Design of the Coronary ARtery DIsease Genome-Wide ...circgenetics.ahajournals.org/content/circcvg/3/5/475.full.pdf · Design of the Coronary ARtery DIsease Genome-Wide Replication

Design of the Coronary ARtery DIsease Genome-WideReplication And Meta-Analysis (CARDIoGRAM) Study

A Genome-Wide Association Meta-analysis Involving More Than 22 000Cases and 60 000 Controls

Michael Preuss, Dipl-Inform Med; Inke R. Konig, Prof Dr rer biol hum; John R. Thompson, PhD;Jeanette Erdmann, Prof Dr rer nat; Devin Absher, PhD; Themistocles L. Assimes, MD, PhD;

Stefan Blankenberg, Prof Dr med; Eric Boerwinkle, PhD; Li Chen, MSc; L. Adrienne Cupples, PhD;Alistair S. Hall, MD, ChB, PhD, FRCP; Eran Halperin, PhD; Christian Hengstenberg, Prof Dr med;

Hilma Holm, MD; Reijo Laaksonen, MD, PhD; Mingyao Li, PhD; Winfried Marz, Prof Dr med;Ruth McPherson, MD, PhD, FRCPC; Kiran Musunuru, MD, PhD, MPH; Christopher P. Nelson, MSc, PhD;

Mary Susan Burnett, PhD; Stephen E. Epstein, MD; Christopher J. O’Donnell, MD, MPH;Thomas Quertermous, MD; Daniel J. Rader, MD; Robert Roberts, MD, FRCP(C), MACC;Arne Schillert, Dr rer hum biol; Kari Stefansson, MD, PhD; Alexandre F.R. Stewart, PhD;

Gudmar Thorleifsson, PhD; Benjamin F. Voight, PhD; George A. Wells, MSc, PhD;Andreas Ziegler, Prof Dr rer nat; Sekar Kathiresan, MD; Muredach P. Reilly, MBBCH, MSCE;

Nilesh J. Samani, FMedSci; Heribert Schunkert, Prof Dr med; on behalf of the CARDIoGRAM Consortium

Background—Recent genome-wide association studies (GWAS) of myocardial infarction (MI) and other forms of coronaryartery disease (CAD) have led to the discovery of at least 13 genetic loci. In addition to the effect size, power to detectassociations is largely driven by sample size. Therefore, to maximize the chance of finding novel susceptibility loci for CADand MI, the Coronary ARtery DIsease Genome-wide Replication And Meta-analysis (CARDIoGRAM) consortium was formed.

Methods and Results—CARDIoGRAM combines data from all published and several unpublished GWAS in individuals withEuropean ancestry; includes �22 000 cases with CAD, MI, or both and �60 000 controls; and unifies samples from theAtherosclerotic Disease VAscular functioN and genetiC Epidemiology study, CADomics, Cohorts for Heart and AgingResearch in Genomic Epidemiology, deCODE, the German Myocardial Infarction Family Studies I, II, and III, LudwigshafenRisk and Cardiovascular Heath Study/AtheroRemo, MedStar, Myocardial Infarction Genetics Consortium, Ottawa HeartGenomics Study, PennCath, and the Wellcome Trust Case Control Consortium. Genotyping was carried out on Affymetrixor Illumina platforms followed by imputation of genotypes in most studies. On average, 2.2 million single nucleotidepolymorphisms were generated per study. The results from each study are combined using meta-analysis. As proof ofprinciple, we meta-analyzed risk variants at 9p21 and found that rs1333049 confers a 29% increase in risk for MI per copy(P�2�10�20).

Conclusion—CARDIoGRAM is poised to contribute to our understanding of the role of common genetic variation on riskfor CAD and MI. (Circ Cardiovasc Genet. 2010;3:475-483.)

Key Words: coronary artery disease � myocardial infarction � meta-analysis � genetics

Recent success in identifying genes involved in complexdiseases such as coronary artery disease (CAD) and

myocardial infarction (MI) has been largely brought about by2 major developments. First, modern array technology nowenables simultaneous measurement of hundreds of thousandsof single nucleotide polymorphisms (SNPs) across the humangenome. Second, collaborations have been formed, bringingtogether large collections of well-phenotyped individuals.With respect to CAD and MI, this effort was established by

highly informative collections of patients with premature dis-ease. Up to the present, these studies have individually identifiedat least 13 chromosomal loci with genome-wide significance forassociation with MI and other forms of CAD.1–10

Editorial see p 396Clinical Perspective on p 483

However, only a small proportion of the heritability of CADhas been explained. One major reason is likely to be the complex

Received November 12, 2009; accepted August 5, 2010.From the Institut fur Medizinische Biometrie und Statistik (M.P., I.R.K., A.S., A.Z.) and Medizinische Klinik II (M.P., J.E., H.S.), Universitat zu Lubeck,

Lubeck, Germany; Department of Health Sciences (J.R.T, C.P.N.), University of Leicester, Leicester, UK; Hudson Alpha Institute (D.A.), Huntsville, Ala;© 2010 American Heart Association, Inc.

Circ Cardiovasc Genet is available at http://circgenetics.ahajournals.org DOI: 10.1161/CIRCGENETICS.109.899443

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nature of atherosclerosis, with multiple genetic factors contrib-uting only small effects to disease manifestation. In fact, for atypical genome-wide association study (GWAS) with �1000cases and controls, the power to detect any effects at stringentsignificance levels is low.11 To increase the power, we formed aconsortium to pool data across all published and multiple unpub-lished GWAS for CAD and MI. Here, we aim to provide a detaileddescription of the structure and functioning of our consortium.

MethodsGeneral Organization of the ConsortiumOur consortium—the Coronary ARtery DIsease Genome-wide Rep-lication And Meta-analysis (CARDIoGRAM) consortium—is basedon a core of previously successful collaborations among singleparticipating studies.4–8 These case-control or prospective cohortstudies both have detailed phenotyping for CAD, MI, or both aspreviously described.8 Control subjects have been derived frompopulation-based studies in most investigations. For all of theparticipating studies, genome-wide scans were performed in theyears 2006 to 2009. Statistical methods have been standardizedacross the studies, and an analysis platform has been created to allowsummarized analyses on CAD, MI, and related phenotypes.

The organizational structure comprises a steering committee of eitherthe principal or another key investigator of the participating studies andrepresentatives from the statistical groups. The analysis team comprises

1 responsible genetic epidemiologist or statistical geneticist from eachstudy. Standard operating procedures were generated to harmonize thedata analyses. A centralized database for aggregated data is provided bythe Cardiogenics consortium (http://www.cardiogenics.eu/web/) to al-low centralized and decentralized access and analysis by all of thestatistical groups (Figure 1). Transparency, with disclosure of any othercollaborations with the potential to create conflict (including follow-upexperiments), has been encouraged in order to sustain a high level oftrust within the consortium. A consensus has been established by thesteering committee to discourage intellectual property claims on aggre-gate findings before publication of results.

Statistical Analysis MethodsDifferent genotyping platforms have been used across the studies.An analysis restricted only to SNPs genotyped on all platformswould have been severely limited. For instance, the estimatedoverlap between the Affymetrix Genome Wide Human SNP Array6.0 and the Illumina Human-1 mol/L chip is only about 250 000SNPs. To allow for combined analyses across different platforms,missing SNPs were imputed by each study.

To summarize the evidence across studies, we used aggregated datafrom association analyses for CAD and MI as well as for importantsubgroups as outlined a priori in the study protocol. The plannedstatistical analysis across all studies ending in the meta-analysis has6 steps, as summarized in Table 1. In brief, after quality control,SNP-wise association tests are computed using log-additive geneticmodels with adjustment for age and sex in every study. After upload

Figure 1. Workflow of CARDIoGRAM. Analyses areperformed in every study separately by the statisti-cal group and then submitted to the central data-base. The analysis group checks the data qualitywithin each study and queries individual studies onsummary data. Once the initial quality control hasbeen performed and data summaries are consis-tent across individual studies, these quality-controlled data are updated in the database andused for meta-analysis.

Department of Medicine (T.L.A., T.Q.), Stanford University School of Medicine, Stanford, Calif; Medizinische Klinik und Poliklinik (S.B.),Johannes-Gutenberg Universitat Mainz, Universitatsmedizin, Mainz, Germany; University of Texas Health Science Center (E.B.), Human GeneticsCenter and Institute of Molecular Medicine, Houston, Tex; The John & Jennifer Ruddy Canadian Cardiovascular Genetics Center (L.C., R.M., R.R.,A.F.R.S., G.W.), University of Ottawa, Ottawa, Ontario, Canada; Department of Biostatistics and Epidemiology (A.C.), Boston University, Boston, Mass;National Heart, Lung, and Blood Institute (A.C., C.J.O.), Framingham Heart Study, Framingham, Mass; LIGHT Research Institute (A.S.H.), Faculty ofMedicine and Health, University of Leeds, Leeds, UK; The Blavatnik School of Computer Science and the Department of Molecular Microbiology andBiotechnology (E.H.), Tel-Aviv University, Tel-Aviv, Israel; International Computer Science Institute (E.H.), Berkeley, Calif; Klinik und Poliklinik furInnere Medizin II (C.H.), Universitat Regensburg, Regensburg, Germany; deCODE Genetics (H.H., G.T., U.T.), 101 Reykjavik, Iceland; Science Center(R.L.), Tampere University Hospital, Tampere, Finland; Biostatistics and Epidemiology (M.L.), University of Pennsylvania, Philadelphia, Pa; SynlabCenter of Laboratory Diagnostics Heidelberg (W.M.), Heidelberg, Germany; Clinical Institute of Medicine and Chemical Laboratory Diagnostics (W.M.),Medical University of Graz, Graz, Austria; Institute of Public Health, Social and Preventive Medicine (W.M.), Faculty of Medicine Mannheim, Universityof Heidelberg, Germany; Cardiovascular Research Center and Cardiology Division (K.M., S.K.), Massachusetts General Hospital, Boston, Mass; Centerfor Human Genetic Research (K.M., B.F.V., S.K.), Massachusetts General Hospital, Boston, Mass; Program in Medical and Population Genetics (K.M.,B.F.V., S.K.), Broad Institute of Harvard and Massachusetts Institute of Technology, Cambridge, Mass; Cardiovascular Research Institute (M.S.B.,S.E.E.), MedStar Research Institute, Washington Hospital Center, Washington, DC; Cardiology Division (C.J.O.), Massachusetts General Hospital,Boston, Mass; The Institute for Translational Medicine and Therapeutics (D.J.R., M.P.R.), School of Medicine, University of Pennsylvania, Philadelphia,Pa; The Cardiovascular Institute (D.J.R., M.P.R.), University of Pennsylvania, Philadelphia, Pa; University of Iceland (U.T.), Faculty of Medicine, 101Reykjavik, Iceland; Department of Medicine (B.F.V.), Harvard Medical School, Boston, Mass; Department of Cardiovascular Sciences (N.J.S.),University of Leicester, Glenfield Hospital, Leicester, UK.

M. Preuss and Drs König, Thompson, and Erdmann contributed equally to this work.Drs Kathiresan, Reilly, Samani, and Schunkert contributed equally to this work.Guest Editor for this article was Barry London, MD, PhD.The online-only Data Supplement is available at http://circgenetics.ahajournals.org/cgi/content/full/CIRCGENETICS.109.899443/DC1.Correspondence to Heribert Schunkert, Prof Dr med, Universitat zu Lubeck, Medizinische Klinik II, Ratzeburger Allee 160, 23538 Lubeck, Germany

(E-mail [email protected]); or Nilesh J. Samani, FMedSci, Department of Cardiovascular Sciences, University of Leicester, Clinical SciencesWing, Glenfield Hospital, Groby Road, Leicester, LE3 9QP, United Kingdom (E-mail [email protected]).

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of the summary data and centralized quality control, a meta-analysisacross all studies is performed for every SNP separately. Here,depending on the heterogeneity between studies, fixed (inversevariance weighting) or random effects (DerSimonian-Laird) modelsare calculated, and outlying studies excluded.

As a proof-of-principle analysis, a number of SNPs in the 9p21region with known association to CAD and MI were analyzedupfront. Specifically, we selected 3 SNPs that were reported as leadSNPs in the first publications by McPherson et al2 (rs2383206),Helgadottir et al3 (rs10757278), and Samani et al4 (rs1333049). Inaddition, we chose the SNP rs10811661 in the same chromosomalregion that was associated with type 2 diabetes but not with CAD.12,13

Given the potential etiologic heterogeneity of CAD, we predefinedseveral subgroup analyses. Specifically, we chose to compare asso-ciation results between female and male sex (female cases withfemale controls versus male cases with male controls) and youngeronset and older onset cases (older cases with all controls versusyounger cases with all controls). In addition, we analyzed age at firstMI as a phenotype in cases only. We illustrate the results of thesepredefined subgroup analyses for the SNP rs1333049 with a knownassociation to CAD and MI.

Replication Strategy and Levels of EvidenceIn addition to providing a powerful meta-analysis, CARDIoGRAM hasintegrated a replication stage into the analysis plan, assembling asubstantial resource of more than 60 000 samples, which are availablefor wet-laboratory or in silico replication. Replication has been pre-defined as being successful if the 1-sided P value remains �0.05 aftercorrection for the testing of the number of genetic loci taken forward.

Because of the size of the primary CARDIoGRAM meta-analysiscombined with the replication sample size, it is unlikely that a similar-scale experiment will be performed in the near future to independentlyverify the findings that emerge from our study. Mindful of this and toaccount for the caveats involved in combining data sets for meta-analysis, the CARDIoGRAM consortium has decided a priori tocategorize the principal findings into different levels of evidence thatdepend on the strength of the association observed in the meta-analysisand replication samples. Establishing such criteria in advance avoids

bias, helps with interpretation of findings, and may help to guide futurework. The criteria we used are as follows:

● Level 1: Regions with SNPs that display (1) genome-wide signif-icance with P�5�10�8 in a joint analysis of the GWAS andwet-laboratory replication stages and (2) display independentlysignificant association in the replication stage at a threshold of0.05 divided by the number of loci tested in replication.

● Level 2: SNPs that display genome-wide significance with P�5�10�8

in a joint analysis of the GWAS and wet-laboratory replication stagesbut do not display independent significant association in the replica-tion stage.

● Level 3: SNPs with levels of association evidence with P�5�10�8

but �10�6 in a joint analysis of the GWAS and wet-laboratoryreplication stages.

These criteria will be applied to the main comparison and to all ofthe subgroup analyses.

ResultsA description of the participants in each study is given inTable 2. Online-only Data Supplement Table 1 summarizesthe genotyping platforms and imputation methods used forthe individual studies.

Collectively, our consortium provides �10 times the numberof cases and controls than the largest published individual CADGWAS. Consequently, our meta-analysis will have increasedpower to detect small genetic effects. For instance, the power is�80% to detect an odds ratio (OR) of only 1.1 at the level ofgenome-wide significance, provided that the mean minor allelefrequency (MAF) is at least 15% (see Figure 2). For thereplication stage, the estimated power is shown in Figure 3.

The results for our proof-of-principle analysis are shown inTable 3 and Figure 4. In agreement with prior studies, the resultsshow a strong association between the SNPs rs1333049,

Table 1. Algorithm Applied for Analysis

Step Description

1. Perform analyses inevery studyseparately.

According to an a priori standard operating procedure, analyses are performed in every study separately. Here, quality-controlindicators and tests for association with CAD are computed. Specifically, a log-additive model frequency test that takes into

account the uncertainty of possibly imputed genotypes is performed with adjustment for age (at first CAD/MI onset for cases orrecruitment for controls) and sex.

2. Upload to a centraldatabase.

The full set of summary data, including aggregated genotypes, quality-control parameters, and results from the associationanalyses are uploaded to a central database.

3. Perform quality controlof data.

Quality control of the data is performed centrally according to previously agreed criteria, including a check of consistency of thegiven alleles across all studies, quality of the imputation, deviation from Hardy-Weinberg equilibrium in the controls, the MAF, and

the call rate. In every study, the variance inflation factor � is computed from genotyped SNPs and used for adjustment.

4. Apply meta-analysisprocedure.

Separately for every SNP from every study that passes the quality criteria, the following procedure is applied for themeta-analysis:

(1) Fixed effects models are calculated together with Q- and I statistics for testing homogeneity. If there is no heterogeneity(P�0.01 for Q), fixed effects models are reported.

(2) If there is evidence for heterogeneity (P�0.01 for Q), an outlier test is performed comparing each study with the average ofthe others.

(a) If outliers are found with P�0.01/(number of studies providing data for the SNP), the most extreme study is excluded,and the procedure is repeated from step (1).

(b) If no outliers are found but heterogeneity was apparent, random effects models are calculated.

5. Compute the overall �on the results of themeta-analysis.

Finally, in addition to the adjustment for each individual study inflation factor �, the overall � is computed on the results of themeta-analysis. Primary statistical evidence is based on individual study � adjustment, but results with an additional adjustment

for the overall � based on the meta-analysis also are reported.

6. Conduct the mainanalyses in at least 2sites.

To ensure high quality of the statistical analysis, the main analyses are conducted in at least 2 sites in parallel andindependently. Importantly, all sites have access to all data sets to provide additional quality checks of the principal findings.

Preuss et al Design of CARDIoGRAM 477



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rs2383206, and rs10757278 and CAD but no evidence forassociation for rs10811661. The SNP rs1333049 was also part ofthe previous meta-analysis by Schunkert et al.19 In that report,the overall OR was 1.29 with a 95% CI of 1.22 to 1.37, virtuallyidentical to our current result.

Results for the subgroup analyses of rs1333049 are shown inFigure 5. Except for the analysis of older cases, results are morehomogenous across study groups so that fixed effects models areselected. Overall, the association effect is strongest amongyounger cases. Moreover, there was an association of the

Table 2. Description of Probands (Cases/Controls) in Participating Studies

Study Full Study Name No. MI, % Female Sex, % Age, y BMI CAD Definition Control Definition Ref

ADVANCE Atherosclerotic Disease,

VAscular functioN, and

genetiC Epidemiology

278/312 50.4 57.9/59.0 45.8 (6.2)/45.3 (5.7) 31.2 (7.1)/26.5 (5.7) Clinical nonfatal CAD (men aged

�45 years, women aged �55

years), including AMI (enzymes),

typical angina with �1 artery

with �50% stenosis, positive

noninvasive test, or PCI or CABG

No history of clinical CAD,

CVA, or PAD

14

CADomics Coronary Artery Disease

and Omics

2078/2952 58.3 21.9/50.5 60.8 (10.1)/55.3 (10.8) 29.4 (5.1)/27.0 (4.7) CAD: �50% stenosis in 1 major

coronary artery and/or MI based

on ECG and enzymes

Population sample with no

history of MI

�

CHARGE Cohorts for Heart and

Aging Research in

Genomic Epidemiology

2287/22 024 48.0 33.4/59.6 60.0 (7.9)/63.1 (8.0) 28.1 (7.4)/27.5 (8.0) CHD: definite or probable MI,

PTCA or CABG, or ECG MI

None of the conditions that

define CAD

15

deCODE 6640/27 611 54.7 36.3/61.9 74.8 (11.8)/53.7 (21.5) 27.7 (4.7)/27.0 (5.4)* MI: MONICA criteria (aged �75

years) or discharge diagnosis of

MI; CAD: PCI or participation in

CVD genetics program with

self-report of CABG or PCI or

discharge diagnosis of angina

pectoris, MI, or chronic

ischemic heart disease

Population sample 3

GERMIFS I German Myocardial

Infarction Family

Studies I

884/1604 100.0 49.4/50.8 50.2 (7.8)/62.6 (10.0)† 27.4 (3.6)/27.7(4.5) MI (�65 years) with �1

first-degree sibling with severe

CAD (PTCA, MI, CABG)

Population sample 4

GERMIFS II German Myocardial

Infarction Family

Studies II

1222/1287 100.0 33.1/48.3 51.4 (7.5)/51.2 (11.9)† 29.0 (3.8)/27.4 (4.6) MI (aged �60 years); 59.4%

with family history of CAD

Population sample 6

GERMIFS III

(KORA)

German Myocardial

Infarction Family

Studies III (Cooperative

Research in the Region

of Augsburg)

1157/1748 100.0 20.1/48.9 58.6 (8.7)/55.9 (10.7)† 27.0 (3.6)/27.1 (4.5) MI (aged �60 years); MONICA

criteria

Population sample �

LURIC/

AtheroRemo 1

Ludwigshafen Risk and

Cardiovascular Heath

Study

652/213 71.9 20.3/46.0 61.0 (11.8)/58.3 (12.1) 27.7 (4.4)/27.4 (4.2) Symptoms of angina pectoris,

NSTEMI, STEMI, or �50%

coronary stenosis

No coronary lesions or

minor stenoses (�20%)

16

LURIC/

AtheroRemo 2

Ludwigshafen Risk and

Cardiovascular Heath

Study

486/296 79.0 23.4/48.6 63.7 (9.4)/56.4 (12.7) 27.1 (3.8)/26.8 (4.0) Symptoms of angina pectoris,

NSTEMI, STEMI, or �50%

coronary stenosis

No coronary lesions or

minor stenoses (�20%)

16

MedStar 874/447‡ 48.1 33.0/54.6 48.9 (6.4)/59.7 (8.9) 31.7 (6.8)/31.3 (7.9) Angiography (�1 coronary

vessel with �50% stenosis);

aged �65 years

Angiography normal, aged

�45 years

5

MIGen Myocardial Infarction

Genetics Consortium

1274/1407 100.0 37.2/39.9 42.4 (6.6)/43.0 (7.8)† 27.6 (5.2)/25.8 (4.4) MI (men aged �50

years/women aged �60 years)

Hospital based, community

based, or nested

case-control

5

OHGS Ottawa Heart Genomics

Study

1542/1455 61.6 24.1/48.0 48.7 (7.3)/75.0 (5.0) 28.5 (4.9)/26.0 (4.0) Angiographic (�50% stenosis) Asymptomatic 17

PennCATH 933/468 50.3 23.7/51.9 52.7 (7.6)/61.7 (9.6) 29.8 (5.6)/28.9 (6.4) Angiography (�1 coronary

vessel with �50% stenosis);

aged �65 years

Angiography normal, men

aged �40 years/women

aged �45 years

5, 18

WTCCC� Wellcome Trust Case

Control Consortium

1926/2938 71.5 20.7/50.0 49.8 (7.7)� 27.6 (4.2)� Validated MI, CABG, PTCA, or

angina with positive noninvasive

testing; aged �66 years

Unselected 1

Age and BMI data are presented as mean (SD). AMI indicates acute myocardial infarction; BMI, body mass index; CABG, coronary artery bypass graft; CHD, coronaryheart disease; CVA, cerebrovascular accident; CVD, cardiovascular disease; MONICA, Multinational MONItoring of trends and determinants in CArdiovascular disease;NSTEMI, non–ST-segment elevated myocardial infarction; PTCA, percutaneous transluminal coronary angioplasty; STEMI, ST-segment elevation myocardial infarction;PAD, peripheral arterial disease; PCI, percutaneous coronary intervention.

*Information on BMI in the deCODE study is available for 81.7% of the cases and 66.8% of the controls.†For cases, age at diagnosis; for controls, age at recruitment.‡Cases, angiographic CAD (�50% stenosis in at least 1 vessel); controls, angiography normal or �10% stenosis in all vessels.�WTCCC controls comprise an equal number of subjects from the 1958 Birth Cohort and from the National Blood Service Donors. The latter were recruited in equal

10-year age bands from age 11 to 70 years. Additional phenotypes are not available for these controls.




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number of the risk-conferring alleles with earlier age of first MI(��0.37; SE�0.12; P�0.0015).

DiscussionThe GWAS approach has proven useful in the identification ofgenetic variants affecting the risk of complex diseases. Specifi-cally, several GWAS investigations have identified genes thatreproducibly demonstrate association with CAD and MI.5–6,8

Given that up to 2.5 million comparisons are carried out inparallel, a limitation of the approach is the clear discriminationbetween true and false associations. Consequently, stringentthresholds for genotyping quality and statistical significanceneed to be passed for reliable demonstration of a true-positiveassociation. Large sample sizes are required to detect modest,but biologically important associations, and replication studiesare required to minimize any remaining doubt about the repro-ducibility and relevance of such findings.

Almost all variants that have so far been associated with CADor MI demonstrate risk ratios between 1.1 and 1.3 per risk allele.Given the small effect sizes, only 1 or few novel chromosomal

loci were identified by each of the published studies. The newlyformed CARDIoGRAM consortium will enhance the statisticalpower to detect true association by increasing the sample size bya factor of 10 for cases and 20 for controls. Indeed, all of thepredefined subgroups are larger than the sample sizes of cur-rently published GWAS. These larger samples are likely tosubstantially enhance the detection of true associations for CADrisk. Furthermore, we have prepared for a substantial replicationphase and defined hierarchical levels of evidence a priori to helpattach an appropriate level of confidence to our various findingsas they emerge.

An unresolved problem in the interpretation of an individualGWAS is the potential for heterogeneity of risk allele effectsacross different individual populations. To meet this challenge,CARDIoGRAM has prespecified methods to uncover outliersand potential false-positive associations. Compared with diseasestates with intermediate or quantitative phenotypes, we antici-pate generally higher levels of heterogeneity in the clinicaldefinition of CAD and MI as a result of greater variation inclinical expression (eg, MI versus angiographic CAD) and in

Figure 2. Estimated power of the meta-analysis.Power for different MAFs and ORs at a P value of5�10�8 (n�22 000 cases and 60 000 controls).

Figure 3. Estimated power for the replication.Power for different MAFs and ORs. Critical P valuefor a 1-sided test, 0.05�2�0.1 adjusted for thetesting of 30 regions (n�15 000 cases and 15 000controls).




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ascertainment (eg, cutoff for age at onset at young age in someclinical samples versus predominantly advanced age of onset inpopulation-based samples). Relevant, but to a lesser degree, maybe local or population-based differences in the genomic struc-ture. We believe that the sample size of CARDIoGRAM willallow us to address this heterogeneity by performing stratifiedanalyses that investigate clinically important questions, includ-ing age of onset of disease and sex.

The CARDIoGRAM consortium wishes to facilitate the insilico replication of findings from other investigators, withindependent samples studying either candidate genes orgenome-wide data. The consortium is limited in its ability toprovide insights into the genetic risk of CAD in nonwhitepopulations. However, CARDIoGRAM will provide a substan-

tial number of validated loci in white individuals that can betested for association in nonwhite populations. Reciprocally, wewill offer a large white race study sample for testing therelevance of novel loci discovered in GWAS of nonwhitepopulations. It will be of particular interest, therefore, to com-

Figure 5. Results for subgroup association analysis for SNPrs1333049. Shown are ORs with 95% CIs comparing femalecases versus female controls, male cases versus male controls,old cases (�50 years) versus all controls, and young cases (�50years) versus all controls. Numbers below the x axis denote Pvalues, fixed effects (FE) or random effects (RE) models, andnumber of cases/controls.

Table 3. Proof-of-Principle Analysis on Chromosome 9p21

SNP OR 95% CI P* Model

rs1333049 1.29 1.22, 1.36 2.06�10�20 RE

rs2383206 1.28 1.22, 1.35 1.64�10�20 RE

rs10757278 1.28 1.21, 1.35 5.79�10�19 RE

rs10811661 1.02 0.98, 1.05 0.3693 FE

The model is either fixed effects or random effects (RE).*Two-sided P value with adjustment for study-wise �.

Figure 4. Forest plots for SNPs on chromosome 9p21. Random effects (RE) models were calculated for SNPs rs1333049 (risk al-lele�C), rs2383206 (risk allele�G), and rs10757278 (risk allele�G); the fixed effects (FE) model was calculated for SNP rs10811661 (riskallele�C). Heterogeneity between the studies is indicated by I2, and for every study, it is indicated whether the respective SNP was ge-notyped (G) or imputed (I) or a mixture of genotyped and imputed (NA).




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pare our findings for individuals of European ancestry with thoseobservations made for other studies recruiting members of otherracial/ethnic groups.

A secondary benefit that we foresee for the future use of theCARDIoGRAM data set and for further study of the results ofplanned meta-analyses will be to explore and strengthen evi-dence for the existence of a causal association of MI, CAD, orboth with biomarkers and other intermediate traits. Many ofthese biomarkers and traits are known to have robust associa-tions with CAD (eg, C-reactive protein, high-density lipoproteincholesterol, hyperhomocysteinemia), and the list is continuallyexpanding. Determining whether these associations are causal(ie, the biomarker or trait is involved in the pathogenesis ofCAD) versus a consequence of reverse causality (ie, the biomar-ker is raised or decreased by the presence of CAD) versus aresult of pleiotropic, but independent effects of another factor onboth the marker and the CAD risk is an important clinicalquestion with particular relevance for identifying therapeutictargets.20 If variants causally affect the level of biomarker ortrait, then one can immediately investigate whether those vari-ants also affect CAD risk to a degree that is comparable to thequantitative association of the biomarker or trait with CADrisk.20 Because the effect of the variant on the trait may bemodest, the impact on CAD risk is likely to be small, and largesample sizes are required to demonstrate or refute an associationof the variant with CAD risk. In this regard, we expectCARDIoGRAM to make a significant contribution.

In summary, we describe the design, structure, and plans ofa large consortium formed to investigate the genetic basis ofCAD and MI using genome-wide association data. Further,we discuss the likely benefits of the resource that will becreated for the cardiovascular genetics community.

AppendixCARDIoGRAM Consortium (affiliations listed in the online-only DataSupplement): Executive Committee: Heribert Schunkert, Nilesh J.Samani, Sekar Kathiresan, Muredach P. Reilly; Executive Secretary:Jeanette Erdmann; Steering Committee: Eric Boerwinkle, JeanetteErdmann, Alistair Hall, Christian Hengstenberg, Sekar Kathiresan, InkeR. Konig, Reijo Laaksonen, Ruth McPherson, Themistocles L. Assimes,Muredach P. Reilly, Nilesh J. Samani, Heribert Schunkert, John R.Thompson, Unnur Thorsteinsdottir, Andreas Ziegler

Statisticians: Inke R. Konig (chair), John R. Thompson (chair),Devin Absher, Li Chen, L. Adrienne Cupples, Eran Halperin,Mingyao Li, Kiran Musunuru, Michael Preuss, Arne Schillert,Gudmar Thorleifsson, Benjamin F. Voight, George A. Wells

ADVANCE: Devin Absher, Themistocles L. Assimes, Stephen Fort-mann, Alan Go, Mark Hlatky, Carlos Iribarren, Joshua Knowles, RichardMyers, Thomas Quertermous, Steven Sidney, Neil Risch, Hua Tang

CADomics: Stefan Blankenberg, Tanja Zeller, Arne Schillert, PhilippWild, Andreas Ziegler, Renate Schnabel, Christoph Sinning, KarlLackner, Laurence Tiret, Viviane Nicaud, Francois Cambien, ChristophBickel, Hans J. Rupprecht, Claire Perret, Carole Proust, Thomas Munzel

CHARGE: Maja Barbalic, Joshua Bis, Eric Boerwinkle, Ida Yii-DerChen, L. Adrienne Cupples, Abbas Dehghan, Serkalem Demissie-Banjaw,Aaron Folsom, Nicole Glazer, Vilmundur Gudnason, Tamara Harris,Susan Heckbert, Daniel Levy, Thomas Lumley, Kristin Marciante,Alanna Morrison, Christopher J. O’Donnell, Bruce M. Psaty, KennethRice, Jerome I. Rotter, David S. Siscovick, Nicholas Smith, AlbertSmith, Kent D. Taylor, Cornelia van Duijn, Kelly Volcik, JaquelineWhitteman, Vasan Ramachandran, Albert Hofman, Andre Uitterlinden

deCODE: Solveig Gretarsdottir, Jeffrey R. Gulcher, Hilma Holm,Augustine Kong, Kari Stefansson, Gudmundur Thorgeirsson, KarlAndersen, Gudmar Thorleifsson, Unnur Thorsteinsdottir

GERMIFS I and II: Jeanette Erdmann, Marcus Fischer, AnikaGrosshennig, Christian Hengstenberg, Inke R. Konig, WolfgangLieb, Patrick Linsel-Nitschke, Michael Preuss, Klaus Stark, StefanSchreiber, H.-Erich Wichmann, Andreas Ziegler, Heribert Schunkert

GERMIFS III (KORA): Zouhair Aherrahrou, Petra Bruse, AngelaDoering, Jeanette Erdmann, Christian Hengstenberg, Thomas Illig,Norman Klopp, Inke R. Konig, Patrick Linsel-Nitschke, ChristinaLoley, Anja Medack, Christina Meisinger, Thomas Meitinger, JanjaNahrstaedt, Annette Peters, Michael Preuss, Klaus Stark, Arnika K.Wagner, H.-Erich Wichmann, Christina Willenborg, AndreasZiegler, Heribert Schunkert

LURIC/AtheroRemo: Bernhard O. Bohm, Harald Dobnig, TanjaB. Grammer, Eran Halperin, Michael M. Hoffmann, Marcus Kleber,Reijo Laaksonen, Winfried Marz, Andreas Meinitzer, Bernhard R.Winkelmann, Stefan Pilz, Wilfried Renner, Hubert Scharnagl, Tat-jana Stojakovic, Andreas Tomaschitz, Karl Winkler

MIGen: Benjamin F. Voight, Kiran Musunuru, Candace Guiducci,Noel Burtt, Stacey B. Gabriel, David S. Siscovick, Christopher J.O’Donnell, Roberto Elosua, Leena Peltonen, Veikko Salomaa, StephenM. Schwartz, Olle Melander, David Altshuler, Sekar Kathiresan

OHGS: Alexandre F. R. Stewart, Li Chen, Sonny Dandona,George A. Wells, Olga Jarinova, Ruth McPherson, Robert Roberts

PennCATH/MedStar: Muredach P. Reilly, Mingyao Li, LimingQu, Robert Wilensky, William Matthai, Hakon H. Hakonarson, JoeDevaney, Mary Susan Burnett, Augusto D. Pichard, Kenneth M.Kent, Lowell Satler, Joseph M. Lindsay, Ron Waksman, ChristopherW. Knouff, Dawn M. Waterworth, Max C. Walker, Vincent Mooser,Stephen E. Epstein, Daniel J. Rader

WTCCC: Nilesh J. Samani, John R. Thompson, Peter S. Braund,Christopher P. Nelson, Benjamin J. Wright, Anthony J. Balmforth,Stephen G. Ball, Alistair S. Hall (members of the WTCCC are listedin the online-only Data Supplement)

Sources of FundingThe ADVANCE study was supported by a grant from the Reynold’sFoundation and National Heart, Lung, and Blood Institute (NHLBI)grant HL087647. Genetic analyses of CADomics were supported bya research grant from Boehringer Ingelheim. Recruitment and analysisof the CADomics cohort was supported by grants from BoehringerIngelheim and PHILIPS Medical Systems; by the Government ofRheinland-Pfalz in the context of the “Stiftung Rheinland-Pfalz furInnovation”; by the research program “Wissen schafft Zukunft”; by theJohannes-Gutenberg University of Mainz in the context of the “Schw-erpunkt Vaskulare Pravention” and the “MAIFOR grant 2001”; and bygrants from the Fondation de France, the French Ministry of Research,and the Institut National de la Sante et de la Recherche Medicale.

The deCODE CAD/MI Study was sponsored by National Institutes ofHealth (NIH) NHLBI grant R01HL089650–02. The GerMIFS I-III(KORA) were supported by the Deutsche Forschungsgemeinschaft andthe German Federal Ministry of Education and Research (BMBF) in thecontext of the German National Genome Research Network (NGFN-2and NGFN-plus) and the European Union (EU)-funded integratedproject Cardiogenics (LSHM-CT-2006–037593). The KORA researchplatform was initiated and financed by the GSF-National ResearchCentre for Environment and Health, which is funded by the GermanFederal Ministry of Education and Research and of the State of Bavaria.LURIC has received funding from the EU framework 6-funded Inte-grated Project “Bloodomics” (LSHM-CT-2004–503485); from the EUframework 7-funded Integrated Project AtheroRemo (HEALTH-F2–2008-201668); and from Sanofi/Aventis, Roche, Dade Behring/Sie-mens, and AstraZeneca. The MIGen study was funded by the US NIHand NHLBI’s STAMPEED genomics research program, and genotyp-ing was partially funded by The Broad Institute Center for Genotypingand Analysis, which is supported by grant U54 RR020278 from theNational Center for Research Resources. Recruitment of PennCATHwas supported by the Cardiovascular Institute of the University ofPennsylvania. Recruitment of the MedStar sample was supported in partby the MedStar Research Institute and the Washington Hospital Centerand a research grant from GlaxoSmithKline. Genotyping of PennCATHand MedStar was performed at the Center for Applied Genomics at theChildren’s Hospital of Philadelphia and supported by GlaxoSmithKline




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through an Alternate Drug Discovery Initiative research alliance award(to Drs Reilly and Rader) with the University of Pennsylvania School ofMedicine. The OHGS was supported by Canadian Institutes of HealthResearch (CIHR) #MOP82810 (to Dr Roberts), Canadian Funds forInnovation (CFI) #11966 (to Dr Roberts), Heart and Stroke Foundationof Ontario #NA6001 (to Dr McPherson), CIHR #MOP172605 (to DrMcPherson), and CIHR #MOP77682 (to Dr Stewart). The WTCCCstudy was funded by the Wellcome Trust. Recruitment of cases for theWTCCC study was carried out by the British Heart Foundation (BHF)Family Heart Study Research Group and supported by the BHF and theUK Medical Research Council. Dr Samani holds a chair funded by theBHF. Dr Samani also is supported by the Leicester National Institute ofHealth Research Biomedical Research Unit in Cardiovascular Disease.

DisclosuresDr Absher reports receiving an NIH research grant for the ADVANCEstudy. Dr Assimes reports receiving an NIH research grant for theADVANCE study. Dr Blankenberg reports receiving research grantsfrom NGFNplus for Atherogenomics and from BMBF for CADomics.Dr Boerwinkle received research support from NIH/National HumanGenome Research Institute (NHGRI), GWA for gene-environmentinteraction effects influencing CGD; NIH/NHLBI, Molecular epidemi-ology of essential hypertension; NIH/NHLBI, Genome-wide associationfor loci influencing coronary heart disease; NIH/NHLBI, Genetics ofhypertension-associated treatment; NIH/NHLBI, Modeling DNA diver-sity in reverse cholesterol transport; NIH/NHLBI, 20-year changes infitness and cardiovascular disease risk; NIH/NHLBI, Genetic epidemi-ology of sodium-lithium countertransport; NIH/National Institute ofGeneral Medical Sciences (NIGMS), Pharmacogenomic evaluation ofantihypertensive responses; NIH/NIGMS, Genomic approaches to com-mon chronic disease; NIH/NHLBI, Genes of the CYP450-derivedeicosanoids in subclinical atherosclerosis; NIH/NHGRI-University ofNorth Carolina, Chapel Hill, Genetic epidemiology of causal variantsacross the life course; and NIH/NHLBI, Building on GWAS forNHLBI-diseases: the CHARGE consortium. Dr Cupples reports receiv-ing research grants from NIH/NHLBI, The Framingham Heart Study;NIH/NHLBI, Genome-wide association study of cardiac structure andfunction; NIH/NHLBI, Functional evaluation of GWAS loci for cardio-vascular intermediate phenotypes; and NIH/NHLBI, Building onGWAS for NHLBI-diseases: the CHARGE consortium. Dr Halperinreports receiving research grants from NIH, subcontract Genome-wideassociation study of Non Hodgkin’s lymphoma; ISF, Efficient designand analysis of disease association studies; EU, consultant AtheroRemo;NSF, Methods for sequencing based associations; BSF, Searching forcausal genetic variants in breast cancer and honoraria from ScrippsInstitute, UCLA. Dr Halperin also reports ownership interest in Navi-genics. Dr Hengstenberg reports receiving research grants for EUCardiogenics. 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 serviceprovided by deCODE Genetics; and employment with deCODE Genet-ics. Dr Li reports receiving research grant R01HG004517 and otherresearch support in the form of coinvestigator on several NIH-fundedgrants and receiving honoraria from National Cancer Institute Divisionof Cancer Epidemiology and Genetics. Dr McPherson reports receivingresearch grants from Heart & Stroke Funds Ontario, CIHR, and CFI. DrRader reports receiving research grant support from GlaxoSmithKline.Dr Roberts reports receiving research grants from the Cystic FibrosisFoundation, NIH, and Cancer Immunology and Hematology Branch;membership on the speakers bureau for AstraZeneca; receiving hono-raria from Several; and serving as consultant/advisory board member forCelera. 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 researchsupport from CFI: Infrastructure support; and honoraria from theInstitute for Biomedical Sciences, Academia Sinica, Taipei, Taiwan. DrThorleifsson is an employee of deCODE Genetics. Dr Thorsteinsdottirreports receiving research grants from NIH and EU; serving as an expertwitness for a US trial; having stock options at deCODE Genetics; andhaving employment with deCODE Genetics. Dr Kathiresan reportsreceiving research grants from Pfizer, Discovery of type 2 diabetes

genes, and Alnylam, Function of new lipid genes, and serving asconsultant/advisory board member for DAIICHI SANKYO Merck. DrReilly reports receiving research grant support from GlaxoSmithKline.Dr Schunkert reports receiving research grants from the EU, projectCardiogenics; 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 noconflicts to disclose.

Genotyping of PennCATH and MedStar was supported by Glaxo-SmithKline. Dawn M. Waterworth, Max C. Walker, and VincentMooser are employees of GlaxoSmithKline. PennCath/MedStarinvestigators acknowledge the support of Eliot Ohlstein, Dan Burnsand Allen Roses at GlaxoSmithKline.

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CLINICAL PERSPECTIVEDespite the recent progress in identification of coronary artery disease (CAD)/myocardial infarction genes, only a relativelylimited fraction (�10%) of the overall genetic risk (heritability) of the disease is explained by the currently identified loci.One part of the explanation is likely to be the limited power of individual genome-wide association studies to detect suchloci. The global Coronary ARtery DIsease Genome-Wide Replication And Meta-analysis (CARDIoGRAM) consortiumwill now analyze genome-wide information from �22 000 cases of CAD and �60 000 controls, and this will undoubtedlyidentify additional loci harboring common variants affecting CAD risk. Indeed, we anticipate a wealth of new informationon heritable aspects of CAD and its risk factors, which likely will open multiple opportunities for scientific exploration.However, such a large experiment requires careful prospective planning of the methodology used. Here we describe howsuch a meta-analysis, including a replication study, could be conducted. In conclusion, CARDIoGRAM is a novel andpowerful consortium poised to contribute to the understanding of common genetic variation affecting the risk for CAD andmyocardial infarction. This information then can be used to derive mechanistic information on biological processes as wellas used to identify potential targets for therapeutic intervention.




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on behalf of the CARDIoGRAM ConsortiumMuredach P. Reilly, Nilesh J. Samani and Heribert Schunkert

Thorleifsson, Benjamin F. Voight, George A. Wells, Andreas Ziegler, Sekar Kathiresan,Rader, Robert Roberts, Arne Schillert, Kari Stefansson, Alexandre F.R. Stewart, Gudmar

Susan Burnett, Stephen E. Epstein, Christopher J. O'Donnell, Thomas Quertermous, Daniel J.Mingyao Li, Winfried März, Ruth McPherson, Kiran Musunuru, Christopher P. Nelson, Mary

Alistair S. Hall, Eran Halperin, Christian Hengstenberg, Hilma Holm, Reijo Laaksonen,Themistocles L. Assimes, Stefan Blankenberg, Eric Boerwinkle, Li Chen, L. Adrienne Cupples,

Michael Preuss, Inke R. König, John R. Thompson, Jeanette Erdmann, Devin Absher,Than 22 000 Cases and 60 000 Controls

(CARDIoGRAM) Study: A Genome-Wide Association Meta-analysis Involving More Design of the Coronary ARtery DIsease Genome-Wide Replication And Meta-Analysis

Print ISSN: 1942-325X. Online ISSN: 1942-3268 Copyright © 2010 American Heart Association, Inc. All rights reserved.

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SUPPLEMENTAL MATERIAL

Design of the Coronary ARtery DIsease Genome-wide Replication And Meta-Analysis

(CARDIoGRAM) Study – A genome-wide association meta-analysis involving more

than 22,000 cases and 60,000 controls

Authors

Michael Preuss*1,2, Dipl.-Inform. Med., Inke R. König*1, Prof. Dr. rer. biol. hum., John R.

Thompson*3, PhD, Jeanette Erdmann*2, Prof. Dr. rer. nat., Devin Absher4, PhD,

Themistocles L. Assimes5, MD, PhD, Stefan Blankenberg6, Prof. Dr. med., Eric Boerwinkle7,

PhD, Li Chen8, MSc, L. Adrienne Cupples9,10, PhD, Alistair S. Hall11, MD, ChB, PhD, FRCP,

Eran Halperin12, PhD, Christian Hengstenberg13, Prof. Dr. med., Hilma Holm14, MD, Reijo

Laaksonen15, MD, PhD, Mingyao Li16, PhD, Winfried März17,18,19, Prof. Dr. med., Ruth

McPherson8, MD, PhD, FRCPC, Kiran Musunuru20,21,22, MD, PhD, MPH, Christopher P.

Nelson3, MSc, PhD, Mary Susan Burnett23, PhD, Stephen E. Epstein23, MD, Christopher J.

O´Donnell24, MD, MPH, Thomas Quertermous5, MD, Daniel J. Rader25,26, MD, Robert

Roberts8, MD, FRCP(C), MACC, Arne Schillert1, Dipl.-Biomath., Kari Stefansson14,27, MD,

PhD, Alexandre F. R. Stewart8, PhD, Gudmar Thorleifsson14, PhD, Unnur

Thorsteinsdottir14,27, PhD, Benjamin F. Voight21,22,28, PhD, George A. Wells8, MSc, PhD,

Andreas Ziegler1, Prof. Dr. rer. nat., Sekar Kathiresan^20,21,22, MD, Muredach P. Reilly^25,26,

MBBCH, MSCE, Nilesh J. Samani^29, FMedSci., Heribert Schunkert^2, Prof. Dr. med., on

behalf of the CARDIoGRAM Consortium

The CARDIoGRAM Consortium

Executive Committee: Heribert Schunkert2, Nilesh J. Samani29, Sekar Kathiresan20,21,22,

Muredach P. Reilly25,26

Executive Secretary: Jeanette Erdmann2

Steering Committee: Eric Boerwinkle7, Jeanette Erdmann2, Alistair Hall11, Christian

Hengstenberg13, Sekar Kathiresan20,21,22, Inke R. König1, Reijo Laaksonen15, Ruth

McPherson8, Themistocles L. Assimes5, Muredach P. Reilly25,26, Nilesh J. Samani29, Heribert

Schunkert2, John R. Thompson3, Unnur Thorsteinsdottir14,27, Andreas Ziegler1

Statisticians: Inke R. König1 (chair), John R. Thompson3 (chair), Devin Absher4, Li Chen8, L.

Adrienne Cupples9,10, Eran Halperin12, Mingyao Li16, Kiran Musunuru20,21,22, Michael

Preuss1,2, Arne Schillert1, Gudmar Thorleifsson14, Benjamin F. Voight21,22,28, George A. Wells8

ADVANCE: Devin Absher4, Themistocles L. Assimes5, Stephen Fortmann5, Alan Go30, Mark

Hlatky5, Carlos Iribarren30, Joshua Knowles5, Richard Myers4, Thomas Quertermous5, Steven

Sidney30, Neil Risch31, Hua Tang32

CADomics: Stefan Blankenberg6, Tanja Zeller6, Arne Schillert1, Philipp Wild6, Andreas

Ziegler1, Renate Schnabel6, Christoph Sinning6, Karl Lackner33, Laurence Tiret34, Viviane

Nicaud34, Francois Cambien34, Christoph Bickel6, Hans J. Rupprecht6, Claire Perret34, Carole

Proust34, Thomas Münzel6

CHARGE: Maja Barbalic35, Joshua Bis36, Eric Boerwinkle7, Ida Yii-Der Chen37, L. Adrienne

Cupples9,10, Abbas Dehghan38, Serkalem Demissie-Banjaw39, Aaron Folsom40, Nicole

Glazer41, Vilmundur Gudnason42, Tamara Harris43, Susan Heckbert44, Daniel Levy10, Thomas

Lumley45, Kristin Marciante46, Alanna Morrison47, Christopher J. O´Donnell24, Bruce M.

Psaty48, Kenneth Rice45, Jerome I. Rotter37, David S. Siscovick49, Nicholas Smith44, Albert

Smith50, Kent D. Taylor37, Cornelia van Duijn38, Kelly Volcik47, Jaqueline Whitteman38, Vasan

Ramachandran51, Albert Hofman52, Andre Uitterlinden52

deCODE: Solveig Gretarsdottir14, Jeffrey R. Gulcher14, Hilma Holm14, Augustine Kong14, Kari

Stefansson14,27, Gudmundur Thorgeirsson53,27, Karl Andersen53,27, Gudmar Thorleifsson14,

Unnur Thorsteinsdottir14,27

GERMIFS I and II: Jeanette Erdmann2, Marcus Fischer13, Anika Grosshennig1,2, Christian

Hengstenberg13, Inke R. König1, Wolfgang Lieb54, Patrick Linsel-Nitschke2, Michael Preuss1,2,

Klaus Stark13, Stefan Schreiber55, H.-Erich Wichmann56,57, Andreas Ziegler1, Heribert

Schunkert2

GERMIFS III (KORA): Zouhair Aherrahrou2, Petra Bruse2, Angela Doering56, Jeanette

Erdmann2, Christian Hengstenberg13, Thomas Illig56, Norman Klopp56, Inke R. König1, Patrick

Linsel-Nitschke2, Christina Loley1,2, Anja Medack2, Christina Meisinger56, Thomas

Meitinger58,59, Janja Nahrstedt1,2, Annette Peters56, Michael Preuss1,2, Klaus Stark13, Arnika

K. Wagner2, H.-Erich Wichmann56,57, Christina Willenborg1,2, Andreas Ziegler1, Heribert

Schunkert2

LURIC/AtheroRemo: Bernhard O. Böhm60, Harald Dobnig61, Tanja B. Grammer17, Eran

Halperin12, Michael M. Hoffmann62, Marcus Kleber63, Reijo Laaksonen15, Winfried März17,18,19,

Andreas Meinitzer18, Bernhard R. Winkelmann64, Stefan Pilz61, Wilfried Renner18, Hubert

Scharnagl18, Tatjana Stojakovic18, Andreas Tomaschitz61, Karl Winkler62

MIGen: Benjamin F. Voight21,22,28, Kiran Musunuru20,21,22, Candace Guiducci22, Noel Burtt22,

Stacey B. Gabriel22, David S. Siscovick49, Christopher J. O’Donnell24, Roberto Elosua65,

Leena Peltonen66, Veikko Salomaa67, Stephen M. Schwartz49, Olle Melander68, David

Altshuler69, Sekar Kathiresan20,21,22

OHGS: Alexandre F. R. Stewart8, Li Chen8, Sonny Dandona8, George A. Wells8, Olga

Jarinova8, Ruth McPherson8, Robert Roberts8

PennCATH/MedStar: Muredach P. Reilly25,26, Mingyao Li16, Liming Qu16, Robert Wilensky26,

William Matthai26, Hakon H. Hakonarson70, Joe Devaney23, Mary Susan Burnett23, Augusto

D. Pichard23, Kenneth M. Kent23, Lowell Satler23, Joseph M. Lindsay23, Ron Waksman23,

Christopher W. Knouff71, Dawn M. Waterworth71, Max C. Walker71, Vincent Mooser71,

Stephen E. Epstein23, Daniel J. Rader25,26

WTCCC: Nilesh J. Samani29, John R. Thompson3, Peter S. Braund29, Christopher P. Nelson3,

Benjamin J. Wright3, Anthony J. Balmforth11, Stephen G. Ball11, Alistair S. Hall11, Wellcome

Trust Case Control Consortium*

Affiliations

1 Institut für Medizinische Biometrie und Statistik, Universität zu Lübeck, Lübeck, Germany; 2

Medizinische Klinik II, Universität zu Lübeck, Lübeck, Germany; 3 Department of Health

Sciences, University of Leicester, Leicester, UK; 4 Hudson Alpha Institute, Huntsville,

Alabama, USA; 5 Department of Medicine, Stanford University School of Medicine, Stanford,

CA, USA; 6 Medizinische Klinik und Poliklinik, Johannes-Gutenberg Universität Mainz,

Universitätsmedizin, Mainz, Germany; 7 University of Texas Health Science Center, Human

Genetics Center and Institute of Molecular Medicine, Houston, TX, USA; 8 The John &

Jennifer Ruddy Canadian Cardiovascular Genetics Centre, University of Ottawa, Ottawa,

Canada; 9 Department of Biostatistics and Epidemiology, Boston University, USA; 10

National Heart Lung and Blood Institute, Framingham Heart Study, Framingham, MA, USA;

11 LIGHT Research Institute, Faculty of Medicine and Health, University of Leeds, Leeds,

UK; 12 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; 13 Klinik und Poliklinik für Innere Medizin II,

Universität Regensburg, Regensburg, Germany; 14 deCODE Genetics, 101 Reykjavik,

Iceland; 15 Science Center, Tampere University Hospital, Tampere, Finland; 16 Biostatistics

and Epidemiology, University of Pennsylvania, Philadelphia, PA, USA; 17 Synlab Center of

Laboratory Diagnostics Heidelberg, Heidelberg, Germany; 18 Clinical Institute of Medical and

Chemical Laboratory Diagnostics, Medical University of Graz, Graz, Austria; 19 Institute of

Public Health, Social and Preventive Medicine, Medical Faculty Manneim, University of

Heidelberg, Germany; 20 Cardiovascular Research Center and Cardiology Division,

Massachusetts General Hospital, Boston, MA, USA; 21 Center for Human Genetic Research,

Massachusetts General Hospital, Boston, MA, USA; 22 Program in Medical and Population

Genetics, Broad Institute of Harvard and Massachusetts Institute of Technology (MIT),

Cambridge, MA, USA; 23 Cardiovascular Research Institute, MedStar Research Institute,

Washington Hospital Center, Washington, DC, USA; 24 National Heart, Lung and Blood

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

Massachusetts General Hospital, Boston, MA, USA; 25 The Institute for Translational

Medicine and Therapeutics, School of Medicine, University of Pennsylvania, Philadelphia,

PA, USA; 26 The Cardiovascular Institute, University of Pennsylvania, Philadelphia, PA,

USA; 27 University of Iceland, Faculty of Medicine, 101 Reykjavik, Iceland; 28 Department of

Medicine, Harvard Medical School, Boston, MA, USA; 29 Department of Cardiovascular

Sciences, University of Leicester, Glenfield Hospital, Leicester, UK; 30 Division of Research,

Kaiser Permanente, Oakland, CA, USA; 31 Institute for Human Genetics, University of

California, San Francisco, San Francisco, CA, USA; 32 Department of Genetics, Stanford

University School of Medicine, Stanford, CA, USA; 33 Institut für Klinische Molekularbiologie,

Christian-Albrechts Universität, Kiel, Germany; 34 INSERM UMRS 937, Pierre and Marie

Curie University (UPMC, Paris 6) and Medical School, Paris, France; 35 University of Texas,

Houston, TX, USA; 36 University of Washington, Department of Medicine, Seattle, WA, USA;

37 Cedars-Sinai Medical Center, Medical Genetics Institute, Los Angeles, CA, USA; 38

Erasmus Medical Center, Department of Epidemiology, Rotterman, The Netherlands; 39

Boston University, School of Public Health, Boston, MA, USA; 40 University of Minnesota,

Division of Epidemiology and Community Health, Minneapolis, MN, USA; 41 University of

Washington, Cardiovascular Health Research Unit and Department of Medicine, Seattle,

WA, USA; 42 Icelandic Heart Association/University of Iceland, Research Institute/Faculty of

Medicine, Kopavogur/Reykjavik, Iceland; 43 National Institute on Aging, Intramural Research

Program, Laboratory of Epidemiology, Demography, and Biometry, Bethesda, MD, USA; 44

University of Washington, Department of Epidemiology, Seattle, WA, USA; 45 University of

Washington, Department of Biostatistics, Seattle, WA, USA; 46 University of Washington,

Department of Internal Medicine, Seattle, WA, USA; 47 University of Texas, School of Public

Health, Houston, TX, USA; 48 Center for Health Studies, Group Health, Departments of

Medicine, Epidemiology, and Health Services, Seattle, WA, USA; 49 Cardiovascular Health

Research Unit, Departments of Epidemiology and General Medicine, University of

Washington, Seattle, WA, USA; 50 Icelandic Heart Association Research Institute,

Kopavogur, Iceland; 51 Boston University Medical Center, Boston, MA, USA; 52 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 Chemie und

Laboratoriumsmediizin, Johannes-Gutenberg Universität Mainz, Universitätsmedizin, Mainz,

Germany; 56 Institute of Epidemiology, Helmholtz Zentrum München – German Research

Center for Environmental Health, Neuherberg, Germany; 57 Institute of Medical Information

Science, Biometry and Epidemiology, Ludwig-Maximilians-Universität München, Germany;

58 Institut für Humangenetik, Helmholtz Zentrum München, Deutsches Forschungszentrum

für Umwelt und Gesundheit, Neuherberg, Germany; 59 Institut für Humangenetik,

Technische Universität München, Germany; 60 Division of Endocrinology and Diabetes,

Graduate School of Molecular Endocrinology and Diabetes, University of Ulm, Ulm,

Germany; 61 Division of Endocrinology, Department of Medicine, , Medical University of

Graz, Austria; 62 Division of Clinical Chemistry, Department of Medicine, Albert Ludwigs

University, Freiburg, Germany; 63 LURIC non profit LLC, Freiburg, Germany; 64 Cardiology

Group Frankfurt-Sachsenhausen, Frankfurt, Germany; 65 Cardiovascular Epidemiology and

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

Salud Pública (CIBERSP), Spain; 66 The Wellcome Trust Sanger Institute, The Wellcome

Trust Genome Campus, Hinxton, Cambridge, UK; 67 Chronic Disease Epidemiology Unit,

Department of Health Promotion and Chronic Disease Prevention, National Public Health

Institute, Helsinki, Finland; 68 Department of Clinical Sciences, Hypertension and

Cardiovascular Diseases, University Hospital Malmö, Lund University, Malmö, Sweden; 69

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

General Hospital, Harvard Medical School, Boston, USA; 70 The Center for Applied

Genomics, Children’s Hospital of Philadelphia, Philadelphia, Pennsylvania, USA; 71

Genetics Division and Drug Discovery, GlaxoSmithKline, King of Prussia, Pennsylvania

19406, USA

Supplementary Table 1. Description of the genotyping in participating studies.

Study Platform Calling Genotyped SNPs Imputation

algorithms / NCBI

build / HapMap

Total SNPs QC at study center

ADVANCE Illumina 550k v3 BeadStudio 561,466 BIMBAM / 36 / r22a 3,732,514 Sample call rate

>0.985

SNP call rate >0.95

HWE p >0.001

CADomics Affymetrix Genome-Wide

Human SNP Array 6.0

Birdseed 602,459 IMPUTE / 36 / r22a 2,588,156 Sample call rate >0.97

SNP call rate >0.98

HWE p(controls) >10-4

MAF >0.01

CHARGE* Illumina HCNV370 Duo

BeadChip

BeadStudio 353,202 MACH/36/ r22 2,533,153 Sample call rate >0.97

SNP call rate >0.97


MAF >0.01

Supplementary Table 1 continued.


algorithms / NCBI

build / HapMap


CHARGE* Affymetrix 6.0 Birdseed 589,253 MACH/35/r21 2,516,204 Sample call rate >0.95

SNP call rate >0.90

HWE p>10-6

Affymetrix 500K (Nsp 250K

and Sty 250K) + MPS 50k

BRLMM 534,982 MACH/36/ r22, 2,543,887 Sample call rate >0.97

SNP call rate >0.97

Subject heterozygosity

<5 SD from mean

No excessive

Mendelian errors

Illumina Infinium

HumanHap 550K

BeadStudio 530,683 MACH/36/ r22, 2,586,725 Subject call rate

>0.975, heterozygosity

<0.336, match on sex,

no IBS outliers, SNP

call rate >=0.98, HWE

p >10-6, MAF >0.01



algorithms / NCBI

build / HapMap


deCODE Illumina HH300/HHCNV370 BeadStudio Impute / 36 Sample call rate >0.98

SNP call rate >0.96

GerMIFS I Affymetrix Mapping 500K

Array Set

BRLMM 262,338(NSP)/

238,378(STY)

MACH / 36 / r22a 2,543,887 Sample call rate >0.97

SNP call rate >0.98


MAF >0.01

GerMIFS II Affymetrix Genome-Wide

Human SNP Array 6.0

Birdseed 909,622 MACH / 36 / r22a 2,543,887 Sample call rate >0.97

SNP call rate >0.98


MAF >0.01

GerMIFS III

(KORA)

Affymetrix Genome-Wide

Human SNP Array 5.0 / 6.0

BRLMM-P 503,590(5.0)/

904,954(6.0)


SNP call rate >0.98


MAF >0.01



algorithms / NCBI

build / HapMap


LURIC/

AtheroRemo 1


Human SNP Array 6.0

Birdseed 905,484 NA 905,484 SNP call rate >0.9†

LURIC/

AtheroRemo 2

Affymetrix Mapping 500K

Array Set

DM-3 492,555 NA 492,555 SNP call rate >0.9†

MedStar Affymetrix Genome-Wide

Human SNP Array 6.0


SNP call rate >0.95

HWE – NA

MAF – NA

MIGen Affymetrix Genome-Wide

Human SNP Array 6.0

Birdseed 727,496 MACH / 35 2,557,744 Sample call rate >0.95

SNP call rate >0.95


MAF >0.01

OHGS Affymetrix Mapping 500K

Array Set / Genome-Wide

Human SNP Array 6.0

BRLMM/

Birdseed

325,040 Impute / 36 / r22 2,469,454 SNP call rate >0.95


MAF >0.05



algorithms / NCBI

build / HapMap


PennCATH Affymetrix Genome-Wide

Human SNP Array 6.0


SNP call rate >0.95

HWE – NA

MAF – NA

WTCCC Affymetrix Mapping 500K

Array Set

CHIAMO 477,459 IMPUTE / 36 2,614,446 Sample call rate >0.97

SNP call rate >0.98


MAF - NA

ADVANCE = Atherosclerotic Disease, VAscular functioN, and genetiC Epidemiology; GerMIFS = German Myocardial Infarction Family Studies; WTCCC = Wellcome Trust Case

Control Consortium; CHARGE = Cohorts for Heart and Aging Research in Genomic Epidemiology; LURIC = Ludwigshafen Risk and Cardiovascular Heath Study; OHGS = Ottawa

Heart Genomics Study

QC = quality control; HWE = test for deviation from Hardy-Weinberg equilibrium; MAF = minor allele frequency

* Four entries for Charge refer to the studies AGES, ARIC, Fram HS, and RS

† Further criteria in LURIC/AtheroRemo: Removed samples based on relatedness, incorrect gender, outliers in the MDS map

SUPPLEMENTAL MATERIAL

Design of the Coronary ARtery DIsease Genome-wide Replication And Meta-Analysis

(CARDIoGRAM) Study – A genome-wide association meta-analysis involving more

than 22,000 cases and 60,000 controls

Authors

Michael Preuss*1,2, Dipl.-Inform. Med., Inke R. König*1, Prof. Dr. rer. biol. hum., John R.

Thompson*3, PhD, Jeanette Erdmann*2, Prof. Dr. rer. nat., Devin Absher4, PhD,

Themistocles L. Assimes5, MD, PhD, Stefan Blankenberg6, Prof. Dr. med., Eric Boerwinkle7,

PhD, Li Chen8, MSc, L. Adrienne Cupples9,10, PhD, Alistair S. Hall11, MD, ChB, PhD, FRCP,

Eran Halperin12, PhD, Christian Hengstenberg13, Prof. Dr. med., Hilma Holm14, MD, Reijo

Laaksonen15, MD, PhD, Mingyao Li16, PhD, Winfried März17,18,19, Prof. Dr. med., Ruth

McPherson8, MD, PhD, FRCPC, Kiran Musunuru20,21,22, MD, PhD, MPH, Christopher P.

Nelson3, MSc, PhD, Mary Susan Burnett23, PhD, Stephen E. Epstein23, MD, Christopher J.

O´Donnell24, MD, MPH, Thomas Quertermous5, MD, Daniel J. Rader25,26, MD, Robert

Roberts8, MD, FRCP(C), MACC, Arne Schillert1, Dipl.-Biomath., Kari Stefansson14,27, MD,

PhD, Alexandre F. R. Stewart8, PhD, Gudmar Thorleifsson14, PhD, Unnur

Thorsteinsdottir14,27, PhD, Benjamin F. Voight21,22,28, PhD, George A. Wells8, MSc, PhD,

Andreas Ziegler1, Prof. Dr. rer. nat., Sekar Kathiresan^20,21,22, MD, Muredach P. Reilly^25,26,

MBBCH, MSCE, Nilesh J. Samani^29, FMedSci., Heribert Schunkert^2, Prof. Dr. med., on

behalf of the CARDIoGRAM Consortium

The CARDIoGRAM Consortium

Executive Committee: Heribert Schunkert2, Nilesh J. Samani29, Sekar Kathiresan20,21,22,

Muredach P. Reilly25,26

Executive Secretary: Jeanette Erdmann2

Steering Committee: Eric Boerwinkle7, Jeanette Erdmann2, Alistair Hall11, Christian

Hengstenberg13, Sekar Kathiresan20,21,22, Inke R. König1, Reijo Laaksonen15, Ruth

McPherson8, Themistocles L. Assimes5, Muredach P. Reilly25,26, Nilesh J. Samani29, Heribert

Schunkert2, John R. Thompson3, Unnur Thorsteinsdottir14,27, Andreas Ziegler1

Statisticians: Inke R. König1 (chair), John R. Thompson3 (chair), Devin Absher4, Li Chen8, L.

Adrienne Cupples9,10, Eran Halperin12, Mingyao Li16, Kiran Musunuru20,21,22, Michael

Preuss1,2, Arne Schillert1, Gudmar Thorleifsson14, Benjamin F. Voight21,22,28, George A. Wells8

ADVANCE: Devin Absher4, Themistocles L. Assimes5, Stephen Fortmann5, Alan Go30, Mark

Hlatky5, Carlos Iribarren30, Joshua Knowles5, Richard Myers4, Thomas Quertermous5, Steven

Sidney30, Neil Risch31, Hua Tang32

CADomics: Stefan Blankenberg6, Tanja Zeller6, Arne Schillert1, Philipp Wild6, Andreas

Ziegler1, Renate Schnabel6, Christoph Sinning6, Karl Lackner33, Laurence Tiret34, Viviane

Nicaud34, Francois Cambien34, Christoph Bickel6, Hans J. Rupprecht6, Claire Perret34, Carole

Proust34, Thomas Münzel6

CHARGE: Maja Barbalic35, Joshua Bis36, Eric Boerwinkle7, Ida Yii-Der Chen37, L. Adrienne

Cupples9,10, Abbas Dehghan38, Serkalem Demissie-Banjaw39, Aaron Folsom40, Nicole

Glazer41, Vilmundur Gudnason42, Tamara Harris43, Susan Heckbert44, Daniel Levy10, Thomas

Lumley45, Kristin Marciante46, Alanna Morrison47, Christopher J. O´Donnell24, Bruce M.

Psaty48, Kenneth Rice45, Jerome I. Rotter37, David S. Siscovick49, Nicholas Smith44, Albert

Smith50, Kent D. Taylor37, Cornelia van Duijn38, Kelly Volcik47, Jaqueline Whitteman38, Vasan

Ramachandran51, Albert Hofman52, Andre Uitterlinden52

deCODE: Solveig Gretarsdottir14, Jeffrey R. Gulcher14, Hilma Holm14, Augustine Kong14, Kari

Stefansson14,27, Gudmundur Thorgeirsson53,27, Karl Andersen53,27, Gudmar Thorleifsson14,

Unnur Thorsteinsdottir14,27

GERMIFS I and II: Jeanette Erdmann2, Marcus Fischer13, Anika Grosshennig1,2, Christian

Hengstenberg13, Inke R. König1, Wolfgang Lieb54, Patrick Linsel-Nitschke2, Michael Preuss1,2,

Klaus Stark13, Stefan Schreiber55, H.-Erich Wichmann56,57, Andreas Ziegler1, Heribert

Schunkert2

GERMIFS III (KORA): Zouhair Aherrahrou2, Petra Bruse2, Angela Doering56, Jeanette

Erdmann2, Christian Hengstenberg13, Thomas Illig56, Norman Klopp56, Inke R. König1, Patrick

Linsel-Nitschke2, Christina Loley1,2, Anja Medack2, Christina Meisinger56, Thomas

Meitinger58,59, Janja Nahrstedt1,2, Annette Peters56, Michael Preuss1,2, Klaus Stark13, Arnika

K. Wagner2, H.-Erich Wichmann56,57, Christina Willenborg1,2, Andreas Ziegler1, Heribert

Schunkert2

LURIC/AtheroRemo: Bernhard O. Böhm60, Harald Dobnig61, Tanja B. Grammer17, Eran

Halperin12, Michael M. Hoffmann62, Marcus Kleber63, Reijo Laaksonen15, Winfried März17,18,19,

Andreas Meinitzer18, Bernhard R. Winkelmann64, Stefan Pilz61, Wilfried Renner18, Hubert

Scharnagl18, Tatjana Stojakovic18, Andreas Tomaschitz61, Karl Winkler62

MIGen: Benjamin F. Voight21,22,28, Kiran Musunuru20,21,22, Candace Guiducci22, Noel Burtt22,

Stacey B. Gabriel22, David S. Siscovick49, Christopher J. O’Donnell24, Roberto Elosua65,

Leena Peltonen66, Veikko Salomaa67, Stephen M. Schwartz49, Olle Melander68, David

Altshuler69, Sekar Kathiresan20,21,22

OHGS: Alexandre F. R. Stewart8, Li Chen8, Sonny Dandona8, George A. Wells8, Olga

Jarinova8, Ruth McPherson8, Robert Roberts8

PennCATH/MedStar: Muredach P. Reilly25,26, Mingyao Li16, Liming Qu16, Robert Wilensky26,

William Matthai26, Hakon H. Hakonarson70, Joe Devaney23, Mary Susan Burnett23, Augusto

D. Pichard23, Kenneth M. Kent23, Lowell Satler23, Joseph M. Lindsay23, Ron Waksman23,

Christopher W. Knouff71, Dawn M. Waterworth71, Max C. Walker71, Vincent Mooser71,

Stephen E. Epstein23, Daniel J. Rader25,26

WTCCC: Nilesh J. Samani29, John R. Thompson3, Peter S. Braund29, Christopher P. Nelson3,

Benjamin J. Wright3, Anthony J. Balmforth11, Stephen G. Ball11, Alistair S. Hall11, Wellcome

Trust Case Control Consortium*

Affiliations

1 Institut für Medizinische Biometrie und Statistik, Universität zu Lübeck, Lübeck, Germany; 2

Medizinische Klinik II, Universität zu Lübeck, Lübeck, Germany; 3 Department of Health

Sciences, University of Leicester, Leicester, UK; 4 Hudson Alpha Institute, Huntsville,

Alabama, USA; 5 Department of Medicine, Stanford University School of Medicine, Stanford,

CA, USA; 6 Medizinische Klinik und Poliklinik, Johannes-Gutenberg Universität Mainz,

Universitätsmedizin, Mainz, Germany; 7 University of Texas Health Science Center, Human

Genetics Center and Institute of Molecular Medicine, Houston, TX, USA; 8 The John &

Jennifer Ruddy Canadian Cardiovascular Genetics Centre, University of Ottawa, Ottawa,

Canada; 9 Department of Biostatistics and Epidemiology, Boston University, USA; 10

National Heart Lung and Blood Institute, Framingham Heart Study, Framingham, MA, USA;

11 LIGHT Research Institute, Faculty of Medicine and Health, University of Leeds, Leeds,

UK; 12 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; 13 Klinik und Poliklinik für Innere Medizin II,

Universität Regensburg, Regensburg, Germany; 14 deCODE Genetics, 101 Reykjavik,

Iceland; 15 Science Center, Tampere University Hospital, Tampere, Finland; 16 Biostatistics

and Epidemiology, University of Pennsylvania, Philadelphia, PA, USA; 17 Synlab Center of

Laboratory Diagnostics Heidelberg, Heidelberg, Germany; 18 Clinical Institute of Medical and

Chemical Laboratory Diagnostics, Medical University of Graz, Graz, Austria; 19 Institute of

Public Health, Social and Preventive Medicine, Medical Faculty Manneim, University of

Heidelberg, Germany; 20 Cardiovascular Research Center and Cardiology Division,

Massachusetts General Hospital, Boston, MA, USA; 21 Center for Human Genetic Research,

Massachusetts General Hospital, Boston, MA, USA; 22 Program in Medical and Population

Genetics, Broad Institute of Harvard and Massachusetts Institute of Technology (MIT),

Cambridge, MA, USA; 23 Cardiovascular Research Institute, MedStar Research Institute,

Washington Hospital Center, Washington, DC, USA; 24 National Heart, Lung and Blood

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

Massachusetts General Hospital, Boston, MA, USA; 25 The Institute for Translational

Medicine and Therapeutics, School of Medicine, University of Pennsylvania, Philadelphia,

PA, USA; 26 The Cardiovascular Institute, University of Pennsylvania, Philadelphia, PA,

USA; 27 University of Iceland, Faculty of Medicine, 101 Reykjavik, Iceland; 28 Department of

Medicine, Harvard Medical School, Boston, MA, USA; 29 Department of Cardiovascular

Sciences, University of Leicester, Glenfield Hospital, Leicester, UK; 30 Division of Research,

Kaiser Permanente, Oakland, CA, USA; 31 Institute for Human Genetics, University of

California, San Francisco, San Francisco, CA, USA; 32 Department of Genetics, Stanford

University School of Medicine, Stanford, CA, USA; 33 Institut für Klinische Molekularbiologie,

Christian-Albrechts Universität, Kiel, Germany; 34 INSERM UMRS 937, Pierre and Marie

Curie University (UPMC, Paris 6) and Medical School, Paris, France; 35 University of Texas,

Houston, TX, USA; 36 University of Washington, Department of Medicine, Seattle, WA, USA;

37 Cedars-Sinai Medical Center, Medical Genetics Institute, Los Angeles, CA, USA; 38

Erasmus Medical Center, Department of Epidemiology, Rotterman, The Netherlands; 39

Boston University, School of Public Health, Boston, MA, USA; 40 University of Minnesota,

Division of Epidemiology and Community Health, Minneapolis, MN, USA; 41 University of

Washington, Cardiovascular Health Research Unit and Department of Medicine, Seattle,

WA, USA; 42 Icelandic Heart Association/University of Iceland, Research Institute/Faculty of

Medicine, Kopavogur/Reykjavik, Iceland; 43 National Institute on Aging, Intramural Research

Program, Laboratory of Epidemiology, Demography, and Biometry, Bethesda, MD, USA; 44

University of Washington, Department of Epidemiology, Seattle, WA, USA; 45 University of

Washington, Department of Biostatistics, Seattle, WA, USA; 46 University of Washington,

Department of Internal Medicine, Seattle, WA, USA; 47 University of Texas, School of Public

Health, Houston, TX, USA; 48 Center for Health Studies, Group Health, Departments of

Medicine, Epidemiology, and Health Services, Seattle, WA, USA; 49 Cardiovascular Health

Research Unit, Departments of Epidemiology and General Medicine, University of

Washington, Seattle, WA, USA; 50 Icelandic Heart Association Research Institute,

Kopavogur, Iceland; 51 Boston University Medical Center, Boston, MA, USA; 52 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 Chemie und

Laboratoriumsmediizin, Johannes-Gutenberg Universität Mainz, Universitätsmedizin, Mainz,

Germany; 56 Institute of Epidemiology, Helmholtz Zentrum München – German Research

Center for Environmental Health, Neuherberg, Germany; 57 Institute of Medical Information

Science, Biometry and Epidemiology, Ludwig-Maximilians-Universität München, Germany;

58 Institut für Humangenetik, Helmholtz Zentrum München, Deutsches Forschungszentrum

für Umwelt und Gesundheit, Neuherberg, Germany; 59 Institut für Humangenetik,

Technische Universität München, Germany; 60 Division of Endocrinology and Diabetes,

Graduate School of Molecular Endocrinology and Diabetes, University of Ulm, Ulm,

Germany; 61 Division of Endocrinology, Department of Medicine, , Medical University of

Graz, Austria; 62 Division of Clinical Chemistry, Department of Medicine, Albert Ludwigs

University, Freiburg, Germany; 63 LURIC non profit LLC, Freiburg, Germany; 64 Cardiology

Group Frankfurt-Sachsenhausen, Frankfurt, Germany; 65 Cardiovascular Epidemiology and

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

Salud Pública (CIBERSP), Spain; 66 The Wellcome Trust Sanger Institute, The Wellcome

Trust Genome Campus, Hinxton, Cambridge, UK; 67 Chronic Disease Epidemiology Unit,

Department of Health Promotion and Chronic Disease Prevention, National Public Health

Institute, Helsinki, Finland; 68 Department of Clinical Sciences, Hypertension and

Cardiovascular Diseases, University Hospital Malmö, Lund University, Malmö, Sweden; 69

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

General Hospital, Harvard Medical School, Boston, USA; 70 The Center for Applied

Genomics, Children’s Hospital of Philadelphia, Philadelphia, Pennsylvania, USA; 71

Genetics Division and Drug Discovery, GlaxoSmithKline, King of Prussia, Pennsylvania

19406, USA

Supplementary Table 1. Description of the genotyping in participating studies.


algorithms / NCBI

build / HapMap


ADVANCE Illumina 550k v3 BeadStudio 561,466 BIMBAM / 36 / r22a 3,732,514 Sample call rate

>0.985

SNP call rate >0.95

HWE p >0.001

CADomics Affymetrix Genome-Wide

Human SNP Array 6.0

Birdseed 602,459 IMPUTE / 36 / r22a 2,588,156 Sample call rate >0.97

SNP call rate >0.98


MAF >0.01

CHARGE* Illumina HCNV370 Duo

BeadChip

BeadStudio 353,202 MACH/36/ r22 2,533,153 Sample call rate >0.97

SNP call rate >0.97


MAF >0.01



algorithms / NCBI

build / HapMap


CHARGE* Affymetrix 6.0 Birdseed 589,253 MACH/35/r21 2,516,204 Sample call rate >0.95

SNP call rate >0.90

HWE p>10-6

Affymetrix 500K (Nsp 250K

and Sty 250K) + MPS 50k

BRLMM 534,982 MACH/36/ r22, 2,543,887 Sample call rate >0.97

SNP call rate >0.97

Subject heterozygosity

<5 SD from mean

No excessive

Mendelian errors

Illumina Infinium

HumanHap 550K

BeadStudio 530,683 MACH/36/ r22, 2,586,725 Subject call rate

>0.975, heterozygosity

<0.336, match on sex,

no IBS outliers, SNP

call rate >=0.98, HWE

p >10-6, MAF >0.01



algorithms / NCBI

build / HapMap


deCODE Illumina HH300/HHCNV370 BeadStudio Impute / 36 Sample call rate >0.98

SNP call rate >0.96

GerMIFS I Affymetrix Mapping 500K

Array Set

BRLMM 262,338(NSP)/

238,378(STY)


SNP call rate >0.98


MAF >0.01

GerMIFS II Affymetrix Genome-Wide

Human SNP Array 6.0


SNP call rate >0.98


MAF >0.01

GerMIFS III

(KORA)


Human SNP Array 5.0 / 6.0

BRLMM-P 503,590(5.0)/

904,954(6.0)


SNP call rate >0.98


MAF >0.01



algorithms / NCBI

build / HapMap


LURIC/

AtheroRemo 1


Human SNP Array 6.0

Birdseed 905,484 NA 905,484 SNP call rate >0.9†

LURIC/

AtheroRemo 2

Affymetrix Mapping 500K

Array Set

DM-3 492,555 NA 492,555 SNP call rate >0.9†

MedStar Affymetrix Genome-Wide

Human SNP Array 6.0


SNP call rate >0.95

HWE – NA

MAF – NA

MIGen Affymetrix Genome-Wide

Human SNP Array 6.0

Birdseed 727,496 MACH / 35 2,557,744 Sample call rate >0.95

SNP call rate >0.95


MAF >0.01

OHGS Affymetrix Mapping 500K

Array Set / Genome-Wide

Human SNP Array 6.0

BRLMM/

Birdseed

325,040 Impute / 36 / r22 2,469,454 SNP call rate >0.95


MAF >0.05



algorithms / NCBI

build / HapMap


PennCATH Affymetrix Genome-Wide

Human SNP Array 6.0


SNP call rate >0.95

HWE – NA

MAF – NA

WTCCC Affymetrix Mapping 500K

Array Set

CHIAMO 477,459 IMPUTE / 36 2,614,446 Sample call rate >0.97

SNP call rate >0.98


MAF - NA

ADVANCE = Atherosclerotic Disease, VAscular functioN, and genetiC Epidemiology; GerMIFS = German Myocardial Infarction Family Studies; WTCCC = Wellcome Trust Case

Control Consortium; CHARGE = Cohorts for Heart and Aging Research in Genomic Epidemiology; LURIC = Ludwigshafen Risk and Cardiovascular Heath Study; OHGS = Ottawa

Heart Genomics Study

QC = quality control; HWE = test for deviation from Hardy-Weinberg equilibrium; MAF = minor allele frequency

* Four entries for Charge refer to the studies AGES, ARIC, Fram HS, and RS

† Further criteria in LURIC/AtheroRemo: Removed samples based on relatedness, incorrect gender, outliers in the MDS map

Documents

Design of the Coronary ARtery DIsease Genome-Wide ...circgenetics.ahajournals.org/content/circcvg/3/5/475.full.pdf · Design of the Coronary ARtery DIsease Genome-Wide Replication