single-nucleotide polymorphisms
associated with ischemic stroke? Stroke 43, 980-6 (2012). 5. van den Herik, E.G., de Lau, L.M., Mohamad, A., Ikram, M.A. & Koudstaal, P.J. Association of
two single nucleotide polymorphisms from genomewide association studies with clinical phenotypes of cerebral ischemia. Int J Stroke 7, 219-23 (2012).
6. Domingues-Montanari, S. et al. The I/D polymorphism of the ACE1 gene is not associated with ischaemic stroke in Spanish individuals. Eur J Neurol 17, 1390-2 (2010).
7. Adams, H.P., Jr. et al. Classification of subtype of acute ischemic stroke. Definitions for use in a multicenter clinical trial. TOAST. Trial of Org 10172 in Acute Stroke Treatment. Stroke 24, 35-41 (1993).
8. Price, A.L. et al. Principal components analysis corrects for stratification in genome-wide association studies. Nat Genet 38, 904-9 (2006).
9. Cheng, Y.C. et al. Genome-wide association analysis of ischemic stroke in young adults. G3 (Bethesda) 1, 505-14 (2011).
10. Matarin, M. et al. A genome-wide genotyping study in patients with ischaemic stroke: initial analysis and data release. Lancet Neurol 6, 414-20 (2007).
11. Meschia, J.F. et al. Genomic risk profiling of ischemic stroke: results of an international genome-wide association meta-analysis. PLoS ONE 6, e23161 (2011).
12. Bellenguez, C. et al. Genome-wide association study identifies a variant in HDAC9 associated with large vessel ischemic stroke. Nat Genet 44, 328-33 (2012).
13. Wichmann, H.E., Gieger, C. & Illig, T. KORA-gen--resource for population genetics, controls and a broad spectrum of disease phenotypes. Gesundheitswesen 67 Suppl 1, S26-30 (2005).
14. Fornage, M. et al. Genome-wide association studies of cerebral white matter lesion burden: the CHARGE consortium. Ann Neurol 69, 928-39 (2011).
15. Rolfs, A. et al. Protocol and methodology of the Stroke in Young Fabry Patients (sifap1) study: a prospective multicenter European study of 5,024 young stroke patients aged 18-55 years. Cerebrovasc Dis 31, 253-62 (2011).
16. Jood, K. et al. Fibrinolytic gene polymorphism and ischemic stroke. Stroke 36, 2077-81 (2005).
17. Rutten-Jacobs, L.C. et al. Risk factors and prognosis of young stroke. The FUTURE study: a prospective cohort study. Study rationale and protocol. BMC Neurol 11, 109 (2011).
18. Muiesan, M.L. et al. Pulse wave velocity and cardiovascular risk stratification in a general population: the Vobarno study. J Hypertens 28, 1935-43 (2010).
19. Wagner, A. et al. High blood pressure prevalence and control in a middle-aged French population and their associated factors: the MONA LISA study. J Hypertens 29, 43-50 (2011).
20. Hofman, A. et al. The Rotterdam Study: 2010 objectives and design update. Eur J Epidemiol 24, 553-72 (2009).
21. Hofman, A., Grobbee, D.E., de Jong, P.T. & van den Ouweland, F.A. Determinants of disease and disability in the elderly: the Rotterdam Elderly Study. Eur J Epidemiol 7, 403-22 (1991).
22. Yadav, S. et al. Bio-Repository of DNA in stroke (BRAINS): a study protocol. BMC Med Genet 12, 34 (2011).
23. Cotlarciuc, I. et al. Bio-Repository of DNA in stroke (BRAINS): A study protocol. JRSM Cardiovascular Disease (2012, in press).
Nature Genetics: doi:10.1038/ng.3154
65
24. Purcell, S. et al. PLINK: a tool set for whole-genome association and population-based linkage analyses. Am J Hum Genet 81, 559-75 (2007).
25. Patterson, N., Price, A.L. & Reich, D. Population structure and eigenanalysis. PLoS Genet 2, e190 (2006).
26. Bacanu, S.A., Devlin, B. & Roeder, K. The power of genomic control. Am J Hum Genet 66, 1933-44 (2000).
27. Debette, S. & Markus, H.S. The genetics of cervical artery dissection: a systematic review. Stroke 40, e459-66 (2009).
28. Buss, A. et al. Functional polymorphisms in matrix metalloproteinases -1, -3, -9 and -12 in relation to cervical artery dissection. BMC Neurol 9, 40 (2009).
29. Jara-Prado, A. et al. MTHFR C677T, FII G20210A, FV Leiden G1691A, NOS3 intron 4 VNTR, and APOE epsilon4 gene polymorphisms are not associated with spontaneous cervical artery dissection. Int J Stroke 5, 80-5 (2010).
30. Longoni, M. et al. The ICAM-1 E469K gene polymorphism is a risk factor for spontaneous cervical artery dissection. Neurology 66, 1273-5 (2006).
31. von Pein, F. et al. Analysis of the COL3A1 gene in patients with spontaneous cervical artery dissections. J Neurol 249, 862-6 (2002).
32. Pezzini, A. et al. Migraine mediates the influence of C677T MTHFR genotypes on ischemic stroke risk with a stroke-subtype effect. Stroke 38, 3145-51 (2007).
33. Pezzini, A. et al. Plasma homocysteine concentration, C677T MTHFR genotype, and 844ins68bp CBS genotype in young adults with spontaneous cervical artery dissection and atherothrombotic stroke. Stroke 33, 664-9 (2002).
34. Arauz, A. et al. Mild hyperhomocysteinemia and low folate concentrations as risk factors for cervical arterial dissection. Cerebrovasc Dis 24, 210-4 (2007).
35. Schievink, W.I., Mokri, B. & Piepgras, D.G. Angiographic frequency of saccular intracranial aneurysms in patients with spontaneous cervical artery dissection. J Neurosurg 76, 62-6 (1992).
36. Conde, L. et al. PupaSuite: finding functional single nucleotide polymorphisms for large-scale genotyping purposes. Nucleic Acids Res 34, W621-5 (2006).
37. Goring, H.H. et al. Discovery of expression QTLs using large-scale transcriptional profiling in human lymphocytes. Nat Genet 39, 1208-16 (2007).
38. Idaghdour, Y. et al. Geographical genomics of human leukocyte gene expression variation in southern Morocco. Nat Genet 42, 62-7 (2010).
39. Heap, G.A. et al. Complex nature of SNP genotype effects on gene expression in primary human leucocytes. BMC Med Genomics 2, 1 (2009).
40. Emilsson, V. et al. Genetics of gene expression and its effect on disease. Nature 452, 423-8 (2008).
41. Fehrmann, R.S. et al. Trans-eQTLs reveal that independent genetic variants associated with a complex phenotype converge on intermediate genes, with a major role for the HLA. PLoS Genet 7, e1002197 (2011).
42. Mehta, D. et al. Impact of common regulatory single-nucleotide variants on gene expression profiles in whole blood. Eur J Hum Genet 21, 48-54 (2013).
43. Zhernakova, D.V. et al. DeepSAGE reveals genetic variants associated with alternative polyadenylation and expression of coding and non-coding transcripts. PLoS Genet 9, e1003594 (2013).
44. Sasayama, D. et al. Identification of single nucleotide polymorphisms regulating peripheral blood mRNA expression with genome-wide significance: an eQTL study in the Japanese population. PLoS One 8, e54967 (2013).
45. Landmark-Hoyvik, H. et al. Genome-wide association study in breast cancer survivors reveals SNPs associated with gene expression of genes belonging to MHC class I and II. Genomics 102, 278-87 (2013).
Nature Genetics: doi:10.1038/ng.3154
66
46. Westra, H.J. et al. Systematic identification of trans eQTLs as putative drivers of known disease associations. Nat Genet 45, 1238-43 (2013).
47. van Eijk, K.R. et al. Genetic analysis of DNA methylation and gene expression levels in whole blood of healthy human subjects. BMC Genomics 13, 636 (2012).
48. The Genotype-Tissue Expression (GTEx) project. Nat Genet 45, 580-5 (2013). 49. Battle, A. et al. Characterizing the genetic basis of transcriptome diversity through RNA-
sequencing of 922 individuals. Genome Res 24, 14-24 (2014). 50. Benton, M.C. et al. Mapping eQTLs in the Norfolk Island genetic isolate identifies candidate
genes for CVD risk traits. Am J Hum Genet 93, 1087-99 (2013). 51. Dixon, A.L. et al. A genome-wide association study of global gene expression. Nat Genet 39,
1202-7 (2007). 52. Liang, L. et al. A cross-platform analysis of 14,177 expression quantitative trait loci derived
from lymphoblastoid cell lines. Genome Res 23, 716-26 (2013). 53. Stranger, B.E. et al. Population genomics of human gene expression. Nat Genet 39, 1217-24
(2007). 54. Kwan, T. et al. Genome-wide analysis of transcript isoform variation in humans. Nat Genet
40, 225-31 (2008). 55. Dimas, A.S. et al. Common regulatory variation impacts gene expression in a cell type-
dependent manner. Science 325, 1246-50 (2009). 56. Cusanovich, D.A. et al. The combination of a genome-wide association study of lymphocyte
count and analysis of gene expression data reveals novel asthma candidate genes. Hum Mol Genet 21, 2111-23 (2012).
57. Grundberg, E. et al. Mapping cis- and trans-regulatory effects across multiple tissues in twins. Nat Genet 44, 1084-9 (2012).
58. Gutierrez-Arcelus, M. et al. Passive and active DNA methylation and the interplay with genetic variation in gene regulation. Elife 2, e00523 (2013).
59. Mangravite, L.M. et al. A statin-dependent QTL for GATM expression is associated with statin-induced myopathy. Nature 502, 377-80 (2013).
60. Fairfax, B.P. et al. Genetics of gene expression in primary immune cells identifies cell type-specific master regulators and roles of HLA alleles. Nat Genet 44, 502-10 (2012).
61. Murphy, A. et al. Mapping of numerous disease-associated expression polymorphisms in primary peripheral blood CD4+ lymphocytes. Hum Mol Genet 19, 4745-57 (2010).
62. Heinzen, E.L. et al. Tissue-specific genetic control of splicing: implications for the study of complex traits. PLoS Biol 6, e1 (2008).
63. Zeller, T. et al. Genetics and beyond--the transcriptome of human monocytes and disease susceptibility. PLoS One 5, e10693 (2010).
64. Fairfax, B.P. et al. Innate immune activity conditions the effect of regulatory variants upon monocyte gene expression. Science 343, 1246949 (2014).
65. Barreiro, L.B. et al. Deciphering the genetic architecture of variation in the immune response to Mycobacterium tuberculosis infection. Proc Natl Acad Sci U S A 109, 1204-9 (2012).
66. Lee, M.N. et al. Common genetic variants modulate pathogen-sensing responses in human dendritic cells. Science 343, 1246980 (2014).
67. Huang, R.S. et al. Population differences in microRNA expression and biological implications. RNA Biol 8, 692-701 (2011).
68. Degner, J.F. et al. DNase I sensitivity QTLs are a major determinant of human expression variation. Nature 482, 390-4 (2012).
69. Greenawalt, D.M. et al. A survey of the genetics of stomach, liver, and adipose gene expression from a morbidly obese cohort. Genome Res 21, 1008-16 (2011).
70. Kompass, K.S. & Witte, J.S. Co-regulatory expression quantitative trait loci mapping: method and application to endometrial cancer. BMC Med Genomics 4, 6 (2011).
71. Li, Q. et al. Integrative eQTL-based analyses reveal the biology of breast cancer risk loci. Cell 152, 633-41 (2013).
Nature Genetics: doi:10.1038/ng.3154
67
72. Webster, J.A. et al. Genetic control of human brain transcript expression in Alzheimer disease. Am J Hum Genet 84, 445-58 (2009).
73. Zou, F. et al. Brain expression genome-wide association study (eGWAS) identifies human disease-associated variants. PLoS Genet 8, e1002707 (2012).
74. Shpak, M. et al. An eQTL analysis of the human glioblastoma multiforme genome. Genomics (2014).
75. Colantuoni, C. et al. Temporal dynamics and genetic control of transcription in the human prefrontal cortex. Nature 478, 519-23 (2011).
76. Liu, C. et al. Whole-genome association mapping of gene expression in the human prefrontal cortex. Mol Psychiatry 15, 779-84 (2010).
77. Kim, S., Cho, H., Lee, D. & Webster, M.J. Association between SNPs and gene expression in multiple regions of the human brain. Transl Psychiatry 2, e113 (2012).
78. Gamazon, E.R. et al. Enrichment of cis-regulatory gene expression SNPs and methylation quantitative trait loci among bipolar disorder susceptibility variants. Mol Psychiatry 18, 340-6 (2013).
79. Gibbs, J.R. et al. Abundant quantitative trait loci exist for DNA methylation and gene expression in human brain. PLoS Genet 6, e1000952 (2010).
80. Zhang, B. et al. Integrated systems approach identifies genetic nodes and networks in late-onset Alzheimer's disease. Cell 153, 707-20 (2013).
81. Schadt, E.E. et al. Mapping the genetic architecture of gene expression in human liver. PLoS Biol 6, e107 (2008).
82. Innocenti, F. et al. Identification, replication, and functional fine-mapping of expression quantitative trait loci in primary human liver tissue. PLoS Genet 7, e1002078 (2011).
83. Schroder, A. et al. Genomics of ADME gene expression: mapping expression quantitative trait loci relevant for absorption, distribution, metabolism and excretion of drugs in human liver. Pharmacogenomics J 13, 12-20 (2013).
84. Wang, X. et al. Mapping of hepatic expression quantitative trait loci (eQTLs) in a Han Chinese population. J Med Genet 51, 319-26 (2014).
85. Grundberg, E. et al. Population genomics in a disease targeted primary cell model. Genome Res 19, 1942-52 (2009).
86. Kabakchiev, B. & Silverberg, M.S. Expression quantitative trait loci analysis identifies associations between genotype and gene expression in human intestine. Gastroenterology 144, 1488-96, 1496 e1-3 (2013).
87. Keildson, S. et al. Expression of phosphofructokinase in skeletal muscle is influenced by genetic variation and associated with insulin sensitivity. Diabetes 63, 1154-65 (2014).
88. Quigley, D.A. et al. The 5p12 breast cancer susceptibility locus affects MRPS30 expression in estrogen-receptor positive tumors. Mol Oncol 8, 273-84 (2014).
89. Curtis, C. et al. The genomic and transcriptomic architecture of 2,000 breast tumours reveals novel subgroups. Nature 486, 346-52 (2012).
90. Hao, K. et al. Lung eQTLs to help reveal the molecular underpinnings of asthma. PLoS Genet 8, e1003029 (2012).
91. Gao, C. et al. HEFT: eQTL analysis of many thousands of expressed genes while simultaneously controlling for hidden factors. Bioinformatics 30, 369-76 (2014).
92. Ding, J. et al. Gene expression in skin and lymphoblastoid cells: Refined statistical method reveals extensive overlap in cis-eQTL signals. Am J Hum Genet 87, 779-89 (2010).
93. Qiu, W. et al. Genetics of sputum gene expression in chronic obstructive pulmonary disease. PLoS One 6, e24395 (2011).
94. Lin, H. et al. Gene expression and genetic variation in human atria. Heart Rhythm 11, 266-71 (2014).
95. Rantalainen, M. et al. MicroRNA expression in abdominal and gluteal adipose tissue is associated with mRNA expression levels and partly genetically driven. PLoS One 6, e27338 (2011).
Nature Genetics: doi:10.1038/ng.3154
68
96. Duan, S. et al. Genetic architecture of transcript-level variation in humans. Am J Hum Genet 82, 1101-13 (2008).
97. Myers, A.J. et al. A survey of genetic human cortical gene expression. Nat Genet 39, 1494-9 (2007).
98. The International Classification of Headache Disorders: 2nd edition. Cephalalgia 24 Suppl 1, 9-160 (2004).
99. Bilguvar, K. et al. Susceptibility loci for intracranial aneurysm in European and Japanese populations. Nat Genet 40, 1472-7 (2008).
100. Low, S.K. et al. Genome-wide association study for intracranial aneurysm in the Japanese population identifies three candidate susceptible loci and a functional genetic variant at EDNRA. Hum Mol Genet 21, 2102-10 (2012).
101. Yasuno, K. et al. Genome-wide association study of intracranial aneurysm identifies three new risk loci. Nat Genet 42, 420-5 (2010).
102. Lemaire, S.A. et al. Genome-wide association study identifies a susceptibility locus for thoracic aortic aneurysms and aortic dissections spanning FBN1 at 15q21.1. Nat Genet 43, 996-1000 (2011).
103. Bown, M.J. et al. Abdominal aortic aneurysm is associated with a variant in low-density lipoprotein receptor-related protein 1. Am J Hum Genet 89, 619-27 (2011).
104. Kathiresan, S. et al. Genome-wide association of early-onset myocardial infarction with single nucleotide polymorphisms and copy number variants. Nat Genet 41, 334-41 (2009).
105. Schunkert, H. et al. Large-scale association analysis identifies 13 new susceptibility loci for coronary artery disease. Nat Genet 43, 333-8 (2011).
106. Samani, N.J. et al. Genomewide association analysis of coronary artery disease. N Engl J Med 357, 443-53 (2007).
107. Deloukas, P. et al. Large-scale association analysis identifies new risk loci for coronary artery disease. Nat Genet 45, 25-33 (2013).
108. Erdmann, J. et al. New susceptibility locus for coronary artery disease on chromosome 3q22.3. Nat Genet 41, 280-2 (2009).
109. Wang, F. et al. Genome-wide association identifies a susceptibility locus for coronary artery disease in the Chinese Han population. Nat Genet 43, 345-9 (2011).
110. Tregouet, D.A. et al. Genome-wide haplotype association study identifies the SLC22A3-LPAL2-LPA gene cluster as a risk locus for coronary artery disease. Nat Genet 41, 283-5 (2009).
111. Coronary_Artery_Disease_(C4D)_Genetics_Consortium. A genome-wide association study in Europeans and South Asians identifies five new loci for coronary artery disease. Nat Genet 43, 339-44 (2011).
112. Gudbjartsson, D.F. et al. Sequence variants affecting eosinophil numbers associate with asthma and myocardial infarction. Nat Genet 41, 342-7 (2009).
113. Reilly, M.P. et al. Identification of ADAMTS7 as a novel locus for coronary atherosclerosis and association of ABO with myocardial infarction in the presence of coronary atherosclerosis: two genome-wide association studies. Lancet 377, 383-92 (2011).
114. Gretarsdottir, S. et al. Risk variants for atrial fibrillation on chromosome 4q25 associate with ischemic stroke. Ann Neurol 64, 402-9 (2008).
115. Lemmens, R. et al. The association of the 4q25 susceptibility variant for atrial fibrillation with stroke is limited to stroke of cardioembolic etiology. Stroke 41, 1850-7 (2010).
116. Gschwendtner, A. et al. Sequence variants on chromosome 9p21.3 confer risk for atherosclerotic stroke. Ann Neurol 65, 531-9 (2009).
117. Holliday, E.G. et al. Common variants at 6p21.1 are associated with large artery atherosclerotic stroke. Nat Genet 44, 1147-1151 (2012).
118. Ikram, M.A. et al. Genomewide association studies of stroke. N Engl J Med 360, 1718-28 (2009).
Nature Genetics: doi:10.1038/ng.3154
69
119. Gudbjartsson, D.F. et al. A sequence variant in ZFHX3 on 16q22 associates with atrial fibrillation and ischemic stroke. Nat Genet 41, 876-8 (2009).
120. Anttila, V. et al. Genome-wide meta-analysis identifies new susceptibility loci for migraine. Nat Genet 45, 912-7 (2013).
121. Freilinger, T. et al. Genome-wide association analysis identifies susceptibility loci for migraine without aura. Nat Genet advance online publication(2012).
122. Chasman, D.I. et al. Genome-wide association study reveals three susceptibility loci for common migraine in the general population. Nat Genet 43, 695-8 (2011).
123. Anttila, V. et al. Genome-wide association study of migraine implicates a common susceptibility variant on 8q22.1. Nat Genet 42, 869-73 (2010).
124. Gauderman, W.J. & Morrison, J.M. QUANTO 1.1: A computer program for power and sample size calculations for genetic-epidemiology studies, http://hydra.usc.edu/gxe. (2006).
Nature Genetics: doi:10.1038/ng.3154