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Keywords:

  • Coronary artery disease;
  • myocardial infarction;
  • apolipoprotein(a);
  • genetics;
  • single nucleotide polymorphism;
  • haplotype

Summary

  1. Top of page
  2. Summary
  3. Introduction
  4. Materials and Methods
  5. Results
  6. Discussion
  7. Acknowledgements
  8. Conflict of Interest
  9. Supporting Information

Relatively low numbers of kringle 4 type 2 repeats in apolipoprotein(a) and specific haplotypes of the SLC22A3-LPAL2-LPA region on chromosome 6 are associated with an increased risk of coronary disease. We examined the possibility that rs3798220 and rs10455872, short variations located in LPA [the apolipoprotein(a) gene], and related to the number of kringle 4 type 2 repeats, may serve as markers for the association between haplotypes and acute myocardial infarction. Genotypes were determined with TaqMan assays in a sample of 2136 cases and 1211 controls. The minor alleles of rs3798220 and rs10455872 were associated with increased risks (rs3798220-C: adjusted OR 2.14, 95% CI, 1.37–3.33, P = 0.00080; rs10455872-G: adjusted OR 1.74, 95% CI 1.36–2.24, P < 0.00001). After adjustments were made for potential confounders, none of nine polymorphisms included in a haplotype analysis were singly related to disease. Two risk haplotypes were identified; one (CCTTGTGTG; OR 1.25, 95% CI 1.08–1.45, P = 0.0022) was correlated with rs3798220-C and the other (CCCTGGATC; OR 1.65, 95% CI 1.14–2.38, P = 0.0074) with rs10455872-G. Thus, the findings allowed for a more precise definition of risk-associated markers: specific nucleotides in LPA instead of standard haplotypes defined by noneffective variants from the extensive SLC22A3-LPAL2-LPA region.


Introduction

  1. Top of page
  2. Summary
  3. Introduction
  4. Materials and Methods
  5. Results
  6. Discussion
  7. Acknowledgements
  8. Conflict of Interest
  9. Supporting Information

Apolipoprotein(a) [apo(a)] is the characteristic protein component of lipoprotein(a) [Lp(a)], a low-density lipoprotein-like plasma lipoprotein particle (Gaubatz et al., 1983; Loscalzo, 1990). While the biological function of Lp(a) remains uncertain (Berglund & Ramakrishnan, 2004; Anuurad et al., 2010), a high level of Lp(a) in plasma has been associated with an increased cardiovascular risk (Danesh et al., 2000; Suk Danik et al., 2006; Ridker et al., 2007; Kamstrup et al., 2008; Clarke et al., 2009; Nicholls et al., 2010).

Apo(a) is highly polymorphic, mainly due to the kringle 4 type 2 polymorphism, a copy number variation defined by a variable number, from 2 to >40, of a cystein-rich sequence resulting in differently sized isoforms, ranging from 250 to 900 kDa (McLean et al., 1987; Lackner et al., 1993). The number of kringle 4 type 2 repeats was found to be inversely related to the level of Lp(a) and the risk of coronary artery disease (CAD) and myocardial infarction (MI) (Holmer et al., 2003; Clarke et al., 2009; Kamstrup et al., 2009; Lanktree et al., 2010). Relatively short isoforms (17–21 repeats) of apo(a) have been correlated with rare allele variants, rs3798220-C and rs10455872-G, in the apo(a) gene (LPA) (Clarke et al., 2009). These alleles were reported to be associated with elevated plasma Lp(a) and increased cardiovascular risk (Luke et al., 2007; Shiffman et al., 2008a, b; Chasman et al., 2009; Clarke et al., 2009).

In addition to the findings on rs3798220-C and rs10455872-G, specific haplotypes of an extended region on the long arm of chromosome 6 (Ch6q26–27) including LPA and two other loci, solute carrier family 22 member 3 (SLC22A3) and Lp(a)-like 2 (LPAL2), have been related to elevated plasma concentrations of Lp(a) and CAD (Trégouët et al., 2009). The haplotypes were based on four single nucleotide polymorphisms (SNPs), rs2048327 (T > C) in SLC22A3, rs3127599 (C > T) in LPAL2, and both rs7767084 (T > C) and rs10755578 (C > G) in LPA, none of which is correlated with rs3798220 (T > C) or rs10455872 (A > G) in LPA or the kringle 4 type 2 polymorphism of apo(a) (Trégouët et al., 2009). An effective variant in the CAD-related four-SNP haplotypes, CTTG and CCTC, could not be identified, because none of the SNPs involved was sufficient to explain the observed association with disease (Trégouët et al., 2009).

We examined the possibility that rs3798220 and rs10455872 in LPA may explain the association between haplotypes in the SLC22A3-LPAL2-LPA region and cardiovascular disease. Compared with a prior study, which included four SNPs (Trégouët et al., 2009), the haplotype analysis was extended to nine SNPs. A large sample of European patients with acute MI (AMI) and controls with angiographically normal coronary arteries and no history of AMI served as a study population.

Materials and Methods

  1. Top of page
  2. Summary
  3. Introduction
  4. Materials and Methods
  5. Results
  6. Discussion
  7. Acknowledgements
  8. Conflict of Interest
  9. Supporting Information

Study Population

Participants were of European, mainly (90%) German, origin. They were recruited consecutively at Deutsches Herzzentrum München or 1. Medizinische Klinik, Klinikum rechts der Isar, Technische Universität München from 1993 to 2002. After coronary angiography, 3605 individuals were deemed eligible for inclusion into the AMI or control group. Written informed consent for genetic analysis was obtained from 97.3% (n = 3508) of these individuals. In no case was consent withdrawn. Blood samples assigned for DNA preparation had been collected from 95.4% (n = 3347) of the individuals who agreed to participate in the study. These individuals, 2136 patients with AMI and 1211 controls, constituted the study population. The study protocol was approved by the institutional ethics committee, and the reported investigations have been carried out in accordance with WMA Declaration of Helsinki – Ethical Principles for Medical Research Involving Human Subjects (http://www.wma.net/en/30publications/10policies/b3/index.html; last accessed May 15, 2012).

The diagnosis of AMI was established in the presence of chest pain lasting >20 min combined with ST-segment elevation or pathological Q waves on a surface ECG. All patients with AMI underwent coronary angiography and had to show either an occluded infarct-related artery or regional wall motion abnormalities corresponding to the electrocardiographic infarct localization, or both. The diagnosis of AMI was also to be confirmed by an increase in the MB fraction of creatine kinase or troponin T above the upper limit of normal. Individuals were considered disease-free and therefore eligible as controls if their coronary arteries showed smooth contours or only slight wall irregularities that did not cause lumen narrowing of more than 10% of the normal diameter, and when they had no previous AMI, no symptoms suggestive of AMI, no electrocardiographic signs of AMI, 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 (Chalmers et al., 1999), 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, according to information provided by the TIMI study group in the document TIMI Definitions for Commonly Used Terms in Clinical Trials (http://www.timi.org/wp-content/uploads/2010/10/TIMI-Definitions.pdf; last accessed May 15, 2012). Persons reporting regular smoking in the previous six 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 individuals on dietary treatment, documentation of an abnormal fasting blood glucose or glucose tolerance test based on the World Health Organization criteria (World Health Organization Study Group, 1985) was required for establishing this diagnosis.

SNP Selection

The haplotype analysis comprised ∼140 kb of the SLC22A3-LPAL2-LPA region, which was approximately the same genomic segment found to be associated with CAD in a prior haplotype study (Trégouët et al., 2009). Haplotype-tagging SNPs were inferred from the HapMap data base adjusted to the CEU population sample (Utah residents with ancestry from northern and western Europe from the Centre d'Etude du Polymorphisme Humain collection) (http://hapmap.ncbi.nlm.nih.gov) (The International HapMap Consortium, 2007). Eight tagging SNPs were identified which captured a total of 56 SNPs at r2 ≥ 0.80 (mean r2 = 0.93). Three of the tagging SNPs (rs2048327, rs3127599, and rs10755578) were also used in the prior analysis (Trégouët et al., 2009). In addition to the tagging SNPs, the haplotype analysis included rs7767084, which was used in the prior study (Trégouët et al., 2009). A plot of linkage disequilibrium in the SLC22A3-LPAL2-LPA region is shown in Figure S1, which includes all the common variation in this region, as provided by HapMap. The SNPs rs3798220 (exon 37; Ile4399Met) and rs10455872 (intron 25) in LPA were also used but were not included in the haplotype analysis. SNP locations are shown in Figure 1.

image

Figure 1. SLC22A3-LPAL2-LPA region on the long arm of chromosome 6 (Ch6q26-q27) and positions of SNPs included in the analysis. SLC22A3 refers to the gene for solute carrier family 22 (extraneuronal monoamine transporter), member 3, LPAL2 denotes the lipoprotein(a)-like 2 gene, and LPA encodes apolipoprotein(a). SNPs depicted in blue were included in a haplotype analysis and SNPs depicted in red were not. Exons are shown as vertical lines or boxes, depending on their size. The graph is based on genome build 36.3 and was designed according to data provided online by the International HapMap Consortium (http://hapmap.ncbi.nlm.nih.gov).

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Genotyping

Genomic DNA was extracted from peripheral leukocyte nuclei with Nucleo-Spin Blood Quick Pure reagents (Macherey-Nagel, Düren, Germany). All genotypes were determined by TaqMan assays, which combine the standard PCR and the 5’ nuclease reaction (Livak, 1999). Probes were allele-specifically labeled with one of the fluorescent dyes FAM (6-carboxy-fluorescein; major allele) and VIC (proprietary dye of Applied Biosystems, Foster City, CA, USA; minor allele) and contained a minor groove binder group and a dark quencher (Kutyavin et al., 2000; de Kok et al., 2002). The sequences of primers and probes were designed in house using Primer Express software (version 2.0; Applied Biosystems) and are shown in Table S1. Primers and probes were synthesized by Applied Biosystems. Reactions were performed in ABsolute QPCR ROX Mix (Thermo Fisher Scientific, Schwerte, Germany). After cycling on a 2720 Thermal Cycler or GeneAmp PCR System 9700 (Applied Biosystems), genotype calling was carried out on an ABI PRISM 7000 Sequence Detection System (Applied Biosystems). With the use of DNA separately prepared from the original blood sample, retyping of 20% of the DNA samples was done to control for correct sample handling and data acquisition.

Using the same primers utilized for PCR in TaqMan assays, the accuracy of genotyping with the new TaqMan systems was evaluated by sequencing of 100 randomly selected DNA samples. PCR amplifications were carried out with Taq DNA Polymerase (Qiagen, Hilden, Germany) followed by digestion of residual primers with NucleoSpin Extract II reagents (Macherey-Nagel). Sequencing reactions were performed with the use of a BigDye Terminator v1.1 Cycle Sequencing Kit (Applied Biosystems). Products were purified with NucleoSEQColumns (Macherey-Nagel) and subsequently separated on an ABI PRISM 3100 Genetic Analyzer (Applied Biosystems). Sequencing results were analyzed with DNA Sequencing Analysis Software (version 5.1; Applied Biosystems).

Sequencing of these samples was also used to inspect the probe-binding sections of the amplicons for the presence of variation in addition to the SNPs under examination. Such testing is necessary, because, with TaqMan assays, the existence of further variation in a probe-binding section might bring about incorrect genotype assignments (Teupser et al., 2001). Details of reaction protocols are available upon request. Genotyping was done by workers who were not aware of the clinical and laboratory data of the study participants and the case/control status of blood and DNA samples.

Statistical Analysis

The analysis consisted of comparing separately allele and haplotype frequencies between the group with AMI and the control group. Discrete variables are expressed as counts (percentage) and compared with the use of the χ2 test. Age is expressed as mean ± SD and compared by means of the unpaired, 2-sided t test. The per-allele odds ratio (OR) and 95% confidence interval (CI) of the minor allele were calculated by logistic regression. The primary hypothesis was the association of individual alleles with AMI and the threshold for significance was adjusted by the 11 SNPs that were tested. Hardy-Weinberg equilibrium tests were run in the controls for each SNP. We tested for the association of SNPs independent from potentially confounding effects in multiple logistic regression models of AMI 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. Haplotypes were reconstructed from genotype data using the software package PHASE (Stephens et al., 2001). The resulting haplotype probabilities for each individual were used to test for association with AMI in a logistic regression analysis (Zaykin et al., 2002) and to calculate haplotype frequencies. The measures of linkage disequilibrium (D’ and r2) among the haplotype-defining SNPs were calculated with the use of the software package Haploview (Barrett et al., 2005). Linkage disequilibrium (r2) between haplotypes and alleles of rs3798220 and rs10455872 was estimated with Multiallelic Interallelic Disequilibrium Analysis Software (MIDAS) (Gaunt et al., 2006) using the best haplotype reconstruction of PHASE.

Results

  1. Top of page
  2. Summary
  3. Introduction
  4. Materials and Methods
  5. Results
  6. Discussion
  7. Acknowledgements
  8. Conflict of Interest
  9. Supporting Information

Baseline Characteristics in Cases and Controls

Baseline characteristics of the AMI group and control group are shown in Table 1. Mean age of the AMI group was higher than that of the control group and the proportion of women was lower among the patients with AMI than among the controls (Table 1). History of arterial hypertension and hypercholesterolemia, current cigarette smoking, and diabetes mellitus occurred more often in the cases than the controls (Table 1).

Table 1. Baseline characteristics of the AMI group and control group
 AMI groupControl group
 (n = 2136)(n = 1211)
  1. AMI, acute myocardial infarction.

  2. Age is mean ± SD; other variables are presented as number (%). The P < 0.0001 for all comparisons.

Age (years)63.0 ± 12.560.3 ± 11.9
Women560 (26.2)598 (49.4)
Arterial hypertension1197 (56.0)589 (48.6)
Hypercholesterolemia1221 (57.2)602 (49.7)
Current cigarette smoking1136 (53.2)184 (15.2)
Diabetes mellitus375 (17.6)65 (5.4)

Association between SNPs in the SLC22A3-LPAL2-LPA Region and AMI

Genotyping in the study population was accomplished with TaqMan assays specifically designed for 11 SNPs located in the SLC22A3-LPAL2-LPA genomic region (Fig. 1). Accuracy of genotyping with the TaqMan assays was ascertained by a comparative test involving DNA sequencing in a fraction (n = 100 per SNP) of the study population. The degree of accuracy was 100% for each of the SNPs. Sequencing also showed that no variation in addition to the SNPs under examination was present in the probe-binding sections of the amplicons. This finding greatly reduced the probability of wrong genotype assignments that may occur with a TaqMan assay if one or more variations in addition to the index SNP existed in the probe-binding region. The results of repeat TaqMan typing in subsets (20% per SNP) of the study participants were in full agreement with the initially assigned genotypes, indicating correct sample handling and data acquisition. Genotype distributions in the case and control groups are shown in Table S2.

Four SNPs were associated with AMI and the alleles related to an elevated risk were rs3127599-T, rs9346818-G, rs3798220-C, and rs10455872-G (Table 2). After adjustments were made for conventional cardiovascular risk markers (age, gender, history of arterial hypertension, history of hyper-cholesterolemia, current cigarette smoking, and diabetes mellitus), a multiple logistic regression analysis of AMI showed associations for the carriers of rs9346818-G (adjusted OR, 1.26; 95% CI, 1.00–1.57; P = 0.047), rs3798220-C (adjusted OR, 2.14; 95% CI, 1.37–3.33; P = 0.00080), and rs10455872-G (adjusted OR, 1.74; 95% CI, 1.36–2.24; P < 0.00001), but not for the carriers of rs3127599-T (adjusted OR, 1.15; 95% CI, 0.94–1.42; P = 0.18). To compensate for the number (n = 11) of inferences made, only the associations of rs3798220-C and rs10455872-G with AMI were deemed significant.

Table 2. Allele frequencies and risk estimates
   AMI group (4272 alleles)Control group (2422 alleles)  
SNP1Position2Alleles3MajorMinorMajorMinorOR (95% CI)4P
  1. SNP, single nucleotide polymorphism; AMI, acute myocardial infarction; OR, odds ratio; CI, confidence interval.

  2. 1SNP identification number according to the National Center for Biotechnology Information SNP data base (http://www.ncbi.nlm.nih.gov/projects/SNP; last accessed May 21, 2012).

  3. 2Position of SNP on chromosome 6 (genome build 36.3).

  4. 3Major and minor allele in the study population. The alleles of rs2048327, rs3127599, rs7767084, and rs10755578 were adopted from Trégouët et al. (2009) and the alleles from all other SNPs were derived from the same DNA strand as those of rs7767084 (and rs10755578). Hence, with the exception of rs2048327 and rs3127599, the alleles of the SNPs were taken from the “Watson” strand (mRNA-like strand of SLC22A3).

  5. 4OR and 95% CI for minor allele.

rs2048327160,783,522T > C2705 (63.3)1567 (36.7)1581 (65.3)841 (34.7)1.09 (0.98–1.21)0.11
rs2076828160,792,776C > G2439 (57.1)1833 (42.9)1360 (56.2)1062 (43.8)0.96 (0.87–1.06)0.46
rs3127599160,827,124C > T2955 (69.2)1317 (30.8)1760 (72.7)662 (27.3)1.18 (1.06–1.32)0.0026
rs3123630160,831,043T > C3482 (81.5)790 (18.5)1949 (80.5)473 (19.5)0.93 (0.82–1.06)0.30
rs9346818160,834,450G > A2623 (61.4)1649 (38.6)1410 (58.2)1012 (41.8)0.88 (0.79–0.97)0.011
rs9457930160,841,556T > G2174 (50.9)2098 (49.1)1179 (48.7)1243 (51.3)0.92 (0.83–1.01)0.082
rs9365168160,860,110G > A2891 (67.7)1381 (32.3)1624 (67.1)798 (32.9)0.97 (0.87–1.08)0.60
rs3798220160,881,127T > C4168 (97.6)104 (2.4)2389 (98.6)33 (1.4)1.81 (1.22–2.68)0.0029
rs7767084160,882,493T > C3584 (83.9)688 (16.1)1999 (82.5)423 (17.5)0.91 (0.79–1.04)0.15
rs10755578160,889,728C > G2242 (52.5)2030 (47.5)1295 (53.5)1127 (46.5)1.04 (0.94–1.15)0.44
rs10455872160,930,108A > G3944 (92.3)328 (7.7)2291 (94.6)131 (5.4)1.45 (1.18–1.79)0.00042

Association between Haplotypes and AMI

Using a set of nine SNPs, nine major haplotypes (frequency >1%) were inferred from genotype data (Table 3). Pairwise measures of allelic association showed little evidence for ancestral recombination, as suggested by high D’ values (Fig. S2), and relatively little correlation among the SNPs, as indicated by low r2 values (Fig. S3). Among the five relatively frequent (>10%) haplotypes, the CCTTGTGTG haplotype (Hap4) was associated with AMI (Table 3). In addition, two relatively rare haplotypes, the CCCTGGATC haplotype (Hap7; frequency 2.3%) and TGTTGTGTC haplotype (Hap9; frequency 1.3%), were associated with AMI (Table 3).

Table 3. Haplotypes and associated risk of AMI
Haplotype    
 AMIControl  
 Allelegroupgroup  
Namecombination1(%)(%)OR (95% CI)P
  1. AMI, acute myocardial infarction; OR, odds ratio; CI, confidence interval.

  2. a

    The order of the alleles in the haplotypes is in accordance with the relative chromosomal positions of the SNPs (from left to right): rs2048327 (T > C), rs2076828 (C > G), rs3127599 (C > T), rs3123630 (T > C), rs9346818 (G > A), rs9457930 (T > G), rs9365168 (G > A), rs7767084 (T > C), and rs10755578 (C > G).

Hap1TGCTGGATC27.028.70.92 (0.82–1.03)0.14
Hap2TCCCATGTC17.818.80.94 (0.82–1.06)0.31
Hap3CCCTAGGCG15.016.40.89 (0.78–1.03)0.11
Hap4CCTTGTGTG15.712.91.25 (1.08–1.45)0.0022
Hap5TGTTGTGTG12.812.61.01 (0.87–1.18)0.85
Hap6CCCTAGGTG2.73.20.83 (0.62–1.12)0.23
Hap7CCCTGGATC2.61.61.65 (1.14–2.38)0.0074
Hap8TCCTATATC1.41.80.79 (0.53–1.17)0.24
Hap9TGTTGTGTC1.50.91.66 (1.02–2.70)0.039
Other 3.63.11.16 (0.88–1.54)0.29

Correlation of Risk Haplotypes with the rs3798220-C and rs10455872-G Risk Alleles

A combined analysis of haplotype and allele data showed that the Hap7 risk haplotype was strongly correlated (r2 = 0.77) with the rs3798220-C risk allele and the Hap4 risk haplotype partially tagged (r2 = 0.28) the rs10455872-G risk allele. None of the other haplotypes were substantially correlated with rs3798220 or rs10455872 (r2 ≤ 0.02). Conceivably, a considerable proportion of the effects of Hap7 and Hap4 could be explained by linkage disequilibrium with the rs3798220-C and rs10455872-G alleles, respectively.

Discussion

  1. Top of page
  2. Summary
  3. Introduction
  4. Materials and Methods
  5. Results
  6. Discussion
  7. Acknowledgements
  8. Conflict of Interest
  9. Supporting Information

These results are consistent with prior evidence to suggest association of specific haplotypes of the SLC22A3-LPAL2-LPA region and the rs3798220-C and rs10455872-G alleles of LPA with cardiovascular disease (Luke et al., 2007; Shiffman et al., 2008a, b; Chasman et al., 2009; Clarke et al., 2009; Trégouët et al., 2009). None of the SNPs used in the present and a prior (Trégouët et al., 2009) haplotype analysis were sufficiently correlated with haplotypes to explain the haplotype-associated risk. Instead, the Hap7 and Hap4 risk haplotypes defined in this work were specifically correlated with rs3798220-C and rs10455872-G, alleles not used in the haplotype scan and not correlated with any of the haplotype-defining alleles. The rs3798220-C and rs10455872-G alleles were reported to be correlated with short isoforms of apo(a) (Clarke et al., 2009), which are associated with CAD and MI (Holmer et al., 2003; Clarke et al., 2009; Kamstrup et al., 2009). Thus, the rs3798220-C and rs10455872-G alleles provide connecting links between genetically defined risk markers, specific haplotypes of the SLC22A3-LPAL2-LPA region, and structural risk markers, short isoforms of apo(a). Differences in age and gender between the AMI group and the control group may be regarded as a limitation of the study.

A haplotype-tagging SNP, rs3127599, associated with AMI in an univariate but not a multiple logistic regression analysis, was not related to CAD in studies conducted by Samani et al. (2007) and Trégouët et al. (2009), each of which combined at least two distinct population samples. Another haplotype-tagging SNP, rs9346818, associated with AMI in univariate and multivariate analyses, but no longer after adjustment for multiple testing, was associated with CAD in the study by Samani et al. (2007), but only in one (Wellcome Trust Case Control Consortium) of two samples examined. An SNP, rs2048327, not associated with AMI was strongly related to CAD in a combined analysis of six samples reported by Trégouët et al. (2009). Corresponding with this finding of no association with AMI, several SNPs were not linked to CAD in prior studies: rs7767084 and rs10755578, an SNP (rs4708869) correlated with rs3123630 (HapMap: r2 = 1.0), and SNPs (rs3127595, rs9365166, and rs9347407) correlated with rs9457930 (HapMap: r2 ≥ 0.86) (Luke et al., 2007; Samani et al., 2007; Trégouët et al., 2009).

Using four SNPs, rs2048327, rs3127599, rs7767084, and rs10755578, which were also included in this analysis, Trégouët et al. (2009) identified the SLC22A3-LPAL2-LPA region as a risk locus for CAD. They defined seven common haplotypes (frequency >1%) and used the most abundant among them, the TCTC haplotype (frequency ∼50%), as a reference for the estimation of risk associated with each of the other six common haplotypes (Trégouët et al., 2009). Two of the haplotypes, the CTTG and CCTC, were associated with a significantly increased risk of CAD, compared to the TCTC haplotype (Trégouët et al., 2009). The CTTG and CCTC haplotypes are fully covered by the risk-associated Hap4 (CCTTGTGTG) and Hap7 (CCCTGGATC) haplotypes in this study, respectively. The Hap9 (TGTTGTGTC) risk haplotype was represented by the TTTC haplotype in the prior report, a haplotype that was not associated with the risk of CAD (Trégouët et al., 2009).

In contrast to LPA, a role for SLC22A3 and LPAL2 in atherogenesis or thrombosis is not evident, and, to our knowledge, SLC22A3 and LPAL2 have not been considered as candidate genes for CAD or MI. SLC22A3 encodes solute carrier family 22 (extraneuronal monoamine transporter) member 3, a polyspecific organic cation transporter, and LPAL2 [Lp(a)-like 2] is a truncated version of LPA (Byrne et al., 1995; Gründemann et al., 1998; Gründemann & Schömig, 2000). These findings of a correlation between risk-conferring haplotypes of the SLC22A3-LPAL2-LPA region and the rs3798220-C and rs10455872-G alleles in LPA are in favor of the assumption that effective variants are rather located in LPA than in SLC22A3 or LPAL2.

Plasma levels of Lp(a) vary substantially within populations, and prospective epidemiological studies have firmly established a positive association of baseline Lp(a) concentration with coronary disease, carotid atherosclerosis, and stroke (Danesh et al., 2000; Bennet et al., 2008; The Emerging Risk Factors Collaboration, 2009; Nordestgaard et al., 2010). Levels of Lp(a) are to a large extent influenced by an established heritable and highly polymorphic variation in apo(a), the kringle 4 type 2 repeats, suggesting a causal relationship between elevated Lp(a) and increased cardio- and cerebrovascular disease risk (Seed et al., 1990; Holmer et al., 2003; Samani et al., 2007; Clarke et al., 2009; Kamstrup et al., 2009). In addition to epidemiological evidence, experimental data pointed to a role for apo(a) and Lp(a) in the development of atherosclerosis and thrombosis (Lawn et al., 1992; Grainger et al., 1993; Grainger et al., 1994; Caplice et al., 1998; Lou et al., 1998; Caplice et al., 2001; Tsimikas et al., 2005). Following transgenic expression of the human LPA gene in mice, a species normally lacking apo(a), the animals were more susceptible than control mice to the development of lipid-staining lesions in the aorta and apo(a) was shown to colocalize with lipid accumulation in the artery walls (Lawn et al., 1992). Fibrin(ogen) depositions were found to be among the major sites to which apo(a) binds in fatty streak-type atherosclerotic lesions in the vessel wall of apo(a) mice, suggesting a cooperation of apo(a) and fibrinogen in atherogenesis (Lou et al., 1998). Apo(a) and Lp(a) were shown to stimulate the proliferation of human vascular smooth muscle cells in culture, thus imitating a process contributing to atherogenesis in humans (Grainger et al., 1993). The growth-promoting effect of apo(a) and Lp(a) resulted from the suppression of plasminogen activation, and consequently the stimulation, by plasmin, of latent transforming growth factor-β, which is an inhibitor of smooth muscle cell proliferation (Grainger et al., 1993; Grainger et al., 1994). Plasma levels of proinflammatory oxidized phospholipids present on apo B-100-containing lipoproteins and predominantly on Lp(a) were reported to be related to the presence and extent of CAD (Tsimikas et al., 2005). A direct prothrombotic effect of Lp(a) may be mediated through its interaction with the tissue factor pathway inhibitor (TFPI), a central regulator of tissue factor (coagulation factor III)-promoted thrombosis (Caplice et al., 1998; Caplice et al., 2001). Lp(a) was shown to bind, via its apo(a) moiety, to the C-terminus of TFPI, a region known to determine TFPI function (Caplice et al., 2001). Apo(a) and TFPI were found to coexist in smooth muscle cell-rich areas within human coronary plaques, suggesting the potential for in vivo interaction in the vessel wall (Caplice et al., 2001). It is not known whether the rs3798220-C and rs10455872-G alleles may exert direct influences on the proatherogenic and prothrombotic processes triggered by apo(a) and Lp(a) or whether the immediate consequence of the rs3798220 variation, a substitution of isoleucine by methionine, may play a functional role.

In conclusion, the findings allowed for a more precise definition of risk-associated genetic markers, that is, specific nucleotides in LPA, rather than standard haplotypes, which span the extensive SLC22A3-LPAL2-LPA region and were defined by noneffective variants, exert a substantial proportion of the risk for AMI.

Acknowledgements

  1. Top of page
  2. Summary
  3. Introduction
  4. Materials and Methods
  5. Results
  6. Discussion
  7. Acknowledgements
  8. Conflict of Interest
  9. Supporting Information

This research was supported by a grant from Deutsches Herzzentrum München. We thank Wolfgang Latz, Marianne Eichinger, Ulrike Weiss, and Birgit Campbell for their excellent technical assistance.

Conflict of Interest

  1. Top of page
  2. Summary
  3. Introduction
  4. Materials and Methods
  5. Results
  6. Discussion
  7. Acknowledgements
  8. Conflict of Interest
  9. Supporting Information

The authors have no financial or other relation that could lead to a conflict of interest.

References

  1. Top of page
  2. Summary
  3. Introduction
  4. Materials and Methods
  5. Results
  6. Discussion
  7. Acknowledgements
  8. Conflict of Interest
  9. Supporting Information
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Supporting Information

  1. Top of page
  2. Summary
  3. Introduction
  4. Materials and Methods
  5. Results
  6. Discussion
  7. Acknowledgements
  8. Conflict of Interest
  9. Supporting Information

Additional Supporting Information may be found in the online version of this article.

FilenameFormatSizeDescription
ahg739-sup-0001-S1.pdf85K

Table S1 Oligonucleotide primers and TaqMan probes used for genotyping.

Table S2 Genotype distributions in the AMI group and control group.

Figure S1 Linkage disequilibrium in the SLC22A3-LPAL2-LPA region and positions of SNPs selected for a haplotype analysis.

Figure S2 Linkage disequilibrium between SNPs shown as D’ values.

Figure S3 Linkage disequilibrium between SNPs shown as r2 values.

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