Genome-Wide Association Study for Incident Myocardial Infarction and Coronary Heart Disease in Prospective Cohort Studies: The CHARGE Consortium
Genome-Wide Association Study for Incident Myocardial Infarction and Coronary Heart Disease in Prospective Cohort Studies: The CHARGE Consortium
Abbas Dehghan 0 1
Joshua C. Bis 0 1
Charles C. White 0 1
Albert Vernon Smith 0 1
Alanna C. Morrison 0 1
L. Adrienne Cupples 0 1
Stella Trompet 0 1
Daniel I. Chasman 0 1
Thomas Lumley 0 1
Uwe Völker 0 1
Brendan M. Buckley 0 1
Jingzhong Ding 0 1
Majken K. Jensen 0 1
Aaron R. Folsom 0 1
Stephen B. Kritchevsky 0 1
Cynthia J. Girman 0 1
Ian Ford 0 1
Marcus Dörr 0 1
Veikko Salomaa 0 1
André G. Uitterlinden 0 1
Gudny Eiriksdottir 0 1
Ramachandran S. Vasan 0 1
Nora Franceschini 0 1
Cara L. Carty 0 1
Jarmo Virtamo 0 1
Serkalem Demissie 0 1
Philippe Amouyel 0 1
Dominique Arveiler 0 1
Susan R. Heckbert 0 1
Jean Ferrières 0 1
Pierre Ducimetière 0 1
Nicholas L. Smith 0 1
Ying A. Wang 0 1
David S. Siscovick 0 1
Kenneth M. Rice 0 1
Per- Gunnar Wiklund 0 1
Kent D. Taylor 0 1
Alun Evans 0 1
Frank Kee 0 1
Jerome I. Rotter 0 1
Juha Karvanen 0 1
Kari Kuulasmaa 0 1
Gerardo Heiss 0 1
Peter Kraft 0 1
Lenore J. Launer 0 1
Albert Hofman 0 1
Marcello R. P. Markus 0 1
Lynda M. Rose 0 1
Kaisa Silander 0 1
Peter Wagner 0 1
Emelia J. Benjamin 0 1
Kurt Lohman 0 1
David J. Stott 0 1
Fernando Rivadeneira 0 1
Tamara B. Harris 0 1
Daniel Levy 0 1
Yongmei Liu 0 1
Eric B. Rimm 0 1
J. Wouter Jukema 0 1
Henry Völzke 0 1
Paul M. Ridker 0 1
Stefan Blankenberg 0 1
Oscar H. Franco 0 1
Vilmundur Gudnason 0 1
Bruce M. Psaty 0 1
Eric Boerwinkle 0 1
Christopher J. O'Donnell 0 1
0 1 Department of Epidemiology, Erasmus University Medical Center , Rotterdam , The Netherlands , 2 Cardiovascular Health Research Unit, Department of Medicine, University of Washington, Seattle, Washington, United States of America, 3 Department of Biostatistics, Boston University School of Public Health , Boston, MA , United States of America, 4 Icelandic Heart Association, Kopavogur, Iceland, 5 University of Iceland , Reykjavik , Iceland , 6 Human Genetics Center, and Division of Epidemiology, Human Genetics, and Environmental Sciences, University of Texas Health Science Center at Houston , Houston, TX , United States of America, 7 Boston University's and National Heart Lung and Blood Institute's Framingham Heart Study , Framingham, MA , United States of America, 8 Department of Cardiology, Leiden University Medical Center , Leiden , The Netherlands , 9 Department of Gerontology and Geriatrics, Leiden University Medical Center , Leiden , The Netherlands , 10 Division of Preventive Medicine, Brigham and Women's Hospital and Harvard Medical School , Boston , Massachusetts, United States of America, 11 Department of Biostatistics, University of Washington , Seattle, WA , United States of America, 12 Department of Statistics, University of Auckland , Auckland , New Zealand , 13 Interfaculty Institute for Genetics and Functional Genomics, University Medicine Greifswald , Greifswald, Germany, 14 DZHK ( German Center for Cardiovascular Research) , partner site, Greifswald, Germany , 15 Department of Pharmacology and Therapeutics, University College , Cork, Ireland , 16 Department of Internal Medicine, Division of Geriatrics, Wake Forest University , Winston-Salem , North Carolina, United States of America, 17 Department of Nutrition, Harvard School of Public Health , Boston, MA , United States of America, 18 Channing Division of Network Medicine, Harvard Medical School , Boston, MA , United States of America, 19 Division of Epidemiology & Community Health, School of Public Health, University of Minnesota, Minneapolis, United States of America, 20 Sticht Center on Aging, Wake Forest School of Medicine , Winston-Salem, NC , United States of America, 21 Department of Epidemiology, Gillings School of Global Public Health, University of North Carolina , Chapel Hill, NC , United States of America, 22 Department of Epidemiology, Merck Research Laboratories, Merck Sharp & Dohme Corp., Whitehouse Station, NJ, United States of America, 23 Robertson Centre for Biostatistics, University of Glasgow , Glasgow , United Kingdom , 24 Department of Internal Medicine B, University Medicine Greifswald , Greifswald, Germany , 25 National Institute for Health and Welfare , Helsinki , Finland , 26 Department of Internal Medicine, Erasmus University Medical Center , Rotterdam , The Netherlands , 27 Department of Epidemiology, Boston University School of Public Health , Boston, MA , United States of America, 28 Department of Medicine, Boston University School of Medicine
1 Editor: Marie-Pierre Dubé, Universite de Montreal , CANADA
Funding: There were no current funding sources for
this study. Merck Sharp & Dohme Corp. provided
support in the form of salary for author Cynthia J.
Girman, but did not have any additional role in the
study design, data collection and analysis, decision to
publish, or preparation of the manuscript. The specific
role of this author is articulated in the ‘author
contributions’ section. Novartis Institutes for
Biomedical Research provided support in the form of
salary for author Ying A. Wang, but did not have any
additional role in the study design, data collection and
analysis, decision to publish, or preparation of the
manuscript. The specific role of this author is
articulated in the ‘author contributions’ section.
Competing Interests: Merck Research Laboratories
supported the genotyping of the Nurses’ Health and
Health Professionals Follow-Up Studies though an
unrestricted grant. Cynthia J. Girman is employed by
and owns shares in Merck & Co. Inc. Ying A. Wang is
currently an employee of Novartis. David J. Stott has
received research funding from Bristol Myers Squibb
for the PROSPER study (included in this report).
Daniel Chasman has received funds from Amgen for
genotyping and collaborative scientific support in the
WGHS cohort. O. H. Franco works in ErasmusAGE,
a center for aging research across the life course
funded by Nestlé Nutrition (Nestec Ltd.), Metagenics
Inc., and AXA. Nestlé Nutrition (Nestec Ltd.),
Metagenics Inc., and AXA had no role in the design
and conduct of the study; collection, management,
analysis, and interpretation of the data; and
preparation, review, or approval of the article. Psaty
serves on the DSMB for a clinical trial of a device
funded by the manufacturer (Zoll LifeCor) and serves
on the Steering Committee of the Yale Open Data
Access Project funded by Johnson & Johnson. David
J. Stott has received research support from Merck
Serono, Kabi-Fresenius, Pfizer, Astra-Zeneca, and
consultancy/speaker fees for Boehringer Ingleheim,
Nestle, Nutricia. There are no patents, products in
development or marketed products to declare. This
does not alter the authors' adherence to all the PLOS
ONE policies on sharing data and materials.
Boston, MA, United States of America, 29 Department of Preventive Medicine, Boston University School of
Medicine, Boston, MA, United States of America, 30 Section of Cardiovascular Medicine, Department of
Medicine, Boston University School of Medicine, Boston, MA, United States of America, 31 Public Health
Sciences, Fred Hutchinson Cancer Research Center, Seattle, WA, United States of America, 32 Department
of Epidemiology and Public Health, Pasteur Institute of Lille, Lille, France, 33 Department of Epidemiology
and Public Health, EA 3430, University of Strasbourg, Strasbourg, France, 34 Department of Epidemiology,
University of Washington, Seattle, WA, United States of America, 35 Group Health Research Institute, Group
Health Cooperative, Seattle, United States of America, 36 Departments of Cardiology and Epidemiology,
Toulouse University Hospital, Toulouse, France, 37 National Institute of Health and Medical Research
(U258), Paris, France, 38 Seattle Epidemiologic Research and Information Center of the Department of
Veterans Affairs Office of Research and Development, Seattle, WA, United States of America, 39 Novartis
Institutes for Biomedical Research, 250 Massachusetts Avenue, Cambridge, MA, United States of America,
40 The New York Academy of Medicine, New York, NY, United States of America, 41 Department of
Medicine, Umeå University Hospital, Umeå, Sweden, 42 Institute for Translational Genomics and Population
Sciences, Los Angeles Biomedical Research Institute, Torrance, CA, United States of America,
43 Department of Pediatrics, Harbor-UCLA Medical Center, Torrance, CA, United States of America,
44 UKCRC Centre of Excellence for Public Health Research (Northern Ireland), Queen’s University of
Belfast, Belfast, United Kingdom, 45 Department of Mathematics and Statistics, University of Jyväskylä,
Jyväskylä, Finland, 46 Department of Epidemiology, Harvard School of Public Health, Boston, MA, United
States of America, 47 Laboratory of Epidemiology, Demography, and Biometry, National Institute on Aging,
National Institutes of Health, Bethesda, MD, United States of America, 48 Institute for Community Medicine,
University Medicine Greifswald, Greifswald, Germany, 49 Institute for Molecular Medicine FIMM, University
of Helsinki, Helsinki, Finland, 50 Department of Epidemiology & Prevention, Public Health Sciences, Wake
Forest School of Medicine, Winston-Salem, NC, 27157, United States of America, 51 Institute of
Cardiovascular and Medical Sciences, Faculty of Medicine, University of Glasgow, Glasgow, United
Kingdom, 52 Netherlands Genomics Initiative (NGI)-sponsored Netherlands Consortium for Healthy Aging
(NCHA), Leiden, The Netherlands, 53 Durrer Center for Cardiogenetic Research, Amsterdam, The
Netherlands, 54 Interuniversity Cardiology Institute of the Netherlands, Utrecht, The Netherlands,
55 Department of General and Interventional Cardiology, University Heart Center Hamburg-Eppendorf,
Hamburg, Germany, 56 Department of Health Services, University of Washington, Seattle, WA, United
States of America, 57 Department of Medicine, Baylor College of Medicine, Houston, Texas, United States of
America, 58 Division of Intramural Research, National Heart, Lung and Blood Institute, Bethesda, MD,
United States of America, 59 Cardiology Section, Department of Medicine, Boston Veteran’s Administration
Healthcare, Boston, MA, United States of America
☯ These authors contributed equally to this work.
‡ These authors also contributed equally to this work.
* . (AD); (CJO)
Data are limited on genome-wide association studies (GWAS) for incident coronary heart
disease (CHD). Moreover, it is not known whether genetic variants identified to date also
associate with risk of CHD in a prospective setting.
We performed a two-stage GWAS analysis of incident myocardial infarction (MI) and CHD in
a total of 64,297 individuals (including 3898 MI cases, 5465 CHD cases). SNPs that passed
an arbitrary threshold of 5×10−6 in Stage I were taken to Stage II for further discovery.
Furthermore, in an analysis of prognosis, we studied whether known SNPs from former GWAS were
associated with total mortality in individuals who experienced MI during follow-up.
In Stage I 15 loci passed the threshold of 5×10−6; 8 loci for MI and 8 loci for CHD, for which
one locus overlapped and none were reported in previous GWAS meta-analyses. We took
2 / 16
60 SNPs representing these 15 loci to Stage II of discovery. Four SNPs near QKI showed
nominally significant association with MI (p-value<8.8×10−3) and three exceeded the
genome-wide significance threshold when Stage I and Stage II results were combined (top
SNP rs6941513: p = 6.2×10−9). Despite excellent power, the 9p21 locus SNP (rs1333049)
was only modestly associated with MI (HR = 1.09, p-value = 0.02) and marginally with CHD
(HR = 1.06, p-value = 0.08). Among an inception cohort of those who experienced MI during
follow-up, the risk allele of rs1333049 was associated with a decreased risk of subsequent
mortality (HR = 0.90, p-value = 3.2×10−3).
QKI represents a novel locus that may serve as a predictor of incident CHD in prospective
studies. The association of the 9p21 locus both with increased risk of first myocardial
infarction and longer survival after MI highlights the importance of study design in investigating
genetic determinants of complex disorders.
There is strong and consistent evidence that coronary heart disease (CHD) is highly heritable
and is influenced by a wide range of genetic factors [
]. Recently genome-wide association
studies (GWAS) identified common genetic variants involved in cardiovascular disease and its
risk factors . The loci reported by the latest and largest GWAS altogether explain around
10% of CHD heritability [
To date, GWAS for CHD have been conducted mostly in cross-sectional case-control setting,
and this design, which uses prevalent cases, typically oversamples those with long post-event
survival times. Although such a design often makes it possible to collect information from a large
number of patients, this approach may incorrectly identify factors that are associated with a high
or low case-fatality rate. For instance, a factor associated with a low case-fatality will be enriched
among surviving cases and may appear to increase the risk of disease when prevalent cases are
compared with controls. This bias is known as incidence-prevalence (Neyman) bias [
major advantage of studying incident cases rather than prevalent cases is that incident cases
properly represent the fatal cases and persons with only brief post-event survival. To date the
strong and reliable evidence for identifying and assessing factors such as LDL-cholesterol and
systolic blood pressure that predict future clinical disease are provided by well-designed
population-based, prospective cohort studies that collect large number of incident cases .
Here we aimed to study genetic variants that affect the incidence of myocardial infarction
(MI) and CHD in prospective, population-based cohorts and whether the genetic variants
identified to date are also associated with risk of CHD in a prospective setting. Moreover, we
investigated whether the known genetic variants are associated with total-mortality after MI.
To this end we used the data from the Cohorts for Heart and Aging Research in Genome
Epidemiology (CHARGE) Consortium [
] and collaborating prospective studies.
We performed our study in two stages. Stage I studies comprised participants from five
prospective cohort studies that form the CHARGE consortium [
]: the Age, Gene Environment
3 / 16
Susceptibility Reykjavik Study (AGES) [
]; the Atherosclerosis Risk in Communities (ARIC)
]; the Cardiovascular Health Study (CHS) [
]; the Framingham Heart Study (FHS)
]; and the Rotterdam Study (RS) [
]. Stage II comprised individuals from: The Health,
Aging, and Body Composition (Health ABC) Study; The Health Professionals Follow-Up
Study (HPFS); The Nurses’ Health Study (NHS); PROSPER/PHASE Study; the Study of Health
in Pomerania (SHIP); The Women’s Genome Health Study (WGHS); the MOnica Risk,
Genetics, Archiving and Monograph (MORGAM) Study comprising the Alpha-Tocopherol,
BetaCarotene (ATBC) Study; The FINRISK Study; The PRIME Study (including the PRIME
cohorts of Belfast, Lille, Strasbourg and Toulouse); The Northern Sweden Study. Participants
in Stages I and II were of European ancestry. Participants with a history of MI or CHD at
baseline were excluded. All studies had protocols approved by local institutional review boards.
Participants provided written informed consent and gave permission to use their DNA for
research purposes. The Supplementary Document provides details about the design and
characteristics for these studies.
Case Definitions for MI and CHD
The definitions of incident MI were consistent among the participating studies, including both
fatal and non-fatal MI. CHD included fatal or non-fatal MI, and in most studies fatal CHD or
sudden death. The definition of MI and CHD for each cohort study is summarized in S1 Table
and S2 Table.
The date of entry to the analysis was the date of cohort entry (AGES, ARIC, CHS, RS) or DNA
collection (FHS). Within each study, Cox proportional hazards regression models were used to
test the association between each SNP and time to incident MI or CHD, while adjusting for sex
and baseline age. FHS adjusted for familial correlation by clustering on pedigree. Analyses in
CHS and ARIC were adjusted for study site and in FHS, for generation and additionally for
ancestry using principal components [
]. The censor date was the time of MI or CHD
diagnosis, the time of death, last date of contact, or at the end of follow-up, whichever came first. For
each SNP, additive genetic models were used to estimate the regression coefficient for the
hazard ratio (HR) for allele dosage and its respective standard error. For each analysis, a genomic
control coefficient (λ) was calculated, which estimated the extent of underlying population
structure. Further information on the analysis methods can be found in S3 Table and S4 Table.
Information regarding the genotyping and imputation as well as genotype quality control
are found in S5 Table and S6 Table. SNPs with a minor allele frequency of less than 1%,
imputation quality less than 0.3 or very large regression coefficients (absolute value larger than 5)
were excluded from meta-analysis. Results from individual studies were meta-analyzed for a
total of 2,543,842 autosomal SNPs based on Phase 2 HapMap. A fixed effects inverse variance
weighted meta-analysis approach was implemented in METAL [
] to combine the regression
coefficients and their standard errors, producing a summary regression coefficient and
standard error from which a p-value was computed. An arbitrary significance threshold for
followup in Stage II was set at 5.0×10−6. When more than one SNP clustered at a locus, we carried
forward four SNPs with smallest p-values in the associated locus for further investigation in
In Stage II, three studies provided data both for incident MI and CHD (HABC, MORGAM,
and WGHS), two studies provided data only for MI (PROSPER, SHIP), and two others
provided data only for CHD (HPFS, NHS). Each Stage II study used the same analytic method as
used in Stage I to examine the association of the 60 SNPs with MI or CHD. As in the Stage I
4 / 16
meta-analysis, we used inverse-variance weighted fixed effects meta-analysis to evaluate the
Stage II results. We applied a Bonferroni correction for 60 SNPs and set 8.3×10−4 as the
significance threshold. Finally, results from all studies in Stage I and II were combined using
inversevariance weighted fixed effects meta-analysis.
We further studied each of the 46 SNPs reported by the CARDIoGRAMplusC4D
], for association with incident events in our meta-analysis of longitudinal cohort
studies. Moreover, the SNPs were combined into a weighted genetic risk score using beta estimates
from the CARDIoGRAMplusC4D Consortium report [
]. The association of each SNP, as well
as the score from the combination of all 46 SNPs, was examined with incident MI and CHD
using the results of Stage I meta-analysis.
We applied a Cox proportional hazards model adjusted for age and sex to examine the
association of the known SNPs with mortality after MI. Five studies including AGES, ARIC, CHS,
FHS and the Rotterdam Study provided data for this analysis and in total 2953 individuals
were followed after incidence of MI of which 1828 died. The median follow up time ranged
from 2.3 years in AGES to 4.7 years in FHS. The baseline characteristics of the study
populations for this analysis are presented in S7 Table. Since this analysis was meant to explore
potential reasons for weak association or lack of association with incident MI and CHD, we limited
the analysis to three SNPs with more than 80% power in Stage I to study its estimated
associations with incident MI and CHD.
Fig 1 describes Stage I and Stage II of the study. The Stage I panel included five prospective
cohort studies comprising a total of 24,024 participants who were free of MI and CHD at
baseline. The average age ± standard deviation ranged from 54.1±5.6 in ARIC to 74.6±5.5 in AGES.
More than half of the participants (54.5%) were women. The basic characteristics of the
participating studies are shown in Table 1. A total of 1570 incident MI events (6.5%) and 2406
incident CHD events (10.0%) occurred over an average of 8.2 years and 8.1 years of follow-up for
MI and CHD, respectively. The average age at the time of MI ranged from 65.2 years in ARIC
to 80.8 years in CHS.
The λ coefficient within each cohort was small ( 1.03), suggesting negligible genomic
inflation. We combined the results of associations for all SNPs across the five cohorts. S1A Fig and
S1B Fig presents the Q-Q plots of combined p-values against the expected p-value distribution
for MI and CHD, respectively. The evidence for population admixture was small, both for MI
(λ = 1.017) and CHD (λ = 1.022). S2A Fig and S2B Fig illustrates the p-values of the
meta-analysis for each of the SNPs across the 22 autosomal chromosomes for MI and CHD, respectively.
In Stage I, 27 SNPs in 8 loci reached our arbitrary threshold of 5×10−6 for MI and 29 SNPs
in 8 loci reached this threshold for CHD (Table 2). The most significant association with MI
was seen for rs6941513 located on chromosome 6 upstream of QKI (Hazard Ratio = 1.22 [95%
Confidence Interval: 1.13, 1.31], p-value = 2.0×10−7). For CHD, rs986080, a SNP located on
chromosome 1 between two genes (SNX7 and PAP2D) showed the strongest association
(HR = 1.19 [95%CI: 1.12, 1.27], p-value = 6.6×10−8).
In Stage II, we sought additional evidence for associations in eight loci for MI (QKI, ODZ3,
DGKB, FOXL1, CALCOCO2, BARD1, COL8A1, ATXN1) and eight loci for CHD (PAP2D,
GPC5, CTNNA3, BARHL2, IGFBP3, LRFN2, ATXN1, SNCA) using four SNPs per locus, for a
total of 60 SNPs in 15 loci (ATXN1 was associated with both MI and CHD). Baseline
characteristics of the participants of Stages II are shown in S8 and S9 Tables. The results for all 60 SNPs
are presented in S10 Table and S11 Table, for MI and CHD, respectively. None of the SNPs
passed the Bonferroni adjusted threshold of 8.3×10−4. The results for the best association in
5 / 16
Fig 1. Study design for identification and validation of SNPs associated with MI and CHD.
6 / 16
Numbers in table are Mean (SD) or percentage. AGES = Age, Gene/Environment Study; ARIC = Atherosclerosis Risk in Communities Study;
CHS = Cardiovascular Health Study; FHS = Framingham Heart Study; HDL = high density lipoprotein; RS = The Rotterdam Study
1 Hypertension was defined as blood pressure 140/90 mmHg or on anti-hypertensive medication
2 Diabetes was defined as fasting blood glucose >125 mg/dL, a random blood glucose of >200 mg/dL, or use of insulin or oral hypoglycemic agents
(Rotterdam: diabetes definition: Using anti-diabetic medication or random glucose or oral glucose test more than 200 mg/dl)
3 Current cigarette smoking was defined as self-reported cigarette smoking of at least 1 cigarette per day for a year at any attended exam
each locus are shown in Table 3. Four SNPs located upstream of QKI showed nominal evidence
in Stage II for association with MI. The analysis of the combined Stage I and Stage II yielded
genome-wide significant associations for three SNPs close to QKI, (rs6941513: HR = 1.21 [95%
CI: 1.13, 1.28], p-value = 6.2×10−9). Fig 2 presents the linkage disequilibrium (LD) and p-values
of regional markers for this locus. We tested for evidence of replication of this association in
8201 African American individuals including 546 incident cases from the PAGE Study [
however, rs6941513 was not significantly associated with risk of MI in this population
(p = 0.49).
We sought evidence for the association of 46 SNPs recently reported in the largest GWAS to
date for coronary artery disease [
] with the incidence of MI and CHD (Table 4). Despite
excellent power, we found only modest evidence for replication of the association with 9p21 locus
(CDKN2A/B), the most established finding from previous cross-sectional case-control GWAS.
The most replicated SNP at 9p21 locus, rs1333049, was nominally associated with MI (HR:
1.09 [95%CI: 1.01, 1.18], p-value = 0.02) and marginally with CHD (HR = 1.06 [95%CI: 0.99,
1.13], p-value = 0.08). The most significant association with MI was found for rs15563, a SNP
in UBE2Z (HR: 1.12 [95%CI: 1.04, 1.20], p-value = 1.9×10−3) and the most significant
association with CHD was found for rs10947789, a SNP within the KCNK5 locus (HR: 1.13 [95%CI:
1.05, 1.22], p-value = 5.6×10−4). We found nominally significant associations (p<0.05) with
SNPs annotated to CDKN2A/B for MI, LIPA for CHD and COL4A2, TCF21, PDGFD, KCNK5,
VAMP8, MRAS, UBE2Z and TCF21 for both MI and CHD (Table 4). A weighted genetic risk
score composed of these 46 SNPs was associated with MI (p-value = 1.3×10−3) and CHD
(pvalue = 1.2×10−3) in the Stage I meta-analysis.
Among individuals who experienced MI during follow-up, the risk allele of rs1333049 was
associated with a significantly decreased risk of mortality (HR: 0.90 [95% CI: 0.84, 0.97],
pvalue = 5.5×10−3) (Table 5). In both SNPs at 9p21 locus the “risk allele” from cross-sectional
8 / 16
Fig 2. Regional plots for the association of SNPs with MI in the region of QKI.
case-control GWAS was associated with longer survival after MI and would have been enriched
in surviving prevalent cases. Fig 3 illustrates the inverse association of 78 top SNPs at the 9p21
locus as reported by CARDIoGRAMplusC4D Consortium [
] with survival after MI. We also
examined the association of rs6941513 with mortality after MI, however, the association was
We performed a GWAS on incident MI and CHD and examined whether the gene variants
identified to date are also associated with risk of CHD in a prospective setting. In a two-stage
design, involving 37,561 participants with 2,328 cases of incident MI, we identified a novel
genome-wide significant locus, QKI, associated with incident MI. This finding requires further
replication. The results also highlighted the difference between the genes identified in
prospective versus cross-sectional case-control studies. The 9p21 locus was associated with both an
increased risk of incident MI and, during follow-up post-MI, a decreased risk of total mortality,
indicating that genetic variants may operate differently in an alternative setting.
9 / 16
In this two-stage design, we found evidence for MI-associated genetic variants nearby QKI
(KH domain containing, RNA binding). The combined p-value for three out of four genetic
variants that were examined in the region exceeded genome-wide significant threshold.
Although these data provide evidence for an association between the QKI locus and incident
MI, this finding should be confirmed by further studies since these variants attained
conventional levels of genome-wide significant p-value only in the combined meta-analysis.
If confirmed, the QKI finding may represent a novel pathway in developing CHD. QKI is
known to be involved in cell cycle regulation, a pathway for which there is emerging evidence for
a key role in developing atherosclerotic plaques and cardiovascular disease [
]. A functional
study has reported that QKI is a central regulator of vascular smooth muscle cell phenotypic
plasticity and that intervention in QKI activity can improve pathogenic fibro-proliferative responses
to vascular injury . Moreover, a recent paper shows that the RNA-binding properties of QKI
play a critical role in regulating human monocyte to macrophage differentiation [
]. de Bruin
and co-workers identified that the conversion of monocytes to both pro- and anti-inflammatory
macrophages with GM-CSF or M-CSF, respectively, markedly increased expression of the QKI,
which all were readily detected in CD68+ macrophages of fibrous cap atheromata and
atherosclerotic lesions with intraplaque hemorrahage. Furthermore, reduced expression of QKI in
monocytes delayed their differentiation into macrophages, perturbed their capacity to become
lipidengorged foam cells, and led to a reduction in monocyte infiltration in atherosclerotic lesions
]. Altogether we propose that QKI is involved in inflammatory responses to injury and could
be a potential thrapeutic target to prevent cardiovascular disease. Further functional investigation
is needed to robustly identify mechanisms involved for this locus.
Prior GWAS which included extremely large sample sizes did not report QKI though they
should have had enough statistical power to detect a locus with such an effect. However
rs6941513 was not associated with CAD in the Cardiogram plusC4D GWAS (OR = 1.01,
pvalue = 0.45). In contrast to former GWAS, we have used a prospective, longitudinal cohort
design to examine genetic association with incident cases of MI and CHD. It is possible that
the magnitude of the effect with prevalent cases is smaller than with incident cases; thus the
locus was not detected by previously published GWAS that primarily use a case-control design.
Although CHD includes MI events by definition, the loci we found for MI and CHD
overlapped only for one locus (ATX1). One reason could be differences in mechanisms involved in
the restrictive diagnosis of MI versus the broader diagnosis of CHD. However, unstable effect
Fig 3. The association of top 79 SNPs with coronary artery disease as reported by CardiogramplusC4D for 9p21 locus and their association with
total mortality after MI.
estimates and p-values due to lack of statistical power could have contributed to this
observation as well.
Despite excellent statistical power, we identified only a modest signal at the 9p21 locus. This
locus, initially identified by GWAS, has been validated by numerous studies in different
geographic and ethnic subgroups. However, our study is not the first study to report a weak signal
or lack of association at this locus. In fact, prominent differences have been observed between
cross-sectional case-control versus longitudinal studies. For instance, in a meta-analysis by
Chan et al [
], cross-sectional analyses of angiographically defined cases and controls show a
strong per allele association with 9p21 (OR: 1.31, 95% CI: 1.20, 1.43). However, in a
meta-analysis of follow-up studies by Patel et al [
], the per allele hazard ratio of the 9p21 variants for
fatal and non-fatal adjudicated MI was 1.09 (95% CI: 1.03–1.16). The latter is the same as what
we report in this study, though the meta-analysis includes earlier reports from some of our
studies. One explanation for this inconsistency is the incidence-prevalence bias. Most GWAS
for coronary artery disease to date have consisted of cross-sectional case-control studies, a
design that over represents patients who survived their MI or CHD event. Using data from five
population based cohort studies we found that the reported risk alleles for this locus are
associated with longer survival after MI. This finding that was previously reported as well [
supports the conjecture. Thus, the high prevalence of the risk allele in various types of
cross12 / 16
sectional analyses may not be due entirely to a high risk of experiencing MI or CHD, but also
to an improved chance of survival after MI.
The molecular biology behind the protective effect of the risk alleles at 9p21 is yet unclear,
however, there is a growing body of evidence to show that 9p21 locus is only increasing the risk
of CHD for the first event and not for the subsequent events. For instance, Patel et al found no
association with subsequent CHD events in a recent meta-analysis of 25,163 individuals with
established CHD [
]. Thus, it could be concluded that 9p21 locus is contributing to the
formation and progression of plaques and not to their instability prior to events; therefore, the
association is merely observed in early stages of the disease. This is in agreement with the report by
] that suggests a diminished effect of 9p21 locus by age, a finding that is confirmed
by Patel et al for secondary events. It should be noted that the mean age of participants was more
than 70 years old in two and more than 60 years old in four of the participating cohorts. In this
context, the older mean age of our population could be another reason why our findings do not
replicate known loci such as 9p21.Our study is the largest collection of population-based
prospective GWAS on incident MI and CHD and includes high quality genotyping and phenotyping
data from well-known cohort studies in the field of cardiovascular disease. Moreover, similar
case definitions for MI and CHD, comparable quality control for genotyped data, harmonized
imputation strategies and collaboratively designed analysis plans are further strengths of our
study. Despite these strengths, there are several limitations that merit discussion. First, nearly all
studies who contributed to our GWAS are also members of the CARDIoGRAMPlusC4D
], however, they have used only their prevalent cases in CARDIoGRAMPlusC4D
project and therefore there is no overlap between the two GWAS. Second, since our sample size
was limited, further susceptibility variants of weaker effects may have been missed in our study.
Third, we have tried to use consistent definitions for MI, however, slight differences exist between
the definitions for CHD. This might have introduced heterogeneity in our case definition. Finally,
our findings may not be directly generalizable to non-European populations.
A potential clinical application of risk alleles identified from GWAS is the prospective
prediction of cardiovascular disease. To date, the totality of evidence from prospective studies
suggests that there is only modest, independent prediction of increased cardiovascular disease risk
using genetic information with small to modest incremental reclassification for prediction
beyond the known clinical CVD risk scores [
]. This lack of success has been attributed to the
small percentage of variance explained by known genetic factors. However, our results also
suggest that genetic risk prediction needs to consider differences in genetic variants that predict
the risk of cardiovascular disease in prospective and cross-sectional settings.
In summary, using the largest collection of population- based prospective genome-wide
association studies we have identified QKI as a potential locus for incident myocardial
infarction. Furthermore, we have shown that the genes associated with risk of cardiovascular disease
may differ in effect size when studied in a cross-sectional case-control versus cohort settings.
The role of 9p21 locus may be complex, increasing the risk of incident MI and decreasing
mortality among those with CHD. This highlights the importance of examining longitudinal cohort
studies in the study of etiology even for genetic factors. These findings may have implications
for application of genetic variants in risk estimation for cardiovascular disease, an effort that so
far has not provided strong evidence for incremental risk prediction by genetic markers.
S1 File. Supplementary document: Methods, acknowledgment and funding for the participating studies.
13 / 16
S1 Fig. a. QQ Plot for Discovery GWAS MI. b. QQ Plot for Discovery GWAS CHD.
S2 Fig. a. Log-plot for Discovery GWAS MI. b. Log-plot for Discovery GWAS CHD.
S1 Table. Phenotype description of the studies in stage I.
S2 Table. Phenotype description of the studies in stage II.
S3 Table. Analysis Logistics of the studies in stage I.
S4 Table. Analysis Logistics of the studies in stage II.
S5 Table. Genotyping/imputation/QC specifics of the studies in stage I.
S6 Table. Genotyping/imputation/QC specifics of the studies in stage II.
S7 Table. Basic description of the study population for the survival after MI analysis.
S8 Table. Basic description of the cohort studies in stage II.
S9 Table. Basic description of the case-control studies in stage II.
S10 Table. Association of the SNPs taken to stage II with MI.
S11 Table. Association of the SNPs taken to stage II with CHD.
Conceived and designed the experiments: DIC ARF SBK CJG VS AGU RSV CLC JV SD PA
DA SRH JF PD NLS YAW DSS PGW KDT AE FK JIR JK KK GH LJL AH MRPM KS PW EJB
TBH DL YL EBR JWJ HV PMR SB OHF VG BMP EB CJO. Performed the experiments: BMB
JD MD MRPM DJS FR. Analyzed the data: AD JCB CCW AVS ACM ST TL UV MKJ IF GE
NF YAW GH PK LMR KL. Contributed reagents/materials/analysis tools: LAC TL IF CLC JV
PA DA KMR PGW AE FK JK KK PK SB. Wrote the paper: AD JCB CCW LAC KK JWJ BMP
14 / 16
15 / 16
1. Lloyd-Jones DM , Nam BH , D'Agostino RB Sr ., Levy D , Murabito JM , Wang TJ , et al. Parental cardiovascular disease as a risk factor for cardiovascular disease in middle-aged adults: a prospective study of parents and offspring . JAMA . 2004 ; 291 ( 18 ): 2204 - 11 . Epub 2004/05/13. doi: 10 .1001/jama.291.18. 2204 291/18/2204 [pii]. PMID: 15138242.
2. Marenberg ME , Risch N , Berkman LF , Floderus B , de Faire U. Genetic susceptibility to death from coronary heart disease in a study of twins . N Engl J Med . 1994 ; 330 ( 15 ): 1041 - 6 . Epub 1994/04/14. doi: 10 . 1056/NEJM199404143301503 PMID: 8127331 .
3. O 'Donnell CJ , Nabel EG . Genomics of cardiovascular disease . N Engl J Med . 2011 ; 365 ( 22 ): 2098 - 109 . Epub 2011/12/02. doi: 10 .1056/NEJMra1105239 PMID: 22129254 .
4. Consortium CAD , Deloukas P , Kanoni S , Willenborg C , Farrall M , Assimes TL , et al. Large-scale association analysis identifies new risk loci for coronary artery disease . Nat Genet . 2013 ; 45 ( 1 ): 25 - 33 . Epub 2012/12/04. ng. 2480 [pii] doi: 10.1038/ng.2480 PMID: 23202125 .
5. Hill G , Connelly J , Hebert R , Lindsay J , Millar W. Neyman's bias re-visited . J Clin Epidemiol . 2003 ; 56 ( 4 ): 293 - 6 . Epub 2003/05/28. S0895435602005711 [pii]. PMID: 12767404.
6. Neyman J . Statistics; servant of all sciences. Science . 1955 ; 122 ( 3166 ): 401 - 6 . Epub 1955/09/02. PMID: 13246647 .
7. Wilson PW . Risk scores for prediction of coronary heart disease: an update . Endocrinol Metab Clin North Am . 2009 ; 38 ( 1 ): 33 - 44 . Epub 2009/02/17. S0889 - 8529 ( 08 ) 00084 - 4 [pii] doi: 10.1016/j.ecl. 2008 . 11 .001 PMID: 19217511 .
8. Psaty BM , O'Donnell CJ , Gudnason V , Lunetta KL , Folsom AR , Rotter JI , et al. Cohorts for Heart and Aging Research in Genomic Epidemiology (CHARGE) Consortium: Design of prospective meta-analyses of genome-wide association studies from 5 cohorts . Circ Cardiovasc Genet . 2009 ; 2 ( 1 ): 73 - 80 . Epub 2009/12/25. 2/1/73 [pii] doi: 10.1161/CIRCGENETICS.108.829747 PMID: 20031568; PubMed Central PMCID : PMC2875693 .
9. Harris TB , Launer LJ , Eiriksdottir G , Kjartansson O , Jonsson PV , Sigurdsson G , et al. Age, Gene/Environment Susceptibility-Reykjavik Study: multidisciplinary applied phenomics . Am J Epidemiol . 2007 ; 165 ( 9 ): 1076 - 87 . Epub 2007/03/14. kwk115 [pii] doi: 10.1093/aje/kwk115 PMID: 17351290; PubMed Central PMCID : PMC2723948 .
10. The Atherosclerosis Risk in Communities (ARIC) Study: design and objectives. The ARIC investigators . Am J Epidemiol . 1989 ; 129 ( 4 ): 687 - 702 . Epub 1989/04/01. PMID: 2646917 .
11. Fried LP , Borhani NO , Enright P , Furberg CD , Gardin JM , Kronmal RA , et al. The Cardiovascular Health Study: design and rationale . Ann Epidemiol. 1991 ; 1 ( 3 ): 263 - 76 . Epub 1991/02/01. PMID: 1669507 .
12. Feinleib M , Kannel WB , Garrison RJ , McNamara PM , Castelli WP . The Framingham Offspring Study. Design and preliminary data . Prev Med . 1975 ; 4 ( 4 ): 518 - 25 . Epub 1975/12/01. PMID: 1208363 .
13. Hofman A , Grobbee DE , de Jong PT, van den Ouweland FA. Determinants of disease and disability in the elderly: the Rotterdam Elderly Study . Eur J Epidemiol . 1991 ; 7 ( 4 ): 403 - 22 . PMID: 1833235 .
14. Hofman A , van Duijn CM , Franco OH , Ikram MA , Janssen HL , Klaver CC , et al. The Rotterdam Study: 2012 objectives and design update . Eur J Epidemiol . 2011 ; 26 ( 8 ): 657 - 86 . PMID: 21877163 . doi: 10 . 1007/s10654-011-9610-5
15. Price AL , Patterson NJ , Plenge RM , Weinblatt ME , Shadick NA , Reich D. Principal components analysis corrects for stratification in genome-wide association studies . Nat Genet . 2006 ; 38 ( 8 ): 904 - 9 . Epub 2006/07/25. ng1847 [pii] doi: 10.1038/ng1847 PMID: 16862161 .
16. Willer CJ , Li Y , Abecasis GR . METAL: fast and efficient meta-analysis of genomewide association scans . Bioinformatics . 2010 ; 26 ( 17 ): 2190 - 1 . Epub 2010/07/10. btq340 [pii] doi: 10.1093/bioinformatics/ btq340 PMID: 20616382; PubMed Central PMCID : PMC2922887 .
17. Matise TC , Ambite JL , Buyske S , Carlson CS , Cole SA , Crawford DC , et al. The Next PAGE in understanding complex traits: design for the analysis of Population Architecture Using Genetics and Epidemiology (PAGE) Study . Am J Epidemiol . 2011 ; 174 ( 7 ): 849 - 59 . Epub 2011/08/13. kwr160 [pii] doi: 10. 1093/aje/kwr160 PMID: 21836165; PubMed Central PMCID : PMC3176830 .
18. Helgadottir A , Thorleifsson G , Manolescu A , Gretarsdottir S , Blondal T , Jonasdottir A , et al. A common variant on chromosome 9p21 affects the risk of myocardial infarction . Science . 2007 ; 316 ( 5830 ): 1491 - 3 . Epub 2007/05/05. 1142842 [pii] doi: 10.1126/science.1142842 PMID: 17478679 .
19. McPherson R , Pertsemlidis A , Kavaslar N , Stewart A , Roberts R , Cox DR , et al. A common allele on chromosome 9 associated with coronary heart disease . Science . 2007 ; 316 ( 5830 ): 1488 - 91 . Epub 2007/05/05. 1142447 [pii] doi: 10.1126/science.1142447 PMID: 17478681; PubMed Central PMCID : PMC2711874 .
20. van der Veer E , de Bruin RG , Kraaijeveld A , de Vries MR , Bot I , Pera T , et al. The RNA-Binding Protein Quaking is a Critical Regulator of Vascular Smooth Muscle Cell Phenotype . Circ Res . 2013 . Epub 2013/08/22. CIRCRESAHA. 113 .301302 [pii] .
21. de Bruin RG , Shiue L , Prins J , de Boer HC , Singh A , Fagg W.S. , et al. Quaking post-transcriptionally promotes monocyte differentiation into pro-atherogenic macrophages by controlling pre-mRNA splicing and gene expression . Nat Commun . 2016 .
22. Chan K , Patel RS , Newcombe P , Nelson CP , Qasim A , Epstein SE , et al. Association between the chromosome 9p21 locus and angiographic coronary artery disease burden: a collaborative meta-analysis . J Am Coll Cardiol . 2013 ; 61 ( 9 ): 957 - 70 . Epub 2013/01/29. S0735 - 1097 ( 12 ) 05975 - X [pii] doi: 10.1016/j. jacc. 2012 . 10 .051 PMID: 23352782; PubMed Central PMCID : PMC3653306 .
23. Patel RS , Asselbergs FW , Quyyumi AA , Palmer TM , Finan CI , Tragante V , et al. Genetic variants at chromosome 9p21 and risk of first versus subsequent coronary heart disease events: a systematic review and meta-analysis . J Am Coll Cardiol . 2014 ; 63 ( 21 ): 2234 - 45 . Epub 2014/03/13. S0735 - 1097 ( 14 ) 01383 - 7 [pii] doi: 10.1016/j.jacc. 2014 . 01 .065 PMID: 24607648; PubMed Central PMCID : PMC4035794 .
24. Szpakowicz A , Kiliszek M , Pepinski W , Waszkiewicz E , Franaszczyk M , Skawronska M , et al. Polymorphism of 9p21 . 3 locus is associated with 5-year survival in high-risk patients with myocardial infarction . PLoS One . 2014 ; 9 ( 8 ): e104635 . Epub 2014 /08/12. doi: 10 .1371/journal.pone.0104635 PONE-D- 14- 14817 [pii]. PMID: 25105296; PubMed Central PMCID : PMC4126747 .
25. Gong Y , Beitelshees AL , Cooper-DeHoff RM , Lobmeyer MT , Langaee TY , Wu J , et al. Chromosome 9p21 haplotypes and prognosis in white and black patients with coronary artery disease . Circ Cardiovasc Genet . 2011 ; 4 ( 2 ): 169 - 78 . Epub 2011/03/05. CIRCGENETICS. 110 .959296 [pii] doi: 10.1161/ CIRCGENETICS.110.959296 PMID: 21372283; PubMed Central PMCID : PMC3101633 .
26. Palomaki GE , Melillo S , Bradley LA . Association between 9p21 genomic markers and heart disease: a meta-analysis . JAMA . 2010 ; 303 ( 7 ): 648 - 56 . Epub 2010/02/18. 303/7/648 [pii] doi: 10.1001/jama. 2010 . 118 PMID: 20159873 .
27. Deloukas P , Kanoni S , Willenborg C , Farrall M , Assimes TL , Thompson JR , et al. Large-scale association analysis identifies new risk loci for coronary artery disease . Nat Genet . 2012 . Epub 2012/12/04. ng. 2480 [pii] doi: 10.1038/ng.2480 PMID: 23202125 .
28. de Vries PS , Kavousi M , Ligthart S , Uitterlinden AG , Hofman A , Franco OH , et al. Incremental predictive value of 152 single nucleotide polymorphisms in the 10-year risk prediction of incident coronary heart disease: the Rotterdam Study . Int J Epidemiol . 2015 ; 44 ( 2 ): 682 - 8 . Epub 2015/05/09. dyv070 [pii] doi: 10.1093/ije/dyv070 PMID: 25953786 .