Identification of a Sudden Cardiac Death Susceptibility
Locus at 2q24.2 through Genome-Wide Association in
European Ancestry Individuals
Dan E. Arking1.*, M. Juhani Junttila2,3., Philippe Goyette4., Adriana Huertas-Vazquez5., Mark
Eijgelsheim6., Marieke T. Blom7., Christopher Newton-Cheh8,9,10., Kyndaron Reinier5, Carmen
Teodorescu5, Audrey Uy-Evanado5, Naima Carter-Monroe11, Kari S. Kaikkonen2, Marja-Leena
Kortelainen2, Gabrielle Boucher4, Caroline Lagace ´4, Anna Moes1, XiaoQing Zhao11, Frank Kolodgie11,
Fernando Rivadeneira6,12,13, Albert Hofman6,13, Jacqueline C. M. Witteman6,13, Andre ´ G.
Uitterlinden6,12,13, Roos F. Marsman7, Raha Pazoki7, Abdennasser Bardai7, Rudolph W. Koster7, Abbas
Dehghan6, Shih-Jen Hwang10, Pallav Bhatnagar1, Wendy Post14, Gina Hilton1, Ronald J. Prineas15, Man
Li16, Anna Ko ¨ttgen16, Georg Ehret1,17, Eric Boerwinkle18, Josef Coresh16,19, W. H. Linda Kao16, Bruce M.
Psaty20,21, Gordon F. Tomaselli14, Nona Sotoodehnia22, David S. Siscovick20, Greg L. Burke15, Eduardo
Marba ´n5, Peter M. Spooner14, L. Adrienne Cupples10,23, Jonathan Jui24, Karen Gunson25, Y. Antero
Kesa ¨niemi2,26, Arthur A. M. Wilde7, Jean-Claude Tardif4, Christopher J. O’Donnell10,27, Connie R.
Bezzina7, Renu Virmani11, Bruno H. Ch. Stricker6,12,13,28,29", Hanno L. Tan7", Christine M. Albert30",
Aravinda Chakravarti1", John D. Rioux4", Heikki V. Huikuri2", Sumeet S. Chugh5"*
1McKusick-Nathans Institute of Genetic Medicine, Johns Hopkins University School of Medicine, Baltimore, Maryland, United States of America, 2Institute of Clinical
Medicine, Department of Internal Medicine, University of Oulu, Oulu, Finland, 3Division of Cardiology, Miller School of Medicine, University of Miami, Miami, Florida,
United States of America, 4Montreal Heart Institute and the Universite ´ de Montre ´al, Montreal, Canada, 5The Heart Institute, Cedars-Sinai Medical Center, Los Angeles,
California, United States of America, 6Department of Epidemiology, Erasmus MC, Rotterdam, The Netherlands, 7Heart Failure Research Center, Department of Clinical and
Experimental Cardiology, Academic Medical Center, University of Amsterdam, Amsterdam, The Netherlands, 8Center for Human Genetic Research, Massachusetts General
Hospital, Boston, Massachusetts, United States of America, 9Cardiology Division, Massachusetts General Hospital, Boston, Massachusetts, United States of America,
10Framingham Heart Study, National Heart, Lung, and Blood Institute, National Institutes of Health, Framingham, Massachusetts, United States of America, 11CVPath
Institute, Gaithersburg, Maryland, United States of America, 12Department of Internal Medicine, Erasmus MC, Rotterdam, The Netherlands, 13Netherlands Consortium
for Healthy Aging (NCHA), Netherlands Genomic Initiative (NGI), Rotterdam, The Netherlands, 14Division of Cardiology, Department of Medicine, Johns Hopkins
University School of Medicine, Baltimore, Maryland, United States of America, 15Division of Public Health Sciences, Wake Forest University School of Medicine, Winston-
Salem, North Carolina, United States of America, 16Department of Epidemiology, Johns Hopkins University, Baltimore, Maryland, United States of America, 17Cardiology,
Department of Medicine, Geneva University Hospital, Geneva, Switzerland, 18University of Texas Health Science Center at Houston, Houston, Texas, United States of
America, 19Departments of Medicine and Biostatistics, Johns Hopkins University, Baltimore, Maryland, United States of America, 20Cardiovascular Health Research Unit,
Departments of Medicine and Epidemiology, University of Washington, Seattle, Washington, United States of America, 21Group Health Research Institute, Group Health
Cooperative, Seattle, Washington, United States of America, 22Cardiovascular Health Research Unit, Division of Cardiology, Department of Medicine, University of
Washington, Seattle, Washington, United States of America, 23Department of Biostatistics, Boston University School of Public Health, Boston, Massachusetts, United
States of America, 24Department of Emergency Medicine, Oregon Health and Science University, Portland, Oregon, United States of America, 25Department of
Pathology, Oregon Health and Science University, Portland, Oregon, United States of America, 26Biocenter Oulu, University of Oulu, Oulu, Finland, 27National Heart,
Lung, and Blood Institute, Bethesda, Maryland, United States of America, 28Department of Medical Informatics, Erasmus MC, Rotterdam, The Netherlands,
29Inspectorate of Health Care, The Hague, The Netherlands, 30Center for Arrhythmia Prevention, Division of Preventive Medicine, Cardiovascular Division, Department
of Medicine, Brigham and Women’s Hospital, Harvard Medical School, Boston, Massachusetts, United States of America
Sudden cardiac death (SCD) continues to be one of the leading causes of mortality worldwide, with an annual incidence
estimated at 250,000–300,000 in the United States and with the vast majority occurring in the setting of coronary disease.
We performed a genome-wide association meta-analysis in 1,283 SCD cases and .20,000 control individuals of European
ancestry from 5 studies, with follow-up genotyping in up to 3,119 SCD cases and 11,146 controls from 11 European ancestry
studies, and identify the BAZ2B locus as associated with SCD (P=1.8610210). The risk allele, while ancestral, has a frequency
of ,1.4%, suggesting strong negative selection and increases risk for SCD by 1.92–fold per allele (95% CI 1.57–2.34). We also
tested the role of 49 SNPs previously implicated in modulating electrocardiographic traits (QRS, QT, and RR intervals).
Consistent with epidemiological studies showing increased risk of SCD with prolonged QRS/QT intervals, the interval-
prolonging alleles are in aggregate associated with increased risk for SCD (P=0.006).
Citation: Arking DE, Junttila MJ, Goyette P, Huertas-Vazquez A, Eijgelsheim M, et al. (2011) Identification of a Sudden Cardiac Death Susceptibility Locus at 2q24.2
through Genome-Wide Association in European Ancestry Individuals. PLoS Genet 7(6): e1002158. doi:10.1371/journal.pgen.1002158
Editor: Mark I. McCarthy, University of Oxford, United Kingdom
Received January 29, 2011; Accepted May 11, 2011; Published June 30, 2011
Copyright: ? 2011 Arking et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits
unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
PLoS Genetics | www.plosgenetics.org1June 2011 | Volume 7 | Issue 6 | e1002158
Funding: AGNES is supported by the Netherlands Heart Foundation (2001D019, 2003T302, 2007B202), the Leducq Foundation (05-CVD), and the Interuniversity
Cardiology Institute of the Netherlands (project 27). ARIC is supported by NHLBI contracts N01-HC-55015, N01-HC-55016, N01-HC-55018 through N01-HC-55022,
R01HL087641, R01HL59367, and R01HL086694; NHGRI contract U01HG004402; and NIH contract HHSN268200625226C. Infrastructure was partly supported by
UL1RR025005. ARREST is supported by the Netherlands Organization for Scientific Research (Mozaiek 017.003.084, ZonMW Vici 918.86.616) and the Dutch Medicines
Evaluation Board MEB/CBG. CHS is supported by N01-HC-80007, N01-HC-85079 through N01-HC-85086, N01-HC-35129, N01 HC-15103, N01 HC-55222, N01-HC-75150,
N01-HC-45133, U01 HL080295 from the NHLBI, with additional contribution from the NINDS. FHS is supported by the NHLBI and Boston University School of Medicine
(N01-HC-25195), its contract with Affymetrix for genotyping services (N02-HL-6-4278), based on analyses by FHS investigators participating in the SHARe project. A
portion of this research was conducted using the Linux Cluster for Genetic Analysis (LinGA-II) funded by the Robert Dawson Evans Endowment at Boston University
School of Medicine and Boston Medical Center. FinGesture is supported the Finnish academy of Science, Helsinki, Finland; Sigrid Juselius Foundation, Helsinki, Finland;
and Foundation Leducq, Paris, France. The genetic work was supported by the Montreal Heart Institute Foundation (www.fondationicm.org). The Harvard Cohorts are
supported by HL068070, HL-26490, HL-34595, HL-34594, HL-35464, HL-46959, HL-080467 (from the NHLBI); by CA-34944, CA 40360, CA55075, CA-87969, CA 97193
(from the NCI); and by a Career Award for Medical Scientists from the Burroughs Wellcome Fund. Oregon-SUDS is funded by the NHLIB (R01 HL105170-01, R01
HL088416, R01 HL088416-03S1 NIH NHLBI). The RS is funded by Erasmus Medical Center and Erasmus University, Rotterdam; Netherlands Organization for Health
Research and Development (ZonMw); the Research Institute for Diseases in the Elderly (RIDE); the Ministry of Education, Culture, and Science; the Ministry for Health,
Welfare, and Sports; the European Commission (DG XII); and the Municipality of Rotterdam. The GWA study was funded by the Netherlands Organization of Scientific
Research NWO Investments (nr. 175.010.2005.011, 911-03-012), the Research Institute for Diseases in the Elderly (014-93-015; RIDE2), and the Netherlands Genomics
Initiative (NGI)/Netherlands Consortium for Healthy Aging (NCHA) project nr. 050-060-810. This publication was made possible by Grant Number 1UL1RR025005 from
the National Center for Research Resources (NCRR). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the
Competing Interests: AC is a paid member of the Scientific Advisory Board of Affymetrix, a role that is managed by the Committee on Conflict of Interest of the
Johns Hopkins University School of Medicine.
* E-mail: email@example.com (DEA); Sumeet.Chugh@cshs.org (SSC)
. These authors contributed equally to this work.
" These authors also contributed equally to this work.
Despite recent progress in treatment and prevention of coronary
heart disease, sudden cardiac death (SCD) remains a major public
health problem, with an annual incidence of SCD that ranges
from 50 to 100 per 100,000 in the general population [1,2]. While
there has been a great deal of focus on SCD in the setting of
Mendelian forms of arrhythmia (e.g. long and short QT
syndromes), the vast majority of SCD events occur in the general
population, with up to 50% of individuals manifesting SCD as a
first sign of disease . An estimated ,80% of all SCDs are
associated with coronary disease, ,10–15% in the setting of
cardiomyopathy and ,5% occur in persons with myocarditis,
coronary anomalies or ion channelopathies (e.g. long QT/
Brugada/short QT syndromes) . Despite this clinical heteroge-
neity, a familial component to SCD risk has been demonstrated
even after adjusting for traditional cardiovascular disease risk
factors [5,6,7,8], suggesting that genetic factors are likely to play
an important role. The critical importance of the genetic
contribution for effective prediction and prevention of SCD has
been emphasized in a recent consensus document from the US
National Heart Lung and Blood Institute .
In this study, we perform a meta-analysis of 5 genome-wide
association studies (GWAS) with follow-up genotyping in up to 11
additional studies of European ancestry to identify genetic variants
that modify susceptibility to community-based SCD. In addition to
an unbiased scan of the genome, we also focus on specific SNPs
that have been previously associated with electrophysiological
traits that, when extreme, are associated with increased risk for
SCD (QRS, QT, and RR intervals).
To identify genetic determinants of SCD, genome-wide
genotyping and imputation of ,2.5 million SNPs was performed
in 1,283 SCD cases and ,20,000 controls drawn from 5 samples
of European ancestry: Atherosclerosis Risk in Communities
(ARIC), Framingham Heart Study (FHS), FinGesture, Oregon
Sudden Unexpected Death Study (Oregon-SUDS), Rotterdam
Study (RS) (Table S1). All individual studies showed minimal test
statistic inflation after post-imputation quality control (genomic
control l,1.03) (see Materials and Methods). Meta-analysis was
performed using inverse variance weighting, with minimal test
statistic inflation observed (l=1.004) and no early departure from
the null expectation (Figure S1A), suggesting that overall there was
good genotyping quality and minimal population substructure.
Two loci exceeded the genome-wide significance threshold of
P,561028(Figure S1B, Table 1).
There were 13 independent loci containing at least 1 SNP with
P,1025(Table S2). We attempted to follow-up all of these loci in
additional independent case-control samples from FinGesture and
Oregon-SUDS, as well as in seven additional cohorts: Cardiovas-
cular Health Study (CHS), CVPath Institute Sudden Cardiac
Death registry (CVPI-SCDr), the Harvard Cohorts (5 combined
cohorts, see Materials and Methods). In total, this follow-up
genotyping included 1,730 cases and 10,530 controls of European
ancestry (Table S3). We were able to design assays and obtain high
quality genotype data for 11 SNPs corresponding to 10 of these
independent loci (Table 1). Nominally significant replication
Family studies have clearly demonstrated a role for genes
in modifying risk for sudden cardiac death (SCD), however
genetic studies have been limited by available samples.
Here we have assembled over 4,400 SCD cases with
.30,000 controls, all of European ancestry, and utilize a
two-stage study design. In the first stage, we conducted an
unbiased genome-wide scan in 1,283 SCD cases and
.20,000 controls, and then performed follow-up geno-
typing in the remainder of the samples. We demonstrate
strong association to a region of the genome not
previously implicated in SCD, the BAZ2B locus, which
contains 3 genes not previously known to play a role in
cardiac biology. In addition, we used the genome-wide
scan data to test a focused hypothesis that genetic
variants that modulate ECG traits associated with SCD
(QT, QRS, and RR intervals) also modify risk for SCD, and we
demonstrate that QT- and QRS-prolonging alleles are, as a
group, associated with increased risk of SCD. Taken
together, these findings begin to elucidate the genetic
contribution to SCD susceptibility and provide important
targets for functional studies to investigate the etiology
and pathogenesis of SCD.
Identification of the SCD-Associated 2q24.2 Locus
PLoS Genetics | www.plosgenetics.org2June 2011 | Volume 7 | Issue 6 | e1002158
results consistent with the GWAS were observed for two highly
correlated SNPs (r2=1 in HapMap CEU samples) in the BAZ2B
(bromodomain adjacent zinc finger domain 2B) locus (rs174230,
P=0.03; rs4665058, P=0.01). On the other hand there was no
significant evidence of replication for the two loci that were of
genome-wide significance in the initial scan (rs2650907 and
rs12601622 located on chromosomes 16 and 17, respectively),
suggesting that they represent either false positive signals in the
initial scan or insufficient power of the replication cohort. In this
context, it is important to note that the imputation quality of
rs12601622 was pooracrossallstudies(imputation quality(r2),0.4).
When combining follow-up results with the discovery GWAS
results, only rs4665058 approached genome-wide significance
(P=5.861028). This marker was therefore further genotyped in
the ARREST study, which includes 719 SCD cases, and in 4,190
population-based controls, and in the recently published AGNES
case-control study , which is a study of 670 individuals with
first MI and ventricular fibrillation (VF) compared to 654 with MI
alone. In the combined follow-up cohorts, including the ARREST
and AGNES studies, this SNP was significant after Bonferroni
correction for the number of loci tested (Pnominal=8.061025;
Pcorrected=8.061024). In a combined analysis of GWAS and all
follow-up genotyping results, rs4665058 well exceeded genome-
wide significance (P=1.8610210) (Figure 1). The risk allele (A
allele) of rs4665058 has a study size weighted frequency of 1.4%,
and increases risk for SCD by 1.92-fold per allele (95% CI 1.57 to
2.34) in the combined analysis (Figure S2), and by 1.65-fold per
allele (95% CI 1.29 to 2.12) in the follow-up samples alone
(Table 1). No significant heterogeneity was observed (Q statistic
=15.6, P=0.11; I2=35.9, 95% CI 0 to 68.5%). No significant
interaction was observed for either sex or age (data not shown). It
is interesting to note that the risk allele is the ancestral allele (based
on non-human primate sequence), and its low frequency in
European ancestry populations suggests strong negative selection,
as fewer than 0.8% of ancestral alleles have reached a frequency of
1.4% or lower (Figure S3).
In addition to performing an unbiased GWAS in the 5 discovery
cohorts, we also used the results from the initial GWAS meta-
analysis to examine the role of SNPs previously reported to be
associated with QRS (ventricular depolarization), QT (ventricular
depolarization/repolarization), and/or RR (inverse heart rate)
intervals [11,12,13,14]. These electrocardiographic measured
traits are associated with cardiovascular mortality and SCD in
both Mendelian settings (e.g. LQTS), and in the general
population [15,16,17]. Overall, there were 49 independent loci
associated with the ECG traits reported (25 for QRS , 16 QT
[12,13], 9 for RR , with one locus overlapping between QRS
and QT intervals) (Tables S4, S5, S6). While in general, we
hypothesize that QRS/QT/heart rate prolonging alleles increase
risk for SCD, recent pleiotropy analyses of ECG traits have shown
inconsistent directions of effects for some alleles (e.g. a QT-
prolonging allele is associated with decreased QRS interval) ,
and thus, a priori we have chosen a two-sided test to assess
significance of association for ECG SNPs with SCD. Nominal
significance was observed for 3 loci, including PLN (QT/QRS,
P=0.013), NOS1AP (QT, P=0.010), and KCNQ1 (QT, P=0.014).
A fourth locus, TKT/CACNA1D/PRKCD (QRS, P=0.0007), was
significant after multiple-test correction for all 49 tested SNPs
(corrected P=0.034) (Table 2). Interestingly, the direction of effect
for TKT/CACNA1D/PRKCD and KCNQ1 is opposite of that
expected: the QRS/QT interval prolonging allele is associated
with decreased risk for SCD. For the remaining 2 loci, the
direction of effect is consistent with a model in which increasing
QRS/QT interval increases risk for SCD. Indeed, looking across
all 49 loci (Tables S4, S5, S6), we find that in aggregate QRS/
QT/heart rate(inverse RR)-prolonging alleles are more often
associated with increased risk for SCD (31/49; P=0.03). This
result is entirely driven by QRS (17/25, P=0.04) (Table S4) and
QT intervals (12/16, P=0.02) (Table S5), with no overrepresen-
tation observed for RR interval (3/9, P=NS) (Table S6).
The results for QRS and QT intervals (combined P=0.006) are
consistent with epidemiological studies, which show increased
QRS/QT interval in the general population is associated with
increased risk of SCD. While the general trend is significant, the
observation that 2/4 of the individual SNPs nominally associated
with SCD actually show the opposite direction of effect, including
Table 1. Summary of GWAS and follow-up genotyping results for association with SCD.
SNP Chr Position
coded allele AFGWAS OR (95% CI) GWAS P
(95% CI) Follow-up PCombined P
rs1742302 159,883,556T/C0.013 2.49 (1.78–3.47)8.58E-081.38 (0.99–1.93)0.03 3.0E-07
rs46650582159,898,455A/C0.014 2.52 (1.80–3.53)7.07E-08 1.48 (1.05–2.08)0.015.8E-08
rs4665058*1.65 (1.29–2.12)8.0E-05 1.8E-10
rs168803954 27,848,761T/C 0.2301.30 (1.16–1.46)5.14E-060.97 (0.87–1.07) 0.270.01
rs21784905 30,875,088G/A0.207 1.32 (1.17–1.48)5.63E-060.90 (0.81–1.00) NA0.11
rs125175785106,008,730 G/C0.2340.76 (0.67–0.85) 4.41E-06 0.94 (0.85–1.04)0.120.0001
rs3193970 1097,061,998 C/T0.4210.78 (0.71–0.86)1.11E-06 0.96 (0.89–1.05)0.20.0001
rs11626637 14 45,792,412G/A 0.100 0.64 (0.52–0.78)9.57E-061.00 (0.87–1.16) NA0.01
rs2650907 1675,950,708 G/C0.4210.75 (0.68–0.83)4.39E-08 1.03 (0.92–1.13)NA 0.0005
rs7218928 1732,338,069 G/A 0.4290.79 (0.72–0.87) 4.34E-061.02 (0.94–1.11) NA0.008
rs126016221773,560,970A/G0.0146.79 (3.43–13.42)3.69E-080.89 (0.64–1.23) NA0.08
rs65075661839,939,723T/G0.3301.26 (1.14–1.40) 7.09E-060.93 (0.85–1.02)NA0.08
Chr, chromosome; AF, allele frequency of coded allele (weighted by study size); OR, odds ratio; CI, confidence interval. Follow-up genotyping results are reported for
1,730 SCD cases and 10,530 controls, with the exception of rs12601622 (1,460 SCD cases, 10.182 controls), which failed genotyping in the Oregon-SUDS follow-up study.
Bold indicates nominal significance (P,0.05) for validation. P-values for validation are reported as one-sided, and NA indicates opposite direction of effect from GWAS.
*Includes ARREST (719 SCD cases, 4,190 controls) and AGNES (670 SCD cases, 654 controls) studies.
Identification of the SCD-Associated 2q24.2 Locus
PLoS Genetics | www.plosgenetics.org3June 2011 | Volume 7 | Issue 6 | e1002158
TKT/CACNA1D/PRKCD, suggests the need to further elucidate
how each of these variants modifies both the underlying QRS/QT
interval trait as well as the potential role in SCD. For example, it is
possible that the risk for SCD is not directly (or entirely) mediated
though the effect on QRS/QT interval, as has been suggested for
Using meta-analysis of GWAS with follow-up genotyping in
independent samples, we demonstrate strong evidence for SCD
susceptibility at locus 2q24.2 in individuals of European ancestry.
Somewhat surprisingly, we identified a relatively rare allele
(MAF=0.014) with a strong effect (OR=1.92), which is in
contrast to most GWAS for complex traits, which have typically
identified alleles with MAF .0.1 and ORs,1.5. Given concerns
about imputation accuracy for rarer alleles, we note that
rs4665058 is almost perfectly in LD with rs174230 (r2=1.0 in
CEU), which is directly genotyped in all the GWAS samples, and
shows almost identical association results (Table 1). Further, we
observe the same effect size for FinGesture GWAS and follow-up
genotyping samples (Figure S2), again suggesting that imputation
does not affect the results. Indeed, it is quite plausible that rare
variants play a large role in risk for SCD given the fatal nature of
SCD (,10% of SCD victims survive) potentially selecting against
more common risk alleles.
Despite the strong evidence for association with SCD across the
entire study, we note that no effect for rs4665058 is observed in the
AGNES and CHS studies. Power was 63% and 52%, respectively,
under an additive genetic model with OR=1.92, requiring a
nominal P=0.05. We observe no significant heterogeneity in the
meta-analysis including all studies (P=0.11), suggesting that the
lack of association in these two cohorts is likely a function of
random chance, magnified by the low frequency of the risk allele.
We would also point out that AGNES has a more narrow
phenotype than the other studies, and included only those who
Figure 1. Regional association plot for rs4655058. Each SNP is plotted with respect to its chromosomal location (x-axis) and P-value (y-axis on
the left). The tall blue spikes indicate the recombination rate (y-axis on the right) at that region of the chromosome. The index SNP is denoted by the
larger diamond, for both the GWAS (red) and combined GWAS and validation results (blue). The dotted black line denotes genome-wide significance
(P,561028). Shading of additional SNPs indicates degree of linkage disequilibrium with the index SNP (red: r2$0.8, orange: 0.5#r2,0.8, grey:
0.2#r2,0.5, white: r2,0.2).
Identification of the SCD-Associated 2q24.2 Locus
PLoS Genetics | www.plosgenetics.org4 June 2011 | Volume 7 | Issue 6 | e1002158
suffered their first MI and survived the initial event to receive in-
hospital care, which could contribute to the lack of association. For
CHS, SCD adjudication was performed along with the ARIC
samples, and thus represents an identical phenotype. The only
difference was the age of the individuals in these two cohorts
(ARIC 45–64 years, CHS .65), however in interaction analyses
we did not observe age to be a significant modifier of the genetic
As with other GWAS, the current study design is not without
limitations. First, association approaches depend upon linkage
disequilibrium to identify associated SNPs, thus the underlying
functional variant at any of the loci is potentially unrecognized.
Indeed, using Pilot 1 data from the 1000 Genomes project
(November 2010 release) , we do not identify any missense
mutations highly correlated with the most strongly associated SNP,
rs4665058 (r2.0.8), suggesting that the functional variant is likely
to be regulatory. To test whether rs4665058 was an eQTL, we
searched the GTEx eQTL database (http://www.ncbi.nlm.nih.
gov/gtex/test/GTEX2/gtex.cgi), which queries lymphoblastoid,
liver and various brain regions. We did not observe any eQTLs for
rs4665058 or any SNPs from the 1000 Genomes data highly
correlated with rs4665058 (r2.0.8). We recognize that this
negative finding is not necessarily informative, given that
expression from heart tissue has not been queried. Second, we
only implicate loci, as opposed to genes, and additional work is
required to definitively identify which gene at a locus is
responsible. Indeed, while the strongest association signal maps
to an intron in BAZ2B, due to linkage disequilibrium, the signal
also extends to the WDSUB1 and TANC1 genes. All three genes
are expressed in human heart (http://www.genecards.org) and the
mouse heart during various key stages during cardiogenesis and
the formation of the autonomic nervous system from neural crest
(Embryonic day, E7.5–10.5) (http://biogps.gnf.org). BAZ2B and
WDSUB1 are essentially uncharacterized, however, TANC1 has
been shown to regulate dendritic spines and excitatory synapses in
both cultured neurons and a mouse knock-out . TANC1 is
most highly expressed in heart in humans, however, no cardiac
phenotype in the TANC1 knock-out mouse was noted. Third,
while we present evidence that in aggregate QRS/QT interval-
prolonging alleles are associated with SCD, only TKT/CACNA1D/
PRKCD exceeds a Bonferroni corrected P-value threshold,
suggesting inadequate power for these analyses. The findings
related to individual QRS/QT interval-prolonging alleles should
therefore be considered exploratory and require replication in
additional populations. Finally, we note that the meta-analysis
consisted of both population-based and case-control studies, with
some of the case-control studies using CAD controls as opposed to
population-based controls (Tables S1 and S3). The inclusion of
different control groups allows us to separate out whether the risk
conferred by the BAZ2B locus acts through increased risk for
CAD, which is present in ,80% of SCD victims. The consistent
results in studies with CAD controls (FinGesture GWAS, Oregon-
SUDS GWAS, Oregon-SUDS replication) (Figure S2) provide
evidence that the risk associated with rs4665058 may be specific to
SCD, rather than a generic risk factor for CAD. Indeed, given the
case mix across the cohorts (primary ventricular fibrillation,
ischemic CAD, non-ischemic CAD, non-CAD), the study is best
powered to identify variants that increase risk of SCD through a
mechanism common across the various subtypes of SCD.
In summary, we have identified the locus including the
bromodomain-containing gene , BAZ2B, as a new SCD
susceptibility locus. The bromodomain is an exclusive protein
domain known to recognize acetyl-lysine residues on proteins and
might play an important role in chromatin remodeling and gene
transcription regulation . The risk allele, while low frequency
in Caucasian populations (MAF =0.014) has a relatively large
effect, increasing risk for SCD by .1.9-fold per allele (95% CI
1.57 to 2.34). While rs4665058 may not be clinically relevant in
the general population in whom the increment in absolute risk
attributable to the variant is modest, exploring its role in high-risk
populations (e.g. heart failure, SQTS/LQTS) may help to identify
those who could benefit from intervention. Beyond the BAZ2B
locus, our study also highlights the role of QRS/QT interval
associated variants in the risk of SCD, and suggests that larger
GWAS of these and other intermediate risk factors may yield
additional SCD loci.
Materials and Methods
Five studies consisting of individuals of European ancestry from
Europe and the United States contributed to the GWAS discovery
phase of this study: Atherosclerosis Risk in Communities (ARIC),
Framingham Heart Study (FHS), FinGesture, Oregon-Sudden
Unexpected Death Study (Oregon-SUDS), and Rotterdam Study
(RS). For follow-up, additional genotyping was performed in
independent samples from FinGesture and Oregon-SUDS, as well
as in 8 additional populations of European ancestry: AmsteRdam
REsuscitation STudies (ARREST), Cardiovascular Health Study
(CHS), CVPath Institute Sudden Cardiac Death registry (CVPI-
SCDr), and Harvard Cohorts (consisting of 5 combined popula-
tions, see below). We also performed a look-up of our top result in
Table 2. Association of QRS/QT interval associated SNPs with SCD.
TraitNearest Gene Index SNPChr Position
coded Allele Trait b
(95% CI)SCD P
2 20.631.27 (1.10–1.45)0.0007 NO
1rs111537306118,774,215 C/T 0.591.13 (1.02–1.25) 0.013 YES
QTNOS1AP rs121438421 160,300,514T/C 2.881.16 (1.03–1.3)0.010 YES
1rs119702866118,787,067T/C 1.641.11 (1.01–1.22) 0.037YES
QT KCNQ1 rs12296050 112,445,918 T/C 1.440.85 (0.76–0.96) 0.014 NO
Chr, chromosome; OR, odds ratio; CI, confidence interval. Results are drawn from the SCD GWAS only (n=1283 case, .20,000 controls). Trait beta estimates (b) are in
milliseconds (ms). P-values are for a two-tailed test. Concordant Effect refers to whether the QRS/QT prolonging allele is associated with increased risk of SCD. Bold
indicates significant after Bonferroni correction for the 49 SNPs tested. QRS results are drawn from  and QT results are drawn from the QTSCD study . 1 These SNP
represent the same genetic effect (r2=0.91).
Identification of the SCD-Associated 2q24.2 Locus
PLoS Genetics | www.plosgenetics.org5June 2011 | Volume 7 | Issue 6 | e1002158
the AGNES study. All studies received approval from the
appropriate institutional review committees, and the subjects in
each cohort provided written informed consent.
from four communities in the United States (Jackson, Mississippi;
Forsyth County, North Carolina; Washington County, Maryland;
suburbs of Minneapolis, Minnesota) enrolled in 1987–1989 and
prospectively followed . Assessment of SCD has been
previously described . Briefly, all cases of fatal CHD that
occurred by December 31, 2002 were reviewed and adjudicated
by a committee of physicians. SCD was operationally defined as a
sudden pulseless condition from a cardiac origin in a previously
stable individual, and the reviewers classified each CHD death as
definite sudden arrhythmic death, possible sudden arrhythmic
death, definite non-sudden death, or unclassifiable. The primary
outcome of SCD described in the present study combines both
definite and possible sudden arrhythmic death. For the present
analysis, participants were censored at time of loss to follow up or
death if the cause of death was other than SCD.
The Framingham Heart Study is a longitudinal cohort
study including individuals recruited from three generations
recruited without regard to phenotype and followed up with
serial clinical examinations, mailed updates and review of medical
records. The SCD adjudication was conducted by three physicians
using previously established criteria. A SCD was defined as a
coronary heart disease death within one hour of the onset of
The FinGesture study started in 1999 aimed at
collecting consecutive victims of out-of-hospital sudden death from
a defined geographical area, Oulu University Hospital District in
northern Finland. All the victims of sudden death were autopsied
at the Department of Forensic Medicine, University of Oulu,
Oulu, Finland. The definition of SCD caused by an acute
coronary event has been previously described in detail . In each
case of sudden death, the mechanism of death was defined and all
patients who were considered to have died due to any cause other
than SCD due to an acute coronary event were excluded from the
study. Of the out-of-hospital SCD victims, those with (1) a
witnessed sudden death within 6 hours of the onset of the
symptoms or within 24 hours of the time that the victim was last
seen alive in a normal state of health and (2) evidence of a
coronary complication, defined as a fresh intracoronary thrombus,
plaque rupture or erosion, intraplaque hemorrhage, or critical
coronary stenosis (.75%) in the main coronary artery were
included in the SCD group. Victims of SCD with other serious
heart diseases, such as severe valve disease or cardiomyopathy,
were excluded. In addition, victims with evidence of non-cardiac
causes and victims with mechanical causes of sudden death, such
as a rupture of the myocardium and/or tamponade, extensive
myocardial necrosis (.50%), rupture of entire papillary muscle,
pulmonary edema, or any cause of death considered to be due to
some reason other than ischemia-induced SCD, were also
For the discovery GWAS, the FinGesture study control
population consisted of MI patients from the same geographical
area than cases and treated in the University of Oulu Hospital
. Acute myocardial infarction was diagnosed according to
ICD-10 classification with at least two of three following findings:
elevated troponin/ckMbm levels, typical angina pectoris, EKG
ST-segment changes typical for MI. All patients who had in-
hospital life-threatening ventricular arrhythmias were excluded
from the study. The replication control group consisted of subjects
The ARIC study includes 15,792 men and women
without a history of coronary heart disease, AMI, or aborted
cardiac arrest from the OPERA (Oulu Project Elucidating Risk of
Atherosclerosis) study . These general population samples
were randomly selected subjects from the social insurance register
covering the entire population of the city of Oulu, Finland. The
mean age of the subjects at the beginning of the study was 51
The Oregon Sudden Unexpected Death
Study (ongoing since 2002), is a community-based study of SCD
among residents of the Portland, Oregon metropolitan area (pop.
approx. 1,000,000). Methods of case ascertainment have been
published earlier [1,26]. In brief, patients with SCD were
ascertained from the regional emergency medical response
system (EMS), the County Medical Examiner, and emergency
departments of the 16 area hospitals. Determination of SCD was
made after in-house adjudication of all cases based on the arrest
circumstances detailed in the EMS incident report or medical
examiner report (available for all cases), medical records (available
for 79% of cases) and autopsy data (available for 15% of cases).
SCD was defined as a sudden unexpected pulse-less condition of
likely cardiac origin and survivors of SCA were included. If un-
witnessed, SCD subjects were included if they were found dead
within 24 hours of having last been seen alive and in normal state
of health. Subjects were excluded if they had a chronic terminal
illness (e.g. terminal cancer), or an identifiable non-cardiac
etiology of sudden death related to trauma, overdose, drowning
or suicide. Cases included in the current GWAS study were white
non-Hispanic SCA cases with DNA available (a blood or tissue
sample was available in 59% of cases). Case subjects were also
required to have documented significant coronary artery disease
(CAD), or, if aged $50 years, were assumed to have CAD (based
on 95% likelihood of CAD in SCA cases aged $50 years) .
Significant CAD was defined as $50% stenosis of a major
coronary artery from an angiogram prior to arrest or at autopsy;
physician report of past MI; history of percutaneous coronary
intervention (PCI) or coronary artery bypass grafting (CABG);
autopsy-identified CAD; or MI by clinical data with any two of the
following three: ischemic symptoms, positive troponins or CKMB;
or pathologic Q waves on ECG. For the discovery GWAS,
controls were drawn from the ARIC cohort, and consisted of
1,208 individuals with prevalent or non-fatal incident CAD. The
ARIC and Oregon-SUDS genotype data was combined before
imputation, and QC for both individuals and SNPs performed on
the combined samples. Oregon-SUDS controls analyzed in the
replication samples are subjects from the same geographic region
who had coronary artery disease but no history of SCD (n=348).
The Rotterdam Study is an ongoing
prospective population-based cohort study of chronic diseases in
Caucasian elderly, which started in 1990. The Medical Ethics
Committee of the Erasmus University approved the study. All
inhabitants of Ommoord, a Rotterdam suburb in the Netherlands,
aged 55 years and over (n=10,278) were invited to participate. Of
them, 78% (n=7,983) gave their written informed consent for
participation. Baseline examinations took place from March 1990
through July 1993. All participants were continuously monitored
for major morbidity and mortality through linkage with general
practitioner and municipality records. Detailed information on
design, objectives and methods of the Rotterdam Study is
described elsewhere . Of all 7,983 participants, 5,974
subjects were genotyped on the Infinium II HumanHap550K
Genotyping BeadChipH version 3 (Illumina) as part of a large
population-based project on genetics of complex traits and
diseases. The ascertainment of SCD cases in the Rotterdam
Study has been described previously . SCD cases were defined
Identification of the SCD-Associated 2q24.2 Locus
PLoS Genetics | www.plosgenetics.org6June 2011 | Volume 7 | Issue 6 | e1002158
as a witnessed natural death attributable to cardiac causes,
heralded by abrupt loss of consciousness, within one hour of onset
of acute symptoms, or as an unwitnessed, unexpected death of a
person seen in a stable medical condition within 24 hours before
death without evidence of a non-cardiac cause.
with a first acute ST-elevation myocardial infarction . AGNES
cases had ECG-registered ventricular fibrillation occurring before
reperfusion therapy for an acute and first ST-elevation myocardial
infarction. AGNES controls were individuals with a first acute ST-
elevation myocardial infarction but without ventricular fibrillation.
All cases and controls were recruited at seven heart centers in The
Netherlands from 2001–2010. We excluded individuals with an
actual non–ST-elevation myocardial infarction, prior myocardial
infarction, congenital heart defects, known structural heart disease,
severe comorbidity, electrolyte disturbances, trauma at presenta-
tion, recent surgery, previous coronary artery bypass graft or use of
class I and III antiarrhythmic drugs. Individuals who developed
ventricular fibrillation during or after percutaneous coronary
intervention were not eligible. Furthermore, because early reper-
fusion limits the opportunity of developing ventricular fibrillation,
potential control subjects undergoing percutaneous coronary
intervention within 2 h after onset of myocardial ischemia
symptoms were not included. This time interval was based on
the observation that .90% of cases developed ventricular
fibrillation within 2 h after onset of the complaint of symptoms.
ARREST is an ongoing prospective population-
based study that covers .95% of all out-of-hospital cardiac arrests
(with ECG documentation) in a contiguous region of the
Netherlands with ,2.4 million inhabitants, that was designed to
study the clinical and genetic determinants of sudden cardiac
death [10,29]. In collaboration with all Emergency Medical
Services (EMS) in this study region, all patients with out-of-
hospital cardiac arrest (OHCA) with ECG-documented ventri-
cular tachycardia/fibrillation (VT/VF) are prospectively included.
To ensure .95% coverage, a data collection infrastructure has
been set up that records all CPR attempts with EMS involvement
for OHCA from ambulance dispatch to discharge from the
hospital or to death according to the Utstein template. This
method presumably reflects the real-life situation better than
studies that only include SCD victims who survive and are
admitted to the hospital. SCD was defined as OHCA due to
cardiac causes with ECG-documentation of VT/VF. Medical
history and current disease diagnosis are retrieved from the
patient’s General Practitioner and/or hospital records, and
medication use prior to the resuscitation is retrieved from the
patient’s pharmacist. For the current study, 719 OHCA cases with
VT/VF were included. Controls were drawn from the RS-II and
RS-III cohorts from the Rotterdam Study .
The CVPath Institute Sudden Cardiac Death
registry samples are received through and ongoing joint
consultation service provided to the Maryland Office of the
Chief Medical Examiner initiated in 1993. Sudden death is
defined as symptoms commencing within 6 hours of death
(witnessed arrest) or death occurring within 24-hours after the
victim was last seen alive in his normal state of health.
Comprehensive analysis of each sample includes coronary artery
histology, and cases of unexpected sudden death are stratified into
cardiac deaths (with coronary disease (CAD): at least 1 epicardial
coronary artery has $75% cross-sectional luminal narrowing by
an atherosclerotic plaque or a lesion with a superimposed
thrombus or evidence of a prior MI and no other cause of
The AGNES case-control set consists of individuals
death; non-CAD: atherosclerosis with less ,75% cross-sectional
luminal narrowing (non-flow limiting CAD) and/or cardiomyo-
pathies) and non-cardiac deaths (e.g., drug overdose, trauma,
seizure disorder, stroke). All samples are genotyped for a panel
of ancestry informative markers, and only those identified as
Caucasian by the CVPI-SCDR with concordant genotype data
are included. For the current study, 259 sudden cardiac deaths
were included, and all non-SCD CHS samples were used as
CHS is a population-based prospective cohort study of
cardiovascular disease, and includes 5,888 participants .65 years
of age identified from four U.S. communities using Medicare
eligibility lists. The original cohort included 5201 participants
recruited in 1989–1990 and 687 additional subjects were recruited
in 1992–1993 to enhance the racial/ethnic diversity of the cohort
. The following exclusion criteria were applied to obtain the
final sample for the present analysis: no consent for genetic
analyses, poor quality DNA (samples with , 60% of genotypes
called), and self-described ethnicity other than White. Assessment
of SCD was identical to that of ARIC, described above, with all
cases of fatal CHD that occurred by July 31, 2002 examined.
The study design is a case-control
investigation sampled from prospective cohorts and clinical
trials, taking advantage of the time-to-event data by matching
cases and controls on follow-up time. The SCD cases from the
Physicians’ Health Study (PHS I and II), the Nurses’ Health Study
(NHS), the Health Professionals Follow-up Study (HPFS), and the
Women’s Antioxidant Cardiovascular Study (WACS) were
included in the present analysis. In all cohorts, cases of sudden
and/or arrhythmic cardiac death are confirmed by medical record
review (hospital, emergency room, autopsy, and emergency
medical services reports) and next-of-kin descriptions of the
circumstances surrounding the death. The definition of SCD has
been previously described . Briefly, a cardiac death is
considered a definite SCD if the death or cardiac arrest that
precipitated death occurred within one hour of symptom onset as
documented by medical records or next-of-kin reports or had an
autopsy consistent with SCD (i.e. acute coronary thrombosis or
severe coronary artery disease without myocardial necrosis or
other pathologic findings to explain death). Deaths were also
classified as arrhythmic based on the definition of Hinkle and
Thaler . Unwitnessed deaths or deaths that occurred during
sleep were considered probable SCDs if the participant was
documented to be symptom free when last observed within the
preceding 24 hours, and circumstances suggested that the death
could have been sudden. A total of 435 confirmed sudden and
arrhythmic cardiac deaths among individuals of self-described
white ancestry were included in the analysis. Controls were
selected using risk-set sampling , with up to three controls for
each case matched on study cohort, sex, age (+/21 year),
ethnicity, smoking status (current, never, past), time and date of
blood sampling, fasting status, and presence or absence of
cardiovascular disease (MI, angina, CABG, or stroke) prior to
For all studies, covariates, measured at baseline for prospective
cohorts, included age and gender, with the following exceptions:
FinGesture GWAS included the top 10 principal components
calculated from pre-imputation genotype data through a multi-
dimensional scaling (MDS) method, as implemented in PLINK
; CVPI-SCDr did not include covariates, as the controls were
population-based controls from CHS.
Identification of the SCD-Associated 2q24.2 Locus
PLoS Genetics | www.plosgenetics.org7June 2011 | Volume 7 | Issue 6 | e1002158
Genome-Wide Genotyping and Imputation
Genotyping was performed using either Affymetrix or Illumina
arrays, depending on the cohort (Table S7). Each study performed
filtering of both individuals and SNPs to ensure robustness for
genetic analysis. SNP genotypes were assessed for quality, and
SNPs failing quality control were removed before imputation
according to specific criteria (Table S7). Each study utilized the
remaining SNP genotypes to impute genotypes for approximately
2.5 million autosomal SNPs based on linkage disequilibrium
patterns observed in the HapMap CEU samples (Utah residents of
Northern and Western European descent). Imputed genotypes
were calculated as dosages, with fractional values between 0 and 2
reflecting the estimated number of copies of a given allele for a
given SNP for each individual. The use of dosages allows for the
incorporation of the uncertainty in the imputations into subse-
quent analysis. All studies used a hidden Markov model as
implemented in the MACH software . All results are reported
on the forward strand. Post-imputation, quantile-quantile (QQ)
plots were generated for each study, stratified by both allele
frequency and imputation quality to identify classes of SNPs that
show strong early departure from the null. Based on these
analyses, for the ARIC and Rotterdam studies, SNPs with minor
allele frequency (MAF) ,0.01 were excluded. For FHS, SNPs with
MAF ,0.02 and/or imputation quality score ,0.40 were
Genotyping was performed using iPlex single base primer
extension with MALDI-TOF mass spectrometry according to
manufacturer protocols (Sequenom Inc., San Diego, CA). SNPs
were excluded from each study if call rate was #90% or Hardy-
Weinberg equilibrium P,0.001. SNPs were excluded from final
meta-analysis if fewer than 4 out of 5 studies reported back results
for that SNP. PCR and extension primer sequences are available
upon request. In ARREST, rs4665058 was genotyped using
TaqMan (Applied Biosystems), and primer and probe sequences
are available upon request. In AGNES, genotypes at rs4665058
were determined by direct genotyping (N=360) using Taqman
assay or imputation from HapMap reference panel (N=969) using
MACH1.0 (Rsq=0.97). Details on genotype imputation have
been described elsewhere .
For prospective community-based samples, associations be-
tween SCD and SNPs were tested using Cox proportional hazards
regression models under the assumption of an additive model of
genotypic effect. For case-control samples, a logistic regression
framework was employed. For the Harvard cohorts, risk set
analysis was used to match cases and controls holding time at risk
stable; conditional logistic regression in risk set sampling avoids
differences in time at risk that otherwise result. These models were
adjusted for age and sex. In family-based cohorts (FHS), linear
mixed modeling was implemented to additionally control for
relatedness . A genomic control correction factor (l),
calculated from all imputed SNPs, was applied on a per-study
basis to account for cryptic population sub-structure and other
potential biases . Regression results were meta-analyzed using
inverse variance weighted fixed-effects models as implemented in
the METAL software package (http://www.sph.umich.edu/csg/
abecasis/metal/). Results were considered statistically significant
at a P-value of 561028, a figure that reflects the estimated testing
burden of one million independent SNPs in samples of European
ancestry . For age by SNP and sex by SNP interactions, we
performed regression analysis as described above separately within
each study, including both main effects and an interaction term in
the model. Meta-analysis of discovery and replication results was
performed using inverse-variance weighting as implemented in the
R package ‘meta’ (R version 2.81, http://www.r-project.org/). To
account for the age range differences across the cohorts, we used a
random-effects model for the age by SNP interaction.
populations of European ancestry. (A) The QQ plot shows no
early departure from the null expectation between observed and
expected P-values; (B) Manhattan plot. Dotted line indicates
threshold for genome-wide significance (P,561028).
Association results for GWAS for SCD in 5
report data for this SNP. Freq incidates the coding allele
frequency, TE indicates the beta estimate, and seTE is the
standard error of the beta estimate.
Forest plot for rs4665058. Note that FHS does not
primate sequence) frequency distribution in HapMap CEU. Red
line indicates allele frequency of the rs4665058 risk allele (A allele).
Ancestral allele (based on comparison to non-human
studies is at baseline.
GWAS cohort chracteristics. Age for prospective
association with SCD. Chr, chromosome; AF, allele frequency of
coded allele (study size weighted average). Follow-up genotyping
results are reported for 1,730 SCD cases and 10,530 controls, with
the exception of rs12601622 (1,460 SCD cases, 10.182 controls),
which failed genotyping in the Oregon-SUDS follow-up study.
Bold indicates nominal significance (P,0.05) for validation. P-
values for validation are reported as one-sided, and NA indicates
opposite direction of effect from GWAS. *Includes ARREST (719
SCD cases, 4,190 controls) and AGNES (670 SCD cases, 654
controls) studies. Follow-up genotyping results are reported for the
11 SNPs which passed genotyping QC.
Summary of GWAS and validation results for
prospective studies is at baseline.
Follow-up genotyping cohort characteristics. Age for
SCD. Chr, chromosome; OR, odds ratio; CI, confidence interval.
Trait beta estimates (b) are in milliseconds (ms). P-values are for a
two-tailed test. Bold indicates nominal significance (P,0.05).
Concordant Effect refers to whether the QRS prolonging allele is
associated with increased risk of SCD. QRS interval results are
drawn from Sootodehnia et al.14.1This SNPs represent the same
genetic effect for QT as rs11970286 in Table S5 (r2=0.91).
Association of QRS interval associated SNPs with
Chr, chromosome; OR, odds ratio; CI, confidence interval. Trait
beta estimates (b) are in milliseconds (ms). P-values are for a two-
tailed test. Bold indicates nominal significance (P,0.05). Concor-
dant Effect refers to whether the QT prolonging allele is associated
with increased risk of SCD. QT results are drawn from the
QTSCD study13, unless otherwise noted. *Genome-wide signifi-
cant results (P,5610-8) are drawn from the QTGEN study12, and
Association of QT interval associated SNPs with SCD.
Identification of the SCD-Associated 2q24.2 Locus
PLoS Genetics | www.plosgenetics.org8June 2011 | Volume 7 | Issue 6 | e1002158
standardized beta estimates and SE were converted to ms using Download full-text
QRS interval as rs11153730 in Table S4 (r2=0.91).
1This SNP represent the same genetic effect for
Chr, chromosome; OR, odds ratio; CI, confidence interval. Trait
beta estimates (b) are in milliseconds (ms). P-values are for a two-
tailed test. Concordant Effect refers to whether the QT prolonging
allele is associated with increased risk of SCD. RR results are
drawn from11.1This SNP is partially correlated with rs11153730
and rs11970286 from Tables S4 and S5, respectively (r2=0.59).
Association of RR interval associated SNPs with SCD.
Study genome-wide genotyping characteristics.
ARIC. The authors thank the staff and participants of the ARIC study for
their important contributions.
ARREST. The authors are very grateful to all EMS staff that participate
RS. The authors are very grateful to the participants and staff from the
Rotterdam Study, the participating general practitioners, and the
pharmacists. We thank Pascal Arp, Mila Jhamai, and Marijn Verkerk for
their help in creating the GWAS database and Yurii Aulchenko, Maksim
Struchalin, and Karol Estrada for their contribution on the imputed data.
Conceived and designed the experiments: DEA SSC CN-C MJJ HVH
HLT EM AAMW CRB RWK AC CMA J-CT JDR PMS. Performed the
experiments: DEA SSC KR CT AU-E KG CJO MJJ HVH KSK M-LK
RFM RP MTB AB AD AC CMA CN-C JCMW BHCS JJ YAK AH RJP
AGU FR PG CL RV FK NC-M AM XZ GH WP NS EB JC GLB.
Analyzed the data: DEA SSC AH-V KR CT AU-E MJJ KG S-JH LAC
RP MTB AD CMA ME FR PG GB ML AK GE PB WHLK. Wrote the
paper: DEA SSC AH-V MJJ HH ME PG GB. Critical revision of the
manuscript: DEA SSC AH-V RJP KR CT AU-E MJJ KG CJO CN-C S-
JH RP MTB HLT CRB RWK CMA CN-C ME BHCS FR PG GB J-CT
JDR GFT BMP DSS JC.
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Identification of the SCD-Associated 2q24.2 Locus
PLoS Genetics | www.plosgenetics.org9 June 2011 | Volume 7 | Issue 6 | e1002158