Identification of the BCAR1-CFDP1-TMEM170A Locus …circgenetics.ahajournals.org/content/circcvg/early/2012/...DOI: 10.1161/CIRCGENETICS.112.963660 1 Identification of the BCAR1-CFDP1-TMEM170A

DOI: 10.1161/CIRCGENETICS.112.963660

1

Identification of the BCAR1-CFDP1-TMEM170A Locus as a Determinant of

Carotid Intima-Media Thickness and Coronary Artery Disease Risk

Running title: Gertow et al.; Genetic locus associated with cIMT and CAD

Karl Gertow, PhD1; Bengt Sennblad, PhD1; Rona J. Strawbridge, PhD1; John Öhrvik, PhD1;

Delilah Zabaneh, PhD2; Sonia Shah, MSc2; Fabrizio Veglia, PhD3; Cristiano Fava, MD, PhD4,5;

Maryam Kavousi, MD, MSc6,7; Stela McLachlan, PhD8; Mika Kivimäki, PhD9; Jennifer L.

Bolton, PhD8; Lasse Folkersen, PhD1,10; Bruna Gigante, MD, PhD11; Karin Leander, PhD11; Max

Vikström, BSc11; Malin Larsson, PhD1; Angela Silveira, PhD1; John Deanfield, MD, PhD12;

Benjamin F. Voight, PhD13,14; Pierre Fontanillas, PhD13,14; Maria Sabater-Lleal, PhD1; Gualtiero

I. Colombo, MD, PhD3; Meena Kumari, PhD9; Claudia Langenberg, PhD9,15; Nick J. Wareham,

MBBS, PhD15; André G. Uitterlinden, PhD6,7,16; Anders Gabrielsen, MD, PhD10; Ulf Hedin, MD,

PhD17; Anders Franco-Cereceda, MD, PhD17; Kristiina Nyyssönen, PhD18; Rainer Rauramaa,

MD, PhD19,20; Tomi-Pekka Tuomainen, MD, PhD18; Kai Savonen, MD, PhD19,20; Andries J.

Smit, MD, PhD21; Philippe Giral, MD, PhD22; Elmo Mannarino, MD, PhD23; Christine M.

Robertson, MBChB8; Philippa J. Talmud, PhD24; Bo Hedblad, MD, PhD4; Albert Hofman, MD,

PhD6,7; Jeanette Erdmann, PhD25*; Muredach P. Reilly, MBBCH, MSCE26,27*; Christopher J.

O’Donnell, MD, MPH28,29,30*; Martin Farrall, FRCPath31,32†; Robert Clarke, MD, PhD33†; Maria

Grazia Franzosi, PhD34†; Udo Seedorf, PhD35†; Ann-Christine Syvänen, PhD36; Göran K.

Hansson, MD, PhD10; Per Eriksson, PhD1; Nilesh J. Samani, MF, FRCP37,38*; Hugh Watkins,

FRCP31,32†; Jacqueline F. Price, MBChB8; Aroon D. Hingorani, MD, PhD9,39; Olle Melander,

MD, PhD4; Jacqueline C.M. Witteman, PhD6,7; Damiano Baldassarre, PhD3,40; Elena Tremoli

PhD3,40; Ulf de Faire, MD, PhD11; Steve E. Humphries, PhD24; Anders Hamsten, FRCP1

on behalf of the *CARDIoGRAM & †PROCARDIS consortiums

1Atherosclerosis Research Unit, 10Experimental Cardiovascular Rsrch Unit, Dept of Medicine, Solna, Karolinska Institutet, Karolinska Univ Hospital Solna, Stockholm, Sweden; 2Univ College London

Genetics Inst, 9Genetic Epidemiology Group, Dept of Epidemiology & Public Health, 24Cardiovascular Genetics, BHF Laboratories, Rayne Building, 39Centre for Clinical Pharmacology, Dept of Medicine,

Univ College London, London, UK; 3Centro Cardiologico Monzino, IRCCS, Milan, Italy; 4Clinical Rsrch

imäki, PhD ; Jenniffiffere

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2

Center, Dept of Clinical Sciences, Lund Univ, Skåne Univ Hospital, Lund, Sweden; 5Division of Internal Medicine C, Dept of Medicine, Univ of Verona, Hospital "Policlinico G.B Rossi", Verona, Italy; 6Dept of Epidemiology, 16Dept of Internal Medicine, Erasmus University Medical Center; 7Netherlands Genomics Initiative - Sponsored Netherlands Consortium for Healthy Ageing, Rotterdam, The Netherlands; 8Centre

for Population Health Sciences, Univ of Edinburgh, Edinburgh, UK; 11Division of Cardiovascular Epidemiology, Inst of Environmental Medicine, 17Dept of Molecular Medicine & Surgery, Karolinska

Institutet, Stockholm, Sweden; 12Cardiothoracic Unit, Great Ormond Street Hospital, London, UK; 13Program in Medical & Population Genetics, Broad Institute, Cambridge, MA; 14Center for Human

Genetic Research & Diabetes Rsrch Center (Diabetes Unit), Massachusetts General Hospital, Boston, MA; 15MRC Epidemiology Unit, Inst of Metabolic Science, Univ of Cambridge, Addenbrooke's Hospital,

Cambridge, UK; 18Inst of Public Health & Clinical Nutrition, Univ of Eastern Finland; 19Kuopio Rsrch Inst of Exercise Medicine, Foundation for Rsrch in Health Exercise & Nutrition; 20Dept of Clinical

Physiology & Nuclear Medicine, Kuopio Univ Hospital, Kuopio, Finland; 21Dept of Medicine, Univ Medical Center Groningen, Groningen, the Netherlands; 22Assistance Publique - Hopitaux de Paris,

Service Endocrinologie-Metabolisme, Groupe Hôpitalier Pitie-Salpetriere, Unités de Prévention Cardiovasculaire, Paris, France; 23Internal Medicine, Angiology & Arteriosclerosis Diseases, Dept of

Clinical & Experimental Medicine, Univ of Perugia, Perugia, Italy; 25Universität zu Lübeck, Medizinische Klinik II, Lübeck, Germany; 26The Inst for Translational Medicine & Therapeutics,

27Cardiovascular Inst, Perelman School of Medicine, Univ of Pennsylvania, Philadelphia, PA; 28National Heart, Lung, and Blood Institute's Framingham Heart Study, Framingham, MA; 29Division of Intramural

Rsrch, NHLBI, Bethesda, MD; 30Cardiology Division, Dept of Medicine, Massachusetts General Hospital, Harvard Medical School, Boston, MA; 31Dept of Cardiovascular Medicine, The Wellcome Trust

Centre for Human Genetics, 33Clinical Trial Service Unit, Univ of Oxford; 32Dept of Cardiovascular Medicine, Univ of Oxford, John Radcliffe Hospital, Headington, Oxford, UK; 34Dept of Cardiovascular

Rsrch, Istituto Mario Negri, Milan, Italy; 35Gesellschaft für Arterioskleroseforschung e.V., Leibniz-Institut für Arterioskleroseforschung an der Universität Münster (LIFA), Münster, Germany; 36Dept of

Medical Sciences, Molecular Medicine & Science for Life Laboratory, Uppsala Univ, Uppsala, Sweden; 37Dept of Cardiovascular Sciences, Univ of Leicester, 38Leicester NIHR Biomedical Rsrch Unit in

Cardiovascular Disease, Glenfield Hospital, Leicester, UK; 40Dipartimento di Scienze Farmacologiche e Biomolecolari, Univ of Milan, Milan, Italy

Corresponding author:

Karl Gertow, PhD

Atherosclerosis Research Unit

Karolinska University Hospital Solna

Center for Molecular Medicine, Building L8:03

S-171 76 Stockholm, Sweden

Tel: +46-8-51773201

Fax: +46-8-311298E

E-mail: [email protected]

Journal Subject Codes: [109] Clinical genetics

osclerosis Diseases, DeDDD pUnUnUnUniviviviverererersisisisitätätätät t t t zuzuzuzu LLLLüüüübebebebeckckckck,,,,üüü

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Abstract:

Background - Carotid intima-media thickness (cIMT) is a widely accepted marker of subclinical

atherosclerosis. To date, large-scale investigations of genetic determinants of cIMT are sparse.

Methods and Results - In order to identify cIMT-associated genes and genetic variants, a

discovery analysis using the Illumina 200K CardioMetabochip was conducted in 3,430 subjects

with detailed ultrasonographic determinations of cIMT from the IMPROVE study. Segment-

specific IMT measurements of common carotid (CC), bifurcation, and internal carotid arteries,

and composite IMT variables considering the whole carotid tree (IMTmean, IMTmax, and IMTmean-

max), were analysed. A replication stage investigating 42 single nucleotide polymorphisms

(SNPs) for association with CC-IMT was undertaken in five independent European cohorts (total

n=11,590). A locus on chromosome 16 (lead SNP rs4888378, intronic in CFDP1) was associated

with cIMT at significance levels passing multiple-testing correction at both stages (array-wide

significant discovery P=6.75x10-7 for IMTmax; replication P=7.24x10-6 for CC-IMT; adjustments

for sex, age and population substructure where applicable; minor allele frequency 0.43 and 0.41,

respectively). The protective minor allele was associated with lower carotid plaque score in a

replication cohort (P=0.04, n=2120), and lower coronary artery disease (CAD) risk in two case-

control studies of subjects with European ancestry (odds ratio [95%CI] 0.83 [0.77-0.90],

P=6.53x10-6; n=13,591, and 0.95 [0.92-0.98], P=1.83x10-4, n=82,297, respectively). Queries of

human biobank datasets (n=126-138) revealed associations of rs4888378 with nearby gene

expression in vascular tissues.

Conclusions - This study identified rs4888378 in the BCAR1-CFDP1-TMEM170A locus as a

novel genetic determinant of cIMT and CAD risk in individuals of European descent.

Key words: atherosclerosis; coronary artery disease; genetics; carotid intima-media thickness

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Introduction

Carotid intima-media thickness (cIMT), as determined by high-resolution ultrasound techniques,

is a well-established marker of subclinical atherosclerosis and is widely used in epidemiological

studies and interventional trials.1, 2 It has been proposed as a surrogate marker for coronary

atherosclerosis, and has been shown to predict incident coronary and cerebrovascular events.3-5

Accordingly, cIMT constitutes an attractive quantitative intermediate disease phenotype for the

study of atherosclerosis-related cardiovascular disease. Genetic association studies of cIMT,

conducted in individuals free of manifest disease, may identify susceptibility genes and pathways

involved in the initiation and early phases of disease, which may be less readily discernible in

studies of late-stage and clinically manifest disease such as myocardial infarction (MI) and

stroke. Nevertheless, large-scale studies of genetic determinants of cIMT remain sparse.

To date, one meta-analysis of single-nucleotide polymorphism (SNP)-based genome-

wide association (GWA) studies of cIMT has been reported, which identified three regions (on

chromosome 8q23.1, 8q24, and 19q13) as being associated with common carotid IMT (CC-

IMT).6 In contrast, candidate gene studies of cIMT have provided inconsistent results,7 and two

genome-wide linkage scans only found regions with suggestive linkage to cIMT.8, 9 In the

present study, we performed a discovery genetic association analysis of cIMT in 3,430

participants of the Pan-European population-based IMPROVE study,10 using a custom

genotyping array (the Illumina CardioMetabochip, also referred to as the Metabochip). The

CardioMetabochip interrogates approximately 200,000 SNPs located in regions identified by

previous GWA studies of metabolic and cardiovascular traits and diseases. In a second stage, we

conducted replication studies in 11,590 participants from five independent population-based

cohorts. One robustly associated cIMT locus was subsequently tested in silico for association

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with coronary artery disease (CAD) risk in two large independent GWA studies of CAD and

with mRNA expression of nearby genes in vascular tissues collected in two biobank

programmes.

Methods

Cohorts and Study Design

The first stage consisted of genetic association analysis of cIMT measurements in the IMPROVE

study (n=3,430).10 SNPs that passed an a priori threshold for statistical association (P<1x10-4 or

P<1x10-5 depending on cIMT phenotype) were then taken forward for replication in the

Whitehall-II study (WH-II, n=2,138),11, 12 the Edinburgh Artery Study (EAS, n=630),13 the

Rotterdam-I and Rotterdam-II studies (RS-I and RS-II, n=4,699 and n=1,980, respectively),14, 15

and the cardiovascular arm of the Malmö Diet and Cancer study (MDC, n=2,141),16, 17 with

subsequent evaluation of results by meta-analysis. Detailed descriptions of the discovery and

replication cohorts are given in online Supplemental Section S1 and in Supplemental Table 1.

A locus which reached significance levels passing correction for multiple testing at both the

discovery and replication stages was further tested for association with carotid plaque score in

the MDC study, with coronary artery calcium (CAC) score in the RS-I and RS-II studies, and for

association with CAD risk in PROCARDIS,18 a large European CAD case-control study to

which additional controls were added from the Wellcome Trust Case-Control Consortium

(WTCCC), in total 5,710 cases and 7,881 controls, and in CARDIoGRAM,19 a large CAD case-

control GWA study meta-analysis consortium comprising 22,233 CAD cases and 64,762

controls of European descent. In addition to replication in independent cohorts, complementary

internal validation by bootstrap analyses was undertaken in IMPROVE in order to corroborate

findings from the discovery stage in relation to IMT phenotypes which were not available in the

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replication cohorts. The replicated cIMT locus was also tested for association with mRNA

expression of nearby genes in vascular tissues collected in the Advanced Study of Aortic

Pathology (ASAP) and the Biobank of Karolinska Endarterectomies (BiKE) studies,20 in order to

explore potential mechanisms underlying the observed cIMT and CAD associations.

Genotyping and Quality Control

A description of the genotyping technologies used for the discovery and replication cohorts

along with quality control (QC) criteria is provided in Supplemental Section S2 and

Supplemental Table 1. Genotyping in IMPROVE, WH-II, EAS and MDC was performed using

the Illumina 200K CardioMetabochip, whereas the RS-I and RS-II studies were genotyped using

the Ilumina HumanHap550 array and imputed to the 1000Genomes CEU Caucasian reference

panel.19 The CardioMetabochip is a custom Illumina iSelect genotyping array that captures DNA

variation at regions identified by meta-analyses of GWA studies for diseases and traits relevant

to metabolic and atherosclerotic/cardiovascular endpoints, comprising approximately 200,000

SNPs. In IMPROVE, individual level exclusion criteria were call rates <0.95, results of identity

by state (IBS) estimations (e.g. unverified cryptic relatedness), verified relatedness, estimated

inbreeding (excessive homozygosity), discrepancy between recorded and genotype-determined

sex, outliers in multi-dimensional scaling (MDS) analysis (Supplemental Section S2 and

Supplemental Figure 1), and self-reported non-Caucasian ethnicity. Exclusion criteria for SNPs

were genotype call rates <0.90, deviation from Hardy-Weinberg equilibrium (P<5x10-7) and

minor allele frequency (MAF) <0.005. Following QC procedures, 3,430 individuals and 127,830

autosomal SNPs were included in the discovery association analysis in IMPROVE.

Ultrasonographic Measurements

The carotid ultrasound protocol applied in IMPROVE and the precision of the ultrasonographic

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measurements have been reported.10 In brief, measurements were made of the mean and

maximum IMT of the common carotid at the 1st cm proximal to the bifurcation (CC-1st cm-

IMTmean and CC-1st cm-IMTmax) and in a segment excluding the 1st cm proximal to the

bifurcation (CC-IMTmean and CC-IMTmax), of the bifurcation (Bif-IMTmean and Bif-IMTmax), and

of the internal carotid arteries (ICA-IMTmean and ICA-IMTmax). Composite IMT variables

considering the whole carotid tree were derived from the segment-specific measurements:

IMTmean, IMTmax, and IMTmean-max (the average of IMT maxima recorded at the different

segments).10 Measures of common carotid IMT were available in all replication cohorts, and in

addition, Bif-IMTmax was measured and a 6-level carotid plaque score was generated in the

MDC cohort (Supplemental Section S1 and Supplemental Table 1).

Statistical Analyses of the Discovery and Replication Cohorts

All cIMT variables were logarithmically transformed prior to statistical analysis due to skewed

distributions. Association analysis was performed using linear regression, adjusting for age and

sex, under the assumption of additive genetic effects using PLINK version 1.07.21 For

IMPROVE, the first three MDS dimensions (based on CardioMetabochip genotype data) were

used to adjust for identified population substructure (Supplemental Section S2 and

Supplemental Figure 1). The a priori threshold for array-wide statistical significance was

established as P<8.39x10-7 through estimation of the total number of uncorrelated SNPs on the

CardioMetabochip (Supplemental Section S3). Selection of index SNPs for replication from

candidate SNPs that obtained an a priori level for statistical association in the discovery analysis

(set to P<10-4 for segment-specific IMT measurements and P<10-5 for composite IMT variables)

was performed by the PLINK clump procedure. A linkage disequilibrium (LD) threshold of r2

>0.8 within a physical distance of 500 kilobases (kb) was used in the clump procedure.

eplication cohorts,,,, anaaa

was gggeneratttt ddded in nnn ththththe

r

A

variables were logarithmically transformed prior to statistical analysis due to skew

n e

the ass mption of additi e genetic effects sing PLINK ersion 1 07 21 For

rt ((((SuSuSupppppplelelememmm ntntnttal Section S1 and Supppplelelemmental Table 1).).).

AnAnAnA ala yses of thheee DDissscovvvveere y anaanddd RRepppliicattioon CoCoCoohohohh rttts

variablblbbleseseses wweree llologagaga iririthththmicacacalllllllly trannnsffsfsforororo memeddd pprioioiorrrr totoo ssstatititisststs icicii llalal anaalylylylysisisiis dudud ee totot sskkkew

ns. Association analyllyl siis was pepp rfffformed dd usinii g gg linear regggressioiii n,,, adjdjdjd ustinggg for aggge

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Power calculations for the replication studies indicated that it would be justifiable to take

up to 50 uncorrelated SNPs forward to replication (Supplemental Section S4).

The replication stage comprised two approaches. The first approach involved meta-analysis of

the replication cohorts, both separately from and jointly with IMPROVE, using a fixed-effect

model with inverse variance weighting as applied in Metal (version 25 March 2011).22 Pooled

regression coefficients with corresponding 95% confidence intervals (CI) and P-values were

calculated. Two IMT phenotypes were studied in the replication stage. Primarily, 39 SNPs

associated with CC-IMT variables (CC-1st cm-IMTmean, CC-1st cm-IMTmax, CC-IMTmean, and/or

CC-IMTmax) and 3 SNPs associated with composite IMT variables (IMTmean, IMTmax, and/or

IMTmean-max) in IMPROVE were examined in relation to CC-IMT in the WH-II, EAS, MDC, RS-

I and RS-II cohorts (total n=11,590). Additionally, 26 SNPs associated with Bif-IMT in

IMPROVE were investigated in relation to Bif-IMT in the MDC cohort (n=1,690).

Given that the composite IMT-measures were unique to IMPROVE and thereby not

assessable by conventional replication in independent cohorts, we applied nonparametric

bootstrap re-sampling to perform internal validation. 23In brief, this method uses a weighted

average over bootstrap replicates of the difference between the effect size estimated from the

observations in the bootstrap sample and the one estimated from the observations not in the

bootstrap sample to estimate the over-estimation of the effect sizes of the most significant SNPs

(Supplemental Section S5).

Secondary Analyses

Regional plots of associations from the original discovery analysis and adjusted analysis where

the lead SNP was included as a covariate in the regression model were generated using

LocusZoom (http://csg.sph.umich.edu/locuszoom/). Secondary analyses to assess potential

MTmax, CC-IMTmeanananan,,, , a

MTmean, IMIMIMIMTTTTmax, anananandddd/

x C

en that the composite IMT-measures were unique to IMPROVE and thereby not

b con entional replication in independent cohorts e applied nonparametric

x) innnn IIIMPMPMPMPROROROROVEVEVEE were examined in relatiiiionoon to CC-IMT in thththhe WH-II, EAS, MDC

ccoc hhhorts (totall nnn=11,,,5990000))).) Addddddid tttioonallllyy, 2626 SNPNPNPs s s s aassooocciateeded wwwitititthhh h Biiiif-f-f-IMMI TTT T innn

wereee ininininvvvev stigigi tatatededd iiinn relatititionononon to BiBiBiB ffff I-IIIMTMTMTT iiinn thhheeee MDMDMDMDCCC coococohohohh ttrtrt (n==11,11 6966690))). fffff

en that the compopp iisiite IIIMTMMT-measures were uniquqq e to IIMPMPMPROROROOVEVEVE and therebyy not

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pleiotropy and possible confounders were performed by investigating genotype associations with

biochemical and clinical parameters (including 13 variables reflecting established cardiovascular

risk factors: waist circumference, systolic and diastolic blood pressure, pulse pressure,

hypertension, LDL-cholesterol, HDL-cholesterol, triglycerides, blood glucose, diabetes, C-

reactive protein, statin treatment, and cumulative life-time smoking expressed as a 5-level

categorical variable according to never-smoker status and quartiles of pack-years), and by further

adjustments of the original model (adjusted for sex, age and MDS) using PASW Statistics

version 18.

CAD Case-Control Studies

To determine whether a locus robustly associated with cIMT is also implicated in the

pathogenesis of clinically manifest CAD, we explored the lead SNP identified at the level of

array-wide statistical significance in relation to cIMT (rs4888378) for associations with CAD in

the PROCARDIS and CARDIoGRAM case-control studies.18, 19 Design features and details of

genotyping, QC and statistical analyses are provided in online Supplemental Sections S1 and

S6.

Association with Coronary Artery Calcium Score

Associations of the lead SNP with CAC score measured by a C-150 Imatron scanner in RS-I 24

and a 16- or 64-slice MDCT scanner in RS-II25 was evaluated by fixed-effect model meta-

analysis with inverse variance weighting using Metal.22

Expression Quantitative Trait Locus (eQTL) Studies

In silico analyses of genotype-gene expression level associations were conducted in the ASAP

and BiKE data sets. Details of design features and methods for ASAP and BiKE have been

reported.20 In ASAP, mRNA extracted from biopsies of ascending thoracic aorta intima-media

n

is of clinical manifest CAD, we ex ored the lead SNP identified at the level o

A

A l

QC and statistical anal ses are pro ided in online S pplemental Sections S1 a

ne wwwheheheethththhererer a lllocococus robustly associated wwwititii h h cIMT is also iiimpmm licated in the

iss oooof clinicallyy mmmannififififest t t t CCAC D,DDD wwwee exxxpploreded thehehe llleeaee dd d SNSSNP idddeeenttit fffif ed aaaat tt thhheee levevvel o

statiiiistststicicicicalalala siggniniifififificacancncee in rrrelelelelation nn tototo ccccIMIMIMMTTT (r(r(( s4s4s48888888883383837888))) fffoforr asassociciciciatatatiiioi nss wititith hhh CCCA

ARDIS and CARDRRDIoII GRGRGRRAMAMAMM case-contr lloll studidd es.1818181 , 19191919 DDe isiigngg fffeatures and detail

QCQC dd statiis iti ll lal iiddedd iin lnliine SSupplleme tnt lal SSectitions S1S1 a by guest on June 18, 2018http://circgenetics.ahajournals.org/

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(n=138), aortic adventitia (n=133), mammary artery (n=89), heart (n=127), and liver (n=211)

from patients undergoing aortic valve surgery20 was analysed with Affymetrix ST 1.0 Exon

arrays. In BiKE, RNA extracted from human plaque tissue (n=126) and peripheral blood

mononuclear cells (PBMC; n=96) obtained from patients referred for surgical treatment of severe

carotid artery stenosis, was analysed with Affymetrix HG-U133 plus 2.0 Genechip arrays.20

Robust Multichip Average (RMA) normalization was performed as implemented in the

Affymetrix Power Tools 1.10.2 package apt-probeset-summarize and processed gene expression

data was returned in a log2-scale.20 For both studies, blood-derived DNA had been genotyped

with Illumina 610w-Quad BeadArrays. Genotype-gene expression associations were investigated

using an additive model. 20

Results

Discovery Analysis

A total of 3,430 subjects (54 to 79 years of age, 48% males) from the IMPROVE study were

included in the discovery analysis. Basic characteristics of IMPROVE study participants are

shown in Supplemental Table 1. Since MDS analysis revealed significant population

substructure in IMPROVE (Supplemental Figure 1), adjustment for the first three MDS

dimensions, in addition to age and sex, was performed in all SNP association analyses with

cIMT measures. Associations with segment-specific mean and maximum IMT were investigated

as well as composite IMT variables reflecting the whole carotid tree (Figure 1 and

Supplemental Figure 2 a-k). One locus on chromosome 16 (lead SNP rs4888378, MAF=0.43,

P=6.75x10-7, beta [SE]= -0.019 [0.004] versus IMTMax for the minor A allele) passed the array-

wide significance threshold of P<8.39x10-7 (Figure 1). Rs4888378 is located in the last 3’ intron

of the CFDP1 gene (encoding cranio-facial development protein-1). A Q-Q plot of observed

NA had been genooootytytytyp

ociattttioiii ns were invevevevest

d 20

A

,430 subjects (54 to 79 years of age, 48% males) from the IMPROVE study wer

dditiiiiveveve mmmodododdeleee . 2020200

Analysis

,4,43030 ssububjejecttctss (5(5(54 44 totot 779 99 yeyeararss offof aagege,, 48488% %% mamaleles))s) ffroromm thhthee IMIMIMPRPRROVOVOVEEE ststududyy wewer


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versus expected P-values from the analysis of IMTmax is presented as an insert in Figure 1. The

genomic inflation factor (lambda) in the analysis of IMTmax was 1.05, indicating an adequate

correction of population substructure by the MDS-adjustment.

SNPs associated with any of the 4 CC-IMT measures at P<1x10-4 (a set of 46 SNPs) or

any composite IMT measure at P<1x10-5 (a set of 4 SNPs) were considered as candidates for

replication with respect to CC-IMT in independent cohorts (i.e a total of 50 SNPs). From these,

index SNPs were selected using the PLINK clump procedure, performed once for each set of

candidate SNPs, resulting in 39 index SNPs selected among the 46 SNPs that were associated

with CC-IMT measures at P<1x10-4 (designated CC-IMT SNPs, Supplemental Table 2) and 3

index SNPs selected among the 4 SNPs that were associated with composite IMT measures at

P<1x10-5 (Table 1) (designated composite IMT SNPs). Similarly, 26 index SNPs associated with

either of the 2 Bif-IMT measures (designated Bif-IMT SNPs) were generated from 52 candidate

SNPs associated at P<1x10-4 (not shown). For SNPs that passed the initial significance criteria

for more than one cIMT variable of the same category (CC-IMT, Bif-IMT, or composite IMT,

respectively), the most significant association was considered for the clump procedure.

Replication and Internal Validation

A replication stage investigating associations with CC-IMT of the identified 39 CC-IMT SNPs

and 3 composite IMT SNPs was undertaken in five independent population-based cohorts from

Sweden, the United Kingdom, and the Netherlands (total n=11,590). Basic characteristics of

participants in the replication cohorts are shown in Supplemental Table 1. In the replication

meta-analysis of the CC-IMT SNPs, no SNP was significantly associated with CC-IMT

(Bonferroni correction for 42 independent tests P<0.0012; lowest observed P=0.007;

Supplemental Table 2). In contrast, the association of the composite IMT SNP rs4888378 with

NPs that were assocccciaiaiai t

pleme tntttallll TTTaT blblblb e 2222)))) a

s s

T e

e d

c t

an one cIMT ariable of the same categor (CC IMT Bif IMT or composite IM

s seeelelelelectcttctedededed aaamomomonngn the 4 SNPs that were asasasassoociated with compmpmposite IMT measures

Taaaabbblb e 1) (desiggnnnateeddd d compmmm osssititite IMMTTT SSNPsPs). SSSimimimiili aarlyyy,, 26 innndexexex SNPNPNPNPs ss asasassoccciaate

e 2 BiBBiBiffff-IMIMMIMT memeasasurureses (((deded sisiisiggggnatededed BBBBifififi -ff IMMMIMTT TT SNSNSNNPsPPsPs))) wewereerere ggeneneeratttedededed ffffrom m 5255252 ccanand

ciated at P<1x10000-444 ((((not hhshown)n)). FFFor SNSNSNS PsPP thhhah t papp ssedddd thheh iii iniii iitiialll sigigii nificance crit

a IcIMTMT iiablbl fof thhe te ((CCCC IMIMTT BBifif IMIMTT iit IMIM by guest on June 18, 2018http://circgenetics.ahajournals.org/

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CC-IMT (P=7.24x10-6; Table 1) passed Bonferroni correction in meta-analysis of the replication

cohorts (i.e. P<0.0012). This index SNP also achieved array-wide significance in combined

meta-analysis of IMPROVE and replication cohorts (P=3.51x10-7). The associations of

rs4888378 with CC-IMT in all individual cohorts and in the meta-analyses are illustrated in

Figure 2. The two other composite IMT SNPs that were selected at the P<1x10-5 level reached

nominal significance in the replication cohorts (Table 1). None of the Bif-IMT index SNPs were

significantly associated with Bif-IMT in the MDC cohort after Bonferroni correction for 26

independent tests (all P>0.0019). The internal validation of the 3 composite IMT SNPs by non-

parametric double bootstrap confirmed significant associations with composite IMT (bootstrap

beta with 98.33% confidence intervals to account for analysis of 3 independent SNPs: -0.0094 [-

0.0200, -0.0034] for rs4888378, -0.0083 [-0.0166, -0.0035] for rs1001861, and 0.0056 [0.0018,

0.0131] for rs200991; Supplemental Figure 3).

Secondary Analyses in the Discovery Cohort

The replicated lead SNP rs4888378 in the chromosome 16 locus was evaluated in greater detail

in the IMPROVE cohort. Regional association plots for IMTmax (Figure 3) indicated the

presence of only one single association signal (rs4888378 in the IMPROVE cohort), centered

over the 3’ end of the CFDP1 gene. Potential pleiotropy and possible confounders were assessed

by investigating associations of rs4888378 with biochemical and clinical parameters. To the best

of our knowledge, rs4888378 was included on the CardioMetabochip due to previous

associations with systolic blood pressure. However, in IMPROVE this association was not

confirmed (P=0.12). Cumulative life-time smoking (pack-year categories) was found to differ

between genotype groups (Kruskal-Wallis P=0.0067). Adjustment for pack-year categories, in

addition to age, sex, the first three MDS dimensions, and 12 additional variables reflecting

posite IMT SNPs bybybyby n

ompositititite IMIMIMIMTT TT (b((b(boooooooottttst

8 0

003 for rs4888378, -0.0083 0.0166, -0.003 for rs1001861, and 0.0056 0.0

ated lead SNP rs4888378 in the chromosome 16 loc s as e al ated in greater d

8.33%3%3% cococonfnfnfnfidenenence intervals to account fofofoforrr analysis of 3 indededeependent SNPs: -0.00

0003034] for rs48888837878788, -00-0.00.0083333 [-000.0166666, -00.0003335]5]5] fffoor rrrs11001818186111,, and ddd 000.0 00055565 [000.0 0

rs2000009909099191919 ; SuSuS pppppplelelememementalalal FFFFiiigi ure ee 3333)).

Analysyy es in the DiDiiscovery yy CoC hhhort

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established cardiovascular risk factors (see Methods) did not have a major impact on the original

cIMT association of rs4888378 (P=2.7x10-6 for IMTMax), indicating that this association signal is

not mediated by established cardiovascular risk factors.

Investigation of rs4888378 in relation to segment-specific IMT measurements in

IMPROVE showed that rs4888378 was most strongly associated with Bif-IMT (P=1.13x10-5 for

Bif-IMTmax), whereas the weakest association was seen with CC-IMT (P-values for the 4 CC-

IMT variables ranging from 0.010 to 0.038), the ICA-IMT association being intermediate

(Supplemental Table 3).

Association with Other Cardiovascular Phenotypes

The lead SNP rs4888378 in the chromosome 16 locus was further investigated for associations

with carotid plaque score in the MDC study, with CAC score in the Rotterdam studies, and with

CAD risk in PROCARDIS and CARDIoGRAM. Since the association of rs4888378 with CC-

IMT was consistent across the investigated cohorts (Table 1, Figure 2), no significant between-

cohort heterogeneity was expected with respect to associations with other related cardiovascular

phenotypes. Accordingly, fixed-effects (rather than random-effects) models were considered

appropriate for meta-analyses of the CAC score in the Rotterdam studies and of CAD in

CARDIoGRAM. The minor A allele of rs4888378 (which was associated with thinner IMT) was

weakly associated with a lower carotid plaque score (beta [SE]=-0.046 [0.023], P=0.04, n=2120)

in the MDC study and showed a tendency towards lower CAC score in meta-analysis of RS-I

and RS-II (beta [SE]=-0.11 [0.06], P=0.06, n=2,948). Furthermore, the thinner-IMT allele was

associated with decreased risk for CAD and MI in PROCARDIS and CARDIoGRAM (odds

ratio [95%CI] 0.83 [0.77-0.90] for all CAD and 0.84 [0.77-0.91] for MI in PROCARDIS, and

0.95 [0.92-0.98] for all CAD and 0.96 [0.93-0.99] for MI in CARDIoGRAM) (Figure 4).

NP rs4888378 in the chromosome 16 locus was further investigated for associati

d plaque score in the MDC stu , with CAC score in the Rotterdam studies, and

n C

o w

rogeneit as e pected ith respect to associations ith other related cardio as

NP rrrs4s4s4888888888383383787878 iiiinn n the chromosome 16 locucucuss was further invevevestigated for associati

d plllaque scoree innn thehehee MDCDCDD stutut dyyy, witthh CACA sscocooorererer in thhe RoRoRotteere dddad m ststtudddieieiei s, annd

n PRRRROCOCOCOCARARARA DIDIDISSSS ananddd CACC RDRDRDDIIIoIoGRRRRAMAMAMAM. SiSiSiincncee thththeeee assssssos ciciciatatatatioionn ofo rs4s4s4s4888888883337878788 wititithhh C

onsistent across thehh iiiinves itiigagg ted ddd cohoh rts (((TaT blbb e 1,11,1 FFFigiigi ure 2222),)),) no signgg ificant betw

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Association with Expression in Target Tissues

Global gene expression data from five different tissues (aortic intima-media, aortic adventitia,

mammary artery intima-media, PBMC, and carotid plaque) were used to link individual genes to

the cIMT-associated locus discovered in this study. We investigated associations between

genotype and expression levels of all genes located within ±200 kb of the lead SNP rs4888378.

A total of 9 genes were contained within this region, 8 of which were captured by the microarray

analysis (Supplemental Figure 4). The most significant allele-specific difference in gene

expression level according to rs4888378 genotype was observed for TMEM170A

(transmembrane protein 170a) (in aortic intima-media, P=0.000569, n=138, and adventitia,

P=0.000576, n=133, respectively; Figure 5) Two more genes were differentially expressed at

nominally significant levels, BCAR1 (breast cancer anti-estrogen resistance-1) in carotid plaque,

P=0.00749, n=126, and LDHD (lactate dehydrogenase D) in aortic intima-media and adventitia,

P=0.0459, n=138, and P=0.00836, n=133, respectively (Figure 5). However, strictly considering

multiple testing of 8 genes in 7 different tissues, a P-value of <0.00089 should be held

statistically significant; this threshold was reached only for TMEM170A. No significant genotype

association was observed with expression levels of CFDP1 (lowest observed P=0.0693, n=133,

for aortic adventitia; Supplemental Figure 4).

Discussion

In this study, we investigated genetic determinants of cIMT, a widely accepted marker of

subclinical atherosclerosis, applying a two-stage discovery and replication study design

involving more than 15,000 subjects. We identified a novel locus on chromosome 16 (lead SNP

rs4888378), the minor allele of which was associated with thinner cIMT and decreased risk of

CAD in subjects of European ancestry. The association with CAD was stronger in PROCARDIS

MEM170A

=138888, and ddd dadddventtttititititiaiaiaia,

6 d

s

, n

n d

sting of 8 genes in 7 different tiss es a P al e of <0 00089 sho ld be held

6, nnn=1=1=133333333, rereresppeecectively; Figure 5) Two mmomorre genes were dddifififffefff rentially expressed

siiiigngngnnificant leveellls, BCBCCARRRR111 (brrrreeassst cannnccer ananti-esesestrtrtrtrogogoo ennn rresisstaaancncnceee-1) inininin cararrotiiddid pl

, n=12112126666, aand LDLDLDL HDHDHDH (((lall ctttatatateeee ddddehyyydrdrdrogogogogennasasee DDD)))) iiiin aaaortititicccc inini tititimma-mememmedidididia ananddd d adddadveven

n=138, ,, and P=000 0.0008080808363636,,, n=11131 3,3,3 respepp ctiiivi lelly yy (((Figugg re 55).).) HoHH wever,,, strictly yy consid

iti ff 88 iin 77 ddififffe nt tiis a PP lal ff 0<0 0000008989 hho lldd bbe hheldld by guest on June 18, 2018http://circgenetics.ahajournals.org/

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than in CARDIoGRAM, which may reflect the fact that PROCARDIS recruited cases from

CAD-enriched families, thereby potentially enhancing the impact of genetic risk factors. We

also identified allele-specific differences in the expression of nearby genes in vascular tissues

according to rs4888378 genotype. Thus, investigation of genetic determinants of cIMT resulted

in discovery of a novel CAD risk locus and novel candidate CAD susceptibility genes that merit

further investigation.

A recent meta-analysis of SNP-based GWA studies of cIMT conducted by the CHARGE

consortium discovered three regions associated with CC-IMT.6 In IMPROVE, the lead SNPs for

the three IMT-associated loci identified by CHARGE were associated with cIMT , albeit at

significance levels that did not qualify for inclusion in the replication stage of our study

(investigated directly or by proxy; data not shown). The fact that our lead SNP rs4888378 was

not identified by the CHARGE consortium may reflect differences in study design and IMT

phenotyping. Specifically, rs4888378 was selected for replication in our study based on its

association with composite IMT, and among individual segments rs4888378 proved to be most

strongly associated with Bif-IMT; neither of these IMT phenotypes were analysed by the

CHARGE consortium.

It is noteworthy that established CAD loci19, 26, 27 neither appeared as major determinants

of cIMT in the current study, nor in the study reported by the CHARGE consortium.6 Thus it

appears that the impact of these loci that confer risk of clinically manifest CAD may not be as

strong in early subclinical atherosclerosis.

Our results suggest that the observed associations may be due to an influence of

rs4888378, or linked variants, on the expression of nearby gene(s) in the arterial wall. The

expression of CFDP1, the gene harbouring rs4888378 in its last 3’ intron, showed no allele-

PROVE, the lead SNSNSNSNP

with hhh cIIIIMTMTMTMT , alblll eieieieittt t at

e

ed direct or proxy; data not shown The fact that our lead SNP rs4888378 w

ed by the CHARGE consortium may reflect differences in study design and IMT

ith composite IMT and among indi id al segments rs4888378 pro ed to be m

e levevevelelelsss thththt atatat didididd not qualify for inclusion nn inin the replicationn stststage of our study

edd d dddirectly or bybyby prroxxxy; dadadad ta nnnot shhownwwn). TThe fffacaccctt tt thththattt oour llleeead dd SSSNS PP rsss488888888 373737378 w

ed bbbby y ththththeee e CHCHCHARARARRGEGEGE cconsooortrtrtrtiiiium mamamay yyy reflflflflecect ttt dididifffffffferrrenenenceeessss inn sstttutudy ddddeesesign n anand ddd IMIMIMT

g.gg Spepp cifically,y,y, r 4s4448888888 8383833787878 was s lellecteddd d fofff r replp icatioiii n iiini our studydydd based on its

ii hth isite IIMTMT dd iindidi iidd lal nt 48488888373788 ded t bbe by guest on June 18, 2018http://circgenetics.ahajournals.org/

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specific association with rs4888378. The biological role of TMEM170A, the expression of which

showed the strongest association with rs4888378, is currently unknown. In silico sequence

analysis predicts that the TMEM170A protein consists of an extracellular N-terminal part, three

transmembrane helices, and a short cytoplasmic C-terminal tail (MEMSAT-SVM;

http://bioinf.cs.ucl.ac.uk). In contrast to TMEM170A, BCAR1 (also known as p130CAS) has been

extensively studied and ascribed important roles in processes such as cellular adhesion,

migration and proliferation/survival e.g. in vascular smooth muscle cells (VSMC) 28, 29, and thus

has a biologically plausible role in atherogenesis. In silico analysis of the genomic sequence

surrounding rs4888378 predicts that rs4888378 may influence the binding of transcription

factors (YY1 and NF-1; TESS software, http://www.cbil.upenn.edu/cgi-bin/tess/tess). YY1 is

expressed in human carotid atherosclerotic lesions and has experimentally been ascribed roles in

VSMC injury responses and neointima formation.30, 31 Accordingly, it is tempting to speculate

that one mechanism underlying the association between rs4888378 and cIMT and CAD risk

would be influence of rs4888378 on YY1-regulated transcription of BCAR1 in VSMC, with

downstream effects on VSMC function. The BCAR1 locus is further indicated by the discovery

stage composite IMT index SNP rs100861 which maps close to the BCAR1 gene. Interestingly,

another intronic SNP in the CFDP1 locus has been associated with markers of chronic

obstructive pulmonary disease (COPD).32 This SNP (rs2865531) is in strong LD with rs4888378

(r2=0.967 in the HapMap CEU reference panel; not present on the CardioMetabochip). The

BCAR1-CFDP1-TMEM170A locus is thus implicated in both atherosclerotic cardiovascular

disease and COPD, two pathologies which both have strong inflammatory components and

involve tissue remodeling including dysregulation of SMC phenotype and function, and that

exhibit pronounced co-morbidity.33

the genomic sequeeeencnnn

ding offff ttttranscripipipi tititiionononon

Y 1

n o

u l

echanism underlying the association between rs4888378 and cIMT and CAD risk

nfl ence of rs4888378 on YY1 reg lated transcription of BCAR1 in VSMC ith

Y1 aaandndndn NNNNF-FF-111; TETETETESS software, http://wwwww.cbc il.upenn.edu/cccgiggg -bin/tess/tess). YY1

nnn huuumu an carottiddd athththherosssscclc erotootiici leesiooonns andnd hhhaas eexee pperrrimmenntaaallyyy bbbbeennn n aaascrcrcriiibi edededd ro

ury resesespopopoponnnsess aa ddndnd nneoeoiiintimimmaaaa fffformmmattatatioioioionnn.3030300,,, 31131 AAAcccccccororordiididinggglyylyly, iiittt iiisis tempmpmmptitititingg tttoo spspececulu

echanism underlyllyl inii g gg hthhhe associatiioi n bbebb tween rs4888888888883737373 8888 and ddd IcIMTMTMT and CAD risk

nffll ff 48488888373788 YYY1Y1 llat ded t iip iti ff BCBCARAR11 ii VSVSMCMC iith by guest on June 18, 2018http://circgenetics.ahajournals.org/

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A major strength of the current study is the extensive and thoroughly standardised

ultrasound examination performed in the discovery cohort (IMPROVE). The fact that SNPs

selected for replication based on their association with composite IMT variables performed better

at the replication stage than those selected based on their associations with CC-IMT, suggests

that these composite variables are of particular value. However, some limitations of the present

study should also be considered. Differences in ultrasonographic protocols exist between the

participating cohorts. For example, CC-IMT was not measured in exactly the same way in all

cohorts. Furthermore, recruitment protocols differed between studies. Whereas the IMPROVE

study recruited high-risk individuals (with at least three established vascular risk factors), the

WH-II study recruited healthy subjects, and the MDC, EAS and Rotterdam studies enrolled

population-based subjects. These between-cohort differences may have obscured associations

that remain undetected in the present study.

Conclusions

This study identified rs4888378 in the BCAR1-CFDP1-TMEM170A locus on chromosome 16 as

a novel genetic determinant of cIMT and CAD risk in individuals of European ancestry. Further

investigations, including experimental studies, are needed to fully clarify the biological

mechanisms underlying the current findings.

Acknowledgments: The authors fully acknowledge the thousands of study participants who volunteered their time to help advance science and the scores of research staff and scientists who have made this research possible. The Edinburgh Artery Study would particularly like to acknowledge all EAS staff and participants. The authors representing the Rotterdam Study are very grateful to the participants and staff from the Rotterdam Study, the participating general practitioners and the pharmacists. Whitehall-II genotyping was in part supported by a MRC-GSK pilot programme grant (ID 85374). David Altshuler, Sekar Kathiresan and "The Pfizer Broad-Massachusetts General Hospital-Broad Genetics Collaboration" are acknowledged for supporting the genotyping in the Malmö Diet and Cancer Study. Members, sources of funding, and

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disclosures of the CARDIoGRAM consortium are listed in Supplemental Section S7. Members of the PROCARDIS consortium are listed in Supplemental Section S8. Members of the writing group and affiliations by participating study are listed in Supplementary Section S9.

Funding Sources:The IMPROVE study was supported by the European Commission (Contract number: QLG1-CT-2002-00896), the Swedish Heart-Lung Foundation, the Swedish Research Council (projects 8691 and 0593), Knut and Alice Wallenberg Foundation, the Torsten and Ragnar Söderberg Foundation, the Swedish Foundation for Strategic Research, the Stockholm County Council (project 562183), the Strategic Cardiovascular and Diabetes Programmes of Karolinska Institutet and Stockholm County Council, Academy of Finland (Grant #110413), Ministry of Education and Culture of Finland, the City of Kuopio, the British Heart Foundation (RG2008/014) and the Italian Ministry of Health (Ricerca Corrente). The UCL Genetics Institute supported D.Z., and S.S., and. S.E.H was supported by the BHF (RG2008/08). The Rotterdam Study was funded by the Erasmus Medical Center and Erasmus University, Rotterdam, 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. The Rotterdam GWA study was funded by the Netherlands Organisation of Scientific Research (NWO, De Nederlandse Organisatie voor Wetenschappelijk Onderzoek) Investments (number 175.010.2005.011, 911-03-012), the Research Institute for Diseases in the Elderly (014-93-015; RIDE2) and the Netherlands Genomics Initiative (NGI)/Netherlands Consortium for Healthy Aging (NCHA) project number 050-060-810. The present work was further supported by an NWO grant (vici, 918-76-619). The Whitehall-IIStudy was supported by the Medical Research Council, the British Heart Foundation, and the US National Institutes of Health (R01HL36310). S.E.H and P.J.T were supported by BHF RG005/014. The Malmö Diet and Cancer Study was supported by the Swedish Research Council, the Swedish Heart-Lung Foundation and the European Research Council (ERC-StG-282255). The Edinburgh Artery Study was financed by the British Heart Foundation and the Chief Scientist Office of the Scottish Executive Health Department. The ASAP Study was supported by the Swedish Research Council (12660), the Swedish Heart-Lung Foundation (20090541), the European Commission (FAD, Health F2 2008 200647) and a donation from Fredrik Lundberg. The BiKE Study was funded by the Swedish Heart-Lung Foundation, the Swedish Research Council, the European Commission (AtheroRemo (FP7-HEALTH-2007-A-201668), the AFA Foundation, and the Torsten and Ragnar Söderberg Foundation. PROCARDIS was supported by the European Community Sixth Framework Program (LSHM-CT- 2007-037273), AstraZeneca, the British Heart Foundation, the Wellcome Trust (Contract No. 090532/Z/09/Z), the Swedish Research Council, the Knut and Alice Wallenberg Foundation, the Swedish Heart-Lung Foundation, the Torsten and Ragnar Söderberg Foundation, the Strategic Cardiovascular Program of Karolinska Institutet and Stockholm County Council, the Foundation for Strategic Research and the Stockholm County Council. M.F and H.W. are supported by the British Heart Foundation Centre of Research Excellence. M S-L is a recipient of a Marie Curie Intra European Fellowship within the 7th Framework Programme of the European Union (PIEF-GA-2009-252361).

Conflict of Interest Disclosures: Stela McLachlan reports stock ownership interest in Pfizer. Mika Kivimäki reports receiving research grant support from NHLBI (R01HL36310; principal

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investigator). John Deanfield reports receiving research grant support from the British Heart Foundation and Medical Research Foundation, and honoraria payment and payment for speaker’s bureau appointments (Novartis, Roche, Merck, Danone, Pfizer). Benjamin Voight reports receiving research grant support from NIH (A GWAS for early onset myocardial infarction; post-doc) and an industry grant (Toward therapeutical markers for MI in a T2D background; post-doc). Meena Kumari reports receiving research grant support from the British Heart Foundation (PG1041133124260, RG1081008), NIH (AG13196), Medical Research Council, and NHLBI (HL36310). Anders Gabrielsen was employed by Bayer after completion of the study. Ulf Hedin reports consultant relationship with Cardoz AB. Kristiina Nyyssönen reports receiving research grant support from the Academy of Finland for the IMPROVE study (Grant #110413). Philippe Giral reports receiving Hospital Clinical Research Programme (PHRC) research grant support, awarded by the French Health Ministry. Robert Clarke reports receiving research grant support from the British Heart Foundation. Maria Grazia Franzosi reports receiving research grant support from the European Commission 6th Framework Programme as collaborator in the PROCARDIS project. Aroon D. Hingorani reports receiving research grant report from the British Foundation. Damiano Baldassarre reports receiving research grant support from the European Commission (Contract number: QLG1-CT-2002-00896) for the IMPROVE study. Elena Tremoli reports receiving research grant support from the European Commission (Contract number: QLG1-CT-2002-00896) for the IMPROVE study. Steve E. Humphries reports receiving research grant support from the British Heart Foundation (RG2008/08, programme and project grants on cardiovascular genetics) and European Commission 7th Framework Programme on diabetes, and discloses speakers' bureau appointment payment (Genzyme meeting on FH, Amsterdam November 2011) and one consultant/advisory board relationship (Store Gene, a CHD-risk genetic testing University College London spin-off company).

References:

1. Simon A, Megnien JL, Chironi G. The value of carotid intima-media thickness for predicting cardiovascular risk. Arterioscler Thromb Vasc Biol. 2010;30:182-185.

2. Stein JH, Korcarz CE, Hurst RT, Lonn E, Kendall CB, Mohler ER, et al. Use of carotid ultrasound to identify subclinical vascular disease and evaluate cardiovascular disease risk: A consensus statement from the american society of echocardiography carotid intima-media thickness task force. Endorsed by the society for vascular medicine. J Am Soc Echocardiogr.2008;21:93-111.

3. Amato M, Montorsi P, Ravani A, Oldani E, Galli S, Ravagnani PM, et al. Carotid intima-media thickness by b-mode ultrasound as surrogate of coronary atherosclerosis: Correlation with quantitative coronary angiography and coronary intravascular ultrasound findings. Eur Heart J.2007;28:2094-2101.

4. Lorenz MW, Markus HS, Bots ML, Rosvall M, Sitzer M. Prediction of clinical cardiovascular events with carotid intima-media thickness: A systematic review and meta-analysis. Circulation.2007;115:459-467.

ngorani reports rececececeiviiirrrre eee rerererepopopoportrtrtrtsss s rerererececececeivivivivinnnngggg ber: QLQLQLQLG1G1G1G1-CTCTCTCT-222000000002222-arch ggggrarararantntntnt ssssupupupuppopopoportrtrtrt fffro

C yumphries reports receiving research grant support from the British Heart Founda

o nGenzyme meeting on FH, Amsterdam November 2011) and one consultant/advisionship (Store Gene, a CHD-risk genetic testing University College London spin

Commmmimimimissssssioioion nn (CCCCoono tract number: QLG1-CTCTCT-2-- 002-00896) foror ttthe IMPROVE studyummmmppphp ries repepepepororrortsttt rececececeieieivivvv ngngng rrrresesese eaeaeaearcrcrch hhh grgrgrg antt ssupppporororort t t t fromomomom ttthehehe BBBBritiiiishshshsh HHHHeartrttrt FFFouououundnnn a8888, ppprp ogrammee aaand ppprojjececece t grrraantss on caardioiovascscululularr geneeneticcsss) aaandnnn Eurururu opopopeaaan

on 777thtt Frameeewowow rrrk PProoogramaammee oonnn ddiabbbeetess, aanddd dddiscsclosesses spsppeaaakekersrsrs' buuurerereaauu appppooinGenzyyymemememe meeetititingngng oonn FHFF , AmAmAmAmsterdaddadammmm NNoNovevembmbmberererer 2220101010 1)1)1) aandndd oonne ccccononono sultttanant/t/t/addad ivivisionship (Store GeGGeG nenenee,, a a a CHCHCHC D-D-D-D-riririsksksksk gggenenenenetete icicic ttesesesstititit nngn UUUUninininivevevev rsrsrssitititity y y CoCoCoC lllllllegegegege London spin


Dow

nloaded from



20

5. van der Meer IM, Bots ML, Hofman A, del Sol AI, van der Kuip DA, Witteman JC. Predictive value of noninvasive measures of atherosclerosis for incident myocardial infarction: The rotterdam study. Circulation. 2004;109:1089-1094.

6. Bis JC, Kavousi M, Franceschini N, Isaacs A, Abecasis GR, Schminke U, et al. Meta-analysis of genome-wide association studies from the charge consortium identifies common variants associated with carotid intima media thickness and plaque. Nat Genet. 2011;43:940-947.

7. Paternoster L, Martinez-Gonzalez NA, Charleton R, Chung M, Lewis S, Sudlow CL. Genetic effects on carotid intima-media thickness: Systematic assessment and meta-analyses of candidate gene polymorphisms studied in more than 5000 subjects. Circ Cardiovasc Genet. 2010;3:15-21.

8. Fox CS, Cupples LA, Chazaro I, Polak JF, Wolf PA, D'Agostino RB, et al. Genomewide linkage analysis for internal carotid artery intimal medial thickness: Evidence for linkage to chromosome 12. Am J Hum Genet. 2004;74:253-261.

9. Wang D, Yang H, Quinones MJ, Bulnes-Enriquez I, Jimenez X, De La Rosa R, et al. Agenome-wide scan for carotid artery intima-media thickness: The mexican-american coronary artery disease family study. Stroke. 2005;36:540-545.

10. Baldassarre D, Nyyssonen K, Rauramaa R, de Faire U, Hamsten A, Smit AJ, et al. Cross-sectional analysis of baseline data to identify the major determinants of carotid intima-media thickness in a european population: The improve study. Eur Heart J. 2010;31:614-622.

11. Kivimaki M, Lawlor DA, Smith GD, Kumari M, Donald A, Britton A, et al. Does high c-reactive protein concentration increase atherosclerosis? The whitehall ii study. PLoS One.2008;3:e3013.

12. Marmot M, Brunner E. Cohort profile: The whitehall ii study. Int J Epidemiol. 2005;34:251-256.

13. Lee AJ, Mowbray PI, Lowe GD, Rumley A, Fowkes FG, Allan PL. Blood viscosity and elevated carotid intima-media thickness in men and women: The edinburgh artery study. Circulation. 1998;97:1467-1473.

14. Bots ML, Hoes AW, Koudstaal PJ, Hofman A, Grobbee DE. Common carotid intima-media thickness and risk of stroke and myocardial infarction: The rotterdam study. Circulation.1997;96:1432-1437.

15. Hofman A, van Duijn CM, Franco OH, Ikram MA, Janssen HL, Klaver CC, et al. The rotterdam study: 2012 objectives and design update. Eur J Epidemiol. 2011;26:657-686.

16. Berglund G, Elmstahl S, Janzon L, Larsson SA. The malmo diet and cancer study. Design and feasibility. J Intern Med. 1993;233:45-51.

La RoRRR sa RRRR, etttt alll. AAAAican amamamamerererericicccanananan ccccororororonoo a

a

s sn dn

ko13

ase ffffamamamilililly y stststudddyy.y Stroke. 2005;36:540-54445.55.

saaaarre ee D, Nyysssonnenn KKKK, RaRaRaR uramaamaaa R, dee Faiaire UUU, , ,, HaHaHammmstten A,A,A, SSSmimmm t AJAJAJAJ, ett aaal. CrCrCrosnalalalyyyysiss s of bbbasaa eeliiine daata ttto o idenentifyfyfy thhehe majajoor dddeeterrmminannannts ofoff cararrotoo iddd iiinntn imimima-memm dn a eurururopopopopeeean popopupupulalal tiititiono : ThThThhee imprprprovovovoveee e sttttuddudy. EuEuEuur HeHeHeH arrrttt JJJJ. 2020200101 ;3;3;3;31:1:1:66616 4-44 62626222.22 JJJJ

ki M, ,, Lawlor DDDA,A,AA SSSS imiii hhth GGGGD,D,D, KKKumari i M,M,M, DDDonald ddd A,A,AA BBriitton AAAA, ,, et al. Does higgh oootein conccenenenentrtrtrt atattatioioioion n n n iniinincrcrcrreaeaeaeaseseese aaaathththererererosossosclcclclererrerososososisisis???? ThThhTheeee whhwhwhitititehhhehalaalall ll iiiiiiii ssstutuutudydyddy. . PLPPLPLoS One.1313 by guest on June 18, 2018

http://circgenetics.ahajournals.org/D

ownloaded from



21

17. Hedblad B, Nilsson P, Janzon L, Berglund G. Relation between insulin resistance and carotid intima-media thickness and stenosis in non-diabetic subjects. Results from a cross-sectional study in malmo, sweden. Diabet Med. 2000;17:299-307.

18. Farrall M, Green FR, Peden JF, Olsson PG, Clarke R, Hellenius ML, et al. Genome-wide mapping of susceptibility to coronary artery disease identifies a novel replicated locus on chromosome 17. PLoS Genet. 2006;2:e72.

19. Schunkert H, Konig IR, Kathiresan S, Reilly MP, Assimes TL, Holm H, et al. Large-scale association analysis identifies 13 new susceptibility loci for coronary artery disease. Nat Genet.2011;43:333-338.

20. Folkersen L, van't Hooft F, Chernogubova E, Agardh HE, Hansson GK, Hedin U, et al.Association of genetic risk variants with expression of proximal genes identifies novel susceptibility genes for cardiovascular disease. Circ Cardiovasc Genet. 2010;3:365-373.

21. Purcell S, Neale B, Todd-Brown K, Thomas L, Ferreira MA, Bender D, et al. Plink: A tool set for whole-genome association and population-based linkage analyses. Am J Hum Genet.2007;81:559-575.

22. Willer CJ, Li Y, Abecasis GR. Metal: Fast and efficient meta-analysis of genomewide association scans. Bioinformatics. 2010;26:2190-2191.

23. Ioannidis JP, Thomas G, Daly MJ. Validating, augmenting and refining genome-wide association signals. Nat Rev Genet. 2009;10:318-329.

24. Vliegenthart R, Oudkerk M, Hofman A, Oei HH, van Dijck W, van Rooij FJ, et al. Coronary calcification improves cardiovascular risk prediction in the elderly. Circulation. 2005;112:572-577.

25. Odink AE, van der Lugt A, Hofman A, Hunink MG, Breteler MM, Krestin GP, et al. Risk factors for coronary, aortic arch and carotid calcification; the rotterdam study. J Hum Hypertens.2010;24:86-92.

26. The Coronary Artery Disease (C4D) Genetics Consortium. A genome-wide association study in europeans and south asians identifies five new loci for coronary artery disease. Nat Genet.2011;43:339-344.

27. Malarstig A, Hamsten A. Genetics of atherothrombosis and thrombophilia. Curr Atheroscler Rep. 2010;12:159-166.

28. Tang DD. P130 crk-associated substrate (cas) in vascular smooth muscle. J Cardiovasc Pharmacol Ther. 2009;14:89-98.

29. Tikhmyanova N, Little JL, Golemis EA. Cas proteins in normal and pathological cell growth control. Cell Mol Life Sci. 2010;67:1025-1048.

t. 2010;3:365-373. . . .

der D,DDD etttt allll. PlPlPlPliniii k:k:k:k: AAAA tses AmAmAmAm JJJJ HHHHumumumum GGGGeenenenet

C

d

nthart R O dkerk M Hofman A Oei HH an Dijck W an Rooij FJ et al Cor

9-5757575.55.

CCCCJJ, Li Y, Abecacaasis GGRGRG . MeMMM taaal:l:l: FFFaast anaand eeffficiiienennt t t t memmm taaa--analllyyysisss ooof geeeennnomemem wiiiiddded scccananns. Bioiiinfnn ororrmaatiiics. 2020202 10;2;2;2; 6::::2212 90000--219911.

dis JP, Thomas GGGG,, DaDaDaDalylylyy MMMJ.JJ. VVVValalallididididatatattininining,g,g, aaauguguggmememem nttttininini g g g g ananana d ddd rererer fififiinininingngngg ggggenome-wide signgg als. Nat Rev GeGG net. 20202000909090 ;1;111000:0 3313 88-88 32323299.9

nthhart RR OO dkdk kk MM HH fof AA OO iei HHHH DDijij kck WW RRo ioijj FJFJ et ll CCo by guest on June 18, 2018http://circgenetics.ahajournals.org/

Dow

nloaded from



22

30. Santiago FS, Ishii H, Shafi S, Khurana R, Kanellakis P, Bhindi R, et al. Yin yang-1 inhibits vascular smooth muscle cell growth and intimal thickening by repressing p21waf1/cip1 transcription and p21waf1/cip1-cdk4-cyclin d1 assembly. Circ Res. 2007;101:146-155.

31. Santiago FS, Lowe HC, Bobryshev YV, Khachigian LM. Induction of the transcriptional repressor yin yang-1 by vascular cell injury. Autocrine/paracrine role of endogenous fibroblast growth factor-2. J Biol Chem. 2001;276:41143-41149.

32. Soler Artigas M, Loth DW, Wain LV, Gharib SA, Obeidat M, Tang W, et al. Genome-wide association and large-scale follow up identifies 16 new loci influencing lung function. Nat Genet.2011;43:1082–1090.

33. Maclay JD, McAllister DA, Macnee W. Cardiovascular risk in chronic obstructive pulmonary disease. Respirology. 2007;12:634-641.


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Table 1. Loci selected for replication based on association with composite IMT variables in the discovery analysis (P<1x10-5)

Composite IMT Discovery

(n=3,428-3,429)

CCA-IMT Discovery

(n=3,427-3,429)

CCA-IMT Replication

(n=11,585-11,587)

SNP Nearest gene Chr Pos

Alleles

effect/other Freq Beta SE P Beta SE P Freq Beta SE P

Heterogeneity

I2 Pheterogeneity

rs4888378 CFDP1 16 73889542 A/G 0.43 -0.0192 0.0038 6.75E-07 -0.0071 0.0028 0.010 0.41 -0.0045 0.0010 7.24E-06 0 0.784

rs1001861 BCAR1 16 73863956 G/A 0.35 -0.0178 0.0040 7.16E-06 -0.0064 0.0028 0.025 0.37 -0.0033 0.0011 0.002 0 0.835

rs200991 HIST1H2BN 6 27923473 A/C 0.12 0.0136 0.0030 8.83E-06 0.0087 0.0028 0.002 0.16 0.0032 0.0013 0.015 41.6 0.144

Discovery and replication meta-analysis P-values and beta-coefficients for the effect allele (minor allele in the discovery cohort) are shown after adjustments for sex, age, and population substructure when applicable (multi-dimensional scaling in the IMPROVE discovery cohort only). Observations are sorted according to discovery P-value. Chr: chromosome, Freq: frequency. SE: standard error. Between-cohort heterogeneity is described by I2 in percent and Q-test P-values. Chromosome positions are given according to NCBI Build 36.

SEEEE P P P P FrFrFrFreqeqeqeq

0028 0000 001010 0000 000 410.43 0.0192 0.0038 6.75E 07 0.0071 0.0028 0.010 0.41

7

6

s c

0.43 0.0192 0.0038 6.75E 07 0.0071 0.0028 0.010 0.41

0.35 -000.017878788 00.00 00000 40404040 7.166EEE-066 -0.0.0.0.000000644 0.00002228 0.020202025 0..37

0.12 0...0101010 36363636 0.00 0000000030303030 8.8.8.8.83838383EEEE--06060 0....0000000087878787 0.0.0.0.000000028282828 0.000 002 0.16

sss and betetetetaaaa----cocococoefefefeffififif cicicicienenenentstststs fffforororor tttthehehehe eeeeffffffffecececect ttt alalallleleleelelelele ((((mimimiminonononor rrr alalalallelelelelelelele iiiin nnn the dissc


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Figure Legends:

Figure 1. Manhattan plot of the association P-values for IMTmax in the IMPROVE study,

adjusted for sex, age and population substructure (multi-dimensional scaling). SNPs are plotted

with their P-values (as -log10 values) as a function of genomic position (NCBI Build 36). The

red line indicates the threshold for array-wide significance (P=8.39x10-7). The lead SNP

rs4888378 is colored in green. Insert: Quantile-quantile (Q-Q) plot for associations with IMTmax

in IMPROVE, adjusted for sex, age, and population substructure. The expected distribution of

the P-values under the null distribution is given by the diagonal, and the empirical distribution of

the observed P-values is given by the open black circles. The lead SNP rs4888378 is colored in

green. The genomic inflation factor lambda equals 1.05.

Figure 2. Plot showing the associations of the minor allele of rs4888378 with common carotid

IMT (CC-IMT) in the discovery and replication cohorts separately, and meta-analysis of the

discovery and replication cohorts combined. Effect size (beta with 95% confidence intervals),

sample size, and minor allele frequency (MAF) of rs4888378 are given.

Figure 3. Regional plots of the genomic region containing the lead SNP rs4888378 (± 500

kilobases). SNPs are plotted with their P-values for the association with IMTmax (as -log10

values) as a function of genomic position (NCBI Build 36). Estimated recombination rates (from

the HapMap project) are plotted to reflect the local LD structure. The lead SNP rs4888378 is

shown as a diamond, and all other SNPs as circles. Correlations between a given SNP and

expected distributtttioioioion

he empipipii iiriical lll didididistriririribubububut

e e

Plot showing the associations of the minor allele of rs4888378 with common caro

MT) in the disco er and replication cohorts separatel and meta anal sis of th

ed PPP-v-v-valalala ueueuess s is gggiviii en by the open black ciriririrclcles. The lead SNPNPNPP rs4888378 is colore

gggennnnomic inflaatititioon faaactoooorrr r llal mbmbmbm daaa eequuualls 1.005.

Plot showing gg the asso iciiatioii ns of fff hhthe miiinor llalllelll le off f rs4848488888888837373778888 wiiii hthh common caro

MTMMT)) iin thhe ddiis dnd lili iti hoh ts at lel dnd eta lal iis ff hth by guest on June 18, 2018http://circgenetics.ahajournals.org/

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rs4888378 are indicated according to a color scheme based on pairwise r2-values from HapMap

(CEU reference panel). A. Original associations from the discovery analysis. B. Secondary

analysis where discovery associations are adjusted for the effect of rs4888378.

Figure 4. Associations of rs4888378 with coronary artery disease (CAD) and myocardial

infarction (MI). Forest plot of odds ratios with 95% confidence interval for the association of the

rs4888378 minor allele with CAD phenotypes in PROCARDIS with additional controls from the

Wellcome Trust Case-Control Consortium (adjusted for age, gender, country, and relatedness)

and in CARDIoGRAM (meta-analysis of 12 CAD and 10 MI case-control studies, respectively,

adjusted for age, gender, the genomic inflation (lambda), and taking into account the uncertainty

of imputed genotypes). Relative study size is reflected by plotted box size.

Figure 5. Associations of rs4888378 with nearby gene expression in human target tissues.

Robust Multichip Average (RMA)-normalized expression levels of TMEM170A and LDHD in

aortic intima-media and adventitia and BCAR1 in carotid plaque according to rs4888378

genotype are shown. Additive model P values are given.

countryy, and relateeeedndndndne

ntrol sttutt didididies, respececececttttiv

r age, gender, the genomic inflation (lambda), and taking into account the uncert

A

ltichip A erage (RMA) normali ed e pression le els ofd TMEM170A and LDHD

r agegege,, gegegendndnddeeer, ththththe genomic inflation (lammmbdbdbb a), and taking iiintntntto account the uncert

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IMPROVE n=3427MAF=0.43

WH-II n=2138MAF=0.41

MDC n=2140MAF=0.40

RS-I n=4699MAF=0.41

RS-II n=1980MAF=0.42

EAS n=630MAF=0.40

Replication n=11587MAF=0.41

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Tremoli, Ulf de Faire, Steve E. Humphries and Anders HamstenAroon D. Hingorani, Olle Melander, Jacqueline C.M. Witteman, Damiano Baldassarre, Elena

Syvänen, Göran K. Hansson, Per Eriksson, Nilesh J. Samani, Hugh Watkins, Jacqueline F. Price,J. O'Donnell, Martin Farrall, Robert Clarke, Maria Grazia Franzosi, Udo Seedorf, Ann-Christine

Philippa J. Talmud, Bo Hedblad, Albert Hofman, Jeanette Erdmann, Muredach P. Reilly, ChristopherTuomainen, Kai Savonen, Andries J. Smit, Philippe Giral, Elmo Mannarino, Christine M. Robertson,

Tomi-PekkaGabrielsen, Ulf Hedin, Anders Franco-Cereceda, Kristiina Nyyssönen, Rainer Rauramaa, Colombo, Meena Kumari, Claudia Langenberg, Nick J. Wareham, André G. Uitterlinden, AndersSilveira, John Deanfield, Benjamin F. Voight, Pierre Fontanillas, Maria Sabater-Lleal, Gualtiero I. Bolton, Lasse Folkersen, Bruna Gigante, Karin Leander, Max Vikström, Malin Larsson, AngelaFabrizio Veglia, Cristiano Fava, Maryam Kavousi, Stela McLachlan, Mika Kivimäki, Jennifer L. Karl Gertow, Bengt Sennblad, Rona J. Strawbridge, John Öhrvik, Delilah Zabaneh, Sonia Shah,

Intima-Media Thickness and Coronary Artery Disease Risk Locus as a Determinant of CarotidBCAR1-CFDP1-TMEM170AIdentification of the

Print ISSN: 1942-325X. Online ISSN: 1942-3268 Copyright © 2012 American Heart Association, Inc. All rights reserved.

TX 75231is published by the American Heart Association, 7272 Greenville Avenue, Dallas,Circulation: Cardiovascular Genetics

published online November 14, 2012;Circ Cardiovasc Genet.

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SUPPLEMENTAL MATERIAL

Identification of the BCAR1-CFDP1-TMEM170A locus as a determinant of

carotid intima-media thickness and coronary artery disease risk

Karl Gertow, PhD; Bengt Sennblad, PhD; Rona J. Strawbridge, PhD; John Öhrvik, PhD; Delilah

Zabaneh, PhD; Sonia Shah, MSc; Fabrizio Veglia, PhD; Cristiano Fava, MD, PhD; Maryam Kavousi,

MD, MSc; Stela McLachlan, PhD; Mika Kivimäki, PhD; Jennifer L. Bolton, PhD; Lasse Folkersen,

PhD; Bruna Gigante, MD, PhD; Karin Leander, PhD; Max Vikström, BSc; Malin Larsson, PhD;

Angela Silveira, PhD; John Deanfield, MD, PhD; Benjamin F. Voight, PhD; Pierre Fontanillas, PhD;

Maria Sabater-Lleal, PhD; Gualtiero I. Colombo, MD, PhD; Meena Kumari, PhD; Claudia

Langenberg, PhD; Nick J. Wareham, MBBS, PhD; André G. Uitterlinden, MD, PhD; Anders

Gabrielsen, MD, PhD; Ulf Hedin, MD, PhD; Anders Franco-Cereceda, MD, PhD; Kristiina

Nyyssönen, PhD; Rainer Rauramaa, MD, PhD; Tomi-Pekka Tuomainen, MD, PhD; Kai Savonen,

MD, PhD; Andries J. Smit, MD, PhD; Philippe Giral, MD, PhD; Elmo Mannarino, MD, PhD;

Christine M. Robertson, MBChB; Philippa J. Talmud, PhD; Bo Hedblad, MD, PhD; Albert Hofman,

MD, PhD; Jeanette Erdmann, PhD*; Muredach P. Reilly, MBBCH, MSCE*; Christopher J.

O’Donnell, MD, MPH*; Martin Farrall, FRCPath†; Robert Clarke, MD, PhD

†; Maria Grazia Franzosi,

PhD†; Udo Seedorf, PhD

†; Ann-Christine Syvänen, PhD; Göran K. Hansson, MD, PhD; Per Eriksson,

PhD; Nilesh J. Samani, MF, FRCP*; Hugh Watkins, FRCP†; Jacqueline F. Price, MBChB; Aroon D.

Hingorani, MD, PhD; Olle Melander, MD, PhD; Jacqueline C.M. Witteman, PhD; Damiano

Baldassarre, PhD; Elena Tremoli PhD; Ulf de Faire, MD, PhD; Steve E. Humphries, PhD; Anders

Hamsten, FRCP

*On behalf of the CARDIoGRAM consortium

†On behalf of the PROCARDIS consortium

CONTENTS

1. SUPPLEMENTAL SECTION S1: COHORT DESCRIPTIONS

2. SUPPLEMENTAL SECTION S2: GENETIC QUALITY CONTROL PROCEDURES IN

THE DISCOVERY COHORT (IMPROVE)

3. SUPPLEMENTAL SECTION S3: ESTABLISHMENT OF THRESHOLD FOR ARRAY-

WIDE STATISTICAL SIGNIFICANCE

4. SUPPLEMENTAL SECTION S4: POWER CALCULATIONS

5. SUPPLEMENTAL SECTION S5: BOOTSTRAPPING PROCEDURE

6. SUPPLEMENTAL SECTION S6: ASSOCIATION TESTING IN CAD CASE-CONTROL

STUDIES

7. SUPPLEMENTAL SECTION S7: CARDIoGRAM MEMBERS, AFFILIATIONS,

FUNDING AND DISCLOSURES

8. SUPPLEMENTAL SECTION S8: PROCARDIS MEMBERS AND AFFILIATIONS

9. SUPPLEMENTAL SECTION S9: WRITING GROUP, STUDY AND INSTITUTIONAL

AFFILIATIONS

10. SUPPLEMENTAL TABLES 1-3

11. SUPPLEMENTAL FIGURE LEGENDS 1-4

12. SUPPLEMENTAL FIGURES 1-4

SUPPLEMENTAL SECTION S1: COHORT DESCRIPTIONS

IMPROVE: The IMPROVE study1 was designed to evaluate the capacity of cross-sectional carotid

intima-media thickness (cIMT) and cIMT progression as predictors of incident vascular events in

European subjects at high risk for cardiovascular disease. The IMPROVE study enrolled 3,711

individuals (age range 54 to 79 years, 48% men) with at least three vascular risk factors but

asymptomatic for cardiovascular diseases, and free of any conditions that might limit longevity (e.g.

cancer) during 2004 and 2005. Subjects were recruited from 7 centres in 5 countries across Europe:

1,050 from two centres in Kuopio, Finland, 501 from Paris, France, 553 from Milan, Italy, 542 from

Perugia, Italy, 532 from Groningen, the Netherlands and 533 from Stockholm, Sweden. Among these,

the common carotid artery could not be properly visualized by ultrasonography in 8 subjects; hence

3,703 individuals were considered. Measurements of mean and maximum IMT of the common carotid

(CC), bifurcation (Bif), and internal carotid arteries (ICA) were obtained. IMT of the CC was

measured at two levels; at the 1st cm proximal to the bifurcation (CC-1

st cm), and in a segment

excluding the 1st cm proximal to the bifurcation (CC-IMT). Composite IMT variables considering the

whole carotid tree (IMTmean, IMTmax, and IMTmean-max [the average of IMT maxima recorded at the

different segments]) were derived from the segment-specific measurements.1 Details of the

ultrasonographic protocol have been described.1

Malmö Diet and Cancer (MDC) study: MDC is a population-based prospective study.2 Between

1991 and 1996, women aged 45 to 73 years and men aged 46 to 73 years residing in Malmö

(approximately 250,000 habitants), Sweden, were invited by mail and by newspaper advertisement to

participate in the study. In all, 28,449 persons from an eligible population of 74,000 participated. The

participants were asked to complete a questionnaire at home, which included lifestyle factors,

medication and previous and current diseases. Measurement of IMT in the distal part of common

carotid artery (mean) and at the bifurcation (max) level was obtained along with detection of carotid

plaques (summarized in a plaque scoring system on a 0-6 scale).3 Ultrasound measurements (along

with measurements of other cardiovascular risk factors) were performed in a random subsample

referred to as the MDC-CVA (n=6,103). Genotyping with the CardioMetabochip was performed in

2,143 non-diabetic participants in the MDC-CVA subset.

Edinburgh Artery Study (EAS): EAS was designed to explore the epidemiology of peripheral

arterial disease in a general population sample. The EAS study enrolled 1,592 men and women (age

range 55 to 74 years), selected in 1988 at random from 10 general practices spread across the city of

Edinburgh, Scotland. Surviving participants were invited to attend a five-year follow-up examination

involving measurements of cIMT. Valid measurements of cIMT and quality-controlled

CardioMetabochip genotyping data were available for 630 subjects for the present study.

Measurement of cIMT was made for both the right and left common carotid arteries 2 cm proximal to

the bifurcation.4

Whitehall-II (WH-II): WH-II recruited 10,308 men and women between 1985 and 1989 from 20

London-based civil service departments.5 Clinical measurements were taken at 5-year intervals.

Currently, clinical data is available from 4 phases (phase 1: 1985-1988, phase 3: 1991-1993, phase

5:1997-1999 and phase 7: 2003-2004). Carotid ultrasound measurements were made at Phase 7 (2003–

2004). cIMT was measured in the right and left common carotid arteries. Longitudinal images of the

CC artery, triggered on the R-wave of the ECG, were magnified and recorded in DICOM format as a

cine loop, on the hard drive of the ultrasound machine for later analysis. The CC-IMT was measured

at its thickest part 1 cm proximal to the bifurcation. The overall coefficient of variation for repeated

measures of cIMT was 4.7% (n=89).6 A total number of 2,138 subjects were included in the present

study.

Rotterdam study-I and Rotterdam study-II (RS-I and RS-II): The Rotterdam Study is a

prospective population-based cohort study aimed at investigating the determinants of chronic diseases

among participants aged 55 years and older.7 Briefly, residents of Ommoord, a district of

Rotterdamthe Netherlands, 55 years of age or older, were asked to participate, of whom 7983

participated (RS-I). The baseline examination was conducted in 1990-1993 and consisted of a home

interview and research centre visit for blood sampling. In 1999, inhabitants who turned 55 years of age

or had moved into the study district since the start of the study participated (RS-II). Ultrasonography

of the common carotid artery of the left and right carotid arteries was performed with a 7.5-MHz

linear-array transducer (ATL UltraMark IV). The maximal cIMT, summarized as the mean of the

maximal measurements from the near and far walls on both the left and right sides, was used for

analysis.8 A total of 4699 subjects from RS-I and 1980 subjects from RS-II had both genotyping and

IMT measurements available and were included in the current study.9

PROCARDIS: The PROCARDIS cohort has been described elsewhere.10

In brief, cases were

included if a diagnosis of coronary artery disease (CAD) was made before the age of 66 years and a

sibling also was diagnosed with CAD before 66 years. Control subjects were defined as being free

from symptoms of CAD up to the age of 66 years and without siblings diagnosed with CAD before 66

years. Subjects were genotyped on the Illumina 1M or 610K genotyping arrays, and imputation was

made using the MACH2 software. Standard quality control procedures were performed.11

PROCARDIS control subjects were supplemented with control subjects from the WTCCC, which has

been described previously.12

Genotyping, imputation and quality control procedures of WTCCC

subjects were comparable with those of PROCARDIS.

CARDIoGRAM: The CARDIoGRAM CAD genome-wide association study (GWAS) meta-analysis

consortium has been described elsewhere.13

In brief, CARDIoGRAM combined GWAS data from 14

CAD case-control studies providing a discovery sample size of 22,233 cases and 64,762 controls.

Only subjects reporting European ancestry were included. Genotyping in individual discovery GWAS

was carried out on Affymetrix or Illumina platforms and approximately 2.3 million imputed genotypes

were generated using the MACH, IMPUTE or BIMBAM algorithms, which has been described

previously.13

Quality control was performed at individual sites and centrally.13

REFERENCES

1. Baldassarre D, Nyyssonen K, Rauramaa R, de Faire U, Hamsten A, Smit AJ, et al. Cross-

sectional analysis of baseline data to identify the major determinants of carotid intima-media

thickness in a european population: The improve study. Eur Heart J. 2010;31:614-622.

2. Berglund G, Elmstahl S, Janzon L, Larsson SA. The malmo diet and cancer study. Design and

feasibility. J Intern Med. 1993;233:45-51.

3. Hedblad B, Nilsson P, Janzon L, Berglund G. Relation between insulin resistance and carotid

intima-media thickness and stenosis in non-diabetic subjects. Results from a cross-sectional

study in malmo, sweden. Diabet Med. 2000;17:299-307.

4. Lee AJ, Mowbray PI, Lowe GD, Rumley A, Fowkes FG, Allan PL. Blood viscosity and

elevated carotid intima-media thickness in men and women: The edinburgh artery study.

Circulation. 1998;97:1467-1473.

5. Marmot M, Brunner E. Cohort profile: The whitehall ii study. Int J Epidemiol. 2005;34:251-

256.

6. Kivimaki M, Lawlor DA, Smith GD, Kumari M, Donald A, Britton A, et al. Does high c-

reactive protein concentration increase atherosclerosis? The whitehall ii study. PLoS One.

2008;3:e3013.

7. Hofman A, van Duijn CM, Franco OH, Ikram MA, Janssen HL, Klaver CC, et al. The

rotterdam study: 2012 objectives and design update. Eur J Epidemiol. 2011;26:657-686.

8. Bots ML, Hoes AW, Koudstaal PJ, Hofman A, Grobbee DE. Common carotid intima-media

thickness and risk of stroke and myocardial infarction: The rotterdam study. Circulation.

1997;96:1432-1437.

9. Bis JC, Kavousi M, Franceschini N, Isaacs A, Abecasis GR, Schminke U, et al. Meta-analysis

of genome-wide association studies from the charge consortium identifies common variants

associated with carotid intima media thickness and plaque. Nat Genet. 2011;43:940-947.

10. Farrall M, Green FR, Peden JF, Olsson PG, Clarke R, Hellenius ML, et al. Genome-wide

mapping of susceptibility to coronary artery disease identifies a novel replicated locus on

chromosome 17. PLoS Genet. 2006;2:e72.

11. Strawbridge RJ, Dupuis J, Prokopenko I, Barker A, Ahlqvist E, Rybin D, et al. Genome-wide

association identifies nine common variants associated with fasting proinsulin levels and

provides new insights into the pathophysiology of type 2 diabetes. Diabetes. 2011;60:2624-

2634.

12. The Coronary Artery Disease (C4D) Genetics Consortium. A genome-wide association study

in europeans and south asians identifies five new loci for coronary artery disease. Nat Genet.

2011;43:339-344.

13. Schunkert H, Konig IR, Kathiresan S, Reilly MP, Assimes TL, Holm H, et al. Large-scale

association analysis identifies 13 new susceptibility loci for coronary artery disease. Nat

Genet. 2011;43:333-338.

SUPPLEMENTAL SECTION S2: GENETIC QUALITY CONTROL PROCEDURES IN THE

DISCOVERY COHORT (IMPROVE)

The IMPROVE study, enrolled 3,711 subjects in five different European countries, 3,615 of which had

both phenotype and CardioMetabochip genotype data available. Individual level quality control (QC)

exclusion criteria were call rates <0.95, results of identity by state (IBS) estimations (e.g. unverified

cryptic relatedness), verified relatedness, estimated inbreeding (excessive homozygosity), and

discrepancy between recorded and genotype-determined sex, resulting in 140 exclusions. Multi-

dimensional scaling (MDS) analysis and calculation of the genomic inflation factor lambda was

performed to evaluate population substructure, using PLINK 1.07.1 MDS analysis was performed

using largely uncorrelated CardioMetabochip SNPs obtained by applying a filter of pair-wise

correlation of r2<0.5 within a 50 SNP window, iteratively shifted 5 SNPs along the sequence, which

revealed significant population substructure (Supplemental Figure 1). An additional 45 individuals

were excluded based on results of the MDS analysis or self-reported non-Caucasian ethnicity. In order

to adjust for the identified population substructure, the first three MDS dimensions were included as

covariates in subsequent association analyses. QC exclusion criteria for SNPs were genotype call rates

<0.90, highly significant deviation from Hardy-Weinberg equilibrium (P<5x10-7

) and minor allele

frequency (MAF) <0.005. Following QC procedures, 3,430 individuals and 127,830 autosomal SNPs

were included in the association analysis.

REFERENCES

1. Purcell S, Neale B, Todd-Brown K, Thomas L, Ferreira MA, Bender D, et al. Plink: A tool set

for whole-genome association and population-based linkage analyses. Am J Hum Genet.

2007;81:559-575.

SUPPLEMENTAL SECTION S3: ESTABLISHMENT OF THRESHOLD FOR ARRAY-WIDE

STATISTICAL SIGNIFICANCE

The a priori threshold for array-wide statistical significance was established as P<8.39x10-7

through

approximation of the total number of uncorrelated SNPs on the CardioMetabochip with the number of

principal components explaining >99.5% of the total SNP variation according to methods proposed by

Gao et al,1, 2

using a block size of 8,192 SNPs. This number was then used for a standard Bonferroni

correction to set the threshold for array-wide significance for a two-sided test at the 5% level.

REFERENCES

1. Gao X, Becker LC, Becker DM, Starmer JD, Province MA. Avoiding the high bonferroni

penalty in genome-wide association studies. Genet Epidemiol. 2010;34:100-105.

2. Gao X, Starmer J, Martin ER. A multiple testing correction method for genetic association

studies using correlated single nucleotide polymorphisms. Genet Epidemiol. 2008;32:361-369.

SUPPLEMENTAL SECTION S4: POWER CALCULATIONS

Power calculations for the replication studies were performed using the Genetic Power Calculator.1

The 90th percentile of the carotid intima-media thickness (cIMT) variable was used as a threshold to

group participants into cases (above the 90th percentile) and controls (at and below the 90

th percentile).

With upwards of 11,000 subjects available in the five replication cohorts, we assigned 1,100 cases.

Furthermore, we assumed a minor allele frequency (MAF) of at least 0.05 and that the proportion of

the cIMT variance attributable to a specific locus would be at least 0. 16%. Based on these conditions

and setting the experimental error rate to at most 5% and the minimum power 80% (to detect a SNP in

the replication studies contributing at least 0.16% of the variation in cIMT), it would be justifiable to

take up to 50 uncorrelated SNPs forward to replication.

REFERENCES

1. Purcell S, Cherny SS, Sham PC. Genetic power calculator: Design of linkage and association

genetic mapping studies of complex traits. Bioinformatics. 2003;19:149-150.

SUPPLEMENTAL SECTION S5: BOOTSTRAPPING PROCEDURE

Given that the composite IMT-measures were unique to IMPROVE and thereby not assessable by

conventional replication in independent cohorts, nonparametric bootstrap re-sampling was applied to

perform internal validation. While this approach cannot evaluate the generality of the results in terms

of population-specificity, it allows adjustment for the effect known as the winner’s curse, i.e. a bias

towards over-estimation of effect sizes among the most significant SNPs.1 A modified version of the

double bootstrap re-sampling method (BR-squared) proposed by Sun et al 2 was used. This method

uses a weighted average over bootstrap replicates of the difference between the effect size estimated

from the observations in the bootstrap sample and the one estimated from the observations not in the

bootstrap sample to estimate the over-estimation. The difference was adjusted for the negative

correlation between the two effect sizes (disjoint events). Since the bias correction in this context may

be large, the standard deviation of the original effect size can be a poor estimate of the bias-corrected

effect size. To resolve this, we performed a double bootstrap. In the outer bootstrap we draw B2

samples from the original sample. The bias corrected effect size was computed by drawing B1 inner

bootstrap samples from each of the B2 samples. The limits of an approximate (1-α)100% confidence

interval (CI) for the bias corrected effect size is then given by the B2*α/2 and the B2*(1- α/2) ordered

bias-corrected effect sizes. A (1-α)100% CI not covering zero corresponds to rejecting the null

hypothesis at significance level α. In case of multiple testing α was adjusted using the Bonferroni

correction. First, results from the meta-analysis of the CC-IMT SNPs in the replication cohorts alone

were compared with the outcome of a bootstrap validation of the same CC-IMT SNPs in IMPROVE

(Supplemental Figure 3a). The agreement between the two approaches was good in terms of both

estimates of effect sizes and their confidence intervals when corrected for sample size (Supplemental

Figure 3a), indicating good performance of the bootstrap approach in adjusting for the winner’s curse

effect. Subsequent bootstrap validation of the 3 composite IMT index SNPs confirmed significant

effects of these SNPs on composite IMT, with similar deflation of effect size estimates as those

observed for the CC-IMT SNPs, indicating an adjustment for the winner’s curse effect similar to that

for the CC-IMT SNPs (Supplemental Figure 3b).

REFERENCES

1. Ioannidis JP, Thomas G, Daly MJ. Validating, augmenting and refining genome-wide

association signals. Nat Rev Genet. 2009;10:318-329.

2. Sun L, Dimitromanolakis A, Faye LL, Paterson AD, Waggott D, Group TDER, et al. Br-

squared: A practical solution to the winner's curse in genome-wide scans. Human genetics.

2011.

SUPPLEMENTAL SECTION S6: ASSOCIATION TESTING IN CAD CASE-CONTROL

STUDIES

In PROCARDIS, associations between the SNP and all CAD (n=13,591), and MI only (n=12,472)

were tested by logistic regression analysis assuming an additive model of allelic association and

adjusting for age, gender, country and relatedness using STATA version 11. In CARDIoGRAM,

associations between rs4888378 and all CAD (n=82,297), and MI only (n=52,226) were tested by

meta-analysis of cohort-specific associations using a fixed-effect model with inverse variance

weighting. In each CARDIoGRAM cohort analyzed (12 cohorts for CAD and 10 cohorts for MI),

associations were tested by logistic regression analysis assuming an additive model of allelic

association and adjusting for age, gender, genomic inflation (lambda), and taking into account the

uncertainty of imputed genotypes.1

REFERENCES

1. Schunkert H, Konig IR, Kathiresan S, Reilly MP, Assimes TL, Holm H, et al. Large-scale

association analysis identifies 13 new susceptibility loci for coronary artery disease. Nat

Genet. 2011;43:333-338.

SUPPLEMENTAL SECTION S7: CARDIoGRAM MEMBERS, AFFILIATIONS, FUNDING

AND DISCLOSURES

The CARDIoGRAM Consortium

Executive Committee: Sekar Kathiresan1,2,3

, Muredach P. Reilly4, Nilesh J. Samani

5,6, Heribert

Schunkert7

Executive Secretary: Jeanette Erdmann7

Steering Committee: Themistocles L. Assimes8, Eric Boerwinkle

9, Jeanette Erdmann

7,79 Alistair

Hall10

, Christian Hengstenberg11

, Sekar Kathiresan1,2,3

, Inke R. König12

, Reijo Laaksonen13

, Ruth

McPherson14

, Muredach P. Reilly4, Nilesh J. Samani

5,6, Heribert Schunkert

7,79, John R. Thompson

15,

Unnur Thorsteinsdottir16,17

, Andreas Ziegler12

Statisticians: Inke R. König12

(chair), John R. Thompson15

(chair), Devin Absher18

, Li Chen19

, L.

Adrienne Cupples20,21

, Eran Halperin22

, Mingyao Li23

, Kiran Musunuru1,2,3

, Michael Preuss12,7

, Arne

Schillert12

, Gudmar Thorleifsson16

, Benjamin F. Voight2,3,24

, George A. Wells25

Writing group: Themistocles L. Assimes8, Panos Deloukas

26, Jeanette Erdmann

7,79, Hilma Holm

16,

Sekar Kathiresan1,2,3

, Inke R. König12

, Ruth McPherson14

, Muredach P. Reilly4, Robert Roberts

14,

Nilesh J. Samani5,6

, Heribert Schunkert7,79

, Alexandre F. R. Stewart14

ADVANCE: Devin Absher18

, Themistocles L. Assimes8, Stephen Fortmann

8, Alan Go

27, Mark

Hlatky8, Carlos Iribarren

27, Joshua Knowles

8, Richard Myers

18, Thomas Quertermous

8, Steven

Sidney27

, Neil Risch28

, Hua Tang29

CADomics: Stefan Blankenberg30

, Tanja Zeller30

, Arne Schillert12

, Philipp Wild30

, Andreas Ziegler12

,

Renate Schnabel30

, Christoph Sinning30

, Karl Lackner31

, Laurence Tiret32

, Viviane Nicaud32

, Francois

Cambien32

, Christoph Bickel30

, Hans J. Rupprecht30

, Claire Perret32

, Carole Proust32

, Thomas Münzel30

CHARGE: Maja Barbalic33

, Joshua Bis34

, Eric Boerwinkle9, Ida Yii-Der Chen

35, L. Adrienne

Cupples20,21

, Abbas Dehghan36

, Serkalem Demissie-Banjaw37,21

, Aaron Folsom38

, Nicole Glazer39

,

Vilmundur Gudnason40,41

, Tamara Harris42

, Susan Heckbert43

, Daniel Levy21

, Thomas Lumley44

,

Kristin Marciante45

, Alanna Morrison46

, Christopher J. O´Donnell47

, Bruce M. Psaty48

, Kenneth Rice49

,

Jerome I. Rotter35

, David S. Siscovick50

, Nicholas Smith43

, Albert Smith40,41

, Kent D. Taylor35

,

Cornelia van Duijn36

, Kelly Volcik46

, Jaqueline Whitteman36

, Vasan Ramachandran51

, Albert

Hofman36

, Andre Uitterlinden52,36

deCODE: Solveig Gretarsdottir16

, Jeffrey R. Gulcher16

, Hilma Holm16

, Augustine Kong16

, Kari

Stefansson16,17

, Gudmundur Thorgeirsson53,17

, Karl Andersen53,17

, Gudmar Thorleifsson16

, Unnur

Thorsteinsdottir16,17

GERMIFS I and II: Jeanette Erdmann7,79

, Marcus Fischer11

, Anika Grosshennig12,7

, Christian

Hengstenberg11

, Inke R. König12

, Wolfgang Lieb54

, Patrick Linsel-Nitschke7, Michael Preuss

12,7, Klaus

Stark11

, Stefan Schreiber55

, H.-Erich Wichmann56,58,59

, Andreas Ziegler12


GERMIFS III (KORA): Zouhair Aherrahrou7,79

, Petra Bruse7,79

, Angela Doering56

, Jeanette

Erdmann7,79

, Christian Hengstenberg11

, Thomas Illig56

, Norman Klopp56

, Inke R. König12

, Patrick

Diemert7, Christina Loley

12,7, Anja Medack

7,79, Christina Meisinger

56, Thomas Meitinger

57,60, Janja

Nahrstedt12,7

, Annette Peters56

, Michael Preuss12,7

, Klaus Stark11

, Arnika K. Wagner7, H.-Erich

Wichmann56,58,59

, Christina Willenborg,7,79

, Andreas Ziegler12


LURIC/AtheroRemo: Bernhard O. Böhm61

, Harald Dobnig62

, Tanja B. Grammer63

, Eran Halperin22

,

Michael M. Hoffmann64

, Marcus Kleber65

, Reijo Laaksonen13

, Winfried März63,66,67

, Andreas

Meinitzer66

, Bernhard R. Winkelmann68

, Stefan Pilz62

, Wilfried Renner66

, Hubert Scharnagl66

, Tatjana

Stojakovic66

, Andreas Tomaschitz62

, Karl Winkler64

MIGen: Benjamin F. Voight2,3,24

, Kiran Musunuru1,2,3

, Candace Guiducci3, Noel Burtt

3, Stacey B.

Gabriel3, David S. Siscovick

50, Christopher J. O’Donnell

47, Roberto Elosua

69, Leena Peltonen

49,

Veikko Salomaa70

, Stephen M. Schwartz50

, Olle Melander26

, David Altshuler71,3

, Sekar Kathiresan1,2,3

OHGS: Alexandre F. R. Stewart14

, Li Chen19

, Sonny Dandona14

, George A. Wells25

, Olga Jarinova14

,

Ruth McPherson14

, Robert Roberts14

PennCATH/MedStar: Muredach P. Reilly4, Mingyao Li

23, Liming Qu

23, Robert Wilensky

4, William

Matthai4, Hakon H. Hakonarson

72, Joe Devaney

73, Mary Susan Burnett

73, Augusto D. Pichard

73,

Kenneth M. Kent73

, Lowell Satler73

, Joseph M. Lindsay73

, Ron Waksman73

, Christopher W. Knouff74

,

Dawn M. Waterworth74

, Max C. Walker74

, Vincent Mooser74

, Stephen E. Epstein73

, Daniel J. Rader75,4

WTCCC: Nilesh J. Samani5,6

, John R. Thompson15

, Peter S. Braund5, Christopher P. Nelson

5,

Benjamin J. Wright76

, Anthony J. Balmforth77

, Stephen G. Ball78

, Alistair S. Hall10

, Wellcome Trust

Case Control Consortium

Affiliations

1 Cardiovascular Research Center and Cardiology Division, Massachusetts General Hospital, Boston,

MA, USA; 2 Center for Human Genetic Research, Massachusetts General Hospital, Boston, MA,

USA; 3 Program in Medical and Population Genetics, Broad Institute of Harvard and Massachusetts

Institute of Technology (MIT), Cambridge, MA, USA; 4 The Cardiovascular Institute, University of

Pennsylvania, Philadelphia, PA, USA; 5 Department of Cardiovascular Sciences, University of

Leicester, Glenfield Hospital, Leicester, UK; 6 Leicester National Institute for Health Research

Biomedical Research Unit in Cardiovascular Disease, Glenfield Hospital, Leicester, LE3 9QP, UK; 7

Medizinische Klinik II, Universität zu Lübeck, Lübeck, Germany; 8 Department of Medicine,

Stanford University School of Medicine, Stanford, CA, USA; 9 University of Texas Health Science

Center, Human Genetics Center and Institute of Molecular Medicine, Houston, TX, USA; 10 Division

of Cardiovascular and Neuronal Remodelling, Multidisciplinary Cardiovascular Research Centre,

Leeds Institute of Genetics, Health and Therapeutics, University of Leeds, UK; 11 Klinik und

Poliklinik für Innere Medizin II, Universität Regensburg, Regensburg, Germany; 12 Institut für

Medizinische Biometrie und Statistik, Universität zu Lübeck, Lübeck, Germany; 13 Science Center,

Tampere University Hospital, Tampere, Finland; 14 The John & Jennifer Ruddy Canadian

Cardiovascular Genetics Centre, University of Ottawa Heart Institute, Ottawa, Canada; 15 Department

of Health Sciences, University of Leicester, Leicester, UK; 16 deCODE Genetics, 101 Reykjavik,

Iceland; 17 University of Iceland, Faculty of Medicine, 101 Reykjavik, Iceland; 18 Hudson Alpha

Institute, Huntsville, Alabama, USA; 19 Cardiovascular Research Methods Centre, University of

Ottawa Heart Institute, 40 Ruskin Street, Ottawa, Ontario, Canada, K1Y 4W7; 20 Department of

Biostatistics, Boston University School of Public Health, Boston, MA USA; 21 National Heart, Lung

and Blood Institute's Framingham Heart Study, Framingham, MA, USA; 22 The Blavatnik School of

Computer Science and the Department of Molecular Microbiology and Biotechnology, Tel-Aviv

University, Tel-Aviv, Israel, and the International Computer Science Institute, Berkeley, CA, USA; 23

Biostatistics and Epidemiology, University of Pennsylvania, Philadelphia, PA, USA; 24 Department

of Medicine, Harvard Medical School, Boston, MA, USA; 25 Research Methods, Univ Ottawa Heart

Inst; 26 Department of Clinical Sciences, Hypertension and Cardiovascular Diseases, Scania

University Hospital, Lund University, Malmö, Sweden; 27 Division of Research, Kaiser Permanente,

Oakland, CA, USA; 28 Institute for Human Genetics, University of California, San Francisco, San

Francisco, CA, USA; 29 Dept Cardiovascular Medicine, Cleveland Clinic; 30 Medizinische Klinik

und Poliklinik, Johannes-Gutenberg Universität Mainz, Universitätsmedizin, Mainz, Germany; 31

Institut für Klinische Chemie und Laboratoriumsmediizin, Johannes-Gutenberg Universität Mainz,

Universitätsmedizin, Mainz, Germany; 32 INSERM UMRS 937, Pierre and Marie Curie University

(UPMC, Paris 6) and Medical School, Paris, France; 33 University of Texas Health Science Center,

Human Genetics Center, Houston, TX, USA; 34 Cardiovascular Health Resarch Unit and Department

of Medicine, University of Washington, Seattle, WA USA; 35 Cedars-Sinai Medical Center, Medical

Genetics Institute, Los Angeles, CA, USA; 36 Erasmus Medical Center, Department of Epidemiology,

Rotterdam, The Netherlands; 37 Boston University, School of Public Health, Boston, MA, USA; 38

University of Minnesota School of Public Health, Division of Epidemiology and Community Health,

School of Public Health (A.R.F.), Minneapolis, MN, USA; 39 University of Washington,

Cardiovascular Health Research Unit and Department of Medicine, Seattle, WA, USA; 40 Icelandic

Heart Association, Kopavogur Iceland; 41 University of Iceland, Reykjavik, Iceland; 42 Laboratory of

Epidemiology, Demography, and Biometry, Intramural Research Program, National Institute on

Aging, National Institutes of Health, Bethesda MD, USA; 43 University of Washington, Department

of Epidemiology, Seattle, WA, USA; 44 University of Washington, Department of Biostatistics,

Seattle, WA, USA; 45 University of Washington, Department of Internal Medicine, Seattle, WA,

USA; 46 University of Texas, School of Public Health, Houston, TX, USA; 47 National Heart, Lung

and Blood Institute, Framingham Heart Study, Framingham, MA and Cardiology Division,

Massachusetts General Hospital, Boston, MA, USA; 48 Center for Health Studies, Group Health,

Departments of Medicine, Epidemiology, and Health Services, Seattle, WA, USA; 49 The Wellcome

Trust Sanger Institute, The Wellcome Trust Genome Campus, Hinxton, Cambridge, UK; 50

Cardiovascular Health Research Unit, Departments of Medicine and Epidemiology, University of

Washington, Seattle; 51 Boston University Medical Center, Boston, MA, USA; 52 Department of

Internal Medicine, Erasmus Medical Center, Rotterdam, The Netherlands; 53 Department of Medicine,

Landspitali University Hospital, 101 Reykjavik, Iceland; 54 Boston University School of Medicine,

Framingham Heart Study, Framingham, MA, USA; 55 Institut für Klinische Molekularbiologie,

Christian-Albrechts Universität, Kiel, Germany; 56 Institute of Epidemiology, Helmholtz Zentrum

München – German Research Center for Environmental Health, Neuherberg, Germany; 57 Institut für

Humangenetik, Helmholtz Zentrum München, Deutsches Forschungszentrum für Umwelt und

Gesundheit, Neuherberg, Germany; 58 Institute of Medical Information Science, Biometry and

Epidemiology, Ludwig-Maximilians-Universität München, Germany; 59 Klinikum Grosshadern,

Munich, Germany; 60 Institut für Humangenetik, Technische Universität München, Germany; 61

Division of Endocrinology and Diabetes, Graduate School of Molecular Endocrinology and Diabetes,

University of Ulm, Ulm, Germany; 62 Division of Endocrinology, Department of Medicine, Medical

University of Graz, Austria; 63 Synlab Center of Laboratory Diagnostics Heidelberg, Heidelberg,

Germany; 64 Division of Clinical Chemistry, Department of Medicine, Albert Ludwigs University,

Freiburg, Germany; 65 LURIC non profit LLC, Freiburg, Germany; 66 Clinical Institute of Medical

and Chemical Laboratory Diagnostics, Medical University Graz, Austria; 67 Institute of Public Health,

Social and Preventive Medicine, Medical Faculty Manneim, University of Heidelberg, Germany; 68

Cardiology Group Frankfurt-Sachsenhausen, Frankfurt, Germany; 69 Cardiovascular Epidemiology

and Genetics Group, Institut Municipal d’Investigació Mèdica, Barcelona; Ciber Epidemiología y

Salud Pública (CIBERSP), Spain; 70 Chronic Disease Epidemiology and Prevention Unit, Department

of Chronic Disease Prevention, National Institute for Health and Welfare, Helsinki, Finland; 71

Department of Molecular Biology and Center for Human Genetic Research, Massachusetts General

Hospital, Harvard Medical School, Boston, USA; 72 The Center for Applied Genomics, Children’s

Hospital of Philadelphia, Philadelphia, Pennsylvania, USA; 73 Cardiovascular Research Institute,

Medstar Health Research Institute, Washington Hospital Center, Washington, DC 20010, USA; 74

Genetics Division and Drug Discovery, GlaxoSmithKline, King of Prussia, Pennsylvania 19406, USA;

75 The Institute for Translational Medicine and Therapeutics, School of Medicine, University of

Pennsylvania, Philadelphia, PA, USA; 76 Department of Cardiovascular Surgery, University of

Leicester, Leicester, UK; 77 Division of Cardiovascular and Diabetes Research, Multidisciplinary

Cardiovascular Research Centre, Leeds Institute of Genetics, Health and Therapeutics, University of

Leeds, Leeds, LS2 9JT, UK; 78 LIGHT Research Institute, Faculty of Medicine and Health,

University of Leeds, Leeds, UK; 79 Nordic Center for Cardiovascular Research (NCCR), Lübeck,

Germany.

Sources of Funding

The ADVANCE study was supported by a grant from the Reynold's Foundation and NHLBI grant

HL087647.

Genetic analyses of CADomics were supported by a research grant from Boehringer Ingelheim.

Recruitment and analysis of the CADomics cohort was supported by grants from Boehringer

Ingelheim and PHILIPS medical Systems, by the Government of Rheinland-Pfalz in the context of the

“Stiftung Rheinland-Pfalz für Innovation”, the research program “Wissen schafft Zukunft” and by the

Johannes-Gutenberg University of Mainz in the context of the “Schwerpunkt Vaskuläre Prävention”

and the “MAIFOR grant 2001”, by grants from the Fondation de France, the French Ministry of

Research, and the Institut National de la Santé et de la Recherche Médicale.

The deCODE CAD/MI Study was sponsored by NIH grant, National Heart, Lung and Blood Institute

R01HL089650-02.

The German MI Family Studies (GerMIFS I-III (KORA)) were supported by the Deutsche

Forschungsgemeinschaft and the German Federal Ministry of Education and Research (BMBF) in the

context of the German National Genome Research Network (NGFN-2 and NGFN-plus), the EU

funded integrated project Cardiogenics (LSHM-CT-2006-037593), and the bi-national BMBF/ANR

funded project CARDomics (01KU0908A).

LURIC has received funding from the EU framework 6 funded Integrated Project “Bloodomics”

(LSHM-CT-2004-503485), the EU framework 7 funded Integrated Project AtheroRemo (HEALTH-

F2-2008-201668) and from Sanofi/Aventis, Roche, Dade Behring/Siemens, and AstraZeneca.

The MIGen study was funded by the US National Institutes of Health (NIH) and National Heart,

Lung, and Blood Institute’s STAMPEED genomics research program through R01 HL087676. Ron

Do from the MIGen study is supported by a Canada Graduate Doctoral Scholarship from the Canadian

Institutes of Health Research.

Recruitment of PennCATH was supported by the Cardiovascular Institute of the University of

Pennsylvania. Recruitment of the MedStar sample was supported in part by the MedStar Research

Institute and the Washington Hospital Center and a research grant from GlaxoSmithKline. Genotyping

of PennCATH and Medstar was performed at the Center for Applied Genomics at the Children’s

Hospital of Philadelphia and supported by GlaxoSmithKline through an Alternate Drug Discovery

Initiative research alliance award (M. P. R. and D. J. R.) with the University of Pennsylvania School

of Medicine.

The Ottawa Heart Genomic Study was supported by CIHR #MOP--82810 (R. R.), CFI #11966 (R.

R.), HSFO #NA6001 (R. McP.), CIHR #MOP172605 (R. McP.), CIHR #MOP77682 (A. F. R. S.).

The WTCCC Study was funded by the Wellcome Trust. Recruitment of cases for the WTCCC Study

was carried out by the British Heart Foundation (BHF) Family Heart Study Research Group and

supported by the BHF and the UK Medical Research Council. N. J. S. and S. G. B. hold chairs funded

by the British Heart Foundation. N. J. S. and A.H.G are also supported by the Leicester NIHR

Biomedical Research Unit in Cardiovascular Disease and the work described in this paper is part of

the research portfolio of the Leicester NIHR Biomedical Research Unit.

The Age, Gene/Environment Susceptibility Reykjavik Study has been funded by NIH contract

N01-AG-12100, the NIA Intramural Research Program, Hjartavernd (the Icelandic Heart Association),

and the Althingi (the Icelandic Parliament).

The Cleveland Clinic GeneBank study was supported by NIH grants P01 HL098055, P01HL076491-

06, R01DK080732, P01HL087018, and 1RO1HL103931-01.

The collection of clinical and sociodemographic data in the Dortmund Health Study was supported

by the German Migraine- & Headache Society (DMKG) and by unrestricted grants of equal share

from Astra Zeneca, Berlin Chemie, Boots Healthcare, Glaxo-Smith-Kline, McNeil Pharma (former

Woelm Pharma), MSD Sharp & Dohme and Pfizer to the University of Muenster. Blood collection

was done through funds from the Institute of Epidemiology and Social Medicine, University of

Muenster.

The EPIC-Norfolk study is supported by the Medical Research Council UK and Cancer Research

UK.

The EpiDREAM study is supported by the Canadian Institutes for Health Research, Heart and Stroke

Foundation of Ontario, Sanofi-Aventis, GlaxoSmithKline and King Pharmaceuticals.

Funding for Andrew Lotery from the LEEDS study was provided by the T.F.C. Frost charity and the

Macular Disease Society.

The Rotterdam Study is supported by the Erasmus Medical Center and Erasmus University

Rotterdam; the Netherlands Organization for Scientific Research; the Netherlands Organization for

Health Research and Development (ZonMw); the Research Institute for Diseases in the Elderly; The

Netherlands Heart Foundation; the Ministry of Education, Culture and Science; the Ministry of Health

Welfare and Sports; the European Commission (DG XII); and the Municipality of Rotterdam. Support

for genotyping was provided by the Netherlands Organization for Scientific Research (NWO)

(175.010.2005.011, 911.03.012), the Netherlands Genomics Initiative (NGI)/ NWO project nr. 050-

060-810 and Research Institute for Diseases in the Elderly (RIDE). This study is further supported by

a grant from NWO (Vici, 918-76-619).

The SAS study was funded by the British Heart Foundation.

The Swedish Research Council, the Swedish Heart & Lung Foundation and the Stockholm County

Council (ALF) supported the SHEEP study.

SMILE was funded by the Netherlands Heart foundation (NHS 92345). Dr Rosendaal is a recipient of

the Spinoza Award of the Netherlands Organisation for Scientific Research (NWO) which was used

for part of this work.

The Verona Heart Study was funded by grants from the Italian Ministry of University and Research,

the Veneto Region, and the Cariverona Foundation, Verona.

The Atherosclerosis Risk in Communities Study is carried out as a collaborative study supported by

National Heart, Lung, and Blood Institute contracts N01-HC-55015, N01-HC-55016, N01-HC-55018,

N01-HC-55019, N01-HC-55020, N01-HC-55021, and N01-HC-55022. The authors thank the staff

and participants of the ARIC study for their important contributions.

The KORA (Kooperative Gesundheitsforschung in der Region Augsburg) research platform was

initiated and financed by the Helmholtz Zentrum München - National Research Center for

Environmental Health, which is funded by the German Federal Ministry of Education, Science,

Research and Technology and by the State of Bavaria. Part of this work was financed by the German

National Genome Research Network (NGFN-2 and NGFNPlus) and within the Munich Center of

Health Sciences (MC Health) as part of LMUinnovativ.

Work described in this paper is part of the research portfolio supported by the Leicester NIHR

Biomedical Research Unit in Cardiovascular Disease.

This work forms part of the research themes contributing to the translational research portfolio of

Barts and the London Cardiovascular Biomedical Research Unit which is supported and funded by the

National Institute of Health Research.

Disclosures

Dr Absher reports receiving an NIH research grant for the ADVANCE study. Dr Assimes reports

receiving an NIH research grant for the ADVANCE study. Dr Blankenberg reports receiving research

grants from NGFNplus for Atherogenomics and from BMBF for CADomics. Dr Boerwinkle received

research support from NIH/National Human Genome Research Institute (NHGRI), GWA for gene-

environment interaction effects influencing CGD; NIH/NHLBI, Molecular epidemiology of essential

hypertension; NIH/NHLBI, Genome-wide association for loci influencing coronary heart disease;

NIH/NHLBI, Genetics of hypertension-associated treatment; NIH/NHLBI, Modeling DNA diversity

in reverse cholesterol transport; NIH/NHLBI, 20-year changes in fitness and cardiovascular disease

risk; NIH/NHLBI, Genetic epidemiology of sodium-lithium countertransport; NIH/National Institute

of General Medical Sciences (NIGMS), Pharmacogenomic evaluation of antihypertensive responses;

NIH/NIGMS, Genomic approaches to common chronic disease; NIH/NHLBI, Genes of the CYP450-

derived eicosanoids in subclinical atherosclerosis; NIH/NHGRI-University of North Carolina, Chapel

Hill, Genetic epidemiology of causal variants across the life course; and NIH/NHLBI, Building on

GWAS for NHLBI-diseases: the CHARGE consortium. Dr Cupples reports receiving research grants

from NIH/NHLBI, The Framingham Heart Study; NIH/NHLBI, Genome-wide association study of

cardiac structure and function; NIH/NHLBI, Functional evaluation of GWAS loci for cardiovascular

intermediate phenotypes; and NIH/NHLBI, Building on GWAS for NHLBI-diseases: the CHARGE

consortium. Dr Halperin reports receiving research grants from NIH, subcontract Genome-wide

association study of Non Hodgkin’s lymphoma; ISF, Efficient design and analysis of disease

association studies; EU, consultant AtheroRemo; NSF, Methods for sequencing based associations;

BSF, Searching for causal genetic variants in breast cancer and honoraria from Scripps Institute,

UCLA. Dr Halperin also reports ownership interest in Navigenics. Dr Hengstenberg reports receiving

research grants for EU Cardiogenics. Dr Holm reports receiving a research grant from NIH; providing

expert witness consultation for the district court of Reykjavik; serving as member of the editorial

board for decodeme, a service provided by deCODE Genetics; and employment with deCODE

Genetics. Dr Li reports receiving research grant R01HG004517 and other research support in the form

of coinvestigator on several NIH-funded grants and receiving honoraria from National Cancer Institute

Division of Cancer Epidemiology and Genetics. Dr McPherson reports receiving research grants from

Heart & Stroke Funds Ontario, CIHR, and CFI. Dr Rader reports receiving research grant support

from GlaxoSmithKline. Dr Roberts reports receiving research grants from the Cystic Fibrosis

Foundation, NIH, and Cancer Immunology and Hematology Branch; membership on the speakers

bureau for AstraZeneca; receiving honoraria from Several; and serving as consultant/advisory board

member for Celera. Dr Stewart reports receiving research grant support from CIHR, Genome-wide

scan to identify coronary artery disease genes, and CIHR, Genetic basis of salt-sensitive hypertension

in humans; other research support from CFI: Infrastructure support; and honoraria from the Institute

for Biomedical Sciences, Academia Sinica, Taipei, Taiwan. Dr Thorleifsson is an employee of

deCODE Genetics. Dr Thorsteinsdottir reports receiving research grants from NIH and EU; serving as

an expert witness for a US trial; having stock options at deCODE Genetics; and having employment

with deCODE Genetics. Dr Kathiresan reports receiving research grants from Pfizer, Discovery of

type 2 diabetes genes, and Alnylam, Function of new lipid genes, and serving as consultant/advisory

board member for DAIICHI SANKYO Merck. Dr Reilly reports receiving research grant support from

GlaxoSmithKline. Dr Schunkert reports receiving research grants from the EU, project Cardiogenics;

NGFN, project Atherogenomics; and CADnet BMBF. M. Preuss, L. Chen, and Drs König, Thompson,

Erdmann, Hall, Laaksonen, März, Musunuru, Nelson, Burnett, Epstein, O’Donnell, Quertermous,

Schillert, Stefansson, Voight, Wells, Ziegler, and Samani have no conflicts to disclose. Genotyping of

PennCATH and MedStar was supported by Glaxo-SmithKline. Dawn M. Waterworth, Max C.

Walker, and Vincent Mooser are employees of GlaxoSmithKline. PennCath/MedStar investigators

acknowledge the support of Eliot Ohlstein, Dan Burns and Allen Roses at GlaxoSmithKline.

SUPPLEMENTAL SECTION S8: PROCARDIS MEMBERS AND AFFILIATIONS

The PROCARDIS Consortium

Project Steering Committee: Hugh Watkins (chair) 1,2

, Anders Hamsten3, Rory Collins

4, Udo

Seedorf5, Maria Grazia Franzosi

6, Jörg Hager

7, Fiona Green

8, Michael Parker

9, Jose Manuel Soria

10,

Elena Tremoli11,12

, Johan Björkegren13

, Anna Walentinsson14

, Carla Finocchiaro15

Department of Cardiovascular Medicine, University of Oxford: Anuj Goel1, Martin Farral

1,2,

Shapour Jalilzadeh1, Theodosios Kyriakou

1, Halit Ongen

1, John F Peden

1, Hugh Watkins

1,2

Atherosclerosis Research Unit, Department of Medicine, Solna, Karolinska Institutet: Per

Eriksson3, Lasse Folkersen

3, Anders Hamsten

3, Mai-Lis Hellénius

3, Ferdinand vant Hooft

3, Jacob

Lagercrantz3, Anders Mälarstig

3, Maria Sabater-Lleal

3, Bengt Sennblad

3, Angela Silveira

3, Rona

Strawbridge3, John Öhrvik

3

Clinical Trial Service Unit, University of Oxford: Robert Clarke4, Rory Collins

4, Jemma C

Hopewell4, Pamela Linksted

4, Sarah Parish

4

Leibniz-Institut für Arterioskleroseforschung an der Universität Münster: Gerd Assmann5,

Stephan Rust5, Udo Seedorf

5

Department of Cardiovascular Research, Istituto Mario Negri: Simona Barlera6, Maria Grazia

Franzosi6, Gianni Tognoni

6

Centre National de Genotypage: Marc Delepine7, Jörg Hager

7, Simon Heath

7, Mark Lathrop

7

Division of Biomedical Sciences, University of Surrey: Fiona Green8

The Ethox Centre, University of Oxford: Paula Boddington9, Michael Parker

9

Institut de Recerca del Hospital de la Santa Creu I Sant Pau: Alfonso Buil10

, Jose Manuel Soria10

,

Juan Carlos Souto10

Centro Cardiologico Monzino and Dipartimento di Scienze Farmacologiche e Biomolecolari,

University of Milan: Cristina Banfi11,12

, Elena Tremoli11,12

Clinical Gene Networks AB: Johan Björkegren13

, Josefin Skogsberg13

, Jesper Tegnér13

CV Genetics and Molecular Biology, AstraZeneca R&D: Per G Olsson14

, Gunnar O Olsson14

, Anna

Walentinsson14

CF consulting S.r.l: Serena Cogoni15

, Carla Finocchiaro15

, Tessa Gronchi15

Affiliations

1 Department of Cardiovascular Medicine, The Wellcome Trust Centre for Human Genetics,

University of Oxford, Oxford, United Kingdom; 2 Department of Cardiovascular Medicine, University

of Oxford, John Radcliffe Hospital, Headington, Oxford, United Kingdom; 3 Atherosclerosis Research

Unit, Department of Medicine, Solna, Karolinska Institutet, Karolinska University Hospital Solna,

Stockholm, Sweden; 4 Clinical Trial Service Unit, University of Oxford, United Kingdom; 5 Leibniz-

Institut für Arterioskleroseforschung an der Universität Münster, Münster, Germany; 6 Department of

Cardiovascular Research, Istituto Mario Negri, Milan, Italy; 7 Centre National de Genotypage,

Institut Genomique, Commisariat L’Energie Atomique, Evry,France; 8 Division of Biomedical

Sciences, Faculty of Health and Medical Sciences, University of Surrey, Surrey, United Kingdom; 9

The Ethox Centre, Institute of Health Sciences, University of Oxford, Oxford, United Kingdom; 10

Institut de Recerca del Hospital de la Santa Creu I Sant Pau, Barcelona, Spain; 11 Centro Cardiologico

Monzino, IRCCS, Milan, Italy; 12 Dipartimento di Scienze Farmacologiche e Biomolecolari,

University of Milan, Milan, Italy; 13 Clinical Gene Networks AB, Stockholm, Sweden; 14 CV

Genetics and Molecular Biology , AstraZeneca R&D, Mölndal, Sweden; 15 CF consulting S.r.l,

Milan, Italy.

SUPPLEMENTAL SECTION S9: WRITING GROUP, STUDY AND INSTITUTIONAL

AFFILIATIONS

Writing Group: Karl Gertow1, Bengt Sennblad

1, Rona J. Strawbridge

1, John Öhrvik

1, Delilah

Zabaneh2, Sonia Shah

2, Fabrizio Veglia

3, Cristiano Fava

4,5, Bruna Gigante

11, Karin Leander

11, Max

Vikström11

, Gualtiero I. Colombo3, Jacqueline F. Price

8, Olle Melander

4, Damiano Baldassarre

3,40,

Elena Tremoli3,40

, Ulf de Faire11

, Steve E. Humphries24

, Anders Hamsten1 (chair)

The following is a list of study and institutional affiliations:

IMPROVE: 1 Atherosclerosis Research Unit (K.G., B.S., R.J.S., J.Ö., M.L., A.S., M.S-L.,

A.Hamsten), Department of Medicine, Solna, Karolinska Institutet, Karolinska University Hospital

Solna, Stockholm, Sweden; 11 Division of Cardiovascular Epidemiology (B.G., K.L., M.V., U.de F.),

Institute of Environmental Medicine, Karolinska Institutet, Stockholm, Sweden; 36 Department of

Medical Sciences (A-C.S.), Uppsala University, Sweden; 2 University College London Genetics

Institute (D.Z., S.S.), University College London, London, United Kingdom; 24 Cardiovascular

Genetics (S.E.H.), BHF Laboratories, Rayne Building, University College London, London, United

Kingdom; 3 Centro Cardiologico Monzino, IRCCS, (F.V., G.I.C., D.B., E.T.), and 40 Dipartimento di

Scienze Farmacologiche e Biomolecolari (D.B., E.T.), University of Milan, Milan, Italy; 18 Institute

of Public Health and Clinical Nutrition (K.N., T-P.T.), University of Eastern Finland, Kuopio,

Finland; 19 Kuopio Research Institute of Exercise Medicine (R.R., K.S.), Foundation for Research in

Health Exercise and Nutrition, Kuopio, Finland; 20 Department of Clinical Physiology and Nuclear

Medicine (R.R., K.S.), Kuopio University Hospital, Kuopio, Finland; 21 Department of Medicine

(A.J.S.), University Medical Center Groningen, Groningen, the Netherlands; 22 Assistance Publique -

Hopitaux de Paris (P.G.), Service Endocrinologie-Metabolisme, Groupe Hôpitalier Pitie-Salpetriere,

Unités de Prévention Cardiovasculaire, Paris, France; 23 Internal Medicine (E.M.), Angiology and

Arteriosclerosis Diseases, Department of Clinical and Experimental Medicine, University of Perugia,

Perugia, Italy.

Rotterdam Study: 6 Department of Epidemiology (M.Kavousi, A.G.U., A.Hofman, J.C.M.W.),

Erasmus University Medical Center, Rotterdam, The Netherlands; 16 Department of Internal Medicine

(A.G.U), Erasmus University Medical Center, Rotterdam, The Netherlands; 7 Netherlands Genomics

Initiative - Sponsored Netherlands Consortium for Healthy Ageing (M.Kavousi, A.G.U, A.Hofman,

J.C.M.W.), Rotterdam, The Netherlands.

Whitehall-II: 2 University College London Genetics Institute (D.Z., S.S.), University College

London, London, United Kingdom; 12 Cardiothoracic Unit (J.D.), Great Ormond Street Hospital,

London, United Kingdom; 9 Genetic Epidemiology Group (M.Kivimäki., M.Kumari, C.L., A.D.H.),

Department of Epidemiology and Public Health, University College London, London, United

Kingdom; 15 MRC Epidemiology Unit (C.L., N.J.W), Institute of Metabolic Science, University of

Cambridge, Addenbrooke's Hospital, Cambridge, United Kingdom; 39 Centre for Clinical

Pharmacology (A.D.H.), Department of Medicine, University College London, London, United

Kingdom; 24 Cardiovascular Genetics (P.J.T., S.E.H.), BHF Laboratories, Rayne Building, University

College London, London, United Kingdom.

Malmö Diet and Cancer Study: 4 Clinical Research Center (C.F., B.H., O.M.), Department of

Clinical Sciences, Lund University, Skåne University Hospital, Lund, Sweden; 5 Division of Internal

Medicine C (C.F.), Department of Medicine, University of Verona, Hospital "Policlinico G.B Rossi",

Verona, Italy; 13 Program in Medical and Population Genetics (B.F.V., P.F.), Broad Institute,

Cambridge, Massachusetts 02142, USA; 14 Center for Human Genetic Research and Diabetes

Research Center (Diabetes Unit) (B.F.V., P.F.), Massachusetts General Hospital, Boston,

Massachusetts 02114, USA.

Edinburgh Artery Study: 8 Centre for Population Health Sciences (S.M., J.L.B., C.M.R., J.F.P),

University of Edinburgh, Edinburgh, United Kingdom.

Advanced Study of Aortic Pathology: 1 Atherosclerosis Research Unit (L.F., P.E.), Department of

Medicine Solna, Karolinska Institutet, Karolinska University Hospital Solna, Stockholm, Sweden; 17

Cardiothoracic Surgery Unit (A.F-C.), Department of Molecular Medicine and Surgery, Karolinska

Institutet, Stockholm, Sweden.

Biobank of Karolinska Endarterectomies: 10 Experimental Cardiovascular Research Unit (L.F.,

A.G., G.K.H.), Department of Medicine Solna, Karolinska Institutet, Karolinska University Hospital,

Stockholm, Sweden; 17 Cardiothoracic Surgery Unit (U.H.), Department of Molecular Medicine and

Surgery, Karolinska Institutet, Stockholm, Sweden.

Precocious Coronary Artery Disease Study: 31 Department of Cardiovascular Medicine (M.F.,

H.W.), The Wellcome Trust Centre for Human Genetics, University of Oxford, Oxford, United

Kingdom; 32 Department of Cardiovascular Medicine (M.F., H.W.), University of Oxford, John

Radcliffe Hospital, Headington, Oxford, United Kingdom; 33 Clinical Trial Service Unit (R.C.),

University of Oxford, United Kingdom; 34 Department of Cardiovascular Research (M.G.F.), Istituto

Mario Negri, Milan, Italy; 35 Gesellschaft für Arterioskleroseforschung e.V. (U.S.), Leibniz-Institut

für Arterioskleroseforschung an der Universität Münster (LIFA), Münster, Germany. Names and

affiliations for all members of the PROCARDIS consortium are provided in Supplemental Section S8.

Coronary Artery Disease Genome Wide Replication and Meta-Analysis Consortium: 25

Universität zu Lübeck, Medizinische Klinik II (J.E.), Lübeck, Germany; 26 The Institute for

Translational Medicine and Therapeutics (M.P.R.), School of Medicine, University of Pennsylvania,

Philadelphia, PA, USA; 27 The Cardiovascular Institute (M.P.R.), University of Pennsylvania,

Philadelphia, PA, USA; 28 National Heart, Lung, and Blood Institute's (NHBLI’s) Framingham Heart

Study (C.J.O), Framingham, Mass, USA; 29 Division of Intramural Research (C.J.O.), NHLBI,

Bethesda, USA; 30 Cardiology Division (C.J.O.), Department of Medicine, Massachusetts General

Hospital, Harvard Medical School, Boston, Mass, USA; 37 Department of Cardiovascular Sciences

(N.J.S.), University of Leicester, Glenfield Hospital, Leicester, United Kingdom; 38 Leicester NIHR

Biomedical Research Unit in Cardiovascular Disease (N.J.S.), Glenfield Hospital, Leicester, United

Kingdom. Names and affiliations for all members of the CARDIoGRAM consortium are provided in

Supplemental Section S7.

SUPPLEMENTAL TABLES 1-3

Supplemental Table 1. Description of the discovery and replication cohorts, genotyping technologies, and quality control criteria

IMPROVE Whitehall-II Edinburgh Artery Study (EAS) Malmö Diet and Cancer (Cardiovascular arm) Rotterdam Study-I Rotterdam Study-II

Prospective Prospective Prospective Prospective Prospective Prospective

European European European (Scotland) Swedish European European

N (%M) 3430 (48) 2138 (77) 630 (46) 2143 (49) 4699 (41) 1980 (46)

Age (years) 64.2 (5.4) 60.74 (5.9) 69.75 (5.68) 57.4 (6.0) 68.9 (8.70) 64.7 (7.9)

BMI kg/m2 mean (sd) 27.2 (4.3) 26.55 (4.11) 26.31 (4.06) 25.1 (3.4) 26.3 (3.6) 27.3 (4.2)

SBP mmHg mean (sd) 142.0 (18.5) 126.82 (15.8) 147.48 (24.13) 139.9 (18.6) 138.9 (22.1) 143.0 (21.2)

DBP mmHg mean (sd) 82.0 (9.7) 73.95 (10.2) 82.19 (12.41) 82.2 (9.5) 73.6 (11.4) 79.0 (11.0)

Current smokers % 14.9 7.4 18.25 25.6 23.4 19.6

Lipid-lowering therapy % 49.5 10.7 N/A 1.8 2.6 12.4

Anti-hypertensive therapy % 56.8 22 20.95 59.4 17.8 21.8

Diabetes % 26.7 1.5 4.6 0 10 10.6

HDL-Chol mmol/L mean (sd) 1.26 (0.36) 1.57 (0.44) 1.45 (0.38) - at baseline 1.39 (0.36) 1.34 (0.36) 1.37 (0.37)

LDL-Chol mmol/L mean (sd) 3.55 (1.00) 3.47 (2.42) 5.36 (1.21) - at baseline 4.13 (0.95) TBC TBC

Total-Chol mmol/L mean (sd) 5.50 (1.13) 5.7 (0.98) 7.11 (1.3) - at baseline 6.08 (1.04) 6.63 (1.21) 5.78 (0.99)

Triglycerides mmol/L mean (sd) 1.59 (1.24) 1.35 (0.84) 1.53 (0.88) - at baseline 1.23 (0.57) - 1.60 (0.88)

Fasting glucose mmol/L mean (sd) 5.92 (1.65) 5.38 (0.9) 5.78 (1.38) - at baseline 4.78 (0.33) 6.89 (2.59) - not fasting 6.0 (1.7)

Common carotid baseline IMT variable(s) CC-IMTmean, CC-IMTmax, CC 1st cm-IMTmean, CC 1st cm-IMTmax CC-IMTmean CC-IMTmean CC-IMTmean (right only) CC-IMTmax CC-IMTmax

Bifurcation baseline IMT variable(s) Bif-IMTmean, Bif-IMTmax - - Bif-IMTmax (right only) - -

Composite baseline IMT variable(s) IMTmean, IMTmax, IMTmean-max - - Plaque score (right only) - -

IMT variable transformation(s) log10 log10 log10 log10 ln (recalculated to log10 for meta-analysis) ln (recalculated to log10 for meta-analysis)

IMT units mm mm mm mm mm mm

Genotyping centre Uppsala , Sweden UCL Genomics and Cambridge Edinburgh Clinical Research Facility- Genetics Core Broad Institute of Harvard and MIT Erasmus MC Rotterdam Erasmus MC Rotterdam

Genotyping platform Illumina CardioMetabochip Illumina CardioMetabochip Illumina CardioMetabochip Illumina CardioMetabochip Illumina HumanHap 550 Illumina HumanHap 550

Genotype calling algorithm GeneCall Illumina GenomeStudio GeneCall BIRDSEED (modified for Illumina Intensity Data) Illumina BeadStudio Illumina BeadStudio

Pre-imputation quality control - - - - MAF>0.01, Call rate>0.98, HWE p>1x10-6

MAF>0.01, Call rate>0.98, HWE p>1x10-6

Imputation software - - - - Mach (v1.0.1.5) Mach (v1.0.1.5)

Call rate criteria ≥95% ≥90% ≥95% ≥95% ≥97.5% ≥97.5%

Exclusion criteria

relatedness (confirmed or cryptic), reported non-European descent,

outliers identified by multi-dimensional scaling, estimated inbreeding

(excessive homozygosity), mismatch between recorded and genotype-

determined sex

cryptic relatedness, outliers

determined by multidimensional

scaling, mismatch between

recorded and genotype-

determined sex

cryptic relatedness, outliers determined by

multidimensional scaling, mismatch between recorded

and genotype-determined sex

excess or deficient homozygosity, excess low-level

identity by descent (IBD) sharing (PIHAT>0.1,

pairwise close kinship (PIHAT>0.2), outliers

identified in principal components space

excess autosomal heterozygosity >0.336

(~FDR<0.1%), mismatch between recorded and

genotype-determined sex, outliers identified by

identity by state (IBS) clustering analysis (>3

standard deviations from population mean), first

or second degree relatives using IBS

probabilities>97%

excess autosomal heterozygosity >0.336

(~FDR<0.1%), mismatch between recorded and

genotype-determined sex, outliers identified by

identity by state (IBS) clustering analysis (>3

standard deviations from population mean), first

or second degree relatives using IBS

probabilities>97%

N SNPs after quality control filtering 127,998 (127,830 autosomal SNPs included in discovery analysis) 131,007 125,888 179,195 530,683 495,478

Call rate criteria ≥90% ≥98% ≥90% ≥95% ≥90% ≥90%

Hardy-Weinberg equilibrium (HWE) criteria* p>5x10-7

p>5x10-7

p>5x10-7

p>1x10-6

p>5x10-5

p>5x10-5

Minor allele frequency (MAF) criteria* ≥0.005 ≥0.005 ≥0.005 ≥0.01 >0.01 >0.01

Adjustments population substructure (multi-dimensional scaling), age, sex age and sex age and sex age and sex age and sex age and sex

Analysis method linear regression linear regression linear regression linear regression linear regression linear regression

Genetic model additive additive additive additive additive additive

Software for analysis plink (version 1.07) plink (version 1.07) plink (version v1.07) plink (version 1.07) R using ProbABEL software (version 1.1) R using ProbABEL software (version 1.1)

Cohort reference (PMID) 19952003 15576467 9576427 8429286 21877163 21877163

Website - - - - http://rotterdamstudy.com/index.html http://rotterdamstudy.com/index.html

Cohort

Study type

Ethnicity

Genotyping

Characteristics

*For the meta-analysis in the current study, HWE and MAF filters were applied centrally.

IMT variables

Sample quality control

References

Imputation

SNP quality control

Analysis

Supplemental Table 2. Loci selected for replication based on association with CC-IMT in the discovery analysis (P<1x10-4

)

Discovery Replication

SNP

Nearest

gene Chr Pos

Alleles

effect/other Freq Beta SE n P Freq Beta SE n P

Heterogeneity

I2 Pheterogeneity

rs4901536 SAMD4A 14 54270284 T/C 0.29 0.013 0.0026 3429 9.97E-07

0.29 -0.0018 0.0011 11586 0.096 0 0.509

rs11256141 GATA3 10 9366107 C/T 0.17 0.0113 0.0024 3429 3.21E-06

0.17 0 0.0013 11583 1.000 38.2 0.167

rs17624670 NRG1 8 32245916 A/G 0.22 0.0128 0.0028 3427 5.62E-06

0.20 0.0004 0.0012 11587 0.732 44.9 0.123

rs11897302 OSR1 2 19637880 C/T 0.15 -0.0116 0.0026 3429 7.78E-06

0.13 0.0004 0.0015 11501 0.769 4.6 0.381

rs2925663 UBXN2B 8 59424423 T/C 0.13 0.011 0.0025 3427 1.07E-05

0.13 0.0002 0.0015 11587 0.888 0 0.952

rs4061073 SSBP3 1 54469331 G/A 0.28 0.0082 0.0019 3427 1.31E-05

0.31 -0.0013 0.0011 11585 0.228 0 0.512

chr18:55963125†‡ PMAIP1 18 55963125 T/C 0.007 0.0623 0.0143 3429 1.38E-05

0.0005 0.0018 0.0667 2768 0.978 0 0.985

rs11850769 BRMS1L 14 35492026 G/A 0.06 0.022 0.0051 3429 1.48E-05

0.06 -0.0027 0.0024 11586 0.257 0 0.775

rs2013056 RUNX1T1 8 93537351 T/C 0.15 0.0165 0.0038 3427 1.63E-05

0.13 0.0031 0.0014 11587 0.033 12.1 0.336

rs2972481 LSAMP 3 117048396 A/G 0.08 0.0186 0.0044 3428 2.07E-05

0.07 0.0006 0.0019 11586 0.750 44.3 0.127

rs17145627 GATA3 10 9336846 A/G 0.14 0.0111 0.0026 3429 2.20E-05

0.14 0.0016 0.0015 11585 0.268 0 0.624

rs4145320 TMEM26 10 62693320 A/G 0.49 -0.0101 0.0024 3423 2.36E-05

0.46 -0.0009 0.0010 11575 0.353 14.6 0.321

rs12453442 HLF 17 50745420 A/G 0.16 0.0158 0.0037 3427 2.39E-05

0.18 0.0012 0.0013 11583 0.348 19.0 0.293

rs13260097 FAM84B 8 127730309 A/G 0.11 -0.0113 0.0027 3427 2.55E-05

0.11 0.0021 0.0019 11587 0.280 0 0.506

rs2584681 PHB 17 44884745 A/G 0.25 -0.013 0.0031 3426 2.66E-05

0.28 -0.0013 0.0012 11587 0.261 0 0.570

chr6:119186493† C6orf204 6 119186493 A/G 0.16 -0.0133 0.0032 3429 3.07E-05

0.19 0.0006 0.0021 4907 0.758 0 0.790

rs7296372 KIAA0528 12 22517338 C/T 0.09 0.0136 0.0033 3429 3.36E-05

0.10 -0.0001 0.0016 11510 0.968 34.9 0.189

rs4723264§ BBS9 7 33164040 G/A 0.02 0.0303 0.0074 3429 3.94E-05

NA NA NA NA NA NA NA

rs12579259 IPO8 12 30395202 A/C 0.13 0.01 0.0025 3421 4.67E-05

0.12 0.0015 0.0015 11580 0.339 0 0.803

rs17164583 OR2A5 7 143370870 T/C 0.19 0.0088 0.0022 3426 4.88E-05

0.18 0.0026 0.0013 11512 0.041 0 0.509

rs1155530 FHIT 3 59702842 A/G 0.22 0.0087 0.0022 3429 4.97E-05

0.22 -0.0007 0.0012 11587 0.559 0 0.427

rs2551968 CCNYL1 2 208244781 A/C 0.45 -0.0096 0.0024 3429 4.98E-05

0.46 -0.0015 0.0010 11586 0.144 12.4 0.335

rs6491467 DOCK9 13 98349266 G/A 0.11 0.0152 0.0038 3429 5.58E-05

0.12 0.0033 0.0016 11587 0.037 0 0.956

rs4791906 GLP2R 17 9736177 T/C 0.35 -0.01 0.0025 3428 5.65E-05

0.35 0.0013 0.0010 11587 0.213 0 0.802

chr20:39103884† TOP1 20 39103884 G/A 0.02 0.0237 0.0059 3427 5.74E-05

0.02 -0.0034 0.0070 4908 0.623 14.2 0.312

rs11863148 RBFOX1 16 7065902 T/G 0.23 -0.0132 0.0033 3423 5.74E-05

0.25 0.0019 0.0011 11585 0.094 18.2 0.299

chr11:47233666* NR1H3 11 47233666 T/C 0.38 0.0111 0.0028 3422 6.29E-05

0.37 0.0025 0.0010 11564 0.016 9.1 0.355

rs2613235* CDH17 8 95262411 T/A 0.27 -0.0108 0.0027 3429 6.55E-05

0.22 -0.0014 0.0012 11584 0.249 27.2 0.240

rs6659255 SERINC2 1 31642256 T/C 0.13 -0.011 0.0028 3429 7.20E-05

0.12 0.0003 0.0015 11588 0.823 57.0 0.054

rs2304977 KIAA0753 17 6454053 A/G 0.35 0.0111 0.0028 3427 7.30E-05

0.38 0.0014 0.0010 11586 0.168 35.1 0.188

rs1040691 FSCB 14 43711385 C/T 0.26 -0.0106 0.0027 3429 7.61E-05

0.26 -0.0003 0.0011 11588 0.759 56.9 0.054

rs4801882 SIGLEC5 19 56818865 A/G 0.44 0.0067 0.0017 3427 7.88E-05

0.45 -0.0003 0.0010 11587 0.762 0.5 0.404

rs9295957 HCG27 6 31265572 A/G 0.18 -0.014 0.0036 3427 8.27E-05

0.21 0.0010 0.0012 11586 0.425 19.0 0.293

rs8085514 HRH4 18 20447234 T/C 0.42 0.0108 0.0027 3427 9.00E-05

0.40 -0.0013 0.0010 11588 0.204 0 0.740

rs10171653 EML6 2 54981105 A/G 0.18 0.0086 0.0022 3427 9.23E-05

0.17 0.0036 0.0013 11499 0.007 9.0 0.355

chr17:61664923† APOH 17 61664923 A/G 0.009 0.0577 0.0147 3427 9.24E-05

0.008 0.0006 0.0091 4908 0.949 0 0.737

rs10757269 CDKN2BAS 9 22062264 A/G 0.49 0.0072 0.0018 3411 9.41E-05

0.54 -0.0020 0.0010 11458 0.044 0 0.952

rs10763757 SVIL 10 30119539 T/C 0.35 -0.0076 0.0019 3426 9.57E-05

0.36 -0.0010 0.0010 11586 0.316 0 0.744

rs1655217 TLE3 15 68339357 A/G 0.29 -0.0078 0.002 3428 9.68E-05 0.32 -0.0009 0.0011 11586 0.424 0 0.436

Supplemental Table 2 Footnote:

Discovery and replication meta-analysis P-values and beta-coefficients for the effect allele (minor allele in the discovery cohort) are shown after adjustments

for sex, age, and population substructure when applicable (multi-dimensional scaling in the IMPROVE discovery cohort only). Observations are sorted

according to discovery P-value. Chr: chromosome, Freq: frequency. SE: standard error. Between-cohort heterogeneity is described by I2 in percent and Q-test

P-values. Chromosome positions are given according to NCBI Build 36. *In the Rotterdam-I and Rotterdam-II studies, rs2157 was used as proxy for

rs2613235 and rs749067 for chr11:47233666. †The SNPs chr6:119186493, chr17:61664923, chr18:55963125, and chr20:39103884 were not imputed in the

Rotterdam-I and Rotterdam-II studies and therefore lost from analysis in these cohorts. ‡The rare SNP chr18:55963125 failed the general minor allele

frequency cut-off in the replication cohorts, however the association result is given for completeness. §The low-frequency SNP rs4723264 was only detected

in the discovery cohort.

Supplemental Table 3. Association of rs4888378 with individual segment-specific IMT phenotypes in IMPROVE

Results are shown from linear regression analysis using an additive model, adjusted for sex, age, and population substructure (multi-dimensional scaling). SE:

standard error. CC-IMTmean: mean IMT of the common carotid excluding the 1st cm proximal to the bifurcation, CC-IMTmax: maximum IMT of the common

carotid excluding the 1st cm proximal to the bifurcation, CC-1

st cm-IMTmean: mean IMT of the 1

st cm of the common carotid proximal to the bifurcation, CC-1

st

cm-IMTmax: maximum IMT of the 1st cm of the common carotid proximal to the bifurcation, Bif-IMTmean: mean IMT of the bifurcation, Bif-IMTmax: maximum

IMT of the bifurcation, ICA-IMTmean: mean IMT of the internal carotid artery, ICA-IMTmax: maximum IMT of the internal carotid artery.

IMT phenotype Beta SE n P

CC-IMTmean -0.0043 0.0017 3427 0.013

CC-IMTmax -0.0071 0.0028 3427 0.010

CC-1st cm-IMTmean -0.0039 0.0019 3429 0.038

CC-1st cm-IMTmax -0.0061 0.0024 3429 0.013

Bif-IMTmean -0.0129 0.0033 3409 1.02E-04

Bif-IMTmax -0.0174 0.0040 3409 1.13E-05

ICA-IMTmean -0.0115 0.0034 3397 8.18E-04

ICA-IMTmax -0.0160 0.0045 3397 3.71E-04

SUPPLEMENTAL FIGURE LEGENDS 1-4

Supplemental Figure 1. Plots of results from multi-dimensional scaling (MDS) analysis of the

CardioMetaboChip genotypes in IMPROVE after exclusion of 45 individuals who reported non-

Caucasian ethnicity or who were outliers in the MDS analysis. MDS analysis was performed using

n=83,856 SNPs obtained by applying a sequential filter of pairwise correlation of r2<0.5 within a

window size of 50 SNPs, and with 5 SNPs overlap between windows, as implemented by the indep-

pairwise function in PLINK. The first three dimensions used for adjustment in association analysis are

plotted. A color scheme according to recruitment centre is applied.

Supplemental Figure 2. Manhattan plots of the association P-values for segment-specific and

composite carotid IMT variables in the IMPROVE study, adjusted for sex, age and population

substructure (multi-dimensional scaling). SNPs are plotted with their P-values (as -log10 values) as a

function of genomic position (NCBI Build 36). The red line indicates the threshold for array-wide

significance (P=8.39x10-7

). The lead SNP rs4888378 is plotted with a larger symbol and colored in

green. Segment-specific variables: CC-IMTmean: mean IMT of the common carotid excluding the 1st

cm proximal to the bifurcation, CC-IMTmax: maximum IMT of the common carotid excluding the 1st

cm proximal to the bifurcation, CC-1st cm-IMTmean: mean IMT of the 1

st cm of the common carotid

proximal to the bifurcation, CC-1st cm-IMTmax: maximum IMT of the 1

st cm of the common carotid

proximal to the bifurcation, Bif-IMTmean: mean IMT of the bifurcation, Bif-IMTmax: maximum IMT of

the bifurcation, ICA-IMTmean: mean IMT of the internal carotid artery, ICA-IMTmax: maximum IMT of

the internal carotid artery. Composite IMT variables: IMTmean: over-all mean of segment-specific

IMT means, IMTmax: over-all maximum of segment-specific IMT maxima, IMTmean-max: mean of the

segment-specific IMT maxima.

Supplemental Figure 3. A. Comparison of results from replication meta-analysis and bootstrap

analysis. Effect sizes (beta with 95% confidence intervals) of associations with common carotid IMT

(CC-IMT) are shown for SNPs that reached P<0.05 in the replication meta-analysis. The bootstrap

confidence intervals are adjusted for sample size (i.e. simulating an equal sample size as that of the

replication meta-analysis) to enable comparison of the two approaches. The corresponding discovery

beta-coefficients are shown for reference. B. Bootstrap validation of top associations with composite

IMT variables in IMPROVE. Bootstrap effect sizes (beta with 98.33% confidence intervals to account

for analysis of 3 independent SNPs) are shown for SNPs that reached P<1x10-5

in relation to

composite IMT variables in the discovery analysis and were taken forward to replication in relation to

common carotid IMT by meta-analysis. The corresponding discovery beta-coefficients are shown for

reference.

Supplemental Figure 4. A. Association of the lead SNP rs4888378 with expression of genes within a

400 kb search window in 7 vascular and non-vascular tissues (mammary artery intima-media, liver,

aortic intima-media, aortic adventitia, heart, carotid plaque, and peripheral blood mononuclear cells;

PBMC). The y-axis shows the -log10 P-value calculated for the association between genotype and

expression level using an additive model. Each colored line shows the association level for one gene

across the different tissue types. The relative expression level of a gene in the respective tissue is

indicated by the dot size. Genes are denoted with the respective gene symbols according to the HUGO

Gene Nomenclature Committee. The CTRB1 gene present in the region was not captured by the

expression microarrays and is therefore missing. B. The relative locations of rs4888378, genes, and

linkage disequilibrium boundaries are indicated by the map below the plot. The dark and light shades

of gray indicate the most distant SNPs in LD with rs4888378 with r2=0.8 and 0.6 in the HapMap CEU

population, respectively. The gene color scheme corresponds to that in A.

SUPPLEMENTAL FIGURES 1-4

Supplemental Figure 1




Documents

Identification of the BCAR1-CFDP1-TMEM170A Locus …circgenetics.ahajournals.org/content/circcvg/early/2012/...DOI: 10.1161/CIRCGENETICS.112.963660 1 Identification of the BCAR1-CFDP1-TMEM170A