Common variation in PHACTR1 is associated with ... · 1 Common variation in PHACTR1 is associated...

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Common variation in PHACTR1 is associated with susceptibility to

cervical artery dissection

Supplementary Appendix

Nature Genetics: doi:10.1038/ng.3154

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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,


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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,


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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).


http://www.strokegenetics.org/

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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).


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


http://www.strokegenetics.org/

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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


http://www.cng.fr/

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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


http://genome.sph.umich.edu/wiki/MaCH

http://genome.sph.umich.edu/wiki/Minimac

17

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.


18

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).


http://genome.sph.umich.edu/wiki/Mach2dat:_Association_with_MACH_output

19

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


20

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


http://pupasuite.bioinfo.cipf.es/

21

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.


22

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


23

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


24

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)


25

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)


26

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).


27

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).


28



29



30



31

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




32

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



33

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


34

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


35

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.


36

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


37

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


38

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.


39

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


http://pngu.mgh.harvard.edu/~purcell/gpc/

40

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


41

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.


42

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


43

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)


44

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.


45

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.


46

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.


47

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


48

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)


49

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


50

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.


51

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


52

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


53

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.


54

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.


55

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




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.


56

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


57

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.


58

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.


59

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


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


120

rs11172113 12q13.3 55813550 LRP1 T 1.28 (1.17-1.40) 4.22x10-8

3.59x10-7

Chasman, Nat Genet 2011


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


60

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


61

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


1,63E-11 12 57533690 1660397 STAT6

rs1466535 2,07E-06 rs1466535 Index Whole blood (22692066) 2,55E-11 12 55820737 ILMN_1763198 STAT6


4,06E-10 12 57531632 1660397 STAT6


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






rs13190774 3,33E-03 rs9349379 0,139 Whole blood (24013639) 3,99E-14 6 13149644 3170139 AL008729.1-1,PHACTR1 T 6 13393053






62










63

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)


64

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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).

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