Carotid intima-media thickness, determined by high-resolution ultrasound techniques, is a well-established marker of subclinical atherosclerosis that has been shown to predict incident coronary and cerebrovascular events and is frequently used in both academic research and clinical trials. Genetic association studies of carotid intima-media thickness in individuals free of manifest cardiovascular disease may identify genes involved in early phases of disease, which may be less readily discernible in studies of late-stage disease. In the present study, we performed a genetic association analysis of carotid intima-media thickness in 3430 participants of the pan-European population-based IMPROVE study, who have undergone uniquely extensive ultrasound examinations of the carotid tree. We used a custom genotyping array (the Illumina CardioMetabochip), which interrogates genomic regions identified by previous genome-wide association studies of metabolic and cardiovascular disease. In the second stage, we conducted replication studies and subsequently tested 1 locus for association with coronary artery disease risk and with mRNA expression of nearby genes in vascular tissue biobanks. We thus identified a locus on chromosome 16, containing the BCAR1, CFDP1, and TMEM170A genes, as a novel genetic determinant of carotid intima-media thickness and coronary artery disease risk. Of these genes, BCAR1 has the most plausible role in atherogenesis based on current literature, whereas TMEM170A, which was highlighted by the biobank expression analysis, is an intriguing lead because its biological function is not known. Thus, this study has identified a novel genetic locus of importance for atherosclerosis-related disease, indicating specific genes that merit further research, results of which may become of future clinical use.
Identification of the BCAR1-CFDP1-TMEM170A Locus as a Determinant of Carotid Intima-Media Thickness and Coronary Artery Disease Risk
Circulation: Cardiovascular Genetics
Abstract
Background—
Carotid intima-media thickness (cIMT) is a widely accepted marker of subclinical atherosclerosis. To date, large-scale investigations of genetic determinants of cIMT are sparse.
Methods and Results—
To identify cIMT-associated genes and genetic variants, a discovery analysis using the Illumina 200K CardioMetabochip was conducted in 3430 subjects with detailed ultrasonographic determinations of cIMT from the IMPROVE (Carotid Intima Media Thickness [IMT] and IMT-Progression as Predictors of Vascular Events in a High Risk European Population) study. Segment-specific IMT measurements of common carotid, bifurcation, and internal carotid arteries, and composite IMT variables considering the whole carotid tree (IMTmean, IMTmax, and IMTmean-max), were analyzed. A replication stage investigating 42 single-nucleotide polymorphisms for association with common carotid IMT was undertaken in 5 independent European cohorts (total n=11 590). A locus on chromosome 16 (lead single-nucleotide polymorphism rs4888378, intronic in CFDP1) was associated with cIMT at significance levels passing multiple testing correction at both stages (array-wide significant discovery P=6.75×10−7 for IMTmax; replication P=7.24×10−6 for common cIMT; adjustments for sex, age, and population substructure where applicable; minor allele frequency 0.43 and 0.41, respectively). The protective minor allele was associated with lower carotid plaque score in a replication cohort (P=0.04, n=2120) and lower coronary artery disease risk in 2 case-control studies of subjects with European ancestry (odds ratio [95% confidence interval] 0.83 [0.77–0.90], P=6.53×10−6, n=13 591; and 0.95 [0.92–0.98], P=1.83×10−4, n=82 297, respectively). Queries of human biobank data sets revealed associations of rs4888378 with nearby gene expression in vascular tissues (n=126–138).
Conclusions—
This study identified rs4888378 in the BCAR1-CFDP1-TMEM170A locus as a novel genetic determinant of cIMT and coronary artery disease risk in individuals of European descent.
Introduction
Carotid intima-media thickness (cIMT), as determined by high-resolution ultrasound techniques, is a well-established marker of subclinical atherosclerosis and is widely used in epidemiological studies and interventional trials.1,2 It has been proposed as a surrogate marker for coronary atherosclerosis and has been shown to predict incident coronary and cerebrovascular events.3–5 Accordingly, cIMT constitutes an attractive quantitative intermediate disease phenotype for the study of atherosclerosis-related cardiovascular disease. Genetic association studies of cIMT, conducted in individuals free of manifest disease, may identify susceptibility genes and pathways involved in the initiation and early phases of disease, which may be less readily discernible in studies of late-stage and clinically manifest disease such as myocardial infarction (MI) and stroke. Nevertheless, large-scale studies of genetic determinants of cIMT remain sparse.
Clinical Perspective on p 665
To date, 1 meta-analysis of single-nucleotide polymorphism (SNP)-based genome-wide association (GWA) studies of cIMT has been reported, which identified 3 regions (on chromosomes 8q23.1, 8q24, and 19q13) as being associated with common carotid IMT (CC-IMT).6 In contrast, candidate gene studies of cIMT have provided inconsistent results,7 and 2 genome-wide linkage scans only found regions with suggestive linkage to cIMT.8,9 In the present study, we performed a discovery genetic association analysis of cIMT in 3430 participants of the Pan-European population-based IMPROVE study10 using a custom genotyping array (the Illumina CardioMetabochip, also referred to as the Metabochip). The CardioMetabochip interrogates ≈200 000 SNPs located in regions identified by previous GWA studies of metabolic and cardiovascular traits and diseases. In the second stage, we conducted replication studies in 11 590 participants from 5 independent population-based cohorts. One robustly associated cIMT locus was subsequently tested in silico for association with coronary artery disease (CAD) risk in 2 large independent GWA studies of CAD and with mRNA expression of nearby genes in vascular tissues collected in 2 biobank programmes.
Methods
Cohorts and Study Design
The first stage consisted of genetic association analysis of cIMT measurements in the IMPROVE study (n=3430).10 SNPs that passed an a priori threshold for statistical association (P<1×10−4 or P<1×10−5 depending on cIMT phenotype) were then taken forward for replication in the Whitehall-II study (n=2138),11,12 the Edinburgh Artery Study (n=630),13 the Rotterdam-I and Rotterdam-II studies (RS-I and RS-II, n=4699 and n=1980, respectively),14,15 and the cardiovascular arm of the Malmö Diet and Cancer (MDC, n=2141) study,16,17 with subsequent evaluation of results by meta-analysis. Detailed descriptions of the discovery and replication cohorts are given in online-only Data Supplement Section S1 and Table SI. A locus that reached significance levels passing correction for multiple testing at both the discovery and replication stages was further tested for association with carotid plaque score in the MDC study, with coronary artery calcium (CAC) score in the RS-I and RS-II studies, and for association with CAD risk in PROCARDIS,18 a large European CAD case-control study to which additional controls were added from the Wellcome Trust Case-Control Consortium, in total 5710 cases and 7881 controls, and in CARDIOGRAM,19 a large CAD case-control GWA study meta-analysis consortium comprising 22 233 CAD cases and 64 762 controls of European descent. In addition to replication in independent cohorts, complementary internal validation by bootstrap analyses was undertaken in IMPROVE to corroborate findings from the discovery stage in relation to IMT phenotypes which were not available in the replication cohorts. The replicated cIMT locus was also tested for association with mRNA expression of nearby genes in vascular tissues collected in the Advanced Study of Aortic Pathology and the Biobank of Karolinska Endarterectomies studies20 to explore potential mechanisms underlying the observed cIMT and CAD associations.
| Composite IMT Discovery (n=3428–3429) | CCA-IMT Discovery (n=3427–3429) | CCA-IMT Replication (n=11 585–11 587) | |||||||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Heterogeneity | |||||||||||||||||
| SNP | Nearest Gene | Chr | Pos | Alleles Effect/Other | Freq | β | SE | P | β | SE | P | Freq | β | SE | P | I2 | Pheterogeneity |
| rs4888378 | CFDP1 | 16 | 73889542 | A/G | 0.43 | −0.0192 | 0.0038 | 6.75E−07 | −0.0071 | 0.0028 | 0.010 | 0.41 | −0.0045 | 0.0010 | 7.24E−06 | 0 | 0.784 |
| rs1001861 | BCAR1 | 16 | 73863956 | G/A | 0.35 | −0.0178 | 0.0040 | 7.16E−06 | −0.0064 | 0.0028 | 0.025 | 0.37 | −0.0033 | 0.0011 | 0.002 | 0 | 0.835 |
| rs200991 | HIST1H2BN | 6 | 27923473 | A/C | 0.12 | 0.0136 | 0.0030 | 8.83E−06 | 0.0087 | 0.0028 | 0.002 | 0.16 | 0.0032 | 0.0013 | 0.015 | 41.6 | 0.144 |
Discovery and replication meta-analysis probability values and β coefficients for the effect allele (minor allele in the discovery cohort) are shown after adjustments for sex, age, and population substructure when applicable (multidimensional scaling in the IMPROVE discovery cohort only). Observations are sorted according to discovery probability value.
SNP indicates single-nucleotide polymorphism; Chr, chromosome; and Freq, frequency.
Between-cohort heterogeneity is described by I2 in percent and Q test probability values. Chromosome positions are given according to NCBI Build 36.
Genotyping and Quality Control
A description of the genotyping technologies used for the discovery and replication cohorts along with quality control criteria is provided in online-only Data Supplement Section S2 and Table SI. Genotyping in IMPROVE, Whitehall-II, Edinburgh Artery Study, and MDC was performed using the Illumina 200K CardioMetabochip, whereas the RS-I and RS-II studies were genotyped using the Ilumina HumanHap550 array and imputed to the 1000 Genomes CEU Caucasian reference panel.19 The CardioMetabochip is a custom Illumina iSelect genotyping array that captures DNA variation at regions identified by meta-analyses of GWA studies for diseases and traits relevant to metabolic and atherosclerotic/cardiovascular end points, comprising ≈200 000 SNPs. In IMPROVE, individual-level exclusion criteria were call rates <0.95, results of identity by state estimations (eg, unverified cryptic relatedness), verified relatedness, estimated inbreeding (excessive homozygosity), discrepancy between recorded and genotype-determined sex, outliers in multidimensional scaling (MDS) analysis (online-only Data Supplement Section 2 and Figure SI), and self-reported non-Caucasian ethnicity. Exclusion criteria for SNPs were genotype call rates <0.90, deviation from Hardy-Weinberg equilibrium (P<5×10−7), and minor allele frequency (MAF) <0.005. After quality control procedures, 3430 individuals and 127 830 autosomal SNPs were included in the discovery association analysis in IMPROVE.
Ultrasonographic Measurements
The carotid ultrasound protocol applied in IMPROVE and the precision of the ultrasonographic measurements have been reported.10 In brief, measurements were made of the mean and maximum IMT of the common carotid at the first cm proximal to the bifurcation (CC-1st cm-IMTmean and CC-1st cm-IMTmax) and in a segment excluding the first cm proximal to the bifurcation (CC-IMTmean and CC-IMTmax), of the bifurcation (Bif-IMTmean and Bif-IMTmax), and of the internal carotid arteries (ICA-IMTmean and ICA-IMTmax). Composite IMT variables considering the whole carotid tree were derived from the segment-specific measurements: IMTmean, IMTmax, and IMTmean-max (the average of IMT maxima recorded at the different segments).10 Measures of CC-IMT were available in all replication cohorts. In addition, Bif-IMTmax was measured, and a 6-level carotid plaque score was generated in the MDC cohort (online-only Data Supplement Section S1 and Table SI).
Statistical Analyses of the Discovery and Replication Cohorts
All cIMT variables were logarithmically transformed before statistical analysis because of skewed distributions. Association analysis was performed using linear regression, adjusting for age and sex, under the assumption of additive genetic effects using PLINK version 1.07.21 For IMPROVE, the first 3 MDS dimensions (based on CardioMetabochip genotype data) were used to adjust for identified population substructure (Section S2 and Figure SI in the online-only Data Supplement). The a priori threshold for array-wide statistical significance was established as P<8.39×10−7 through estimation of the total number of uncorrelated SNPs on the CardioMetabochip (online-only Data Supplement Section S3). Selection of index SNPs for replication from candidate SNPs that obtained an a priori level for statistical association in the discovery analysis (set to P<10−4 for segment-specific IMT measurements and P<10−5 for composite IMT variables) was performed by the PLINK clump procedure. A linkage disequilibrium threshold of r2 >0.8 within a physical distance of 500 kilobases (kb) was used in the clump procedure.
Power calculations for the replication studies indicated that it would be justifiable to take up to 50 uncorrelated SNPs forward to replication (online-only Data Supplement Section S4).
The replication stage comprised 2 approaches. The first approach involved meta-analysis of the replication cohorts, both separately from and jointly with IMPROVE, using a fixed-effect model with inverse variance weighting as applied in Metal (version 25 March 2011).22 Pooled regression coefficients with corresponding 95% confidence intervals and probability values were calculated. Two IMT phenotypes were studied in the replication stage. Primarily, 39 SNPs associated with CC-IMT variables (CC-1st cm-IMTmean, CC-1st cm-IMTmax, CC-IMTmean, and CC-IMTmax) and 3 SNPs associated with composite IMT variables (IMTmean, IMTmax, and IMTmean-max) in IMPROVE were examined in relation to CC-IMT in the Whitehall-II, Edinburgh Artery Study, MDC, RS-I, and RS-II cohorts (total n=11 590). Additionally, 26 SNPs associated with Bif-IMT in IMPROVE were investigated in relation to Bif-IMT in the MDC cohort (n=1690).
Given that the composite IMT measures were unique to IMPROVE and thereby not assessable by conventional replication in independent cohorts, we applied nonparametric bootstrap resampling to perform internal validation.23 In brief, this method uses a weighted average over bootstrap replicates of the difference between the effect size estimated from the observations in the bootstrap sample and the one estimated from the observations not in the bootstrap sample to estimate the overestimation of the effect sizes of the most significant SNPs (online-only Data Supplement Section S5).
Secondary Analyses
Regional plots of associations from the original discovery analysis and adjusted analysis where the lead SNP was included as a covariate in the regression model were generated using LocusZoom (http://csg.sph.umich.edu/locuszoom/). Secondary analyses to assess potential pleiotropy and possible confounders were performed by investigating genotype associations with biochemical and clinical parameters (including 13 variables reflecting established cardiovascular risk factors: waist circumference, systolic and diastolic blood pressure, pulse pressure, hypertension, low-density lipoprotein-cholesterol, high-density lipoprotein-cholesterol, triglycerides, blood glucose, diabetes mellitus, C-reactive protein, statin treatment, and cumulative life-time smoking expressed as a 5-level categorical variable according to never-smoker status and quartiles of pack-years), and by further adjustments of the original model (adjusted for sex, age, and MDS) using PASW Statistics version 18.
CAD Case-Control Studies
To determine whether a locus robustly associated with cIMT is also implicated in the pathogenesis of clinically manifested CAD, we explored the lead SNP identified at the level of array-wide statistical significance in relation to cIMT (rs4888378) for associations with CAD in the PROCARDIS and CARDIOGRAM case-control studies.18,19 Design features and details of genotyping, quality control, and statistical analyses are provided in online-only Data Supplement Sections S1 and S6.
Association With CAC Score
Expression Quantitative Trait Locus Studies
In silico analyses of genotype-gene expression-level associations were conducted in the Advanced Study of Aortic Pathology and Biobank of Karolinska Endarterectomies data sets. Details of design features and methods for the Advanced Study of Aortic Pathology and Biobank of Karolinska Endarterectomies have been reported.20 In the Advanced Study of Aortic Pathology, mRNA extracted from biopsies of ascending thoracic aorta intima-media (n=138), aortic adventitia (n=133), mammary artery (n=89), heart (n=127), and liver (n=211) from patients undergoing aortic valve surgery20 was analyzed with Affymetrix ST 1.0 Exon arrays. In the Biobank of Karolinska Endarterectomies study, RNA extracted from human plaque tissue (n=126) and peripheral blood mononuclear cells (n=96) obtained from patients referred for surgical treatment of severe carotid artery stenosis was analyzed with Affymetrix HG-U133 plus 2.0 Genechip arrays.20 Robust Multichip Average normalization was performed as implemented in the Affymetrix Power Tools 1.10.2 package apt-probeset-summarize, and processed gene expression data were returned in a log2-scale.20 For both studies, blood-derived DNA had been genotyped with Illumina 610w-Quad BeadArrays. Genotype-gene expression associations were investigated using an additive model.20
Results
Discovery Analysis
A total of 3430 subjects (54–79 years of age, 48% males) from the IMPROVE study were included in the discovery analysis. Basic characteristics of IMPROVE study participants are shown in online-only Data Supplement Table SI. Because MDS analysis revealed significant population substructure in IMPROVE (online-only Data Supplement Figure SI), adjustment for the first 3 MDS dimensions, in addition to age and sex, was performed in all SNP association analyses with cIMT measures. Associations with segment-specific mean and maximum IMT were investigated as well as composite IMT variables reflecting the whole carotid tree (Figure 1 and online-only Data Supplement Figure SIIA–IIK). One locus on chromosome 16 (lead SNP rs4888378, MAF=0.43, P=6.75×10−7, β [SE]= −0.019 [0.004] versus IMTMax for the minor A allele) passed the array-wide significance threshold of P<8.39×10−7 (Figure 1). Rs4888378 is located in the last 3′ intron of the CFDP1 gene (encoding cranio-facial development protein-1). A Q-Q plot of observed versus expected probability values from the analysis of IMTmax is presented as an insert in Figure 1. The genomic inflation factor (λ) in the analysis of IMTmax was 1.05, indicating an adequate correction of population substructure by the MDS adjustment.
SNPs associated with any of the 4 CC-IMT measures at P<1×10−4 (a set of 46 SNPs) or any composite IMT measure at P<1×10−5 (a set of 4 SNPs) were considered as candidates for replication with respect to CC-IMT in independent cohorts (ie, 50 SNPs). From these, index SNPs were selected using the PLINK clump procedure, performed once for each set of candidate SNPs, resulting in 39 index SNPs selected among the 46 SNPs that were associated with CC-IMT measures at P<1×10−4 (designated CC-IMT SNPs, online-only Data Supplement Table SII), and 3 index SNPs selected among the 4 SNPs that were associated with composite IMT measures at P<1×10−5 (Table 1) (designated composite IMT SNPs). Similarly, 26 index SNPs associated with either of the 2 Bif-IMT measures (designated Bif-IMT SNPs) were generated from 52 candidate SNPs associated at P<1×10−4 (not shown). For SNPs that passed the initial significance criteria for >1 cIMT variable of the same category (CC-IMT, Bif-IMT, or composite IMT, respectively), the most significant association was considered for the clump procedure.
Replication and Internal Validation
A replication stage investigating associations with CC-IMT of the identified 39 CC-IMT SNPs and 3 composite IMT SNPs was undertaken in 5 independent population-based cohorts from Sweden, the United Kingdom, and the Netherlands (total n=11 590). Basic characteristics of participants in the replication cohorts are shown in online-only Data Supplement Table SI. In the replication meta-analysis of the CC-IMT SNPs, no SNP was significantly associated with CC-IMT (Bonferroni correction for 42 independent tests P<0.0012; lowest observed P=0.007; Table SII in the online-only Data Supplement). In contrast, the association of the composite IMT SNP rs4888378 with CC-IMT (P=7.24×10−6; Table 1) passed Bonferroni correction in meta-analysis of the replication cohorts (ie, P<0.0012). This index SNP also achieved array-wide significance in combined meta-analysis of IMPROVE and replication cohorts (P=3.51×10−7). The associations of rs4888378 with CC-IMT in all individual cohorts and in the meta-analyses are illustrated in Figure 2. The 2 other composite IMT SNPs that were selected at the P<1×10−5 level reached nominal significance in the replication cohorts (Table 1). None of the Bif-IMT index SNPs was significantly associated with Bif-IMT in the MDC cohort after Bonferroni correction for 26 independent tests (all P>0.0019). The internal validation of the 3 composite IMT SNPs by nonparametric double bootstrap confirmed significant associations with composite IMT (bootstrap β with 98.33% confidence intervals to account for analysis of 3 independent SNPs: −0.0094 [−0.0200, −0.0034] for rs4888378, −0.0083 [−0.0166, −0.0035] for rs1001861, and 0.0056 [0.0018, 0.0131] for rs200991; online-only Data Supplement Figure SIII).
Secondary Analyses in the Discovery Cohort
The replicated lead SNP rs4888378 in the chromosome 16 locus was evaluated in greater detail in the IMPROVE cohort. Regional association plots for IMTmax (Figure 3) indicated the presence of only 1 association signal (rs4888378 in the IMPROVE cohort), centered over the 3′ end of the CFDP1 gene. Potential pleiotropy and possible confounders were assessed by investigating associations of rs4888378 with biochemical and clinical parameters. To the best of our knowledge, rs4888378 was included on the CardioMetabochip because of previous associations with systolic blood pressure. However, in IMPROVE, this association was not confirmed (P=0.12). Cumulative life-time smoking (pack-year categories) was found to differ between genotype groups (Kruskal-Wallis P=0.0067). Adjustment for pack-year categories, in addition to age, sex, the first 3 MDS dimensions, and 12 additional variables reflecting established cardiovascular risk factors (see Methods), did not have a major impact on the original cIMT association of rs4888378 (P=2.7×10−6 for IMTMax), indicating that this association signal is not mediated by established cardiovascular risk factors.
Investigation of rs4888378 in relation to segment-specific IMT measurements in IMPROVE showed that rs4888378 was most strongly associated with Bif-IMT (P=1.13×10−5 for Bif-IMTmax), whereas the weakest association was seen with CC-IMT (probability values for the 4 CC-IMT variables ranging from 0.010–0.038), the ICA-IMT association being intermediate (online-only Data Supplement Table SIII).
Association With Other Cardiovascular Phenotypes
The lead SNP rs4888378 in the chromosome 16 locus was further investigated for associations with carotid plaque score in the MDC study, with CAC score in the Rotterdam studies, and with CAD risk in PROCARDIS and CARDIOGRAM. Because the association of rs4888378 with CC-IMT was consistent across the investigated cohorts (Table 1 and Figure 2), no significant between-cohort heterogeneity was expected with respect to associations with other related cardiovascular phenotypes. Accordingly, fixed-effects (rather than random-effects) models were considered appropriate for meta-analyses of the CAC score in the Rotterdam studies and of CAD in CARDIOGRAM. The minor A allele of rs4888378 (which was associated with thinner IMT) was weakly associated with a lower carotid plaque score (β [SE]=−0.046 [0.023], P=0.04, n=2120) in the MDC study and showed a tendency toward lower CAC score in meta-analysis of RS-I and RS-II (β [SE]=−0.11 [0.06], P=0.06, n=2948). Furthermore, the thinner-IMT allele was associated with decreased risk for CAD and MI in PROCARDIS and CARDIOGRAM (odds ratio [95% confidence interval] 0.83 [0.77–0.90] for all CAD and 0.84 [0.77–0.91] for MI in PROCARDIS, and 0.95 [0.92–0.98] for all CAD and 0.96 [0.93–0.99] for MI in CARDIOGRAM) (Figure 4).
Association With Expression in Target Tissues
Global gene expression data from 7 different tissues (aortic intima-media, aortic adventitia, mammary artery intima-media, heart, liver, peripheral blood mononuclear cells, and carotid plaque) were used to link individual genes to the cIMT-associated locus discovered in this study. We investigated associations between genotype and expression levels of all genes located within ±200 kb of the lead SNP rs4888378. Nine genes were contained within this region, 8 of which were captured by the microarray analysis (online-only Data Supplement Figure SIV). The most significant allele-specific difference in gene expression level according to rs4888378 genotype was observed for TMEM170A (transmembrane protein 170a) (in aortic intima-media, P=0.000569, n=138, and adventitia, P=0.000576, n=133, respectively; Figure 5). Two more genes were differentially expressed at nominally significant levels, BCAR1 (breast cancer antiestrogen resistance-1) in carotid plaque, P=0.00749, n=126, and LDHD (lactate dehydrogenase D) in aortic intima-media and adventitia, P=0.0459, n=138, and P=0.00836, n=133, respectively (Figure 5). However, strictly considering multiple testing of 8 genes in 7 different tissues, a probability value of <0.00089 should be held statistically significant; this threshold was reached only for TMEM170A. No significant genotype association was observed with expression levels of CFDP1 (lowest observed P=0.0693, n=133, for aortic adventitia) (online-only Data Supplement Figure SIV).
Discussion
In this study, we investigated genetic determinants of cIMT, a widely accepted marker of subclinical atherosclerosis, applying a 2-stage discovery and replication study design involving >15 000 subjects. We identified a novel locus on chromosome 16 (lead SNP rs4888378), the minor allele of which was associated with thinner cIMT and decreased risk of CAD in subjects of European ancestry. The association with CAD was stronger in PROCARDIS than in CARDIOGRAM, which may reflect the fact that PROCARDIS recruited cases from CAD-enriched families, thereby potentially enhancing the impact of genetic risk factors. We also identified allele-specific differences in the expression of nearby genes in vascular tissues according to rs4888378 genotype. Thus, investigation of genetic determinants of cIMT resulted in the discovery of a novel CAD risk locus and novel candidate CAD susceptibility genes that merit further investigation.
A recent meta-analysis of SNP-based GWA studies of cIMT conducted by the CHARGE consortium discovered 3 regions associated with CC-IMT.6 In IMPROVE, the lead SNPs for the 3 IMT-associated loci identified by CHARGE were associated with cIMT, although at significance levels that did not qualify for inclusion in the replication stage of our study (investigated directly or by proxy; data not shown). The fact that our lead SNP rs4888378 was not identified by the CHARGE consortium may reflect differences in study design and IMT phenotyping. Specifically, rs4888378 was selected for replication in our study based on its association with composite IMT, and among individual segments, rs4888378 proved to be the most strongly associated with Bif-IMT; neither of these IMT phenotypes were analyzed by the CHARGE consortium.
It is noteworthy that established CAD loci19,26,27 neither appeared as major determinants of cIMT in the current study nor in the study reported by the CHARGE consortium.6 Thus, it appears that the impact of these loci that confer risk of clinically manifest CAD may not be as strong in early subclinical atherosclerosis.
Our results suggest that the observed associations may be because of an influence of rs4888378, or linked variants, on the expression of nearby gene(s) in the arterial wall. The expression of CFDP1, the gene harbouring rs4888378 in its last 3′ intron, showed no allele-specific association with rs4888378. The biological role of TMEM170A, the expression of which showed the strongest association with rs4888378, is currently unknown. In silico sequence analysis predicts that the TMEM170A protein consists of an extracellular N-terminal part, 3 transmembrane helices, and a short cytoplasmic C-terminal tail (MEMSAT-SVM; http://bioinf.cs.ucl.ac.uk). In contrast to TMEM170A, BCAR1 (also known as p130CAS) has been extensively studied and ascribed important roles in processes such as cellular adhesion, migration, and proliferation/survival, eg, in vascular smooth muscle cells,28,29 and thus has a biologically plausible role in atherogenesis. In silico analysis of the genomic sequence surrounding rs4888378 predicts that rs4888378 may influence the binding of transcription factors (YY1 and NF-1; TESS software, http://www.cbil.upenn.edu/cgi-bin/tess/tess). YY1 is expressed in human carotid atherosclerotic lesions and has experimentally been ascribed roles in vascular smooth muscle cell injury responses and neointima formation.30,31 Accordingly, it is tempting to speculate that 1 mechanism underlying the association between rs4888378 and cIMT and CAD risk would be the influence of rs4888378 on YY1-regulated transcription of BCAR1 in vascular smooth muscle cells, with downstream effects on vascular smooth muscle cell function. The BCAR1 locus is further indicated by the discovery stage composite IMT index SNP rs100861, which maps closely to the BCAR1 gene. Interestingly, another intronic SNP in the CFDP1 locus has been associated with markers of chronic obstructive pulmonary disease.32 This SNP (rs2865531) is in strong linkage disequilibrium with rs4888378 (r2=0.967 in the HapMap CEU reference panel; not present on the CardioMetabochip). The BCAR1-CFDP1-TMEM170A locus is thus implicated in both atherosclerotic cardiovascular disease and chronic obstructive pulmonary disease, 2 pathologies which both that have strong inflammatory components and involve tissue remodeling, including dysregulation of smooth muscle cell phenotype and function, and that exhibit pronounced comorbidity.33
A major strength of the current study is the extensive and thoroughly standardized ultrasound examination performed in the discovery cohort (IMPROVE). The fact that SNPs selected for replication based on their association with composite IMT variables performed better at the replication stage than those selected based on their associations with CC-IMT suggests that these composite variables are of particular value. However, some limitations of the present study should also be considered. Differences in ultrasonographic protocols exist among the participating cohorts. For example, CC-IMT was not measured in exactly the same way in all cohorts. Furthermore, recruitment protocols differed between studies. Although the IMPROVE study recruited high-risk individuals (with at least 3 established vascular risk factors), the Whitehall-II study recruited healthy subjects, and the MDC, Edinburgh Artery Study, and Rotterdam studies enrolled population-based subjects. These among-cohort differences may have obscured associations that remain undetected in the present study.
Conclusions
This study identified rs4888378 in the BCAR1-CFDP1-TMEM170A locus on chromosome 16 as a novel genetic determinant of cIMT and CAD risk in individuals of European ancestry. Further investigations, including experimental studies, are needed to fully clarify the biological mechanisms underlying the current findings.
Appendix
From the Atherosclerosis Research Unit (K.G., B.S., R.J.S., J.O., L.F., M.L., A.S., M.S.-L, P.E., A.Ha), Experimental Cardiovascular Research Unit (L.F., A.G., G.K.H.), Department of Medicine, Solna, Karolinska Institutet, Karolinska University Hospital Solna, Stockholm, Sweden; University College London Genetics Institute (D.Z., S.S.), Genetic Epidemiology Group, Department of Epidemiology and Public Health (M.Ki., M.Ku., C.L., A.D.H.), Cardiovascular Genetics, BHF Laboratories, Rayne Building (P.J.T., S.E.H.), Centre for Clinical Pharmacology, Department of Medicine, University College London, London, UK (A.D.H.); Centro Cardiologico Monzino, IRCCS, Milan, Italy (F.V., G.I.C., D.B., E.T.); Clinical Research Center, Department of Clinical Sciences, Lund University, Skåne University Hospital, Lund, Sweden (C.F., B.H., O.M.); Division of Internal Medicine C, Department of Medicine, University of Verona, Hospital Policlinico G.B. Rossi, Verona, Italy (C.F.); Department of Epidemiology (M.Ka, A.G.U., A.Ho, J.C.M.W.), Department of Internal Medicine, Erasmus University Medical Center, Rotterdam, The Netherlands (A.G.U.); Netherlands Genomics Initiative–Sponsored Netherlands Consortium for Healthy Ageing, Rotterdam, The Netherlands (M.Ka, A.G.U., A.Ho, J.C.M.W.); Centre for Population Health Sciences, University of Edinburgh, Edinburgh, UK (S.M., J.L.B., C.M.R., J.F.P.); Division of Cardiovascular Epidemiology, Institute of Environmental Medicine (B.G., K.L., M.V., U.d.F.), Department of Molecular Medicine and Surgery, Karolinska Institutet, Stockholm, Sweden (U.H., A.F.-C.); Cardiothoracic Unit, Great Ormond Street Hospital, London, UK (J.D.); Program in Medical and Population Genetics, Broad Institute, Cambridge, MA (B.F.V., P.F.); Center for Human Genetic Research and Diabetes Research Center (Diabetes Unit), Massachusetts General Hospital, Boston, MA (B.F.V., P.F.); MRC Epidemiology Unit, Institute of Metabolic Science, University of Cambridge, Addenbrooke’s Hospital, Cambridge, UK (C.L., N.J.W.); Institute of Public Health and Clinical Nutrition, University of Eastern Finland, Kuopio, Finland (K.N., T.-P.T.); Kuopio Research Institute of Exercise Medicine, Foundation for Research in Health Exercise and Nutrition, Kuopio, Finland (R.R., K.S.); Department of Clinical Physiology and Nuclear Medicine, Kuopio University Hospital, Kuopio, Finland (R.R., K.S.); Department of Medicine, University Medical Center Groningen, Groningen, The Netherlands (A.J.S.); Assistance Publique–Hopitaux de Paris, Service Endocrinologie-Metabolisme, Groupe Hôpitalier Pitie-Salpetriere, Unités de Prévention Cardiovasculaire, Paris, France (P.G.); Internal Medicine, Angiology and Arteriosclerosis Diseases, Department of Clinical and Experimental Medicine, University of Perugia, Perugia, Italy (E.M.); Universität zu Lübeck, Medizinische Klinik II, Lübeck, Germany (J.E.); The Institute for Translational Medicine and Therapeutics, University of Pennsylvania, Philadelphia, PA (M.P.R.); Cardiovascular Institute, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA (M.P.R.); National Heart, Lung, and Blood Institute’s Framingham Heart Study, Framingham, MA (C.J.O.D.); Division of Intramural Research, NHLBI, Bethesda, MD (C.J.O.D.); Cardiology Division, Department of Medicine, Massachusetts General Hospital, Harvard Medical School, Boston, MA (C.J.O.D.); Department of Cardiovascular Medicine, The Wellcome Trust Centre for Human Genetics (M.F., H.W.), Clinical Trial Service Unit, University of Oxford, Oxford, UK (R.C.); Department of Cardiovascular Medicine, University of Oxford, John Radcliffe Hospital, Headington, Oxford, UK (M.F., H.W.); Department of Cardiovascular Research, Instituto Mario Negri, Milan, Italy (M.G.F.); Gesellschaft für Arterioskleroseforschung e.V., Leibniz-Institut für Arterioskleroseforschung an der Universität Münster (LIFA), Münster, Germany (U.S.); Department of Medical Sciences, Molecular Medicine and Science for Life Laboratory, Uppsala University, Uppsala, Sweden (A.-C.S.); Department of Cardiovascular Sciences, University of Leicester, Leicester, UK (N.J.S.); Leicester NIHR Biomedical Research Unit in Cardiovascular Disease, Glenfield Hospital, Leicester, UK (N.J.S.); and Dipartimento di Scienze Farmacologiche e Biomolecolari, University of Milan, Milan, Italy (D.B., E.T.). On behalf of the *CARDIOGRAM and †PROCARDIS consortiums.
Acknowledgments
The authors fully acknowledge the thousands of study participants who volunteered their time to help advance science and the scores of research staff and scientists who have made this research possible. The Edinburgh Artery Study would particularly like to acknowledge all staff and participants. The authors representing the Rotterdam Study are grateful to the participants and staff, the participating general practitioners, and the pharmacists. Whitehall-II genotyping was, in part, supported by a Medical Research Council-GlaxoSmithKline pilot program grant (ID 85374). David Altshuler, Sekar Kathiresan, and “The Pfizer Broad-Massachusetts General Hospital-Broad Genetics Collaboration” are acknowledged for supporting the genotyping in the MDC study. Members, sources of funding, and disclosures of the CARDIOGRAM consortium are listed in online-only Data Supplement Section S7. Members of the PROCARDIS consortium are listed in online-only Data Supplement Section S8. Members of the writing group and affiliations by participating study are listed in online-only Data Supplement Section S9.
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© 2012 American Heart Association, Inc.
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History
Received: 15 February 2012
Accepted: 31 October 2012
Published online: 14 November 2012
Published in print: December 2012
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Disclosures
Dr McLachlan reports stock ownership interest in Pfizer. Dr Kivimäki reports receiving research grant support from the National Heart, Lung and Blood Institute (R01HL36310; principal investigator). Dr Deanfield reports receiving research grant support from the BHF and Medical Research Foundation and honoraria payment and payment for speakers’ bureau appointments (Novartis, Roche, Merck, Danone, Pfizer). Dr Voight reports receiving research grant support from the National Institutes of Health (A Genome-Wide Association Study for Early Onset MI; postdoc) and an industry grant (Toward Therapeutical Markers for MI in a T2D Background; postdoc). Dr Kumari reports receiving research grant support from the BHF (PG1041133124260, RG1081008), the National Institutes of Health (AG13196), the Medical Research Council, and the National Heart, Blood and Lung Institute (HL36310). Dr Gabrielsen was employed by Bayer after completion of the study. Dr Hedin reports consultant relationship with Cardoz AB. Dr Nyyssönen reports receiving research grant support from the Academy of Finland for the IMPROVE study (Grant 110413). Dr Giral reports receiving Hospital Clinical Research Program research grant support, awarded by the French Health Ministry. Dr Clarke reports receiving research grant support from the BHF. Dr Franzosi reports receiving research grant support from the European Commission 6th Framework Program as collaborator in the PROCARDIS project. Dr Hingorani reports receiving research grant support from the BHF. Dr Baldassarre reports receiving research grant support from the European Commission (Contract No. QLG1-CT-2002-00896) for the IMPROVE study. Dr Tremoli reports receiving research grant support from the European Commission (Contract No. QLG1-CT-2002-00896) for the IMPROVE study. Dr Humphries reports receiving research grant support from the BHF (RG2008/08, program and project grants on cardiovascular genetics) and the European Commission Seventh Framework Program on Diabetes, and discloses speakers’ bureau appointment payment (Genzyme meeting on FH, Amsterdam, November 2011) and 1 consultant/advisory board relationship (Store Gene, a Coronary Heart Disease risk genetic testing University College London spin-off company).
Sources of Funding
The IMPROVE study was supported by the European Commission (Contract No. QLG1-CT-2002-00896), the Swedish Heart-Lung Foundation, the Swedish Research Council (projects 8691 and 0593), the Knut and Alice Wallenberg Foundation, the Torsten and Ragnar Söderberg Foundation, the Swedish Foundation for Strategic Research, the Stockholm County Council (project 562183), the Strategic Cardiovascular and Diabetes Programmes of Karolinska Institutet and Stockholm County Council, Academy of Finland (Grant 110413), the Ministry of Education and Culture of Finland, the City of Kuopio, the British Heart Foundation (BHF) (RG2008/014), and the Italian Ministry of Health (Ricerca Corrente). The University College London Genetics Institute supported Dr Zabaneh and S. Shah, and Dr Humphries was supported by the BHF (RG2008/08). The Rotterdam Study was funded by the Erasmus Medical Center and Erasmus University, Rotterdam, Netherlands Organization for the Health Research and Development (ZonMw), the Research Institute for Diseases in the Elderly, the Ministry of Education, Culture and Science, the Ministry for Health, Welfare and Sports, the European Commission (DG XII), and the Municipality of Rotterdam. The Rotterdam Genome-Wide Association study was funded by the Netherlands Organization of Scientific Research (De Nederlandse Organisatie voor Wetenschappelijk Onderzoek) Investments (number 175.010.2005.011, 911-03-012), the Research Institute for Diseases in the Elderly (014-93-015; RIDE2) and the Netherlands Genomics Initiative/Netherlands Consortium for Healthy Aging (project 050-060-810). The present work was further supported by a Netherlands Organization of Scientific Research grant (vici, 918-76-619). The Whitehall-II study was supported by the Medical Research Council, the BHF, and the National Institutes of Health (R01HL36310). Drs Humphries and Talmud were supported by BHF RG005/014. The Malmö Diet and Cancer Study was supported by the Swedish Research Council, the Swedish Heart-Lung Foundation, and the European Research Council (ERC-StG-282255). The Edinburgh Artery Study was financed by the BHF and the Chief Scientist Office of the Scottish Executive Health Department. The Advanced Study of Aortic Pathology Study was supported by the Swedish Research Council (12660), the Swedish Heart-Lung Foundation (20090541), the European Commission (FAD, Health F2 2008 200647), and a donation from Fredrik Lundberg. The Biobank of Karolinska Endarterectomies Study was funded by the Swedish Heart-Lung Foundation, the Swedish Research Council, the European Commission (AtheroRemo; FP7-HEALTH-2007-A-201668), the AFA Insurance Foundation, and the Torsten and Ragnar Söderberg Foundation. PROCARDIS was supported by the European Community Sixth Framework Program (LSHM-CT-2007–037273), AstraZeneca, the BHF, the Wellcome Trust (Contract No. 090532/Z/09/Z), the Swedish Research Council, the Knut and Alice Wallenberg Foundation, the Swedish Heart-Lung Foundation, the Torsten and Ragnar Söderberg Foundation, the Strategic Cardiovascular Program of Karolinska Institutet and Stockholm County Council, the Foundation for Strategic Research and the Stockholm County Council. M. Farall and H. Watkins are supported by the BHF Center of Research Excellence. Dr Sabater-Lleal is a recipient of a Marie Curie Intra European Fellowship within the seventh Framework Program of the European Union (PIEF-GA-2009-252361).
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