Abstract

A role of thrombospondins (TSPs) in atherosclerosis and thrombosis was suggested by associations of single nucleotide polymorphisms in the genes coding for TSP-1 (rs2228262; Asn700Ser), TSP-2 (rs8089; 3′ untranslated region), and TSP-4 (rs1866389; Ala387Pro) with myocardial infarction (MI). However, these findings were not consistently confirmed in replication studies. We determined the genotypes related to these polymorphisms in a large case–control sample of MI and performed a meta-analysis of data obtained in the present sample and available from prior studies that included Europeans or Americans of European origin. In the population examined here, the carriers of the minor allele of the polymorphism in the TSP-2 gene (GG and TG genotypes) had a mildly statistically significant higher risk of MI than the homozygous carriers of the major allele (TT genotype) [adjusted odds ratio (OR) 1.19; 95% confidence interval (CI), 1.02 to 1.39]. In similar comparisons, no associations of the polymorphisms in the TSP-1 (adjusted OR 1.12; 95% CI, 0.93 to 1.35) and TSP-4 (adjusted OR 0.99; 95% CI, 0.85 to 1.16) genes with MI were observed. The meta-analysis included 6388 (TSP-1), 4930 (TSP-2), and 6978 (TSP-4) cases. None of the polymorphisms was found to be linked with the risk of MI. Thus, despite associations in certain individual studies, the synthesis of available evidence did not suggest that the TSP polymorphisms included in this study were associated with MI.

INTRODUCTION

The thrombospondins (TSPs) constitute a group of five structurally related extracellular matrix proteins (TSP-1–5) that affect processes as disparate as synaptogenesis in the developing central nervous system and aggregation of activated platelets in hemostasis (1–3). A role for TSPs in atherosclerosis and thrombosis was proposed because of their involvement in the proliferation and migration of cells, interactions among cells and between cells and the extracellular matrix, remodeling of the extracellular matrix, wound-healing, inflammatory reactions, and immune responses (1,2,4–13).

Further evidence for such a relationship was provided by Topol et al. (14), who were the first to report that single nucleotide polymorphisms (SNPs) in TSP genes affecting the amino acid sequences of TSP-1 (Asn700Ser, 2210A/G, rs2228262) and TSP-4 (Ala387Pro, 1186G/C, rs1866389), and an SNP (3949T/G, rs8089) in the 3′ untranslated region (UTR) of the gene coding for TSP-2 (THBS2) were linked with myocardial infarction (MI).

In support of these association findings, some properties of TSP-1 and TSP-4 proteins and THBS2 RNA were shown to exhibit isoform-specific or allele-specific differences (15–20). Compared with the predominant Asn700 isoform, the Ser700 variant of TSP-1 has been associated with lower Ca2+-binding capacity and conformational stability, increased presence and enhanced interaction with fibrinogen on platelet surfaces, and a higher rate and extent of platelet aggregation (15–17). In contrast to the Ala387 isoform, TSP-4 with Pro387 has been reported to interfere with adhesion properties and proliferation of vascular endothelial cells, and, in processes mediated by the TSP-4 receptor Mac-1 (αMβ2-integrin), the Pro387 isoform appeared to be a stronger activator of neutrophil responses than the Ala387 isoform (18,19). The T and G alleles of the rs8089 SNP in the 3′ UTR of THBS2 have been related to different secondary structures of RNA in a modeling experiment (20).

The original results (14) about the association between SNPs in TSP genes and MI were not consistently corroborated in replication studies (21–33). Compared with the AA genotype, the GG genotype and G allele of the SNP in THBS1 have been related with increased MI risks (14,31), but associations were not always observed (21,27,32). Depending on the study, the GG genotype of the SNP in THBS2 has been linked with a higher or lower risk of MI than the TT genotype (14,21,25,32), and the CC genotype of the SNP in THBS4 has been implicated with a higher or lower risk of MI than the GG genotype (21,24,26,30). Alternatively, the GC genotype of the SNP in THBS4 has been connected with MI (14,25) or an association was not found (22,23,28,29,32).

Because of the potential importance of TSPs in thrombotic disease and existing uncertainty about the effect of variations in their genes on the risk of MI, we have determined the genotypes related to the SNPs in THBS1, THBS2, and THBS4 in a large case–control sample of MI and performed a meta-analysis of data from the present and prior studies.

RESULTS

The main baseline characteristics in the group of patients with MI (n = 3657) and the control group (n = 1211) are shown in Table 1. Mean age of the patients was higher than that of the control group, the proportion of women was lower in the patient group than in the control group, and history of arterial hypertension and hypercholesterolemia, current cigarette smoking, and diabetes mellitus were more prevalent in the patient group than in the control group (P < 0.0001 for all comparisons; Table 1).

Table 1.

Baseline clinical characteristics of the myocardial infarction (MI) group and control group

 MI group (n = 3657) Control group (n = 1211) 
Age (years) 64.0 ± 12.0 60.3 ± 11.9 
Women 885 (24.2) 598 (49.4) 
Arterial hypertension 2246 (61.4) 589 (48.6) 
Hypercholesterolemia 2067 (56.5) 602 (49.7) 
Current cigarette smoking 1849 (50.6) 184 (15.2) 
Diabetes mellitus 754 (20.6) 65 (5.4) 
 MI group (n = 3657) Control group (n = 1211) 
Age (years) 64.0 ± 12.0 60.3 ± 11.9 
Women 885 (24.2) 598 (49.4) 
Arterial hypertension 2246 (61.4) 589 (48.6) 
Hypercholesterolemia 2067 (56.5) 602 (49.7) 
Current cigarette smoking 1849 (50.6) 184 (15.2) 
Diabetes mellitus 754 (20.6) 65 (5.4) 

Age is mean ± SD; other variables are presented as number (%) of individuals.

We determined the genotypes of the SNPs in THBS1 (rs2228262), THBS2 (rs8089) and THBS4 (rs1866389) and observed that their distributions in the study sample were consistent with those expected for samples in Hardy–Weinberg equilibrium (P ≥ 0.17). Genotype distributions and allele frequencies of the SNPs in THBS1 and THBS4 were not significantly different between the MI group and the control group (Table 2). With the SNP in THBS2, the differences were slightly above (allele frequencies) and below (genotype distributions) the threshold of significance (Table 2). No substantial sex-related differences of genotype frequencies between the case and control groups were detected (Table 3). Among the younger (<50 years) participants of the study, the genotype distributions were not significantly different between the women of the MI and control groups (n = 136; P ≥ 0.26) and between the men of the two groups (n = 581; P ≥ 0.33).

Table 2.

Genotype distributions and allele frequencies of the thrombospondin gene (THBS) single nucleotide polymorphisms (SNPs) in the myocardial infarction (MI) group and control group

Gene SNP IDa Genotype MI group (n = 3657) Control group (n = 1211) P Allele MI group (7314 alleles) Control group (2422 alleles) P 
THBS1 rs2228262 AA 2873 (78.6) 961 (79.4) 0.20 6495 (88.8) 2154 (88.9) 0.86 
  AG 749 (20.5) 232 (19.2)  819 (11.2) 268 (11.1)  
  GG 35 (1.0) 18 (1.5)      
THBS2 rs8089 TT 2072 (56.7) 734 (60.6) 0.054 5509 (75.3) 1878 (77.5) 0.027 
  TG 1365 (37.3) 410 (33.9)  1805 (24.7) 544 (22.5)  
  GG 220 (6.0) 67 (5.5)      
THBS4 rs1866389 GG 2295 (62.8) 746 (61.6) 0.37 5770 (78.9) 1905 (78.7) 0.81 
  GC 1180 (32.3) 413 (34.1)  1544 (21.1) 517 (21.3)  
  CC 182 (5.0) 52 (4.3)      
Gene SNP IDa Genotype MI group (n = 3657) Control group (n = 1211) P Allele MI group (7314 alleles) Control group (2422 alleles) P 
THBS1 rs2228262 AA 2873 (78.6) 961 (79.4) 0.20 6495 (88.8) 2154 (88.9) 0.86 
  AG 749 (20.5) 232 (19.2)  819 (11.2) 268 (11.1)  
  GG 35 (1.0) 18 (1.5)      
THBS2 rs8089 TT 2072 (56.7) 734 (60.6) 0.054 5509 (75.3) 1878 (77.5) 0.027 
  TG 1365 (37.3) 410 (33.9)  1805 (24.7) 544 (22.5)  
  GG 220 (6.0) 67 (5.5)      
THBS4 rs1866389 GG 2295 (62.8) 746 (61.6) 0.37 5770 (78.9) 1905 (78.7) 0.81 
  GC 1180 (32.3) 413 (34.1)  1544 (21.1) 517 (21.3)  
  CC 182 (5.0) 52 (4.3)      

Variables are presented as number (%) of patients and control individuals.

aSNP identification number (http://www.ncbi.nlm.nih.gov/projects/SNP).

Table 3.

Genotype distributions of the thrombospondin gene (THBS) single nucleotide polymorphisms (SNPs) in the women and men of the myocardial infarction (MI) and control groups

Gene SNP IDa Genotype Women Men 
   MI group (n = 885) Control group (n = 598) P MI group (n = 2772) Control group (n = 613) P 
THBS1 rs2228262 AA 680 (76.8) 475 (79.4) 0.33 2193 (79.1) 486 (79.3) 0.42 
  AG 195 (22.0) 114 (19.1)  554 (20.0) 118 (19.2)  
  GG 10 (1.1) 9 (1.5)  25 (0.9) 9 (1.5)  
THBS2 rs8089 TT 495 (55.9) 361 (60.4) 0.19 1577 (56.9) 373 (60.8) 0.16 
  TG 333 (37.6) 207 (34.6)  1032 (37.2) 203 (33.1)  
  GG 57 (6.4) 30 (5.0)  163 (5.9) 37 (6.0)  
THBS4 rs1866389 GG 554 (62.6) 364 (60.9) 0.064 1741 (62.8) 382 (62.3) 0.90 
  GC 282 (31.9) 214 (35.8)  898 (32.4) 199 (32.5)  
  CC 49 (5.5) 20 (3.3)  133 (4.8) 32 (5.2)  
Gene SNP IDa Genotype Women Men 
   MI group (n = 885) Control group (n = 598) P MI group (n = 2772) Control group (n = 613) P 
THBS1 rs2228262 AA 680 (76.8) 475 (79.4) 0.33 2193 (79.1) 486 (79.3) 0.42 
  AG 195 (22.0) 114 (19.1)  554 (20.0) 118 (19.2)  
  GG 10 (1.1) 9 (1.5)  25 (0.9) 9 (1.5)  
THBS2 rs8089 TT 495 (55.9) 361 (60.4) 0.19 1577 (56.9) 373 (60.8) 0.16 
  TG 333 (37.6) 207 (34.6)  1032 (37.2) 203 (33.1)  
  GG 57 (6.4) 30 (5.0)  163 (5.9) 37 (6.0)  
THBS4 rs1866389 GG 554 (62.6) 364 (60.9) 0.064 1741 (62.8) 382 (62.3) 0.90 
  GC 282 (31.9) 214 (35.8)  898 (32.4) 199 (32.5)  
  CC 49 (5.5) 20 (3.3)  133 (4.8) 32 (5.2)  

Variables are presented as number (%) of women and men in the patient and control groups.

aSNP identification number (http://www.ncbi.nlm.nih.gov/projects/SNP).

Carriers of the minor allele of the SNP in THBS2 (TG and GG genotypes) were present somewhat more frequently in the patient group than in the control group [odds ratio (OR) 1.18; 95% confidence interval (CI), 1.03 to 1.34], whereas the proportions of minor allele carriers of the SNPs in THBS1 and THBS4 were not substantially different between the study groups (THBS1: OR 1.05; 95% CI, 0.89 to 1.23; THBS4: OR 0.95; 95% CI, 0.83 to 1.09). After adjustments were made for conventional cardiovascular risk markers (age, gender, history of arterial hypertension, history of hypercholesterolemia, current cigarette smoking and diabetes mellitus), multiple logistic regression analyses showed similar results: in comparison with homozygosity of the major allele, a mildly increased risk was present in the minor allele carriers of the SNP in THBS2 (adjusted OR 1.19; 95% CI, 1.02 to 1.39), but no significant effects were detected in the minor allele carriers of the SNPs in THBS1 (adjusted OR 1.12; 95% CI, 0.93 to 1.35) and THBS4 (adjusted OR 0.99; 95% CI, 0.85 to 1.16).

Associations of the same SNPs with coronary artery disease, MI, or the acute coronary syndrome (unstable angina pectoris or MI) were previously examined in different samples of Europeans or Americans of European ancestry (Table 4). A consensus risk allele or risk genotype of each of the three SNPs has not emerged. With the use of results provided in reports of these studies, we compared the homozygous and heterozygous carriers of the minor allele with the homozygous carriers of the major allele (risk model 1), the homozygous carriers of the minor allele with the carriers of the major allele (risk model 2), and the homozygous carriers of the minor allele with the homozygous carriers of the major allele (risk model 3). ORs and 95% CIs obtained with risk model 1 in the individual study samples are shown in Figure 1, together with data obtained in the present sample. In the analysis of each SNP with risk model 1, a significant effect was found only once among the different study groups, including the SNP in THBS2 in the present sample (Fig. 1). Heterogeneous results among studies were also observed with risk model 2 and risk model 3. In examinations of combined effects, no associations of the SNPs and MI were detected with the use of risk model 1 (Fig. 1), risk model 2 and risk model 3.

Figure 1.

Estimated risks (with 95% confidence intervals) in G allele carriers (AG or GG genotype) versus AA genotype carriers of the rs2228262 single nucleotide polymorphism (SNP) in THBS1 (five studies), G allele carriers (TG or GG genotype) versus TT genotype carriers of the rs8089 SNP in THBS2 (four studies), C allele carriers (GC or CC genotype) versus GG genotype carriers of the rs1866389 SNP in THBS4 (eight studies). Descriptive information for each included study sample is given in Table 4. From the samples investigated by Topol et al. (14) and Boekholdt et al. (21), the subgroups with myocardial infarction (MI) were included in the analyses. The horizontal axis is plotted on a log doubling scale and the sizes of the data markers (squares) are proportional to the weights of the individual studies. Weights were calculated from random effects analysis. Rhombs indicate the effect sizes that resulted from SNP-specific combinations of individual risk measures. NHS Nurses’ Health Study; HPFS Health Professionals Follow-up Study.

Figure 1.

Estimated risks (with 95% confidence intervals) in G allele carriers (AG or GG genotype) versus AA genotype carriers of the rs2228262 single nucleotide polymorphism (SNP) in THBS1 (five studies), G allele carriers (TG or GG genotype) versus TT genotype carriers of the rs8089 SNP in THBS2 (four studies), C allele carriers (GC or CC genotype) versus GG genotype carriers of the rs1866389 SNP in THBS4 (eight studies). Descriptive information for each included study sample is given in Table 4. From the samples investigated by Topol et al. (14) and Boekholdt et al. (21), the subgroups with myocardial infarction (MI) were included in the analyses. The horizontal axis is plotted on a log doubling scale and the sizes of the data markers (squares) are proportional to the weights of the individual studies. Weights were calculated from random effects analysis. Rhombs indicate the effect sizes that resulted from SNP-specific combinations of individual risk measures. NHS Nurses’ Health Study; HPFS Health Professionals Follow-up Study.

Table 4.

Studies included in the meta-analysis

Study (Country) Cases/controls Women (%) Cases/controls Phenotype in casesa Age selection of cases (Age at onset) 
Present (Germany) 3657/1211 24.2/49.4 Myocardial infarction (MI) no 
Topol et al. (14) (United States) 352/418 30.1/56.5 Familial CAD (54% MI) yes (women: <50 years; men: <45 years) 
Boekholdt et al. (21) (The Netherlands) 503/1071 19.3/23.7 Symptomatic CAD (64% MI) yes (<50 years) 
Wessel et al. (26) (United States) 474/472 19.6/62.3 MI no 
Asselbergs et al. (29) (United States)b 247/486 100/100 Non-fatal MI or fatal CHD no 
Asselbergs et al. (29) (United States)c 264/528 0/0 Non-fatal MI or fatal CHD no 
Cui et al. (30) (Canada) 1032/1014 38.0/53.0 MI no 
Zwicker et al. (31) (Italy) 1425/1425 data not shown MI yes (<45 years) 
Morgan et al. (32) (United States) 811/650 32.2/39.4 ACS (73% MI) no 
Study (Country) Cases/controls Women (%) Cases/controls Phenotype in casesa Age selection of cases (Age at onset) 
Present (Germany) 3657/1211 24.2/49.4 Myocardial infarction (MI) no 
Topol et al. (14) (United States) 352/418 30.1/56.5 Familial CAD (54% MI) yes (women: <50 years; men: <45 years) 
Boekholdt et al. (21) (The Netherlands) 503/1071 19.3/23.7 Symptomatic CAD (64% MI) yes (<50 years) 
Wessel et al. (26) (United States) 474/472 19.6/62.3 MI no 
Asselbergs et al. (29) (United States)b 247/486 100/100 Non-fatal MI or fatal CHD no 
Asselbergs et al. (29) (United States)c 264/528 0/0 Non-fatal MI or fatal CHD no 
Cui et al. (30) (Canada) 1032/1014 38.0/53.0 MI no 
Zwicker et al. (31) (Italy) 1425/1425 data not shown MI yes (<45 years) 
Morgan et al. (32) (United States) 811/650 32.2/39.4 ACS (73% MI) no 

aCAD, coronary artery disease; CHD, coronary heart disease; ACS, acute coronary syndrome.

bNurses’ Health Study.

cHealth professionals follow-up study.

DISCUSSION

In the case–control sample examined here, a weak association of the minor allele (G allele) of the SNP in THBS2 with MI was suggested by the combination of a nonsignificant trend in the overall genotype distribution, a marginally (∼2%) higher frequency of the minor allele in the MI group than in the control group, and, in univariate and multivariate analyses, mildly elevated risks of MI in the carriers of the minor allele when compared with the homozygous carriers of the major allele. Despite the nominal statistical significance of this finding, chance can not be ruled out, and a possible influence of multiple hypothesis testing should be considered. Similar comparisons showed an absence of measurable relationships of the SNPs in THBS1 and THBS4 with MI. Most likely, the lack of association cannot be attributed to the sample size, because the cohort was sufficiently large to achieve 99% power for detecting a 20% increase in the risk of MI (two-sided α-error 0.05).

To compare SNP-disease relations observed in this and prior studies, the combined frequencies of the genotypes with the minor allele were opposed to that of the genotype homozygous for the major allele. Associations were not found in most study groups (Fig. 1). Exceptions were significantly increased risks of MI linked with the minor allele of the SNP in THBS1 in the sample examined by Zwicker et al. (31), the SNP in THBS2 in the present sample, and the SNP in THBS4 in the sample analyzed by Topol et al. (14) (Fig. 1). Combined examination of the results of the different studies strongly suggested that the SNPs in THBS1, THBS2 and THBS4 were not associated with disease (Fig. 1). The SNPs in the TSP genes have been related to premature MI (14,21,31,33,34). Among the younger women and men of the present sample, no such associations were found.

Because all subjects of the present control sample had some indication for coronary angiography, they did not represent a typical sample of healthy controls. Nevertheless, we consider this group to be especially suitable as a control group in the setting of this study, because the absence of MI was rigorously established on the basis of history, electrocardiography, left ventricular angiography, and coronary angiography. The control group studied here appears not to be fundamentally different from typical control groups, because the genotype distributions were not significantly different from the genotype distributions observed in other control groups that consisted of white individuals (21,26,29,30,32).

Data from the sample presented here and from prior reports (14,21–32) showed differences in the relationship of variations in TSP genes and disease: the identity of the risk genotype or allele was inconsistent between studies or there was no association at all. Among the possible reasons for these differences were small sample sizes in some instances, lack of correction for multiple hypothesis testing, application of different risk models, different age ranges of study participants, assessment of related but not identical disease phenotypes, different definitions and procedures for establishment of case status, and different recruitment strategies for control individuals (14,21–32). To greatly reduce the potential influence of differences based on ethnic factors, inclusion in the meta-analysis was restricted to data obtained from studies with participants of European descent. The combined analyses were limited by some heterogeneity of the disease phenotype, because the genotype distributions in the patients with MI were not separately shown in two reports of studies that also included patients without MI in the case groups (29,32).

With the examination of only one of the many known SNPs located within each of the TSP genes (http://www.ncbi.nlm.nih.gov/sites/entrez/), merely a portion of the existing variation of these genes was addressed in this and prior (14,21–32) studies. Data about allelic associations established by the International HapMap consortium suggest the use of three (THBS1), 19 (THBS2) and nine (THBS4) tag SNPs to capture the genetic variety at these loci (http://www.hapmap.org) (35).

Conclusion: while results from individual studies, including ours, suggest associations of SNPs in THBS1 (rs2228262), THBS2 (rs8089), and THBS4 (rs1866389) with MI, the synthesis of the available data does not provide evidence for such relationships. The findings do not support the use of these SNPs as risk markers of MI. However, comprehensive haplotype analysis across the regions of the TSP genes may reveal links between these genes and atherosclerotic diseases in coronary arteries.

MATERIALS AND METHODS

Patients and controls

Participants were recruited from Southern Germany and examined at Deutsches Herzzentrum München or 1. Medizinische Klinik rechts der Isar der Technischen Universität München from 1993 to 2002. After catheterization, 5264 individuals were deemed eligible for inclusion into the MI or control group. Written informed consent for genetic analysis was obtained from 97.1% (n = 5111) of these individuals. In no case, consent was withdrawn. Blood samples assigned for DNA preparation had been collected from 95.2% (n = 4868) of the individuals who agreed to participate in the study. These individuals, 3657 patients with MI and 1211 controls, constituted the study population. The study protocol was approved by the institutional ethics committee and the reported investigations were in accordance with the principles of the current version of the Declaration of Helsinki (http://www.wma.net/e/policy/b3.htm).

Definitions

The diagnosis of MI was established in the presence of chest pain lasting>20 min combined with ST-segment elevation or pathological Q waves on a surface electrocardiogram. Patients with MI had to show either an angiographically occluded infarct-related artery or regional wall motion abnormalities corresponding to the electrocardiographic infarct localization, or both. Individuals were considered disease-free and therefore eligible as controls when their coronary arteries were angiographically normal and when they had no history of MI, no symptoms suggestive of MI, no electrocardiographic signs of MI, and no regional wall motion abnormalities. Coronary angiography in the control individuals was performed for the evaluation of chest pain. Systemic arterial hypertension was defined as a systolic blood pressure of ≥140 mmHg and/or a diastolic blood pressure of ≥90 mmHg (36), on at least two separate occasions, or antihypertensive treatment. Hypercholesterolemia was defined as a documented total cholesterol value ≥240 mg/dl (≥6.2 mmol/l) or current treatment with cholesterol-lowering medication. Persons reporting regular smoking in the previous 6 months were considered as current smokers. Diabetes mellitus was defined as the presence of an active treatment with insulin or an oral antidiabetic agent; for patients on dietary treatment, documentation of an abnormal fasting blood glucose or glucose tolerance test based on the World Health Organisation criteria (37) was required for establishing this diagnosis.

DNA genotyping

TaqMan allelic discrimination systems (38) were designed and used for genotyping of the SNPs in THBS1 (rs2228262), THBS2 (rs8089) and THBS4 (rs1866389) (http://www.ncbi.nlm.nih.gov/entrez/). The sequences of primers and probes are shown in Table 5. Presence of the fluorogenic dyes FAM (6-carboxy-fluorescein) and VIC (proprietary dye of Applied Biosystems) at the 5′ ends of the probe molecules accomplished allele-specific signaling (38). Minor groove binder groups were conjugated with the 3′ ends of the probe oligonucleotides to facilitate formation of stable duplexes between the probes and their single-stranded DNA targets (39). TaqMan reactions were analyzed on the ABI Prism 7000 Sequence Detection System (Applied Biosystems). About 20% of the DNA samples were re-typed to control for correct sample handling and data acquisition. With each polymorphism, 100 different DNA samples were examined by sequencing to verify the accuracy of TaqMan genotyping and to test whether one or more additional polymorphisms were present in the probe-binding section of the amplicons, because they may interfere with TaqMan reactions and result in wrong genotype assignments. BigDye Terminator Cycle Sequencing reagents and the ABI Prism 3100 Genetic Analyzer (Applied Biosystems) were used for sequence analysis. Genotypes determined by sequencing and TaqMan reactions were fully corresponding. Sequencing results showed also that the known polymorphisms were the only sequence variabilities in the probe-binding regions, which implicated that the probability of genotyping errors because of possible further sequence variations was relatively low. Clinicians responsible for diagnosis were not aware of the genetic data. All genetic analyses were done without knowledge of the case–control status of the DNA samples.

Table 5.

Primers and TaqMan probes used for genotyping of the thrombospondin gene (THBS) single nucleotide polymorphisms (SNPs)

Gene Location SNP IDa Polymorphism Primer (5′→3′) TaqMan probe (5′→3′)b 
THBS1 15q15 rs2228262 2210A/G (Asn700Ser) CTGCGGGGAGGACACAGA FAM-TGGCCCAATGAGAA 
    TTAGCCTGTGTTTTGATAAGGTGATG VIC-CTGGCCCAGTGAGA 
THBS2 6q27 rs8089 3949T/G (3′-UTRcCATAAAGTCTCATATATCACCAAACTGGTT FAM-TTCATCTCTGAGTTCCATT 
    CCTTGACCTTAACTCTGATGGTTCTT VIC-TCATCTCTGCGTTCCA 
THBS4 5q12-q13 rs1866389 1186G/C (Ala387Pro) CTAGGTCTGCACTGACATTGATGAGT FAM-ACGCACGCTCCAT 
    ACCAAAGTATTAACGCAGATCGAGTT VIC-AACGCACGGTCCAT 
Gene Location SNP IDa Polymorphism Primer (5′→3′) TaqMan probe (5′→3′)b 
THBS1 15q15 rs2228262 2210A/G (Asn700Ser) CTGCGGGGAGGACACAGA FAM-TGGCCCAATGAGAA 
    TTAGCCTGTGTTTTGATAAGGTGATG VIC-CTGGCCCAGTGAGA 
THBS2 6q27 rs8089 3949T/G (3′-UTRcCATAAAGTCTCATATATCACCAAACTGGTT FAM-TTCATCTCTGAGTTCCATT 
    CCTTGACCTTAACTCTGATGGTTCTT VIC-TCATCTCTGCGTTCCA 
THBS4 5q12-q13 rs1866389 1186G/C (Ala387Pro) CTAGGTCTGCACTGACATTGATGAGT FAM-ACGCACGCTCCAT 
    ACCAAAGTATTAACGCAGATCGAGTT VIC-AACGCACGGTCCAT 

aSNP identification number (http://www.ncbi.nlm.nih.gov/projects/SNP).

bAllele-specific nucleotides are underlined.

c3′ untranslated region.

Meta-analysis: data sources, inclusion criteria, data analysis

To identify data to be included in the meta-analysis, the National Library of Medicine (PubMed, at http://www.pubmed.gov) and reference lists of identified studies and any relevant published reviews were searched up to November 2007. Searches addressed case–control studies that investigated associations of one or more of the rs2228262 (Asn700Ser) polymorphism in THBS1, the rs8089 polymorphism in THBS2, and the rs1866389 (Ala387Pro) polymorphism in THBS4 with MI. The terms used in the PubMed search were combinations of ‘TSP’, ‘THBS1’, ‘THBS2’, or ‘THBS4’ with ‘polymorphi*’ or ‘SNP’, or with ‘MI’, ‘coronary artery disease’, or ‘coronary heart disease’. To ensure a high degree of genetic homogeneity, only data of studies with Europeans or Americans of European origin were included in the meta-analysis. Papers were excluded that reported about results identical to eligible data already published elsewhere or studies with participants also contained in a larger sample considered eligible for inclusion. Data from a study presented only in the form of an abstract were not included, because genotype distributions and the number of patients with MI were not shown (40). Results from eight different study samples that were published in seven reports fulfilled the inclusion criteria (Table 4). Four studies addressed the polymorphism in THBS1 (14,21,31,32), three studies the polymorphism in THBS2 (14,21,32), and six studies the polymorphism in THBS4 (14,21,26,29,30,32). In addition to published results, data from the present case–control study were included in the meta-analysis. Individual analyses involved 6388 cases and 4736 controls (THBS1), 4930 cases and 3277 controls (THBS2), and 6978 cases and 5745 controls (THBS4). The meta-analysis was performed in accordance with the MOOSE guidelines (41).

Statistical analysis

The analysis consisted of comparing separately genotype distributions and allele frequencies of the SNPs in the MI group and the control group. In addition, separate analyses of genotype distributions were performed in the groups of men and women because the possibility of sex-specific associations was suggested from results obtained with the SNP in THBS4 in two independent populations (29,30). Age is expressed as mean±SD and compared by means of the unpaired, two-sided t-test. Discrete variables are expressed as counts (percentage) and compared with the use of the χ2-test. Independent genetic association effects were tested in multiple logistic regression models of MI that included as covariates age, gender, history of arterial hypertension, history of hypercholesterolemia, current cigarette smoking, and diabetes mellitus. Adjusted ORs and 95% Wald CIs were calculated on the basis of these models. Heterogeneity across trials was assessed by calculating the I2-statistic (describing the percentage of total variation across trials that was due to heterogeneity rather than chance). Pooled ORs and 95% CIs from the different studies were calculated by the DerSirmonian and Laird method for random effects (42). The meta-analysis was performed with Stata software version 9.2 (Stata). Statistical significance was accepted for P-values < 0.05.

FUNDING

This work was funded by a grant from Deutsches Herzzentrum München (KKF 1.4-05) to W.K.

ACKNOWLEDGEMENTS

The authors wish to thank Claudia Ganser, Wolfgang Latz and Marianne Eichinger for expert technical assistance.

Conflict of Interest statement. None declared.

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