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Human Molecular Genetics Pages 1453-1462  

Array-based multiplex analysis of candidate genes reveals two independent and additive genetic risk factors for myocardial infarction in the Finnish population
Introduction
Results
   The genotyping procedure
   The candidate gene analyses
Discussion
Materials And Methods
   Subjects
   DNA samples and primers
   Array preparation
   PCR amplification and ssDNA preparation
   Minisequencing reactions on the primer arrays
   Signal detection
   Reference method
   ACE I/D genotyping
   Statistical analyses
Acknowledgements
References

Array-based multiplex analysis of candidate genes reveals two independent and additive genetic risk factors for myocardial infarction in the Finnish population

Array-based multiplex analysis of candidate genes reveals two independent and additive genetic risk factors for myocardial infarction in the Finnish population

T. Pastinen1,2,+,*, M. Perola1,2,+, P. Niini3, J. Terwilliger4, V. Salomaa5, E. Vartiainen5, L. Peltonen1,2 and A.-C. Syvänen1,6

1Department of Human Molecular Genetics and 5Department of Epidemiology and Health Promotion, National Public Health Institute, Mannerheimintie 166, FIN 00300, Helsinki, Finland, 2University of Helsinki, Department of Medical Genetics, Helsinki, Finland, 3Microelectronics Laboratory, Technical Research Center of Finland, Espoo, Finland, 4Columbia University, Department of Psychiatry, New York, NY, USA and 6Uppsala University, Department of Medical Sciences, Molecular Medicine, Uppsala, Sweden

Received May 9, 1998; Revised and Accepted June 29, 1998

We analysed common variants of eight genes implicated previously as risk factors for coronary heart disease or myocardial infarction (MI) in a cross-sectional study on patients with a history of MI and in carefully matched controls from the Finnish population. The most common low density lipoprotein receptor mutations in Finland were also included in our analysis. Multiplex genotyping of the target genes was performed using a specific and efficient array-based minisequencing system. The 4G allele of the plasminogen activator inhibitor gene (P < 0.05) and the PlA2 allele of the glycoprotein IIIa gene (P < 0.01) were associated with an increased risk of MI in our study population. We analysed the combined effect of these risk alleles and found that the concurrent carrier status of the two genetic variants conferred a high risk for the development of MI in our sample (OR = 4.5, P = 0.001), which was particularly prominent in male subjects (OR = 6.4, P = 0.0005). This study demonstrates the application of a new powerful tool for genome analysis to yield information on the inherited determinants of susceptibility to MI. The observation of two separate genes contributing an additive risk of developing MI exemplifies the advantages of multiplex analysis of genetic variation.

INTRODUCTION

Coronary heart disease (CHD) and acute coronary syndromes such as acute myocardial infarction (MI) are leading causes of morbidity and mortality in developed countries. CHD is a typical example of a multifactorial trait, the phenotype of which results from a combination of both environmental and inherited risk factors (1,2). Genetic susceptibility to CHD and its complications is most probably conferred by allelic variation that is relatively common in the population. Thus, multiple genes predisposing to CHD must be identified before their true significance for public health can be evaluated. Detailed knowledge of the inherited component of CHD would provide insight into the molecular pathogenesis of the disorder and might allow the design of prevention and treatment based on the specific risk factors possessed by each individual.

Common genetic variants of proteins participating in lipid (3) and homocysteine (4) metabolism, platelet adhesion (5), the renin-angiotensinogen system (6-8), blood coagulation (9) and thrombolysis (10) have been suggested to be associated with the development of CHD and MI. However, the results obtained in a number of subsequent association studies have been controversial. This controversy may be explained by differences in study design, e.g. in the definition of disease phenotypes or in the matching of cases and controls, and by genetic heterogeneity within and between the populations from which the samples were derived. Some variant gene products, such as those of apolipoprotein E (ApoE) (3), have an established role in the development of CHD, and interactions between others, such as angiotensin type 1 receptor (AT1R) and angiotensin-converting enzyme (ACE) (7), have been suggested to be involved in CHD. Mostly, however, only single genes were analysed in most of the studies cited above and no attempts to monitor the influence of other genes were made.

In our study design, we approached the issue of genetic heterogeneity by analysing cases and controls from the genetically homogenous population of Finland, and the issue of combined effects of multiple genes was addressed by including several of the previously characterized genetic variants in our analysis. Hence, it was possible to analyse not only the association of a single polymorphism with MI but also to identify possible interactions between genes and increased risk effects of combinations of the genes found to affect the risk of MI. We studied common allelic variants of eight genes containing polymorphisms that classically have been associated with CHD, such as the apoE gene, as well as more recently reported associations such as the coagulation factor XIII (FXIII) gene (9). Additionally, four mutations in the low density lipoprotein receptor (LDLR) gene that account for at least 75% of the familial hypercholesterolaemia (FH) cases in Finland (11) were analysed. Simultaneous, multiplex genotyping of the target genes with suggested roles in the pathogenesis of MI was performed using an array-based minisequencing system that we recently have developed (12). The principle of this method is to identify the polymorphic nucleotides in PCR-amplified DNA templates by specific extension of detection primers arrayed on a small glass surface with a labelled nucleotide using a high-fidelity DNA polymerase enzyme.

The selected MI case and control samples were matched carefully for age, sex and regional origin. The Finnish population has been considered as a genetic isolate through the enrichment of several monogenic disorders that are rare or absent elsewhere in the world (13,14). Most of the chromosomal regions containing the disease genes demonstrate a significant linkage disequilibrium that often reaches over several cM, and usually a major allelic variant is responsible for the majority of the disease cases (14). Therefore, the Finnish population may be advantageous also for studying the genetic background of multifactorial disorders such as CHD (15). Despite a recent decline in CHD mortality due to environmental factors (16), the prevalence of CHD and MI is still relatively high in Finland.

We found evidence of association of the glycoprotein IIIa (GPIIIa) gene and the plasminogen activator inhibitor (PAI-1) gene with MI in Finnish subjects. A concurrent carrier status of both these associated genetic variants increased the risk of MI even more than expected by the single gene results. The apoE4 allele was also enriched in MI cases. These data demonstrate the advantages of multiplex genotyping in studying genetic risk factors of a common disease as well as the feasibility of our minisequencing microarray system to carry out such analyses efficiently.

RESULTS

The genotyping procedure

The simultaneous genotyping of 12 common polymorphisms or mutations in eight genes with a suggested association with MI was performed by minisequencing on primer arrays. Minisequencing primers for detection of the polymorphisms in the GPIIIa, methylenetetrahydrofolate (MTHFR), the AT1R, the angiotensinogen (ATG), the PAI-1, the FXIII and the apoE genes, and for the four most common Finnish FH-causing mutations were designed to anneal immediately 5[prime] to the site of the variant nucleotide in the gene sequences (Table 1). The primers were covalently immobilized on a small, 3.5 mm2 glass surface using a custom-built printing robot that allows rapid and reproducible manufacturing of primer arrays. In addition to the capacity for multiplex genotyping, the miniaturized assay format has the advantages of small reaction volumes, fast reaction rates and easy handling of multiple samples in parallel.

Table 1. Primers for the oligonucleotide array-based detection of CHD-associated genetic variants
Gene PCR primers (5[prime] to 3[prime])a Minisequencing primer (5[prime] to 3[prime])b Variantc
GPIIIa B-TTG CTG GAC TTC TCT TTG GGTTC AGG TCA CAG CGA GGT G ACT TAC AGG CCC TGC CTC PlA1/A2(T1565->C, M57482)
MTHFR GGA GAA GGT GTC TGC GGG AB-AAG CTG CGT GAT GAT GAA AT GCT GCG TGA TGA TGA AAT CG MTHFR 677C->T (U09806)
AT1R B-GAG GTT GAG TGA CAT GTT CGGAA AAG TCG GTT CAG TCC AC CAC TTC ACT ACC AAA TGA GC AT1R 1166A->C (Z11162)
ATG B-TTC ATG CAG GCT GTG ACA GGTTG CCT TAC CTT GGA AGT GG AAG ACT GGC TGC TCC CTG A ATGM235T (exon 2nt278T->C, S78530)
PAI-1 B-GAG AGC CCT CAG GGG CACCCT CCG ATG ATA CAC GGC T GAG AGT CTG GAC ACG TGG GG PAI-14G/5G (ins/del-675G/A, X13323)
FXIII B-GAA GAT GAC CTG CCC ACA GATG CTC ATA CCT TGC AGG TT CCA CAG TGG AGC TTC AGG GC FXIIIVal34Leu(exon 2nt163G->T, M21987)
ApoE B-GAA CAA CTG ACC CCG GTG GCG G GCG CGG AGA TGG AGG ACG TG apoE112 (C3745->T)
CTC GCG GGC CCC GGC CTG GTA CA ATG CCG ATG ACC TGC AGA AG apoE158 (C3883->T, X00143)
LDLR AAC AGT TCT TGC CCT CTT TTGCTG AGA CAC CCG GTT ACC TTB-ATC CCA ACA CAC ACG ACA GA GAA CTG AGA AAG TGC AAG GAG FHHki (del 9.5 kb, intron 15 to exon 18, ref. 59)
B-GCA TCA CCC TGG ACA AAG TCGCA AGC CGC CTG CAC CGA GAC TCA C GCC GGG ACT GGT CAG ATG AA FHNK (exon 6 delnt108-116, L00350)
GGT CCA CAT TTG CCA CAA CCB-CAC TCA CCG AGG GGT AGC TG CAC CGA GGG GTA GCT GTA G FHTku (A2533->G,L00350)
B-CGG ACC CCC AGG CTC CATTCG TGC CGG TTG GTG AAG AA CAG GCT CCA TCG CCT ACC FHPori (T1202->A, L00343)
aThe sequence of the forward PCR primer is given above that of the reverse primer, and the 5[prime] biotin residue is marked by the letter B. The unbiotinylated PCR primer of each pair contained the non-specific tail sequence TTC TAA TAC GAC TCA CTA TAG in its 5[prime]end, and the 5[prime] end of the biotinylated PCR primer contained the tail sequence GCG GTC CCA AAA GGG TCA GT.
bThe polarity of the minisequencing detection primer is the same as that of the corresponding biotinylated PCR primer. The minisequencing detection primers contained a spacer sequence of 15 T residues 5[prime] of the gene specific sequence and a 5[prime] amino group.
cThe polymorphisms and mutations are denoted according to the predominantly used nomenclature, and the exact nucleotide position either in the genomic or mRNA sequence is shown in parentheses, followed by the GenBank or EMBL sequence accession number.

In the miniaturized minisequencing assay, the amplified templates spanning the polymorphic nucleotides are annealed to the primers on the array, and the nucleotides at each of the variable sites are identified by four separate primer extension reactions catalysed by a high-fidelity DNA polymerase (12). In the reactions, each of the primers becomes extended with the labelled dideoxynucleotide that is complementary to the nucleotide at the polymorphic site. A given primer becomes extended with one labelled dideoxynucleotide in the case of a homozygous genotype, whereas two signals are detected at heterozygous positions. Figure 1A shows the signals visualized on the primer array when typing four samples. The genotypes at each polymorphic site are assigned most conveniently by calculating the numeric ratio between the signal obtained from the incorporation of the normal (or expected) nucleotide and that obtained from the corresponding variant nucleotide.


Figure 1. (A) Images of four samples typed by minisequencing on DNA arrays. The phosphor scanning images of the A, C, G and T extension reactions for samples 1-4 are shown. The detection primers are immobilized in a 3×4 array, with the four LDLR mutation-specific primers in the upper row (from left to right): FHHki, FHNK, FHTku and FHPori; the middle row contains the apoE112, apoE158, GPIIIa and MTHFR primers; and the PAI-1, FXIII, ATG and AT1R primers are located in the bottom row of the arrays. Sample number 2 is the single patient found to carry a LDLR mutation (FHNK), which is evident by a signal from the A reaction and the C reaction at the second primer site of the top row. Numerically, heterozygosity is seen as a mutant to normal signal ratio of ~1 (B). (B) Genotype assignment for the samples in (A). The mutations/polymorphisms are shown on the left with the nucleotides expected to be incorporated at each site in parentheses. After subtraction of a local background value from the signals, the signal intensity ratio between the two possible nucleotides incorporated at each site is calculated. In the preliminary typing experiments, the signal intensity ratios defining the genotypes were determined. The signal ratio for a homozygous genotype for the first allele is >8, for a heterozygous genotype 0.5-2.0 and for a homozygous genotype at the second allele <0.13. A polymorphism was retyped if the signal intensity ratio did not fall into one of the three categories defining the genotypes. The grey bars are signal intensity ratios for heterozygous genotypes, the white and black bars illustrate the homozygous genotypes for the first and second allele, respectively. The scale is logarithmic and values >10 or <0.1 are not shown.

First, the detection of genetic variants associated with MI using minisequencing on DNA arrays was validated by typing samples with previously known genotypes according to the reference method, or previously sequenced patient samples in the case of FH mutations, to obtain reference values for the signal ratios that are used for defining the genotypes. At each site, the signal ratios fell into three distinctive categories that unequivocally define the genotypes (Fig. 1B).

In the actual sample comprising 152 MI survivors and 152 controls, we analysed 12 loci using the minisequencing arrays for genotyping (Table 2), which yielded >3600 genotypes. About 5% of the genotypes remained unassigned in the initial array-based screening primarily due to failure of the multiplex PCR amplification. The samples yielding equivocal genotypes were retyped using the well established reference method (17). In addition, 32 randomly chosen samples were retyped at each polymorphic site in individual reactions by the reference method. These results were in complete agreement with the results from the array-based method, proving its reliability. The Alu insertion/deletion polymorphism in the ACE gene had been typed previously in the sample material using a conventional method (18).

The candidate gene analyses

In the primary analyses by [chi]2 test, the PlA2 allele of the GPIIIa gene (P = 0.005) and the 4G allele of the PAI-1 gene (P = 0.04) were associated with MI. The differences in allele and genotype frequencies between the cases and controls remained significant (P = 0.04 for GPIIIa and P = 0.001 for PAI-I) after restricting the study subjects to males only. The differences in allele or genotype frequencies of the other monitored gene variants did not reach the level of statistical significance but, for the apoE gene, a clear trend of an MI risk-increasing effect of the apoE4 allele was seen (Table 2). In a logistic regression analysis, the unadjusted odds ratio (OR) for the GPIIIa variant was 2.11 (95% CI: 1.25-3.63) and for the PAI-1 variant it was 1.40 (95% CI: 0.84-2.42). When the analysis was restricted to males, the unadjusted OR for the GPIIIa variant was 1.73 (95% CI: 0.96-3.19) and for the PAI-1 variant it was 2.08 (95% CI: 1.15-3.84). Adjustment of the gene effect by high density lipoprotein (HDL)-cholesterol and triglyceride levels, body mass index (BMI) and smoking did not affect the results significantly.

Table 2. Genotypes and alleles of the eight candidate genes in subjects with and without MI
Genea Genotype and allele distributionsb
MI cases (%) Controls (%) P-valuec Male MI cases (%) Male controls (%) P-value
GPIIIa, T1565->C genotypes
TT 102 (68) 123 (81)   87 (71) 99 (81)  
TC 44 (29) 26 (17)   31 (25) 22 (18)  
CC 5 (3) 2 (1) 0.006 4 (3) 1 (1) 0.07
GPIIIa, alleles
T (PlA1) 248 (82) 272 (90)   205 (84) 220 (90)  
C (PlA2) 54 (18) 30 (10) 0.005 39 (16) 24 (10) 0.04
MTHFRd 677C->T genotypes
GG 95 (63) 81 (54)   76 (62) 64 (52)  
GA 49 (32) 58 (38)   41 (34) 47 (39)  
AA 7 (5) 12 (8) 0.20 5 (4) 11 (9) 0.16
MTHFR, alleles
G (677C) 239 (79) 220 (73)   193 (79) 175 (72)  
A (677T) 63 (21) 82 (27) 0.07 51 (21) 69 (28) 0.06
AT1R, 1166A->C genotypes
AA 87 (58) 95 (63)   72 (59) 76 (62)  
AC 54 (36) 49 (32)   42 (34) 40 (33)  
CC 10 (7) 7 (5) 0.57 8 (7) 6 (5) 0.80
AT1R, alleles
A (1166A) 228 (75) 239 (79)   186 (76) 192 (79)  
C (1166C) 74 (25) 63 (21) 0.28 58 (24) 52 (21) 0.51
ATG, exon2 nt278T->C genotypes
TT 48 (32) 53 (35)   37 (30) 43 (35)  
TC 66 (44) 64 (42)   55 (45) 51 (42)  
CC 37 (25) 34 (23) 0.41 30 (25) 28 (23) 0.67
ATG, alleles
T (235 Met) 162 (54) 170 (56)   129 (53) 137 (56)  
C (235 Thr) 140 (46) 132 (44) 0.43 115 (47%) 107 (44) 0.46
PAI-1, ins/del-675G/A genotypes
GG 31 (21) 40 (27)   22 (18) 38 (31)  
GA 74 (49) 80 (53)   57 (47) 60 (50)  
AA 46 (30) 30 (20) 0.09 43 (35) 23 (19) 0.006
PAI-1, alleles
G (5G) 136 (45) 160 (53)   101 (41) 136 (56)  
A (4G) 166 (55) 140 (47) 0.04 143 (59) 106 (44) 0.006
FXIII, exon2 nt163G->T genotypes
GG 101 (67) 107 (71)   86 (70) 82 (67)  
GT 42 (28) 41 (27)   30 (25) 37 (30)  
TT 8 (5) 3 (2) 0.29 6 (5) 3 (2) 0.40
FXIII, alleles
G (Val34) 244 (81) 255 (84)   202 (83) 201 (82)  
T (Leu34) 58 (19) 47 (16) 0.23 42 (17) 43 (18) 0.90
Table 2. Continued
Genea Genotype and allele distributionsb
MI cases (%) Controls (%) P-valuec Male MI cases (%) Male controls (%) P-value
ACE, genotypes (deletion/insertione)
DD 31 (21) 28 (19)   22 (18) 22 (18)  
ID 56 (37) 69 (46)   45 (37) 56 (46)  
II 64 (42) 54 (36) 0.56 55 (45) 44 (36) 0.30
ACE, alleles
D 118 (39) 125 (41)   89 (36) 100 (41)  
I 184 (61) 177 (59) 0.34 155 (64) 144 (59) 0.31
ApoEf
2 2 1 (1) 1 (1)   0 (0) 1 (1)  
2 3 12 (8) 16 (11)   10 (8) 14 (11)  
2 4 4 (3) 5 (3)   4 (3) 4 (3)  
3 3 76 (50) 86 (57)   60 (49) 67 (55)  
3 4 53 (35) 39 (26)   44 (36) 32 (26)  
4 4 5 (3) 4 (3)   4 (3) 4 (3)
Allele 4 carriers 62 (41) 48 (32)   52 (43) 40 (32)  
Non-carriers 89 (59) 103 (68) 0.09 70 (57) 82 (68) 0.11
ApoE, alleles
2 18 (6) 23 (8)   14 (6) 20 (8)  
3 217 (72) 227 (75)   174 (71) 180 (74)  
4 67 (22) 52 (17) 0.25 56 (23) 44 (18) 0.27
aThe commonly used names of the alleles and the position of the variant nucleotide either in the genomic or mRNA are shown. For GenBank or EMBL sequence accession numbers see Table 1.
bThe number of genotypes or alleles and the percentage (in parentheses) are given.
cThe significance level of a [chi]2-test of a 2 × 2 or a 3 × 2 table (see Materials and Methods).
dThe non-coding strand of the MTHFR gene was analysed.
eA 288 bp insertion/deletion (I/D) polymorphism in intron 16 of the ACE gene (18).
fSingle nucleotide polymorphisms C3745->T and C3883->T detecting amino acid substitutions at residues 112 and 158 responsible for the ApoE genotypes.

Because a significant association of GPIIIa and PAI-1 with an increased risk of MI was seen, we analysed the combined effect of the risk alleles of these two genes. The OR for the individuals carrying three or four of the risk-increasing GPIIIa and PAI-1 alleles when compared with those carrying none or one allele was 4.5 (95% CI: 1.82-12.72, P = 0.001). An even more pronounced effect of OR = 6.4 in the male subgroup was observed (95% CI: 2.28-23.07, P = 0.0005). These results suggest an additive effect for the risk alleles of the GPIIIa and PAI-1 genes in conferring risk of MI (Fig. 2), although the number of individuals carrying three or four alleles was low: 20 cases in comparison with six controls for the total study group and 18 cases and four controls for the group of males only (Fig. 2). Adjustment of the gene effect by HDL-cholesterol and triglyceride levels, BMI and smoking did not affect the results significantly, neither did the incorporation of the apoE4 allele as a risk factor in the analysis.


Figure 2. The additive effect of the GPIIIa PlA2 and PAI-1 4G `risk alleles' (A) for the total study group and (B) for males only. The vertical bars represent the number of individuals carrying 0-4 risk alleles. The black bars are the cases with a history of MI and the white bars are the controls. The P-values give the statistical significance levels of the differences in the history of MI between those individuals carrying no or one `risk alleles' and those carrying three or four `risk alleles'.

We also analysed the combined effect of the ACE D allele and the AT1R genotype, as suggested by Tiret et al. (7), who observed a risk-increasing effect of the ACE D allele only in carriers of the AT1R 1166C allele. Also, the suggested interaction between FXIII and PAI-1 was analysed as suggested by Kohler et al. (9). No significant associations were found in the total study group nor in the male subgroup. In addition, division of the MI cases into smokers and non-smokers, diabetics and non-diabetics or into groups with high or low lipid values or blood pressure did not reveal new associations with any of the genes analysed here. The data from these subgroup analyses can be obtained at our web site (http://www.ktl.fi/molbio/wwwpub/mi_chip ).

DISCUSSION

We describe a system based on minisequencing in a miniaturized array format, which can be established easily in the laboratory and applied for multiplex genotyping of large samples. In our system, sequence-specific primer extension with a single labelled nucleotide by a DNA polymerase is utilized to distinguish between the genotypes. As we previously have shown experimentally, the `minisequencing' primer extension reaction results in one order of magnitude better discrimination between heterozygous and homozygous genotypes than hybridization with allele specific oligonucleotides in an array format (12). To circumvent the problem of limited genotype discrimination using hybridization-based arrays, multiple redundant probes for each site to be analysed must be included in the arrays (19), which results in high-density arrays and makes the manufacturing of probe arrays beyond the capacity of most laboratories. In the minisequencing method, only a single primer is required per polymorphic site, and consequently medium density arrays are applicable for analysing a large number of polymorphisms simultaneously. Thus, it is feasible to manufacture the arrays using modified low-cost industrial robots for an application of choice. Moreover, reliable genotype assignment using the hybridization-based arrays require the inclusion of a reference sample in each reaction and the application of complex allele scoring algorithms (20). Because of the high sequence specificity of the minisequencing reaction, the genotypes can be assigned by simple calculation of the ratios between signal intensities from the sequence variants obtained by commercially available phosphorimaging instruments and image processing software. In our experience, the use of a low-energy isotope such as 33P followed by detection using a phosphorimager is highly sensitive and rapid in the screening of large numbers of samples.

In the present study, we have shown the practical feasibility of our genotyping system by analysing several genes coding for proteins participating in platelet adhesion and thrombus formation, coagulation or fibrinolysis for their association with MI in a well-defined Finnish case-control material. Our data suggest that common variants of the PAI-1 gene and the GPIIIa gene that encode proteins involved in fibrinolysis and platelet adhesion, respectively, are genetic risk factors for MI. Furthermore, we found that the concurrent presence of these two genetic variants increased an individual's risk of MI significantly. However, the number of MI patients and controls contributing to this result was low, and thus the actual risk ratio and an actual gene-gene interaction remain to be determined by a larger, preferably prospective epidemiological study as well as by studies on the molecular level of this suggested interaction.

In an early study, it was shown that high PAI-1 activity in plasma correlates with an increased risk of recurrent MI (21). Later, a single nucleotide insertion in the promotor region of the PAI-1 gene was found to affect the plasma levels of the protein, thus influencing the risk of developing MI in the Swedish population (10). Although the association of the PAI-1 promotor polymorphism with MI could not be reproduced initially in a study on French and Irish persons (22), recent studies have found a significant enrichment of the predisposing genotype in MI patients (23) and in individuals with a family history of CHD (24) from different Caucasian populations. Evidence that the PAI-1 polymorphism is an indicator of CHD progression has also been presented recently (25). The glycoprotein IIb-IIIa receptor complex mediates platelet aggregation (26), and a particular allele of the GPIIIa gene was found to be associated with MI (5). A number of studies on this GPIIIa variant in Caucasian populations have yielded contradictory results either confirming its association with MI (27,28) or failing to detect an association (29-31). A recent study suggested that the GPIIIa genotype would be a significant factor for predicting the thrombosis of coronary stents (32), and a significant association of the GPIIIa variant with coronary thrombosis was seen in a Finnish autopsy material (J. Mikkelsson, M. Perola, P. Laippala, V. Savolainen, J. Pajarinen, K. Lalu, A. Penttilä and P.J. Karhunen, in preparation). Physiologically, PAI-1 and GPIIIa are involved in arterial thrombus formation, which is the final event in the cascade leading to acute coronary syndromes. Thus, it is plausible that the genes encoding PAI-1 and GPIIIa appear as significant genetic risk factors in our study, where MI was the phenotype according to which the subjects were selected.

In our sample, the frequency of the apoE4 allele was similar to that reported earlier from the Finnish population (33,34), and the apoE4 allele was slightly enriched in MI-affected individuals. It has been shown that the apoE4 allele predisposes to CHD (35), affects the severity of CHD (34) and increases the risk of death by CHD (36) in the Finnish population. The OR obtained for the apoE4 allele in the present study agrees well with these previous reports. That our finding was not statistically significant may be due to its limited statistical power and cross-sectional design leading to survival bias. By combining the previous data with our results, it is seems likely that ApoE4 is related to the development of CHD and its complications in Finland. However, the apoE4 allele did not increase the additive risk of MI in combination with the GPIIIa and PAI-1 risk alleles.

The analysis of the other polymorphisms apart from those of the PAI-1 and GPIIIa genes failed to reveal any significant association with MI, either because they are truly insignificant for the disease or because they have a minor role in the analysed sample that remains undetectable with our current sample size. The intragenic polymorphisms of the renin-angiotensin system genes ACE, AGT and AT1R have been shown to be associated with increased risk of MI (6-8), but the association has been less clear or contradicted in other studies (37-41). ACE genotype did not prove to be a risk factor for CHD in two previous Finnish studies (42,43), and no evidence for association of the previously suggested risk alleles of the ACE, AGT or AT1R genes with MI were found in our study. Similarly to the genes of the renin-angiotensin system, the data concerning the role of the variation of the MTHFR gene for CHD are contradictory. Homozygosity for the thermolabile form of MTHFR initially was shown to contribute to the development of premature CHD (4,44,45), but several later studies failed to replicate these results in Caucasian populations (46-50). The results of our study are in accordance with the latter studies, since there was no increase of the suggested MTHFR risk genotype in the MI cases. A polymorphism in the FXIII gene has been suggested to confer protection against the development of MI, particularly in cases with angiographic evidence of CHD (9). There was no significant difference in the distribution of the FXIII variants between the MI cases and controls of the total study group, nor in the male subjects of our study.

In an average Caucasian population, the heterozygosity rate for LDLR mutations causing FH is one in 500, and the relative risk for the development of CHD conferred by FH is as high as 35 (51,52). An exceptionally high frequency of FH mutations (9%) has been reported in young CHD patients originating from Northeastern Finland (53). We included the common LDLR mutations covering the majority of Finnish FH cases in our analysis to study the incidence of the disorder in MI patients of unselected age and to exclude potential individuals with such a strong genetic predisposition from our study. Only one patient (1/152) was found to have FH based on the mutation analysis, suggesting that the disease is not particularly enriched in sporadic MI cases aged <65 years.

The dissection of our sample material into subgroups by environmental risk factors or geographical origin did not reveal any association with MI of the variants of the renin-angiotensin system-related genes, the MTHFR gene or the FXIII genes, nor an increase in the risk of MI conferred by the PAI-1 or GPIIIa genes. A larger, prospective study would be needed to observe such interactions between genes and the environment. However, our multiplex analysis of genetic variation revealed a previously uncharacterized additive and independent effect of the GPIIIa and PAI-1 genes contributing to the risk of developing MI. Thus, our study provides novel information on the inherited determinants of susceptibility to MI and suggests an important role for GPIIIa and PAI-1 in the progression of acute coronary events. Our study exemplifies how the new powerful tools for genome analysis in combination with the increasing knowledge of genetic variation within candidate genes can provide new insight into the complex questions of genetic risk factors behind multifactorial diseases.

MATERIALS AND METHODS

Subjects

This study was designed to be a cross-sectional case-control study of MI cases and controls. The study participants were selected from the subjects of the FINRISK 1992 survey, which is one in the continuum of FINRISK studies that are carried out at 5 year intervals and designed to assess the levels of coronary risk factors in random population samples in different geographical areas of Finland (16). The FINRISK study design is a cross-sectional population survey stratified to contain at least 250 subjects of each sex and 10 year age group (25-34, 35-44, 45-54 and 55-64 years of age) from each area. Of those invited, 77% participated in the survey. All study subjects were screened for assessment of the most established environmental cardiovascular risk factors and filled out a questionnaire where, among other questions, history of MI was investigated. The protocol is the same as that established by the MONICA project, an international study conducted under the auspices of the World Health Organization to monitor trends and determinants of cardiovascular disease (54). The Finnish hospital discharge register was used to find all patients in the FINRISK 1992 survey with a history of MI according to International Classification of Diseases 9 (ICD-9) diagnoses 410 and 412. There were 152 cases with a history of MI and available DNA (30 females and 122 males). A control for each case matched for age, sex and study area was obtained from the same study setting. The controls had no history of MI according to the hospital discharge register (no ICD-9 diagnoses 410 and 412), and did not report a history of MI in the FINRISK questionnaire. Table 3 shows the clinical characteristics of the MI cases and controls. One individual who was found to be a carrier of a mutation in the LDLR gene (FHNK) in the array-based screening, and the corresponding control were excluded from the further data analyses.

DNA samples and primers

The DNA was prepared from the blood samples by a standard method (55). The sequences of the PCR and minisequencing primers are shown in Table 1. The non-specific tail sequences were added to the primers to facilitate multiplex amplification of the target genes by unifying their template annealing kinetics (56). The primers were synthesized by Interactiva Biotechnologie GmbH (Ulm, Germany).

Table 3. Clinical characteristics of the study individuals
  MI cases
n = 151		 Controls
n = 151		 P-valuea Male MI cases
(n = 122)		 Male controls
(n = 122)		 P-valuea
Age 58.1 (4.9)b 58.1 (4.9) 1.0 57.7 (4.9) 57.7 (4.9) 0.97
BMI (kg/m2) 28.8 (4.1) 27.6 (3.9) 0.004 28.5 (3.8) 27.5 (3.5) 0.03
Smoking (ever/never) 100/51c 86/65 0.10 90/32 78/44 0.10
Diabetes (yes/no) 24/126 14/136 0.08 15/106 13/108 0.69
Total cholesterol 6.1 (1.3) 6.1 (1.1) 0.90 6. (1.2) 6.0 (1.1) 0.55
HDL-cholesterol 1.16 (0.30) 1.31 (0.33) 0.0001 1.14 (0.28) 1.27 (0.34) 0.001
Triglycerides 2.31 (1.69) 1.77 (1.02) 0.0009 2.32 (1.54) 1.79 (1.08) 0.002
aSignificance level for the distribution of characteristics.
bMeans and standard deviations in parentheses for continuous variables and cnumber of subjects for non-continuous variables are given.

Array preparation

Microscopic glass slides (Erie Scientific, Portsmouth, NH) with 12 wells of 5 mm in diameter were treated with epoxysilane as described previously (12). Fifteen nanolitres of 20 µM solutions of the 12 detection primers in 0.1 M NaOH were printed in each of the 12 wells of the slides as spots of 300 µm in diameter in a 3×4 array with 750 µm spacing between the spots. The printing device was modified from an Isel EP 1090/4 (Eiterfeld, Germany) robot by manufacturing a tweezer-like printing tip (57) to deliver the detection primer solutions from microwell plate wells to the array surface. The printing robot was operated using Windows-compatible custom-built software.

PCR amplification and ssDNA preparation

The DNA fragments spanning the 12 polymorphic or mutant sites were amplified in four PCR reactions. The four LDLR mutations were amplified from 20 ng of DNA at a dNTP concentration of 0.2 mM with 3 U of AmpliTaq Gold (Perkin Elmer, Branchburg, NJ), 15 pmol of each biotinylated (B) primer and 30 pmol of each non-biotinylated primer, in a final volume of 50 µl of DNA polymerase buffer in microtitre plate wells. The AT1R, ATG, FXIII, GPIIIa and PAI-1 fragments were amplified under the same conditions as the LDLR mutations, but with the following modified amounts of primers: 25 pmol of B-AT1R, 50 pmol of AT1R; 15 pmol of B-FXIII and B-ATG; 30 pmol of FXIII and ATG; 20 pmol of B-PAI-1 and B-GPIIIa; and 40 pmol of PAI-1 and GPIIIa. After a heating step of 11 min at 95°C to activate the enzyme, the thermocycling was in a PTC-225 Peltier Thermal Cycler (MJ Research, Watertown, MA) for 35 cycles of 30 s at 95°C, 30 s at 58°C and 2.5 min at 72°C. The MTHFR and ApoE genes were amplified individually using 20 ng of DNA at a dNTP concentration of 0.2 mM with 1.25 U of AmpliTaq Gold. The MTHFR reaction contained 40 pmol of B-MTHFR and 80 pmol of MTHFR, the ApoE reaction contained 25 pmol of B-ApoE, 50 pmol of ApoE and 10% (v/v) dimethylsulfoxide in a final volume of 50 µl of DNA polymerase buffer. The thermocycling parameters were 11 min at 95°C followed by 35 cycles of 30 s at 95° C, 30 s at 55°C and 1 min at 72°C. The (biotinylated) PCR products were combined and captured on 1 mg of avidin-coated polystyrene microparticles (Idexx Research Products, Westbrook, ME) for 30 min at 20°C. The unbiotinylated strands of the products were collected in 10 µl of H2O by heat denaturation.

Minisequencing reactions on the primer arrays

One quarter (2.5 µl) of the concentrated single-stranded DNA templates in a final volume of 10 µl of 0.2 M NaCl was annealed to the primers on four parallel arrays for 15 min at 35°C. Each minisequencing reaction mixture contained a ddNTP mix with 0.09 µCi (0.06 pmol) 33P of one of the four ddNTP analogues (Amersham) and 0.6 pmol of the same unlabelled ddNTP, 1.2 pmol of the three other corresponding unlabelled ddNTPs (Pharmacia Biotech, Uppsala, Sweden) and 1 U of DyNASeq DNA polymerase (Finnzymes, Helsinki, Finland) in a final volume of 10 µl of 26 mM Tris-HCl, pH 9.5, 6.5 mM MgCl2, 1.8% Triton X-100. The reaction was allowed to proceed for 15 min at 65°C in a humid chamber. The slides were rinsed once with distilled water and once with 0.1% SDS. All the steps of the procedure were carried out in a format compatible with multichannel pipettors.

Signal detection

The arrays were exposed to an imaging plate (Fuji, Kanagawa, Japan) for 2 h. Up to 480 arrays (120 samples) were exposed simultaneously to a single imaging plate. The plate was scanned using a Fuji BAS 1500 Bioimaging Analyzer, and the signals were measured using the Tina 2.10 software package (Raytest, Straubenhardt, Germany).

Reference method

The results of the multiplex genotyping on arrays were confirmed by retyping 10% of the samples randomly chosen from the study material using our conventional microtitre plate-based minisequencing assay for each genetic variant individually (58). The minisequencing primers were designed to detect either the complementary or the same strand as in the array-based system.

ACE I/D genotyping

The ACE polymorphism was genotyped as described (18), using PCR amplification and electrophoretic separation on 2% agarose gels to discriminate between the two alleles.

Statistical analyses

Student's t-test was used for comparing the mean values of the continuous variables. The [chi]2 test was used for comparing the genotype and allele frequencies between cases and controls. If the number of homozygotes or the expected number of a particular genotype was <5, these homozygotes were pooled with the heterozygotes and the genotype difference was calculated in a 2 × 2 table, whereas otherwise a 2 × 3 table was used. The combined effect of GPIIIa and PAI-1 were analysed by comparing individuals carrying three or four alleles of PlA2 or 4G with those having only one or no such alleles. A logistic regression model including age, sex, total cholesterol, HDL-cholesterol and triglyceride levels, BMI and smoking was utilized to control for environmental effects. Computer programs Statistica/Mac (StatSoft, Tulsa, OK) and SAS 6.1 (SAS Institute, Cary, NC) were used for the statistical analyses. The control population was in Hardy-Weinberg equilibrium for all alleles and subgroups studied. Because there were only 29 female MI cases and controls, only males were analysed separately.

ACKNOWLEDGEMENTS

We thank Dr Kimmo Kontula for providing the FH patient samples, and Ms Päivi Tainola for her excellent technical assistance. This work was supported by grants from the Technology Development Centre of Finland, the Instrumentarium Foundation, the Rinnekoti Research Foundation, the Duodecim Foundation, and by the EC Biomed2 Contract no. BMH4-972013.

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*To whom correspondence should be addressed. Tel: +358 9 47 44 8496; Fax: +358 9 47 44 8480; Email: tomi.pastinen@ktl.fi
+These authors contributed equally to this work

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