Conceived and designed the experiments: DS JJD. Performed the experiments: ARA JC. Analyzed the data: DS JZL. Contributed reagents/materials/analysis tools: JPK DR MM SGE. Wrote the paper: DS ARA JJD.
DS, JL, AA, DR, JC and JD are current employees of Celera. J.K. and M.M. received funding from the University of California Discovery Grant Program which is jointly funded by the University of California and the State of California with matching funds from Celera. S.E. had been a paid consultant of Celera.
Myocardial infarction (MI) is a common complex disease with a genetic component. While several single nucleotide polymorphisms (SNPs) have been reported to be associated with risk of MI, they do not fully explain the observed genetic component of MI. We have been investigating the association between MI and SNPs that are located in genes and have the potential to affect gene function or expression. We have previously published studies that tested about 12,000 SNPs for association with risk of MI, early-onset MI, or coronary stenosis. In the current study we tested 17,576 SNPs that could affect gene function or expression. In order to use genotyping resources efficiently, we staged the testing of these SNPs in three case–control studies of MI. In the first study (762 cases, 857 controls) we tested 17,576 SNPs and found 1,949 SNPs that were associated with MI (P<0.05). We tested these 1,949 SNPs in a second study (579 cases and 1159 controls) and found that 24 SNPs were associated with MI (1-sided P<0.05) and had the same risk alleles in the first and second study. Finally, we tested these 24 SNPs in a third study (475 cases and 619 controls) and found that 5 SNPs in 4 genes (
Myocardial infarction (MI) is a prevalent and often fatal consequence of coronary heart disease. Each year approximately 865 thousand Americans are diagnosed with MI and about 180 thousand die from the disease
Risk factors for MI include age, sex, elevated LDL-cholesterol, hypertension, low HDL-cholesterol, smoking, type 2 diabetes, and family history of cardiovascular disease. Risk of MI has a genetic component as evidenced by large twin studies
The identification of genetic variants that are associated with MI is a challenging task, because variants that are associated with MI are expected to only modestly increase the risk of MI, and because a large number of variants could potentially be tested. Thus, very large studies are needed to detect modest association and account for multiple testing confidently
To identify genetic polymorphisms associated with MI, we interrogated three case–control studies comprising cases with a history of MI and controls without a history of MI. The first two case–control studies (Study 1 and Study 2) identified SNPs nominally associated with MI. The hypotheses that these SNPs are associated with MI were tested in Study 3. We determined the allele frequency of each SNP in pools of case and control DNA prior to determining the genotype of a smaller number of SNPs for all individual DNA samples.
Participants in Study 1 and Study 2 were enrolled between July 1989 and May 2005 by the University of California, San Francisco (UCSF) Genomic Resource in Arteriosclerosis. UCSF samples received at the Celera genotyping facility by May 2004 were considered for inclusion in Study 1. Samples that arrived past that date were considered for Study 2. Cases in Study 1 and Study 2 included patients who had undergone diagnostic or interventional cardiac catheterization and patients of the UCSF Lipid Clinic. Controls were enrolled by the UCSF Genomic Resource in Arteriosclerosis and included UCSF staff, patients of UCSF Clinics, and senior citizens who participated in physical activities at regional community centers and events for senior citizens. A history of MI for Study 1 cases was verified by a clinical chart review or by
Participants in Study 3 were patients of the Cleveland Clinic Foundation (CCF) Heart Center who had undergone diagnostic or interventional cardiac catheterization between July 2001 and March 2003 and enrolled in the Genebank at Cleveland Clinic Study. A history of MI was verified by electrocardiogram, cardiac enzymes, or perfusion imaging. Controls had less than 50% coronary luminal narrowing. All participants in Study-3 selected North European, Eastern European, or ‘other Caucasian’ as the description of both their mother and father on the enrollment questionnaire. The demographic and risk factor characteristics of the participants in the 3 studies are presented in
Study 1 | Study 2 | Study 3 | ||||
Cases (n = 762) | Controls (n = 857) | Cases (n = 579) | Controls (n = 1159) | Cases (n = 475) | Controls (n = 619) | |
Male, % | 60 | 41 | 81 | 42 | 61 | 62 |
Age at enrollment, median (range) | 62 (29–87) | 65 (24–100) | 66(28–88) | 58 (45–97) | 60 (32–86) | 58 (37–88) |
Age at MI, median (range) | 52 (27–82) | NA | 57 (21–70) | NA | 53 (29–77) |
NA |
Smoking, % | 66 | 45 | 68 | 40 | 73 | 54 |
Diabetes, % | 20 | 0 |
25 | 0 |
38 | 10 |
Dyslipidemia |
84 | 53 | 84 | 61 | 95 | 56 |
Hypertension |
61 | 32 | 66 | 33 | 96 | 78 |
BMI (kg/m2), mean±SD | 28±5 | 26±5 | 28±5 | 26±5 | 31±6 | 30±7 |
Data available for 254 cases.
Individuals with diabetes were excluded from control group.
Dyslipidemia was defined in Study 1 and Study 2 to be self-reported history of a physician diagnosis of dyslipidemia or the use of lipid lowering prescription medication(s) and defined in Study 3 to be the use of lipid lowering prescription medication(s), LDL cholesterol >129 mg/dL, triglycerides >149 mg/dL or HDL cholesterol <45 mg/dL .
Hypertension was defined in Study 1 and Study 2 to be a self–reported history of a physician diagnosis of hypertension or use of antihypertensive prescription medication(s) and defined in Study 3 to be the use of antihypertensive prescription medication(s), systolic blood pressure >160 mmHg, or diastolic blood pressure >90 mmHg.
NA; not applicable.
The 17,576 SNPs investigated in Study 1 are located in 10,152 genes. Of these 17,576 SNPs, 2767 were tested in at least one of 3 previously reported studies
Initially, the allele frequency for each individual SNP was determined for all the cases and all the controls in pools of DNA. Pools were made by mixing equal volumes of standardized DNA from each individual member of the pool. Prior to pooling, DNA concentration for each sample was determined in triplicate using Picogreen fluorescent detection (Invitrogen). Measurements were repeated for samples which had high variation of fluorescence values (5% or greater coefficient of variation). DNA concentrations were determined from mean fluorescence values using a standard curve of salmon sperm DNA. DNA samples were then diluted to 6 ng/µL using automated liquid handling robotics (Beckman Coulter Fx, or Perkin Elmer Multiprobe II). The final concentration was confirmed using Picogreen fluorescent detection. Typically, several unique pools of DNA were made for cases and controls, made up of about 50 cases or controls. For each SNP, two real-time PCR reactions were performed, using 3 ng of pooled DNA in each reaction and allele-specific primers. The allele frequency in each pool was calculated from amplification curves for each allele. Genotyping of individual DNA samples was done by performing two real-time PCR reactions for each individual sample, using 0.3 ng DNA from each sample and allele specific primers.
Subjects of all three studies gave written informed consent and completed questionnaire approved by the Institutional Review Board of UCSF (Study 1 and Study 2) or the Cleveland Clinic Foundation (Study 3).
We assessed association between MI status and allele frequencies by two-tailed χ2 tests, and between MI status and genotype by logistic regression using an additive inheritance model (Wald test). In Study 2 and Study 3, since we tested a single prespecified risk allele for each SNP, we present one-sided P values and 90% confidence intervals (for odds ratios greater than one, there is 95% confidence that the true risk estimate is greater than the lower bound of a 90% confidence interval). We used a P threshold value of 0.05 in all three studies, and adjusted for multiple testing by calculating the False discovery rate (FDR) in Study 3. FDR was calculated using the MULTTEST procedure (SAS statistical package Version 9.1); for SNPs that were in the same gene, only the SNP with the higher (less significant) P value was included in the calculation.
We measured the allele frequencies of 17,576 putative functional SNPs in Study 1 cases and controls using pooled DNA samples and identified 1,949 SNPs that were associated with MI (P<0.05) and had minor allele frequency estimates that were greater than 1%. For these 1,949 SNPs, we determined allele frequencies in Study 2 cases and controls using pooled DNA samples and verified that the risk allele identified in Study 1 was also associated with risk of MI in Study 2. For those SNPs that were associated with MI and had the same risk alleles in both pooling studies, we then confirmed the association of the SNP with MI in Study 1 and Study 2 by genotyping individual DNA samples. We found that the risk alleles of 24 SNPs in 23 genes were associated with MI in both studies using an additive inheritance model (
SNP | Gene Symbol | Risk Allele | Study 1 | Study 2 | ||||||
Risk Allele Freq. | P value | OR | 95%CI | Risk Allele Freq. | P value |
OR | 90%CI | |||
rs11568658 | G | 0.97 | 0.005 | 1.98 | 1.24–3.16 | 0.97 | 0.01 | 1.81 | 1.19–2.77 | |
rs16875009 | A | 0.13 | 0.005 | 1.33 | 1.09–1.62 | 0.13 | 0.005 | 1.29 | 1.10–1.51 | |
rs25651 | T | 0.29 | 0.0001 | 1.36 | 1.17–1.58 | 0.31 | 0.02 | 1.17 | 1.03–1.33 | |
rs439401 | T | 0.35 | 0.03 | 1.17 | 1.01–1.35 | 0.37 | 0.01 | 1.19 | 1.05–1.35 | |
rs867852 | T | 0.78 | 0.03 | 1.22 | 1.02–1.45 | 0.78 | 0.04 | 1.17 | 1.01–1.37 | |
rs28372907 | A | 0.18 | 0.03 | 1.21 | 1.02–1.44 | 0.18 | 0.01 | 1.22 | 1.05–1.42 | |
rs11553576 | T | 0.60 | 0.03 | 1.17 | 1.02–1.35 | 0.60 | 0.03 | 1.16 | 1.02–1.31 | |
rs1325920 | A | 0.80 | 0.02 | 1.24 | 1.03–1.48 | 0.80 | 0.007 | 1.26 | 1.08–1.48 | |
rs31208 | G | 0.11 | 0.03 | 1.27 | 1.03–1.57 | 0.12 | 0.006 | 1.30 | 1.09–1.55 | |
rs3793456 | G | 0.56 | 0.01 | 1.20 | 1.05–1.39 | 0.55 | 0.03 | 1.15 | 1.02–1.30 | |
rs10890 | T | 0.43 | 0.004 | 1.23 | 1.07–1.42 | 0.43 | 0.03 | 1.15 | 1.02–1.30 | |
rs35410698 | G | 0.93 | 0.02 | 1.43 | 1.06–1.91 | 0.94 | 0.006 | 1.53 | 1.16–2.03 | |
rs1136141 | G | 0.86 | 0.05 | 1.24 | 1.00–1.53 | 0.86 | 0.02 | 1.26 | 1.05–1.52 | |
rs7928656 | A | 0.84 | 0.04 | 1.23 | 1.01–1.51 | 0.84 | 0.004 | 1.32 | 1.11–1.57 | |
rs3740918 | G | 0.69 | 0.003 | 1.26 | 1.08–1.46 | 0.70 | 0.01 | 1.20 | 1.05–1.37 | |
rs725660 | A | 0.34 | 0.006 | 1.23 | 1.06–1.42 | 0.35 | 0.0009 | 1.26 | 1.12–1.43 | |
rs3798220 | C | 0.02 | 0.04 | 1.59 | 1.03–2.48 | 0.02 | 0.008 | 1.72 | 1.19–2.49 | |
rs11711953 | T | 0.07 | 0.03 | 1.34 | 1.03–1.73 | 0.07 | 0.01 | 1.35 | 1.09–1.67 | |
rs4907956 | G | 0.60 | 0.03 | 1.18 | 1.02–1.36 | 0.61 | 0.01 | 1.19 | 1.05–1.34 | |
rs2290819 | T | 0.38 | 0.03 | 1.17 | 1.01–1.35 | 0.38 | 0.008 | 1.20 | 1.06–1.35 | |
rs3204635 | A | 0.25 | 0.02 | 1.20 | 1.03–1.40 | 0.26 | 0.03 | 1.16 | 1.02–1.33 | |
rs1866389 | C | 0.20 | 0.03 | 1.21 | 1.02–1.43 | 0.20 | 0.03 | 1.19 | 1.03–1.37 | |
rs3812475 | T | 0.50 | 0.03 | 1.16 | 1.01–1.34 | 0.52 | 0.04 | 1.13 | 1.01–1.28 | |
rs862708 | C | 0.02 | 0.003 | 1.88 | 1.24–2.83 | 0.03 | 0.03 | 1.45 | 1.05–2.00 |
1 sided P value
Age and Sex adjusted | Fully Adjusted | ||||||||
SNP (gene symbol) | Genotype | Cases n (%) | Controls n (%) | OR | 90% CI | P value | OR | 90% CI | P value |
rs1325920 | AA | 327 (71) | 394 (65) | 1.61 | 0.92–2.81 | 0.08 | 1.28 | 0.62–2.62 | 0.3 |
( |
GA | 120 (26) | 186 (31) | 1.25 | 0.70–2.22 | 0.3 | 1.20 | 0.57–2.54 | 0.3 |
GG | 14 (3) | 27 (4) | ref | ref | |||||
Additive | 1.28 | 1.06–1.55 | 0.01 | 1.09 | 0.85–1.38 | 0.3 | |||
rs10890 | TT | 117 (25) | 98 (16) | 1.52 | 1.14–2.04 | 0.009 | 1.49 | 1.02–2.18 | 0.04 |
( |
CT | 201 (44) | 325 (54) | 0.79 | 0.62–0.99 | 0.9 | 0.85 | 0.63–1.16 | 0.8 |
CC | 143 (31) | 183 (30) | ref | ref | |||||
Additive | 1.18 | 1.02–1.37 | 0.03 | 1.18 | 0.98–1.42 | 0.07 | |||
rs3793456 | GG | 174 (38) | 180 (30) | 1.33 | 0.98–1.80 | 0.06 | 1.50 | 1.01–2.22 | 0.04 |
( |
AG | 205 (45) | 319 (53) | 0.88 | 0.66–1.18 | 0.8 | 1.00 | 0.69–1.45 | 0.5 |
AA | 78 (17) | 107 (18) | ref | ref | |||||
Additive | 1.21 | 1.04–1.40 | 0.02 | 1.26 | 1.05–1.53 | 0.02 | |||
rs35410698 | GG | 426 (92) | 539 (89) | 1.56 | 1.09–2.22 | 0.02 | 2.07 | 1.31–3.27 | 0.004 |
( |
GA | 36 (8) | 70 (11) | ref | ref | ||||
Additive | 1.46 | 1.03–2.06 | 0.04 | 1.79 | 1.14–2.81 | 0.02 | |||
rs3798220 | CT | 41 (9) | 12 (2) | 4.63 | 2.67–8.03 | <0.001 | 3.52 | 1.85–6.69 | 0.001 |
( |
TT | 416 (91) | 573 (98) | ref | ref | ||||
Additive | 4.63 | 2.67–8.03 | <0.001 | 3.52 | 1.85–6.69 | 0.001 |
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 (
The first SNP is located in
Two of the SNPs are in the
The fourth SNP (rs3798220 in
Lastly, the fifth SNP is in
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
We identified 5 SNPs in 4 genes that are likely associated with MI. These SNPs merit investigation in additional studies of MI.
The authors are grateful to the subjects of the genetic association studies.