We conducted an analysis of 17,576 SNPs that could potentially affect gene function or expression in three case-control studies of MI and identified 5 SNPs in four genes (ENO1, FXN (2 SNPs), HLA-DPB2, and LPA) that were associated with MI. The false discovery rate for this group of 5 SNPs was 0.23, indicating that several of these SNPs are expected to be associated with MI.
The first SNP is located in ENO1,
a gene that encodes α-enolase, a glycolytic enzyme that catalyzes the conversion of 2-phospho-D-glycerate to phosphoenolpyruvate. α-enolase is also known to be a plasminogen receptor on the surface of hematopoietic cells and endothelial cells 
. Thus, α-enolase could contribute to fibrinolysis, hemostasis, and arterial thrombus formation–processes that are critical in the pathophysiology of MI. The SNP in ENO1
(rs1325920) is located about 1 kb upstream of the gene and could be involved in transcriptional regulation.
Two of the SNPs are in the FXN
gene. The FXN
gene encodes Frataxin, a mitochondrial protein involved in maintaining cellular iron homeostasis 
. Expanded GAA triplet repeats in intron 1 of FXN
leads to silencing of the FXN
gene and to accumulation of iron in the mitochondria, which makes mitochondria sensitive to oxidative stress 
. These changes lead to Friedreich's ataxia, an autosomal recessive disease of the central nervous system that is frequently associated hypertrophic cardiomyopathy 
. The two SNPs in FXN
found to be associated with MI are located in the 3′ untranslated region of FXN
(rs10890) and in a putative transcription factor binding site (rs3793456), thus one or both of these SNPs could have an effect on FXN
gene expression. These two SNPs are in linkage disequilibrium (r2
0.57 in Study 1) and thus, are not independent of one another. Whether these SNPs are associated with increased sensitivity of mitochondria to oxidative stress or to other mild manifestations of Friedreich's Ataxia symptoms is not known.
The fourth SNP (rs3798220 in LPA
) encodes a isoleucine to methionine substitution at amino acid 4399 of apolipoprotein(a). We have previously shown that this SNP is associated with coronary artery narrowing and with increased levels of plasma lipoprotein(a) in case-control studies 
. This SNP was also associated with incident myocardial infarction in the Cardiovascular Health Study, a population–based prospective study of about 5000 individuals aged 65 or older 
. The low minor allele frequency of this SNP in LPA (1% in the European American population of CHS 
) suggests that this SNP accounts for only a small fraction of the total variability of plasma Lp(a) levels. Our previous data showed that Lp(a) levels were 5.9-fold higher in carriers of the 4399 methionine allele than in noncarriers 
; and high levels of Lp(a) are associated with an increased risk for MI 
. Additionally, one can speculate that the association of the 4399 methionine allele with increased risk of disease could also be due to the isoleucine to methionine change in apolipoprotein(a) that may result in a more deleterious form of Lp(a).
Lastly, the fifth SNP is in HLA-DPB2
(rs35410698) is also associated with MI in this study. HLA-DPB2
is a pseudogene in the Human Leukocyte Antigen (HLA) region. The HLA region is highly polymorphic, gene rich region. Linkage disequilibrium in this region can extend across hundred kilobases and encompass HLA as well as non-HLA genes 
. Therefore, additional genotyping of SNPs in this region would be needed in order to know which gene variant in this region could be associated with MI.
We analyzed case–control studies that were retrospectively collected and did not include fatal cases of MI. Therefore, SNPs specifically associated with fatal MI would not have been identified. There were some differences between the participants in these three studies, specifically, Study 3 controls were recruited from patients who underwent coronary catheterization, whereas Study 1 and Study 2 controls were recruited from a lipid clinic population and from community centers. Thus, SNPs that were associated with MI in Study 1 and Study 2 but not in Study 3 might be explained by the differences between these studies. For example, a SNP in THBS4
(rs1866389) that was found to be associated with MI in Study 1 and Study 2 but not in Study 3, has been previously reported to be associated with premature MI 
. However, the power to detect the association of THBS4
with MI in Study 3 was limited (40% power), thus the lack of association in Study 3 may represent a false negative result. The false discovery rate for the 5 SNPs that were associated with MI in Study 3 was 0.23. Thus we expect that some of the SNPs we identified could be false positive associations (type 1 error); replication from additional studies is required to validate the observed associations. We have looked for support for these associations in the published data from the Welcome Trust Case-Control Consortium data 
, unfortunately, none of the 5 SNPs we report here was genotyped in that study. Finally, although the SNPs in this study could potentially affect gene function, additional linkage disequilibrium analysis would be needed in order to determine if other SNPs in these region could better account for the associations with MI we observed.
We identified 5 SNPs in 4 genes that are likely associated with MI. These SNPs merit investigation in additional studies of MI.