University of Groningen
Meta-Analysis of Genome-Wide Association Studies for Abdominal Aortic Aneurysm Identifies
Four New Disease-Specific Risk Loci
Jones, Gregory T.; Tromp, Gerard; Kuivaniemi, Helena; Gretarsdottir, Solveig; Baas, Annette
F.; Giusti, Betti; Strauss, Ewa; van't Hof, Femke N. G.; Webb, Thomas R.; Erdman, Robert
Published in:
Circulation research
DOI:
10.1161/CIRCRESAHA.116.308765
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Jones, G. T., Tromp, G., Kuivaniemi, H., Gretarsdottir, S., Baas, A. F., Giusti, B., Strauss, E., van't Hof, F.
N. G., Webb, T. R., Erdman, R., Ritchie, M. D., Elmore, J. R., Verma, A., Pendergrass, S. A., Kullo, I. J.,
Zy, Z. Y., Peissig, P. L., Gottesman, O., Verma, S. S., ... Int Consortium Blood Pressure (2017). Meta-
Analysis of Genome-Wide Association Studies for Abdominal Aortic Aneurysm Identifies Four New
Disease-Specific Risk Loci.
Circulation research
,
120
(2), 341-353.
https://doi.org/10.1161/CIRCRESAHA.116.308765
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Circulation Research is available at http://circres.ahajournals.org DOI: 10.1161/CIRCRESAHA.116.308765
Original received March 23, 2016; revision received October 28, 2016; accepted November 21, 2016. In October 2016, the average time from submission
to first decision for all original research papers submitted to Circulation Research was 15.7 days.
For the author affiliations, please see the Appendix.
The online-only Data Supplement is available with this article at http://circres.ahajournals.org/lookup/suppl/doi:10.1161/CIRCRESAHA.
116.308765/-/DC1.
Correspondence to Matthew J. Bown, MBBCh, MD, Cardiovascular Sciences, University of Leicester Robert Kilpatrick Bldg, Leicester, LE2 7LX,
United Kingdom. E-mail m.bown@le.ac.uk; or Gregory T. Jones, PhD, Surgery Department, University of Otago, Dunedin 9054, New Zealand. E-mail
greg.jones@otago.ac.nz
Meta-Analysis of Genome-Wide Association Studies
for Abdominal Aortic Aneurysm Identifies Four
New Disease-Specific Risk Loci
Gregory T. Jones, Gerard Tromp, Helena Kuivaniemi, Solveig Gretarsdottir, Annette F. Baas, Betti Giusti, Ewa Strauss,
Femke N.G. van‘t Hof, Thomas R. Webb, Robert Erdman, Marylyn D. Ritchie, James R. Elmore, Anurag Verma,
Sarah Pendergrass, Iftikhar J. Kullo, Zi Ye, Peggy L. Peissig, Omri Gottesman, Shefali S. Verma, Jennifer Malinowski,
Laura J. Rasmussen-Torvik, Kenneth M. Borthwick, Diane T. Smelser, David R. Crosslin, Mariza de Andrade,
Evan J. Ryer, Catherine A. McCarty, Erwin P. Böttinger, Jennifer A. Pacheco, Dana C. Crawford, David S. Carrell,
Glenn S. Gerhard, David P. Franklin, David J. Carey, Victoria L. Phillips, Michael J.A. Williams, Wenhua Wei,
Ross Blair, Andrew A. Hill, Thodor M. Vasudevan, David R. Lewis, Ian A. Thomson, Jo Krysa, Geraldine B. Hill,
Justin Roake, Tony R. Merriman, Grzegorz Oszkinis, Silvia Galora, Claudia Saracini, Rosanna Abbate, Raffaele Pulli,
Carlo Pratesi, The Cardiogenics Consortium, The International Consortium for Blood Pressure, Athanasios Saratzis,
Ana R. Verissimo, Suzannah Bumpstead, Stephen A. Badger, Rachel E. Clough, Gillian Cockerill, Hany Hafez,
D. Julian A. Scott, T. Simon Futers, Simon P.R. Romaine, Katherine Bridge, Kathryn J. Griffin, Marc A. Bailey,
Alberto Smith, Matthew M. Thompson, Frank M. van Bockxmeer, Stefan E. Matthiasson, Gudmar Thorleifsson,
Unnur Thorsteinsdottir, Jan D. Blankensteijn, Joep A.W. Teijink, Cisca Wijmenga, Jacqueline de Graaf,
Lambertus A. Kiemeney, Jes S. Lindholt, Anne Hughes, Declan T. Bradley, Kathleen Stirrups, Jonathan Golledge,
Paul E. Norman, Janet T. Powell, Steve E. Humphries, Stephen E. Hamby, Alison H. Goodall, Christopher P. Nelson,
Natzi Sakalihasan, Audrey Courtois, Robert E. Ferrell, Per Eriksson, Lasse Folkersen, Anders Franco-Cereceda,
John D. Eicher, Andrew D. Johnson, Christer Betsholtz, Arno Ruusalepp, Oscar Franzén, Eric E. Schadt,
Johan L.M. Björkegren, Leonard Lipovich, Anne M. Drolet, Eric L. Verhoeven, Clark J. Zeebregts,
Robert H. Geelkerken, Marc R. van Sambeek, Steven M. van Sterkenburg, Jean-Paul de Vries, Kari Stefansson,
John R. Thompson, Paul I.W. de Bakker, Panos Deloukas, Robert D. Sayers, Seamus C. Harrison,
Andre M. van Rij, Nilesh J. Samani, Matthew J. Bown
Rationale: Abdominal aortic aneurysm (AAA) is a complex disease with both genetic and environmental risk
factors. Together, 6 previously identified risk loci only explain a small proportion of the heritability of AAA.
Objective: To identify additional AAA risk loci using data from all available genome-wide association studies.
Methods and Results: Through a meta-analysis of 6 genome-wide association study data sets and a validation
study totaling 10 204 cases and 107 766 controls, we identified 4 new AAA risk loci: 1q32.3 (SMYD2), 13q12.11
(LINC00540), 20q13.12 (near PCIF1/MMP9/ZNF335), and 21q22.2 (ERG). In various database searches, we
observed no new associations between the lead AAA single nucleotide polymorphisms and coronary artery disease,
blood pressure, lipids, or diabetes mellitus. Network analyses identified ERG, IL6R, and LDLR as modifiers of
MMP9, with a direct interaction between ERG and MMP9.
Conclusions: The 4 new risk loci for AAA seem to be specific for AAA compared with other cardiovascular
diseases and related traits suggesting that traditional cardiovascular risk factor management may only have
limited value in preventing the progression of aneurysmal disease.
(Circ Res. 2017;120:341-353. DOI: 10.1161/CIRCRESAHA.116.308765.)
Key Words: aortic aneurysm, abdominal ■ computational biology ■ genetics ■ genome-wide association study
■ matrix metalloproteinases ■ meta-analysis
Downloaded from http://ahajournals.org by on December 16, 2019
342 Circulation Research January 20, 2017
Nonstandard Abbreviations and Acronyms
AAA abdominal aortic aneurysm
CAD coronary artery disease
eQTL expression quantitative trait locus
GWAS genome-wide association study
IL interleukin
IPA ingenuity pathway analysis
LDLR low-density lipoprotein receptor
LRP1 low-density lipoprotein receptor related protein 1
SMYD2 SET and MYND domain containing 2 (SET domain-containing
proteins, such as catalyze lysine methylation)
SNP single nucleotide polymorphism
TNF tumor necrosis factor
A
bdominal aortic aneurysms (AAAs; MIM100070) are
a significant cause of mortality and morbidity in the
Western world. Although much less common than ischemic
heart disease or stroke, AAA is responsible for
≈11 000
deaths/y in the United States, with no clinical treatment oth-
er than expensive, high-risk surgery.
1
The US Preventative
Services taskforce recommends AAA screening by ultrasound
for all men aged 65 to 75 years who have ever smoked.
2
The
UK NHS AAA Screening Program screens all men at the age
of 65 years irrespective of smoking history yielding a preva-
lence of AAA (>29 mm) of 1.2%.
3
Editorial, see p 259
AAA is an enigmatic complex disease. Although shar-
ing risk factors for, and often coexisting with atherosclerosis,
AAA can be considered to be a distinct entity from atheroscle-
rosis. Smoking, a positive family history of AAA, and male
sex have been consistently identified as the strongest risk fac-
tors for AAA. There is uncertainty over the influence of other
traditional cardiovascular risk markers such as hypertension
and hyperlipidemia. Furthermore, diabetes mellitus has been
found to be negatively associated with AAA and is strongly
protective against disease progression (AAA growth).
1
Heritability of AAA is >0.7,
4
and individuals with a first-
degree relative with AAA have a 2-fold higher risk of develop-
ing an AAA.
5
Genome-wide association studies (GWAS) have
identified 3 AAA risk loci on chromosomes 9 (DAB2IP
6
[DAB2
interacting protein]), 12 (LRP1
7
[low-density lipoprotein receptor
related protein 1]), and 19 (LDLR
8
[low-density lipoprotein re-
ceptor]). Further AAA risk loci on chromosomes 1 (SORT1
9
[sor-
tilin 1] and IL6R
10
[interleukin 6 receptor]) and 9 (CDKN2BAS1/
ANRIL
11
[also known as CDKN2B-AS1, CDKN2B antisense
RNA 1]) were identified by candidate gene/locus approaches.
Together, these explain only a small proportion of the heritability
of AAA.
Overall, the high heritability estimates for AAA and the
small number of loci identified suggest that there are further
risk loci yet to be found. In the current study, we performed
a meta-analysis of 6 available GWAS data sets for AAA on
4972 cases and 99 858 controls and confirmed the findings
within validation data sets of 5232 cases and 7908 controls.
This resulted in identification of 4 novel validated loci for
AAA. We followed up positive results with extensive bioin-
formatics analyses and used data available from various data-
bases to elucidate the potential biological significance of our
findings to the pathobiology of AAA.
Methods
Detailed Methods are available in the Online
Data Supplement.
Expanded Aneurysm Consortium
All known studies with AAA genome-wide genotyping (Online
Methods; Online Table I) were invited to join the International
Aneurysm Consortium. Additional samples (Online Methods; Online
Table II) were used for the validation study. All AAA cases had an
infrarenal aortic diameter of >30 mm. AAAs secondary to connec-
tive tissue diseases were excluded. The use of the samples in each
study cohort was approved by local Ethics Committees or Institutional
Review Boards.
What Is Known?
• Abdominal aortic aneurysm (AAA) has a prevalence of ≈1.5% in men
aged >65 years.
• Positive family history of AAA is a strong risk factor for AAA; however,
only 6 robust and independently validated AAA genetic loci have been
identified to date.
What New Information Does This Article Contribute?
• Four novel genetic loci associated with AAA were identified.
• Pathway analysis highlighted the potential importance of lipoprotein
metabolism, inflammation, and matrix metalloproteinases in AAA
pathobiology.
• Potentially novel mechanisms, involving genes such as ERG, PLTP, and
FGF9, were implicated.
AAA is a significant health burden, particularly among elderly
males. It has a strong heritable component; however, previously
identified risk loci explain only a small proportion of this effect.
No current effective medical therapies that slow AAA growth
exist, highlighting the need to better understand factors influ-
encing pathogenesis and disease progression. This study is
the first meta-analysis of genome-wide association studies for
AAA (10 204 cases). Four novel loci were identified and 5 of the
6 previous AAA genetic associations were confirmed. The new
loci showed no significant associations with other arterial disease
phenotypes, potentially suggesting associations more specific to
AAA than known loci (such as CDKN2BAS1, SORT1, and LDLR).
Associations were consistent with known AAA pathobiology, im-
plicating lipoprotein metabolism, inflammation, and matrix me-
talloproteinases but also identified potentially novel mechanisms
relating to genes such as ERG and FGF9. This study has identified
novel, potentially disease-specific, genetic associations with AAA.
Further functional studies, investigating the translational potential
of these observations, will be required.
Novelty and Significance
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Jones et al Meta-GWAS for Abdominal Aortic Aneurysm 343
Meta-Analysis
The discovery phase of the meta-GWAS was conducted using the
METAL (a tool for meta-analysis of genome-wide association scans)
software package
12
on the 6 cohorts detailed in Online Table I, com-
prising 4972 AAA cases and 99 858 controls. An effective sample
number (N
eff
) weighted analysis
12
was conducted because of case/
control asymmetry within some of the contributing cohorts. Quality
control included assessments for population stratification in each data
set and adjustment was performed if necessary. The analysis of each
contributing GWAS had been performed independently, and there was
therefore no uniform analysis plan across all data sets. The individual
GWAS data sets from Iceland and the Netherlands were adjusted for
genomic inflation before inclusion in the meta-analysis. The over-
all meta-analysis was then adjusted for genomic inflation (
λ; Online
Table I; Online Figure I). An initial (λ-adjusted) discovery threshold
of P<5×10
−6
was used to identify single nucleotide polymorphisms
(SNPs) for subsequent validation genotyping. SNPs with high hetero-
geneity (P
het
<0.005 or I
2
>70%) were not taken forward for validation.
The lead SNPs [or their proxies in high linkage disequilibrium],
identified in the discovery analyses, were then genotyped in a further
8 independent cohorts with 5,232 cases and 7,908 controls (Online
Table II). Allele association analysis of each individual validation
study cohort was carried out using the SHEsis (software platform
for analyses of linkage disequilibrium, haplotype construction, and
genetic association at polymorphism loci) web-based software pack-
age.
13
A combined (discovery-validation) fixed effect meta-analysis
was performed using a Maentel–Haenzel method with the genome-
wide P-value significance threshold being set at 5×10
−8
. Random-
effects (Han-Eskin method
14
) meta-analysis was also performed
to determine whether any results were sensitive to between-study
heterogeneity.
SNP Lookup in GWAS for Other Traits
Associated With AAA
GWAS data sets for other traits were searched for associations
with the AAA-associated SNPs to determine whether the associa-
tions were unique to AAA or related to generalized cardiovascular
disease. Results were obtained from meta-analyses of multiple pri-
mary GWAS data sets for each trait. Summary data for each AAA
associated SNP (P value and effect size) were extracted. P values
<5×10
−8
were considered to be significant. Results were available for
type 2 diabetes mellitus
15
(DIAGRAM [a consortium called DIAbetes
Genetics Replication And Meta-analysis] consortium;
http://www.
diagram-consortium.org/index.html
), coronary artery disease
(CAD; CARDIoGRAM consortium (a consortium called Coronary
ARtery DIsease Genome wide Replication and Meta-analysis)
16
;
www.CARDIOGRAMPLUSC4D.ORG), lipids (the Global Lipids
Genetics Consortium
17
;
http://csg.sph.umich.edu/abecasis/public/lip-
ids2013
), and blood pressure (the International Consortium for Blood
Pressure
18
;
http://www.ncbi.nlm.nih.gov/projects/gap/cgi-bin/study.
cgi?study_id=phs000585.v1.p1
).
Search for Other Associated Traits and Diseases
Using GWAS Databases
The Phenotype-Genotype Integrator
19
(
http://www.ncbi.nlm.nih.
gov/gap/phegeni#GenomeView
), the GWAS catalog (http://www.
gwascentral.org/index
), and the NHLBI GRASP (The Genome-
wide Repository of Associations between SNPs and Phenotypes)
catalog (GRASP v2.0;
http://grasp.nhlbi.nih.gov/Overview.aspx)
20
were searched for diseases and traits associated with the lead SNPs
at the AAA loci.
Phenome-Wide Association Study Analysis
We performed a phenome-wide association study (PheWAS)
21,22
exploring associations between the 9 AAA-associated SNPs and an
extensive group of diagnoses to identify novel associations and un-
cover potential pleiotropy. For the PheWAS, we used data from the
eMERGE (electronic Medical Records and Genomics) Network
23
with a total of 27 077 unrelated patients of European ancestry aged
>19 years. We divided these samples into 2 data sets by propor-
tional sampling based on eMERGE site, sex, and genotyping plat-
form (13 559 and 13 518 individuals in sets 1 and 2, respectively).
We calculated associations between the 9 AAA-associated SNPs
and case or control status based on the extensive set of 9th edition of
the International Statistical Classification of Diseases and Related
Health Problems diagnoses (2408 and 2385 in sets 1 and 2, respec-
tively) where for a specific diagnosis, individuals with the diagnosis
are considered cases. Associations were adjusted for sex, site, geno-
typing platform, and the first 3 principal components to account for
global ancestry.
Annotation of AAA Associated SNPs Using the
University of California Santa Cruz Genome
Browser, Pupasuite, and GWAS3D
Confirmed AAA-associated loci were manually annotated using the
University of California Santa Cruz Genome Browser (
http://genome.
ucsc.edu/cgi-bin/hgGateway
) on the hg19 human genome assembly.
For the Pupasuite analyses SNPs in linkage disequilibrium (r
2
>0.5)
and with lead SNPs at the novel AAA risk loci identified were ex-
tracted from the 1000 Genomes data and then entered into Pupasuite
v3.1.
24
In addition, all known (novel and previously identified) AAA-
associated SNPs were entered into the GWAS3D (bioinformatics
tool detecting human regulatory variants by integrative analysis of
genome-wide associations, chromosome interactions, and histone
modifications)
25
web-portal (
http://jjwanglab.org/gwas3d) to identify
functional SNPs.
Bioinformatic Identification of Candidate AAA
Genes and Pathways Using DEPICT (Data-Driven
Expression-Prioritized Integration for Complex
Traits)
An integrated gene function analysis was performed using the
DEPICT tool (version 1.1).
26
Two separate runs were performed
using either all independent SNPs with discovery meta-GWAS
P<5×10
−6
or just those 9 SNPs that reached P<5×10
−8
in the com-
bined analysis. Both nominal P values and false discovery rates were
calculated.
Experimental Evidence for Functional Variants
at AAA Loci
SNPs at loci confirmed to be associated with AAA were examined
for functional effects using multiple methods (Online Methods).
(1) To search for evidence of functional effects of SNPs at AAA
associated loci 2 expression quantitative trait locus (eQTL) data
sets based on publically available data, and a broad range of tis-
sues with relatively large sample sizes were examined. First, in-
dex and proxy SNPs were queried in a collected database of
published expressed SNP results. The collected expressed SNP
results met criteria for statistical thresholds for association with
gene transcript levels as described in the original publications.
Second, additional eQTL data were integrated from online sources
including ScanDB (SNP and CNV Annotation Database), the
Broad Institute The Genotype-Tissue Expression browser, and the
Pritchard Laboratory (eqtl.uchicago.edu). (2) To search for vascu-
lar tissue-specific effects, eQTL data were also obtained from the
ASAP (Advanced Study of Aortic Pathology) data set
27
and RNA-seq
(whole-genome RNA-sequence generated by high-throughput meth-
ods) data were from the Stockholm-Tartu Atherosclerosis Reverse
Network Engineering Task (STARNET) database
28
(
http://www.
mountsinai.org/profiles/johan-bjorkegren
). (3) Because some genes
at AAA loci were associated with monocyte function and AAA is
known to be an inflammatory disease,
29
data from an eQTL analysis
of peripheral blood monocytes were obtained from the Cardiogenics
Consortium (
http://www.cardiogramplusc4d.org/). (4) Finally to
search for effects in AAA tissue specifically, mRNA expression pro-
files of all the GWAS3D predicted distal targets, as well as SNP prox-
imity implicated genes, were examined using a previously published
genome-wide expression data set on human aorta (GSE57691),
30
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344 Circulation Research January 20, 2017
from which 49 AAA samples were compared with 10 organ donor
control aortic samples. Transcription factor (TF) binding data were
also obtained from a previous study,
31
which described chromatin-
immunoprecipitation (ChIP)-chip for TFs ELF1, ETS2, RUNX1, and
STAT5 using human aortic tissue in AAAs and healthy control aorta.
Network Analysis
We investigated whether most of the loci could be connected into
a single network through intermediate nodes and interactions. A
network integrating most of the loci would suggest mechanisms
by which the loci could act in concert, whether synergistically or
antagonistically, to affect the phenotype. The network(s) would
also provide hypotheses for future investigation. Using the genes
harboring AAA-associated SNPs as a starting set, we analyzed po-
tential interactions between the proteins and known intermediates
(proteins, noncoding RNA, and metabolites) using 2 independent
analysis tools, Ingenuity Pathway Analysis (IPA) tool version 9.0
(Qiagen’s Ingenuity Systems, Redwood City, CA;
www.ingenu-
ity.com
) and Consensus PathDB (http://cpdb.molgen.mpg.de/
CPDB
).
32,33
The analyzed gene set had 14 genes because 2 of the
9 AAA loci included clusters of 3 genes and tumor necrosis factor
(TNF) was added because of the recent literature demonstrating
the strong effect of SMYD2 (SET and MYND domain containing 2
[SET domain-containing proteins, such as catalyze lysine methyla-
tion]) on interleukin-6 (IL6) and TNF production
34,35
(see Online
Table XIV for SNP annotations and Online Methods).
Results
Meta-Analysis of 6 GWAS Data sets for AAA
Followed by a Validation Study Reveals 4 New
AAA Susceptibility Loci
The meta-analysis of 6 GWAS data sets (4972 AAA cases;
99 858 controls; Online Table I) revealed 19 loci of interest
(P<1×10
−6
, Online Tables III and IV; Figure 1). Lead SNPs
from these loci, including the 6 AAA risk loci reported pre-
viously, were analyzed in a validation study of 5232 AAA
cases and 7908 controls (Online Tables II, V, VI, and VII).
Four new loci were independently significant (P<0.05) in the
validation cohort, had a direction of effect consistent with the
discovery cohort and when combined with the discovery co-
hort had a P value that surpassed a genome-wide significance
(5×10
−8
): 1q32.3 (SMYD2), 13q12.11 (LINC00540 [long
intergenic nonprotein coding RNA 540]), 20q13.12 (near
PCIF1 [C-terminal inhibiting factor 1 of a protein called pan-
creatic and duodenal homeobox 1]/MMP9 [matrix metallo-
proteinase 9]/ZNF335 [zinc finger protein 335]), and 21q22.2
(ERG [v-ets avian erythroblastosis virus E26 oncogene ho-
molog]; Table 1; Online Tables V, VI, and VII; Figure 2). All
previously reported associations with AAA were confirmed at
genome-wide significance (Table 1; Online Table VII; Online
Figure II) with the exception of 12q13.3 (LRP1), where the
lead SNP identified in this meta-analysis and tested in our
validation study only demonstrated a borderline association
with AAA in the combined analysis (P=6.4×10
−7
). There was
evidence of significant heterogeneity in the results observed
for rs1795061 (near SMYD2) and rs2836411 (ERG) (Online
Table VII). A random-effects model sensitivity analysis (Han-
Eskin
14
method) demonstrated minimal effect on the results
for these 2 loci (Online Table VIII). The lead SNPs at 2 loci
that were both below the threshold for genome-wide signifi-
cance under the fixed-effects model (rs6516091, 20p12.3, near
FERMT1 and rs5954362, Xq27.2, SPANXA1) were significant
Figure 1. Whole-genome association plot for the primary meta-analysis of genome-wide association studies of abdominal
aortic aneurysm (AAA). Data represent a meta-analysis of 4972 AAA cases and 99 858 controls. The horizontal line indicates the P
value threshold of 5×10
−6
used to select loci for validation studies. The 9 subsequently validated AAA loci are indicated along with the
previously identied LRP1 locus, which fell to P=6.4×10
–7
in the combined discovery/ validation analysis (Online Tables III and IV).
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