ARTICLE
GWAS and colocalization analyses implicate
carotid intima-media thickness and carotid
plaque loci in cardiovascular outcomes
Nora Franceschini1, Claudia Giambartolomei et al.#
Carotid artery intima media thickness (cIMT) and carotid plaque are measures of subclinical
atherosclerosis associated with ischemic stroke and coronary heart disease (CHD). Here, we
undertake meta-analyses of genome-wide association studies (GWAS) in 71,128 individuals
for cIMT, and 48,434 individuals for carotid plaque traits. We identify eight novel suscept-
ibility loci for cIMT, one independent association at the previously-identified PINX1 locus, and
one novel locus for carotid plaque. Colocalization analysis with nearby vascular expression
quantitative loci (cis-eQTLs) derived from arterial wall and metabolic tissues obtained from
patients with CHD identifies candidate genes at two potentially additional loci, ADAMTS9 and
LOXL4. LD score regression reveals significant genetic correlations between cIMT and plaque
traits, and both cIMT and plaque with CHD, any stroke subtype and ischemic stroke. Our
study provides insights into genes and tissue-specific regulatory mechanisms linking ather-
osclerosis both to its functional genomic origins and its clinical consequences in humans.
i i, Claudia Giambartolomei et al.#
https://doi.org/10.1038/s41467-018-07340-5 OPEN
Correspondence and requests for materials should be addressed to J.L.M.B. (email: johan.bjorkegren@mssm.edu)
or to C.J.O. (email: Christopher.ODonnell@va.gov). #A full list of authors and their affiliations appears at the end of the paper.
NATURE COMMUNICATIONS |          (2018) 9:5141 | https://doi.org/10.1038/s41467-018-07340-5 | www.nature.com/naturecommunications 1
12
34
56
78
9
0
()
:,;
Atherosclerosis is characterized by an accumulation oflipid-rich and inflammatory deposits (plaques) in the sub-intimal space of medium and large arteries. Plaque
enlargement leads to blood flow limitation, organ ischemia, and/
or tissue necrosis. Plaque rupture can lead to abrupt vascular
occlusion, which underlies clinical cardiovascular events,
including myocardial infarction and ischemic stroke. Coronary
heart disease (CHD) accounts for one in seven deaths, and stroke
accounts for one in 20 deaths in the US1. Because atherosclerosis
has a long pre-clinical phase, early detection of atherosclerosis
using non-invasive methods may help identify individuals at risk
for atherosclerotic clinical events2, and provides an opportunity
for prevention. Subclinical atherosclerosis can be detected by B-
mode ultrasound measurement of common carotid artery intima-
media thickness (cIMT) or carotid plaques1.
Subclinical and clinical atherosclerosis has known genetic
components3. Genome-wide association studies (GWAS) of
subclinical atherosclerosis have previously identified three loci
significantly associated with cIMT at ZHX2, APOC1, and PINX1,
and two loci associated with common carotid artery plaque at
PIK3CG and EDNRA4. An exome-wide-association study iden-
tified significant associations of the APOE ε2 allele with cIMT and
coronary artery calcification5. The APOE single nucleotide poly-
morphism (SNP) rs7412 is in linkage disequilibrium (LD) with
the APOC1 variant, thus representing the same signal. Additional
GWAS-identified associations were reported for carotid plaque at
the 9p21 and SFXN2 loci6, and for cIMT at the CFDP1-
TMEM170A locus7. However, these prior studies were of limited
sample size and genomic coverage, and failed to investigate the
etiological role that subclinical atherosclerosis may have on
atherosclerotic clinical events.
Herein, we perform a large meta-analysis of GWAS of sub-
clinical atherosclerosis by analyzing 1000 Genomes imputed
genotype data obtained from collaborations between the Cohorts
for Heart and Aging Research in Genomic Epidemiology
(CHARGE) consortium8 and the University College London-
Edinburgh-Bristol (UCLEB) consortium9. One of the greatest
challenges in the translation of GWAS findings to biological
understanding is related to the limited access to RNA expression
data from disease-relevant tissues. Consequently, we sought to
reliably identify the tissue-specific gene regulatory functions
responsible for the GWAS signals by prioritizing candidate genes
for established and novel loci of cIMT and carotid plaque using
statistical methods for colocalization10. These methods integrate
identified loci with expression quantitative loci (eQTLs) inferred
from cardiovascular disease-relevant genetics of RNA expression,
the Stockholm-Tartu Atherosclerosis Reverse Network Engi-
neering Task (STARNET) study, where arterial wall and
metabolic-related RNA samples were collected from up to 600
patients with CHD11. We also evaluate the relationships of cIMT
and carotid plaque with clinically apparent CHD and stroke using
summary data from two large consortia. In summary, our study
sequentially assesses the genetic epidemiology and tissue-specific
patterns of gene regulation involved in the formation of sub-
clinical atherosclerosis traits across cardiovascular disease-related
tissues.
Results
Study description. The study design is shown in Fig. 1. We
undertook meta-analysis of GWAS in individuals of European
ancestry for cIMT (up to 71,128 participants from 31 studies) and
carotid plaque (up to 48,434 participants from 17 studies; 21,540
with defined carotid plaque) (Supplementary Table 1). cIMT and
plaque were evaluated using high-resolution B-mode ultra-
sonography and reading protocols as previously reported4. Car-
otid plaque was defined by atherosclerotic thickening of the
common carotid artery wall or the proxy measure of luminal
stenosis greater than 25% (Supplementary Table 2). Each cohort
performed association analyses using standardized protocols
(Methods) for variants imputed based on the 1000 Genomes
Project (1000G) phase 1 v3 reference. Extensive quality control
(QC) was applied to data, and there was little evidence for
population stratification in any of the studies for either trait
(Supplementary Table 3). The study-specific results were com-
bined using fixed-effect meta-analyses, given the low hetero-
geneity across studies (0% heterogeneity)12.
GWAS meta-analyses of cIMT and carotid plaque. For cIMT,
11 loci had at least one SNP association that reached the genome-
wide association threshold (p < 5 × 10−8), of which eight were
newly described and three have been previously reported
(Table 1). The closest genes for the eight loci were: 1q32.2
intergenic (rs201648240), ATP6AP1L (rs224904), AIG1
(rs6907215), PIK3CG (rs13225723), MCPH1 (rs2912063),
SGK223 (rs11785239), VTI1 (rs1196033), and CBFA2T3
(rs844396). For three loci previously reported, the closest genes
were ZHX2 (rs148147734), PINX1 (rs200482500), and APOE
(rs7412).
The PIK3CG is a newly described locus for cIMT, but has been
previously reported in a GWAS of carotid plaque4. The two
cIMT/Plaque, AOR/MAM cis-eQTLs: four genes at three loci
chr3:63561280-65833136 (ADAMTS9)
chr10:99017729-101017321 (LOXL4) 
chr7:105299372-107743409 (CCDC71L, PRKAR2B)
Colocalization
Subclinical GWAS and cis-eQTLs in AOR/MAM
Subclinical trait GWAS: cIMT and carotid plaque
11 cIMT loci: 8 novel 
chr1:208953176 -indel, chr5:81637916, chr6:143608968, 
chr7:106416467, chr8:6486033, chr8:8205010, 
chr10:114410998, chr16:88966667, chr8:123401537-indel, 
chr8:10606223-indel, chr19:45412079
Discovery CHARGE and UCLEB consortia 
LD score regression
Subclinical GWAS and CHD/stroke GWAS
Shared genetic basis
Genome-wide, with clinical outcomes
a
b Local, with gene expression
cIMT/plaque and CHD cIMT and any stroke
cIMT and ischemic stroke
CARDIoGRAMPlusC4D
Significant correlations 
5 plaque loci: 1 novel
chr19:11189298, chr4:148395284-indel, 
chr7:106411858, chr9:22072301, 
chr16:75432686-indel
MEGASTROKE
Fig. 1 Overall study design. a GWAS meta-analyses of cIMT and carotid plaque for gene discovery. b Local and genome-wide shared genetic basis using
gene expression and clinical outcomes GWAS data
ARTICLE NATURE COMMUNICATIONS | https://doi.org/10.1038/s41467-018-07340-5
2 NATURE COMMUNICATIONS |          (2018) 9:5141 | https://doi.org/10.1038/s41467-018-07340-5 | www.nature.com/naturecommunications
signals on chromosome 8 near MCPH1 (rs2912063) and SGK223
(rs11785239) were confirmed to be independent through
conditional analysis (Supplementary Table 4). At the PINX1
locus, the lowest association p-value variant (rs200482500) was
not in LD with the previously reported associated variant in the
region (rs6601530, r2= 0.0, Table 1), thus representing an
independent signal at this locus. Two additional loci for cIMT
had an SNP that reached suggestive evidence for association (p <
1.0 × 10−7) including an SNP nearby APOB (rs515135) and an
intronic low frequency variant at ATG4B (rs139302128, minor
allele frequency [MAF] = 0.03) (Supplementary Table 5).
The GWAS meta-analysis for carotid plaque identified five loci,
of which one has not been previously described (nearby gene
LDLR) (Table 1). At four known loci associated with carotid
plaque (nearby genes EDNRA, PIK3CG, CFDP1-TMEM170A, and
at the 9p21 region), the most significantly associated variants
were in LD with the previously reported SNPs (Table 1)4,6,7,
indicating that these SNPs mark the same association at each
locus. Two suggestive loci (p < 10−7) were also identified nearby
the genes TMCO5B and STEAP2-AS1 (Supplementary Table 5).
Conditional analyses confirmed the presence of a single
independent signal at each locus. Manhattan and QQ plots from
the meta-analysis of cIMT and carotid plaque are shown in
Supplementary Figure 1 and regional plots in Supplementary
Figure 2. Forest Plots for all loci are shown in Supplementary
Figure 3.
Regulatory annotations of GWAS SNPs for cIMT/carotid
plaque. To better define potentially causal variants within the
identified genetic risk loci, we jointly analyzed the GWAS data
with functional genomic information such as annotations on
active transcription sites or open chromatin regions (i.e., per-
formed a fine-mapping functional genome-wide association
analysis using fGWAS13). Only variants in the PINX1 region were
found to have a high probability that its association with cIMT is
driven by SNPs that fall within transcription sites in adipose-
derived mesenchymal stem cells at a DNaseI-hypersensitive site
(Supplementary Figure 4), a finding that provides a down-stream
mechanistic explanation for the cIMT signal in the PINX1 locus.
To further explore the regulatory functions of variants in the
identified loci for cIMT and carotid plaque, we investigated
whether the identified lead SNPs were also eQTLs using vascular
RNAseq data from GTEx (aorta, coronary and tibial arteries,
heart atrial appendage, and heart left ventricle) and from the
coronary artery disease cohort of STARNET (i.e., from the
atherosclerotic-lesion-free internal mammary artery [MAM] and
atherosclerotic aortic root [AOR]). Lead SNP associated with
cIMT and carotid plaque (rs13225723) in the PIK3CG locus was
found to be vascular-specific eQTLs for CCDC71L and PRKAR2B
in GTEx aorta as well as in STARNET AOR and MAM tissues
(Table 2, Fig. 2), suggesting that the genetic regulation of these
two genes are responsible for risk variation in cIMT and carotid
plaque development in this locus.
Colocalization analysis of GWAS data and STARNET eQTLs.
To identify further candidate genes in tissues affected by ather-
osclerosis that had strong evidence of sharing the same variant for
cIMT and carotid plaque as found in our GWAS, we conducted
pairwise colocalization analysis of these genetic variants with cis-
eQTLs in the STARNET study10.
The pairwise colocalization analysis is based on coloc, a
Bayesian statistical methodology that tests pairwise colocalization
of SNPs in GWAS with eQTLs and, in this fashion, generates
posterior probabilities for each locus weighting the evidence for
competing hypothesis of either no colocalization or sharing of a
distinct SNP at each locus10. We used summary statistics from all
SNPs within a 200-kb window around each gene covered by the
eQTL datasets (N= 18,705, see Methods), and analyzed each
Table 1 Loci significantly associated with cIMT and plaque GWAS
SNP Chr:position Nearest coding
gene
Alleles (effect/
other)
Effect allele
freq.
Beta (SE) p N
Newly identified loci for cIMT
rs201648240 1:208953176-indel LINC01717 −/AA 0.83 −0.0062 (0.0011) 4 × 10−9 54,752
rs224904 5:81637916 ATP6AP1L C/G 0.95 −0.0088 (0.0016) 5 × 10−8 68,962
rs6907215 6:143608968 AIG1 T/C 0.60 −0.0040
(0.0007)
5 × 10−8 64,586
rs13225723 7:106416467 PIK3CG A/G 0.22 0.0052 (0.0009) 3 × 10−9 68,070
rs2912063 8:6486033 MCPH1 A/G 0.71 0.0045 (0.0008) 9 × 10−9 67,401
rs11785239 8:8205010 SGK223 T/C 0.65 −0.0043
(0.0008)
9 × 10−9 67,107
rs11196033 10:114410998 VTI1A A/C 0.48 0.0042 (0.0008) 4 × 10−8 57,995
rs844396 16:88966667 CBFA2T3 T/C 0.30 −0.0051 (0.0009) 6 × 10−9 50,377
Newly identified loci for plaque
rs200495339 19:11189298-indel LDLR −/G 0.11 −0.1023 (0.0179) 1 × 10−8 36,569
Known loci for cIMT
rs148147734a 8:123401537-indel ZHX2 −/G 0.54 0.0050 (0.0007) 3 × 10
−11
58,141
rs200482500a 8:10606223-indel PINX1 −/GTACC 0.52 0.0056 (0.0008) 7 × 10−12 58,141
rs7412a 19:45412079 APOE T/C 0.08 −0.0119 (0.0015) 1 × 10−14 44,607
Known loci for plaque
rs11413744b 4:148395284-indel EDNRA −/T 0.86 −0.1586 (0.0253) 4 × 10
−10
39,577
rs17477177b 7:106411858 PIK3CG T/C 0.79 −0.1305 (0.0197) 4 × 10−11 47,863
rs9632884b 9:22072301 9p21 C/G 0.48 0.1127 (0.0163) 5 × 10−12 45,943
rs113309773b 16:75432686-indel CFDP1- TMEM170A −/C 0.46 −0.1259 (0.0194) 9 × 10−11 37,104
p= p-values of association from linear regression analysis, N= total number in meta-analyses
aPublished cIMT SNP in LD with our most significant SNP: rs11781551 (r2= 0.95 with rs148147734), rs6601530 (r2= 0 with rs200482500), and rs445925 (r2= 0.60 with rs7412)
bPublished plaque SNP in LD with our most significant SNP: rs1878406 (r2= 0.98 with rs11413744), rs17398575 (r2= 0.8 with rs17477177), rs9644862 (r2= 0.79 with rs9632884), and rs4888378 (r2
= 0.94 with rs113309773)
NATURE COMMUNICATIONS | https://doi.org/10.1038/s41467-018-07340-5 ARTICLE
NATURE COMMUNICATIONS |          (2018) 9:5141 | https://doi.org/10.1038/s41467-018-07340-5 | www.nature.com/naturecommunications 3
eQTL-GWAS dataset pair (Supplementary Table 6). A posterior
probability of ≥75% was considered strong evidence of the tissue-
specific eQTL-GWAS pair influencing both the expression and
GWAS trait at a particular region. Results for this analysis are
shown in Table 3 and Supplementary Figure 5. The strongest
evidence for an effect on gene expression within the regions
identified in our standard GWAS meta-analysis was for the
CCDC71L and PRKAR2B genes at the previously described
chromosome 7 cIMT locus (PIK3CG in Table 2, Fig. 2). These
genes showed evidence of colocalization for both cIMT and
carotid plaque in AOR and MAM tissues (Table 3, Fig. 3).
CCDC71L had the highest probability (>95%) for colocalization
for cIMT, and MAM and AOR tissue eQTLs, and for carotid
plaque, and MAM and AOR tissue eQTLs. We found a low
probability of colocalization of the SNP with the PIK3CG gene
expression (<1%).
Table 2 Gene expression results for significant SNPs in GTEx and STARNET tissues
SNP eQTLa (Gene, p) GTEx eQTLa (Gene, p) STARNET tissues
AORb HEART (ATR/VEN)c AOR MAM
rs201648240 CAMK1G, 0.0094
AL031316.1, 0.0040
CD34,0.00532
TRAF3IP3, 0.0097
rs6907215 AL023584.1, 0.005384704 (VEN) ENSG00000217648,
0.00046
ENSG00000217648, 0.8 × 10−5
rs13225723 AC005050.1, 1 × 10−10
ENSG00000177820.5, 7.0 × 10−5
CCDC71L, 5 × 10−6
PRKAR2B, 4 × 10−8
PIK3CG, 10 × 10−3
CCDC71L, 6 × 10−36
PRKAR2B, 7 × 10−7
SYPL1, 0.0043
CCDC71L, 3 × 10−33
PRKAR2B, 6 × 10−8
NAMPT, 6 × 10−6
rs2912063 MCPH1, 0.0041 ENSG00000271743.1, 0.0093
(VEN)
MCPH1-AS1, 0.0020
rs11785239 AC022784.1, 0.0078 (VEN) ERI1, 0.0069 PPP1R3B, 0.0036
rs844396 ENSG00000141012.8, 0.003
AC092384.2, 0.001
CBFA2T3, 1 × 10−7
ZNF469, 0.004 (ATR)
AC092384.3, 5 × 10−6 (ATR)
AC092384.1, 0.002 (ATR)
CBFA2T3, 0.0004 (ATR)
ZNF469, 0.002 (VEN)
AC138028.4, 0.001 (VEN)
ENSG00000224888.3, 0.009
(VEN)
PIEZO1, 0.0004 (VEN)
GALNS, 0.004 (VEN)
RPL13, 0.0024
ZNF276, 0.0070
TRAPPC2L, 0.0091
TRAPPC2L, 0.0040
ZNF276, 0.0059
rs200495339 ENSG00000267105.1, 0.0005
(VEN)
rs148147734 DERL1, 0.0082
rs200482500 AF131215.6, 0.005
AF131215.5, 0.001
AF131215.5, 0.002 (ATR)
AF131215.6, 0.003 (VEN)
AF131215.5, 0.004 (VEN)
rs7412 ENSG00000267163.1, 0.007
rs11413744 PRMT9, 0.004
rs17477177 ENSG00000267052.1, 6 × 10−11
ENSG00000177820.5, 5 × 10−6
CCDC71L, 4 × 10−7
PRKAR2B, 2 × 10−8
BCAP29, 0.002 (ATR) CCDC71L, 2 × 10−37
PRKAR2B, 6 × 10−7
SYPL1, 0.0091
CCDC71L,1 × 10−33
PRKAR2B, 2 × 10−8
NAMPT, 1 × 10−5
rs9632884 DMRTA1, 0.007 (ATR) CDKN2B, 2 × 10−3 CDKN2B, 2 × 10−3
rs113309773 BCAR1, 6 × 10−11
ENSG00000261783.1, 2 × 10−16
GABARAPL2, 0.004
ENSG00000261783.1, 1 × 10−5
(ATR)
ENSG00000166822.8, 0.005
(ATR)
ENSG00000261783.1, 0.0003
(VEN)
ZFP1, 4 × 10−4
AC009078.2, 0.002
BCAR1, 3 × 10−12
CFDP1, 0.002
TMEM170A, 0.009
p= p-values of association from linear regression analysis
aThe lead SNP from GWAS is considered an eQTL if the cis-association has a nominal p-value of association <0.01. Multiple but not all lead SNPs reach genome-wide significance (p < 10−4).
bThis includes aorta (AOR)
cThis includes heart atrial (ATR) and heart left ventricle (VEN)
ADAMTS9
LOXL4
PRKAR2B
CCDC71L
CI
M
T−
AO
R
CI
M
T−
M
AM
CI
M
T−
SF
CI
M
T−
SK
LM
CI
M
T−
VA
F
CI
M
T−
LI
V
CI
M
T−
Bl
oo
d
PL
AQ
UE
−A
OR
PL
AQ
UE
−M
AM
PL
AQ
UE
−S
F
PL
AQ
UE
−S
KL
M
PL
AQ
UE
−V
AF
PL
AQ
UE
−L
IV
PL
AQ
UE
−B
loo
d
−1.0
−0.5
0.0
0.5
1.0
Posterior
probability
Fig. 2 Pairwise colocalization results for genes identified for cIMT and
carotid plaque GWAS meta-analysis with STARNET expression datasets.
Red indicates a high posterior probability of colocalization and blue a high
probability of no colocalization of the same SNP with tissue eQTLs
ARTICLE NATURE COMMUNICATIONS | https://doi.org/10.1038/s41467-018-07340-5
4 NATURE COMMUNICATIONS |          (2018) 9:5141 | https://doi.org/10.1038/s41467-018-07340-5 | www.nature.com/naturecommunications
The eQTL associations at two additional loci (ADAMTS9,
LOXL4) in MAM or AOR showed evidence of colocalization with
cIMT or carotid plaque, although GWAS association p-values at
these loci did not meet the genome-wide significance threshold
(Table 3, Supplementary Figure 5). Albeit with weaker magni-
tudes, the expression of these two genes were also associated with
the top colocalizing SNPs as detected in RNAseq data in GTEx
aorta (rs17676309, chr3:64730121, ADAMTS9, p= 0.0003 and
rs55917128, chr10:100023359, LOXL4, p= 0.0005).
Colocalization of CHD and stroke GWAS and STARNET
eQTLs. We next assessed if the four genes (CCDC71L, PRKAR2B,
ADAMTS9, LOXL4) identified through colocalization of cIMT/
carotid plaque with tissue-specific eQTLs also showed evidence
for colocalization with CHD and stroke traits (Supplementary
Data 1 and Supplementary Figure 6). We used GWAS summary
data for CHD (CARDIoGRAMPlusC4D), and stroke subtypes
(MEGASTROKE) and AOR and MAM STARNET tissue eQTLs
for these analyses. CCDC71L and PRKAR2B had suggestive evi-
dence of sharing the same variant with large vessel disease stroke
in both AOR and MAM tissues (probability of colocalization
≥20%, Supplementary Data 1). In contrast, there was strong
evidence (≥75%) to reject a shared variant for CHD and eQTLs at
this locus, thus suggesting there is atherosclerotic outcome spe-
cificity at vascular level for this locus (Supplementary Figure 5).
Three of these genes, CCDC71L, PRKAR2B, and ADAMTS9,
showed evidence for shared genetic influences of cIMT or carotid
plaque on CHD/stroke outcomes when testing the joint associa-
tion using moloc, a multiple-trait extension of coloc14 (Supple-
mentary Table 7). We also highlight the expression of KIAA1462
gene in MAM, carotid plaque/cIMT, and CHD, which were
positively correlated (Supplementary Figure 7). This gene has
suggestive evidence of pairwise colocalization with carotid plaque
(67% of probability of shared variant between carotid plaque and
eQTL in MAM), as well as a high probability of shared variant
between MAM eQTL expression of this gene, GWAS carotid
plaque or cIMT, and CHD traits (Supplementary Table 7). We
note, however, that the GWAS signal for outcomes across the
datasets did not reach genome-wide significance and larger
sample sizes may be needed to strengthen the evidence for
involvement in disease outcomes.
Genetic correlations of cIMT/carotid plaque and clinical out-
comes. To provide etiological insights into the role of measures of
subclinical atherosclerosis and major atherosclerotic disease
outcomes such as CHD and ischemic stroke, we quantified the
genetic correlation using cross-trait LD score regression, a
method that estimates genetic correlation across different traits
using summary level data15. We used summary statistics between
cIMT/carotid plaque with CHD and stroke meta-analysis of
GWAS. Both cIMT and carotid plaque had positive significant
genetic correlations with CHD (all p < 0.05 after adjusting for
multiple testing), though the magnitude of the correlation was
twice as strong for carotid plaque (0.52) as for cIMT (0.20)
(Table 4). There was also evidence for genetic correlations
between cIMT with any stroke and ischemic stroke subtype.
Pathway analysis and druggability. Gene Ontology (GO) ana-
lyses of genes identified in the loci for cIMT and carotid plaque
according to our meta-analysis of GWAS (Table 1 and Supple-
mentary Table 5) and in the colocalization analyses (Table 3,
Supplementary Table 7) showed that cIMT genes are enriched in
lipoprotein-related terms and cholesterol efflux, whereas carotid
plaque genes are enriched in terms associated with fibroblast
apoptosis (Supplementary Figure 8). Analysis of the cIMT genes
using a GO Slim additionally identified several of the genes that
were associated with terms describing cardiovascular develop-
ment, cell adhesion, and immune processes, processes already
considered relevant to atherosclerosis. Specifically, there is cor-
roborating evidence from GO that CCDC71L, PRKAR2B, and
TWIST1 are associated with cIMT/carotid plaque as they are
involved in lipid metabolism, with similar support that
ADAMTS9, CDH13, and KIAA1462 are associated with cIMT or
carotid plaque risk as they are all involved in cell adhesion and,
together with TWIST1, in cardiovascular system development
(Supplementary Data 2).
From the loci associated with cIMT and carotid plaque, we
identified seven genes (ATG4B, ALPL, LDLR, APOB, EDNRA,
APOE, and ADAMTS9) whose encoded proteins are targets at
various stages of the drug development process (Supplementary
Tables 8 and 9). ADAMTS9 gene encodes a protein likely to be
druggable16. ATG4B, ALPL, and LDLR are proteins being targeted
by compounds in pre-clinical phase (tier 2), while APOB and
EDNRA are proteins targeted by drugs in clinical phase or
licensed (tier 1). APOB is the target of an approved FDA drug for
treatment of familial hypercholesterolemia. EDNRA gene encodes
for endothelin A receptor, against which several antagonists have
been developed for the treatment of pulmonary arterial
Table 3 Colocalization of cIMT and plaque with eQTLs in tissues from patients with CHD in STARNET tissues for genes/tissues
combinations that have more than 75% probability to share the same associated variant
Region (chr:start-stop) Trait Gene SNP with best joint probability p, BETA (SE), Tissue posterior probability (PPA)a Direction of effect GWAS/eQTL
cIMT /plaque GWAS AOR eQTL MAM eQTL
chr3:63561280-65833136 cIMT ADAMTS9 rs17676309 (T/C) 2 × 10−6,
-0.0035 (0.0007)
2 × 10−25,
−0.65 (0.06)
PPA=0.93
1 × 10−23,
−0.61 (0.06)
PPA=0.89
−/−
chr10:99017729-101017321 cIMT LOXL4 rs55917128 (T/C) 5 × 10−7,
0.0037 (0.0007)
6 × 10−8,
0.33 (0.06)
PPA=0.79
+/+
chr7:105299372-107743409 cIMT CCDC71L
PRKAR2B
rs12705390 (A/G) 5 × 10−9,
0.0049 (0.0008)
2 × 10−37,
0.81 (0.06)
PPA=0.97
6 × 10−7,
0.34 (0.07)
PPA=0.93
1 × 10−33,
0.755 (0.06)
PPA=0.97
2 × 10−8,
0.368 (0.06)
PPA=0.96
+/+
+/+
Plaque CCDC71L
PRKAR2B
rs12705390 (A/G) 4 × 10−8,
0.12 (0.022)
2 × 10−37,
0.80 (0.06)
PPA=0.97
6 × 10−7,
0.33 (0.07)
PPA=0.93
1 × 10−33,
0.75 (0.06)
PPA=0.97
2 × 10−8,
0.37 (0.06)
PPA=0.96
+/+
+/+
PPA posterior probability of sharing same SNP higher than 75%, cIMT common carotid artery intima-media thickness, AOR aorta, MAM mammary artery
aThis signal reaches genome-wide significance in cIMT/plaque, and reaches a high probability of being mediated by the genes in AOR and MAM
NATURE COMMUNICATIONS | https://doi.org/10.1038/s41467-018-07340-5 ARTICLE
NATURE COMMUNICATIONS |          (2018) 9:5141 | https://doi.org/10.1038/s41467-018-07340-5 | www.nature.com/naturecommunications 5
hypertension or which are in advanced clinical phase develop-
ment for non-small cell lung cancer and diabetic nephropathy.
Discussion
We provide results of a large meta-analysis of GWAS of sub-
clinical atherosclerosis and we integrate our results with tissue-
specific gene expression data using eQTLs from both the early
(MAM) and late advanced (AOR) atherosclerotic arterial wall
from the STARNET study to enable reliable discovery of genes
with biological evidence of an increased probability for conferring
inherited risk of atherosclerosis development. Our discovery
approach using GWAS meta-analyses identified 16 loci sig-
nificantly associated with either cIMT or carotid plaque, of which
nine are novel.
The integration of GWAS and tissue-specific cis-eQTLs for the
joint analyses of tissue-specific eQTLs from CHD patients iden-
tified two potentially additional loci colocalizing with cIMT or
carotid plaque: chr3:63561280-65833136 (ADAMTS9),
chr10:99017729-101017321 (LOXL4). ADAMTS9 is a metallo-
proteinase involved in thrombosis and angiogenesis and has been
associated with cardiometabolic traits (waist-to-hip ratio, waist
circumference, and type 2 diabetes) in GWAS, and with coronary
artery calcification in a gene-by-smoking interaction GWAS17,18.
LOXL4 encodes a lysyl oxidase involved in crosslinks of collagen
and elastin in the extracellular matrix. This family of proteins are
involved in the development of elastic vessels and mechanical
strength of the vessel wall, and their inhibition was associated
with the development of abdominal aortic aneurysms and more
severe atherosclerosis in experimental models19.
cIMT Plaque
eQTL in aorta eQTL in SF
106.5106.4
7:106410777
7:106416467
7:106241491
7:106410777
106.3
Position on chr7 (Mb)
106.2106.1
106.5106.4106.3
Position on chr7 (Mb)
106.2106.1
0
10
20
–L
og
10
 (
ex
pr
es
si
on
 in
 A
O
R
 P
-v
al
ue
)
–L
og
10
 (
ex
pr
es
si
on
 in
 S
F
 P
-v
al
ue
)
30
0
2
4
6
8
10
0
2
4
6
8
10
–L
og
10
 (
cI
M
T
 P
-v
al
ue
)
–L
og
10
 (
P
la
qu
e 
P
-v
al
ue
)
0
2
4
6
8
10
a
c
b
d
40
0.2
0.4
0.6
0.8
0.2
0.4
0.6
0.8
0.2
0.4
0.6
0.8
r2
0.2
0.4
0.6
0.8
106.5106.4106.3
Position on chr7 (Mb)
106.2106.1
106.5106.4106.3
Position on chr7 (Mb)
106.2106.1
CCDC71L CCDC71L
CCDC71L CCDC71L
r2
r2
r2
Fig. 3 Association results at the CCDC71L locus (chromosome 7), showing a high posterior probability of a shared variant for cIMT and carotid plaque in
AOR and MAM eQTLs. −log10(p) SNP association p-values for cIMT (plot A) and carotid plaque (plot B), and eQTL in AOR (plot C) and eQTL in SF (plot
D). Association results in SF tissue have a low probability of a shared signal with cIMT and carotid plaque, possibly indicating a different mechanism in this
tissue. eQTLs in MAM are identical to AOR and not shown. The p-values were calculated by fitting a linear regression model with cIMT or plaque as
dependent variable and imputed SNPs as independent variables. Each dot is an SNP and the color indicates linkage disequilibrium (r2) with the best hit (in
purple)
ARTICLE NATURE COMMUNICATIONS | https://doi.org/10.1038/s41467-018-07340-5
6 NATURE COMMUNICATIONS |          (2018) 9:5141 | https://doi.org/10.1038/s41467-018-07340-5 | www.nature.com/naturecommunications
Some loci identified in our meta-analysis of GWAS include
genes in known pathways for atherosclerosis, including LDLR,
which is related to lipid pathways and CHD, and identified for
associations with carotid plaque in our study. For most of the loci,
however, the underlying gene implicated in signals are unknown.
Our colocalization approach found both CCDC71L and
PRKAR2B as the most likely genes at the chromosome 7 locus,
where PIK3CG was previously the suggested gene. This finding is
in agreement with a targeted sequencing study of subclinical
atherosclerosis15. An additional SNP (rs342286) at this locus has
been associated with platelets volume and reactivity, and cardi-
ovascular traits. However, rs342286 is not in LD with our most
significant SNP and it is not associated with cIMT or carotid
plaque in our studies (p= 0.49 and 0.01, respectively). Of interest,
the variant we identified in this study showed evidence for
colocalization with cIMT/carotid plaque and large vessel disease
stroke but not CHD, therefore showing tissue and outcome-
specificity. CCDC71L has unknown function. PRKAR2B codes for
one of the several regulatory subunits of cAMP-dependent pro-
tein kinase and its expression is ubiquitous. In vitro studies have
shown that adenosine-induced apoptosis of arterial smooth
muscle cells involves a cAMP-dependent pathway20.
Measures of cIMT and carotid plaque reflect vascular patho-
physiologic and atherosclerosis processes, respectively, with car-
otid plaque more strongly reflecting atherosclerotic clinical
events. An important contribution of this study is the supporting
evidence for overall genetic correlations of CHD and stroke (any
cause and ischemic stroke) with subclinical atherosclerosis traits,
estimated using LD score methods. Further highlighting the
potential biological relevance of our findings, the genetic corre-
lations estimates for CHD were stronger for carotid plaque than
for cIMT. However, cIMT and carotid plaque GWAS were cor-
related, and the genetic correlations estimates with stroke were
similar for cIMT or carotid plaque, and not significant for carotid
plaque. The colocalization analyses provided additional insights
in the relationships between subclinical atherosclerosis, clinical
outcomes, and tissue-specific regulation at specific genomic
regions. For example, our suggestive top gene association in
multi-trait colocalization for KIAA1462 included MAM eQTLs,
carotid plaque, and CHD, supporting the shared genetic effects at
this locus of atherosclerosis in carotid and coronary arteries.
KIAA1462 has been previously reported in the same locus iden-
tified by GWAS for CHD21. This gene encodes a protein involved
in cell–cell junctions in endothelial cells22, which was recently
shown to be involved in pathologic angiogenic process in in vitro
and in vivo experimental models23. These findings suggest that
there may be important differences in vascular bed regulation at
distinctive regions for atherosclerotic cardiovascular and stroke
outcomes that may help to identify genes and specific targets for
CHD or stroke prevention and treatment.
Additional studies in diverse and large samples across the
multiple datasets are needed to explore these results further. As
more summary statistics become available for other clinical end-
points beyond stroke and CHD (both in terms of larger sample
size and richer genome coverage), and as further refinements in
clinical phenotypes emerge (e.g. from CHD to acute coronary
syndrome sub-components), strategies to integrate this knowl-
edge using methods such as moloc10 and eCAVIAR24 will con-
tinue to be essential for harnessing genome-wide findings in the
drug-discovery process.
In summary, our study is a large GWAS meta-analysis of cIMT
and carotid plaque. Through a sequential approach of discovery
and colocalization studies, we provide deeper insights into disease
causal genes of subclinical cIMT and carotid plaque formation.
We confirmed three loci and identified nine novel loci in the
meta-analyses of cIMT and carotid plaque. Additionally, we
provide strong evidence for the role of three novel genes from our
integrative analysis of GWAS and eQTL data. Moreover, the
identified correlations with CHD and stroke highlight novel
biological pathways that merit further assessments as novel tar-
gets for drug development.
Methods
Ethics statement. All human research was approved by the relevant institutional
review boards for each study, and conducted according to the Declaration of
Helsinki. All participants provided written informed consent.
Populations and phenotypes. The discovery GWAS in this study consists of a
collaboration between the CHARGE 8 and the UCLEB consortia9, for genetic
studies of cIMT and carotid plaque among individuals of European ancestry
(Supplementary Note 1). All studies followed standardized protocols for phenotype
ascertainment and statistical analyses. The descriptive characteristics of partici-
pating studies are shown in Supplementary Table 1.
cIMT and carotid plaque measures were evaluated using high-resolution B-
mode ultrasonography and reading protocols as previously reported4. We used
data from the baseline examination or the first examination in which carotid
ultrasonography was obtained. cIMT was defined by the mean of the maximum of
several common carotid artery measurements, measured at the far wall or the near
wall. For most studies, this was an average of multiple measurements from both the
left and right arteries. We also examined a carotid plaque phenotype, defined by
atherosclerotic thickening of the carotid artery wall or the proxy measure of
luminal stenosis greater than 25% (Supplementary Table 2).
Genotyping, imputation, and study-level quality control. Genotyping arrays and
QC pre-imputation are shown in Supplementary Table 3. Each GWAS study
Table 4 Genetic correlation between CHD and stroke traits with cIMT and plaque, and cIMT with plaque using LD score and
meta-GWAS
Cardiovascular disease trait Subclinical atherosclerosis trait Genetic correlation SE z p
CHDa cIMT 0.20 0.05 4.1114 4 × 10−5
Any stroke cIMT 0.30 0.07 4.2301 2.3 × 10−5
Ischemic strokeb cIMT 0.31 0.07 4.646 3.4 × 10−6
Cardio-embolic strokeb cIMT 0.10 0.09 1.0729 0.28
Small vessel disease strokeb cIMT 0.33 0.18 1.8728 0.06
CHDa Carotid plaque 0.52 0.08 6.4263 1.3 × 10−10
Any strokeb Carotid plaque 0.28 0.10 2.7097 0.007
Ischemic strokeb Carotid plaque 0.27 0.10 2.6578 0.008
Cardio-embolic strokeb Carotid plaque 0.06 0.14 0.4684 0.64
Small vessel disease strokeb Carotid plaque −0.03 0.24 −0.1344 0.89
Plaque cIMT 0.40 0.10 3.9667 7.3 × 10−5
aCARDIoGRAMPlusC4D
bMEGASTROKE consortium. Unable to estimate the genetic correlations with large vessel disease
NATURE COMMUNICATIONS | https://doi.org/10.1038/s41467-018-07340-5 ARTICLE
NATURE COMMUNICATIONS |          (2018) 9:5141 | https://doi.org/10.1038/s41467-018-07340-5 | www.nature.com/naturecommunications 7
conducted genome-wide imputation using a Phase 1 integrated (March 2012
release) reference panel from the 1000G Consortium using IMPUTE225 or MaCH/
minimac26, and used Human Reference Genome Build 37. Sample QC was per-
formed with exclusions based on call rates, extreme heterozygosity, sex dis-
cordance, cryptic relatedness, and outlying ethnicity. SNP QC excluded variants
based on call rates across samples and extreme deviation from Hardy–Weinberg
equilibrium (Supplementary Table 3). Non-autosomal SNPs were excluded from
imputation and association analysis.
Pre-meta-analysis GWAS study-level QC was performed using EasyQC
software27. This QC excluded markers absent in the 1000G reference panel; non A/
C/G/T/D/I markers; duplicate markers with low call rate; monomorphic SNPs and
those with missing values in alleles, allele frequency, and beta estimates; SNPs with
large effect estimates or standard error (SE) ≥10; and SNPs with allele frequency
difference >0.3 compared to 1000G reference panel. There was a total of 9,574,088
SNPs for the cIMT meta-analysis and 8,578,107 SNPs for the carotid plaque meta-
analysis.
Statistical analyses. Within each study, we used linear and logistic regression to
model cIMT and carotid plaque, respectively, and an additive genetic model (SNP
dosage) adjusted for age, sex, and up to 10 principal components. We combined
summary estimates from each study and each trait using an inverse variance
weighted meta-analysis. Additional filters were applied during meta-analyses
including imputation quality (MACH r2 < 0.3 and IMPUTE info <0.4), a minor
allele frequency (MAF) <0.01, and SNPs that were not present in at least four
studies. The genome-wide significance threshold was considered at p < 5.0 × 10−8.
To assess the evidence for independent associations at each locus attaining
genome-wide significance, we performed conditional analysis in a 1-Mb genomic
interval flanking the lead SNP using GCTA28. This approach uses summary meta-
analysis statistics and a LD matrix from an ancestry-matched sample to perform
approximate conditional SNP association analysis. The estimated LD matrix was
based on 9713 unrelated individuals of European ancestry from the ARIC study,
which was genotyped using an Affymetrix 6.0 array and imputed to the 1000G
panel using IMPUTE225.
Gene expression analysis using GTEx. GTEx Analysis V6 (dbGaP Accession
phs000424.v6.p1) eQTL results were downloaded from GTEx portal for 44 tissues,
and then mapped to SNPs listed in Table 1. We used a false discovery rate (FDR) of
≤0.05.
Colocalization analyses using eQTLs. We integrated our GWAS results with cis-
eQTL data using a Bayesian method (coloc)10. This method evaluates whether the
GWAS and eQTL associations best fit a model in which the associations are due to
a single shared variant (summarized by the posterior probability). We used gene
expression datasets from multiple tissues from patients with CHD of the STAR-
NET study, including blood, MAM, AOR, subcutaneous fat (SF), visceral fat
(VAF), skeletal muscle (SKLM), and liver (LIV) obtained from 600 patients during
open heart surgery11. Pairwise colocalization was tested between these expression
disease tissue datasets and GWAS results from our cIMT/carotid plaque GWAS
meta-analysis. We used GWAS and eQTL summary statistics of SNPs within a
200-kb window around each gene covered by the eQTL datasets. A posterior
probability of colocalization ≥0.75 was considered a strong evidence for a causal
gene. Next, we reported the gene(s) in the STARNET datasets that had the
strongest evidence of sharing the same variant with cIMT or carotid plaque
genome-wide. In an alternative analysis, we also tested loci with an SNP that
reached a threshold of significant or suggestive genome-wide significance for cIMT
or carotid plaque (reported in Table 1, Supplementary Table 5). For each region
200kb around the SNP with the lowest association p-value, we report the gene with
the highest probability of being responsible for the GWAS signal (Supplementary
Table 6).
Pairwise colocalization for these genes was also tested for publicly available
GWAS for CHD case-controls (CARDIoGRAMPlusC4D) and stroke case-controls
(MEGASTROKE consortium). The MEGASTROKE dataset uses genotypes
imputed to the 1000G phase I haplotype panel. The European ancestry sample used
to generate these results consisted of 40,585 stroke cases and 406,111 controls from
15 cohorts and two consortia: the METASTROKE and CHARGE consortia29. The
phenotypes used in this analysis were any stroke (n= 39,067 cases, total n=
442,142), ischemic stroke (IS, n= 32,686 cases, total n= 423,266), and etiologic
stroke subtypes:cardioembolic stroke (CE, n= 6,820 cases, total n= 314,368), large
vessel disease (n= 4,113, total n= 202,263), and small vessel disease (SVD, n=
4,975, total n= 242,250). To explore multi-trait colocalizations, we used moloc14
with prior probabilities of 10−4 for GWAS/GWAS/eQTL, 10−6 for GWAS+eQTL/
GWAS or GWAS+GWAS/eQTL, and 10−7 for colocalization of all three
association signals.
Functional annotation and epigenetic enrichment analyses. From the Epigen-
ome Roadmap Project30,31, we obtained regulatory information using broad classes
of chromatin states (n= 127 tissues) capturing promoter-associated, transcription-
associated, active intergenic, and large-scale repressed and repeat-associated states.
From ENCODE32, we obtained chromatin states, uniformly processed transcrip-
tion factor (TF) Chip assays and DNaseI Hypersensitivity sites (DHS) for nine cells
lines. From FANTOM533, we used information from expression of enhancers in
each tissue (n= 112), and enhancers that are positively differentially expressed
against any other tissue (n= 110).
We used fGWAS13 to identify genomic annotations that are enriched within the
cIMT results and to select the variants with support for a functional role based on
the most informative annotations. We only considered cIMT for these analyses
because of the small number of identified loci for carotid plaque. We first estimated
the enrichment parameters for each annotation individually and identified the set
of annotations with significant marginal associations. We then applied 10-fold
cross-validation likelihood and forward selection to identify the set of annotations
that significantly improve the model fit, and reverse selection of each annotation
included in the model, as suggested in the fGWAS workflow. We reported the
model with the highest cross-validation likelihood and SNPs that have regional
posterior probability of association (PPA) >0.9 and directly overlap the genomic
annotations considered.
Overall genetic correlation analysis. Genetic correlation between cIMT/carotid
plaque, CHD, and stroke traits were calculated using LD score regression approach
LD-score, which uses GWAS summary statistics and is not affected by sample
overlap. This method relies on the fact that the χ2 association statistic for a given
SNP includes the effects of all SNPs that are in LD with it and it calculates genetic
correlation by partitioning the SNP heritabilities15. Genetic correlations between
stroke traits (IS, CE, large vessel disease, and SVD) and cIMT and carotid plaque
were calculated using software available at http://github.com/bulik/ldsc with
GWAS summary statistics for our cIMT/carotid plaque GWAS, CARDIO-
GRAMPlusC4D data, and stroke GWAS. We used the LD-scores15, which are
based on the 1000 Genomes European population and estimated within 1-cM
windows. Based on ten tests performed (two subclinical traits and five outcomes),
we set the significance threshold to p= 0.005.
PATHWAY ANALYSES. Methods for GO Slim: The Ensembl identifiers of all
protein-coding genes identified as in LD with the 12 variants for cIMT and 15
variants for carotid plaque (including variants from main and suggestive signals,
Table 1 and Supplementary Table 5), and five genes for which there is strong
evidence of colocalization (Table 3), were mapped to UniProt accession numbers,
using the UniProt ID mapping service (http://www.uniprot.org/uploadlists/). A GO
Slim analysis was performed on this list using QuickGO (www.ebi.ac.uk/QuickGO)
and the Generic GO Slim. The GO terms used in the final slim analysis were
further refined by adding/removing GO terms to provide more detailed
information about the processes covered.
Methods for GO term enrichment analysis: The VLAD gene list analysis and
visualization tool (http://proto.informatics.jax.org/prototypes/vlad/) was used to
perform a GO term enrichment analysis on the same UniProt accessions as listed
for the GO Slim. The background annotation set was obtained from the
goa_human.gaf file (dated 21 November 2017, downloaded from ftp://ftp.ebi.ac.uk/
pub/databases/GO/goa/HUMAN/) and the ontology data was obtained from the
go-basic.obo file provided in the VLAD tool (analysis run 28 November 2017).
The LD block around top SNPs associated with cIMT and carotid plaque was
constructed using LD information from the 1000 Genomes panel, as previously
outlined in Finan et al.16. Briefly, the boundaries of the LD region were defined as
the positions of the variants furthest upstream and downstream of a GWAS SNP
with an r2 value of ≥0.5 and within a 1-Mbp flank on either side of the GWAS
variant. Associated variants that were not present in the 1000 Genomes panel that
were not in LD with any other variants were given a nominal flank of 2.5 kbp on
either side of the association. Gene annotations using Ensembl version 79 were
then overlapped to the LD region.
Druggable genes. We examined the druggability status for the nearest coding
genes identified in our GWAS analysis on cIMT and carotid plaque, including
significant (novel and replicated) and suggestive ones, as well as genes identified
through colocalization analysis. The druggable gene set was calculated using the
previously described criteria: novel targets of first-in-class drugs licensed since
2005; the targets of drugs currently in late phase clinical development; pre-clinical
phase small molecules with protein binding measurements reported in the
ChEMBL database; and genes encoding secreted or plasma membrane proteins that
are targets of monoclonal antibodies and other bio-therapeutics16. We defined
three tiers of druggable gene sets based on their drug development. In Tier 1, 1427
genes were targets of approved small molecules and biotherapeutic drugs and
clinical-phase drug candidates. Tier 2 comprised 682 genes encoding targets with
known bioactive drug-like small molecule binding partners and those with sig-
nificant sequence similarity to approved drug targets. Tier 3 contained 2370 genes
encoding secreted or extracellular proteins, proteins with more distant similarity to
approved drug targets, and druggable genes not included in Tier 1 or 2 such as
GPCRs, nuclear hormone receptors, ion channels, kinases, and phosphodiesterases.
URLs. For GTEx, see http://gtexportal.org/. For Coloc, see https://cran.r-project.
org/web/packages/coloc/coloc.pdf. For, Moloc, see https://github.com/clagiamba/
ARTICLE NATURE COMMUNICATIONS | https://doi.org/10.1038/s41467-018-07340-5
8 NATURE COMMUNICATIONS |          (2018) 9:5141 | https://doi.org/10.1038/s41467-018-07340-5 | www.nature.com/naturecommunications
moloc/blob/master/man/moloc-package.Rd. For CARDIoGRAMPlusC4D, see
www.cardiogramplusc4d.org/. For LD scores, www.broadinstitute.org/~bulik/
eur_ldscores/. For UniProt ID, www.uniprot.org/uploadlists/. For QuickGO,
www.ebi.ac.uk/QuickGO. For VLAD tool, see http://proto.informatics.jax.org/
prototypes/vlad/.
Data availability
All relevant summary statistics data that support the findings of this study have
been deposit in the database of Genotypes and Phenotypes (dbGaP) under the
CHARGE acquisition number (https://www.ncbi.nlm.nih.gov/projects/gap/cgi-bin/
study.cgi?study_id=phs000930.v6.p1; accession phs000930.v6.p1). GWAS data for
most US studies are already available in dbGAP.
Received: 19 December 2017 Accepted: 24 September 2018
References
1. Mozaffarian, D. et al. Heart Disease and Stroke Statistics-2016 Update: a
report from the American Heart Association. Circulation 133, e38–e360
(2016).
2. Frieden, T. R. & Berwick, D. M. The Million Hearts initiative--preventing
heart attacks and strokes. N. Engl. J. Med. 365, e27 (2011).
3. O’Donnell, C. J. & Nabel, E. G. Genomics of cardiovascular disease. N. Engl. J.
Med. 365, 2098–2109 (2011).
4. Bis, J. C. et al. Meta-analysis of genome-wide association studies from the
CHARGE consortium identifies common variants associated with carotid
intima media thickness and plaque. Nat. Genet. 43, 940–947 (2011).
5. Natarajan, P. et al. Multiethnic Exome-Wide Association Study of Subclinical
Atherosclerosis. Circ. Cardiovasc. Genet. 9, 511–520 (2016).
6. Pott, J. et al. Genome-wide meta-analysis identifies novel loci of plaque burden
in carotid artery. Atherosclerosis 259, 32–40 (2017).
7. Gertow, K. et al. Identification of the BCAR1-CFDP1-TMEM170A locus as a
determinant of carotid intima-media thickness and coronary artery disease
risk. Circ. Cardiovasc. Genet. 5, 656–665 (2012).
8. Psaty, B. M. 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. 2,
73–80 (2009).
9. Shah, S. et al. Causal relevance of blood lipid fractions in the development of
carotid atherosclerosis: Mendelian randomization analysis. Circ. Cardiovasc.
Genet. 6, 63–72 (2013).
10. Giambartolomei, C. et al. Bayesian test for colocalisation between pairs of
genetic association studies using summary statistics. PLoS Genet. 10, e1004383
(2014).
11. Franzen, O. et al. Cardiometabolic risk loci share downstream cis- and trans-
gene regulation across tissues and diseases. Science 353, 827–830 (2016).
12. Pfeiffer, R. M., Mitchell, H. G. & Pee, D. On combining data from genome-
wide assocition studies to discover disease-associated SNPs. Stat. Sci. 24,
547–560 (2009).
13. Pickrell, J. K. Joint analysis of functional genomic data and genome-wide
association studies of 18 human traits. Am. J. Hum. Genet. 94, 559–573
(2014).
14. Giambartolomei, C. et al. A Bayesian framework for multiple trait colo-
calization from summary association statistics. Bioinformatics 34, 2538–2545
(2018).
15. Finucane, H. K. et al. Partitioning heritability by functional annotation using
genome-wide association summary statistics. Nat. Genet. 47, 1228–1235
(2015).
16. Finan, C. et al. The druggable genome and support for target identification
and validation in drug development. Sci. Transl. Med. 9, eaag1166 (2017).
17. Zeggini, E. et al. Meta-analysis of genome-wide association data and large-
scale replication identifies additional susceptibility loci for type 2 diabetes.
Nat. Genet. 40, 638–645 (2008).
18. Polfus, L. M. et al. Genome-wide association study of gene by smoking
interactions in coronary artery calcification. PLoS ONE 8, e74642 (2013).
19. Remus, E. W. et al. The role of lysyl oxidase family members in the
stabilization of abdominal aortic aneurysms. Am. J. Physiol. Heart Circ.
Physiol. 303, H1067–H1075 (2012).
20. Peyot, M. L. et al. Extracellular adenosine induces apoptosis of human arterial
smooth muscle cells via A(2b)-purinoceptor. Circ. Res. 86, 76–85 (2000).
21. Erdmann, J. et al. Genome-wide association study identifies a new locus for
coronary artery disease on chromosome 10p11.23. Eur. Heart J. 32, 158–168
(2011).
22. Akashi, M., Higashi, T., Masuda, S., Komori, T. & Furuse, M. A coronary
artery disease-associated gene product, JCAD/KIAA1462, is a novel
component of endothelial cell-cell junctions. Biochem. Biophys. Res. Commun.
413, 224–229 (2011).
23. Hara, T. et al. Targeted disruption of JCAD (Junctional Protein Associated
With Coronary Artery Disease)/KIAA1462, a coronary artery disease-
associated gene product, inhibits angiogenic processes in vitro and in vivo.
Arterioscler. Thromb. Vasc. Biol. 37, 1667–1673 (2017).
24. Hormozdiari, F., Kostem, E., Kang, E. Y., Pasaniuc, B. & Eskin, E. Identifying
causal variants at loci with multiple signals of association. Genetics 198,
497–508 (2014).
25. Howie, B. N., Donnelly, P. & Marchini, J. A flexible and accurate genotype
imputation method for the next generation of genome-wide association
studies. PLoS Genet. 5, e1000529 (2009).
26. Howie, B., Fuchsberger, C., Stephens, M., Marchini, J. & Abecasis, G. R. Fast
and accurate genotype imputation in genome-wide association studies
through pre-phasing. Nat. Genet. 44, 955–959 (2012).
27. Winkler, T. W. et al. Quality control and conduct of genome-wide association
meta-analyses. Nat. Protoc. 9, 1192–1212 (2014).
28. Yang, J., Lee, S. H., Goddard, M. E. & Visscher, P. M. GCTA: a tool for
genome-wide complex trait analysis. Am. J. Hum. Genet. 88, 76–82 (2011).
29. Malik, R. et al. Multiancestry genome-wide association study of
520,000 subjects identifies 32 loci associated with stroke and stroke subtypes.
Nat. Genet. 50, 524–537 (2018).
30. Roadmap Epigenomics, Consortium et al. Integrative analysis of 111 reference
human epigenomes. Nature 518, 317–330 (2015).
31. Zhu, L. J. Integrative analysis of ChIP-chip and ChIP-seq dataset. Methods
Mol. Biol. 1067, 105–124 (2013).
32. ENCODE Project Consortium. An integrated encyclopedia of DNA elements
in the human genome. Nature 489, 57–74 (2012).
33. Andersson, R. et al. An atlas of active enhancers across human cell types and
tissues. Nature 507, 455–461 (2014).
Acknowledgements
The work was supported by the following grants: National Institute of Health grants:
R21HL123677, R21-HL140385, DK104806-01A1, R01-MD012765-01A1 (NF), National
Institutes of Health awards R01HG009120, R01HG006399, U01CA194393,
T32NS048004 (CG), the American Heart Association Grant #17POST33350042 (PV),
the British Heart Foundation (RG/13/5/30112) and the National Institute for Health
Research University College London Hospitals Biomedical Research Centre (RCL and
RPH), the British Heart Foundation FS/14/55/30806 (JCH), the German Federal Ministry
of Education and Research (BMBF) in the context of the e:Med program (e:Ather-
oSysMed), the DFG as part of the CRC 1123 (B3), and the FP7/2007-2103 European
Union project CVgenes@target (grant agreement number Health-F2-2013-601456). We
thank Li-Ming Gan for assistance with the STARNET study and Jon White for assistance
with UCLEB analyses. Additional acknowledgements are included in Supplementary
Note 2.
Author contributions
N.F., C.G., J.C.B., M.K., C.D., M.S., S.K.M., U.S., W.P., A.B.Z., A.H., A.T., A.G.U., A.B.N.,
B.W.P., C.D.L., E.T., F.R., H.V., I.J.D., L.J.L., M.D., O.R., O.H.F., R.S., R.M., T.B.H., T.L.,
U.F., W.P., A.D., A.S., A.H., C.M.D., D.A.L., D.O.M.-K., D.W.B., H.S., J.F.W., J.G.W., J.I.
R., J.M.W., M.L., M.K.E., S.E.H., U.V., V.G., A.D.H., J.P.C., and C.J.O. contributed to
study concept and design. C.D., M.S., S.K.M., U.S., W.P., A.I., A.C., A.B.Z., A.T., A.G.U.,
A.W., A.J.S., A.B., A.R., B.H., B.W.P., C.F., D.B., D.H.O., D.T., D.K., E.T., E.M., E.G., E.B.,
E.E.S., E.I., F.R., F.B., G.H., H.C., H.V., H.S.M., I.J.D., J.W.J., J.G., J.P., J.T., J.E., K.D.T., L.
K., L.L., L.J.L., L.H., M.D., M.S., M.K., M.K., M.A.N., O.M., O.R., P.H.W., P.G., P.A., R.R.,
R.B., R.H., R.S., R.M., R.W.M.,. S.G.W., S.M.L., S.T., S.K., S.R.H., S.R., T.B.H., T.L., T.G.,
T.S., U.F., V.P., W.R., W.P., X.Z., A.S., A.H., B.M.P., C.M.D., D.A.L., D.O.M.-K., D.W.B.,
H.S., J.F.W., J.G.W., J.I.R., J.C.H., J.M.W., J.D., J.H., M.K.E., S.E.H., U.V., V.G., A.D.H., J.
L.M.B., J.P.C., and C.J.O. contributed to acquisition of genotyping or phenotypic data. N.
F., C.G., C.F., J.C.B., R.P.H., R.C.L., S.M.T., T.W.W., M.G., M.K., C.D., A.V.S., E.H., E.M.
L., I.M.N., L.L., M.S., M.S., N.P., O.F., P.K.J., R.N., R.E.M., S.-J.H., S.K.M., U.S., A.I., A.T.,
K.R., A.J.C., B.S., C.D.L., C.W., F.V., G.C., H.S., J.P., J.L., K.G., L.M.R., M.T., M.A.N., O.
M., P.R., P.A., Q.W., R.J.S., S.H., S.S., S.M.L., T.S., X.Z., X.Z., X.G., Y.S., and L.-.P.L.
contributed to statistical analysis and interpretation of the data. N.F., C.G., P.S.V., J.C.B.,
M.K., S.K.M., A.D.H., and J.P.C. contributed to drafting of the manuscript. All authors
contributed to the critical revision of the manuscript.
Additional information
Supplementary Information accompanies this paper at https://doi.org/10.1038/s41467-
018-07340-5.
Competing interests: C.F. received a fee for speaking at a course by Springer Healthcare/
Malesci. E.I. is a scientific advisor for Precision Wellness, Cellink and Olink Proteomics
NATURE COMMUNICATIONS | https://doi.org/10.1038/s41467-018-07340-5 ARTICLE
NATURE COMMUNICATIONS |          (2018) 9:5141 | https://doi.org/10.1038/s41467-018-07340-5 | www.nature.com/naturecommunications 9
for work unrelated to the present project. M.A.N.’s participation in this project was
supported by a consulting contract between Data Tecnica International and the National
Institute on Aging, NIH, Bethesda, MD, USA. M.A.N. also consults for Illumina Inc., the
Michael J. Fox Foundation, and University of California Healthcare. B.M.P. serves on the
DSMB of a clinical trial funded by Zoll LifeCor and on the Steering Committee of the
Yale Open Data Access Project funded by Johnson & Johnson. D.A.L. has received
support from Roche Diagnostics and Medtronic for biomarker research unrelated to this
paper. J.P.C. has received funding from GSK regarding methodological work around
electronic health records, and -omics for drug discovery. All remaining authors declare
no competing interests.
Reprints and permission information is available online at http://npg.nature.com/
reprintsandpermissions/
Publisher’s note: Springer Nature remains neutral with regard to jurisdictional claims in
published maps and institutional affiliations.
Open Access This article is licensed under a Creative Commons
Attribution 4.0 International License, which permits use, sharing,
adaptation, distribution and reproduction in any medium or format, as long as you give
appropriate credit to the original author(s) and the source, provide a link to the Creative
Commons license, and indicate if changes were made. The images or other third party
material in this article are included in the article’s Creative Commons license, unless
indicated otherwise in a credit line to the material. If material is not included in the
article’s Creative Commons license and your intended use is not permitted by statutory
regulation or exceeds the permitted use, you will need to obtain permission directly from
the copyright holder. To view a copy of this license, visit http://creativecommons.org/
licenses/by/4.0/.
© The Author(s) 2018
Nora Franceschini1, Claudia Giambartolomei 2, Paul S. de Vries3, Chris Finan4, Joshua C. Bis5,
Rachael P. Huntley 4, Ruth C. Lovering 4, Salman M. Tajuddin 6, Thomas W. Winkler 7, Misa Graff1,
Maryam Kavousi8, Caroline Dale9, Albert V. Smith10,11, Edith Hofer12,13, Elisabeth M. van Leeuwen8,
Ilja M. Nolte 14, Lingyi Lu15, Markus Scholz 16,17, Muralidharan Sargurupremraj18, Niina Pitkänen19,
Oscar Franzén20,21, Peter K. Joshi 22, Raymond Noordam23, Riccardo E. Marioni24,25, Shih-Jen Hwang26,27,
Solomon K. Musani28, Ulf Schminke29, Walter Palmas30, Aaron Isaacs 8,31, Adolfo Correa 28,
Alan B. Zonderman6, Albert Hofman8,32, Alexander Teumer 33,34, Amanda J. Cox35,36,
André G. Uitterlinden8,37, Andrew Wong 38, Andries J. Smit39, Anne B. Newman 40, Annie Britton41,
Arno Ruusalepp21,42,43, Bengt Sennblad 44,45, Bo Hedblad46, Bogdan Pasaniuc2,47, Brenda W. Penninx48,
Carl D. Langefeld15, Christina L. Wassel49, Christophe Tzourio18, Cristiano Fava46,50, Damiano Baldassarre 51,52,
Daniel H. O’Leary53, Daniel Teupser17,54, Diana Kuh 38, Elena Tremoli52,55, Elmo Mannarino56, Enzo Grossi57,
Eric Boerwinkle3,58, Eric E. Schadt 20,21, Erik Ingelsson59,60,61, Fabrizio Veglia52, Fernando Rivadeneira 8,37,
Frank Beutner62, Ganesh Chauhan18,63, Gerardo Heiss1, Harold Snieder14, Harry Campbell22, Henry Völzke33,34,
Hugh S. Markus64, Ian J. Deary24,65, J. Wouter Jukema 66, Jacqueline de Graaf67, Jacqueline Price22,
Janne Pott16,17, Jemma C. Hopewell68, Jingjing Liang69, Joachim Thiery17,70, Jorgen Engmann4, Karl Gertow44,
Kenneth Rice 71, Kent D. Taylor72, Klodian Dhana 73, Lambertus A.L.M. Kiemeney74, Lars Lind75,
Laura M. Raffield76, Lenore J. Launer6, Lesca M. Holdt17,54, Marcus Dörr34,77, Martin Dichgans 78,79,
Matthew Traylor 64, Matthias Sitzer80, Meena Kumari41,81, Mika Kivimaki 41, Mike A. Nalls82,83,
Olle Melander46, Olli Raitakari19,84, Oscar H. Franco8,85, Oscar L. Rueda-Ochoa8,86, Panos Roussos 20,87,88,
Peter H. Whincup89, Philippe Amouyel 90,91,92, Philippe Giral93, Pramod Anugu28, Quenna Wong94,
Rainer Malik78, Rainer Rauramaa95,96, Ralph Burkhardt 17,97,98, Rebecca Hardy38, Reinhold Schmidt12,
Renée de Mutsert99, Richard W. Morris 100, Rona J. Strawbridge44,101, S. Goya Wannamethee102,
Sara Hägg 103, Sonia Shah4, Stela McLachlan22, Stella Trompet23,66, Sudha Seshadri104, Sudhir Kurl105,
Susan R. Heckbert5,106, Susan Ring107,108, Tamara B. Harris6, Terho Lehtimäki109,110, Tessel E. Galesloot74,
Tina Shah4, Ulf de Faire111,112, Vincent Plagnol113, Wayne D. Rosamond1, Wendy Post114, Xiaofeng Zhu69,
Xiaoling Zhang27,115, Xiuqing Guo72,116, Yasaman Saba117, MEGASTROKE Consortium,
Abbas Dehghan8,118, Adrie Seldenrijk119, Alanna C. Morrison3, Anders Hamsten44, Bruce M. Psaty106,120,
Cornelia M. van Duijn8,68, Deborah A. Lawlor 107,108, Dennis O. Mook-Kanamori99,121, Donald W. Bowden122,
Helena Schmidt117, James F. Wilson 22,123, James G. Wilson124, Jerome I. Rotter72,116,
Joanna M. Wardlaw 24,125, John Deanfield4, Julian Halcox126, Leo-Pekka Lyytikäinen 109,110,
ARTICLE NATURE COMMUNICATIONS | https://doi.org/10.1038/s41467-018-07340-5
10 NATURE COMMUNICATIONS |          (2018) 9:5141 | https://doi.org/10.1038/s41467-018-07340-5 | www.nature.com/naturecommunications
Markus Loeffler16,17, Michele K. Evans6, Stéphanie Debette18, Steve E. Humphries127, Uwe Völker34,128,
Vilmundur Gudnason 10,11, Aroon D. Hingorani4, Johan L.M. Björkegren20,21,42,129,
Juan P. Casas9 & Christopher J. O’Donnell130,131,132
1Department of Epidemiology, University of North Carolina, Chapel Hill, NC 27516, USA. 2Department of Pathology and Laboratory Medicine,
University of California (UCLA), Los Angeles, Los Angeles, CA 90095, USA. 3Human Genetics Center, Department of Epidemiology, Human
Genetics, and Environmental Sciences, School of Public Health, The University of Texas Health Science Center at Houston, Houston, TX 77030,
USA. 4Institute of Cardiovascular Science, University College London, London WC1 6BT, UK. 5Cardiovascular Health Research Unit, Department of
Medicine, University of Washington, Seattle, WA 98101, USA. 6Laboratory of Epidemiology and Population Sciences, National Institute on Aging,
National Institutes of Health, Baltimore, MD 20892, USA. 7Department of Genetic Epidemiology, University of Regensburg, Regensburg 93053,
Germany. 8Department of Epidemiology, Erasmus Medical Center, Rotterdam 3015, The Netherlands. 9Institute of Health Informatics, University
College London, London WC1E 6BT, UK. 10Icelandic Heart Association, Kopavogur IS-201, Iceland. 11University of Iceland, Reykjavik 101, Iceland.
12Clinical Division of Neurogeriatrics, Department of Neurology, Medical University of Graz, Graz 8036, Austria. 13Institute for Medical Informatics,
Statistics and Documentation, Medical University of Graz, Graz 8036, Austria. 14Department of Epidemiology, University of Groningen, University
Medical Center Groningen, Groningen 3015, The Netherlands. 15Department of Biostatistical Sciences, Wake Forest University School of Medicine,
Winston-Salem, NC 27157, USA. 16Institute for Medical Informatics, Statistics and Epidemiology, , University of Leipzig, Leipzig 04107, Germany.
17LIFE Research Center for Civilization Diseases, University of Leipzig, Leipzig 04107, Germany. 18Univ. Bordeaux, Inserm, Bordeaux Population
Health Research Center, UMR 1219, CHU Bordeaux, F-33000 Bordeaux, France. 19Research Centre of Applied and Preventive Cardiovascular
Medicine, University of Turku, Turku 20520, Finland. 20Department of Genetics and Genomic Sciences, The Icahn Institute for Genomics and
Multiscale Biology Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA. 21Clinical Gene Networks AB, Stockholm 104 62, Sweden.
22Usher Institute of Population Health Sciences and Informatics, University of Edinburgh, Edinburgh EH8 9AG, UK. 23Department of Internal
Medicine, Section of Gerontology and Geriatrics, Leiden University Medical Center, Leiden 2300 RC, The Netherlands. 24Centre for Cognitive
Ageing and Cognitive Epidemiology, University of Edinburgh, Edinburgh EH8 9JZ, UK. 25Medical Genetics Section, Centre for Genomic and
Experimental Medicine, Institute of Genetics and Molecular Medicine, University of Edinburgh, Edinburgh EH4 2XU, UK. 26Population Sciences
Branch, Division of Intramural Research, NHLBI, NIH, Framingham, MA 01702-5827, USA. 27National Heart, Lung and Blood Institute’s Intramural
Research Program, Framingham Heart Study, Framingham, MA 01702-5827, USA. 28Department of Medicine, University of Mississippi Medical
Center, Jackson, MS 39216, USA. 29Department of Neurology, University Medicine Greifswald, Greifswald 17475, Germany. 30Department of
Medicine, Columbia University, New York, NY 10032, USA. 31Department of Biochemistry, Maastricht Centre for Systems Biology (MaCSBio),
CARIM School for Cardiovascular Diseases, Maastricht University, Maastricht 6229, The Netherlands. 32Department of Epidemiology, Harvard T.H.
Chan School of Public Health, Boston, MA 02115, USA. 33Institute for Community Medicine, University Medicine Greifswald, Greifswald 17475,
Germany. 34DZHK (German Center for Cardiovascular Research), partner site Greifswald, Greifswald 17475, Germany. 35Center for Diabetes
Research, Wake Forest School of Medicine, Winston-Salem, NC 25157, USA. 36Menzies Health Institute Queensland, Griffith University, Southport,
QLD 4222, Australia. 37Department of Internal Medicine, Erasmus Medical Center, University Medical Center Rotterdam, Rotterdam 3015, The
Netherlands. 38MRC Unit for Lifelong Health and Ageing at UCL, London WC1E 6BT, UK. 39Department of Medicine, University of Groningen,
University Medical Center Groningen, Groningen 2300, The Netherlands. 40Department of Epidemiology, and School of Medicine, Division of
Geriatric Medicine, University of Pittsburgh, Pittsburgh, PA 15213, USA. 41Department of Epidemiology and Public Health, University College
London, London WC1E 6BT, UK. 42Department of Pathophysiology, Institute of Biomedicine and Translation Medicine, University of Tartu,
Biomeedikum, Tartu 51010, Estonia. 43Department of Cardiac Surgery, Tartu University Hospital, Tartu 51010, Estonia. 44Cardiovascular Medicine
Unit, Department of Medicine Solna, Karolinska Institutet, Stockholm 17177, Sweden. 45Department of Cell and Molecular Biology, National
Bioinformatics Infrastructure Sweden, Science for Life Laboratory, Uppsala University, Uppsala 75108, Sweden. 46Department of Clinical Sciences in
Malmö, Lund University, Malmö SE-205 02, Sweden. 47Department of Human Genetics, University of California (UCLA), Los Angeles, CA 90095,
USA. 48Department of Psychiatry, EMGO Institute for Health and Care Research and Neuroscience Campus Amsterdam, VU University Medical
Center, Amsterdam 1081 HL, The Netherlands. 49Applied Sciences, Premier, Inc., Charlotte, NC 28277, USA. 50Department of Medicine, University
of Verona, Verona 37134, Italy. 51Department of Medical Biotechnology and Translational Medicine, Università di Milano, Milan 20133, Italy.
52Centro Cardiologico Monzino, IRCCS, Milan 20138, Italy. 53St. Elizabeth’s Medical Center, Tufts University School of Medicine, Boston, MA
02135, USA. 54Institute of Laboratory Medicine, University Hospital Munich, LMU Munich 80539, Germany. 55Dipartimento di Scienze
Farmacologiche e Biomolecolari, Università di Milano, Milan 20133, Italy. 56Department of Clinical and Experimental Medicine, Internal Medicine,
Angiology and Arteriosclerosis Diseases, University of Perugia, Perugia 06123, Italy. 57Centro Diagnostico Italiano, Milan 20147, Italy. 58Human
Genome Sequencing Center, Baylor College of Medicine, Houston, TX 77030-3411, USA. 59Department of Medicine, Division of Cardiovascular
Medicine, Stanford University School of Medicine, Stanford, CA 94309, USA. 60Department of Medical Sciences, Molecular Epidemiology, Uppsala
University, Uppsala 75185, Sweden. 61Stanford Cardiovascular Institute, Stanford University, Stanford, CA G1120, USA. 62Heart Center Leipzig,
Leipzig 04103, Germany. 63Centre for Brain Research, Indian Institute of Science, Bangalore 560012, India. 64Stroke Research Group, Department of
Clinical Neurosciences, University of Cambridge, Cambridge CB2 0QQ, UK. 65Department of Psychology, University of Edinburgh, Edinburgh EH8
9JZ, UK. 66Department of Cardiology, Leiden University Medical Center, Leiden 2300 RC, The Netherlands. 67Department of Internal Medicine,
Radboud University Medical Center, Nijmegen 6525 GA, The Netherlands. 68Clinical Trial Service Unit and Epidemiological Studies Unit, Nuffield
Department of Population Health, University of Oxford, Oxford OX3 7LF, UK. 69Department of Population and Quantitative Health Sciences, School
of Medicine, Case Western Reserve University, Cleveland, OH 44106, USA. 70Institute for Laboratory Medicine, University of Leipzig, Leipzig
04109, Germany. 71Department of Biostatistics, University of Washington, Seattle, WA 98105, USA. 72Institute for Translational Genomics and
Population Sciences, Los Angeles Biomedical Research Institute at Harbor-UCLA Medical Center, Torrance, CA 90502, USA. 73Department of
Internal Medicine, Rush University Medical Center, Chicago, IL 60612, USA. 74Radboud Institute for Health Sciences, Radboud University Medical
Center, Nijmegen, GA 6525, The Netherlands. 75Department of Medical Sciences, Cardiovascular Epidemiology, Uppsala University, Uppsala 751
05, Sweden. 76Department of Genetics, University of North Carolina, Chapel Hill, NC 27516, USA. 77Department of Internal Medicine B, University
Medicine Greifswald, Greifswald 17475, Germany. 78Institute for Stroke and Dementia Research (ISD), University Hospital, Ludwig-Maximilians-
University (LMU), Munich 80539, Germany. 79Munich Cluster for Systems Neurology (SyNergy), Munich 81377, Germany. 80Department of
Neurology, Center for Neurology and Neurosurgery, Johann Wolfgang Goethe-University, Frankfurt am Main 60323, Germany. 81Institute for Social
and Economic Research, Essex University, Colchester CO4 3SQ, UK. 82Laboratory of Neurogenetics, National Institute on Aging, National Institutes
of Health, Bethesda, MD 20892, USA. 83Data Tecnica International, Glen Echo, MD 20812, USA. 84Department of Clinical Physiology and Nuclear
NATURE COMMUNICATIONS | https://doi.org/10.1038/s41467-018-07340-5 ARTICLE
NATURE COMMUNICATIONS |          (2018) 9:5141 | https://doi.org/10.1038/s41467-018-07340-5 | www.nature.com/naturecommunications 11
Medicine, Turku University Hospital, Turku 20521, Finland. 85Institute of Social and Preventive Medicine (ISPM), University of Bern, Bern 3012,
Switzerland. 86Electrocardiography Research Group, School of Medicine, Universidad Industrial de Santander, Bucaramanga, Santander 680003,
Colombia. 87Department of Psychiatry and Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA. 88Mental
Illness Research Education and Clinical Center (MIRECC), James J. Peters VA Medical Center, Bronx, New York, NY 10468, USA. 89Population
Health Research Institute, St George’s, University of London, London SW17 0RE, UK. 90Inserm U1167, F-59000 Lille, France. 91Institut Pasteur de
Lille, U1167, F-59000 Lille, France. 92Université de Lille, U1167 - RID-AGE & Centre Hospitalier Universitaire de Lille, U1167, F-59000 Lille, France.
93Sorbonne Université, Cardiovascular Prevention Unit, Pitié Salpétrière Hospital, Paris 75013, France. 94Collaborative Health Studies Coordinating
Center, Department of Biostatistics, University of Washington, Seattle, WA 98195, USA. 95Foundation for Research in Health Exercise and
Nutrition, Kuopio Research Institute of Exercise Medicine, Kuopio 70100, Finland. 96Department of Clinical Physiology and Nuclear Medicine,
Kuopio University Hospital, Kuopio 70210, Finland. 97Institute of Laboratory Medicine, University of Leipzig, Leipzig 04109, Germany. 98Institute of
Clinical Chemistry and Laboratory Medicine, University Hospital Regensburg, Regensburg 93053, Germany. 99Department of Clinical Epidemiology,
Leiden University Medical Center, Leiden 2333, The Netherlands. 100Department of Population Health Sciences, Bristol Medical School, University
of Bristol, Bristol BS8 1QU, UK. 101Mental Health and Wellbeing, Institute of Health and Wellbeing, University of Glasgow, Glasgow G12 0XH, UK.
102Department of Primary Care & Population Health, University College London, London WC1E 6BT, UK. 103Department of Medical Epidemiology
and Biostatistics, Karolinska Institutet, Stockholm SE-171 77, Sweden. 104Department of Neurology, Boston University School of Medicine, Boston,
MA 02118, USA. 105Institute of Public Health and Clinical Nutrition, University of Eastern Finland, Kuopio Campus, Kuopio FI-70210, Finland.
106Kaiser Permanente Washington Health Research Institute, Seattle, WA 98101, USA. 107Population Health Science, Bristol Medical School,
University of Bristol, Bristol BS8 1QU, UK. 108MRC Integrative Epidemiology Unit at the University of Bristol, Bristol BS8 1TH, UK. 109Department of
Clinical Chemistry, Fimlab Laboratories, Tampere 33014, Finland. 110Department of Clinical Chemistry, University of Tampere School of Medicine,
Tampere 33014, Finland. 111Division of Cardiovascular Epidemiology, Institute of Environmental Medicine, Karolinska Institutet, Stockholm S-171 77,
Sweden. 112Department of Cardiology, Karolinska University Hospital, Stockholm S-171 77, Sweden. 113Genetics Institute, University College London,
London WC1E 6BT, UK. 114Departments of Medicine and Epidemiology, Johns Hopkins University, Baltimore, MD 21205, USA. 115Section of
Biomedical Genetics, School of Medicine, Boston University, Boston, MA 02215, USA. 116Department of Pediatrics, Los Angeles Biomedical
Research Institute at Harbor-UCLA Medical Center, Torrance, CA 90502, USA. 117Institute of Molecular Biology and Biochemistry, Centre for
Molecular Medicine, Medical University of Graz, Graz 8010, Austria. 118Department of Epidemiology & Biostatistics, Imperial College London,
London SW7 2AZ, UK. 119GGZ inGeest and Amsterdam Public Health Research Institute, Department of Psychiatry, Amsterdam University Medical
Center, Amsterdam 1081 HV, The Netherlands. 120Cardiovascular Health Research Unit and Departments of Medicine, Epidemiology, and Health
Services, University of Washington, Seattle, WA 98195, USA. 121Department of Public Health and Primary Care, Leiden University Medical Center,
Leiden 2333 ZA, The Netherlands. 122Center for Human Genomics, Wake Forest University School of Medicine, Winston-Salem, NC 27157, USA.
123MRC Human Genetics Unit, Institute of Genetics and Molecular Medicine, University of Edinburgh, Western General Hospital, Edinburgh EH4
2XU, UK. 124Department of Physiology and Biophysics, University of Mississippi Medical Center, Jackson, MS 39216, USA. 125Centre for Clinical
Brain Sciences, and UK Dementia Research Institute at the University of Edinburgh, Edinburgh EH16 4SB, UK. 126Swansea University Medical School,
Swansea SA2 8PP, UK. 127Centre for Cardiovascular Genetics, Institute Cardiovascular Science, University College London, London WC1E 6BT, UK.
128Interfaculty Institute for Genetics and Functional Genomics, University Medicine Greifswald, Greifswald 17475, Germany. 129Integrated Cardio
Metabolic Centre, Department of Medicine, Karolinska Institutet, Karolinska Universitetssjukhuset, Huddinge SE-141 57, Sweden. 130Intramural
Administration Management Branch, National Heart, Lung, and Blood Institute, NIH, Bethesda, MD 20892, USA. 131Cardiology Section, Boston
Veteran’s Administration Healthcare, Boston, MA 02130, USA. 132Harvard Medical School, Boston, MA 02115, USA. These authors contributed
equally: Nora Franceschini, Claudia Giambartolomei. A full list of consortium members can be found at the end of the article.
MEGASTROKE Consortium
Yukinori Okada133,134,135, Aniket Mishra136,137, Loes Rutten-Jacobs138, Anne-Katrin Giese139,140,
Sander W. van der Laan141, Solveig Gretarsdottir142, Christopher D. Anderson140,143,144, Michael Chong145,
Hieab H.H. Adams8, Tetsuro Ago146, Peter Almgren147, Philippe Amouyel148,149, Hakan Ay140,150,
Traci M. Bartz5,71, Oscar R. Benavente151, Steve Bevan152, Giorgio B. Boncoraglio153, Robert D. Brown Jr.154,
Adam S. Butterworth155,156, Caty Carrera157,158, Cara L. Carty159,160, Daniel I. Chasman132,161, Wei-Min Chen162,
John W. Cole163, Ioana Cotlarciuc164, Carlos Cruchaga165,166, John Danesh155,156,167, Paul I.W. de Bakker168,169,
Anita L. DeStefano27,170, Marcel den Hoed171, Qing Duan172, Stefan T. Engelter173,174, Guido J. Falcone144,175,
Rebecca F. Gottesman176, Raji P. Grewal177, Stefan Gustafsson60, Jeffrey Haessler178, Tamara B. Harris6,
Ahamad Hassan179, Aki S. Havulinna180,181, Elizabeth G. Holliday182,183, George Howard184, Fang-Chi Hsu15,
Hyacinth I. Hyacinth185, M. Arfan Ikram8, Marguerite R. Irvin186, Xueqiu Jian187, Jordi Jiménez-Conde188,
Julie A. Johnson189,190, J. Wouter Jukema66, Masahiro Kanai2, Keith L. Keene191,192, Brett M. Kissela193,
Dawn O. Kleindorfer193, Charles Kooperberg178, Michiaki Kubo194, Leslie Lange195, Carl D. Langefeld196,
Claudia Langenberg172, Jin-Moo Lee197, Robin Lemmens198,199, Didier Leys200, Cathryn M. Lewis201,202,
Wei-Yu Lin203,204, Arne G. Lindgren205,206, Erik Lorentzen207, Patrik K. Magnusson103, Jane Maguire208,
Ani Manichaikul162, Patrick F. McArdle209, James F. Meschia210, Thomas H. Mosley211,212,
Toshiharu Ninomiya213, Martin J. O’Donnell145,214, Sara L. Pulit215, Kristiina Rannikmäe216,
Alexander P. Reiner178,217, Kathryn M. Rexrode218, Stephen S. Rich162, Paul M. Ridker132,161, Natalia S. Rost139,140,
ARTICLE NATURE COMMUNICATIONS | https://doi.org/10.1038/s41467-018-07340-5
12 NATURE COMMUNICATIONS |          (2018) 9:5141 | https://doi.org/10.1038/s41467-018-07340-5 | www.nature.com/naturecommunications
Peter M. Rothwell219, Tatjana Rundek220, Ralph L. Sacco220, Saori Sakaue3,221, Michele M. Sale162,
Veikko Salomaa180, Bishwa R. Sapkota222, Reinhold Schmidt223, Carsten O. Schmidt224, Ulf Schminke217,
Pankaj Sharma164, Agnieszka Slowik225, Cathie L.M. Sudlow226, Christian Tanislav227, Turgut Tatlisumak228,229,
Vincent N.S. Thijs230,231, Gudmar Thorleifsson142, Unnur Thorsteinsdottir142, Steffen Tiedt78, Stella Trompet23,
Matthew Walters232, Nicholas J. Wareham172, Sylvia Wassertheil-Smoller233, Kerri L. Wiggins5, Qiong Yang170,
Salim Yusuf145, Tomi Pastinen234, Arno Ruusalepp21,42,43, Eric E. Schadt20, Simon Koplev20,
Veronica Codoni235,236, Mete Civelek162,237, Nick Smith5,106,238, David A. Trégouët235,236,
Ingrid E. Christophersen144,239,240, Carolina Roselli144, Steven A. Lubitz144,239, Patrick T. Ellinor144,239,
E. Shyong Tai241, Jaspal S. Kooner242, Norihiro Kato243, Jiang He244, Pim van der Harst245, Paul Elliott246,
John C. Chambers118,247, Fumihiko Takeuchi243, Andrew D. Johnson27, Dharambir K. Sanghera222,248,249,
Olle Melander46, Christina Jern250, Daniel Strbian251,252, Israel Fernandez-Cadenas157,158, W.T. Longstreth Jr5,253,
Arndt Rolfs147, Jun Hata213, Daniel Woo193, Jonathan Rosand140,143,144, Guillaume Pare145, Danish Saleheen254,
Kari Stefansson142,255, Bradford B. Worrall256, Steven J. Kittner163, Joanna M.M. Howson155 &
Yoichiro Kamatani133,257
133Laboratory for Statistical Analysis, RIKEN Center for Integrative Medical Sciences, Yokohama 230-0045, Japan. 134Department of Statistical
Genetics, Osaka University Graduate School of Medicine, Osaka 565-0871, Japan. 135Laboratory of Statistical Immunology, Immunology Frontier
Research Center (WPI-IFReC), Osaka University, Suita 565-0871, Japan. 136INSERM U1219 Bordeaux Population Health Research Center, Bordeaux
F-33000, France. 137University of Bordeaux, Bordeaux F-33000, France. 138Stroke Research Group, Division of Clinical Neurosciences, University of
Cambridge, Cambridge CB2 1TN, UK. 139Department of Neurology, Massachusetts General Hospital, Harvard Medical School, Boston, MA 02114,
USA. 140J. Philip Kistler Stroke Research Center, Department of Neurology, MGH, Boston, MA 02215, USA. 141Laboratory of Experimental
Cardiology, Division of Heart and Lungs, University Medical Center Utrecht, Utrecht 3584 CX, Netherlands. 142deCODE genetics/AMGEN inc,
Reykjavik 101, Iceland. 143Center for Genomic Medicine, Massachusetts General Hospital (MGH), Boston, MA 02114, USA. 144Program in Medical
and Population Genetics, Broad Institute, Cambridge, MA 02142, USA. 145Population Health Research Institute, McMaster University, Hamilton L8L
2X2, Canada. 146Department of Medicine and Clinical Science, Graduate School of Medical Sciences, Kyushu University, Fukuoka 819-0935, Japan.
147Albrecht Kossel Institute, University Clinic of Rostock, Rostock 18147, Germany. 148INSERM U1167, Institut Pasteur de Lille, Lille F-59000, France.
149Department of Public Health, Lille University Hospital, Lille F-59000, France. 150Department of Radiology, Massachusetts General Hospital,
Harvard Medical School, AA Martinos Center for Biomedical Imaging, Boston, MA 02129, USA. 151Division of Neurology, Faculty of Medicine, Brain
Research Center, University of British Columbia, Vancouver 170-637, Canada. 152School of Life Science, University of Lincoln, Lincoln LN6 7TS, UK.
153Department of Cerebrovascular Diseases, Fondazione IRCCS Istituto Neurologico Carlo Besta, Milano 20133, Italy. 154Department of Neurology,
Mayo Clinic Rochester, Rochester, MN 55905, USA. 155MRC/BHF Cardiovascular Epidemiology Unit, Department of Public Health and Primary
Care, University of Cambridge, Cambridge CB2 1TN, UK. 156The National Institute for Health Research Blood and Transplant Research Unit in Donor
Health and Genomics, University of Cambridge, Cambridge CB2 1TN, UK. 157Neurovascular Research Laboratory, Vall d’Hebron Institut of Research,
Neurology and Medicine Departments-Universitat Autònoma de Barcelona, Vall d’Hebrón Hospital, Barcelona 08193, Spain. 158Stroke
Pharmacogenomics and Genetics, Fundacio Docència i Recerca MutuaTerrassa, Terrassa 08222, Spain. 159Children’s Research Institute, Children’s
National Medical Center, Washington, DC 20052, USA. 160Center for Translational Science, George Washington University, Washington, DC
20052, USA. 161Division of Preventive Medicine, Brigham and Women’s Hospital, Boston, MA 02115, USA. 162Department of Public Health Sciences,
Center for Public Health Genomics, University of Virginia School of Medicine, Charlottesville, VA 22904-4259, USA. 163Department of Neurology,
University of Maryland School of Medicine and Baltimore VAMC, Baltimore, MD 21201, USA. 164Institute of Cardiovascular Research, Royal
Holloway University of London, Egham TW20 OEX, UK. 165Department of Psychiatry,The Hope Center Program on Protein Aggregation and
Neurodegeneration (HPAN), Washington University, School of Medicine, St. Louis, MO 98195, USA. 166Department of Developmental Biology,
Washington University School of Medicine, St. Louis, MO 98195, USA. 167Wellcome Trust Sanger Institute, Hinxton CB10 1SA, UK. 168Department
of Medical Genetics, University Medical Center Utrecht, Utrecht 3584 CX, The Netherlands. 169Department of Epidemiology, Julius Center for
Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht 3584 CX, The Netherlands. 170Boston University School of Public
Health, Boston, MA 02118, USA. 171Department of Immunology, Genetics and Pathology and Science for Life Laboratory, Uppsala University,
Uppsala 751 05, Sweden. 172MRC Epidemiology Unit, University of Cambridge School of Clinical Medicine, Institute of Metabolic Science,
Cambridge Biomedical Campus, Cambridge CB2 0SL, UK. 173Department of Neurology and Stroke Center, Basel University Hospital, Basel 4031,
Switzerland. 174Neurorehabilitation Unit, University and University Center for Medicine of Aging and Rehabilitation Basel, Felix Platter Hospital,
Basel 4055, Switzerland. 175Department of Neurology, Yale University School of Medicine, New Haven, CT 06510, USA. 176Department of
Neurology, Johns Hopkins University School of Medicine, Baltimore, MD 21205, USA. 177Neuroscience Institute, SF Medical Center, Trenton, NJ
08629, USA. 178Division of Public Health Sciences, Fred Hutchinson Cancer Research Center, Seattle, WA 98109-1024, USA. 179Department of
Neurology, Leeds General Infirmary, Leeds Teaching Hospitals NHS Trust, Leeds LS1 3EX, UK. 180National Institute for Health and Welfare, Helsinki
FI-00271, Finland. 181FIMM - Institute for Molecular Medicine Finland, Helsinki FI-00271, Finland. 182Public Health Stream, Hunter Medical Research
Institute, New Lambton NSW 2305, Australia. 183Faculty of Health and Medicine, University of Newcastle, Newcastle 2308, Australia. 184School of
Public Health, University of Alabama at Birmingham, Birmingham, AL 35487, USA. 185Aflac Cancer and Blood Disorder Center, Department of
Pediatrics, Emory University School of Medicine, Atlanta, GA 30322, USA. 186Epidemiology, School of Public Health, University of Alabama at
Birmingham, Birmingham 35487, USA. 187Brown Foundation Institute of Molecular Medicine, University of Texas Health Science Center at Houston,
Houston, TX 77030, USA. 188Neurovascular Research Group (NEUVAS), Neurology Department, Institut Hospital del Mar d’Investigació Mèdica,
Universitat Autònoma de Barcelona, Barcelona 08193, Spain. 189Department of Pharmacotherapy and Translational Research and Center for
Pharmacogenomics, University of Florida, College of Pharmacy, Gainesville, FL 32611, USA. 190Division of Cardiovascular Medicine, College of
NATURE COMMUNICATIONS | https://doi.org/10.1038/s41467-018-07340-5 ARTICLE
NATURE COMMUNICATIONS |          (2018) 9:5141 | https://doi.org/10.1038/s41467-018-07340-5 | www.nature.com/naturecommunications 13
Medicine, University of Florida, Gainesville, FL 32611, USA. 191Department of Biology, East Carolina University, Greenville, NC 27858, USA. 192Center
for Health Disparities, East Carolina University, Greenville, NC 27858, USA. 193University of Cincinnati College of Medicine, Cincinnati, OH 45220,
USA. 194RIKEN Center for Integrative Medical Sciences, Yokohama 230-0045, Japan. 195University of Colorado, Denver, CO 80203, USA. 196Center
for Public Health Genomics and Department of Biostatistical Sciences, Wake Forest School of Medicine, Winston-Salem, NC 27157, USA.
197Department of Neurology, Radiology, and Biomedical Engineering, Washington University School of Medicine, St. Louis, MO 98195, USA.
198Department of Neurosciences, Experimental Neurology, KU Leuven – University of Leuven, Leuven 3000, Belgium. 199VIB Center for Brain &
Disease Research, University Hospitals Leuven, Department of Neurology, Leuven 3000, Belgium. 200University of Lille, INSERM U1171, CHU Lille,
Lille F-59000, France. 201Department of Medical and Molecular Genetics, King’s College London, London WC2R 2LS, UK. 202SGDP Centre, Institute
of Psychiatry, Psychology & Neuroscience, King’s College London, London WC2R 2LS, UK. 203Cardiovascular Epidemiology Unit, Department Public
Health & Primary Care, University of Cambridge, Cambridge CB1 8RN, UK. 204Northern Institute for Cancer Research, Paul O’Gorman Building,
Newcastle University, Newcastle NE2 4AD, UK. 205Department of Clinical Sciences Lund, Neurology, Lund University, Lund 221 00, Sweden.
206Department of Neurology and Rehabilitation Medicine, Skåne University Hospital, Lund 222 29, Sweden. 207Bioinformatics Core Facility,
University of Gothenburg, Gothenburg 405 30, Sweden. 208University of Technology Sydney, Faculty of Health, Ultimo NSW 2007, Australia.
209Department of Medicine, University of Maryland School of Medicine, Baltimore, MD 21201, USA. 210Department of Neurology, Mayo Clinic,
Jacksonville, FL 32224, USA. 211Division of Geriatrics, School of Medicine, University of Mississippi Medical Center, Jackson, MS 39216, USA.
212Memory Impairment and Neurodegenerative Dementia Center, University of Mississippi Medical Center, Jackson, FL 39216, USA. 213Department
of Epidemiology and Public Health, Graduate School of Medical Sciences, Kyushu University, Fukuoka 819-0395, Japan. 214Clinical Research Facility,
Department of Medicine, NUI Galway, Galway H91 TK33, Ireland. 215Department of Neurology, Brain Center Rudolf Magnus, University Medical
Center Utrecht, Utrecht 3584, The Netherlands. 216Centre for Clinical Brain Sciences, University of Edinburgh, Edinburgh EH4 2XU, UK.
217Department of Neurology, University Medicine Greifswald, Greifswald 17489, Germany. 218Department of Medicine, Brigham and Women’s
Hospital, Boston, MA 02115, USA. 219Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford OX3 9DU, UK. 220Department of
Neurology, Miller School of Medicine, University of Miami, Miami, FL 33136, USA. 221Department of Allergy and Rheumatology, Graduate School of
Medicine, the University of Tokyo, Tokyo 13-8654, Japan. 222Department of Pediatrics, College of Medicine, University of Oklahoma Health
Sciences Center, Oklahoma City, OK 73104, USA. 223Department of Neurology, Medical University of Graz, Graz 8036, Austria. 224University
Medicine Greifswald, Institute for Community Medicine, SHIP-KEF, Greifswald 17489, Germany. 225Department of Neurology, Jagiellonian
University, Krakow 31-007, Poland. 226University of Edinburgh, Edinburgh EH8 9JZ, UK. 227Department of Neurology, Justus Liebig University,
Giessen 35390, Germany. 228Department of Clinical Neurosciences/Neurology, Institute of Neuroscience and Physiology, Sahlgrenska Academy at
University of Gothenburg, Gothenburg SE-405, Sweden. 229Sahlgrenska University Hospital, Gothenburg SE-405, Sweden. 230Stroke Division,
Florey Institute of Neuroscience and Mental Health, Heidelberg VIC 3084, Australia. 231Austin Health, Department of Neurology, Heidelberg,
Victoria 3084, Australia. 232School of Medicine, Dentistry and Nursing at the University of Glasgow, Glasgow G12 8QQ, UK. 233Department of
Epidemiology and Population Health, Albert Einstein College of Medicine, Bronx, NY 10461, USA. 234Department of Human Genetics, McGill
University, Montreal H3A 0G4, Canada. 235Sorbonne Universités, UPMC Univ. Paris 06, INSERM, UMR_S 1166, Team Genomics & Pathophysiology
of Cardiovascular Diseases, Paris 75006, France. 236ICAN, Institute for Cardiometabolism and Nutrition, Paris 75013, France. 237Department of
Biomedical Engineering, University of Virginia, Charlottesville, VA 22904-4259, USA. 238Seattle Epidemiologic Research and Information Center,
VA Office of Research and Development, Seattle, WA 98108, USA. 239Cardiovascular Research Center, Massachusetts General Hospital, Boston,
MA 02114, USA. 240Department of Medical Research, Bærum Hospital, Vestre Viken Hospital Trust, Rud 3004, Norway. 241Saw Swee Hock School
of Public Health, National University of Singapore and National University Health System, Singapore 119077, Singapore. 242National Heart and Lung
Institute, Imperial College London, London SW7 2AZ, UK. 243Department of Gene Diagnostics and Therapeutics, Research Institute, National
Center for Global Health and Medicine, Tokyo 162-8655, Japan. 244Department of Epidemiology, Tulane University School of Public Health and
Tropical Medicine, New Orleans, LA 70112, USA. 245Department of Cardiology, University Medical Center Groningen, University of Groningen,
Groningen 9700 RB, Netherlands. 246Department of Epidemiology and Biostatistics, Imperial College London, MRC-PHE Centre for Environment
and Health, School of Public Health, London W2 1PG, UK. 247Department of Cardiology, Ealing Hospital NHS Trust, Southall HA1 3UJ, UK.
248Department of Pharmaceutical Sciences, College of Pharmacy, University of Oklahoma Health Sciences Center, Oklahoma City, OK 73104, USA.
249Oklahoma Center for Neuroscience, Oklahoma City, OK 73104, USA. 250Department of Pathology and Genetics, Institute of Biomedicine, The
Sahlgrenska Academy at University of Gothenburg, Gothenburg SE-405, Sweden. 251Department of Neurology, Helsinki University Hospital,
Helsinki FI-00029, Finland. 252Clinical Neurosciences, Neurology, University of Helsinki, Helsinki FI-00029, Finland. 253Department of Neurology,
University of Washington, Seattle, WA 98195, USA. 254Department of Genetics, Perelman School of Medicine, University of Pennsylvania,
Philadelphia, PA 19104, USA. 255Faculty of Medicine, University of Iceland, Reykjavik 201, Iceland. 256Departments of Neurology and Public Health
Sciences, University of Virginia School of Medicine, Charlottesville, VA 22908, USA. 257Center for Genomic Medicine, Kyoto University Graduate
School of Medicine, Kyoto 606-8501, Japan
ARTICLE NATURE COMMUNICATIONS | https://doi.org/10.1038/s41467-018-07340-5
14 NATURE COMMUNICATIONS |          (2018) 9:5141 | https://doi.org/10.1038/s41467-018-07340-5 | www.nature.com/naturecommunications