Genome-wide association analysis of soluble ICAM-1 concentration reveals novel associations at the NFKBIK, PNPLA3, RELA, and SH2B3 loci.
ABSTRACT Soluble ICAM-1 (sICAM-1) is an endothelium-derived inflammatory marker that has been associated with diverse conditions such as myocardial infarction, diabetes, stroke, and malaria. Despite evidence for a heritable component to sICAM-1 levels, few genetic loci have been identified so far. To comprehensively address this issue, we performed a genome-wide association analysis of sICAM-1 concentration in 22,435 apparently healthy women from the Women's Genome Health Study. While our results confirm the previously reported associations at the ABO and ICAM1 loci, four novel associations were identified in the vicinity of NFKBIK (rs3136642, P = 5.4 × 10(-9)), PNPLA3 (rs738409, P = 5.8 × 10(-9)), RELA (rs1049728, P = 2.7 × 10(-16)), and SH2B3 (rs3184504, P = 2.9 × 10(-17)). Two loci, NFKBIB and RELA, are involved in NFKB signaling pathway; PNPLA3 is known for its association with fatty liver disease; and SH3B2 has been associated with a multitude of traits and disease including myocardial infarction. These associations provide insights into the genetic regulation of sICAM-1 levels and implicate these loci in the regulation of endothelial function.
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ABSTRACT: Grant sponsor The Belgian "National Fund for Scientific Research" (FNRS) The incidence of hepatocellular carcinoma (HCC) is increasing in Western countries. Although several clinical factors have been identified, many individuals never develop HCC, suggesting a genetic susceptibility. However, only a few single nucleotide polymorphisms have been reproducibly shown to be linked to HCC onset to date. A variant (rs738409 C>G, encoding for p.I148M) in the PNPLA3 gene is associated with liver damage in chronic liver diseases. Interestingly, several studies have reported that the minor rs738409[G] allele is more represented in HCC cases in chronic hepatitis C (CHC) and alcoholic liver disease (ALD). However, a significant association with HCC related to CHC has not been consistently observed and the strength of the association between rs738409 and HCC remains unclear. We performed a meta-analysis of individual participant data including 2,503 European cirrhotic patients to assess the association between rs738409 and HCC, particularly in ALD and CHC. We found that rs738409 was strongly associated with overall HCC (OR per G allele, additive model= 1.77 95% CI [1.42-2.19], P= 2.78 x 10(-7) ).This association was more pronounced in ALD (OR=2.20 95% CI [1.80-2.67], P=4.71 x 10(-15) ) than in CHC patients (OR=1.55 95% CI [1.03-2.34], P= 3.52 x 10(-2) ). After adjustment for age, sex and body mass index the variant remained strongly associated with HCC. Conclusion: Overall, these results suggest that rs738409 exerts a marked influence on hepatocarcinogenesis in cirrhotic patients of European descent and provide a strong argument for performing further mechanistic studies to better understand the role of PNPLA3 in HCC development. (Hepatology 2013;).Hepatology 10/2013; · 12.00 Impact Factor
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ABSTRACT: Genetic linkage analyses, genome-wide association studies of single nucleotide polymorphisms, copy number variation surveys, and mutation screenings found the human chromosomal 12q24 locus, with the genes SH2B3 and ATXN2 in its core, to be associated with an exceptionally wide spectrum of disease susceptibilities. Hematopoietic traits of red and white blood cells (like erythrocytosis and myeloproliferative disease), autoimmune disorders (like type 1 diabetes, coeliac disease, juvenile idiopathic arthritis, rheumatoid arthritis, thrombotic antiphospholipid syndrome, lupus erythematosus, multiple sclerosis, hypothyroidism and vitiligo), also vascular pathology (like kidney glomerular filtration rate deficits, serum urate levels, plasma beta-2-microglobulin levels, retinal microcirculation problems, diastolic and systolic blood pressure and hypertension, cardiovascular infarction), furthermore obesity, neurodegenerative conditions (like the polyglutamine-expansion disorder spinocerebellar ataxia type 2, Parkinson's disease, the motor-neuron disease amyotrophic lateral sclerosis, and progressive supranuclear palsy), and finally longevity were reported. Now it is important to clarify, in which ways the loss or gain of function of the locally encoded proteins SH2B3/LNK and ataxin-2, respectively, contribute to these polygenic health problems. SH2B3/LNK is known to repress the JAK2/ABL1 dependent proliferation of white blood cells. Its null mutations in human and mouse are triggers of autoimmune traits and leukemia (acute lymphoblastic leukemia or chronic myeloid leukemia-like), while missense mutations were found in erythrocytosis-1 patients. Ataxin-2 is known to act on RNA-processing and trophic receptor internalization. While its polyglutamine-expansion mediated gain-of-function causes neuronal atrophy in human and mouse, its deletion leads to obesity and insulin resistance in mice. Thus, it is conceivable that the polygenic pathogenesis of type 1 diabetes is enhanced by an SH2B3-dysregulation-mediated predisposition to autoimmune diseases that conspires with an ATXN2-deficiency-mediated predisposition to lipid and glucose metabolism pathology.World journal of diabetes. 06/2014; 5(3):316-327.
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ABSTRACT: Metabolic syndrome (MetS) has become a health and financial burden worldwide. The MetS definition captures clustering of risk factors that predict higher risk for diabetes mellitus and cardiovascular disease. Our study hypothesis is that additional to genes influencing individual MetS risk factors, genetic variants exist that influence MetS and inflammatory markers forming a predisposing MetS genetic network. To test this hypothesis a staged approach was undertaken. (a) We analyzed 17 metabolic and inflammatory traits in more than 85,500 participants from 14 large epidemiological studies within the Cross Consortia Pleiotropy Group. Individuals classified with MetS (NCEP definition), versus those without, showed on average significantly different levels for most inflammatory markers studied. (b) Paired average correlations between 8 metabolic traits and 9 inflammatory markers from the same studies as above, estimated with two methods, and factor analyses on large simulated data, helped in identifying 8 combinations of traits for follow-up in meta-analyses, out of 130,305 possible combinations between metabolic traits and inflammatory markers studied. (c) We performed correlated meta-analyses for 8 metabolic traits and 6 inflammatory markers by using existing GWAS published genetic summary results, with about 2.5 million SNPs from twelve predominantly largest GWAS consortia. These analyses yielded 130 unique SNPs/genes with pleiotropic associations (a SNP/gene associating at least one metabolic trait and one inflammatory marker). Of them twenty-five variants (seven loci newly reported) are proposed as MetS candidates. They map to genes MACF1, KIAA0754, GCKR, GRB14, COBLL1, LOC646736-IRS1, SLC39A8, NELFE, SKIV2L, STK19, TFAP2B, BAZ1B, BCL7B, TBL2, MLXIPL, LPL, TRIB1, ATXN2, HECTD4, PTPN11, ZNF664, PDXDC1, FTO, MC4R and TOMM40. Based on large data evidence, we conclude that inflammation is a feature of MetS and several gene variants show pleiotropic genetic associations across phenotypes and might explain a part of MetS correlated genetic architecture. These findings warrant further functional investigation.Molecular Genetics and Metabolism 05/2014; · 2.83 Impact Factor
Genome-Wide Association Analysis of Soluble ICAM-1
Concentration Reveals Novel Associations at the NFKBIK,
PNPLA3, RELA, and SH2B3 Loci
Guillaume Pare ´1,2,3*, Paul M Ridker1,2, Lynda Rose1,2, Maja Barbalic4, Jose ´e Dupuis5,6, Abbas Dehghan7,8,
Joshua C. Bis9, Emelia J. Benjamin5,10,11, Dov Shiffman12, Alexander N. Parker13, Daniel I. Chasman1,2
1Center for Cardiovascular Disease Prevention, Brigham and Women’s Hospital, Harvard Medical School, Boston, Massachusetts, United States of America, 2Donald W.
Reynolds Center for Cardiovascular Research, Brigham and Women’s Hospital, Harvard Medical School, Boston, Massachusetts, United States of America, 3McMaster
University, Hamilton, Canada, 4Human Genetics Center and Institute of Molecular Medicine, University of Texas Health Science Center at Houston, Houston, Texas, United
States of America, 5National Heart, Lung, and Blood Institute’s and Boston University’s Framingham Heart Study, Framingham, Massachusetts, United States of America,
6Department of Biostatistics, Boston University School of Public Health, Boston, Massachusetts, United States of America, 7Department of Epidemiology, Erasmus
Medical Center, Rotterdam, The Netherlands, 8The Netherlands Consortium on Healthy Aging (NCHA), Leiden, The Netherlands, 9Cardiovascular Health Research Unit,
Department of Medicine, University of Washington, Seattle, Washington, United States of America, 10Section of Preventive Medicine and Epidemiology, Department of
Medicine, Boston University School of Medicine, Boston, Massachusetts, United States of America, 11Department of Epidemiology, Boston University School of Public
Health, Boston, Massachusetts, United States of America, 12Celera, Alameda, California, United States of America, 13Amgen, Cambridge, Massachusetts, United States of
Soluble ICAM-1 (sICAM-1) is an endothelium-derived inflammatory marker that has been associated with diverse conditions
such as myocardial infarction, diabetes, stroke, and malaria. Despite evidence for a heritable component to sICAM-1 levels,
few genetic loci have been identified so far. To comprehensively address this issue, we performed a genome-wide
association analysis of sICAM-1 concentration in 22,435 apparently healthy women from the Women’s Genome Health
Study. While our results confirm the previously reported associations at the ABO and ICAM1 loci, four novel associations
were identified in the vicinity of NFKBIK (rs3136642, P=5.461029), PNPLA3 (rs738409, P=5.861029), RELA (rs1049728,
P=2.7610216), and SH2B3 (rs3184504, P=2.9610217). Two loci, NFKBIB and RELA, are involved in NFKB signaling pathway;
PNPLA3 is known for its association with fatty liver disease; and SH3B2 has been associated with a multitude of traits and
disease including myocardial infarction. These associations provide insights into the genetic regulation of sICAM-1 levels
and implicate these loci in the regulation of endothelial function.
Citation: Pare ´ G, Ridker PM, Rose L, Barbalic M, Dupuis J, et al. (2011) Genome-Wide Association Analysis of Soluble ICAM-1 Concentration Reveals Novel
Associations at the NFKBIK, PNPLA3, RELA, and SH2B3 Loci. PLoS Genet 7(4): e1001374. doi:10.1371/journal.pgen.1001374
Editor: Michel Georges, University of Lie `ge, Belgium
Received May 27, 2010; Accepted March 15, 2011; Published April 21, 2011
Copyright: ? 2011 Pare ´ 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: This study was supported by grants from the National Heart, Lung, and Blood Institute and the National Cancer Institute (Bethesda, MD), the Donald W.
Reynolds Foundation (Las Vegas, NV), the Doris Duke Charitable Foundation, and the Foundation Leducq (Paris, FR), with collaborative scientific and genotypic
support provided by Amgen. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
Competing Interests: DS is an employee of Celera.
* E-mail: pareg@McMaster.ca
A member of the immunoglobulin superfamily of adhesion
receptors, ICAM-1 is expressed on endothelial cells where it serves
as a receptor for the leukocyte integrins LFA-1 and Mac-1 . A
soluble form of ICAM-1 (sICAM-1) is present in plasma and is
thought to arise from proteolytic cleavage of the extra-cellular
domains of ICAM-1. Although the physiologic function of soluble
ICAM-1 remains to be fully defined, plasma concentration of
sICAM-1 have a predictive value for the risk of myocardial
infarction, ischemic stroke, peripheral arterial disease and
noninsulin-dependent diabetes mellitus in epidemiological studies
We recently described a genome-wide association study of
sICAM-1 in 6,578 apparently healthy women from the Women’s
Genome Health Study (WGHS), which confirmed a known
association at the ICAM1 locus and identified a novel association
at the ABO locus . These results were subsequently replicated
in large-scale genomics studies from Barbalic  et al. and Qi 
et al. Nevertheless, the total variance explained by these
associations remained low (8.4%) as compared to the relatively
high heritability estimates (from 0.34 to 0.59) [8,9] for sICAM-1.
We therefore hypothesized that other, weaker, common genetic
determinants of sICAM-1 remained to be discovered. To explore
this issue, we performed a larger genome-wide association study
(GWAS), evaluating 334,295 SNPs in 22,435 apparently healthy
women of European ancestry from the WGHS.
We found that 67 SNPs passed our pre-specified threshold of
genome-wide significance of P,561028for association with
sICAM-1 (Table S1 and Figure 1A). These SNPs clustered within
5 loci in the vicinity of ABO (9q34.2), RELA (11q13.1), SH2B3
(12q24.12), ICAM1 (19p13.2) and PNPLA3 (22q13.31). The
ICAM1 [10,11] and ABO  loci have previously been identified
PLoS Genetics | www.plosgenetics.org1April 2011 | Volume 7 | Issue 4 | e1001374
as contributing to sICAM-1 levels, but the SH2B3, RELA and
PNPLA3 loci were not previously shown to be associated with
sICAM-1. The genomic context of these three latter loci is
illustrated in Figure 2A, 2B and 2C.
In order to determine whether more than one non-redundant
association signal could be detected at each of these five loci, we
applied a model selection algorithm. The SNP with the lowest P-
value for association was the only one retained at every locus with
the exception of the ICAM1 locus, where 5 SNPs were selected by
the model (Table 1). Interestingly, model selected SNPs at the
ICAM1 locus showed lower P-value when they were all included in a
single multivariate model than when considered separately. Three
of the model selected SNPs at the ICAM1 locus (rs281437,
rs1801714 and rs11575074) were not significant at a genome-wide
level of significance in a univariate analysis. We performed two
analyses to determine if the multiple SNPs selected at the ICAM1
locus were the result of an underlying association with a known but
untyped variant. First, we tested all imputed SNPs (using MACH)
within 1.5 Mb of rs1799969 (the lead SNP at that locus) for
association with adjusted sICAM-1 levels. No imputed SNP was
more significant than the directly genotyped rs1799969. Second, we
tested the same set of imputed SNPs after additional adjustment of
sICAM-1 levels for the effect of model selected SNPs. No additional
SNP was associated at genome-wide significance. The 5 SNPs at the
ICAM1 locus selected by our algorithm were also used in haplotype
analysis using WHAP , as implemented in PLINK 
(Table 2). The estimate of the proportion of variance attributable
to haplotypes, as well as their regression coefficients, is consistent
with the linear model of these same SNPs, reinforcing the adequacy
of an additive model to explain the association.
Next we tested whether any additional SNPs are associated with
sICAM-1 levels after adjustment for the model selected SNPs (see
Figure 1B). A single SNP was associated with sICAM-1 at genome-
wide significance (P=5.461029; 24.1 ng/mL per minor allele) in
the vicinity of the NFKBIB locus at 19q13.2 (Figure 2D). This SNP,
rs3136642, is intronic to NFKBIB and had a minor allele frequency
of 0.38.The modelselection algorithmretained no otherSNPat the
NFKBIB locus. Further adjustment of sICAM-1 values for
rs3136642 did not identify any additional SNP with genome-wide
significant association with sICAM-1. We also performed GWAS
analysis using imputed genotypes (using MACH). Because no new
locus reached genome-wide significance after adjustment for model
selected SNPs, only results of directly genotyped SNPs are
presented. These results were essentially unchanged when the first
10 components of a principal component analysis were included as
covariates to account for sub-Caucasian stratification. All 4 novel
loci identified in WGHS were replicated (one-sided P,0.05) in
9,813 individuals from the CHARGE consortium  (Table 3).
Collectively, the 5 SNPs at the ICAM1 gene locus explained
6.5% of sICAM1 total variance, whereas the other loci explained
from 0.1 to 1.4% of the variance. In comparison, clinical
covariates explained 19.5% of the variance (Table 4). For 4 of
the loci, there was no strong evidence for non-additive effects of
the minor allele as judged by lack of significance for a likelihood
ratio test comparing the additive regression model to an
alternative genotype model with an additional degree of freedom.
However, the non-additive component was significant for
rs507666 (P=9.361026) at the ABO locus with a tendency
toward a dominant effect (mean sICAM-1 of 362.1, 342.4 and
335.4 ng/mL for 0, 1 and 2 minor alleles, respectively). The
PNPLA3 SNP rs738409 also showed evidence of non-additive
association (P=4.661025) with a tendency toward a recessive
model (mean sICAM-1 of 352.8, 356.0 and 367.7 ng/mL for 0, 1
and 2 minor alleles, respectively). In spite of these non-additive
Figure 1. Quantile-quantile plot of association with sICAM-1. The quantile-quantile plot of sICAM-1 association P-values is shown on the left.
On the right, the same quantile-quantile plot is shown, but after adjusting sICAM-1 values for the 9 SNPs retained by the model selection algorithm.
Soluble Intercellular Adhesion Molecule 1 (sICAM-1) is an
inflammatory marker that has been associated with several
common diseases such as diabetes, heart disease, stroke,
and malaria. While it is known that blood concentrations of
sICAM-1 are at least partially genetically determined, our
current knowledge of which genes mediate this effect is
limited. Taking advantage of technologies allowing us to
interrogate genetic variation on a whole-genome basis, we
found that variation in the NFKBIK, PNPLA3, RELA, and
SH2B3 genes are important determinant of sICAM-1 blood
concentrations. The NFKBIB and RELA genes are involved in
regulation of inflammation. These observations are signif-
icant because this is the first report of genetic association
within these extensively studied inflammation genes. The
PNPLA3 gene has previously been associated with liver
disease, and the SH2B3 gene has been associated with a
multitude of traits including cardiovascular disease.
Extension of these associations to sICAM-1 adds to the
intriguing diversity of effects of these genes.
Association of sICAM-1 with 4 Novel Loci
PLoS Genetics | www.plosgenetics.org2April 2011 | Volume 7 | Issue 4 | e1001374
trends, no additional locus reached genome-wide significance
when a genotypic test, which does not assume an additive model of
association, was conducted.
Model selected SNPs were tested for association with other
available inflammation markers (C-reactive protein and fibrino-
gen). No significant association was noted (P.0.01) after adjusting
Figure 2. Genomic context of novel associations. Genomic context for each of the four novel loci with significant association with sICAM-1
concentration. (A) RELA locus (11q13.1); (B) SH2B3 locus (12q24.12); (C) PNPLA3 locus (22q13.31); and (D) NFKBIB locus (19q13.2). Upper panel: Genes
from RefSeq release 25. Only one isoform is shown when multiple splicing variants are known. Lower Panel: SNPs are shown according to their
physical location and –log10P-values for association with sICAM-1 (red dots). The red line represents the genome-wide significance threshold of
561028. Also shown is the genetic distance in cM from the lowest P-value SNP (light grey line) along with the position of recombination hotspots
(light grey vertical bars). Recombination rates and hotspots are based on HapMap data, as described by McVean et al.  and Winckler et al. .
Table 1. SNPs retained by the model selection algorithm.
rs5076669q34.2 135139.20.200.00072A intronABO -17.33.0E-91-16.8 4.2E-32
rs104972811q13.165177.7 0.060.79C39 UntranslatedRELA-11.5 2.7E-16-11.23.2E-88
rs3184504 12q24.12110369.00.49 0.01T coding-
ICAM1-24.9 1.3E-120-41.5 1.3E-15
rs5498 19p13.2 10256.7 0.430.13G coding-
ICAM1 13.8 5.7E-89 30.55.0E-249
rs1801714 19p13.210256.20.030.66A coding-
rs28143719p13.210258.20.29 0.48A intronICAM1-1.8 1.6E-02 7.6 7.2E-16
rs1157507419p13.210262.1 0.05 0.09A intronICAM5 7.31.8E-0611.01.2E-11
rs313664219q13.244090.30.38 0.49GIntron NFKBIB-3.8 7.9E-08 -4.15.4E-09
rs73840922q13.31 42656.10.220.00001G coding-
PNPLA3 4.95.8E-095.0 6.4E-10
Association of sICAM-1 with 4 Novel Loci
PLoS Genetics | www.plosgenetics.org3April 2011 | Volume 7 | Issue 4 | e1001374
for multiple hypothesis testing. Model selected SNPs were also
tested for association with incident cardiovascular events (myo-
cardial infarction, coronary revascularization, stroke and total
cardiovascular event) over a mean follow-up period of 14 years. A
Cox proportional hazard model was used adjusting for age at
study entry. Only the SH2B3 SNP rs3184504 was associated with
incident myocardial infarction (315 events), with each minor allele
increasing the risk (P=0.011; OR 1.23 95% CI 1.05–1.43). The
association remained significant after further adjustment for
sICAM-1 levels (P=0.028; OR 1.20 95% CI 1.02–1.41). Given
the known association of sICAM-1 with cardiovascular risk and
the association of selected SNPs with sICAM-1, we estimated the
power to detect an association between the SH2B3 SNP rs3184504
and myocardial infarction to be 6%, for alpha=0.05. In
comparison, power varied from 5% (rs281437) to 11% (rs5498)
for other SNPs. The PNPLA3 SNP rs738409 was tested for
association with triglyceride, LDL cholesterol, HDL cholesterol
and BMI as this gene is known to be involved in lipid metabolism
and association with BMI has been previously suggested . No
significant association was observed.
Since smoking accounts for a large fraction of the variation in
sICAM-1 levels, we tested associated SNPs for interaction with
smoking. A significant interaction was observed for the ICAM1
SNP rs1799969 (interaction P=1.661029) whereby current
smokers had a stronger genetic association, as we previously
reported . A novel interaction was also observed with the ABO
SNP rs507666, again with a stronger genetic association in current
smokers (P=0.0003). When restricting the GWAS analysis to
current smokers, an additional association was observed with
rs8034191 (P=3.561028). This latter SNP is located on
chromosome 15 near the nicotinic acetylcholine receptor subunit
genes CHRNA3 and CHRNA5. This locus is known to be
associated with smoking behavior [17,18] and rs8034191 has
recently been associated with smoking quantity . No novel
association was observed when restricting the GWAS analysis to
non-smokers after adjustment for the previously described loci.
We also tested whether multiple variants of individually weak
effect could contribute to sICAM-1 levels. In cross-validation
procedures, no increase in variance explained was observed when
using P-value cut-offs less significant than 1028for inclusion of
SNPs in gene scores (see Figure 3). In other words, selection of
SNPs on the basis of P-value alone was not able to identify more of
the genetic variance than could be explained by the SNPs with
association P-value ,1028.
Six loci – ABO, ICAM1, NFKBIK, PNPLA3, RELA and SH2B3 –
have been identified in this report for association with sICAM-1.
While the ABO  and ICAM1 [10,11] loci had been previously
reported, we extended the number of non-redundantly associated
variants at the ICAM1 locus by demonstrating association of
rs11575074 and rs1801714 in multivariate analysis along with the
known rs1799969, rs5498 and rs281437 SNPs . Neither
rs1801714 nor rs11575074 are predicted eQTL (http://eqtl.
uchicago.edu/Home.html), but rs1801714 is a missense variant
(P352L) and rs11575074 is located in a predicted binding site for
several transcription factors including PPARG . The NFKBIK,
PNPLA3, RELA and SH2B3 associations are novel. No strong
contribution of weakly associated variants was observed in the
polygene analysis whereby SNPs of varying statistical significance
were included in gene scores.
Nuclear factor kB (NF-kB) proteins are a family of transcription
factors involved in a number of physiological processes that
include cell survival, proliferation, and activation. The NF-kB
proteins (NFKB1 or NFKB2) are bound to REL, RELA, or RELB
to form the NF-kB complex. These complexes are typically
localized in the cytoplasm, where they are trapped by binding to
Table 2. Haplotype Analysis of rs1799969, rs1801714, rs5498, rs281437, and rs11575074 (19p13.2; ICAM1 locus).
HaplotypeFrequency Beta (ng/mL)
rs1799969rs1801714rs5498 rs281437 rs11575074
GGGGG 0.29 12.21
GGAGG 0.27 -18.29
Omnibus (5df) P-value ,102300.
Table 3. Replication of novel loci in CHARGE (N=9,813).
SNPNearest Gene Minor Allele in WGHSAllele Frequency Effect (log-sICAM-1)Standard ErrorP-value (one sided)
rs4802998* NFKBIBG 0.380.0070.0040.048
20.0630.016 3.7 E-5
*The NFKBIB SNP rs3136642 reported in WGHS was not available in CHARGE. Consequently, rs4802998 was chosen for replication as this SNP had the second strongest
association P-value at this locus in WGHS (p=1.361026).
Association of sICAM-1 with 4 Novel Loci
PLoS Genetics | www.plosgenetics.org4April 2011 | Volume 7 | Issue 4 | e1001374
IkB inhibitory proteins NFKBIA or NFKBIB. Upon inflammatory
simulation, IkB kinase A and B phosphorylate IkB inhibitory
proteins and mark them for degradation via the ubiquitination
pathway, thereby allowing activation of the NF-kappa-B complex.
Activated NF-kB complexes translocate into the nucleus and bind
to NF-kB DNA binding motifs. NF-kB triggers transcription of
various genes critical to inflammation, such as cytokines,
chemokines and cell adhesion molecules including ICAM1
[21,22]. Remarkably, two of the novel associations involve genes
physically interacting with NF-kB. No genetic interaction,
however, was noted between these two SNPs (data not shown).
Taken together, these results emphasize the importance of the
NFKB pathway in the regulation of sICAM-1 levels.
PNPLA3 encodes a protein of unknown function that belongs to
the patatin-like phospholipase family. Members of that family are
believed to complement hormone sensitive lipase for adipocyte
triacylglycerol lipase activity. The methionine allele of the
missense PNPLA3 SNP rs738409 (Ile148Met) has recently been
associated with increased hepatic fat levels, hepatic inflammation
and plasma levels of liver enzymes (traits linked to insulin
resistance and obesity) [23,24]. Nevertheless, rs738409 has been
shown not to be associated with insulin resistance  although a
previous study demonstrated an association with insulin secretion
in response to oral glucose tolerance test . Levels of the
inflammatory marker sICAM-1 are known to be correlated with
insulin resistance and obesity . Consistent with rs738409
modulating the response to insulin resistance and associated
phenotypes, the risk allele for fatty liver disease was associated with
increased sICAM-1 levels.
SH2B3 encodes Lnk, an adaptor protein that mediates the
interaction between extra-cellular receptors, such as the T-cell
receptor and the thrombopoietin receptor MPL, and intracellular
signaling pathways. Cells from Lnk-deficient mice show an
increased sensitivity to several cytokines and altered activation of
the RAS/MAPK pathway in response to IL3 and stem cell factor
. The same SH2B3 SNP rs3184504 identified in our study has
previously been associated with multiple other traits, including
blood pressure [27,28], blood eosinophil number , myocardial
infarction , celiac disease , type I diabetes , LDL-
cholesterol , asthma , blood platelet number ,
hemoglobin concentration  and hematocrit . Furthermore,
rs3184504 is a non-synonymous SNP (Arg262Trp) whose derived
allele (Trp) is part of a haplotype that has been suggested to have
been introduced 3,400 years ago and selectively swept in
European populations . The derived allele is the risk allele
for coronary artery disease and was the allele associated with
higher sICAM-1 concentration. Association of rs3184504 with
sICAM-1 further demonstrates the remarkable pleiotropy of that
genetic variant by extending its effect to endothelial cell adhesion
molecules. An interesting hypothesis is whether changes in
sICAM-1 are mediated through increased sub-clinical atheroscle-
rosis, but further studies will be needed to address this question.
In this report, we demonstrate genetic association of sICAM-1
with the ABO, ICAM1, NFKBIK, PNPLA3, RELA and SH2B3 loci.
These findings broaden our current knowledge of the genetic
architecture of sICAM-1 with identification of four novel loci. The
novel association at PNPLA3 reinforces the importance of insulin
resistance-related processes in the regulation of sICAM-1 levels.
The observed associations also provide evidence of functional
genetic variation at two genes – NFKBIK and RELA – well known
for their implication in the NF-kB pathway, therefore providing a
basis for the study of these polymorphisms in other conditions
where this same pathway is involved. The results also extend the
effect of the SH2B3 SNP rs3184504 to endothelial function.
All analyses were performed with approval of the institutional
review board of the Brigham and Women’s Hospital. All members of
the WGHS cohort were participants in the WHS who provided an
Figure 3. Polygene analysis. Variance explained (adjusted R2) by
gene scores using varying P-value thresholds for inclusion of SNPs. Each
P-value threshold was tested 5 times using a 5-fold cross-validation
Table 4. Variance explained.
Clinical covariates Age 0.0149
RELA rs1049728 0.00250.0025
SH2B3 rs31845040.0026 0.0026
Association of sICAM-1 with 4 Novel Loci
PLoS Genetics | www.plosgenetics.org5 April 2011 | Volume 7 | Issue 4 | e1001374
adequate baseline blood sample for plasma and DNA analysis and
who gave consent for blood-based analyses and long-term follow-up.
Study Sample and sICAM-1 Measurements
All participants in this study were part of the Women’s Genome
Health Study (WGHS) . Briefly, participants in the WGHS
include North American women from the Women’s Health Study
(WHS) with no prior history of cardiovascular disease, diabetes,
cancer, or other major chronic illness who also provided a baseline
blood sample at the time of study enrollment. For all WGHS
participants, EDTA anticoagulated plasma samples were collected at
baseline and stored in vapor phase liquid nitrogen (2170uC).
Circulating plasma sICAM-1 concentrations were determined using
a commercial ELISA assay (R&D Systems, Minneapolis, Minn.); the
assay used is known not to recognize the K56M (rs5491) variant of
ICAM-1  and the 82 Caucasian carriers of this mutation were
variation was 6.7% and the reported intra-individual coefficient of
review board of the Brigham and Women’s Hospital. Additional
clinical characteristics of this sample are provided in Table S2.
Samples were genotyped with the Infinium II technology from
Illumina. Either the HumanHap300 Duo-Plus chip or the
combination of the HumanHap300 Duo and I-Select chips was
used. In either case, the custom content was identical and
consisted of candidate SNPs chosen without regard to allele
frequency to increase coverage of genetic variation with impact on
biological function including metabolism, inflammation or cardio-
vascular diseases. Genotyping at 318,237 HumanHap300 Duo
SNPs and 45,571 custom content SNPs was attempted, for a total
of 363,808 SNPs. Genetic context for all annotations are derived
from human genome build 36.1 and dbSNP build 126.
SNPs with call rates ,90% were excluded from further analysis.
Likewise, all samples with percentage of missing genotypes higher
than 2% were removed. Among retained samples, SNPs were
further evaluated for deviation from Hardy-Weinberg equilibrium
using an exact method  and were excluded when the P-value
was lower than 1026. Samples were further validated by
comparison of genotypes at 44 SNPs that had been previously
ascertained using alternative technologies. SNPs with minor allele
frequency .1% in Caucasians were used for analysis. After quality
control, 334,295 SNPs were left for analysis.
Because population stratification can result in inflated type I
error in a GWAS, a principal component analysis using 1443
ancestry informative SNPs was performed using PLINK  to
confirm self-reported ancestry. Briefly, these SNPs were chosen
based on Fst .0.4 in HapMap populations (YRB, CEU,
CHB+JPT) and inter-SNP distance at least 500 kb in order to
minimize linkage disequilibrium. Different ethnic groups were
clearly distinguished with the two first components. 31 self-
identified Caucasian women were removed from analysis because
they did not cluster with other Caucasians, leaving 22,435 non-
diabetic participants with non-missing sICAM-1 information for
analysis. To rule out the possibility that residual stratification
within Caucasians was responsible for the associations observed, a
principal component analysis  was performed in Caucasians
(only) using 64,205 SNPs chosen to have pair-wise linkage
disequilibrium lower than r2=0.2. The first ten components were
then used as covariates in the association analysis. As adjustment
by these covariates did not change the conclusions, we present
analysis among Caucasian participants without further correction
for sub-Caucasian ancestry unless stated otherwise.
Plasma concentrations of sICAM-1 were adjusted for age,
smoking, menopause and body mass index using a linear
regression model in R to reduce the impact of clinical covariates
on sICAM-1 variance. The adjusted sICAM-1 values were then
tested for association with SNP genotypes by linear regression in
PLINK , assuming an additive contribution of each minor
allele. A conservative P-value cut-off of 561028was used to
correct for the roughly 1,000,000 independent statistical tests
thought to correspond to all the common genetic variation of the
human genome [40,41].
Model Selection Algorithm
To investigate whether more than one SNP in each locus is
independently associated with sICAM-1, a forward selection multiple
linear regression model was used. For each locus with at least one
genome-wide significant SNP (i.e. P,561028), all genotyped SNPs
within 1.5 Mb of the most significantly associated SNP and passing
quality control requirements were selected for potential inclusion in
our model. The forward selection algorithm then proceeded in two
steps. In the first step, all SNPs not yet included in the multiple
regression model were tested for association with sICAM-1. In step
multiple regression P-value was less than 561028. We then repeated
steps one and two, such that a single SNP was added to the multiple
regression model at each iteration. The algorithm was stopped when
no more SNP passed the P,561028requirement.
To test whether multiple genetic variants of individually weak
effect could explain a substantial fraction of sICAM-1 variance, we
performed a ‘‘polygene’’ experiment as previously described .
Briefly, we randomly divided our dataset in 5 equal parts. We then
tested SNPs for association with sICAM-1 using 4 out the 5 parts
and performed linkage disequilibrium pruning as implemented in
PLINK (r2.0.05 and distance ,1 Mb). We then derived a gene
score with non-redundant associated SNPs using varying P-value
thresholds and weighting each SNP for its beta coefficient. Finally,
we tested the gene score for association with sICAM-1 in the
remaining one fifth of the total sample and calculated the adjusted
R2. This experiment was repeated 5 times using each one of the
five parts as the gene score validation group alternatively.
Replication of Novel Associations in CHARGE
We sought to replicate the 4 novel loci identified in 9,813
individuals from the Cohorts for Heart and Aging Research in
Genome Epidemiology (CHARGE) consortium  for whom
plasma sICAM-1 concentration and genotypes were available.
The CHARGE sample consists of 4 meta-analyzed cohorts: the
Framingham Heart Study, the Cardiovascular Health Study, the
Atherosclerosis Risk in Communities study, and the Rotterdam
Study. Complete information on each study is available as Text
S1. Association analyses were performed on imputed genotypes
using an additive genetic model on age and sex adjusted log-
transformed sICAM-1 values.
Found at: doi:10.1371/journal.pgen.1001374.s001 (0.12 MB
Genome-wide significant associations with sICAM-1.
Association of sICAM-1 with 4 Novel Loci
PLoS Genetics | www.plosgenetics.org6 April 2011 | Volume 7 | Issue 4 | e1001374
Found at: doi:10.1371/journal.pgen.1001374.s002 (0.03 MB
Clinical characteristics of WGHS.
Found at: doi:10.1371/journal.pgen.1001374.s003 (0.05 MB
Description of study cohorts.
Conceived and designed the experiments: GP PMR ANP DIC. Performed
the experiments: GP ANP DIC. Analyzed the data: GP LR MB.
Contributed reagents/materials/analysis tools: GP PMR MB JD AD
JCB EJB DS ANP DIC. Wrote the paper: GP.
1. van de Stolpe A, van der Saag PT (1996) Intercellular adhesion molecule-1.
J Mol Med 74: 13–33.
2. Ridker PM, Hennekens CH, Roitman-Johnson B, Stampfer MJ, Allen J (1998)
Plasma concentration of soluble intercellular adhesion molecule 1 and risks of
future myocardial infarction in apparently healthy men. Lancet 351: 88–92.
3. Pradhan AD, Rifai N, Ridker PM (2002) Soluble intercellular adhesion
molecule-1, soluble vascular adhesion molecule-1, and the development of
symptomatic peripheral arterial disease in men. Circulation 106: 820–825.
4. Song Y, Manson JE, Tinker L, Rifai N, Cook NR, et al. (2007) Circulating levels
of endothelial adhesion molecules and risk of diabetes in an ethnically diverse
cohort of women. Diabetes 56: 1898–1904.
5. Pare G, Chasman DI, Kellogg M, Zee RY, Rifai N, et al. (2008) Novel
association of ABO histo-blood group antigen with soluble ICAM-1: results of a
genome-wide association study of 6,578 women. PLoS Genet 4: e1000118.
6. Barbalic M, Dupuis J, Dehghan A, Bis JC, Hoogeveen RC, et al. (2010) Large-
scale genomic studies reveal central role of ABO in sP-selectin and sICAM-1
levels. Hum Mol Genet 19: 1863–1872.
7. Qi L, Cornelis MC, Kraft P, Jensen M, van Dam RM, et al. (2010) Genetic
variants in ABO blood group region, plasma soluble E-selectin levels and risk of
type 2 diabetes. Hum Mol Genet 19: 1856–1862.
8. Bielinski SJ, Pankow JS, Foster CL, Miller MB, Hopkins PN, et al. (2007)
Circulating soluble ICAM-1 levels shows linkage to ICAM gene cluster region
on chromosome 19: The NHLBI Family Heart Study follow-up examination.
9. Kent JW, Jr., Mahaney MC, Comuzzie AG, Goring HH, Almasy L, et al. (2007)
Quantitative trait locus on Chromosome 19 for circulating levels of intercellular
adhesion molecule-1 in Mexican Americans. Atherosclerosis 195: 367–373.
10. Ponthieux A, Lambert D, Herbeth B, Droesch S, Pfister M, et al. (2003)
Association between Gly241Arg ICAM-1 gene polymorphism and serum
sICAM-1 concentration in the Stanislas cohort. Eur J Hum Genet 11: 679–686.
11. Puthothu B, Krueger M, Bernhardt M, Heinzmann A (2006) ICAM1 amino-
acid variant K469E is associated with paediatric bronchial asthma and elevated
sICAM1 levels. Genes Immun 7: 322–326.
12. Purcell S, Daly MJ, Sham PC (2007) WHAP: haplotype-based association
analysis. Bioinformatics 23: 255–256.
13. Purcell S, Neale B, Todd-Brown K, Thomas L, Ferreira MA, et al. (2007)
PLINK: a tool set for whole-genome association and population-based linkage
analyses. Am J Hum Genet 81: 559–575.
14. Psaty BM, O’Donnell CJ, Gudnason V, Lunetta KL, Folsom AR, et al. (2009)
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.
15. Johansson LE, Lindblad U, Larsson CA, Rastam L, Ridderstrale M (2008)
Polymorphisms in the adiponutrin gene are associated with increased insulin
secretion and obesity. Eur J Endocrinol 159: 577–583.
16. Pare G, Cook NR, Ridker PM, Chasman DI (2010) On the use of variance per
genotype as a tool to identify quantitative trait interaction effects: a report from
the Women’s Genome Health Study. PLoS Genet 6: e1000981.
17. Thorgeirsson TE, Gudbjartsson DF, Surakka I, Vink JM, Amin N, et al. (2010)
Sequence variants at CHRNB3-CHRNA6 and CYP2A6 affect smoking
behavior. Nat Genet 42: 448–453.
18. Tobacco and Genetics Consortium (2010) Genome-wide meta-analyses identify
multiple loci associated with smoking behavior. Nat Genet 42: 441–447.
19. Liu JZ, Tozzi F, Waterworth DM, Pillai SG, Muglia P, et al. (2010) Meta-
analysis and imputation refines the association of 15q25 with smoking quantity.
Nat Genet 42: 436–440.
20. Xu Z, Taylor JA (2009) SNPinfo: integrating GWAS and candidate gene
information into functional SNP selection for genetic association studies. Nucleic
Acids Res 37: W600–605.
21. Collins T, Read MA, Neish AS, Whitley MZ, Thanos D, et al. (1995)
Transcriptional regulation of endothelial cell adhesion molecules: NF-kappa B
and cytokine-inducible enhancers. FASEB J 9: 899–909.
22. Ledebur HC, Parks TP (1995) Transcriptional regulation of the intercellular
adhesion molecule-1 gene by inflammatory cytokines in human endothelial cells.
Essential roles of a variant NF-kappa B site and p65 homodimers. J Biol Chem
23. Romeo S, Kozlitina J, Xing C, Pertsemlidis A, Cox D, et al. (2008) Genetic
variation in PNPLA3 confers susceptibility to nonalcoholic fatty liver disease.
Nat Genet 40: 1461–1465.
24. Yuan X, Waterworth D, Perry JR, Lim N, Song K, et al. (2008) Population-
based genome-wide association studies reveal six loci influencing plasma levels of
liver enzymes. Am J Hum Genet 83: 520–528.
25. Kantartzis K, Peter A, Machicao F, Machann J, Wagner S, et al. (2009)
Dissociation between fatty liver and insulin resistance in humans carrying a
variant of the patatin-like phospholipase 3 gene. Diabetes 58: 2616–2623.
26. Velazquez L, Cheng AM, Fleming HE, Furlonger C, Vesely S, et al. (2002)
Cytokine signaling and hematopoietic homeostasis are disrupted in Lnk-deficient
mice. J Exp Med 195: 1599–1611.
27. Newton-Cheh C, Johnson T, Gateva V, Tobin MD, Bochud M, et al. (2009)
Genome-wide association study identifies eight loci associated with blood
pressure. Nat Genet.
28. Levy D, Ehret GB, Rice K, Verwoert GC, Launer LJ, et al. (2009) Genome-
wide association study of blood pressure and hypertension. Nat Genet.
29. Gudbjartsson DF, Bjornsdottir US, Halapi E, Helgadottir A, Sulem P, et al.
(2009) Sequence variants affecting eosinophil numbers associate with asthma
and myocardial infarction. Nat Genet 41: 342–347.
30. Hunt KA, Zhernakova A, Turner G, Heap GA, Franke L, et al. (2008) Newly
identified genetic risk variants for celiac disease related to the immune response.
Nat Genet 40: 395–402.
31. Todd JA, Walker NM, Cooper JD, Smyth DJ, Downes K, et al. (2007) Robust
associations of four new chromosome regions from genome-wide analyses of type
1 diabetes. Nat Genet 39: 857–864.
32. Talmud PJ, Drenos F, Shah S, Shah T, Palmen J, et al. (2009) Gene-centric
association signals for lipids and apolipoproteins identified via the HumanCVD
BeadChip. Am J Hum Genet 85: 628–642.
33. Soranzo N, Spector TD, Mangino M, Kuhnel B, Rendon A, et al. (2009) A
genome-wide meta-analysis identifies 22 loci associated with eight hematological
parameters in the HaemGen consortium. Nat Genet 41: 1182–1190.
34. Ganesh SK, Zakai NA, van Rooij FJ, Soranzo N, Smith AV, et al. (2009)
Multiple loci influence erythrocyte phenotypes in the CHARGE Consortium.
Nat Genet 41: 1191–1198.
35. Ridker PM, Chasman DI, Zee RY, Parker A, Rose L, et al. (2008) Rationale,
Design, and Methodology of the Women’s Genome Health Study: A Genome-
Wide Association Study of More Than 25 000 Initially Healthy American
Women. Clin Chem 54: 249–255.
36. Register TC, Burdon KP, Lenchik L, Bowden DW, Hawkins GA, et al. (2004)
Variability of serum soluble intercellular adhesion molecule-1 measurements
attributable to a common polymorphism. Clin Chem 50: 2185–2187.
37. Eschen O, Christensen JH, Dethlefsen C, Schmidt EB (2008) Cellular Adhesion
Molecules in Healthy Subjects: Short Term Variations and Relations to Flow
Mediated Dilation. Biomark Insights 3: 57–62.
38. Wigginton JE, Cutler DJ, Abecasis GR (2005) A note on exact tests of Hardy-
Weinberg equilibrium. Am J Hum Genet 76: 887–893.
39. Price AL, Patterson NJ, Plenge RM, Weinblatt ME, Shadick NA, et al. (2006)
Principal components analysis corrects for stratification in genome-wide
association studies. Nat Genet 38: 904–909.
40. Frazer KA, Ballinger DG, Cox DR, Hinds DA, Stuve LL, et al. (2007) A second
generation human haplotype map of over 3.1 million SNPs. Nature 449:
41. Pe’er I, Yelensky R, Altshuler D, Daly MJ (2008) Estimation of the multiple
testing burden for genomewide association studies of nearly all common variants.
Genet Epidemiol 32: 381–385.
42. Purcell SM, Wray NR, Stone JL, Visscher PM, O’Donovan MC, et al. (2009)
Common polygenic variation contributes to risk of schizophrenia and bipolar
disorder. Nature 460: 748–752.
43. McVean GA, Myers SR, Hunt S, Deloukas P, Bentley DR, et al. (2004) The
fine-scale structure of recombination rate variation in the human genome.
Science 304: 581–584.
44. Winckler W, Myers SR, Richter DJ, Onofrio RC, McDonald GJ, et al. (2005)
Comparison of fine-scale recombination rates in humans and chimpanzees.
Science 308: 107–111.
Association of sICAM-1 with 4 Novel Loci
PLoS Genetics | www.plosgenetics.org7 April 2011 | Volume 7 | Issue 4 | e1001374