Genome-Wide Association Scan Identifies a Risk Locus
for Preeclampsia on 2q14, Near the Inhibin, Beta B Gene
Matthew P. Johnson1, Shaun P. Brennecke2, Christine E. East2, Harald H. H. Go ¨ring1, Jack W. Kent Jr.1,
Thomas D. Dyer1, Joanne M. Said2, Linda T. Roten3, Ann-Charlotte Iversen3, Lawrence J. Abraham4,
Seppo Heinonen5, Eero Kajantie6,7, Juha Kere8,9,10, Katja Kivinen11, Anneli Pouta12, Hannele
Laivuori10,13for the FINNPEC Study Group, Rigmor Austgulen3, John Blangero1, Eric K. Moses1,14*
1Department of Genetics, Texas Biomedical Research Institute, San Antonio, Texas, United States of America, 2Department of Perinatal Medicine/Department of
Obstetrics and Gynaecology, Royal Women’s Hospital and University of Melbourne, Parkville, Victoria, Australia, 3Department of Cancer Research and Molecular Medicine,
Norwegian University of Science and Technology, Trondheim, Norway, 4The School of Biomedical Biomolecular and Chemical Sciences, The University of Western
Australia, Perth, Western Australia, Australia, 5Department of Obstetrics and Gynecology, Kuopio University Hospital, and University of Eastern Finland, Kuopio, Finland,
6Department of Chronic Disease Prevention, Diabetes Prevention Unit, National Institute for Health and Welfare, Helsinki, Finland, 7Children’s Hospital, Helsinki
University Central Hospital and University of Helsinki, Helsinki, Finland, 8Department of Biosciences and Nutrition, and Science for Life Laboratory, Karolinska Institutet,
Stockholm, Sweden, 9Folkha ¨lsan Institute of Genetics, Helsinki, Finland, 10Haartman Institute, Department of Medical Genetics, University of Helsinki, Helsinki, Finland,
11Wellcome Trust Sanger Institute, Wellcome Trust Genome Campus, Hinxton, Cambridge, United Kingdom, 12Department of Children, Young People and Families,
National Institute for Health and Welfare, Oulu, Finland, 13Research Programs Unit, Women’s Health, University of Helsinki, Helsinki, Finland, 14The Centre for Genetic
Epidemiology and Biostatistics, The University of Western Australia, Perth, Western Australia, Australia
Elucidating the genetic architecture of preeclampsia is a major goal in obstetric medicine. We have performed a genome-wide
12 BeadChip to successfully genotype 648,175 SNPs in 538 preeclampsia cases and 540 normal pregnancy controls. Two SNP
associations (rs7579169, p=3.5861027, OR=1.57; rs12711941, p=4.2661027, OR=1.56) satisfied our genome-wide significance
threshold (modified Bonferroni p,5.1161027). These SNPs reside in an intergenic region less than 15 kb downstream from the 39
terminus of the Inhibin, beta B (INHBB) gene on 2q14.2. They are in linkage disequilibrium (LD) with each other (r2=0.92), but not
(r2,0.80) with any other genotyped SNP 6250 kb. DNA re-sequencing in and around the INHBB structural gene identified an
additional 25 variants. Of the 21 variants that we successfully genotyped back in the case-control cohort the most significant
association observed was for a third intergenic SNP (rs7576192, p=1.4861027, OR=1.59) in strong LD with the two significant
GWAS SNPs (r2.0.92). We attempted to provide evidence of a putative regulatory role for these SNPs using bioinformatic analyses
and found that they all reside within regions of low sequence conservation and/or low complexity, suggesting functional
importanceislow.Wealsoexplored themRNA expression in deciduaofgenes6500 kbofINHBB andfoundanominallysignificant
correlation between a transcript encoded by the EPB41L5 gene, ,250 kb centromeric to INHBB, and preeclampsia (p=0.03). We
were unable to replicate the associations shown by the significant GWAS SNPs in case-control cohorts from Norway and Finland,
leading us to conclude that it is more likely that these SNPs are in LD with as yet unidentified causal variant(s).
Citation: Johnson MP, Brennecke SP, East CE, Go ¨ring HHH, Kent JW Jr, et al. (2012) Genome-Wide Association Scan Identifies a Risk Locus for Preeclampsia on
2q14, Near the Inhibin, Beta B Gene. PLoS ONE 7(3): e33666. doi:10.1371/journal.pone.0033666
Editor: Struan Frederick Airth Grant, The Children’s Hospital of Philadelphia, United States of America
Received November 15, 2011; Accepted February 14, 2012; Published March 14, 2012
Copyright: ? 2012 Johnson et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits
unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Funding: National Institutes of Health grants supported the Australian GWAS (HD049847 to E.K.M., S.P.B. and J.B.) and MEDUSA, the super computer cluster at
Texas Biomed (S10RR029392 to J.B.). The AT&T Genomics Computing Center at Texas Biomed is supported by the AT&T Foundation. Transcriptional profiling was
supported by the Faye L. and William L. Cowden Charitable Foundation (to M.P.J.). The Norwegian cohort study was supported by the Functional Genomic
Programme (FUGE) of the Norwegian Research Council (to R.A.). The FINNPEC study was supported by Jane and Aatos Erkko Foundation, Pa ¨ivikki and Sakari
Sohlberg Foundation, Academy of Finland, Research Funds of the University of Helsinki, Government Special Subsidiary for Health Sciences (EVO funding) at
Helsinki and Uusimaa Hospital District. Novo Nordisk Foundation, Finnish Foundation for Pediatric Research, Emil Aaltonen Foundation, and Sigrid Juse ´lius
Foundation. M.P.J. is supported, in part, by an American Heart Association National Scientist Development grant (09SDG2350008). J.M.S. was supported by a
Cornelius Regan Trust Award from the University of Melbourne. This investigation was conducted in facilities constructed with support from Research Facilities
Improvement Program grant RR017515 from the National Center for Research Resources, National Institutes of Health. The funders had no role in study design,
data collection and analysis, decision to publish, or preparation of the manuscript.
Competing Interests: The authors have declared that no competing interests exist.
* E-mail: firstname.lastname@example.org
Preeclampsia is a common and serious complication of human
pregnancy affecting 3–5% of all primigravid women [1–3].
Delivery of the fetus and placenta is the only intervention for
adequate resolution of severe symptoms. It is a major cause of
maternal mortality in developing countries, accounting for 50,000
maternal deaths yearly . The maternal and fetal morbidity and
mortality associated with preeclampsia and in particular with the
adverse consequences of pre-term delivery are a major health
burden in the developed world [5–7].
The pathophysiology of preeclampsia is thought to involve two
main stages [8,9]. In stage one abnormal fetal-derived cytotro-
phoblast invasion in the uterine wall in early pregnancy is
PLoS ONE | www.plosone.org1March 2012 | Volume 7 | Issue 3 | e33666
associated with failed remodeling of the maternal spiral arteries
perfusing the placenta. This is thought to be a ‘root’ cause. As a
result of hypoxia and/or oxidative stress to the placenta there is
release of syncytiotrophoblast-derived factors into the maternal
circulation that give rise to the second stage of the maternal
syndrome. The known placental factor of most relevance to this
second stage is the soluble receptor for vascular endothelial growth
factor, sVEGFR-1, also called sFlt-1. When present in excess, as in
preeclampsia, sFlt-1 binds to, and activates, VEGF, a key survival
factor for endothelium , and thereby induces systemic
The principal diagnostic features of preeclampsia are new onset
hypertension and proteinuria after 20 weeks gestation . The
hypertension is now recognized to be secondary to diffuse
endothelial dysfunction , and the proteinuria is associated
with glomerular endotheliosis [10,13]. Preeclampsia is therefore
primarily characterized by endothelial dysfunction, which is also
one of the principal pathogenic mechanisms in atherosclerotic
vascular diseases such as coronary artery disease and stroke.
Consistent with their shared pathogenesis, atherosclerosis and
preeclampsia share many common risk factors including hyper-
tension, obesity, insulin resistance, diabetes mellitus, metabolic
syndrome, general inflammation, thrombophilia, and family
history . A history of preeclampsia increases the risk of future
hypertension, ischemic heart disease, stroke and venous thrombo-
embolism. This is true especially for women with a history of early-
onset preeclampsia (,34 weeks gestation) than those women who
have preeclampsia at term . A popular theory is that
pregnancy provides a metabolic stress test to unmask underlying
risk of cardiovascular disease .
These data have led several investigators to speculate [17,18]
that the genetic risk factors for preeclampsia will also be relevant to
cardiovascular disease, providing increased impetus and justifica-
tion for their discovery [19,20]. By far the most effort to date has
been focused on candidate genes, primarily those for which a
plausible role in the known underlying pathophysiology could be
argued, and in particular blood pressure regulation, endothelial
dysfunction, lipid metabolism, thrombophilia, placental develop-
ment and function, and the inflammatory response . There
have been many nominal associations reported with a lack of
reproducibility a common theme, in many cases most likely due to
a lack of uniformity in diagnosis and underpowered study designs.
In our attempts to identify risk factors for preeclampsia we have
primarily focused on positional cloning strategies, making no a
priori assumptions about the nature of genes involved. We initially
performed genome-wide linkage mapping studies in multiple
affected families from Australia and New Zealand, identifying
putative susceptibility loci on chromosomes 2q22, 5q and 13q
[22,23], with several plausible positional candidate susceptibility
genes identified, including the activin receptor gene ACVR2A on
2q22 [24,25], the aminopeptidase gene ERAP2 on 5q , and the
cytokine encoding TNFSF13B gene on 13q . We now report
on our continued positional cloning efforts using genome-wide
association mapping in a large Caucasian case-control cohort from
Australia. We herein report a significant novel SNP association on
chromosome 2q14.2, close to the Inhibin, beta B (INHBB) gene.
Genome-wide association with preeclampsia.
of 1,078 unrelated Australian samples (538 preeclampsia cases, 540
normal pregnancy controls) that passed our quality control criteria,
the observed distribution of p-values for 648,175 successfully
In the set
genotyped SNPs exhibited minimal deviation from the expected
distribution (Figure 1). As such, this indicates minimal test statistic
bias or underlying population structure (l=1.002). The 2log10
transformation of observed p-values across the genome are displayed
in Figure 2 and SNPs with a p-value of 1026or less are presented in
Table 1. By accounting for the extent of SNP linkage disequilibrium
(LD), per chromosome, the number of independent SNPs
(SNPINDEP) across the genome were estimated (Table S1). The
estimated number of independent SNPs, specific to the Australian
case-control cohort, was used in a modified Bonferroni procedure to
generate an adjusted target alpha level (0.05/SNPINDEP). The two
most significant SNP associations satisfied our genome-wide
significance threshold (modified
(Figure 2). The SNP showing the strongest association (rs7579169;
0.340) is intergenic and resides ,8.3 kb downstream from the 39
terminus of the INHBB gene on chromosome 2q14.2. The next
strongest association is also for an intergenic SNP (rs12711941;
0.342) downstream (,13.5 kb) from the 39 terminus of the INHBB
gene. Both of these SNPs are strongly correlated with each other
(r2=0.92), but not (r2,0.80) with any other genotyped SNP
6250 kb(Figure3).The SNP
accommodated two SNPs within the INHBB gene locus itself, a
these INHBB locus SNPs did not reach nominal significance
(p=0.43 and p=0.22, respectively), nor were they correlated with
rs7579169 (r2=0.03 and r2=0.02, respectively). The sample
genotype success rates for rs7579169 and rs12711941 were 0.9981
and 1.0, respectively. Furthermore, sample genotype concordance
rates for rs7579169 and rs12711941 genotyped on both the Illumina
and Sequenom platforms (see methods) were 0.987 and 0.993,
HapMap CEU proxy SNPs.
proxy SNPs to rs7579169, we used the latest (19-Apr-2009)
HapMap CEU linkage disequilibrium (LD) data arising from
phases I+II+III (rel #27, NCBI B36). Based on current HapMap
parameters the search for SNPs flanking rs7579169 was restricted
to 6200 kb. One additional proxy SNP (rs7576192) was identified
to be strongly correlated with rs7579169 in the CEU genotype
data (r2=1) (Table S2). The rs7576192 SNP also resides
downstream from the 39 terminus of the INHBB gene and is
93 bp from rs7579169 (Table S2). In the CEU samples, rs7576189
is also strongly correlated with rs12711941 (r2=0.96) and not
correlated with rs13419301 (r2=0.04) or rs11902591 (r2=0.03)
(Table S2). These data are concordant with our Australian GWAS
cohort. In addition, a second INHBB nearGene-5 SNP (rs7578624)
genotyped in the CEU samples was not correlated with rs7579169
(r2=0.02) (Table S2).
SNP degree of dominance.
inheritance of our top two SNP associations by estimating the
degree of dominance index (h) . We report negligible deviation
from additivity and hence, a significant additive effect for both the
rs7579169 (h=20.04) and rs12711941 (h=20.13) SNPs. The
application of PLINK’s  –model option confirms a stronger
additive effect than a dominant or recessive effect for either SNP
(data not shown).
chip usedin this study
To investigate other potential
We investigated the mode of
INHBB locus sequencing
In an effort to identify other potentially causal variants at the
INHBB locus we re-sequenced the entire INHBB structural gene
(NM_002193.2), ,2.5 kb upstream of the INHBB translation start
site and ,3 kb downstream of the INHBB STOP codon. We also
sequenced a region flanking rs7579169 (,2.2 kb upstream and
Preeclampsia GWAS Identifies Risk Locus on 2q14
PLoS ONE | www.plosone.org2March 2012 | Volume 7 | Issue 3 | e33666
,0.6 kb downstream) that exhibited evolutionary conservation
amongst the rhesus monkey (Macaca mulatta), dog (Canis familiaris)
and mouse (Mus musculus). Sequencing experiments were conduct-
ed in 96 individuals from the Australian GWAS cohort (48
preeclampsia cases, 48 normal pregnancy controls). These
individuals were selected on the basis of carrying two copies of
the rare allele at both the rs7579169 and rs12711941 SNP loci. A
total of 19 SNPs (9 known, 10 novel) plus six novel deletions were
identified in our Australian cohort subset (Table 2). Due to the
rare ‘T’ allele for rs7579169 being concordant with our reference
sequence template, this SNP locus was not highlighted in our list of
identified INHBB locus variants.
INHBB variant genotyping and association analysis
Of the 25 INHBB locus variants identified by re-sequencing, 21
were successfully genotyped with a mean (range) genotyping success
rate of 0.975 (0.960–0.999) (Table 2). Of the remaining variants, one
deletion failed assay design, two deletions failed the assay and one
SNP was non-polymorphic due to a discordance between the
preeclampsia data set allele and the reference template allele. We
observed nominal genetic associations for a novel SNP 2,434 bp
upstream of the INHBB translation start site (ss469271203; p=0.02,
MAF(cases)=0.028, MAF(controls)=0.013) and for a rare novel SNP
within INHBB’s 39UTR (ss469271208; p=0.01, MAF(cases)=0.009,
MAF(controls)=0.001) (Table 2). A genome-wide significant associa-
tion was observed for another intergenic SNP residing downstream
OR=1.59, MAF(cases)=0.449, MAF(controls)=0.339) (Table 2). SNP
rs7576192 is in close proximity to, and strongly correlated with the
two significant GWAS SNPs (r2.0.92) (Table S3). The genotypic
correlation data between rs7576192, rs7579169 and rs12711941 in
our Australian cohort is concordant with the reported HapMap
Analysis of gene expression at the INHBB locus
To investigate whether expression of the INHBB locus was
correlated with preeclampsia or the significant GWAS SNPs
exhibited regulatory potential, total RNA from decidual basalis
tissue of 60 individuals from the Australian case-control cohort (25
preeclampsia cases and 35 normal pregnancy controls) were
hybridized onto Illumina’s HumanHT-12 v4 Expression Bead-
Chips. Our analysis of differential mRNA expression at the INHBB
locus (ILMN_1685714) was extended 6500 kb of INHBB, also
bringing into consideration several other genes, including PTPN4
(ILMN_1793549), EPB41L5 (ILMN_1770245, ILMN_2043306),
TMEM185B (ILMN_2231020, ILMN_2231021), RALB (ILMN_
1676358) and GLI2 (ILMN_1727577). One transcript (ILMN_
2043306) was not significantly detected (FDR p.0.05) and an
additional transcript (ILMN_1727577) had a mean expression level
consistent with background noise (mean average raw signal ,50)
(Table 3). We observed a nominally significant correlation with the
ILMN_1770245 (EPB41L5) transcript and preeclampsia (p=0.03)
(Table 3). The independent addition of our GWAS SNPs
(rs7579169, rs12711941) yielded neither as significant predictor
variables in the ‘transcript,preeclampsia’ regression model
(p.0.05) (Table 3).
Bioinformatic analysis of associated SNPs
Using the UCSC genome browser (Human, Feb. 2009
[GRCh37/hg19]) we conducted bioinformatic analyses on the
two significantly associated GWAS SNPs (rs7579169, rs12711941)
and the rs7576192 SNP identified by re-sequencing to see if they
resided within (1) regulatory elements (histone mark H3K4Me1 or
Figure 1. Quantile-quantile (Q-Q) plot of the observed GWAS p-values (2 2log10P).
Preeclampsia GWAS Identifies Risk Locus on 2q14
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Figure 2. The genome-wide distribution of asymptotic p-values for each of the quality control filtered SNPs in the Australian cohort
(n=648,175). Our adjusted genome-wide significant and suggestive thresholds were set at p,5.1148361027and p,1.0229761026, respectively.
Table 1. SNP associations with preeclampsia (p#1026).
Function Gene Alleles2
P-value OR3(95% CI)
2 rs7579169 121118124intergenicC/T 0.44710.3401 3.5861027
2 rs12711941121123383 intergenic G/T 0.44820.3420 4.2661027
15 rs245327444774202 intronic CTDSPL2 C/T0.04620.09892.3261026
13rs12431203 77488736intergenic G/A0.2061 0.1303 2.4561026
21 rs282653822188735intergenicT/C 0.19540.27825.9361026
2 rs9332419 26322040 intronicRAB10 G/A0.3562 0.45186.1761026
2 rs4952830 46746963UTR-5 ATP6V1E2A/G 0.33430.4284 6.7461026
2 rs6542736 108415702 intergenicG/A0.4094 0.31677.3161026
3rs2279720 87276699synonymous CHMP2BG/A 0.05920.11288.6661026
3 rs104449987299075synonymousCHMP2BA/C 0.05920.11288.6661026
2 rs11126375 26344787intronic RAB10A/G 0.1645 0.2412 9.1361026
4rs767752398965696 intronicC4orf37 T/C0.0833 0.0379 9.5161026
3 rs17024019 87257423intergenic A/G0.06010.1137 9.5761026
1Physical coordinate based on NCBI reference assembly build 37.2.
2Major allele/minor allele.
3Odds ratio for the minor allele.
Preeclampsia GWAS Identifies Risk Locus on 2q14
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DNase I hypersensitive sites from ENCODE) or (2) transcription
factor (TF) binding sites (ChIP-seq data from ENCODE).
Additional TF binding site analysis was performed using P-Match
[30,31] and AliBaba 2.1 [30,32]. Histone marks in the regions of
rs7579169 and rs7576192 suggest some promoter/enhancer
activity to be present, and highest in human umbilical vein
endothelial cell (HUVEC) lines. AliBaba indicated a Sp1
(stimulating protein 1) TF binding site in the presence of the
minor ‘T’ allele for rs7579169. This would suggest the minor allele
for rs7579169 to be affiliated with higher transcriptional activity/
expression. Conversely, AliBaba indicated a Sp1 TF binding site in
the presence of the major ‘G’ allele for rs12711941. This would
suggest the minor allele for rs12711941 to be affiliated with lower
transcriptional activity. No TF binding sites were identified in the
presence of the major or minor allele for rs7576192. The
rs7579169, rs12711941 and rs7576192 SNPs all reside within
regions of low sequence conservation and/or low complexity,
suggesting functional importance is low. It is therefore more likely
that these SNPs are in LD with an as yet unidentified
polymorphism of greater functional significance.
Norwegian and Finnish replication cohorts
in the Australian cohort were genotyped in two independent case-control
cohorts from Norway (1,134 preeclampsia cases, 2,263 normal pregnancy
controls) and Finland (760 preeclampsia cases, 759 normal pregnancy
controls). The rs7579169 SNP association for the minor ‘T’ allele was not
replicated in the Norwegian (p=0.29, MAF(cases)=0.424, MAF(controls)=
0.438) or Finnish (p=0.60, MAF(cases)=0.391, MAF(controls)=
0.382) cohorts (Table S4). Similarly, the rs12711941 SNP
association for the minor ‘T’ allele was not replicated in the
Norwegian (p=0.35, MAF(cases)=0.422, MAF(controls)=0.434) or
Finnish (p=0.50, MAF(cases)=0.387, MAF(controls)=0.375) cohorts
The determination of the genetic contributions to risk of
preeclampsia has proven difficult. In this first GWAS for
preeclampsia we have obtained strong evidence for a risk locus
on chromosome 2q14.2 defined by significant genetic association
with two intergenic SNPs located within 15 kb of the 39 terminus
of the Inhibin, beta B (INHBB) gene. Our subsequent re-
sequencing of the INHBB locus in a small sample of affected and
unaffected individuals from our Australian cohort identified a third
intergenic SNP, also residing within 15 kb from the INHBB 39
terminus, to be significantly associated with preeclampsia. While
all three intergenic SNPs are in strong LD with each other they are
not in LD with any other genotyped SNP within 6250 kb.
Our preliminary bioinformatic and transcriptional profiling
analyses have not provided compelling data to implicate these
SNP variants and/or genes in preeclampsia etiology, and we did
not replicate these significant SNP associations in either Norwe-
gian or Finnish case-control cohorts. While successful replication
can provide an important and independent verification of a
putative genetic association, which helps to prevent the discovery
of spurious associations, failure to replicate in a population
different from that used in the initial finding does not necessarily
invalidate the original observation. The reasons why true
associations may not replicate across independent data sets has
received considerable attention over the last five years with genetic
heterogeneity, environmental interactions, age-dependent effects,
epistasis and inadequate statistical power given as possible reasons
[33–36]. In this context it is perhaps noteworthy that in our earlier
linkage-based positional cloning studies in Australian families
where we reported the likely involvement of the activin type 2A
receptor (ACVR2A) gene [24,37] and the endoplasmic reticulum
aminopeptidase 2 (ERAP2) gene  in risk of preeclampsia, we
were also unable to replicate our gene-specific SNP associations in
the same Norwegian case-control cohort as that used in this
current study. In the case of ACVR2A and ERAP2 we subsequently
were able to demonstrate association with preeclampsia in the
Norwegian population using other SNPs in these genes, providing
evidence of different allele frequencies and LD patterns at these
loci [25,26]. These data may be consistent with the existence of as
yet unidentified/untyped rare risk variants that exhibit different
patterns of linkage disequilibrium in our Australian, Norwegian
and Finnish population samples.
While we have not presented compelling functional data to
implicate any genes at the 2q14.2 locus marked by our SNP
associations, we are encouraged by the striking plausibility of the
INHBB gene as a positional candidate susceptibility gene for
preeclampsia. This is supported by a body of substantive biological
data that is consistent with the involvement of the activins, inhibins
and other members of the TGF-b superfamily in the development
of preeclampsia [38–45]. It is worth noting that, during pregnancy
activins and inhibins are produced in the human endometrium,
decidua and placenta and are thought to inactivate matrix
metalloproteases in human endometrial stromal cells during
decidualization thereby affecting remodeling of the maternal
spiral arteries by the invading cytotrophoblasts . Failed
remodeling of these vessels is regarded as an early defining event
in the pathophysiology of preeclampsia [47–49]. The fact that
INHBB is biologically connected to ACVR2A leads us to speculate
that our positional cloning studies in the Australian Caucasian
population, originally using linkage mapping in families and now
GWAS in unrelated individuals, have revealed positional candi-
date genes that define a key pathway involved in susceptibility to
preeclampsia. We now propose to focus our efforts on the
Figure 3. Association plot of the chromosome 2 region
reaching genome-wide significance with preeclampsia suscep-
tibility (rs7579169±250 kb). Observed p-values are plotted as
2log10 values as a function of the SNPs physical location (NCBI Build
37.2). Estimated recombination rates were extracted from HapMap data.
The local linkage disequilibrium structure is based on the observed
allele frequency data in the Australian cohort (red dots, r2$0.8; orange
dots, 0.5#r2,0.8; yellow dots, 0.2#r2,0.5; clear dots, r2,0.2). Gene
annotations were obtained from the UCSC genome browser (Human,
Feb. 2009 [GRCh37/hg19]).
Preeclampsia GWAS Identifies Risk Locus on 2q14
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Table 2. INHBB locus variants identified in a sample of the Australian cohort (n=96).
ss469271203121101331 nearGene-5 G/A 0.02770.01331.2661029
ss469271213121101394 nearGene-5 AAG/- 0.0009 0.00191.0 1.0*
ss469271214 121101650 nearGene-5AGCTGG/- Failed assay
ss469271215121101736 nearGene-5 CGCCGCAGCGCC/-Failed assay
rs7578624121102479 nearGene-5 C/G 0.04870.0427 1.0 0.5080
rs13419301 121102572nearGene-5 T/C0.05800.0655 0.28 0.4803
ss469271216121104688intronicTG/- Failed assay design
ss469271204121105292 intronic C/G0.0066 0.00381.0 0.5478*
rs11902591 121106003intronic A/G 0.06730.07310.81 0.6068
rs4328642 121106850 synonymousC/T 0.03280.03631.0 0.6662
ss469271205121106946 synonymousG/A 0.00090.0 1.0 0.4929*
ss469271217121107784UTR-3 AGTC/-0.00090.01.0 0.4962*
ss469271206 121107831 UTR-3G/A0.0009 0.01.00.4929*
rs45624437121108182UTR-3 T/C0.0162 0.00831.0 0.0987
ss469271207 121108506 UTR-3G/A 0.00.00091.0 1.0*
ss469271208121108585 UTR-3 T/C0.0087 0.00090.02 0.0111*
rs57802235121109444nearGene-3 G/A 0.04160.0466 0.72 0.5788
ss469271209 121109737nearGene-3C/T0.0048 0.00191.00 0.2813*
rs10183524 121109878 nearGene-3 G/A 0.04910.0493 0.740.9829
ss469271210121110151 nearGene-3 G/A0.0039 0.01.00.0602*
ss469271218121116483 intergenic A/-0.00280.0009 1.00.6247*
ss469271211121116625 intergenic G/A0.0009 0.0 1.01.0*
ss469271212121116672 intergenicC/T 0.0029 0.00091.0 0.3709*
rs7576192 121118031 intergenicG/A0.4499 0.33920.131.4861027
Identified variants were genotyped in Australian individuals passing GWAS quality control criteria (n=1,078). Novel variants submitted to dbSNP are assigned with their
‘ss’ submission ID number.
*Fisher’s exact test p-value.
1Major allele/Minor allele.
2Hardy-Weinberg equilibrium p-value.
3Preeclampsia dataset allele discordant to reference template allele.
Table 3. Gene expression results of the INHBB structural locus 6500 kb.
PTPN4 ILMN_179354959 1.7610277
89.6 0.03 0.900.57
ILMN_20433063 0.570.91 1.0 NA8
TMEM185BILMN_2231020 59 1.7610277
278.60.48 0.25 0.19
141.3 0.290.22 0.06
RALB ILMN_167635858 1.9610274
INHBB ILMN_168571457 1.1610271
GLI2ILMN_1727577 110.00015 0.00031 8.9NA8
1Number of samples with a GenomeStudio ‘pDetection’ p-value#0.05.
2Computed transcript detection p-value.
3False discovery rate detection p-value.
4Mean average raw signal.
5‘transcript,preeclampsia’ regression p-value.
6‘transcript,preeclampsia+rs7579169’ regression p-value.
7‘transcript,preeclampsia+rs12711941’ regression p-value.
8No regression analyses performed.
Preeclampsia GWAS Identifies Risk Locus on 2q14
PLoS ONE | www.plosone.org6March 2012 | Volume 7 | Issue 3 | e33666
identification of probable rare and as yet unidentified variants in
the inhibins, activins and their receptors as such variation is likely
to be critical to the development of preeclampsia in many
Materials and Methods
recruitment of the Australian women was granted by the RWH
Research and Ethics Committees, Melbourne, Australia. Written
informed consent was obtained from study participants prior to
them being phlebotomized. Permission was also granted from the
Australian case-control cohort women to access and examine their
medical records in order to confirm/validate Caucasian ancestry
and relevant preeclampsia diagnostic criteria. Ethical approval to
conduct molecular and statistical analyses of the Australian
samples was obtained from the Institutional Review Board (IRB)
of the University of Texas Health Science Center at San Antonio
Norwegian replication cohort.
provided written informed consent when recruited to the study.
Prior approval to use the Norwegian case-control cohort for
genetic studies was obtained by the Regional Committee for
Medical Research Ethics, Norway and approved by the National
Data Inspectorate and The Directorate of Health and Social
Welfare. Ethical approval for the molecular and statistical analysis
of the Norwegian samples was obtained from the IRB of the
Finnish replication cohort.
informed consent. The FINNPEC study protocol was approved by
the coordinating Ethics Committee of the Hospital District of
Helsinki and Uusimaa. The Southern Finnish participant study
was approved by the local ethical review committee at the Helsinki
University Hospital. Ethical approval for the molecular and
statistical analysis of the Finnish samples was in addition obtained
from the IRB of the UTHSCSA.
All HUNT participants
All subjects provided a written
GWAS case-control sample population
The Australian case-control cohort of 1,092 unrelated women
used in this GWAS included 1,018 women of confirmed
Caucasian ancestry (471 preeclampsia cases and 547 normal
pregnancy controls) retrospectively ascertained from a larger
Australian case-control cohort of 1,774 women that were recruited
at the Royal Women’s Hospital (RWH), Melbourne, Australia
over a five period from 2007 to 2011. The Australian population
seen at the RWH in Melbourne is ,70% Caucasian and for this
study the focus was on the recruitment of Caucasian subjects. The
additional 74 women were unrelated preeclampsia cases from our
Caucasian Australian and New Zealand family cohort that has
been described in detail elsewhere [22–24,26].
Replication case-control sample populations
The most promising SNPs from the Australian GWAS were
assessed in two independent case-control cohorts from Norway
All Norwegian samples were retrospectively selected
from a large multipurpose health survey conducted over a three
period from 1995 to 1997 in Nord-Trøndelag County in Norway
. More than 65,000 inhabitants participated. The people living
in the Nord-Trøndelag County are considered to be representative
of the Norwegian population, and are well suited for genetic
studies because of their ethnic homogeneity (,3% non-
Caucasians) [50,51]. Information pertaining to all pregnancies
and deliveries has been registered in the Medical Birth Registry of
Norway (MBRN) since 1968. The MBRN has established formal
classifications of different diseases in pregnancy. The unique 11-
digit national identification numbers from HUNT2 women
participants were cross referenced with the information registries
of the MBRN to identify case-control cohorts. The HUNT study
population used to study preeclampsia has been described in detail
The Finnish patient samples used in this study
originate from the Finnish Genetics of Preeclampsia Consortium
(FINNPEC) study cohort and the Southern Finland preeclampsia
study cohort. FINNPEC is an ongoing multicentre study where
DNA samples and data have been collected prospectively at all
university hospitals in Finland (i.e. Helsinki, Turku, Tampere,
Kuopioand Oulu) from2008.
preeclampsia, the next available woman giving birth at the same
hospital, with no preeclampsia, is invited as a control. After initial
review of hospital records by a research nurse, each diagnosis is
confirmed by a study physician based on criteria described below.
Information pertaining to the Southern Finnish case-control
cohort was obtained from discharge records from the Helsinki
University Central Hospital. These records were used to
January 1988 and April 1998 . These women were healthy
prior to their first pregnancy with no evidence of renal or
autoimmune disease. Blood samples were collected between
January 1997 and April 1998 after the index pregnancy [53,54].
During the same period, blood samples from non-preeclamptic
(control) patients who had given birth in the same hospital were
For eachwoman with
determined by qualified clinicians using criteria set by the
Australasian Society for the Study of Hypertension in Pregnancy
[55,56], and the Society of Obstetric Medicine of Australia and
New Zealand for the management of hypertensive diseases of
pregnancy . Women were considered preeclamptic if they
were previously normotensive and if they, on at least two occasions
six or more hours apart, had after 20 weeks gestation (i) a rise in
systolic blood pressure (SBP) of at least 25 mmHg and/or a rise
from baseline diastolic blood pressure (DBP) of at least 15 mmHg,
or (ii) SBP$140 mmHg and/or DBP$90 mmHg. Additionally,
significant new onset proteinuric levels were either $0.3 g/l in a
24 hour specimen, at least a ‘2+’ proteinuria dipstick reading from
a random urine collection or a spot protein:creatine ratio
$0.03 g/mmol. Preeclamptic women who also experienced
convulsions or unconsciousness in their perinatal period were
hypertension or other medical conditions known to predispose
for preeclampsia (e.g. renal disease, diabetes, twin pregnancies or
fetal chromosomal abnormalities) were excluded. Of the 1,774
unrelated Australian women initially recruited for this study, 1,018
women were of confirmed Caucasian ancestry, meeting our
inclusion criteria. Of these, 471 were confirmed, by medical
records, as having preeclampsia (cases) and 547 were confirmed as
having a normal pregnancy (controls). An additional 74 unrelated
preeclamptic (case) women selected for inclusion in our GWAS
sample were the probands and/or founders of our previously
described 74 preeclampsia families [22–24,26,27,37,58].
classification of preeclampsia used for the Norwegian samples
was established by the MBRN based on previously reported
guidelines . The MBRN definition for preeclampsia was
Preeclampsia GWAS Identifies Risk Locus on 2q14
PLoS ONE | www.plosone.org7March 2012 | Volume 7 | Issue 3 | e33666
defined as an increase in SBP to at least 140/90 mmHg (or an
increase in SBP$30 mmHg, or in DBP$15 mmHg from the level
measured before the 20thweek of gestation), combined with
proteinuria (protein excretion of at least 0.3 g per 24 hours or
$1+ on a dip stick). Based on these diagnostic criteria there were
1,179 women registered with preeclampsia (cases) and 2,358
women with a history of a normal, healthy pregnancy (controls).
Of these registered women, blood samples were available for 1,134
cases and 2,263 controls at the HUNT Biobank and included for
Finnish replication cohort.
preeclamptic pregnancy and had no medical history of chronic
hypertension, type 1 diabetes, or renal disease were eligible for the
study as cases. Diagnostic criteria used for the FINNPEC study
cohort were SBP$140 mmHg and/or DBP$90 mmHg on at
least two occasions with new onset proteinuria ($0.3 g/24 hrs, or
$0.3 g/L,orin the absence
measurement, at least a ‘2+’ or more, or two ‘1+’ proteinuria
dipstick readings) after 20 weeks gestation in a previously
normotensive woman. Preeclampsia in the Southern Finnish
case/control cohort was defined as two SBP/DBP measurements
at least6 hrs apart
measurement $0.3 g in a 24 hour urine collection, or at least a
‘1+’ dipstick reading after 20 weeks gestation . A total of 760
preeclamptic (case) women and 664 control women from the
FINNPEC study cohort, and 95 control women from the Southern
Finland preeclampsia study cohort were included in this study.
Finnish women who suffered a
of concurrent quantitative
The isolation of genomic DNA (gDNA) from the Australian
case-control blood samples was achieved using Qiagen’s Blood &
Cell Culture DNA Midi Kit (Qiagen Pty Ltd, Doncaster, VIC,
Australia). The individual gDNA samples (n=1,092) were
genotyped using Illumina’s Human OmniExpress-12 BeadChip
(Illumina Inc., San Diego, CA) containing 731,442 loci derived
from phases I, II and III of the International HapMap project [59–
61]. A total of 200 ng of gDNA (4 ml at 50 ng/ml) for each sample
was processed according to Illumina’s Infinium HD Assay Ultra
protocol. BeadChips were imaged on Illumina’s iScan System with
iScan Control Software (v3.2.45). Normalization of raw image
intensity data, genotype clustering and individual sample genotype
calls were performed using Illumina’s GenomeStudio software
(v2010.2), Genotyping Module (v1.7.4). Illumina’s pre-defined
genotype cluster boundaries were used to denote SNP genotype
cluster positions (HumanOmniExpress-12v1_C.egt). Additionally,
genotype clusters for all SNPs of interest were visually inspected
(Figures S1 & S2). Genotype assay quality control measures were
assessed with Illumina’s internal assay performance metrics.
Individual SNP loci and individual sample quality control
performance measures were assessed using PLINK . Individ-
ual SNP loci were excluded, (i) if genotype success rates were
,0.95 (n=4,742); (ii) for deviation from Hardy-Weinberg
equilibrium in the control samples with a criterion of p,0.0001
(n=1,676); (iii) if the observed copies of the minor allele in the
population sample (i.e. cases and controls, collectively) was ,10
(n=77,286). This quality control metric equates to a minor allele
frequency (MAF) being less than 0.009 (10/1,092); (iv) for any
residual non-autosomal or X-linked loci (n=380 XY-linked loci).
Given our female only data set, X-linked loci were retained in our
analyses. Individual samples were excluded, (i) if genotype call
rates were ,0.9 (1 case, 1 control); (ii) if PLINK’s sex check to
estimate X chromosome inbreeding (homozygosity) rates (F) was
$0.2 (3 cases, 1 control). For this quality control metric a female
call is made if F,0.2 and was conducted to identify probable
random genotype error(s); (iii) using PLINK’s cryptic relatedness
metric to examine the possibility of unknown, distant familial
relationships amongst the Australian GWAS sample set by
estimating the proportion of alleles shared identical by descent
(^ p p). Eight pairs of DNA samples putatively exhibited a distant
familial relationship (^ p p$0.125), of which 3 cases and 5 controls
were excluded from subsequent data analyses. These SNP loci and
sample quality control metric thresholds resulted in the passing of
648,175 SNPs to be analyzed in 1,078 unrelated Australian
women (538 preeclampsia cases, 540 normal pregnancy controls).
The mean (range) genotyping success rate of the quality control
filtered data set was 0.9986 (0.9499–1).
Gene-centric and/or conserved intergenic regions flanking
prioritized SNPs were sequenced in 96 unrelated Australian
samples (48 preeclampsia cases, 48 normal pregnancy controls).
These samples were a subset of the final GWAS sample set
(n=1,078) that passed our quality control cleaning. Conserved
intergenic regions were identified using the ECR Browser .
Genomic DNA sequence reference templates were obtained from
the UCSC Genome Bioinformatics database (Human, Feb. 2009
[GRCh37/hg19]). All primers were designed using Primer 3
(v0.4.0) and BLASTed to assess their uniqueness to the human
genome. Contiguous primer pairs were designed to overlap by
,100–150 bp. Standard PCR was performed with 20 ng of
gDNA in a 10 ml reaction volume. If standard PCR optimization
conditions failed, FailSafe PCR pre-mixes (Epicentre Biotechnol-
ogies, Madison, WI) were used in lieu. GeneAmp 9700 thermal
cyclers (Life Technologies, Foster City, CA) were used for PCR
amplification. PCR amplicons were purified with ExoSAP-IT
(USB Corp., Cleveland, OH) according to manufacturers’
instructions. Independent sequencing reactions for both the sense
and anti-sense strands were performed on the purified PCR
amplicons (1 ml) using AB BigDye Terminator v3.1 chemistry (Life
Technologies) in a 5 ml reaction volume. Sequence reaction
amplification was performed on a GeneAmp 9700 thermal cycler
using standard cycling conditions. Amplified sequence products
were purified with AB BigDye XTerminator purification kits
according to manufacturers’ instructions (Life Technologies).
Purified sequence reactions were electrophoretically separated on
an AB 3730xl DNA Analyzer (Life Technologies). Sequence
reaction quality was assessed using Sequencing Analysis software
v5.1.1 and sequence variant identification was performed using
SeqScape v2.6 (Life Technologies).
Replication and targeted loci genotyping
Additional genotyping in the Australian cohort and replication
genotyping in the Norwegian and Finnish cohorts was performed
using Sequenom-based MassArray technology (Sequenom, San
Diego, CA). SNP assays were designed using Sequenom’s online
design tools in conjunction with Assay Designer v4.0. Variant
specific PCR and single-base extension primers were supplied by
Integrated DNA Technologies (IDT, Coralville, IA). For each
sample, 20 ng of gDNA was used and assayed in accordance with
the iPLEX Gold Reaction protocol using the MassARRAY Matrix
Liquid Handler. Samples were spotted onto a 384-sample
SpectroCHIP II using the MassARRAY Nanodispenser RS1000.
SpectroCHIPs were loaded into the MassARRAY Analyzer 4 and
the nucleotide mass time-of-flight was recorded using Spectro-
ACQUIRE software (v18.104.22.168). Genotype clustering and individ-
ual sample genotype calls were generated using Sequenom’s
TyperAnalyzer (v4.0.5). To assess the accuracy of the GWAS
Preeclampsia GWAS Identifies Risk Locus on 2q14
PLoS ONE | www.plosone.org8March 2012 | Volume 7 | Issue 3 | e33666
genotypes we re-genotyped our prioritized SNPs back in the
Australian GWAS cohort.
Transcriptional profiling in decidua
Of the 1,078 unrelated Australian women that passed our
GWAS quality control decidual basalis tissue was also available
from 25 preeclampsia cases and 35 healthy pregnancy controls.
These decidual samples were collected at the time of delivery by
Caesarean section, from the placental bed by suction curettage, as
previously described . Total RNA isolation and quality
assessment, and anti-sense RNA (aRNA) synthesis, amplification
and purification were performed as previously described .
Purified aRNA was hybridized to Illumina’s HumanHT-12 v4
Expression BeadChips in accordance with Illumina’s Whole-
Genome Gene Expression Direct Hybridization assay protocol. All
samples were scanned on the Illumina iScan System with iScan
Control software (v3.2.45). Illumina’s GenomeStudio software
(v2010.2), Gene Expression Module (v1.7.0) was used to generate a
control summary report to assess assay performance and quality
control metrics. One control sample failed the image scan and was
subsequently omitted prior to data analysis. The remaining 59
tissue samples yielded high quality expression profile data, without
any samples showing a marked reduction in the number of probes
detected, in mean average raw signal, or in mean correlation (in
raw expression level across probes) with the other samples.
structure within the Australian GWAS samples passing quality
control (n=1,078), principal components analysis (PCA) was
conducted in R (prcomp) using a subset of quality control filtered
SNPs (n=246,406). The subset of common SNPs (MAF$0.05) for
PCA was generated using PLINK to compute the genotypic
correlation (r2) between SNP pairs within a 50 SNP window. Each
SNP window progressed forward by 5 SNPs prior to re-computing
pairwise genotypic correlations. One SNP from a pair of SNPs was
excluded if r2.0.5. PCA revealed very minimal population
structure in the Australian GWAS samples, so principle
components correction was not used in the association analysis.
The absence of false positive association due to population
structure was confirmed by the calculated genomic inflation factor
(l) of 1.002.
Genome-wide data analysis.
structure, asymptotic p-values for each of the quality control
filtered SNPs (n=648,175) were computed to assess minor-allele
association with the disease trait (i.e. preeclampsia) using PLINK.
The Manhattan plot displaying the 2log10 transformation of
observed p-values was generated using the mhtplot function of
theR package ‘gap’.The
transformations of observed p-values as a function of expected
p-values was generated using R base graphics. The asplot
function of the R package ‘gap’ was used to generate a regional
association plot for loci of interest (6250 kb) based on,
recombination rate (HapMap 2006-10_rel21_phaseI+II), PLINK
computed pairwise genotypic correlations between all genotyped
SNPs in the Australian samples (–ld-window-r2 0) and PLINK
generated point-wise, asymptotic association test p-values.
Genome-wide multiple testing correction.
adjusted genome-wide significant and suggestive thresholds we
first imputed sporadic missing genotype data using BEAGLE .
An effective number of independent SNP tests across our GWAS
data set were approximated using the solid spine of linkage
disequilibrium (SSLD) measure implemented in HAPLOVIEW
, as previously described . An approximated effective
To account for potential population
Due to minimal population
Q-Q plot depicting
number of independent SNP tests were used to calculate modified
Bonferroni-adjusted significant and suggestive thresholds. Briefly,
juxtaposed chromosome specific SNP windows containing at most
3,000SNPs werefirst generated
HAPLOVIEW, the number of SNP blocks and interblock SNPs
were determined with a minimum D9 value of 0.8. Pairwise
comparisons of SNPs more than 500 kb apart were ignored.
Quality control filtered SNPs that did not satisfy HAPLOVIEW’s
SSLD default parameters (i.e. MAF,0.01; HWE p,0.001), or
were not assigned a chromosomal bp coordinate with the Illumina
SNP chip annotation were treated as independent SNPs akin to
the interblock SNPs. These additional independent SNPs are
herein referred to as ‘residual SNPs’. For each chromosome the
sum of SNP blocks, interblock SNPs and residual SNPs
approximate the effective number of independent SNP tests.
The estimated number of independent SNPs (SNPINDEP), specific
to the Australian case-control cohort, was used to generate an
adjusted target alpha level (0.05/SNPINDEP). For this study, the
adjusted genome-wide significant and suggestive thresholds were
set at 5.1148361027(0.05/97,755) and 1.0229761026(0.1/
97,755), respectively (Table S1).
Targeted loci data analysis.
in the Australian cohort and replication association analyses in the
Norwegian and Finnish cohorts were performed in PLINK
assuming an additive model of gene action. Extremely rare
variants (MAF,0.01) were analyzed in PLINK using the
conservative Fisher’s Exact Test .
Gene expression data analysis.
sample quality we computed the mean expression signal across
all detected probes for each sample independently. We then
computed, for each sample, the mean correlation with all other
samples across the raw average signals of all detected probes. All
59 samples passing the initial scan were retained. Using the
‘‘pDetection’’ p-values generated by Illumina’s GenomeStudio
Gene Expression module, and computing the probability that as
many or more tissue samples as observed would yield a p-
value#0.05 by chance, expression of 24,647 probes (52.2% of all
probes) was significantly detected. The raw expression levels of
these probes (after background subtraction using GenomeStudio)
were shifted upwards to force positive values (minimum expression
level of 1.0 across all samples and detected probes), log2
transformed, and quantile normalized. To investigate whether
expressed probes in the identified candidate 2q14.2 region (INHBB
structural locus 6500 kb) are significantly correlated with
preeclampsia, and whether the identified candidate SNPs
(expression quantitative trait nucleotides), we preformed linear
regression analysis, using disease status (preeclampsia or no
preeclampsia) and/or SNP genotype (coded additively as the
number of copies of the minor allele present in a person) as
predictors of fully processed expression level.
Additional association analyses
To further scrutinize
GenomeStudio genotype cluster plot for rs7579169.
GenomeStudio genotype cluster plot for rs12711941.
Estimated number of independent GWAS SNPs.
flanking the strongest associated preeclampsia SNP (rs75791696
200 kb). bp, distance from rs7579169; SNP Chip, SNPs present
Genotypic correlations (r2) of HapMap CEU SNPs
Preeclampsia GWAS Identifies Risk Locus on 2q14
PLoS ONE | www.plosone.org9March 2012 | Volume 7 | Issue 3 | e33666
(yes) or absent (no) on the Human OmniExpress-12 BeadChip used
in this study;#INHBB nearGene-5 SNP; *INHBB intronic SNP.
other 20 re-sequenced INHBB locus variants, plus the two GWAS
SNPs; rs7579169 and rs12711941.
Genotypic correlations between rs7576192 and the
case-control cohorts. Alleles are listed as major/minor. r2denotes
the genotypic correlation between rs7579169 and rs12711941.
Replication genotyping in Norwegian and Finnish
For technical assistance we thank Hao-Chang Lan, Yvonne Garcia, Janelle
Bentz (Texas Biomed), Anthony Borg (RWH, Melbourne), and Divya
Neelam (Sequenom). We sincerely acknowledge the support of the clinical
research mid-wives Karen Reidy and Sue Duggan (RWH, Melbourne)
who contributed to this study. The HUNT study is a collaboration between
HUNT Research Centre, Faculty of Medicine at NTNU, the Norwegian
Institute of Public Health and the Nord-Trøndelag County Council. We
are indebted to all the Australian, Norwegian and Finnish women whose
participation made this work possible.
The Finnish Genetics of Pre-eclampsia Consortium (FINN-
We appreciate the collaboration with the following members of the
FINNPEC Study Group: Eeva Ekholm (Turku University Central
Hospital, Turku, Finland), Kaarin Ma ¨kikallio-Anttila, (Oulu University
Hospital, Oulu, Finland), Reija Hietala, Susanna Sainio and Terhi Saisto
(Helsinki University Central Hospital, Helsinki, Finland), Jukka Uotila,
(Tampere University Hospital, Tampere, Finland) Tia Aalto-Viljakainen,
Miira Klemetti and Anna Inkeri Lokki (University of Helsinki, Helsinki,
Finland), and Leena Georgiadis (Kuopio University Hospital, Kuopio,
Finland). The expert technical assistance of Elina Huovari, Eija
Kortelainen, Satu Leminen, Aija La ¨hdesma ¨ki, Susanna Mehta ¨la ¨, and
Christina Salmen is gratefully acknowledged.
Conceived and designed the experiments: MPJ SPB JB EKM. Performed
the experiments: MPJ EKM. Analyzed the data: MPJ HHHG JWK TDD
LJA JB. Contributed reagents/materials/analysis tools: MPJ SPB JMS
LTR ACI SH EK JK KK AP HL RA JB EKM. Wrote the paper: MPJ
HHHG EKM. Reviewed Australian case-control medical records: SPB
CEE. Conceived FINNPEC Study: EK JK HL. FINNPEC Study Core
Investigators: SH EK JK KK AP HL.
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Preeclampsia GWAS Identifies Risk Locus on 2q14
PLoS ONE | www.plosone.org11 March 2012 | Volume 7 | Issue 3 | e33666