Lack of association between the Trp719Arg polymorphism in kinesinlike protein 6 and coronary artery disease in 19 case-control studies
Themistocles L Assimes MD PhD1, Hilma Hólm MD2, Sekar Kathiresan MD 3-6, Muredach P Reilly
MD7,8, Gudmar Thorleifsson PhD2, Benjamin F Voight PhD4,5,10, Jeanette Erdmann PhD11, Christina
Willenborg11,12, Dhananjay Vaidya MBBS PhD MPH13, Changchun Xie PhD14 , Chris C Patterson
PhD15, Thomas M Morgan MD16, Mary-Susan Burnett PhD17, Mingyao Li PhD18, Mark A Hlatky MD1,
Joshua Knowles MD PhD1, John R Thompson PhD19, Devin Absher PhD20, Carlos Iribarren MD MPH PhD21,
Alan Go MD21, Stephen Fortmann MD 1, Steven Sidney MD21, Neil Risch PhD22, Hua Tang PhD23, Richard M
Myers PhD20, Klaus Berger MD24, Monika Stoll PhD25, Svati H. Shah MD MHS26, Gudmundur Thorgeirsson
MD PhD27, Karl Andersen MD PhD27, Aki S Havulinna MSc28, J. Enrique Herrera MS13, Nauder Faraday
MD29, Yoonhee Kim PhD30, Brian J. Kral MD MPH13, Rasika Mathias ScD13, Ingo Ruczinski PhD31, Bhoom
Suktitipat MD32, Alexander F Wilson PhD30, Lisa R. Yanek MPH13, Lewis C Becker MD13, Patrick LinselNitschke MD11, Wolfgang Lieb MD11, Inke R König PhD12, Christian Hengstenberg MD33, Marcus Fischer
MD33, Klaus Stark MD33, Wibke Reinhard MD33, Janina Winogradow33, Martina Grassl33, Anika
Grosshennig11,12, Michael Preuss11,12, Stefan Schreiber MD34 , H-Erich Wichmann MD35-37, Christa Meisinger
MD MPH35,38 , Jean Yee BS39,40, Yechiel Friedlander PhD41, Ron Do MSc42, Goran Berglund MD43, James B
Meigs MD MPH6,44 , Gordon Williams MD6,45 , David M Nathan MD6,46, Calum A MacRae MD PhD3,6, Liming
Qu18, Robert Wilensky MD7,8, William H. Matthai Jr. MD7, Atif Qasim MD8, Hakon H Hakonarson MD PhD47,
Augusto D Pichard MD17, Kenneth M Kent MD PhD17, Lowell Satler MD17, Joseph M Lindsay MD17, Ron
Waksman MD17, Christopher W Knouff48, Dawn M Waterworth PhD48, Max C Walker48, Vincent Mooser
MD48, Jaume Marrugat MD PhD49, Gavin Lucas49, Isaac Subirana49, Joan Sala50, Rafael Ramos51, Nicola
Martinelli MD52, Oliviero Olivieri MD52, Elisabetta Trabetti53, Giovanni Malerba53 , Pier Franco Pignatti53 ,
Aarti Surti5, Candace Guiducci5, Daniel Mirel5, Melissa Parkin5, Noel Burtt5, Stacey B Gabriel PhD5, Joel N
Hirschhorn MD PhD5,54, Rosanna Asselta PhD55, Stefano Duga PhD55, Kiran Musunuru MD PhD MPH3-6,
Mark J Daly PhD4-6, Shaun Purcell PhD4,5,56, Myocardial Infarction Genetics consortium68, Peter S Braund58,
1
Benjamin J Wright PhD19, Anthony J Balmforth PhD59, Stephen G Ball FRCP59, Wellcome Trust Case Control
Consortium68, Willem Ouwehand MD PhD60, Panos Deloukas PhD61, Michael Scholz62, Francois Cambien
MD63, Cardiogenics68, Andreas Huge25, Thomas Scheffold PhD64, Veikko Salomaa MD PhD28, Domenico
Girelli MD PhD52, Christopher B. Granger MD65, Leena Peltonen MD PhD5,61,66, Pascal P McKeown15,
David Altshuler MD PhD4-6,10,54 , Olle Melander MD PhD67, Joseph M Devaney PhD17, Stephen E Epstein
MD17, Daniel J Rader MD8,9, Roberto Elosua MD PhD49, James C Engert PhD42,68, Sonia Anand MD
PhD14, Alistair S Hall FRCP59, Andreas Ziegler PhD12, Christopher J O’Donnell MD MPH3,6,69, John A
Spertus MD MPh70, David Siscovick MD MPH39, Stephen M Schwartz MD PhD39,40, Diane Becker MPH
ScD13, Unnur Thorsteinsdottir PhD2,27, Kari Stefansson MD PhD2, 27, Heribert Schunkert MD11, Nilesh J
Samani F.Med.Sci. 58, Thomas Quertermous MD1
Members of the writing group for this manuscript are in bold (manuscript
preparation, analysts, and Principle Investigators from each of the 19 studies).
Running Title: KIF6 Trp719Arg and CAD in 19 case control studies
WORD COUNT: Text and References ~5200
List of Affiliations
1
Department of Medicine, Stanford University School of Medicine, Stanford, California 94305, USA.
deCODE Genetics, 101 Reykjavik, Iceland.
3
Cardiovascular Research Center and Cardiology Division, Massachusetts General Hospital, Boston,
Massachusetts 02114, USA.
4
Center for Human Genetic Research, Massachusetts General Hospital, Boston, Massachusetts 02114, USA.
5
Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, Massachusetts
02142, USA.
6
Department of Medicine, Harvard Medical School, Boston, Massachusetts 02115, USA.
7
The Cardiovascular Institute, University of Pennsylvania, Philadelphia, Pennsylvania 19104, USA
8
The Institute for Translational Medicine and Therapeutics, School of Medicine,
University of Pennsylvania, Philadelphia, Pennsylvania 19104, USA.
9
The Cardiovascular Institute, University of Pennsylvania, Philadelphia, Pennsylvania 19104, USA.
10
Department of Molecular Biology, Massachusetts General Hospital, Boston, Massachusetts, 02114, USA.
11
Medizinische Klinik II, Universität zu Lübeck, 23538 Lübeck, Germany.
12
Institut für Medizinische Biometrie und Statistik, Universität zu Lübeck, 23538 Lübeck, Germany.
13
Department of Medicine, The Johns Hopkins School of Medicine, Baltimore, MD, USA
2
2
14
Population Health Research Institute, Hamilton Health Sciences and Departments of Medicine and Clinical
Epidemiology and Biostatistics, McMaster University, Hamilton, L8L 2X2 Ontario, Canada.
15
Centre for Public Health, Queen’s University Belfast, Institute of Clinical Science, Belfast, BT126BJ,
Northern Ireland, UK.
16
Department of Pediatrics, Vanderbilt University School of Medicine, Nashville, Tennessee 37232, USA.
17
Cardiovascular Research Institute, MedStar Research Institute, Washington Hospital
Center, Washington, DC 20010, USA.
18
Biostatistics and Epidemiology, University of Pennsylvania, Philadelphia, Pennsylvania 19104, USA.
19
Department of Health Sciences, University of Leicester, Leicester, LE1 7RH, UK.
20
HudsonAlpha Institute for Biotechnology, Huntsville, Alabama 35806, USA
21
Division of Research, Kaiser Permanente, Oakland, CA, USA
22
Institute for Human Genetics, University of California, San Francisco, San Francisco, CA, USA
23
Department of Genetics, Stanford University School of Medicine, Stanford, CA, USA 94305
24
Institute of Epidemiology and Social Medicine, University Münster 48129 Münster, Germany.
25
Leibniz-Institute for Arteriosclerosis Research, University Münster, 48149 Münster, Germany.
26
Department of Medicine and Center for Human Genetics, Duke University Medical Center, Durham, NC,
USA
27
Faculty of Medicine, University of Iceland, and Department of Medicine, Landspitali University Hospital,101
Reykjavık, Iceland.
28
Chronic Disease Epidemiology Unit, Department of Health Promotion and Chronic Disease Prevention,
National Public Health Institute, Helsinki 00300, Finland.
29
Department of Anesthesia and Critical Care Medicine, The Johns Hopkins School of Medicine
30
Genometrics Section, Inherited Disease Research Branch, National Human Genome Research Institute,
National Institutes of Health,
31
Department of Biostatistics, The Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA.
32
Department of Epidemiology, The Johns Hopkins Bloomberg School of Public Health
33
Klinik und Poliklinik für Innere Medizin II,Universität Regensburg, 93042 Regensburg, Germany.
34
Institut für Klinische Molekularbiologie, Christian-Albrechts Universität, 24105 Kiel, Germany
35
Institute of Epidemiology, Helmholtz Zentrum München, German Research Center for Environmental Health,
85764 Neuherberg, Germany.
36
Institute of Medical Informatics, Biometry and Epidemiology, Ludwig-Maximilians-Universität Munich,
81377 Munich,Germany
37
Klinikum Grosshadern, Munich, Germany
38
Klinikum Augsburg, Germany
39
Cardiovascular Health Research Unit, Departments of Medicine and Epidemiology, University of
Washington, Seattle, Washington 98101, USA.
40
Department of Epidemiology, University of Washington, Seattle, Washington 98195, USA.
41
Unit of Epidemiology, Hebrew University-Hadassah School of Public Health, Jerusalem 91120, Israel.
42
Department of Human Genetics, McGill University, Montréal, Québec H3A 1A1, Canada.
43
Department of Clinical Sciences, Internal Medicine, University Hospital Malmö, Lund University, Malmö
20502, Sweden.
44
General Medicine Division, Department of Medicine, Massachusetts General Hospital, Boston,
Massachusetts, 02114, USA.
45
Cardiovascular Endocrinology Section, Division of Endocrinology, Diabetes, and Hypertension, Brigham and
Women’s Hospital, Boston, Massachusetts 02115, USA.
46
Diabetes Center, Massachusetts General Hospital, Boston, Massachusetts 02114, USA.
47
The Center for Applied Genomics,Children’s Hospital of Philadelphia, Philadelphia, Pennsylvania 19104,
USA.
48
Genetics Division and Drug Discovery, GlaxoSmithKline, King of Prussia, Pennsylvania 19406, USA
49
Cardiovascular Epidemiology and Genetics, Institut Municipal D’investigacio Medica, and CIBER
Epidemiologı´a y Salud Pu´ blica, 08003 Barcelona, Spain.
3
50
Servei de Cardiologia i Unitat Corona`ria, Hospital de Girona JosepTrueta and Institut de Investigacio´
Biomedica de Girona, 17007 Girona, Spain.
51
Unitat de Recerca i Unitat Docent de Medicina de Familia de Girona, IDIAP Jordi Gol,
Institut Catala` de la Salut, 08007 Barcelona, Spain.
52
Department of Medicine, University of Verona, 37134 Verona, Italy.
53
Department of Mother and Child, Biology and Genetics, Section of Biology and Genetics, University of
Verona, 37134 Verona, Italy.
54
Department of Genetics, Harvard Medical School, Boston, Massachusetts 02115, USA.
55
Department of Biology and Genetics for Medical Sciences, University of Milan, 20133 Milan, Italy.
56
Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge,
Massachusetts 02142, USA.
57
A full list of members is provided in the Supplementary Note online.
58
Department of Cardiovascular Sciences, University of Leicester, Glenfield Hospital, LE39QP, UK.
59
LIGHT Research Institute, Faculty of Medicine and Health, University of Leeds,
Leeds, LS1 3EX, UK.
60
Department of Haematology, University of Cambridge,Long Road, Cambridge, CB2 2PT, UK.
61
Wellcome Trust Sanger Institute, CambridgeCB10 1SA, UK.
62
Trium Analysis Online GmbH, 81677 Munich, Germany.
63
INSERM UMR_S 525, Universite Pierre et Marie Curie –Paris 6,
Paris 75634, France.
64
Institute for Heart and Circulation Research of the University of Witten/Herdecke, 44227 Dortmund,
Germany.
65
Department of Medicine and Duke Clinical Research Institute, Duke University Medical Center, Durham, NC,
USA
66
Institute for Molecular Medicine, University of Helsinki, Helsinki 00029, Finland.
67
Department of Clinical Sciences, Hypertension and Cardiovascular Diseases, University Hospital Malmö,
Lund University, Malmö 20502, Sweden.
68
Department of Medicine, McGill University, Montreal, H3A 1A1 Quebec, Canada.
69
National, Heart, Lung, and Blood Institute and its Framingham Heart Study, Framingham, Massachusetts
01702, USA.
70
Mid-America Heart Institute and University of Missouri-Kansas City, Kansas City, Missouri 64111, USA.
DISCLOSURES
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. Recruitment of the Medstar sample was supported by a
research grant from GlaxoSmithKline and genotyping of the PennCATH and Medstar samples was supported
by GlaxoSmithKline. Four co-authors of this study are employees of deCODE genetics, a for-profit company
that develops SNP based diagnostic tests for various diseases including coronary artery disease.
Address for Correspondence:
4
Themistocles L. Assimes, MD PhD
Falk Cardiovascular Research Building
Stanford University School of Medicine
Stanford, CA 94305-5406
Tel: 650-498-4154
Fax: 650-475-5650
Email: tassimes@stanford.edu
5
Abstract
Objectives
We sought to replicate the association between the kinesin-like protein 6 (KIF6) Trp719Arg polymorphism
(rs20455) and clinical coronary artery disease (CAD).
Background
Recent prospective studies suggest that carriers of the 719Arg allele in KIF6 are at increased risk of clinical
CAD compared with non-carriers.
Methods
The KIF6 Trp719Arg polymorphism (rs20455) was genotyped in nineteen case-control studies of non-fatal
CAD either as part of a genome-wide association study or in a formal attempt to replicate the initial positive
reports.
Results
Over 17 000 cases and 39 000 controls of European descent as well as a modest number of South Asians,
African Americans, Hispanics, East Asians, and admixed cases and controls were successfully genotyped.
None of the nineteen studies demonstrated an increased risk of CAD in carriers of the 719Arg allele compared
with non-carriers. Regression analyses and fixed effect meta-analyses ruled out with high degree of confidence
an increase of ≥2% in the risk of CAD among European 719Arg carriers. We also observed no increase in the
risk of CAD among 719Arg carriers in the subset of Europeans with early onset disease (<50 years of age for
males and <60 years for females) compared with similarly aged controls as well as all non-European subgroups.
Conclusions
The KIF6 Trp719Arg polymorphism was not associated with the risk of clinical CAD in this large replication
study.
ABSTRACT # OF WORDS: 219
MANUSCRIPT WORD COUNT (Text and References): 4951
6
ABBREVIATIONS USED
SNP: Single Nucleotide Polymorphism
CAD: Coronary Artery Disease
MI: Myocardial Infarction
CKMB: Creatine Kinase, myocardial band
GWAS: Genome Wide Association Study
ECG: Electrocardiography
OR: Odds Ratio
95% CI: 95% Confidence Interval
7
Introduction
Recent prospective observational studies suggest an association between the Trp719Arg SNP in kinesin-like
protein 6 (rs20455) and the development of clinical coronary artery disease (CAD)(1-5). Carriers of the 719Arg
alleles were found to have a modest increase in the risk in the Atherosclerosis Risk in Communities study
(ARIC) (Hazard Ratio and 95% CI for log additive model: 1.11, 1.02-1.21, p = 0.02)(1), the Cardiovascular
Health Study (CHS) (HR and 95% CI for dominant model: 1.29, 1.1-1.52, p = 0.005)(5), and the Women’s
Health Study (WHS)(HR and 95% CI for dominant model: 1.24, 1.04-1.46, p = 0.01)(4).
An increase in the risk of CAD was also observed in the placebo arm of two statin trials: the West of
Scotland Coronary Prevention Study (WOSCOPS) (Odds Ratio for incident CAD events and 95% CI for
dominant model: 1.55, 1.14-2.09, p = 0.005) and the Cholesterol and Recurrent Events (CARE) trial (HR for
recurrent myocardial infarction and 95% CI for dominant model: 1.50 (1.05-2.15), p = 0.03) (3). Curiously,
carriers of the 719Arg allele were not at increased risk of CAD or recurrent MI in the pravastatin arms of these
two trials. Furthermore, in the Pravastatin or Atorvastatin Evaluation and Infection Therapy Thrombolysis in
Myocardial Infarction 22 (PROVE IT-TIMI 22) trial, carriers in the pravastatin arm were also not at increased
risk of CAD while carriers in the atorvastatin arm had a decreased risk compared with non-carriers (adjusted
HR 0.65, 95% CI 0.48 to 0.88; p = 0.005)(2). The discrepant results between the two arms of these three statin
trials were deemed a consequence of a differential effect of genotype on the benefit derived from the use of
statins with carriers benefiting to a larger degree from statin therapy than non-carriers (2,3). Because carriers
randomized to the more potent statin were actually at decreased risk compared with non-carriers in the PROVE
IT-TIMI 22 trial, it was hypothesized that the degree of incremental benefit from statin use among carriers was
a function of the intensity of lipid lowering therapy(2).
The results of these initial studies have been used to justify the development of a KIF6 Trp719Arg
variant pharmacogenomic test (“Statincheck”)(6). This test is currently being marketed to health care
professionals as an aid to identifying subjects at high risk of incident or recurrent CAD events who stand to gain
8
the most from the use of statins(6,7). However, in recent GWAS for CAD and/or MI, SNPs at the KIF6 locus
were not among the associations reaching genome wide significance(8-12). Furthermore, in the ongoing
Ottawa Heart Genomics Study, no association was found between the KIF6 Trp719Arg SNP and
angiographically defined CAD in a subset of 1540 cases and 1455 controls(13). These discrepant results
demand examination of this association in additional populations to substantiate the use of the KIF6 test in the
management of subjects at risk of clinical CAD.
In this study, we report association analyses for the KIF6 Trp719Arg SNP in 19 case-control studies of
CAD that have recently genotyped this SNP using various genotyping platforms either as part of a GWA study
or in a formal attempt to replicate the initial positive reports (1-5).
Methods
Study Populations
We included subjects participating in 19 different case-control studies of CAD conducted around the world: the
Atherosclerotic Disease, VAscular functioN, and genetiC Epidemiology study (ADVANCE)(14) of northern
California, USA, the AMI Gene Study/Dortmund Health (AMI Gene)(15) in Germany, the CATHGEN
Research Project (CATHGEN)(16) in North Carolina, USA, the deCODE CAD study (deCODE)(10) in
Iceland, the National Finrisk study (FINRISK)(17) in Finland, the sibling Genetic Study of Atherosclerosis
Risk (GeneSTAR)(18) in Maryland, USA, the German Myocardial Infarction Family studies (GerMIFS I and
GerMIFS II)(8) in Germany, the Heart Attack Risk in Puget Sound (HARPS)(19) study in Washington state,
USA, the international INTERHEART study (INTERHEART)(20,21) coordinated by Population Health
Research Institute of McMaster University in Hamilton, Ontario, the Irish Family Study (IFS)(22) in Ireland,
the Malmo Diet and Cancer study (MDC)(23) in Malmo, Sweden, the Medstar/Washington Hospital Center
(MEDSTAR)(10) study in Washington, D.C., the Massachusetts General Hospital Premature CAD study (MGH
PCAD)(24) in Boston, USA, the Mid-America Heart Institute study (MAHI)(25,26) in Kansas City, Missouri,
the University of Pennsylvania Medical Center Cardiac catheterization cohort study (PennCATH)(10) in
Philadelphia, the Registre Gironı´ del Cor (REGICOR)(27) study in Gerona, Spain, the Verona Heart Study
9
(VHS)(28) in Verona, Italy, and the Wellcome Trust Case Control Consortium study of CAD (WTCCC CAD)
(11) in the United Kingdom. All participants gave written informed consent and local ethics committees
approved all studies.
Investigators from several of these 19 studies are members of consortia with a primary interest of
identifying novel genetic determinants of CAD through the use of GWAS technology. The consortia include the
PennCATH/Medstar consortium comprised of the PennCATH and Medstar studies, the Myocardial Infarction
Genetics consortium (MIGen)(12) comprised of the HARPS, REGICOR, MGH PCAD, FINRISK, and MDC
studies, the Cardiogenics consortium(8) comprised of the WTCCC, GerMIFS I, GerMIFS II studies, and the
Coronary ARtery Disease Genome-wide Replication And Meta-analysis consortium (CARDIoGRAM)
comprised of the ADVANCE study, the deCODE study, and the Penn/Medstar, MIGen, and CARDIOGENICS
consortia.
Each study used standard criteria to identify cases with myocardial infarction (MI) established by
international organizations during the 1990s or more recently. While some of these studies restricted their
enrollment of cases to subjects with at least one MI, others included cases diagnosed with clinically significant
coronary atherosclerosis without MI. Elevation of cardiac markers (CKMB or troponin) accompanied with
symptoms and/or ECG suggestive of cardiac ischemia were typical criteria used to identify MI cases. Non-MI
cases included subjects with angina and confirmatory tests for ischemia, unstable angina based on symptoms
and ECG changes without elevation of cardiac enzymes, or revascularization procedures that may have
occurred in the presence or absence of symptoms. For the current analyses, both cases with and without a
diagnosis of MI were considered in order to emulate the outcome used in the majority of the prospective studies
published to date (1-5).
Most of the studies either only enrolled subjects of European descent or restricted their genotyping
efforts to Europeans. However, in addition to Europeans, the ADVANCE study genotyped a modest number of
African Americans, Hispanics, East Asians, and admixed individuals, the CATHGEN and GENESTAR studies
genotyped a modest number of African Americans, and the INTERHEART study genotyped a large number of
South Asians.
10
More details on the design of each study including the precise criteria used by each of the 19 studies to
ascertain cases and controls can be found in the Supplementary Appendix and related references.
Genotyping
The Trp719Arg polymorphism in kinesin-like protein 6 (rs20455) was directly genotyped in all studies and no
imputation of the genotype was necessary. For the GerMIFS I and WTCCC CAD studies, the genotyping data
were extracted from the Affymetrix 500k array(29). For the deCODE, ADVANCE, and GeneSTAR studies,
the genotyping data were extracted from the Illumina Infinium HumanHap317/370, 550, and 1M chip arrays,
respectively(29). For the GerMIFS II, FINRISK, HARPS, MDC, MGH PCAD, PennCATH, Medstar, and
REGICOR studies, the genotyping data were extracted from the Affymetrix 6.0 array(29). For the AMI Gene,
IFS, INTERHEART, and MAHI studies, the SNP was genotyped using the iPLEX MassARRAY platform
(Sequenom) platform(29). Finally, for CATHGEN and VHS, the SNP was genotyped using the Centaurus
platform(29). All genotype data generated from the various platforms passed extensive quality control
measures including tests of Hardy Weinberg Equilibrium (p > 0.001 in controls).
Clinical measures
The age of onset of clinical CAD (for cases) and sex for cases and controls were documented in all studies. The
presence of other traditional risk factors was documented in most but not all studies. In the deCODE study, risk
factor data other than age, sex, and BMI were not collected. Furthermore, the presence of other traditional risk
factors was defined in various ways and the timing of enrollment of cases hampered the accurate measurement
of these risk factors in some studies. For example, several studies enrolled cases weeks to months (and
sometimes years) after their initial CAD event, making it difficult to reliably discern whether certain
medications being taken by a participant at the time of enrollment were prescribed to treat risk factors present
prior to their first ever clinical manifestation of CAD vs. for secondary prevention reasons or to treat CAD
symptoms (e.g. beta blockers, calcium channel blockers, ACE inhibitors, and statins). No attempt was made to
standardize risk factor definitions across all studies.
11
Statistical Analysis
We first calculated the crude Odds Ratios (OR) and 95% confidence interval (95% CI) based on the 2x2 table of
KIF6 Trp719Arg allele-by-trait counts for each case control study. In case control studies with more than one
race/ethnic group, we calculated an OR for each race/ethnic group. We then compared the distribution of the
raw genotype counts in each case-control stratum with Armitage’s trend test(30). Next, we calculated OR and
95% CI adjusted for age of onset of CAD and sex using standard unconditional logistic regression. Because of
the difficulties in ascertaining risk factors other than age and sex, we did not adjust OR for any additional CAD
risk factors. Log additive and dominant OR were calculated as both modes of inheritance were reported to date
and the 719Trp allele served as the reference allele given the 719Arg allele was identified as the high risk allele
in prior publications. Analyses were repeated after excluding cases with an age of onset of CAD ≥50 years for
males and ≥60 years for females. Controls for this subgroup analysis were also restricted to those in the same
sex specific age range as cases at the time of enrollment. The GeneSTAR family study analyses were
performed using generalized linear latent and mixed models (GLLAMM) with the logistic link and family
membership as the random effect in order to account for family structure while the Irish Family Study used
likelihood based association statistics incorporated in the UNPHASED software package(31) to account for
family structure. In the latter family study, only the crude OR for the additive model using all subjects could be
calculated.
Lastly, we combined crude and adjusted OR, 95% CI, and p values using a Mantel-Haenszel model in
which the groups were allowed to have different population frequencies for genotypes but were assumed to
have common relative risks(32). We weighed the results from each study by the standard error of the effect
derived from the p value of the OR (see Supplementary Appendix for details). Using this meta-analytic
approach, we calculated the overall combined OR, 95% CI, and p value separately for all case control studies of
Europeans and all case-control studies of African Americans. The meta-analyses were repeated for the
subgroup of cases in each study with early onset disease and their respective controls. For each meta-analysis,
we also performed two standard heterogeneity tests (Cohran’s Q and Higgin’s I2) to assess the appropriateness
of a fixed effects model over a random effects model(33).
12
From previous work, we know that one of the two admixed race/ethnic strata in the ADVANCE study,
the admixed non-Hispanics, has significant differences in the degree of admixture between cases and
controls(14). Specifically, cases in this stratum are, on average, significantly more European (and less African
American) than controls. Therefore, we included a covariate in the multivariate regression model indicating the
proportion of white ancestry for each individual estimated by the program STRUCTURE (34,35) in this
stratum. The other admixed stratum in the ADVANCE study, the admixed Hispanics, did not show differences
in degree of admixture between cases and controls. Therefore, no additional covariates were added to the
regression model for this stratum.
Some Icelandic affected individuals and controls are related, both within and between groups, causing
the chi square test statistic to have a mean >1 and a median larger than 0.6752. The inflation factor, lambda (λ),
was estimated at 1.21 using a method of genomic control(36) by calculating the average of the observed chi
statistics for the genome-wide SNP set, which accounts for relatedness and for potential population
stratification. The 95% CI and p values presented for deCODE are based on adjusting the chi square statistic by
dividing it by this inflation factor. The inflation factor was also found to be high in the GerMIFS I study (λ
~1.27), therefore p values and 95% CI for this study were also adjusted in the same manner. For all other
studies with genome wide data, a genomic control correction was not applied to the data as the inflation factor
was found to be very low (λ ≤1.05).
We performed sensitivity analyses in Europeans only by repeating all analyses described above after
first excluding non-MI cases in the subset of case-control studies which included both MI and non-MI cases of
CAD.
Results
Table 1 summarizes the ascertainment scheme and the inclusion criteria for phenotype, age, sex, and race
stratified by study and case-control status. The distribution and frequency of all traditional risk factors of CAD
stratified by study and case-control status can be found in the Supplementary appendix (Table 1S). Out of the
19 case control studies, 12 enrolled only cases with MI and 14 also restricted enrollment or genotyping to early
13
onset disease (when considering the traditional age cutoffs of ≤ 65 years for females and ≤ 55 years for males at
the time of first onset of disease). However, the largest study (deCODE) enrolled both MI and non MI cases of
CAD and included CAD cases with any age of onset. The proportion of cases that were female varied from
study to study and was influenced by the ascertainment scheme and matching (either one to one or frequency
matching). The overall weighted average age of onset of CAD was 55.7 years.
Table 2 summarizes the genotype counts stratified by study, case control status, and race/ethnic group as
well as the adjusted OR, 95% CI, and p values for the association between KIF6 rs20455 SNP and CAD. Crude
ORs were not materially different from their respective ORs adjusted for age and sex. Therefore, only ORs
adjusted for age and sex are presented. Among Europeans, only one out of the 19 studies (deCODE)
demonstrated a nominally significant association between the KIF6 polymorphism and CAD but in the opposite
direction of the published literature (the 719Arg allele inversely associated with risk: log additive OR of 0.93,
95% CI 0.88-0.99, log dominant OR of 0.91, 0.85-0.99). The meta-analysis produced a point estimate of the
log additive OR near unity with very tight confidence intervals (log additive OR 0.98, 95% CI 0.95-1.02, log
dominant OR 0.97, 0.93-1.01). We also found no significant association between the KIF6 rs20455 SNP and
CAD among South Asian participants in the INTERHEART study (log additive OR 1.02, 95% CI 0.91-1.14,
log dominant OR 1.04, 0.87-1.24), African American participants in the ADVANCE, CATHGEN, and
GENESTAR studies (fixed effects meta analysis log additive OR 0.91, 95% CI 0.73-1.13, log dominant OR
0.81, 0.42-1.55), and a smaller number of Hispanic, East Asian, and admixed individuals participating in the
ADVANCE study (see Table for details).
Table 3 shows genotype counts and association analyses restricted to the subgroup of cases with very
early onset disease (<50 years for males and <60 years for females) and controls within the same age range at
the time of enrollment. Among this subgroup, we also observed no increase in the risk of CAD in either the
European carriers of the 719Arg allele compared with non-carriers (fixed effects meta-analysis log additive OR
0.99, 95% CI 0.95-1.04, log dominant OR 1.01, 0.95-1.08) or the non-European subjects (see Table for details).
Lastly, our sensitivity analyses in Europeans restricting cases to those defined on the basis of an MI in
all 19 case control studies also revealed no increased risk of CAD in European carriers of the 719Arg allele
14
compared with non-carriers both for the overall analysis (fixed effects meta-analysis log additive OR 0.99, 95%
CI 0.96-1.03, log dominant OR 0.98(0.94-1.02) and the subgroup analysis of early onset MI cases (fixed effects
meta-analysis log additive OR 1.03, 95% CI 0.98-1.09, log dominant OR 1.03, 0.97-1.11). Additional details
for these analyses are provided in Tables 2S and 3S of the Supplementary Appendix.
None of the Q tests revealed heterogeneity in our meta-analyses (lowest p value = 0.252) and all metaanalyses in Europeans had an I2 value of zero percent suggesting that the variability in effect sizes in Europeans
was due entirely to sampling error within the studies (see Table 4S in Supplementary Appendix). Thus,
performing random effects model meta-analyses was not necessary in Europeans as the results would be
identical to the fixed-effects model. Only the log dominant model for CAD overall among African Americans
demonstrated an I2 value > 0%. For this stratum, the random effect model (DerSimonian and Laird method)
revealed an OR that was similar to the fixed effects model but with wider confidence intervals (Table 4S).
Discussion
The principal finding of this study was the uniform lack of elevated risk of clinical CAD among carriers of the
KIF6 719Arg allele compared with non-carriers in 19 case control studies performed around the world. Our
meta-analysis involving a very large number of subjects with European ancestry (over 17000 cases and 39000
controls) suggests that the risk of CAD among European carriers of the 719Arg allele is unlikely to be increased
by more than 2% compared with non-carriers.
Our study has two strengths in addition to the very high power conferred by studying a large number of
European subjects. First, we studied a large number of early onset CAD cases (<50 years of age for males and
<60 years of age for females) but observed no association with KIF6 Trp719Arg and CAD despite the
expectation that susceptibility alleles would be more prevalent in this subgroup(37). In this subgroup with early
onset disease, we were able to rule out an increase in risk of ≥8% in Europeans. Second, this is the only study
to date that examines several non-European racial/ethnic groups. In each of non-European case-control strata,
we also found no significant associations between the KIF6 Trp719Arg polymorphism and CAD. However.
the point estimates of the OR for these non-European subgroups have substantially wider confidence intervals
15
than those derived in Europeans due to relatively small sample sizes (particularly in our Hispanic, East Asians,
admixed Hispanic, and admixed non Hispanics groups). Thus, further study of all of these non-European
race/ethnic groups is needed to rule out more modest effects on risk.
Our study has three important limitations related to the case-control design. The first limitation is the
potential selection bias by studying only non-fatal cases of CAD. If the 719Arg allele increases the risk of
incident fatal CAD more than the risk of incident non-fatal CAD, the exclusion of incident fatal CAD cases
could conceivably bias the OR towards the null (i.e. OR = 1). However, the difference in relative risk between
these two subgroups of cases would have to be quite large to result in a substantially biased OR in our study
given the majority of incident CAD events are not fatal especially among subjects with early onset disease (e.g.
in the ARIC surveillance study 20 to 35% of subjects aged 60 years or greater and 10 to 20% of subjects under
the age of 60 years died as a consequence of a complication of their initial presentation of CAD(38)). The
second potential limitation is our inability to measure traditional risk factors as robustly as they are measured in
prospective studies. We therefore made no attempt to fully adjust ORs for all traditional risk factors of CAD.
However, prospective studies published to date suggest that traditional risk factors are not correlated with the
KIF6 Trp719Arg polymorphism making adjustment unnecessary (1-5). Lastly, our study does not allow us to
explore whether statin use modifies the effect of the 719Arg allele on risk as was done in the WOSCOPS,
CARE, and PROVE IT TIMI 22 trials as reliable information on the use of statins in relation to the incident
event was not available for most studies. However, we believe it unlikely that our null results are a
consequence of a high prevalence of the use of statins at the time of the event in cases. In fact, we suspect that
the overall prevalence of the use of statins in our set of 19 case control studies is actually lower than the
prevalence of use observed at the last follow up for participants in the ARIC, CHS, and WHS studies(39-41)
because a majority of our case-control studies restricted their recruitment and/or genotyping efforts to very early
onset cases and young controls. For example, in the ADVANCE study, which focused on very early onset
CAD (<45 years for males, <55 years for females), access to the electronic pharmacy records confirmed that
only a small proportion of cases (~14.4%) and controls (~4.7%) were on statins during the appropriate time
window of exposure.
16
Is it possible that other less obvious sources of bias are responsible for the differences in associations
observed between case control and cohort studies of KIF6? While we cannot rule out this possibility, we also
deem this scenario unlikely given such cryptic biases, if they exist, have not influenced associations between
SNPs at the 9p21.3 locus and CAD in the same manner(9,10). In fact, many of the case-control studies
included in this report have produced OR of disease for the 9p21.3 locus equivalent to or larger than the hazard
ratios derived from cohort studies(9,10,12,14,42-44). These cohort studies include the two largest cohort
studies to date (ARIC and WHS) reporting on the association between KIF6 and CAD. Thus, we are left with
the real possibility that the initial reports falsely suggested an association between variation in KIF6 and CAD.
The reasons for this are unclear but include the possibility of chance findings or inadequate correction for
multiple testing(45-47). The lack of biologic studies implicating KIF6 in the pathogenesis of coronary
atherosclerosis combined with the lack of consistent evidence of expression of this gene in relevant tissues such
as the vasculature (48,49) could also argue for a false positive association. However, many recent population
genetic studies of complex traits have clearly demonstrated that such mechanistic data are not necessary to
validate highly significant genetic associations uncovered through GWAS (50).
Our findings question not only the usefulness of the KIF6 test in identifying subjects at increased risk of
incident or recurrent CAD but also its usefulness in identifying subjects most likely to benefit from statins.
Although we could not test the latter hypothesis directly, the previously reported interaction between genotype
and benefit from statins is largely dependent on the validity of the association among subjects not on statins
which could not be replicated in this study. We also call attention to the fact that the interaction term p value
between genotype and statin use was only marginally significant in the WOSCOPS (p = 0.021) and PROVEITTIMI22 (p = 0.018) trials and not significant in the CARE trial (adjusted p = 0.39)(2,3). Despite these
observations, additional high quality prospective cohort studies of the effect of the KIF6 variant on CAD risk
among users and non users of statins are needed before any firm conclusions can be made regarding the validity
of this interaction.
In conclusion, we were unable to confirm an increased risk of CAD in carriers of the KIF6 719Arg allele
compared with non-carriers in a very large number of European subjects. We also observed no compelling
17
evidence of an association between the KIF6 Trp719Arg SNP and CAD in multiple other race/ethnic groups
and among subjects with early onset CAD. Our null results are unlikely to be a consequence of selection bias, a
high prevalence of use of statins among cases, or other cryptic biases. These findings do not support the clinical
utility of testing for the KIF6 Trp719Arg polymorphism in the primary prevention of CAD and indirectly
question whether genotype information at this locus is able to identify subjects most likely to benefit from the
use of statins.
18
ACKNOWLEDGMENTS
ADVANCE study. The ADVANCE study was funded by the US National Institutes of Health (NIH) and
National Heart, Lung, and Blood Institute’s STAMPEED genomics research program through a grant to T.Q
(R01HL087647-03).
HARPS. The HARPS study was supported by the grants (R01HL056931, P30ES007033) and a contract
(N01HD013107) from US National Institutes of Health.
REGICOR. The REGICOR study was partially funded by the Ministerio de Sanidad y Consumo,
Instituto de Salud Carlos III (Red HERACLES RD06/0009), the CIBER Epidemiologı´a y Salud Pu´ blica, the
FIS and AGAUR Generalitat de Catalunya. Massachusetts General Hospital.
The deCODE CAD/MI Study was sponsored by NIH grant, National Heart, Lung and Blood Institute
(R01HL089650-02).
MIGen: The MIGen consortium study was funded by the US National Institutes of Health (NIH) and
National Heart, Lung, and Blood Institute’s STAMPEED genomics research program through a grant to D.A
(R01HL087676-02). S.K. is supported by a Doris Duke Charitable Foundation Clinical Scientist Development
Award, a charitable gift from the Fannie E. Rippel Foundation, the Donovan Family Foundation, a career
development award from the NIH and the Department of Medicine and Cardiovascular Research Center at
Massachusetts General Hospital. J.B.M. is supported by grant K24 DK080140 from the NIH. Broad Institute.
Genotyping was partially funded by The Broad Institute Center for Genotyping and Analysis, which is
supported by grant U54 RR020278 from the National Center for Research Resources.
FINRISK. V.S. was supported by the Sigrid Juselius Foundation. L.P. was supported by the Center of
Excellence in Complex Disease Genetics of the Academy of Finland, the Nordic Center of Excellence in
Disease Genetics and the Finnish Foundation for Cardiovascular Research.
WTCCC Study. The 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
19
supported by the BHF and the UK Medical Research Council. N.J.S. and S.G.B. hold chairs funded by the BHF.
N.J.S. is supported by the Leicester NIHR Biomedical Research Unit in Cardiovascular Disease.
PennCATH/Medstar. Recruitment of the PennCATH cohort was supported by the Cardiovascular
Institute of the University of Pennsylvania. Recruitment of the Medstart study sample was supported by the
MedStar Research Institute and the Washington Hospital Center as well as a research grant from
GlaxoSmithKline. Genotyping 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 (to M.P.R. and D.J.R.) with the University of Pennsylvania School of Medicine. D.J.R. was
supported by a Doris Duke Charitable Foundation Distinguished Clinical Scientist Award.
GeneSTAR. The GeneSTAR study was funded by the US National Institutes of Health (NIH) and
National Heart, Lung, and Blood Institute’s STAMPEED genomics research program through a grant to L.C. B
(R01HL087698-03).
Verona Heart Study. The study was supported by a grant from the Italian Ministry of University and
Research and grants from the Veneto Region and the Cariverona Foundation, Verona, Italy.
Mid-America Heart Institute. T.M. is supported by a career development grant
from the NIH.
Irish Family Study. We thank the clinical staff members for their valuable
contribution to the collection of families for this study. The research was supported by the Northern Ireland
Research and Development Office, a Royal Victoria Hospital Research Fellowship, the Northern Ireland Chest,
Heart and Stroke Association, and the Heart Trust Fund (Royal Victoria Hospital).
GerMIFS I and II. The German Study was supported by the Deutsche Forschungsgemeinschaft and the
German Federal Ministry of Education and Research in the context of the German National Genome Research
Network.
Cardiogenics. Cardiogenics is an EU-funded integrated project (LSHM-CT- 2006-037593).
INTERHEART.
20
.We acknowledge the contribution of S. Yusuf who initiated and, together with the Steering Committee,
supervised the conduct of the INTERHEART study. We thank members of the Project Office, S. Rangarajan
(study coordinator) and K. Hall (laboratory manager), for their assistance in coordinating the genetics
component of the INTERHEART project. S.A. holds the Michael G. DeGroote and Heart and Stroke
Foundation of Ontario Chair in Population Health and the May Cohen Eli Lilly Endowed Chair in Women’s
Health Research, McMaster University
Contributions are as follows:
Manuscript preparation: Themistocles L Assimes, Hilma Holm, Sekar Kathiresan, Chris C Patterson,
Thomas Morgan, Mark Hlatky, Joshua Knowles, Thomas Quertermous, Diane Becker, Heribert Shunkert ,
Jeanette Erdmann, Stephen M Schwartz, David S Siscovick, Christopher J O’Donnell, Muredach P Reilly ,
John A Spertus, James C Engert, Pascal P McKeown, Nilesh J Samani
Statistical Analysis: Themistocles L Assimes, Gudmar Thorleifsson, Benjamin F Voight, Christina
Willenborg, Dhananjay Vaidya, Changchun Xie, Chris C Patterson, Thomas Morgan, Mingyao Li, John R
Thompson
Atherosclerotic Disease, VAscular functioN, and genetiC Epidemiology. Themistocles L. Assimes, Devin
Absher, Joshua Knowles, Carlos Iribarren, Alan Go, Stephen Fortmann, Steven Sidney, Neil Risch, Hua Tang,
Richard M. Myers, Mark Hlatky, Thomas Quertermous
Acute Myocardial Infarction Gene Study/Dortmund Health Study. Thomas Scheffold, Klaus Berger,
Monika Stoll, Andreas Huge
CATHGEN. Svati H. Shah, Christopher B. Granger
deCODE Study. Hilma Holm, Gudmar Thorleifsson, Gudmundur Thorgeirsson, Karl Andersen, Unnur
Thorsteinsdottir, Kari Stefansson
FINRISK. Veikko Salomaa, Aki S Havulinna, Leena Peltonen
GeneSTAR. Dhananjay Vaidya, Nauder Faraday, J. Enrique Herrera, Yoonhee Kim, Brian J. Kral, Rasika
Mathias, Ingo Ruczinski, Bhoom Suktitipat, Alexander Wilson, Lisa R. Yanek, Lewis C Becker, Diane Becker
21
German MI Family Studies I and II. Heribert Schunkert, Jeanette Erdmann, Patrick Linsel-Nitschke,
Wolfgang Lieb, Andreas Ziegler, Inke R König, Christian Hengstenberg, Marcus Fischer, Klaus Stark, W.
Reinhard, J. Winogradow, M. Grassl, Anika Grosshennig, Michael Preuss, Stefan Schreiber , KORA: H-Erich
Wichmann, Christa Meisinger
Heart Attack Risk in Puget Sound. Stephen M Schwartz, David S Siscovick, Jean Yee, Yechiel Friedlander
INTERHEART. James C Engert, Ron Do, Changchun Xie, Sonia Anand
Irish Family Study. Pascal P McKeown, Chris C Patterson
Malmo Diet and Cancer Study. Olle Melander, Goran Berglund
Massachusetts General Hospital Premature Coronary Artery Disease Study. Sekar Kathiresan, James B
Meigs, Gordon Williams, David M Nathan, Calum A MacRae, Christopher J O’Donnell
Mid-America Heart Institute. Thomas Morgan, John A Spertus
PennCath/Medstar. Muredach P Reilly, Mingyao Li, Liming Qu, Robert Wilensky, William Matthai, Atif
Qasim, Hakon H Hakonarson, Joseph M Devaney, Mary-Susan Burnett, Augusto D Pichard, Kenneth M Kent,
Lowell Satler, Joseph M Lindsay, Ron Waksman, Christopher W Knouff, Dawn M Waterworth, Max C Walker,
Vincent Mooser, Stephen E Epstein, Daniel J Rader
Registre Gironi del COR. Roberto Elosua, Jaume Marrugat, Gavin Lucas, Isaac Subirana, Joan Sala, Rafael
Ramos
Verona Heart Study. Domenico Girelli, Nicola Martinelli, Oliviero Olivieri, Elisabetta Trabetti, Giovanni
Malerba, Pier Franco Pignatti
Myocardial Infarction Genetics consortium cohorts: Aarti Surti, Candace Guiducci, Lauren Gianniny,
Daniel Mirel, Melissa Parkin, Noel Burtt, Stacey B Gabriel, Benjamin F Voight, Sekar Kathiresan, Joel N
Hirschhorn, Rosanna Asselta, Stefano Duga, Marta Spreafico, Kiran Musunuru, Mark J Daly, Shaun Purcell,
Stephen M Schwartz, Jean Yee, Gavin Lucas, Isaac Subirana, David Siscovick, Christopher J O’Donnell, Nilesh
J Samani, Olle Melander, Roberto Elosua, Leena Peltonen, Veikko Salomaa, Stephen M Schwartz, David
Altshuler.
22
Wellcome Trust Case Control Consortium study of CAD. Nilesh J Samani, John R Thompson, Peter S
Braund, Benjamin J Wright, Anthony J Balmforth, Stephen G Ball, Alistair S Hall
CARDIOGENICS consortium: Heribert Schunkert, Nilesh J Samani, Jeanette Erdmann, Willem Ouwehand,
Christian Hengstenberg, Panos Deloukas, Michael Scholz, Francois Cambien
23
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Supplementary Appendix
Lack of association between the Trp719Arg polymorphism in kinesinlike protein 6 and coronary artery disease in 19 case-control studies
Themistocles L. Assimes et al.
(see main text for full list of authors and affiliations)
Address for Correspondence:
Themistocles L. Assimes, MD PhD
Falk Cardiovascular Research Building
Stanford University School of Medicine
Stanford, CA 94305-5406
29
SUPPLEMENTARY METHODS
Study Populations
Detailed description of the eligibility criteria and the source population for all groups in the multi ethnic
ADVANCE study can be found elsewhere (1-4). Briefly, between October 28, 2001 and December 31, 2003,
3179 member of the Kaiser Permanente of Northern California health maintenance organization were enrolled
into three CAD groups and two control groups. The case groups included subjects with “early onset” CAD (as
defined by a CAD qualifying event captured by the electronic databases at any time after Jan 1, 1999 including
AMI, angina with at least one angiographic stenosis of >50%, or revascularization procedure in men 18 to 45 or
women 18 to 55 years of age at the time of the event), incident stable exertional angina at an older age, and
incident non fatal AMI at an older age. The young control group included de novo recruited subjects between
the ages of 30 and 45 for men or 30 and 55 for women as well as a subset of 479 participants in the same age
range from the Coronary Artery Risk Development in Young Adults (CARDIA) study originally recruited at the
Oakland field center and attending the study’s Year 15 examination in 2000–2001(5). Older controls included
men and women aged 60–69 years at the time of identification in June, 2001. All controls were free of clinical
CAD, cerebrovascular disease (CVD) and peripheral arterial disease (PAD) at the time of recruitment. Thus, the
total study sample included 3658 subjects. Only a subset of all subjects was used for the genome wide
association study. Cases included all “early onset” CAD cases (n = 470) and a small number of cases (n = 43)
in the older incident exertional angina group that met the age criterion for “early onset” CAD but not all other
criteria to be included in the “early onset” case group. Controls included a stratified subset of all young controls
(n = 527) that best matched the age at first onset of clinically significant CAD, race/ethnic group mix, and
gender of cases.
The AMI Gene Study/Dortmund Health Study(6) is a prospective multi-centre registry involving 4 heart
centres and 6 cardiologic departments in North-Rhine-Westphalia, Lower Saxony, and Mecklenburg-Western
Pomerania. Between 2004 and 2006, we enrolled a cohort of 809 consecutive men younger than 65 and
suffering from non-ST-segment elevation MI or ST segment elevation MI with an onset of symptoms less than
30
24 hours before admission. All cases underwent cardiac catheterization and interventional or surgical
revascularization. After informed consent personal data collection and blood sampling where carried out at
admission. Information on history, traditional risk factors and co-morbidities were documented after
interventional procedures and clinical stabilization of the patients. Controls came from the Dortmund Health
Study(6,7), a population-based survey conducted in the city of Dortmund with the aim to determine the
prevalence of headache types, cardiovascular and other chronic diseases and their risk factors in the general
population. Sampling for the study was done randomly from the city’s population register stratified by fiveyear
age group and gender. History of MI and other cardiovascular conditions was assessed in face-to-face
interviews. The study was conducted in 2003-2004. The recruitment protocols and study procedures were
approved by the ethics committees of the University of Witten-Herdecke and the University of Muenster,
Germany, respectively.
CATHGEN study participants were enrolled at Duke University Medical Center (Durham, NC) through
the CATHGEN biorepository, consisting of subjects greater than 18 years of age, recruited sequentially through
the cardiac catheterization laboratories from 2001-2005(8). Biological samples and extensive clinical,
angiographic, and longitudinal follow-up data were collected on all subjects consenting to participation. Blood
samples were obtained from the femoral artery at initiation of the procedure. For purposes of this study, cases
were defined as those having a history of MI (by self-report and corroborated by review of medical records), or
having suffered an MI during the study follow-up period. Controls from this cohort, defined as those with no
history of MI prior or subsequent to the index cardiac catheterization, as well as no history of percutaneous or
surgical coronary revascularization procedure; no subsequent percutaneous or surgical coronary
revascularization procedures; ejection fraction on left ventriculogram greater than 40%; and no or minimal
CAD on coronary angiography (defined as a CAD index less than or equal to 23 and no coronary vessel with
clinically significant CAD (stenosis greater than 50%)). CAD index is a numerical summary of angiographic
data directly related to outcome(9).
The GWA study of CAD in Icelanders by deCODE has been previously described (10). However, this
report includes an additional 2706 cases of CAD and 18224 controls. Briefly, over the last 10 years, MI
31
patients have been recruited through the cardiovascular disease (CVD) genetics program at deCODE.
Individuals who suffered an MI were identified from a registry of over 10,000 individuals who: a) had an MI
before the age of 75 in Iceland in the years 1981 to 2002 and satisfy the MONICA criteria(11)or had MI
discharge diagnosis from the major hospitals in Reykjavik in the years 2003 to 2005. MI diagnoses of all
individuals in the registry follow strict diagnostic criteria based on signs, symptoms, electrocardiograms,
cardiac enzymes and necropsy findings(11,12). Additional subjects with coronary artery disease, but that are
without known history of MI, were identified from a list of those who have undergone percutaneous coronary
intervention (PCI) in the major hospital in Reykjavik in the years 1993 to 2003 or from those who have
participated in the CVD genetics program at deCODE and have a self reported history of coronary artery bypass
graft (CABG) or PCI. The controls used for the study were recruited as a part of various genetic programs at
deCODE. The medical history of the controls were unknown unless they had also participated in any of the
CVD genetic programs (i.e. MI, stroke, peripheral vascular disease, type II diabetes, obesity, familial combined
hyperlipidemia, coronary restenosis, and hypertension). Individuals with known MI, stroke, peripheral vascular
or coronary artery disease were excluded as controls. The 24951 controls used break down approximately as
follows: neurologic disorders ~3000, psychiatric disorders ~4900, cancers ~3400, gynecologic disoders ~1500,
pulmonary disorders ~2300, psoriasis ~800, type II diabetes ~800, osteoporosis ~2100, preeclampsia ~700,
rheumatoid arthritis ~500, longevity ~1000, benign prostatic hyperplasia ~400, enuresis ~751, infectious
diseases ~1700, glaucoma 300, population controls ~800.
FINRISK(13) contributed cases of early-onset MI and controls. Cases were defined as men aged ≤50
and women aged ≤60 and hospitalized with MI or died of MI, and were obtained from a national populationbased survey of risk factors for cardiovascular disease. FINRISK surveys are conducted every 5 years, and from
the 23,188 individuals enrolled in FINRISK 1992, 1997, and 2002, we assembled cases and controls.
Hospitalization data and mortality data for MI were obtained from the Finnish National Hospital Discharge
Register and the Finnish National Causes-of-Death Register as of December 31, 2004 based on ICD-9 code of
410 and ICD-10 codes of I20 and I21. These registers have excellent validity. Cases included MI prevalent at
baseline examination and incident events on follow-up. Controls were randomly selected from participants of
32
the three FINRISK cohorts, who survived until the event date of the case without hospitalization for CHD or for
stroke, matched to cases based on age, sex, study area, and FINRISK cohort year. Data collected in both cases
and controls at a baseline study examination include risk factors (based on questionnaire and blood samples),
anthropometry, and blood pressure measurement.
The GeneSTAR (Genetic Study of Atherosclerosis Risk)(14) famly study cases were identified from
probands hospitalized in 10 Baltimore area hospitals with documented CHD (myocardial infarction, angina
with revascularization, angina with documented obstructive coronary stenoses without revascularization, or
documented obstructive coronary stenoses with or without revascularization) prior to 60 years of age. The
classification of all cases followed strict diagnostic criteria based on electrocardiograms, cardiac enzymes and
necropsy findings. All CHD events were adjudicated by 2 cardiologists using standard criteria applied to data
obtained from medical records. The controls used for the study were unaffected siblings, spouses and offspring
of CHD cases followed over a 22 year time period (from 1983 to 2006). Medical history of the controls was
gathered directly during participation in the GeneSTAR Study. Controls that developed incident CHD (defined
as all of the above as well as sudden cardiac death) during the 22 year follow up time period were excluded
from the analysis.
German MI Family Study I (GerMIFS I) has been recently described(15). The recruitment for the
German MI Family Study II (GerMIFS II) was similar to that for German MI Family Study I. All 1,222
patients had a validated MI with a strong genetic component as documented by an early age of onset (prior to
the age of 60 years). Moreover, a positive family history for CAD was documented in 726 (59.4 %) of patients.
Patients were identified following their admission for acute treatment of MI or in cardiac rehabilitation clinics.
Population-based controls were derived from the MONICA/KORA Augsburg survey S4 (n=820)(16) and the
PopGen blood donor sample (PopGen-BSP) (n=478)(17).
The Heart Attack Risk in Puget Sound (HARPS) (18) study is a population-based case-control study of
cases of early-onset MI and controls matched on age and sex. Eligible case patients were men aged <50 and
women <60 diagnosed with a first MI between 1991 to 2002. Cases were ascertained through medical record
review at all acute care facilities in King, Pierce, and Snohomish counties, Washington, US. Controls were
33
identified using random digit dialing from these counties (1991-2002) and had no history of cardiovascular
disease. Data collected through in-person interviews with each case and control include information on medical
and lifestyle risk factors, and a self-administered food frequency questionnaire.
The INTERHEART(19,20) is a global case/control study of risk factors for acute MI involving 27,098
individuals recruited from 262 centers in 52 countries. Informed written consent to obtain the baseline
information and to collect and store the genetic and other biologic specimens was obtained from 21,508
individuals (including all individuals analyzed in this study). To identify incident cases of acute MI, all patients,
irrespective of age, admitted to the coronary care unit (or an equivalent cardiology ward) within 24 hours of
symptom onset were screened. Cases were eligible if they had characteristic symptoms plus electrocardiogram
changes indicative of a new MI (new pathologic Q waves, at least 1 mm ST elevation in any two or more
contiguous limb leads or a new left bundle branch block, or new persistent ST-T wave changes diagnostic of a
non-Q wave MI) or a plasma level of cardiac troponin level above that considered normal in the
hospital/institution where the patient was registered. For each case, at least one control of the same age (±5
years) and sex was recruited from the same centre. Controls were defined as individuals who had no previous
diagnosis of heart disease or history of exertional chest pain. Eligible controls were classified as i) hospitalbased, defined as patients attending the hospital or outpatient clinics for the following reasons: refraction and
cataracts, physical check-up, routine pap smear, routine breast exam, elective minor surgery for conditions that
were not obviously related to CHD or its risk factors, elective orthopedic surgery (eligibility dependent on
ability to complete physical measures) or patients attending the hospital or outpatient clinics for: outpatient
fractures, arthritic complaints, plastic surgery, hemorrhoids, hernias, hydroceles, routine colon cancer screening,
endoscopy, minor dermatologic disorders; or ii) community-based, defined as visitors or relatives of a patient
from a non-cardiac ward, or an unrelated (not first-degree relative) visitor of a cardiac patient. Of the controls in
INTERHEART, 58% were hospital-based and 36% of controls were community-based, and results were similar
with both types of controls. In the remainder of the controls, 3% were from an undocumented source, and 3%
were recruited through the WHO MONICA study in Göteborg, Sweden. Exclusion criteria for controls were
identical to those described for cases. Structured questionnaires were administered to all cases and controls to
34
obtain information on demographic factors (including self-reported ethnicity) as well as socioeconomic and
health status. For this project, we analyzed individuals with self-reported ethnicity defined as “South Asian” or
“European” regardless of their geographic locations.
Recruitment for the Irish Family Study (IFS)(21) took place between August 1999 and October 2004.
All subjects were Caucasian whose four grandparents were born in Ireland. Each family had at least one
member affected with early-onset MI (disease onset ≤55 years for males and ≤60 years for females) and at least
one unaffected sibling and / or both parents surviving. Unaffected siblings were required to: (i) be older than the
affected sibling was at the onset of coronary heart disease; (ii) have no symptoms of angina or possible MI by
World Health Organization questionnaire assessments; (iii) have no history of coronary heart disease diagnosed
by a doctor; and (iv) have a resting 12 lead ECG record showing no evidence of ischemia or previous MI.
The Malmo Diet and Cancer study (MDC)(22) contributed cases of early-onset MI and controls matched
for age and sex. Cases were defined as men aged ≤50 and women aged ≤60 and hospitalized with MI, and were
obtained from a population-based cohort study of 28,098 men and women living in Malmo, Sweden, between
1991 and 1996. Data on MI were obtained by record linkage to the Swedish National Hospital Discharge
Register as of December 31, 2000 based on ICD-9 code 410 and ICD-10 code I21. Cases included MI prevalent
at baseline examination and incident events on follow-up. Controls were randomly selected among the 28,098
participants and were free of MI (criteria above) at baseline examination and during follow-up. Data collected
in cases and controls at a baseline examination include risk factors (based on questionnaire and blood samples),
anthropometry, dietary assessment, and a physical examination.
The Washington Hospital Center catheterization (MEDSTAR)(10) study is a Washington Hospital
Center based angiographic study of 1,500 subjects specifically designed for biomarker and genetic association
studies of acute and chronic coronary atherosclerosis. MedStar is a cross sectional study of coronary
atherosclerosis in a consecutive cohort of selected patients undergoing cardiac catheterization at Washington
Hospital between August 2004 and March 2007. All subjects have been enrolled in a Washington Hospital
Institutional Review Board approved protocol and all subjects gave written informed consent. Enrollment
criteria include any clinical indication for cardiac catheterization and ability to give informed consent. The
35
following data were extracted from the medical record; age, gender, race/ethnicity, past medical, social, family
and medication history, cardiovascular risk factors (diabetes, smoking, and hypertension), physical exam
including vital signs, weight and height (for BMI), and cardiovascular findings. Ethnicity information was self
reported. Data from cardiac catheterization including coronary angiography were recorded. Coronary
angiograms were scored on the day by the interventional cardiologist who performed the procedure and
reviewed by a second cardiologist at a later date. Blood was drawn in a 12-hour fasting state (except in those
with acute MI), at the time of the initial catheter insertion prior to the administration of any contrast dye for
plasma, serum and buffy coat DNA isolation. All demographic, anthropometric, clinical, cardiac catheterization,
laboratory, and genetic data are integrated into study database that exclude personal identifiers. A case-control
GWAS similar to PennCATH (see description below) was performed in MedStar (N=1,322 Caucasians)
composed of controls (N=447) who on coronary angiography showed no evidence of CAD and CAD cases
(N=874) with one or more coronary vessels demonstrating ≥50% stenosis. These cases were divided into stable
CAD cases without history of MI and CAD cases with a history of MI. Controls were aged over 45 in men and
women.
The Massachusetts General Hospital of Premature CAD study (MGH PCAD) (23) is a hospital-based
case-control study of cases of early-onset MI and controls matched on age and sex. Eligible case patients were
men aged ≤50 and women aged ≤60 who were hospitalized at MGH with MI between 1999 and 2004. An
attempt was made to recruit all patients admitted to MGH meeting the case definition. Controls were recruited
from Boston and its vicinity through a general newspaper advertisement and eligible if they reported no history
of cardiovascular disease or cardiac medications. Data collected through in-person interviews and examination
for each case and control include risk factors, anthropometry, and blood pressure.
The Mid-America Heart Institute (MAHI)(24,25) patients (N = 811) were recruited in successive
prospective cohort studies designed to investigate clinical outcomes among survivors of acute coronary
syndrome. Age, sex, and BMI-matched control subjects without known CAD (N = 650) consisted of
ambulatory outpatients from the same geographic area presenting for routine laboratory testing. All 1,461
36
subjects submitted for genotyping were of self-reported white/mixed European ancestry. There were no
restrictions on age.
The Penn-CATH(10) cohort is a University of Pennsylvania Medical Center based angiographic study
of over 3,800 subjects that has been used previously for replication of novel genes and risk factors for
atherosclerotic cardiovascular disease and type 2 diabetes(26-29). Between July 1998 and March 2003,
PennCATH recruited a consecutive cohort of patients undergoing cardiac catheterization at Penn. A total of
3,850 subjects were recruited. Enrollment criteria included any clinical indication for cardiac catheterization
and ability to give informed consent. The following data were extracted from the medical record; age, gender,
race/ethnicity, past medical (including diabetes, hypertension, dyslipidemia, prior MI and cardiac events),
social, family and medication history, cardiovascular risk factors, physical exam including vital signs, weight
and height (for BMI). Ethnicity information was self-reported. Data from cardiac catheterization including
coronary angiography were recorded. Blood was drawn in a fasting state, DNA (buffy coat) and plasma was
isolated, and lipoproteins and glucose were assayed on all samples. A GWAS was recently performed in a
subset of PennCATH participants including 468 European subjects who on coronary angiography showed no
evidence of CAD (“controls”) and 933 subjects with one or more coronary vessels demonstrating ≥50%
stenosis (“cases”). These cases were equally selected for stable CAD cases without history of MI and CAD
cases with a history of MI. Controls were aged over 40 in men and 45 in women.
The Registre Gironı´ del Cor (REGICOR) study(30) is a population-based case-control study of cases of
early-onset MI and controls matched on age and sex. Specifically, eligible case patients were consecutive
patients hospitalized with a first MI to the only coronary care unit in the catchment area of Gerona, Spain.
Controls were subjects randomly selected from a cross-sectional study of cardiovascular risk factors in the
catchment area and were deemed free of MI by history, physical examination, and ECG. Data collected through
in-person interviews and examination include risk factors (based on questionnaire and blood samples),
anthropometry, and blood pressure.
The Verona Heart Study (VHS)(31) is an ongoing study aimed at identifying new risk factors for CAD
and MI in a population of subjects with angiographic documentation of their coronary vessel. The CAD group
37
had angiographically documented severe coronary atherosclerosis, the majority of them being candidates for
coronary artery bypass grafting or percutaneous coronary intervention. Control subjects were selected such that
they had normal coronary arteries, being submitted to coronary angiography for reasons other than CAD.
Controls with history or clinical evidence of atherosclerosis in vascular territories beyond the coronary bed were
excluded. Information on MI diagnoses was gathered through medical records showing diagnostic
electrocardiogram and enzyme changes, and/or the typical sequelae of MI on ventricular angiography and on
echocardiography. The local Ethical Committee approved the study. Informed consent was obtained from all the
patients after a full explanation of the study.
Wellcome Trust Case Control CAD study (WTCCC CAD) (15) (32) has been recently described.
Briefly, cases comprised subjects with a validated history of MI or CAD before the age of 66 years and also a
family history of premature CAD in a first degree relative. Controls were derived in equal numbers from two
sources: (i) the 1958 UK Birth Cohort and (ii) the National Blood Service Collection (NBS) recruited
specifically for the WTCCC Study.
Statistical Analysis
Fixed meta analysis for the additive model were performed using the following R script:
pv<-c(P1, P2, P3, etc…)
rr<-c(OR1, OR2, OR3, etc…)
zscore<-abs(qnorm(pv))
lrr<-log(rr)
wt<-zscore/lrr
lr<-sum(wt**2*lrr)/(sum(wt**2))
r<-exp(lr)
z<-sum(wt*zscore)/sqrt(sum(wt**2))
z1<-abs(z)
p<-2*pnorm(-z1)
se<-lr/z
38
ci<-exp(c(lr-1.96*se,lr+1.96*se))
r<-exp(lr)
p
r
ci
where P1, P2, P3, etc represent the P-values from the respective studies that have been changed to one-sided pvalues, i.e. p/2 if OR > 1, else 1 - p/2. The different cohorts are weighed by the standard error of the OR
represented by OR1, OR2, OR3 etc….
Informed consent.
All participants in the 19 studies gave written informed consent in accordance with the guidelines of
local ethical committees.
39
Membership of the Wellcome Trust Case Control Consortium
Management Committee: Paul R Burton1, David G Clayton2, Lon R Cardon3, Nick Craddock4, Panos
Deloukas5, Audrey Duncanson6, Dominic P Kwiatkowski3,5, Mark I McCarthy3,7, Willem H Ouwehand8,9,
Nilesh J Samani10, John A Todd2, Peter Donnelly (Chair)11
Data and Analysis Committee: Jeffrey C Barrett3, Paul R Burton1, Dan Davison11, Peter Donnelly11, Doug
Easton12, David M. Evans3, Hin-Tak Leung2, Jonathan L Marchini11, Andrew P Morris3, Chris CA Spencer11,
Martin D Tobin1, Lon R Cardon (Co-chair)3, David G Clayton (Co-chair)2
UK Blood Services & University of Cambridge Controls: Antony P Attwood5,8, James P Boorman8,9,
Barbara Cant8, Ursula Everson13, Judith M Hussey14, Jennifer D Jolley8, Alexandra S Knight8, Kerstin Koch8,
Elizabeth Meech15, Sarah Nutland2, Christopher V Prowse16, Helen E Stevens2, Niall C Taylor8, Graham R
Walters17, Neil M Walker2, Nicholas A Watkins8,9, Thilo Winzer8, John A Todd2, Willem H Ouwehand8,9
1958 Birth Cohort Controls: Richard W Jones18, Wendy L McArdle18, Susan M Ring18, David P Strachan19,
Marcus Pembrey18,20
Bipolar Disorder (Aberdeen): Gerome Breen21, David St Clair21; (Birmingham): Sian Caesar22, Katherine
Gordon-Smith22,23, Lisa Jones22; (Cardiff): Christine Fraser23, Elaine K Green23, Detelina Grozeva23, Marian L
Hamshere23, Peter A Holmans23, Ian R Jones23, George Kirov23, Valentina Moskvina23, Ivan Nikolov23, Michael
C O’Donovan23, Michael J Owen23, Nick Craddock23; (London): David A Collier24, Amanda Elkin24, Anne
Farmer24, Richard Williamson24, Peter McGuffin24; (Newcastle): Allan H Young25, I Nicol Ferrier25
Coronary Artery Disease (Leeds): Stephen G Ball26, Anthony J Balmforth26, Jennifer H Barrett26, D Timothy
Bishop26, Mark M Iles26, Azhar Maqbool26, Nadira Yuldasheva26, Alistair S Hall26; (Leicester): Peter S
Braund10, Paul R Burton1, Richard J Dixon10, Massimo Mangino10, Suzanne Stevens10, Martin D Tobin1, John R
Thompson1, Nilesh J Samani10
Crohn’s Disease (Cambridge): Francesca Bredin27, Mark Tremelling27, Miles Parkes27; (Edinburgh): Hazel
Drummond28, Charles W Lees28, Elaine R Nimmo28, Jack Satsangi28; (London): Sheila A Fisher29, Alastair
Forbes30, Cathryn M Lewis29, Clive M Onnie29, Natalie J Prescott29, Jeremy Sanderson31, Christopher G
Mathew29; (Newcastle): Jamie Barbour32, M Khalid Mohiuddin32, Catherine E Todhunter32, John C
Mansfield32; (Oxford): Tariq Ahmad33, Fraser R Cummings33, Derek P Jewell33
Hypertension (Aberdeen): John Webster34; (Cambridge): Morris J Brown35, David G Clayton2; (Evry,
France): G Mark Lathrop36; (Glasgow): John Connell37, Anna Dominiczak37; (Leicester): Nilesh J Samani10;
(London): Carolina A Braga Marcano38, Beverley Burke38, Richard Dobson38, Johannie Gungadoo38, Kate L
Lee38, Patricia B Munroe38, Stephen J Newhouse38, Abiodun Onipinla38, Chris Wallace38, Mingzhan Xue38,
Mark Caulfield38; (Oxford): Martin Farrall39
Rheumatoid Arthritis: Anne Barton40, Ian N Bruce40, Hannah Donovan40, Steve Eyre40, Paul D Gilbert40,
Samantha L Hider40, Anne M Hinks40, Sally L John40, Catherine Potter40, Alan J Silman40, Deborah PM
Symmons40, Wendy Thomson40, Jane Worthington40
Type 1 Diabetes: David G Clayton2, David B Dunger2,41, Sarah Nutland2, Helen E Stevens2, Neil M Walker2,
Barry Widmer2,41, John A Todd2
Type 2 Diabetes (Exeter): Timothy M Frayling42,43, Rachel M Freathy42,43, Hana Lango42,43, John R B
Perry42,43, Beverley M Shields43, Michael N Weedon42,43, Andrew T Hattersley42,43; (London): Graham A
Hitman44; (Newcastle): Mark Walker45; (Oxford): Kate S Elliott3,7, Christopher J Groves7, Cecilia M
Lindgren3,7, Nigel W Rayner3,7, Nicholas J Timpson3,46, Eleftheria Zeggini3,7, Mark I McCarthy3,7
Tuberculosis (Gambia): Melanie Newport47, Giorgio Sirugo47; (Oxford): Emily Lyons3, Fredrik Vannberg3,
Adrian VS Hill3
Ankylosing Spondylitis: Linda A Bradbury48, Claire Farrar49, Jennifer J Pointon48, Paul Wordsworth49,
Matthew A Brown48,49
AutoImmune Thyroid Disease: Jayne A Franklyn50, Joanne M Heward50, Matthew J Simmonds50, Stephen CL
Gough50
Breast Cancer: Sheila Seal51, Michael R Stratton51,52, Nazneen Rahman51
Multiple Sclerosis: Maria Ban53, An Goris53, Stephen J Sawcer53, Alastair Compston53
40
Gambian Controls (Gambia): David Conway47, Muminatou Jallow47, Melanie Newport47, Giorgio Sirugo47;
(Oxford): Kirk A Rockett3, Dominic P Kwiatkowski3,5
DNA, Genotyping, Data QC and Informatics (Wellcome Trust Sanger Institute, Hinxton): Suzannah J
Bumpstead5, Amy Chaney5, Kate Downes2,5, Mohammed JR Ghori5, Rhian Gwilliam5, Sarah E Hunt5, Michael
Inouye5, Andrew Keniry5, Emma King5, Ralph McGinnis5, Simon Potter5, Rathi Ravindrarajah5, Pamela
Whittaker5, Claire Widden5, David Withers5, Panos Deloukas5; (Cambridge): Hin-Tak Leung2, Sarah Nutland2,
Helen E Stevens2, Neil M Walker2, John A Todd2
Statistics (Cambridge): Doug Easton12, David G Clayton2; (Leicester): Paul R Burton1, Martin D Tobin1;
(Oxford): Jeffrey C Barrett3, David M Evans3, Andrew P Morris3, Lon R Cardon3; (Oxford): Niall J Cardin11,
Dan Davison11, Teresa Ferreira11, Joanne Pereira-Gale11, Ingeleif B Hallgrimsdóttir11, Bryan N Howie11,
Jonathan L Marchini11, Chris CA Spencer11, Zhan Su11, Yik Ying Teo3,11, Damjan Vukcevic11, Peter Donnelly11
PIs: David Bentley5,54, Matthew A Brown48,49, Lon R Cardon3, Mark Caulfield38, David G Clayton2, Alistair
Compston53, Nick Craddock23, Panos Deloukas5, Peter Donnelly11, Martin Farrall39, Stephen CL Gough50,
Alistair S Hall26, Andrew T Hattersley42,43, Adrian VS Hill3, Dominic P Kwiatkowski3,5, Christopher G
Mathew29, Mark I McCarthy3,7, Willem H Ouwehand8,9, Miles Parkes27, Marcus Pembrey18,20, Nazneen
Rahman51, Nilesh J Samani10, Michael R Stratton51,52, John A Todd2, Jane Worthington40
1
Genetic Epidemiology Group, Department of Health Sciences, University of Leicester, Adrian Building,
University Road, Leicester, LE1 7RH, UK; 2 Juvenile Diabetes Research Foundation/Wellcome Trust Diabetes
and Inflammation Laboratory, Department of Medical Genetics, Cambridge Institute for Medical Research,
University of Cambridge, Wellcome Trust/MRC Building, Cambridge, CB2 0XY, UK; 3 Wellcome Trust Centre
for Human Genetics, University of Oxford, Roosevelt Drive, Oxford OX3 7BN, UK; 4 Department of
Psychological Medicine, Henry Wellcome Building, School of Medicine, Cardiff University, Heath Park,
Cardiff CF14 4XN, UK; 5 The Wellcome Trust Sanger Institute, Wellcome Trust Genome Campus, Hinxton,
Cambridge CB10 1SA, UK; 6 The Wellcome Trust, Gibbs Building, 215 Euston Road, London NW1 2BE, UK; 7
Oxford Centre for Diabetes, Endocrinology and Medicine, University of Oxford, Churchill Hospital, Oxford,
OX3 7LJ, UK; 8 Department of Haematology, University of Cambridge, Long Road, Cambridge, CB2 2PT, UK;
9
National Health Service Blood and Transplant, Cambridge Centre, Long Road, Cambridge, CB2 2PT, UK; 10
Department of Cardiovascular Sciences, University of Leicester, Glenfield Hospital, Groby Road, Leicester,
LE3 9QP, UK; 11 Department of Statistics, University of Oxford, 1 South Parks Road, Oxford OX1 3TG, UK; 12
Cancer Research UK Genetic Epidemiology Unit, Strangeways Research Laboratory, Worts Causeway,
Cambridge CB1 8RN, UK; 13 National Health Service Blood and Transplant, Sheffield Centre, Longley Lane,
Sheffield S5 7JN, UK; 14 National Health Service Blood and Transplant, Brentwood Centre, Crescent Drive,
Brentwood, CM15 8DP, UK; 15 The Welsh Blood Service, Ely Valley Road, Talbot Green, Pontyclun, CF72
9WB, UK; 16 The Scottish National Blood Transfusion Service, Ellen’s Glen Road, Edinburgh, EH17 7QT, UK;
17
National Health Service Blood and Transplant, Southampton Centre, Coxford Road, Southampton, SO16
5AF, UK; 18 Avon Longitudinal Study of Parents and Children, University of Bristol, 24 Tyndall Avenue,
Bristol, BS8 1TQ, UK; 19 Division of Community Health Services, St George’s University of London, Cranmer
Terrace, London SW17 0RE, UK; 20 Institute of Child Health, University College London, 30 Guilford St,
London WC1N 1EH, UK; 21 University of Aberdeen, Institute of Medical Sciences, Foresterhill, Aberdeen,
AB25 2ZD, UK; 22 Department of Psychiatry, Division of Neuroscience, Birmingham University, Birmingham,
B15 2QZ, UK; 23 Department of Psychological Medicine, Henry Wellcome Building, School of Medicine,
Cardiff University, Heath Park, Cardiff CF14 4XN, UK; 24 SGDP, The Institute of Psychiatry, King's College
London, De Crespigny Park Denmark Hill London SE5 8AF, UK; 25 School of Neurology, Neurobiology and
Psychiatry, Royal Victoria Infirmary, Queen Victoria Road, Newcastle upon Tyne, NE1 4LP, UK; 26 LIGHT and
LIMM Research Institutes, Faculty of Medicine and Health, University of Leeds, Leeds, LS1 3EX, UK; 27 IBD
Research Group, Addenbrooke's Hospital, University of Cambridge, Cambridge, CB2 2QQ, UK; 28
Gastrointestinal Unit, School of Molecular and Clinical Medicine, University of Edinburgh, Western General
Hospital, Edinburgh EH4 2XU UK; 29 Department of Medical & Molecular Genetics, King's College London
School of Medicine, 8th Floor Guy's Tower, Guy's Hospital, London, SE1 9RT, UK; 30 Institute for Digestive
Diseases, University College London Hospitals Trust, London, NW1 2BU, UK; 31 Department of
41
Gastroenterology, Guy's and St Thomas' NHS Foundation Trust, London, SE1 7EH, UK; 32 Department of
Gastroenterology & Hepatology, University of Newcastle upon Tyne, Royal Victoria Infirmary, Newcastle upon
Tyne, NE1 4LP, UK; 33 Gastroenterology Unit, Radcliffe Infirmary, University of Oxford, Oxford, OX2 6HE,
UK; 34 Medicine and Therapeutics, Aberdeen Royal Infirmary, Foresterhill, Aberdeen, Grampian AB9 2ZB, UK;
35
Clinical Pharmacology Unit and the Diabetes and Inflammation Laboratory, University of Cambridge,
Addenbrookes Hospital, Hills Road, Cambridge CB2 2QQ, UK; 36 Centre National de Genotypage, 2, Rue
Gaston Cremieux, Evry, Paris 91057.; 37 BHF Glasgow Cardiovascular Research Centre, University of
Glasgow, 126 University Place, Glasgow, G12 8TA, UK; 38 Clinical Pharmacology and Barts and The London
Genome Centre, William Harvey Research Institute, Barts and The London, Queen Mary’s School of Medicine,
Charterhouse Square, London EC1M 6BQ, UK; 39 Cardiovascular Medicine, University of Oxford, Wellcome
Trust Centre for Human Genetics, Roosevelt Drive, Oxford OX3 7BN, UK; 40arc Epidemiology Research Unit,
University of Manchester, Stopford Building, Oxford Rd, Manchester, M13 9PT, UK; 41 Department of
Paediatrics, University of Cambridge, Addenbrooke’s Hospital, Cambridge, CB2 2QQ, UK; 42 Genetics of
Complex Traits, Institute of Biomedical and Clinical Science, Peninsula Medical School, Magdalen Road,
Exeter EX1 2LU UK; 43 Diabetes Genetics, Institute of Biomedical and Clinical Science, Peninsula Medical
School, Barrack Road, Exeter EX2 5DU UK; 44 Centre for Diabetes and Metabolic Medicine, Barts and The
London, Royal London Hospital, Whitechapel, London, E1 1BB UK; 45 Diabetes Research Group, School of
Clinical Medical Sciences, Newcastle University, Framlington Place, Newcastle upon Tyne NE2 4HH, UK; 46
The MRC Centre for Causal Analyses in Translational Epidemiology, Bristol University, Canynge Hall,
Whiteladies Rd, Bristol BS2 8PR, UK; 47 MRC Laboratories, Fajara, The Gambia; 48 Diamantina Institute for
Cancer, Immunology and Metabolic Medicine, Princess Alexandra Hospital, University of Queensland,
Woolloongabba, Qld 4102, Australia; 49 Botnar Research Centre, University of Oxford, Headington, Oxford
OX3 7BN, UK; 50 Department of Medicine, Division of Medical Sciences, Institute of Biomedical Research,
University of Birmingham, Edgbaston, Birmingham B15 2TT, UK; 51 Section of Cancer Genetics, Institute of
Cancer Research, 15 Cotswold Road, Sutton, SM2 5NG, UK; 52 Cancer Genome Project, The Wellcome Trust
Sanger Institute, Wellcome Trust Genome Campus, Hinxton, Cambridge CB10 1SA, UK; 53 Department of
Clinical Neurosciences, University of Cambridge, Addenbrooke’s Hospital, Hills Road, Cambridge CB2 2QQ,
UK; 54 PRESENT ADDRESS: Illumina Cambridge, Chesterford Research Park, Little Chesterford, Nr Saffron
Walden, Essex, CB10 1XL, UK.
42
Membership of the Cardiogenics consortium
Associazione per lo Studio della Trombosi in Cardiologia, 27100 Pavia, Italy: Diego Ardissino
EUROIMMUN AG, 23560 Lübeck, Germany: Christian Krüger, Lars Komorowski, Christian Probst, Ulf Steller
Helmholtz Institut Zentrum München, 85764 Neuherberg, Germany: Thomas Meitinger, Erich Wichmann,
Peter Lichtner
Institut National de la Santé et de la Recherche Médicale (INSERM), 75654 Paris, France: Francois Cambien,
Laurence Tiret
Johannes Gutenberg-University of Mainz, 55099 Mainz, Germany: Stefan Blankenberg, Karl Lackner, Thomas
Münzel
Queen’s University of Belfast, Belfast BT71NN, UK: Alun Evans, Frank Kee
Sanquin Blood Supply Foundation, 1066 CX Amsterdam, The Netherlands: Ellen van der Schoot, Jaap Jan
Zwaginga
Technische Universität München, 80333 München, Germany: Dietlind Zohlnhoefer
TRIUM Analysis Online GmbH, 81677 München, Germany: Martin Daumer, Michael Scholz
The Chancellor, Master and Scholar of the University of Cambridge, Cambridge CB2 1TS, UK: Willem
Ouwehand, Richard Farndale, Nicholas Watkins
University of Leeds, Leeds LS2 9JT Leeds, UK: Alistair Hall, Stephen Ball, Tim Bishop
University of Leicester, Leicester LE1 7RH, UK: Nilesh Samani, Alison Goodall, John R Thompson
University of Lübeck, 23538 Lübeck, Germany: Heribert Schunkert, Jeanette Erdmann, Andreas Ziegler
University of Regensburg, 93053 Regensburg, Germany: Christian Hengstenberg, Gerd Schmitz
University of Uppsala, 75105 Uppsala, Sweden: Ann-Christin Syvänen, Tomas Axelsson, Ulrika Liljedahl
Wellcome Trust Sanger Institute, Hinxton Cambridge CB10 1SA, UK: Panos Deloukas, Kate Rice
43
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47
Table 1. Study design of case control studies included in the meta-analysis of the Trp719Arg polymorphism (rs20455) in kinesin-like
protein 6 and basic demographic characteristics of participants stratified by case control status
ADVANCE
AMI Gene
CATHGEN
deCODE
FinRisk
GENESTAR
GerMIFS I
GerMIFS II
HARPS
INTERHEART
(Europeans)
N
Country of study
Ascertainment scheme
Qualifyin
g event
505
U.S.
population-based
CAD
*
Study
Controls
514
U.S.
population-based
--
45.6±5.7
61.3
39.5
Cases
793
Germany
hospital-based
MI
men <65
52.2±8.2
0
0.0
Controls
1121
Germany
hospital-based
--
--
52.6±13.7
53.1
0.0
Cases
1575
U.S.
hospital-based
MI
³18 men or women
61.9±11.9
28.9
17.6
Controls
970
U.S.
hospital-based
--
³18 men or women
56.6±11.8
52.4
24.7
Cases
4313
Iceland
population-based
CAD
no age criterion
68.9±12.1
30.8
0.0
Controls
24952
Iceland
population-based
--
Cases
167
Finland
MI
Controls
172
Finland
drawn from population-based
cohort
nested case-cohort
--
men £50 or women £
60
--
Cases
378
U. S.
hospital-based
CAD
Controls
2652
U. S.
unaffected siblings of cases
--
Cases
722
Germany
hospital-based
MI
Controls
1,643
Germany
population-based
--
Cases
1182
Germany
hospital-based
MI
Controls
1280
Germany
hospital-based
--
45.4±6.5
61.5
45.7
49.2±21.7
63.2
0.0
47.1 ± 6.2
33.5
0.0
47.1 ± 6.0
31.4
0.0
men and women < 60
46.9±7.0
26.2
24.6
--
47.2±13.1
58.2
40.5
men £60 or women £
65
--
50.2±7.9
32.5
0.0
62.5±10.1
50.5
0.0
51.3±7.6
20.3
0.0
51.2±11.9
47.9
0.0
46.0 ± 6.9
51.1
0.0
--
men £60 or women £
65
--
U.S.
community-based
559
U.S.
community-based
--
men £50 or women £
60
--
45.2 ± 7.3
55.5
0.0
789
U.S. and Europe
hospital-based
MI
no age criterion
61.6±12.3
29
0.0
859
U.S. and Europe
hospital and community-based
--
61.2±12.2
30.7
0.0
hospital-based
MI
no age criterion
51.4±10.8
10.1
100.0
hospital and community-based
--
--
49.8 ± 11.0
9.1
100.0
46.0±6.3
20.1
0.0
55.2±8.0
55.2
0.0
48.5 ± 4.4
41.9
0.0
48.7 ± 4.6
42.4
0.0
Cases
505
Controls
Cases
Controls
MI
Cases
482
Indian
subcontinent**
Indian
subcontinent**
Northern Ireland
hospital-based
MI
Controls
622
Northern Ireland
older siblings of cases
--
Cases
86
Sweden
MI
Controls
99
Sweden
drawn from population-based
cohort
nested case-cohort
Cases
1092
(South Asians)
Controls
1187
MDC
non
Mean age in Female
European
years ± sd sex (%)
(%)
men £45 or women £
55
--
Cases
INTERHEART
IFS
Age and sex
criterion
--
men £55 or women £
60
-men £50 or women £
60
--
MedStar
MGH PCAD
MAHI
PennCATH
REGICOR
VHS
WTCCC CAD
Cases
875
U.S.
hospital-based
CAD
men and women <65
48.9 ± 6.4
18.2
0.0
Controls
447
U.S.
hospital-based
--
men and women ≥45
59.8 ± 8.9
48.8
0.0
Cases
204
U.S.
hospital-based
Controls
260
U.S.
hospital-based
--
men £50 or women £
60
--
53.8 ± 11.1
33.5
0.0
Cases
807
U.S.
hospital-based
MI
no age criterion
61.5±12.7
32.1
0.0
Controls
637
U.S.
outpatients
--
--
60.7±12.4
39
0.0
Cases
933
U.S.
hospital-based
CAD
≤66 men and women
52.7 ± 7.6
24.3
0.0
Controls
468
U.S.
hospital-based
--
≥45 men and women
61.7 ± 9.6
51.7
0.0
45.9 ± 5.8
20.2
0.0
Cases
312
MI
47.0 ± 6.1
29.9
0.0
Spain
hospital-based
MI
--
men £50 or women £
60
--
46.0 ± 5.6
21.5
0.0
CAD
no age criterion
61.4±10.0
20.4
0.0
--
58.0±12.3
35
0.0
<66 years
49.3±7.9
20.2
0.0
--
44.7±9.3
49.2
0.0
Controls
317
Spain
Cases
1106
Italy
drawn from community-based
cohort
hospital-based
Controls
383
Italy
hospital-based
Cases
1,926
U.K.
community-based
Controls
2,954
U.K.
community-based
CAD
*total number of subjects with successful genotyping of rs20455
CAD = coronary artery disesae, MI = myocardial infarction, ADVANCE = Atherosclerotic Disease, VAscular functioN, and genetiC
Epidemiology, AMI Gene = AMI Gene Study/Dortmund Health, CATHGEN = CATHGEN Research Project, deCODE = deCODE
genetics CAD study, FINRISK = National Finrisk study (FINRISK), GeneSTAR = Genetic Study of Atherosclerosis Risk, GerMIFS I
and GerMIFS II = German Myocardial Infarction Family studies I and II, HARPS = Heart Attack Risk in Puget Sound, INTERHEART =
international INTERHEART study coordinated by Population Health Research Institute of McMaster University, IFS = Irish Family
Study, MDC = Malmo Diet and Cancer, MEDSTAR = Washington Hospital Center/Medstar angiographic CAD study, MGH PCAD =
Massachusetts General Hospital of Premature CAD study, MAHI = Mid-America Heart Institute, PennCATH = University of
Pennsylvania Medical center angiographic CAD study, REGICOR = Registre Gironı´ del Cor study, VHS = Verona Heart Study,
**India, Pakistan, Bangladesh, Sri Lanka, Nepal
Table 2. Kinesin-like protein 6 Trp719Arg polymorphism (rs20455) allele Frequencies, genotypes counts, and Odds Ratios adjusted for age and sex in 19 case-control
studies of CAD, stratified by race/ethnic group
CASES (CAD)
No.
subjects
Europeans
ADVANCE
275
AMI Gene
793
CATHGEN
1298
4313
deCODE†
FinRisk
167
GeneSTAR
285
GerMIFS I*
722
GerMIFS II
1182
HARPS
505
INTERHEART
789
482
IFS†‡
MDC
86
MedStar
875
MGH PCAD
204
MAHI
807
PennCath
933
REGICOR
312
VHS
1106
WTCCC CAD
1922
Total - Meta Analysis 17056
Non Europeans
ADVANCE admixed Hisp.
ADVANCE admixed non
Hisp.
ADVANCE Afr.Am.
ADVANCE East Asians
ADVANCE Hisp.
CathGEN Afr.Am.
34
74
49
45
28
277
CONTROLS
Odds Ratios, 95% confidence intervals and P v
Allele
Allele
719Trp/ 719Arg/ 719Arg/
No.
719Trp/ 719Arg/ 719Arg/
Freq
Freq
719Trp 719Trp 719Arg subjects
719Trp 719Trp 719Arg
719Arg
719Arg
Log Additive
Mode of
Inheritance
P
0.345
0.369
0.361
0.300
0.374
0.368
0.367
0.368
0.404
0.362
0.344
0.372
0.349
0.353
0.367
0.379
0.348
0.378
0.356
119
311
545
2131
64
106
293
472
187
335
203
35
370
89
322
359
134
437
792
122
379
570
1779
81
148
328
551
228
337
226
38
399
86
377
441
139
501
890
34
103
183
403
22
31
101
159
90
117
53
13
106
29
108
133
39
168
240
311
1121
730
24952
172
1579
1643
1280
559
859
622
99
447
260
637
468
317
383
2933
39372
0.378
0.381
0.355
0.312
0.355
0.365
0.368
0.360
0.381
0.354
0.346
0.394
0.364
0.348
0.359
0.358
0.339
0.372
0.355
122
430
297
11813
73
626
662
522
216
354
261
33
174
114
256
194
141
145
1242
143
528
347
10689
76
752
753
595
260
402
292
54
221
111
304
213
137
191
1299
46
163
86
2450
23
201
228
163
83
103
69
12
52
35
77
61
39
47
392
0.87(0.69-1.1)
0.89(0.75-1.04)
1.02(0.89-1.18)
0.93(0.88-0.99)
1.09(0.8-1.49)
0.99(0.82-1.21)
0.97(0.84-1.13)
1.01(0.89-1.14)
1.1(0.92-1.31)
1.03(0.9-1.19)
1.03(0.81-1.30)
0.91(0.6-1.39)
0.99(0.8-1.22)
1.04(0.82-1.31)
1.04(0.89-1.21)
1.04(0.86-1.26)
1.02(0.78-1.34)
1.03(0.87-1.22)
1.02(0.93-1.12)
0.98(0.95-1.02)
0.249
0.142
0.749
0.018
0.596
0.939
0.742
0.893
0.279
0.671
0.835
0.667
0.919
0.878
0.647
0.666
0.747
0.757
0.624
0.349
0.412
0.419
0.745
0.356
0.375
0.762
12
27
4
19
11
16
16
32
17
20
13
100
6
15
28
6
4
161
22
37
87
35
22
240
0.455
0.622
0.816
0.486
0.364
0.783
7
10
5
9
8
9
10
8
22
18
12
86
5
19
60
8
2
145
0.77(0.34-1.67)
0.83(0.44-1.57)
0.7(0.39-1.23)
0.6(0.3-1.13)
1.07(0.45-2.58)
0.89(0.66-1.21)
0.507
0.555
0.207
0.114
0.876
0.465
GENESTAR Afr.Am.
93
0.796
2
34
57
1073
0.786
53
353
INTERHEART (South Asians) 1092
0.451
351
498
243
1187
0.449
389
531
Afr.Am. Total-Meta Analysis 419
1400
Please see footnote of table 1 for resolution of study acronym. Afr.Am. = African Americans Hisp. = Hispanics
†
Adjusted for relatedness
*P values adjusted by genomic control method as λ=1.27
‡Approach to analysis unable to produce dominant model odds ratios
667
267
1.05(0.72-1.51) 0.813
1.02(0.91-1.14) 0.791
0.91(0.73-1.13) 0.380
or age and sex in 19 case-control
95% confidence intervals and P values
Log Dominant
Mode of
Inheritance
P
0.84(0.6-1.16)
0.9(0.71-1.13)
0.96(0.79-1.17)
0.91(0.85-0.99)
1.18(0.76-1.82)
1.09(0.83-1.43)
0.97(0.88-1.08)
0.99(0.91-1.08)
1.07(0.83-1.37)
0.95(0.78-1.15)
0.73(0.4-1.33)
0.91(0.68-1.22)
1.03(0.69-1.52)
1.01(0.82-1.25)
1.03(0.79-1.33)
1.06(0.77-1.45)
0.93(0.73-1.19)
1.09(0.96-1.24)
0.97(0.93-1.01)
0.289
0.349
0.674
0.020
0.458
0.543
0.661
0.770
0.602
0.573
0.305
0.527
0.904
0.919
0.850
0.716
0.568
0.189
0.137
0.73(0.21-2.36)
1.44(0.49-4.41)
0.7(0.18-3)
0.48(0.18-1.25)
0.88(0.27-2.82)
0.58(0.25-1.37)
0.607
0.506
0.619
0.135
0.831
0.213
2.35(0.56-9.83) 0.241
1.04(0.87-1.24) 0.669
0.81(0.42-1.55) 0.520
Table 3. Kinesin-like protein 6 Trp719Arg polymorphism (rs20455) allele Frequencies, genotypes counts, and Odds Ratios adjusted for age and sex in 19 case-control
of CAD, stratified by race/ethnic group and restricted to early onset disease (age of onset of CAD <50 years of age for males and <60 years of age for females) and sim
aged controls
CASES (CAD)
CONTROLS
Odds Ratios, 95% confidence intervals and
No.
subjects
Allele
Allele
719Arg
719Trp/ 719Arg/ 719Arg/
No.
719Trp/ 719Arg/
Freq
Freq
/719Ar
719Trp 719Trp 719Arg subjects
719Trp 719Trp
719Arg
719Arg
g
Log Additive
Mode of
Inheritance
P
Europeans
ADVANCE
AMI Gene
CATHGEN
275
296
585
0.345
0.380
0.366
119
117
233
122
133
276
34
46
76
311
193
295
0.378
0.415
0.359
122
68
121
143
90
136
46
35
38
0.87(0.69-1.1) 0.025
0.98(0.74-1.3) 0.875
0.99(0.78-1.28) 0.990
deCODE†
750
0.292
375
312
63
12548
0.312
5955
5366
1227
0.91(0.72-1.16) 0.450
FinRisk
GeneSTAR
GerMIFS I*
GerMIFS II
HARPS
INTERHEART
IFS†‡
MDC
MedStar
MGH PCAD
MAHI
PennCath
REGICOR
VHS
WTCCC CAD
Total - Meta Analysis
204
201
468
621
875
195
371
167
601
312
227
354
505
197
1085
8289
0.353
0.358
0.372
0.364
0.349
0.364
0.342
0.374
0.351
0.348
0.392
0.383
0.404
0.409
0.359
89
79
190
253
370
85
154
64
255
134
81
134
187
71
444
86
100
208
284
399
78
180
81
270
139
114
169
228
91
503
29
22
70
84
106
32
37
22
76
39
32
51
90
35
138
260
1579
923
869
447
216
309
172
151
317
190
128
559
113
2612
22192
0.348
0.365
0.362
0.361
0.364
0.343
0.353
0.355
0.358
0.339
0.363
0.359
0.381
0.420
0.355
114
626
373
359
174
92
129
73
61
141
77
53
216
33
1101
111
752
432
393
221
100
142
76
72
137
88
58
260
65
1167
35
201
118
117
52
24
38
23
18
39
25
17
83
15
344
1.09(0.8-1.49)
0.95(0.76-1.18)
0.99(0.8-1.23)
0.97(0.81-1.16)
1.1(0.92-1.31)
1.09(0.82-1.44)
0.86(0.63-1.16)
0.91(0.6-1.39)
0.98(0.74-1.29)
1.04(0.82-1.31)
1.14(0.85-1.51)
1.11(0.82-1.49)
1.02(0.78-1.34)
0.88(0.56-1.38)
1.04(0.94-1.16)
0.99(0.94-1.04)
0.596
0.627
0.949
0.760
0.279
0.559
0.315
0.667
0.878
0.878
0.386
0.508
0.747
0.570
0.444
0.729
34
74
49
45
0.412
0.419
0.745
0.356
12
27
4
19
16
32
17
20
6
15
28
6
22
37
87
35
0.455
0.622
0.816
0.486
7
10
5
9
10
8
22
18
5
19
60
8
0.77(0.34-1.67)
0.83(0.44-1.57)
0.7(0.39-1.23)
0.6(0.3-1.13)
0.507
0.555
0.207
0.114
Non Europeans
ADVANCE admixed Hisp.
ADVANCE admixed non
Hisp.
ADVANCE
Afr.Am.
ADVANCE East Asians
ADVANCE Hisp.
28
0.375
11
13
4
22
0.364
8
12
2
1.07(0.45-2.58) 0.876
CathGEN Afr.Am.
157
0.755
7
63
87
128
0.781
5
46
77
1.02(0.64-1.61) 0.950
GENESTAR Afr.Am.
70
0.807
2
23
45
1073
0.786
53
353
667 1.15(0.75-1.76) 0.532
INTERHEART (South Asians) 515
0.458
163
232
120
633
0.453
203
286
144 1.03(0.88-1.21) 0.701
Afr.Am. Total-Meta Analysis
276
1288
1.06(0.76-1.49) 0.721
Please see footnote of table 1 for resolution of study acronym. Afr.Am. = African Americans Hisp. = Hispanics
Controls are restricted to males < 50 years of age and females < 60 years of age at the time of blood draw to match the sex specific age range of cases.
†
Adjusted for relatedness
*P values adjusted by genomic control method as λ=1.27
‡Approach to analysis unable to produce dominant model odds ratios
age and sex in 19 case-control studies
ars of age for females) and similarly
95% confidence intervals and P values
Log Dominant
Mode of
Inheritance
P
0.84(0.6-1.16) 0.289
1.02(0.67-1.54) 0.929
0.98(0.7-1.37) 0.900
0.95(0.69-1.29)
0.730
1.18(0.76-1.82)
0.99(0.73-1.35)
0.99(0.87-1.16)
0.97(0.86-1.1)
1.07(0.83-1.37)
0.96(0.65-1.42)
0.458
0.951
0.999
0.611
0.602
0.837
0.73(0.4-1.33) 0.305
0.91(0.62-1.33) 0.612
1.03(0.69-1.52) 0.904
1.12(0.8-1.79) 0.374
1.16(0.73-1.83) 0.539
1.06(0.77-1.45) 0.716
0.72(0.38-1.37) 0.320
1.10(0.95-1.27 0.216
1.01(0.95-1.08) 0.767
0.73(0.21-2.36)
1.44(0.49-4.41)
0.7(0.18-3)
0.48(0.18-1.25)
0.607
0.506
0.619
0.135
0.88(0.27-2.82) 0.831
0.89(0.23-3.46) 0.860
1.79(0.43-7.5) 0.427
1.04(0.81-1.34) 0.735
1.05(0.87-1.26) 0.609
range of cases.
Table 1S. Summary statistics of traditional risk factors of CAD in each of the 19
studies included in the Trp719Arg polymorphism in kinesin-like protein 6 meta
analysis, stratified by case control status
*
Study
ADVANCE
AMI Gene
CATHGEN
deCODE
FinRisk
GENESTAR
GerMIFS I
N
Cases
506
Ever
Diabetes Hyperchol Body mass
Hyperten
index
cigarette
mellitus esterolemi
sion (%)
smoking (%)
(%)
a (%)
(kg/m2)
58.3
34.2
21.7
28.7
31.7±7.1
Controls
514
34.8
19.3
6.6
19.8
28.0±6.8
Cases
809
65.9
71.8
14.6
60.6
28.0±4.7
Controls
1132
55.9
62.6
8
68.1
27.5±4.9
Cases
1575
65.7
72.8
30.7
71.4
NA
Controls
970
43.8
61.9
21.3
45.4
NA
Cases
4313
34.8
N/A
N/A
N/A
27.8±4.7
Controls
24952
34.8
N/A
N/A
N/A
26.9±5.4
Cases
167
74.4
72.5
17.7
75.2
29.6 ± 5.0
Controls
172
58.2
68
5.9
48.2
27.7 ± 4.0
Cases
378
82
83.1
43.3
80.4
30.8±6.3
Controls
2652
52.6
37
10.2
33.4
29.9±7.0
Cases
875
70.3
86.5
12.3
76.1
27.4±3.6
Controls
1,644
49.3
62.7
11
78
28.1±4.5
Cases
1222
64.7
88.1
15.5
80.8
27.9±4.0
Controls
1298
55.6
41.7
3.5
67.3
27.4±4.6
Cases
505
73.9
50.5
14.9
43.7
29.2 ± 6.8
Controls
559
41.7
30.8
3
26
26.9 ± 5.7
Cases
789
67.1
44.4
15
21.6
27.3±4.7
Controls
859
52.2
31.8
7.1
15.8
26.9±4.1
Cases
1092
61.4
28.3
20.4
21.7
24.9±4.3
(South Asians) Controls
1187
44.3
13.4
8.9
13.9
24.8±4.1
IFS
Cases
482
82.5
27.9
9.4
85.8
28.5±4.3
Controls
622
57.6
28.9
5.6
8.6
28.2±5.0
GerMIFS II
HARPS
INTERHEART
(Europeans)
INTERHEART
MDC
MedStar
MGH PCAD
MAHI
PennCATH
REGICOR
VHS
WTCCC CAD
Cases
86
87.2
81.4
4.7
37.2
26.9 ± 4.2
Controls
99
61.6
62.6
1
1
25.7 ± 4.3
Cases
875
52.6
76.3
30.1
88.4
31.7 ± 6.8
Controls
447
49.8
60.6
19
65.1
31.7± 7.9
Cases
204
74.9
33.5
19.2
79
30.0 ± 7.0
Controls
260
57.3
25.3
0.4
31.3
27.9 ± 6.5
Cases
811
71
60.1
23.9
58.8
29.4
Controls
650
50.2
51.1
11.9
50.4
27.9
Cases
933
45.8
61.2
24.4
80.1
29.7 ± 5.6
Controls
468
34.8
49.2
11.2
61.3
28.9 ± 6.4
Cases
312
82.8
38
14.8
48.9
27.5 ± 4.2
Controls
317
61.9
31.5
6.1
33.1
27.0 ± 3.9
Cases
1106
68.7
66.1
19.4
74.8
26.8±3.5
Controls
383
43.5
40.1
7
66
25.4±3.4
Cases
1,414
78.3
41.1
10.9
79.3
27.6±4.3
Controls
2,938
NA
NA
NA
NA
NA
*total number of subjects with successful genotyping of rs20455
CAD = coronary artery disesae, MI = myocardial infarction, ADVANCE =
Atherosclerotic Disease, VAscular functioN, and genetiC Epidemiology, AMI Gene =
AMI Gene Study/Dortmund Health, CATHGEN = CATHGEN Research Project,
deCODE = deCODE genetics CAD study, FINRISK = National Finrisk study
(FINRISK), GeneSTAR = Genetic Study of Atherosclerosis Risk, GerMIFS I and
GerMIFS II = German Myocardial Infarction Family studies I and II, HARPS = Heart
Attack Risk in Puget Sound, INTERHEART = international INTERHEART study
coordinated by Population Health Research Institute of McMaster University, IFS =
Irish Family Study, MDC = Malmo Diet and Cancer, MEDSTAR = Washington
Hospital Center/Medstar angiographic CAD study, MGH PCAD = Massachusetts
General Hospital of Premature CAD study, MAHI = Mid-America Heart Institute,
PennCATH = University of Pennsylvania Medical center angiographic CAD study,
REGICOR = Registre Gironı´ del Cor study, VHS = Verona Heart Study, WTCCC
Table 2S. Kinesin-like protein 6 Trp719Arg polymorphism (rs20455) allele Frequencies, genotypes counts, and Odds Ratios adjusted for age and sex in 19 casecontrol studies of CAD, restricted to European subgroup of cases with documented myocardial infarction
CASES (MI)
CONTROLS
Odds Ratios, 95% confidence intervals and P values
Allele
Allele
Log Additive
719Trp/ 719Arg/ 719Arg/
No.
719Trp/ 719Arg/ 719Arg/
Freq
Freq
Mode of
P
719Trp 719Trp 719Arg subjects
719Trp 719Trp 719Arg
719Arg
719Arg
Inheritance
ADVANCE
151
0.341
67
65
19
311
0.378
122
143
46
0.98(0.69-1.4)
0.927
AMI Gene
793
0.369
311
379
103
1121
0.381
430
528
163 0.89(0.75-1.04) 0.142
CATHGEN
1298
0.361
545
570
183
730
0.355
297
347
86
1.02(0.89-1.18) 0.749
0.93 (0.87deCODE†
3577
0.299
1771
1475
331
24952
0.312 11813 10689
2450
0.023
0.99)
FinRisk
167
0.374
64
81
22
172
0.355
73
76
23
1.09(0.8-1.49)
0.596
GeneSTAR
164
0.399
52
93
19
1579
0.365
626
752
201 1.14 (0.89-1.45) 0.301
GerMIFS I*
722
0.367
293
328
101
1643
0.368
662
753
228 0.97(0.84-1.13) 0.742
GerMIFS II
1182
0.368
472
551
159
1280
0.360
522
595
163 1.01(0.89-1.14) 0.893
HARPS
505
0.404
187
228
90
559
0.381
216
260
83
1.1(0.92-1.31)
0.279
INTERHEART
789
0.362
335
337
117
859
0.354
354
402
103
1.03(0.9-1.19)
0.671
†‡
482
0.344
203
226
53
622
0.346
261
292
69
1.03(0.81-1.30) 0.835
IFS
MDC
86
0.372
35
38
13
99
0.394
33
54
12
0.91(0.6-1.39)
0.667
MedStar
421
0.356
171
200
50
447
0.364
174
221
52
1.16(0.88-1.51) 0.299
MGH PCAD
204
0.353
89
86
29
260
0.348
114
111
35
1.04(0.82-1.31) 0.878
MAHI
807
0.367
322
377
108
637
0.359
256
304
77
1.04(0.89-1.21) 0.647
PennCath
468
0.386
175
225
68
468
0.358
194
213
61
1.01(0.81-1.23) 0.900
REGICOR
312
0.348
134
139
39
317
0.339
141
137
39
1.02(0.78-1.34) 0.747
VHS
644
0.392
244
295
105
383
0.372
145
191
47
1.10(0.91-1.33) 0.316
WTCCC CAD
1373
0.359
562
636
175
2933
0.355
1242
1299
392 1.04(0.94-1.15) 0.447
Total - Meta Analysis 14145
39372
0.99(0.96-1.03) 0.625
Please see footnote of table 1 for resolution of study acronym. MI = myocardial infarction
For this analysis, the Odds Ratios of 12 out of 19 studies are unchanged compared to the primary analysis shown in the main text for the CAD outcome (Table 2)
because cases in these studies has already been defined on the basis of a documented MI.
No.
subjects
†
Adjusted for relatedness
*P values adjusted by genomic control method as λ=1.27
‡Approach to analysis unable to produce dominant model odds ratios
usted for age and sex in 19 case-
95% confidence intervals and P values
Log Dominant
Mode of
P
Inheritance
0.95(0.58-1.53) 0.818
0.9(0.71-1.13) 0.349
0.96(0.79-1.17) 0.674
0.91 (0.84-0.99) 0.028
1.18(0.76-1.82)
1.37 (0.98-1.99)
0.97(0.88-1.08)
0.99(0.91-1.08)
1.07(0.83-1.37)
0.95(0.78-1.15)
0.73(0.4-1.33)
1.18(0.81-1.70)
1.03(0.69-1.52)
1.01(0.82-1.25)
1.02(0.75-1.40)
1.06(0.77-1.45)
1.02 (0.78-1.34)
1.11(0.97-1.28)
0.98(0.94-1.02)
0.458
0.064
0.661
0.770
0.602
0.573
0.305
0.386
0.904
0.919
0.850
0.716
0.875
0.126
0.422
t for the CAD outcome (Table 2)
Table 3S. Kinesin-like protein 6 Trp719Arg polymorphism (rs20455) allele Frequencies, genotypes counts, and Odds Ratios adjusted for age and sex in 19 case-contro
studies of CAD, restricted to European subgroup of cases with early onset myocardial infarction (age of onset of MI <50 years of age for males and <60 years of age fo
females) ans similarly aged controls
CASES (MI)
CONTROLS
Odds Ratios, 95% confidence intervals and P value
Allele
Allele 719Trp
Log Additive
719Trp/ 719Arg/ 719Arg/
No.
719Arg/ 719Arg/
Freq
Freq
/719Tr
Mode of
P
719Trp 719Trp 719Arg subjects
719Trp 719Arg
719Arg
719Arg
p
Inheritance
ADVANCE
151
0.341
67
65
19
311
0.378
122
143
46
0.98(0.69-1.4)
0.927
AMI Gene
296
0.380
117
133
46
193
0.415
68
90
35
0.98(0.74-1.3)
0.875
CATHGEN
585
0.366
233
276
76
295
0.359
121
136
38
0.99(0.78-1.28) 0.990
750
0.292
375
312
63
12548
0.312
5955
5366
1227 0.91(0.72-1.16) 0.450
deCODE†
FinRisk
204
0.353
89
86
29
260
0.348
114
111
35
1.09(0.8-1.49)
0.596
GeneSTAR
125
0.392
42
68
15
1579
0.365
626
752
201 1.09 (0.83-1.44) 0.521
GerMIFS I*
468
0.372
190
208
70
923
0.362
373
432
118
0.99(0.8-1.23)
0.949
GerMIFS II
621
0.364
253
284
84
869
0.361
359
393
117
0.97(0.81-1.16) 0.760
HARPS
875
0.349
370
399
106
447
0.364
174
221
52
1.1(0.92-1.31)
0.279
INTERHEART
195
0.364
85
78
32
216
0.343
92
100
24
1.09(0.82-1.44) 0.559
371
0.342
154
180
37
309
0.353
129
142
38
0.86(0.63-1.16) 0.315
IFS†‡
MDC
167
0.374
64
81
22
172
0.355
73
76
23
0.91(0.6-1.39)
0.667
MedStar
311
0.360
125
148
38
151
0.358
61
72
18
1.23(0.87-1.72) 0.240
MGH PCAD
312
0.348
134
139
39
317
0.339
141
137
39
1.04(0.82-1.31) 0.878
MAHI
227
0.392
81
114
32
190
0.363
77
88
25
1.14(0.85-1.51) 0.386
PennCath
201
0.410
68
101
32
128
0.359
53
58
17
1.27(0.85-1.87) 0.241
REGICOR
505
0.404
187
228
90
559
0.381
216
260
83
1.02(0.78-1.34) 0.747
VHS
197
0.409
71
91
35
113
0.420
33
65
15
0.88(0.56-1.38) 0.570
WTCCC CAD
826
0.360
333
391
102
2612
0.355
1101
1167
344
1.06(0.94-1.19) 0.325
Total - Meta Analysis 7387
22192
1.03(0.98-1.09) 0.243
Please see footnote of table 1 for resolution of study acronym
For this analysis, the Odds Ratios of 13 out of 19 studies are unchanged compared to the primary analysis of the CAD outcome shown in the main text (Table 3)
because cases in these studies has already been defined on the basis of a documented MI.
Controls are restricted to males < 50 years of age and females < 60 years of age at the time of blood draw to match the sex specific age range of cases.
†
Adjusted for relatedness
*P values adjusted by genomic control method as λ=1.27
No.
subjects
‡Approach to analysis unable to produce dominant model odds ratios
ed for age and sex in 19 case-control
ge for males and <60 years of age for
95% confidence intervals and P values
Log Dominant
Mode of
Inheritance
0.95(0.58-1.53)
1.02(0.67-1.54)
0.98(0.7-1.37)
0.95(0.69-1.29)
1.18(0.76-1.82)
1.27 (0.86-1.88)
0.99(0.87-1.16)
0.97(0.86-1.1)
1.07(0.83-1.37)
0.96(0.65-1.42)
0.818
0.929
0.900
0.730
0.458
0.233
0.999
0.611
0.602
0.837
0.73(0.4-1.33)
1.25(0.79-1.98)
1.03(0.69-1.52)
1.12(0.8-1.79)
1.40(0.81-2.44)
1.06(0.77-1.45)
0.72(0.38-1.37)
1.14(0.97-1.34)
1.03(0.97-1.11)
0.305
0.340
0.904
0.374
0.230
0.716
0.320
0.106
0.263
own in the main text (Table 3)
c age range of cases.
P
Table 4S. Summary of Results of Heterogeneity Tests for Meta Analyses (Q value and I2)
Heterogeneity Tests
Q-value
df (Q)
P-value
I-squared
All CAD Log Additive Model Europeans
All CAD Log Dominant Model Europeans
All CAD Log Additive Model African Americans
All CAD Log Dominant Model African Americans*
11.24
10.98
1.36
2.76
18
17
2
2
0.884
0.858
0.507
0.252
0.0
0.0
0.0
27.4
Early Onset CAD Log Additive Model Europeans
Early Onset CAD Log Dominant Model Europeans
Early Onset CAD Log Additive Model African Americans
Early Onset CAD Log Dominant Model African Americans
7.13
7.23
1.86
0.90
18
17
2
2
0.989
0.980
0.394
0.637
0.0
0.0
0.0
0.0
All AMI Log Additive Model Europeans
13.07
18
0.788
All AMI Log Dominant Model Europeans
14.05
17
0.663
Early Onset AMI Log Additive Model Europeans
7.85
18
0.981
Early Onset AMI Log Dominant Model Europeans
9.47
17
0.924
*Random effects model for this stratum (DerSimonian and Laird method): OR, 95%CI,
0.86(0.39,1.91), p value 0.71
0.0
0.0
0.0
0.0