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Heart Rhythm. Author manuscript; available in PMC Dec 1, 2010.
Published in final edited form as:
PMCID: PMC2789271
NIHMSID: NIHMS142677
Association of TGFBR2 Polymorphism with Risk of Sudden Cardiac Arrest in Patients with Coronary Artery Disease
Zian H Tseng, MD, MAS,* Eric Vittinghoff, PhD, MPH,# Stacy L. Musone, BA, Feng Lin, MS,# Dean Whiteman, BS,* Ludmila Pawlikowska, PhD,^ Pui-Yan Kwok, MD, PhD,§ Jeffrey E Olgin, MD, FHRS,*§ and Bradley E. Aouizerat, PhD, MAS
*Section of Cardiac Electrophysiology, Division of Cardiology, Department of Medicine, University of California, San Francisco
#Department of Epidemiology and Biostatistics, University of California, San Francisco
Institute for Human Genetics, University of California, San Francisco
§Cardiovascular Research Institute, University of California, San Francisco
^Department of Anesthesia and Perioperative Care, University of California, San Francisco
Department of Physiologic Nursing, University of California, San Francisco
Correspondence to: Zian H. Tseng, M.D., M.A.S., Section of Cardiac Electrophysiology, 500 Parnassus Avenue, Box 1354, University of California, San Francisco, San Francisco, CA 94143-1354. 415-476-5706 (phone), 415-476-6260 (fax), zhtseng/at/medicine.ucsf.edu
Background:
Transforming growth factor β (TGFβ) signaling has been shown to promote myocardial fibrosis and remodeling with coronary artery disease (CAD), and previous studies demonstrate a major role for fibrosis in the initiation of malignant ventricular arrhythmias (VA) and sudden cardiac arrest (SCA). Common single nucleotide polymorphisms (SNPs) in TGFß pathway genes may be associated with SCA.
Objective:
We examined the association of common SNPs among 12 candidate genes in the TGFß pathway with the risk of SCA.
Methods:
SNPs (n=617) were genotyped in a case-control study comparing 89 patients with CAD and SCA due to VA to 520 healthy controls.
Results:
Nineteen SNPs among 5 genes (TGFB2, TGFBR2, SMAD1, SMAD3, SMAD6) were associated with SCA after adjustment for age and sex. After permutation analysis to account for multiple testing, a single SNP in TGFBR2 (rs9838682) was associated with SCA (OR = 1.66, 95% CI = 1.08-2.54, p=0.02).
Conclusion:
We demonstrate an association between a common TGFBR2 polymorphism and risk of SCA due to VA in the setting of CAD. If validated, these findings support the role of genetic variation in TGFß signaling in SCA susceptibility.
Keywords: Sudden cardiac arrest, ventricular tachycardia, ventricular fibrillation, coronary artery disease, transforming growth factor ß, genetics
Sudden cardiac arrest (SCA) remains a major public health problem, accounting for up to 450,000 deaths per year in the U.S.1 Previous studies suggest that approximately 80% of sudden cardiac deaths (SCD) occur in the setting of coronary artery disease (CAD).2 Ventricular tachycardia (VT) or ventricular fibrillation (VF) is the initiating event in the majority of SCA cases.3 Ejection fraction (EF) remains the only major criterion to stratify patients for risk of SCD and implantable cardioverterdefibrillator (ICD) implantation, but this strategy alone is insensitive and nonspecific.4 Genetic susceptibility to SCA in the setting of CAD is supported by several epidemiologic studies demonstrating that a family history of SCD is an independent risk factor for SCD and primary VF.5, 6, 7
Transforming growth factor ß (TGFß) is a potent profibrotic cytokine. Three structurally similar isoforms (TGF-ß1, ß2, and ß3) encoded by three distinct genes transduce their signal through combinations of transmembrane type I and type II receptors (TGFBR1and TGFBR2) and their downstream effectors, the SMAD proteins.8, 9 TGFß is expressed at high levels in the heart during embryonic development and adult life, localizing in both cardiomyocytes and the extracellular matrix.10 TGFß2 knockout mice exhibit perinatal mortality and a wide range of developmental, including cardiac, defects.11 Regulatory mutations in the TGFß3 gene cause arrhythmogenic right ventricular cardiomyopathy, an autosomal dominant clinical syndrome of ventricular arrhythmias, SCD, and fibrofatty replacement of the right ventricle.12 Members of the TGFß family are markedly induced in infarcted myocardium and play a central role in infarct healing, cardiac repair, and left ventricular remodeling through mechanisms of cardiomyocyte growth, fibroblast activation, and extracellular matrix deposition.13 Although these basic and genetic studies suggest a central role for the TGFß signaling pathway in ischemic remodeling, genetic variation in members of the pathway have not been investigated for association with risk of ventricular arrhythmias and SCA in the setting of CAD.
We examined the potential role of polymorphisms in 12 genes in the TGFß signaling pathway in a group of patients with a history of myocardial infarction (MI) and aborted SCD with documented VT/VF compared to three healthy control groups without SCA or ventricular arrhythmias.
The UCSF Committee on Human Research approved all protocols. Informed consent was obtained from all participants for DNA isolation and plasma collection.
Study Design
Consecutive cases of SCA presenting to UCSF Medical Center Emergency Department or inpatient cases of SCA at UCSF Medical Center between January 2000 and June 2008 were screened. SCA was defined as a cardiac arrest with documented sustained monomorphic VT or VF requiring cardioversion or defibrillation, exclusive of torsades de pointes (drug-induced QT prolongation or otherwise). Because we were interested in the most common phenotype of SCA, that occurring in the setting of CAD, only patients with a history of MI were included in this study. Although all SCA cases had a history of MI, thirty-six SCA cases occurred in the setting of acute ischemia while 53 SCA cases occurred in the absence of active ischemia. Thus, SCA cases consisted of 89 Caucasian non-Hispanic patients with aborted SCD and a history of MI.
The control population originated from three resources.14 These were randomly selected, healthy Caucasian controls without specific information regarding CAD. Recent large genome-wide association studies using shared controls of this type have met with success.14-16 Sixty unrelated controls employed by the International HapMap Project (http://www.HapMap.org) were selected from the Coriell Institute for Medical Research (http://ccr.coriell.org) Human Genetic Data Collection (sample identifiers are available on request). 216 healthy Caucasian controls were derived from a large genome wide association study of narcolepsy.14 The remaining 244 controls were healthy Caucasian renal transplant donors from an ongoing study of the genomics of renal tranplantation (NIH U19 AI063603). Informed consent was obtained from all participants. Protocols were approved by the local institution review boards at all participating institutions.
Gene Selection:
Figure 1 describes the TGFß-related signaling pathway. The SMAD mediators can be divided into two main intracellular pathways: SMAD2/3 or SMAD1/5.17 Members of the TGFß family (TGFß1/2/3) bind TGFBR2, Type 2 receptor, a specific serine/threonine receptor kinase which catalyzes the phosphorylation of Type 1 receptor, TGFBR1, activating the protein. TGFß Type 3 receptor, TGFBR3, may enhance the binding of TGFß ligands to TGFBR2 by binding TGFβ and presenting it to TGFBR2. Activated type I receptors phosphorylate specific receptor regulated R-SMADs, the intracellular effectors of TGFß signaling, either SMAD2/3 or SMAD 1/5. Activated RSMADs form heteromeric complexes with SMAD4 that translocate to the nucleus, where they regulate the expression of target genes in conjunction with transcription factors. Inhibitory SMADs, SMAD6 and SMAD7, antagonize TGFß signaling by inhibiting activation of R-SMADs. We examined twelve genes (TGFB2, TGFB3, TGFBR1, TGFBR2, TGFBR3, SMAD1, SMAD2, SMAD3, SMAD4, SMAD5, SMAD6, SAMD7) in the TGFß signaling pathway to examine for association with risk of SCA. TGFB1 was not included since the only variant on the array (see below) did not meet our criteria for a common variant (allele frequency of <0.05) and thus was not analyzed.
Figure 1
Figure 1
Gene selection in the TGFß signaling pathway. Schematic representation of genes examined in the TGFß signaling pathway. The SMAD mediators can be divided into two main intracellular pathways: SMAD2/3 or SMAD1/5.17 Members of the TGFß (more ...)
Blood collection and genotyping
Blood samples were obtained by venipuncture and genomic DNA was extracted from peripheral blood lymphocytes (Invitrogen, Carlsbad, CA) for the SCA cases. Genotyping was performed blinded to clinical status; positive and negative controls were included. DNA samples were quantitated with a Nanodrop Spectrophotometer (ND-1000) and normalized to a concentration of 50 ng/μL (diluted in 10 mM Tris/1 mM EDTA). Samples were genotyped using the Genome-Wide SNP Array 6.0 (Affymetrix, Santa Clara, CA) and processed according to the standard Affymetrix automated protocol. This involved fragmentation, whole genome amplification, precipitation, resuspension in hybridization buffer and hybridization to the Genome-Wide SNP Array 6.0. After hybridization the arrays were processed and imaged on an Affymetrix GeneChip® Scanner. Genotype data for the controls were obtained using the same protocols in the same laboratory as described for SCA cases.
SNP Selection
Common variants (defined as having a minor allele frequency ≥ 0.05 in the combined sample of cases and controls) mapping to the candidate genes of interest were extracted from the array .cel files. Data files (Affymetrix “.cel” files) were obtained for all samples (n=608) and genotype calls were assigned using the Birdseed-dev algorithm for Affy 6.0 (Affymetrix Power Tools apt-1.8.5). In order to ensure robust genetic association analyses, we performed quality control filtering of both samples and SNPs. For each Birdseed calling run, SNPs with call rates <95%, or Hardy-Weinberg p<0.001 in controls were excluded. A total of 617 SNPs among the 12 candidate genes (SMAD1: 25 SNPs; SMAD2: 22 SNPs; SMAD3: 65 SNPs; SMAD4: 7 SNPs; SMAD5: 18 SNPs; SMAD6: 147 SNPs; SMAD7: 21 SNPs; TGFB2: 47 SNPs; TGFB3: 7 SNPs; TGFBR1: 24 SNPs; TGFBR2: 125 SNPs; TGFBR3: 109 SNPs) passed all quality control filters and were included in the genetic association analyses. SNP selection was performed using the HelixTree SNP & Variation Suite 7 (GoldenHelix, Bozeman, MT). Potential functional roles of SNPs associated with SCA were examined using PUPASuite 2.0,18 a comprehensive search engine that tests a series of functional effects (i.e., non-synonymous changes, altered transcription factor binding sites, exonic splicing enhancing or silencing, splice site alterations, microRNA target alterations).
Statistical Analyses
To examine for differences in the distribution of demographic factors between cases and controls, we used the unequal-variance t-test for age and a chi-square test for sex. These analyses were carried out using STATA 9.0.
Allele and genotype frequencies were determined by gene counting. Hardy-Weinberg equilibrium was assessed by the chi-square exact test. Measures of linkage disequilibrium, D′ and r2, were computed from the case and control genotypes with Haploview 4.1 (http://www.broad.mit.edu/mpg/haploview/) and heat maps of pairwise D′ values are presented in Supplemental Figures 1-11. Homogeneity in ancestry between cases and shared controls was verified by cluster and principal component analysis (PCA).19 To investigate other biases that could be introduced by using controls shared with other studies such as batch effects due to differences in instrumentation,20 we assessed the potential effect of substructure with the genomic-control method21 and with PCA, as implemented in HelixTree (GoldenHelix, Bozeman, MT). Briefly, the number of principal components were sought which minimized the value of the genome control test statistic. Population substructure was found to be negligible, with no discernible batch effects and PCA-adjusted tests of association that varied little as compared with unadjusted tests (data not shown).
For association tests, four genetic models were assessed for each SNP: dominant, recessive, log additive, and codominant. Barring trivial improvements (delta<10%) the genetic model that best fit the data, by maximizing the significance of the p-value was selected for each SNP. Both un-adjusted and adjusted associations were calculated; logistic regression was used to control for age and sex. Genetic model fit and both unadjusted and age- and sex-adjusted odds ratios were estimated using the SAS software package, version 9.1.3 (SAS Inc, Cary, NC).
Permutation tests were used to protect the type-I error rate against inflation due to testing of multiple SNPs within each gene. To account for multiple comparisons, case-control status was permuted 10,000 times to determine the likelihood that our findings were due to chance. Permutated significance tests are presented in Supplemental Tables 1-12. Permutation analyses were done using the PLINK software package (v1.06).22
Study Population
Compared with controls (Table 1), SCA cases were older (73.0 ± 12.7 versus 52.7 ± 15.6 years; p<0.0001) and predominantly male (90.9% versus 51.9%, p<0.0001). SCA cases had a mean BMI of 25.7 ± 4.4 kg/m2 and a mean EF of 35.7 ± 14.5%. History of congestive heart failure (CHF) was present in 76.5% of cases; diabetes mellitus was present in 28.4% of cases, and hypertension in 81.6% of cases. Clinical characteristics were not available for the anonymous healthy controls.
Table 1
Table 1
Demographic Characteristics of SCA Cases and Healthy Controls
Genetic Association of TGFBR2 rs9838682 with SCA
Genotype frequencies for each SNP for each of the 12 candidate genes are shown in Supplemental Tables 1-12. No SNP deviated from Hardy-Weinberg expectations. Initially, evidence of genetic association with SCA was identified for forty-five SNPs among 8 genes (SMAD1: 3 SNPs; SMAD3: 12 SNPs; SMAD5: 1 SNP; SMAD6: 6 SNPs; TGFB2: 5 SNPs; TGFBR1: 1 SNP; TGFBR2: 14 SNPs; TGFBR3: 3 SNPs) in the unadjusted analyses (Table 2). Following adjustment for age and sex, nineteen SNPs among 5 genes retained their association with SCA (SMAD1: 2 SNPs; SMAD3: 7 SNPs; SMAD6: 3 SNPs; TGFB2: 2 SNPs; TGFBR2: 5 SNPs). Of note, adjustment for age and sex generally resulted in modest effects on the odds ratio (OR) point estimates. Permutation was employed to account for the number of false positives that are likely given the number of comparisons made, resulting in a single SNP in TGFBR2 (rs9838682) remaining statistically significant with an adjusted p<0.05. An additional SNP in SMAD3 (rs11637581) exhibited a permuted p-value of 0.056 that, although suggestive, did not meet the a priori significance threshold for additional analyses (Supplemental Table 3).
Table 2
Table 2
TGFß Pathway SNPs Associated with SCA.
TGFBR2 rs9838682 is a G-to-A transition polymorphism with the frequency of the minor allele (A) of 33.1% in controls. The frequency of the “A” allele was significantly elevated in SCA cases (46.6%, p<0.001). The additive genetic model fit the data best. The “A” allele was associated with SCA with an Odds Ratio (OR) of 1.77 (95% Confidence Interval [CI]: 1.28-2.45; p=0.0006). Adjustment for age and sex yielded a similar OR point estimate: 1.66 (95% CI: 1.08-2.54, p=0.02). The association remained statistically significant following permutation testing (p=0.045). Of note, limiting the analysis to the 36 SCA cases occurring during active ischemia (adjusted OR 1.58, 95% CI: 0.89-2.80, p=0.12) or the 53 SCA cases in the absence of ischemia (adjusted OR 1.71, 95% CI: 0.99-2.95, p=0.054) reduced power but resulted in similar point estimates and overlapping confidence intervals for the OR.
TGFBR2 rs9838682 lies in a region of high linkage disequilibrium (LD, Figure 2) in a haplotype block spanning more than 43.6 thousand basepairs (kB). Although other SNPs in this region of LD were associated with SCA (Table 2), none reached statistical significance after permutation testing and were thus not examined in haplotype analyses, as such analyses have limited power to detect meaningful effects.23 The functional impact of sequence variation in rs9838682 was assessed using PUPASuite 2.0, with no impact predicted for the minor allele.24 Similarly, none of the tested SNPs in LD (D′ ≥ 0.80) with rs9838682 were predicted to impact function.
Figure 2
Figure 2
Linkage disequilibrium map of TGFBR2 Gene. D′ values are displayed within each diamond. Color scheme gradient indicates r2 values with red indicating complete LD (D′ = 1.0) to shades of red to pink (D′ < 1.0). Blue and (more ...)
In the present study, we demonstrate an association between a common TGFBR2 polymorphism (rs9838682) and risk of SCA due to malignant ventricular arrhythmias (VA) in the setting of CAD. Rigorously phenotyped cases were collected, with documented presence or absence of CAD and VA. Compared to healthy controls, adjusted for gender and age differences, the rs9838682 minor allele was associated with increased odds of SCA.
The TGFBR2 gene lies on chromosome 3p22 with 7 exons and encodes for the type II TGFß receptor, a 565-amino acid transmembrane tyrosine kinase with a calculated molecular mass of approximately 60 kD.25 In gastrointestinal tissues, TGFBR2 acts as a tumor suppressor gene and approximately 30% of colorectal cancers carry TGFBR2 mutations.26 In the cardiovascular system, mutations resulting in abnormal splicing in TGFBR2 lead to loss of function of TGFß signaling activity on extracellular matrix formation and cause Marfan syndrome.27 Interestingly, tissues derived from affected individuals show increased expression of collagen.
An important substrate for ventricular arrhythmias that leads to SCD post-MI is altered conduction, particularly in the infarct border zone, due to structural remodeling. TGFß plays a key role in the development of cardiac fibrosis.28 In experimental models, ventricular fibrosis increases the incidence of VF initiated by triggered activity.29 After MI, TGFß signaling is altered and plays a central role in infarct healing and cardiac remodeling.13 TGFß1 and TGFß2 are induced early after MI, while TGFß3 shows delayed and prolonged upregulation. 30, 31 SMAD2, 3, and 4 protein expression is upregulated in the scar and border zone area.32
Recent work from our laboratory demonstrates mitigation of cardiac fibrosis and reduction in vulnerability to atrial and ventricular arrhythmias with an antifibrotic agent (pirfenidone) which is known to inhibit TGFß1 signaling.33 TGFBR2 binds all three TGFß ligands, and represents the first critical intracellular step in this complex signaling cascade.17 Genetic variation in TGFBR2 that may affect function may be expected to play a central role in altered downstream effects, such as enhancing collagen and extracellular matrix synthesis,34 and inducing conversion of fibroblasts to myofibroblasts,35 and thus risk for VT/VF, particularly in the setting of CAD.
The TGFBR2 rs9838682 SNP found to be associated with SCA in the setting of CAD is a common polymorphism. This SNP is intronic, lying between exons 3 and 4, thus it may be in LD with the actual functional variant, or it may impact gene splicing, transcription, or regulation. However, limited data are available on its specific functional effects, therefore further study is necessary to fine-map the association signal, identify causative functional variants, and elucidate their impact on TGFBR2 function or levels, downstream signaling, and whether they ultimately affect development of ventricular arrhythmias.
There are notable limitations to our study. We examined 12 genes in the TGFß signaling pathway but a number of other genes, in particular TGFB1, in the complex cascade were not explored. In addition, we selected only common SNPs captured on the Affymetrix 6.0 DNA SNP Array, thus rarer polymorphisms and insertion/deletion variants were not assessed by our study. Stringent control for the number of statistical tests substantially reduces power, in particular given the number of SCA cases, such that some associations in the examined SNPs may have been missed. Furthermore, because only Caucasian cases and controls were examined, further studies are needed to determine whether the association with TGFBR2 SNP can be generalized to SCAs in other ethnicities. Healthy controls were used, thus a confounding association with CAD or other intermediate phenotypes such as CHF or hypertension cannot be ruled out. These results may also represent an association between SCA and other variants (in TGFBR2 or other genes) in LD with this SNP. Finally and most importantly, the possibility that this association is due to chance should be considered, thus validation of this finding in independent populations is critical.
The strengths of our study include the rigorously phenotyped and adjudicated SCA cases, compared to healthy controls without SCD. Because of our requirement for documented VA in all SCA cases, the possibility of association with other causes of sudden death is avoided. Because we used shared controls, principal components analysis was employed to exclude the possibility of population stratification as a cause of spurious association.
Conclusion
In summary, our results suggest that genetic variation in TGFBR2 may be associated with risk for SCA associated with CAD. These findings contribute to accumulating evidence for the influence of TGFß signaling in fibrosis and risk for malignant ventricular arrhythmias. If these results are validated, further investigation of the TGFß pathway in the development of ventricular arrhythmias is warranted.
Supplementary Material
01
Acknowledgements
This research was funded by a grant from the National Center for Research Resources (NCRR), a component of the National Institutes of Health (NIH) and NIH Roadmap for Medical Research (KL2 RR024130). We are gratefully indebted to Emmanuel Mignot, M.D., Ph.D. and Daniel Salomon, M.D. for providing anonymous control genotypes. We thank Annie Poon and Matthew Akana for assistance with genotyping experiments.
Financial Support: This research was funded by grants from the National Center for Research Resources (NCRR), a component of the National Institutes of Health (NIH) and NIH Roadmap for Medical Research (KL2 RR024130).
Glossary of Abbreviations
SNPsingle nucleotide polymorphism
LDlinkage disequilibrium
TGFßTransforming growth factor beta (ß)
VAventricular arrhythmia
VTventricular tachycardia
VFventricular fibrillation
SCAsudden cardiac arrest
SCDsudden cardiac death
ICDimplantable cardioverter-defibrillator
CADcoronary artery disease
MImyocardial infarction
CHFcongestive heart failure
ORodds ratio
CIconfidence interval

Footnotes
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No conflicts of interest for any authors
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