Association of Chromosome 9p21 With Subsequent Coronary Heart Disease Events

Influence of Chromosome 9p21.3 rs1333049 Variant on Telomere Length and Their Interactive Impact on the Prognosis of Coronary Artery Disease

BACKGROUND

Both telomere shortening and the chromosome 9p21.3 (Chr9p21) rs1333049 (G/C) variant are involved in coronary artery disease (CAD) risk, likely affecting mechanisms related to cell cycle arrest and vascular senescence. The aim of the study was to examine the link between Chr9p21 rs1333049 variant and leucocyte telomere length (LTL), as well as their interactive effect on the risk of major adverse cardiovascular events (MACEs).

METHODS

A cohort of 472 patients with angiographically proven and clinically stable CAD were included in the study. At baseline, the LTL, biochemical parameters, and genotype analysis of Chr9p21 rs1333049 variant were measured in all patients. The primary endpoint of this study was the occurrence of MACE defined as a composite of coronary-related death, nonfatal MI, and coronary revascularization.

RESULTS

On multivariable linear regression analysis, age (p = 0.02) and Chr9p21 rs1333049 variant (p = 0.002) were the only independent predictors of LTL levels. Carriers of the CC genotype of this SNP had shorter telomeres than GC carriers (p = 0.02) and GG carriers (p = 0.0005). After a follow-up with a mean period of 62 ± 19 months, 90 patients (19.1%) had MACE. Short LTL was an independent prognostic factor of MACE incidence (HR:2.2; 95% CI: 1.3-3.7; p = 0.005) after adjustment for potential confounders. There was a significant interaction (p = 0.01) between the LTL and rs1333049 variant, with patients with risk-allele C and short LTL having a higher risk (HR:5.8; 95% CI: 1.8-19.2; p = 0.004).

CONCLUSION

A strong relationship between LTL and Chr9p21 rs1333049 variant was identified, and they interactively affect the risk of poor prognosis in CAD patients.

Genetic Risk Factors Related to Coronary Artery Disease and Role of Transforming Growth Factor Beta 1 Polymorphisms

Coronary artery disease (CAD) is one of the leading causes of mortality globally and has long been known to be heritable; however, the specific genetic factors involved have yet to be identified. Recent advances have started to unravel the genetic architecture of this disease and set high expectations about the future use of novel susceptibility variants for its prevention, diagnosis, and treatment. In the past decade, there has been major progress in this area. New tools, like common variant association studies, genome-wide association studies, meta-analyses, and genetic risk scores, allow a better understanding of the genetic risk factors driving CAD. In recent years, researchers have conducted further studies that confirmed the role of numerous genetic factors in the development of CAD. These include genes that affect lipid and carbohydrate metabolism, regulate the function of the endothelium and vascular smooth muscles, influence the coagulation system, or affect the immune system. Many CAD-associated single-nucleotide polymorphisms have been identified, although many of their functions are largely unknown. The inflammatory process that occurs in the coronary vessels is very important in the development of CAD. One important mediator of inflammation is TGFβ1. TGFβ1 plays an important role in the processes leading to CAD, such as by stimulating macrophage and fibroblast chemotaxis, as well as increasing extracellular matrix synthesis. This review discusses the genetic risk factors related to the development of CAD, with a particular focus on polymorphisms of the transforming growth factor β (TGFβ) gene and its receptor.

9p21 Locus Polymorphism Is A Strong Predictor of Metabolic Syndrome and Cardiometabolic Risk Phenotypes Regardless of Coronary Heart Disease

The world population is genetically predisposed to metabolic syndrome (MetS) and its components, also known as cardiometabolic risk phenotypes, which can cause severe health complications including coronary heart disease (CHD). Genetic variants in the 9p21 locus have been associated with CHD in a number of populations including Pakistan. However, the role of the 9p21 locus in MetS and cardiometabolic risk phenotypes (such as obesity, hypertension, hyperglycemia, and dyslipidemia) in populations with CHD or no established CHD has not been explored. Therefore, the present study was designed to explore the association of the minor/risk allele (C) of 9p21 locus SNP rs1333049 with MetS or its risk phenotypes regardless of an established CHD, in Pakistani subjects. Genotyping of rs1333049 (G/C) was performed on subjects under a case-control study design; healthy controls and cases, MetS with CHD (MetS-CHD⁺) and MetS with no CHD (MetS-CHD^-), respectively. Genotype and allele frequencies were calculated in all study groups. Anthropometric and clinical variables (Means ± SD) were compared among study groups (i.e., controls, MetS + CHD and MetS-CHD) and minor/risk C allele carriers (GC + CC) vs. non-carriers (Normal GG genotype). Associations of the risk allele of rs1333049 SNP with disease and individual metabolic risk components were explored using adjusted multivariate logistic regression models (OR at 95% CI) with a threshold p-value of ≤0.05. Our results have shown that the minor allele frequency (MAF) was significantly high in the MAF cases (combined = 0.63, MetS-CHD⁺ = 0.57 and MetS-CHD^- = 0.57) compared with controls (MAF = 0.39). The rs1333049 SNP significantly increased the risk of MetS, irrespective of CHD (MetS-CHD⁺ OR = 2.36, p < 0.05 and MetS-CHD^- OR = 4.04, p < 0.05), and cardiometabolic risk phenotypes; general obesity, central obesity, hypertension, and dyslipidemia (OR = 1.56-3.25, p < 0.05) except hyperglycemia, which lacked any significant association (OR = 0.19, p = 0.29) in the present study group. The 9p21 genetic locus/rs1333049 SNP is strongly associated with, and can be a genetic predictor of, MetS and cardiometabolic risks, irrespective of cardiovascular diseases in the Pakistani population.

Associations of Polymorphisms in the Peroxisome Proliferator-Activated Receptor Gamma Coactivator-1 Alpha Gene With Subsequent Coronary Heart Disease: An Individual-Level Meta-Analysis

Background: The knowledge of factors influencing disease progression in patients with established coronary heart disease (CHD) is still relatively limited. One potential pathway is related to peroxisome proliferator–activated receptor gamma coactivator-1 alpha (PPARGC1A), a transcription factor linked to energy metabolism which may play a role in the heart function. Thus, its associations with subsequent CHD events remain unclear. We aimed to investigate the effect of three different SNPs in the PPARGC1A gene on the risk of subsequent CHD in a population with established CHD.

Methods: We employed an individual-level meta-analysis using 23 studies from the GENetIcs of sUbSequent Coronary Heart Disease (GENIUS-CHD) consortium, which included participants (n = 80,900) with either acute coronary syndrome, stable CHD, or a mixture of both at baseline. Three variants in the PPARGC1A gene (rs8192678, G482S; rs7672915, intron 2; and rs3755863, T528T) were tested for their associations with subsequent events during the follow-up using a Cox proportional hazards model adjusted for age and sex. The primary outcome was subsequent CHD death or myocardial infarction (CHD death/myocardial infarction). Stratified analyses of the participant or study characteristics as well as additional analyses for secondary outcomes of specific cardiovascular disease diagnoses and all-cause death were also performed.

Results: Meta-analysis revealed no significant association between any of the three variants in the PPARGC1A gene and the primary outcome of CHD death/myocardial infarction among those with established CHD at baseline: rs8192678, hazard ratio (HR): 1.01, 95% confidence interval (CI) 0.98–1.05 and rs7672915, HR: 0.97, 95% CI 0.94–1.00; rs3755863, HR: 1.02, 95% CI 0.99–1.06. Similarly, no significant associations were observed for any of the secondary outcomes. The results from stratified analyses showed null results, except for significant inverse associations between rs7672915 (intron 2) and the primary outcome among 1) individuals aged ≥65, 2) individuals with renal impairment, and 3) antiplatelet users.

Conclusion: We found no clear associations between polymorphisms in the PPARGC1A gene and subsequent CHD events in patients with established CHD at baseline.

Association of Myocardial Infarction with CDKN2B Antisense RNA 1 (CDKN2B-AS1) rs1333049 Polymorphism in Slovenian Subjects with Type 2 Diabetes Mellitus

Background: We examined the role of rs1333049 polymorphism of the CDKN2B Antisense RNA 1 (CDKN2B-AS1) on the prevalence of myocardial infarction (MI) in Slovenian subjects with type 2 diabetes mellitus (T2DM). Methods: A total of 1071 subjects with T2DM were enrolled in this retrospective cross-sectional case-control study. Of the subjects, 334 had a history of recent MI, and 737 subjects in the control group had no clinical signs of coronary artery disease (CAD). With logistic regression, we performed a genetic analysis of rs1333049 polymorphism in all subjects. Results: The C allele of rs1333049 polymorphism was statistically more frequent in MI subjects (p = 0.05). Subjects with CC genotype had a higher prevalence of MI than the control group in the co-dominant (AOR 1.50, CI 1.02–2.21, p = 0.04) and recessive (AOR 1.38, CI 1.09–1.89, p = 0.04) genetic model. Conclusions: According to our study, the C allele and CC genotype of rs1333049 polymorphism of CDKN2B-AS1 are possible markers of MI in T2DM subjects in the Slovenian population.

Cardiovascular Family History Increases the Risk of Disease Recurrence After a First Myocardial Infarction

Background

Family history of atherosclerotic cardiovascular disease (ASCVD) is easily accessible and captures genetic cardiovascular risk, but its prognostic value in secondary prevention is unknown.

Methods and Results

We followed 25 615 patients registered in SWEDEHEART (Swedish Web‐System for Enhancement and Development of Evidence‐Based Care in Heart Disease Evaluated According to Recommended Therapies) from their 1‐year revisit after a first‐time myocardial infarction during 2005 to 2013, until December 31, 2018. Data on relatives, diagnoses and socioeconomics were extracted from national registers. The association between family history and recurrent ASCVD was studied with Cox proportional‐hazard regression, adjusting for risk factors and socioeconomics. A family history of ASCVD was defined as hospitalization due to myocardial infarction, angina with coronary revascularization, stroke, or cardiovascular death in ≥1 parent or full sibling, with early‐onset defined as disease‐onset before 55 years in men and 65 in women. The additional discriminatory value of family history to Thrombolysis in Myocardial Infarction Risk Score for Secondary Prevention was assessed with Harrell’s C‐index difference and reclassification was studied with continuous net reclassification improvement. Family history of early‐onset ASCVD in ≥1 first‐degree relative was present in 2.3% and was associated with recurrent ASCVD (hazard ratio [HR] 1.31; 95% CI, 1.17–1.47), fully adjusted for risk factors (HR, 1.22; 95% CI, 1.05–1.42). Early‐onset family history improved the discriminatory ability of the Thrombolysis in Myocardial Infarction Risk Score for Secondary Prevention, with Harrell’s C improving 0.003 points (95% CI, 0.001–0.005) from initial 0.587 (95% CI, 0.576–0.595) and improved reclassification (continuous net reclassification improvement 2.1%, P<0.001).

Conclusions

Family history of early‐onset ASCVD is associated with recurrent ASCVD after myocardial infarction, independently of traditional risk factors and improves secondary risk prediction. This may identify patients to target for intensified secondary prevention.

Genetic variants of PON1, GSTM1, GSTT1, and locus 9p21.3, and the risk for premature coronary artery disease in Yucatan, Mexico

OBJECTIVE

Genetic variants of PON1, rs70587, rs662, rs854560, GSTM1, and GSTT1 and two single nucleotide polymorphisms (SNP) at 9p21.3 locus, rs1333049, and rs2383207; were evaluated in association with the risk for premature coronary artery disease (CAD) in a population of Yucatan, Mexico. These genes are involved in the inactivation of pro-oxidants and pro-inflammatory mediators, lipid and xenobiotic metabolism, detoxification of reactive oxygen species, and regulation of cellular proliferation playing key roles in the pathogenesis of atherosclerosis.

METHODS

We conducted a matched case-control study with 98 CAD cases and 101 healthy controls. Genotyping of PON1 and 9p21.2 SNP was performed by real time-PCR and for GSTM1 and GSTT1 with multiplex-PCR. Odds ratios (OR) were calculated to estimate association and generalized multifactor dimensionality reduction (GMDR) algorithm to identify gene-gene and gene-environment interactions.

RESULTS

The distribution of all allele/genotype frequencies in controls was within Hardy-Weinberg expectations (p > .05) except for GSTM1. The allele/genotype frequencies of the GSTT1 null were significantly higher in CAD cases than in controls, suggesting association with higher risk for developing CAD. The other SNPs did not show any significant independent association with premature CAD. GMDR revealed a significant interaction between GSTT1 and LL55 genotype. Likewise, the body mass index (BMI) and smoking also showed an interaction with GSTT1.

CONCLUSION

The GSTT1 null allele/genotype is associated with an increased risk of developing premature CAD, the effect of which is not modified by cardiovascular risk factors in the population of Yucatan.

Risk Prediction of Cardiovascular Events by Exploration of Molecular Data with Explainable Artificial Intelligence

Cardiovascular diseases (CVD) annually take almost 18 million lives worldwide. Most lethal events occur months or years after the initial presentation. Indeed, many patients experience repeated complications or require multiple interventions (recurrent events). Apart from affecting the individual, this leads to high medical costs for society. Personalized treatment strategies aiming at prediction and prevention of recurrent events rely on early diagnosis and precise prognosis. Complementing the traditional environmental and clinical risk factors, multi-omics data provide a holistic view of the patient and disease progression, enabling studies to probe novel angles in risk stratification. Specifically, predictive molecular markers allow insights into regulatory networks, pathways, and mechanisms underlying disease. Moreover, artificial intelligence (AI) represents a powerful, yet adaptive, framework able to recognize complex patterns in large-scale clinical and molecular data with the potential to improve risk prediction. Here, we review the most recent advances in risk prediction of recurrent cardiovascular events, and discuss the value of molecular data and biomarkers for understanding patient risk in a systems biology context. Finally, we introduce explainable AI which may improve clinical decision systems by making predictions transparent to the medical practitioner.

Clinical Conditions and Their Impact on Utility of Genetic Scores for Prediction of Acute Coronary Syndrome

BACKGROUND

Acute coronary syndrome (ACS) is a clinically significant presentation of coronary heart disease. Genetic information has been proposed to improve prediction beyond well-established clinical risk factors. While polygenic scores (PS) can capture an individual's genetic risk for ACS, its prediction performance may vary in the context of diverse correlated clinical conditions. Here, we aimed to test whether clinical conditions impact the association between PS and ACS.

METHODS

We explored the association between 405 clinical conditions diagnosed before baseline and 9080 incident cases of ACS in 387 832 individuals from the UK Biobank. Results were replicated in 6430 incident cases of ACS in 177 876 individuals from FinnGen.

RESULTS

We identified 80 conventional (eg, stable angina pectoris and type 2 diabetes) and unconventional (eg, diaphragmatic hernia and inguinal hernia) associations with ACS. The association between PS and ACS was consistent in individuals with and without most clinical conditions. However, a diagnosis of stable angina pectoris yielded a differential association between PS and ACS. PS was associated with a significantly reduced (interaction P=2.87×10^-8) risk for ACS in individuals with stable angina pectoris (hazard ratio, 1.163 [95% CI, 1.082-1.251]) compared with individuals without stable angina pectoris (hazard ratio, 1.531 [95% CI, 1.497-1.565]). These findings were replicated in FinnGen (interaction P=1.38×10^-6).

CONCLUSIONS

In summary, while most clinical conditions did not impact utility of PS for prediction of ACS, we found that PS was substantially less predictive of ACS in individuals with prevalent stable coronary heart disease. PS may be more appropriate for prediction of ACS in asymptomatic individuals than symptomatic individuals with clinical suspicion for coronary heart disease.

Genome-Wide Variants Associated With Longitudinal Survival Outcomes Among Individuals With Coronary Artery Disease

Objective

Coronary artery disease (CAD) is an age-associated condition that greatly increases the risk of mortality. The purpose of this study was to identify gene variants associated with all-cause mortality among individuals with clinically phenotyped CAD using a genome-wide screening approach.

Approach and Results

We performed discovery (n = 684), replication (n = 1,088), and meta-analyses (N = 1,503) for association of genomic variants with survival outcome using secondary data from White participants with CAD from two GWAS sub-studies of the Duke Catheterization Genetics Biorepository. We modeled time from catheterization to death or last follow-up (median 7.1 years, max 12 years) using Cox multivariable regression analysis. Target statistical screening thresholds were p × 10^–8 for the discovery phase and Bonferroni-calculated p-values for the replication (p < 5.3 × 10^–4) and meta-analysis (p < 1.6 × 10^–3) phases. Genome-wide analysis of 785,945 autosomal SNPs revealed two SNPs (rs13007553 and rs587936) that had the same direction of effect across all three phases of the analysis, with suggestive p-value association in discovery and replication and significant meta-analysis association in models adjusted for clinical covariates. The rs13007553 SNP variant, LINC01250, which resides between MYTIL and EIPR1, conferred increased risk for all-cause mortality even after controlling for clinical covariates [HR 1.47, 95% CI 1.17–1.86, p(adj) = 1.07 × 10^–3 (discovery), p(adj) = 0.03 (replication), p(adj) = 9.53 × 10^–5 (meta-analysis)]. MYT1L is involved in neuronal differentiation. TSSC1 is involved in endosomal recycling and is implicated in breast cancer. The rs587936 variant annotated to DAB2IP was associated with increased survival time [HR 0.65, 95% CI 0.51–0.83, p(adj) = 4.79 × 10^–4 (discovery), p(adj) = 0.02 (replication), p(adj) = 2.25 × 10^–5 (meta-analysis)]. DAB2IP is a ras/GAP tumor suppressor gene which is highly expressed in vascular tissue. DAB2IP has multiple lines of evidence for protection against atherosclerosis.

Conclusion

Replicated findings identified two candidate genes for further study regarding association with survival in high-risk CAD patients: novel loci LINC01250 (rs13007553) and biologically relevant candidate DAB2IP (rs587936). These candidates did not overlap with validated longevity candidate genes. Future research could further define the role of common variants in survival outcomes for people with CAD and, ultimately, improve longitudinal outcomes for these patients.

Purine metabolite-based machine learning models for risk prediction, prognosis, and diagnosis of coronary artery disease

Alterations in xanthine oxidase activity are known to be pathologically influential on coronary artery disease (CAD), but the association between purine-related blood metabolites and CAD has only been partially elucidated. We performed global metabolomics profiling and network analysis on blood samples from the Wonju and Pyeongchang (WP) cohort study (n = 2055) to elucidate the importance of purine related metabolites associated with potential CAD risk. Then, 5 selected serum metabolites were quantified from the WP cohort, Shinchon cohort (n = 259), and Shinchon case control (n = 424) groups to develop machine learning models for 10-year risk prediction, relapse within 10 years and diagnosis of the disease via 100 repeated 5-fold cross-validations of logistic models. The combination of purine metabolite levels or only xanthine levels in blood could be applied for machine learning model development for major adverse cardiac and cerebrovascular event (MACCE, cerebrovascular death, nonfatal myocardial infarction, percutaneous transluminal coronary angioplasty, coronary artery bypass graft, and stroke) risk prediction, relapse of MACCEs among patients with myocardial infarction history and diagnosis of stable CAD. In particular, our research provided initial evidence that blood xanthine and uric acid levels play different roles in the development of machine learning models for primary/secondary prevention or diagnosis of CAD. In this research, we determined that purine-related metabolites in blood are applicable to machine learning model development for CAD risk prediction and diagnosis. Also, our work advances current CAD biomarker discovery strategies mainly relying on clinical features; emphasizes the differential biomarkers in first/secondary prevention or diagnosis studies.

Polygenic risk scores for coronary artery disease and subsequent event risk amongst established cases

BACKGROUND

There is growing evidence that polygenic risk scores (PRSs) can identify individuals with elevated lifetime risk of coronary artery disease (CAD). Whether they can also be used to stratify the risk of subsequent events among those surviving a first CAD event remain uncertain, with possible biological differences between CAD onset and progression, and the potential for index event bias.

METHODS

Using two baseline subsamples of UK Biobank: prevalent CAD cases (N = 10 287) and individuals without CAD (N = 393 108), we evaluated associations between a CAD PRS and incident cardiovascular and fatal outcomes.

RESULTS

A 1 SD higher PRS was associated with an increased risk of incident myocardial infarction (MI) in participants without CAD (OR 1.33; 95% CI 1.29, 1.38), but the effect estimate was markedly attenuated in those with prevalent CAD (OR 1.15; 95% CI 1.06, 1.25) and heterogeneity P = 0.0012. Additionally, among prevalent CAD cases, we found an evidence of an inverse association between the CAD PRS and risk of all-cause death (OR 0.91; 95% CI 0.85, 0.98) compared with those without CAD (OR 1.01; 95% CI 0.99, 1.03) and heterogeneity P = 0.0041. A similar inverse association was found for ischaemic stroke [prevalent CAD (OR 0.78; 95% CI 0.67, 0.90); without CAD (OR 1.09; 95% CI 1.04, 1.15), heterogeneity P < 0.001].

CONCLUSIONS

Bias induced by case stratification and survival into UK Biobank may distort the associations of PRS derived from case-control studies or populations initially free of disease. Differentiating between effects of possible biases and genuine biological heterogeneity is a major challenge in disease progression research.

2018 George Lyman Duff Memorial Lecture: Genetics and Genomics of Coronary Artery Disease: A Decade of Progress

Recent studies have led to a broader understanding of the genetic architecture of coronary artery disease and demonstrate that it largely derives from the cumulative effect of multiple common risk alleles individually of small effect size rather than rare variants with large effects on coronary artery disease risk. The tools applied include genome-wide association studies encompassing over 200 000 individuals complemented by bioinformatic approaches including imputation from whole-genome data sets, expression quantitative trait loci analyses, and interrogation of ENCODE (Encyclopedia of DNA Elements), Roadmap Epigenetic Project, and other data sets. Over 160 genome-wide significant loci associated with coronary artery disease risk have been identified using the genome-wide association studies approach, 90% of which are situated in intergenic regions. Here, I will describe, in part, our research over the last decade performed in collaboration with a series of bright trainees and an extensive number of groups and individuals around the world as it applies to our understanding of the genetic basis of this complex disease. These studies include computational approaches to better understand missing heritability and identify causal pathways, experimental approaches, and progress in understanding at the molecular level the function of the multiple risk loci identified and potential applications of these genomic data in clinical medicine and drug discovery.

When drug treatments bias genetic studies: Mediation and interaction

Background

Increasingly, genetic analyses are conducted using information from subjects with established disease, who often receive concomitant treatment. We determined when treatment may bias genetic associations with a quantitative trait.

Methods

Graph theory and simulated data were used to explore the impact of drug prescriptions on (longitudinal) genetic effect estimates. Analytic derivations of longitudinal genetic effects are presented, accounting for the following scenarios: 1) treatment allocated independently of a genetic variant, 2) treatment that mediates the genetic effect, 3) treatment that modifies the genetic effect. We additionally evaluate treatment modelling strategies on bias, the root mean squared error (RMSE), coverage, and rejection rate.

Results

We show that in the absence of treatment by gene effect modification or mediation, genetic effect estimates will be unbiased. In simulated data we found that conditional models accounting for treatment, confounding, and effect modification were generally unbiased with appropriate levels of confidence interval coverage. Ignoring the longitudinal nature of treatment prescription, however (e.g. because of incomplete records in longitudinal data), biased these conditional models to a similar degree (or worse) as simply ignoring treatment.

Conclusion

The mere presence of (drug) treatment affecting a GWAS phenotype is insufficient to bias genetic associations with quantitative traits. While treatment may bias associations through effect modification and mediation, this might not occur frequently enough to warrant general concern at the presence of treated subjects in GWAS. Should treatment by gene effect modification or mediation be present however, current GWAS approaches attempting to adjust for treatment insufficiently account for the multivariable and longitudinal nature of treatment trajectories and hence genetic estimates may still be biased.

Analysis of Polymorphism rs1333049 (Located at 9P21.3) in the White Population of Western Siberia and Associations with Clinical and Biochemical Markers

The 9p21.3 chromosomal region is a marker of the risk of cardiovascular diseases. The aim of this study was to analyze single-nucleotide polymorphism rs1333049 (chr9:22125504) in the population of Western Siberia (Russia) and possible associations with clinical and biochemical parameters. The population included in the analyses was selected from a sample surveyed within the framework of the Health, Alcohol and Psychosocial Factors In Eastern Europe (HAPIEE) study (9360 participants, >90% white, aged 45–69, males: 50%). In total, 2729 randomly selected patients were included. Plasma lipid levels were determined by standard enzymatic assays. Rs1333049 was analyzed by RT-PCR (BioLabMix, Russia). Frequencies of rs1333049 genotypes C/C (homozygote), C/G (heterozygote), and G/G were 0.22, 0.51, and 0.27 in this population. The Allele G frequency was 0.53. We found an association of allele G with total cholesterol and low-density lipoprotein cholesterol levels among male participants (p = 0.004 and p = 0.002, respectively). Allele C was significantly associated with the risk of myocardial infarction among the male participants (odds ratio 1.96, 95% confidence interval 1.14–3.38, p =  0.017) and the study population (odds ratio 1.83, 95% confidence interval 1.23–2.72, p  =  0.004). Thus, rs1333049 is associated with myocardial infarction in the white population of Western Siberia (Russia).