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Article

A Comprehensive Genetic and Bioinformatic Analysis Provides Evidence for the Engagement of COVID-19 GWAS-Significant Loci in the Molecular Mechanisms of Coronary Artery Disease and Stroke

by
Alexey Loktionov
1,2,
Ksenia Kobzeva
2,
Anna Dorofeeva
2,
Maryana Babkina
2,
Elizaveta Kolodezhnaya
2 and
Olga Bushueva
2,3,*
1
Department of Anesthesia and Critical Care, Institute of Continuing Education, Kursk State Medical University, 305004 Kursk, Russia
2
Laboratory of Genomic Research, Research Institute for Genetic and Molecular Epidemiology, Kursk State Medical University, 305004 Kursk, Russia
3
Department of Biology, Medical Genetics and Ecology, Kursk State Medical University, 305004 Kursk, Russia
*
Author to whom correspondence should be addressed.
J. Mol. Pathol. 2024, 5(3), 385-404; https://doi.org/10.3390/jmp5030026
Submission received: 29 July 2024 / Revised: 26 August 2024 / Accepted: 12 September 2024 / Published: 14 September 2024
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Versions Notes

Abstract

:
Cardiovascular diseases (CVDs) significantly exacerbate the severity and mortality of COVID-19. We aimed to investigate whether GWAS-significant SNPs correlate with CVDs in severe COVID-19 patients. DNA samples from 199 patients with severe COVID-19 hospitalized in intensive care units were genotyped using probe-based PCR for 10 GWAS SNPs previously implicated in severe COVID-19 outcomes. SNPs rs17713054 SLC6A20-LZTFL1 (risk allele A, OR = 2.14, 95% CI 1.06–4.36, p = 0.03), rs12610495 DPP9 (risk allele G, OR = 1.69, 95% CI 1.02–2.81, p = 0.04), and rs7949972 ELF5 (risk allele T, OR = 2.57, 95% CI 1.43–4.61, p = 0.0009) were associated with increased risk of coronary artery disease (CAD). SNPs rs7949972 ELF5 (OR = 2.67, 95% CI 1.38–5.19, p = 0.003) and rs61882275 ELF5 (risk allele A, OR = 1.98, 95% CI 1.14–3.45, p = 0.01) were linked to a higher risk of cerebral stroke (CS). No associations were observed with AH. Bioinformatics analysis revealed the involvement of GWAS-significant loci in atherosclerosis, inflammation, oxidative stress, angiogenesis, and apoptosis, which provides evidence of their role in the molecular mechanisms of CVDs. This study provides novel insights into the associations between GWAS-identified SNPs and the risk of CAD and CS.

1. Introduction

The COVID-19 pandemic has had a profound impact worldwide, claiming over 7 million lives as of May 2024 (https://data.who.int/dashboards/covid19/deaths?n=c accessed on 20 May 2024). As the pandemic unfolds, extensive research efforts have been directed toward unraveling the factors that contribute to the severity, outcomes, and treatment of COVID-19 infections [1,2,3].
Genome-wide association studies (GWAS) have identified numerous causal genes associated with severe COVID-19, including SLC6A20, LZTFL1, IFNAR2, DPP9, CCHCR1, and ELF5 [4,5,6,7,8,9]. Furthermore, while the disease manifests with mild symptoms in most individuals, severe COVID-19 disproportionately affects those with comorbidities such as cardiovascular diseases (CVDs), exacerbating symptoms and elevating the risk of adverse outcomes, including mortality [10,11,12].
Arterial hypertension (AH) and coronary artery disease (CAD) are among the most common CVDs and are known risk factors for poor prognosis in COVID-19 patients, contributing to higher mortality rates, severe disease progression, ICU (intensive care units) admission, and overall disease progression [13]. Additionally, cerebral stroke (CS) is a significant CVD associated with COVID-19, as the infection can trigger hypercoagulation, leading to cerebral thrombosis and/or thromboembolism [14].
Observational studies have suggested that COVID-19, AH, CAD, and CS may share common genetic variants. Several GWAS have identified specific SNPs associated with COVID-19 susceptibility, including ACE2 and ABO genes, which have also been implicated in these CVDs [15,16,17,18]. Despite the significant threats posed by the intersection of COVID-19 and CVDs to patient outcomes, the shared pathology between these conditions remains largely unexplored.
This study seeks to expand on these findings by examining whether SNPs previously identified as genetic risk factors for severe COVID-19 by GWAS are also associated with CVDs, including CAD, CS, and AH in patients with severe COVID-19. By focusing on these overlapping genetic risk factors, we aim to uncover shared genetic pathways that might contribute to the comorbidities of severe COVID-19 and CVDs, therefore providing insights into their shared pathology.

2. Materials and Methods

The study involved 199 hospitalized COVID-19 patients from Central Russia. The Ethical Review Committee of Kursk State Medical University approved the study protocol (protocol No. 1 from 11 January 2022), and all participants provided written informed consent. To be included in the study, participants had to self-declare Russian ancestry and be born in Central Russia.
An outline of the study design, along with the main methods and materials used in our study, are depicted in Figure 1.
The patients were enrolled during the COVID-19 pandemic from 2020 to 2022 at the intensive care units (ICU) of Kursk Regional Hospital No. 6 and Kursk Regional Tuberculosis Dispensary [19]. All patients had a PCR-confirmed diagnosis of COVID-19. All patients with severe COVID-19 were divided into subgroups depending on the presence/absence of comorbid CVDs.
Table S1 presents the baseline and clinical characteristics of the studied groups, and Table 1 highlights the parameters with significant differences between studied groups.

2.1. Selection of Genes and Polymorphisms

To select genes and polymorphisms for our study, we utilized data from the largest GWAS meta-analysis of severe COVID-19, published in 2023 [4]. Loci were chosen based on a significance threshold of p ≤ 1 × 10−19, as established in this meta-analysis. We excluded SNPs with a minor allele frequency (MAF) of less than 0.05 and loci where probe development for genotyping using the TaqMan-based PCR method was not feasible due to methodological constraints (such as low CG composition, presence of GC clamps, and runs of identical nucleotides). After these exclusions, a total of 10 SNPs were included for genotyping: rs143334143 CCHCR1, rs111837807 CCHCR1, rs17078346 SLC6A20-LZTFL1, rs17713054 SLC6A20-LZTFL1, rs7949972 ELF5, rs61882275 ELF5, rs12585036 ATP11A, rs67579710 THBS3, THBS3-AS1, rs12610495 DPP9, rs9636867 IFNAR2.

2.2. Genetic Analysis

The Laboratory of Genomic Research at the Research Institute for Genetic and Molecular Epidemiology of Kursk State Medical University (Kursk, Russia) performed genotyping. Up to 5 mL of venous blood from each participant was collected from a cubital vein, put into EDTA-coated tubes, and kept at −20 °C until it was processed. Defrosted blood samples were used to extract genomic DNA using the standard methods of phenol/chloroform extraction and ethanol precipitation. The purity, quality, and concentration of the extracted DNA samples were assessed using a NanoDrop spectrophotometer (Thermo Fisher Scientific, Waltham, MA, USA).
Genotyping of the SNPs was performed using allele-specific probe-based polymerase chain reaction (PCR) according to the protocols designed in the Laboratory of Genomic Research at the Research Institute for Genetic and Molecular Epidemiology of Kursk State Medical University. The Primer3 software was used for primer design. A real-time PCR procedure was performed in a 25 µL reaction solution containing 1.5 units of Hot Start Taq DNA polymerase (Biolabmix, Novosibirsk, Russia), approximately 10 ng of DNA, and following concentrations of reagents: 0.25 μM of each primer; 0.1 μM of each probe; 250 μM of each dNTP; 3 mM MgCl2 for rs7949972, 3.5 mM MgCl2 for rs61882275, 2 mM MgCl2 for rs12610495 and 2.5 mM MgCl2 for the remaining SNPs; 1xPCR buffer (67 mM Tris-HCl, pH 8.8, 16.6 mM (NH4)2SO4, 0.01% Tween-20). The PCR procedure comprised an initial denaturation for 10 min at 95 °C, followed by 39 cycles of 92 °C for 30 s and 57 °C, 59 °C, 60 °C, 61 °C, 62 °C, 63 °C, 65 °C, 66 °C for 1 min (for rs12610495, rs17078346, rs17713054, rs111837807, rs9636867, rs143334143 and rs7949972, rs12585036 and rs61882275, rs67579710, respectively). A total of 10% of the DNA samples were genotyped twice, blinded to the case-control status, to assure quality control. Over 99% of the data were concordant.

2.3. Statistical and Bioinformatic Analysis

The statistical analysis was performed using STATISTICA software (v13.3, USA). The normality of quantitative data distribution was assessed with the Shapiro–Wilk test. As most quantitative parameters deviated from a normal distribution, they were presented as medians (Me) with first and third quartiles [Q1, Q3]. For comparisons between two independent groups, the Mann–Whitney test was also used. Categorical variables were analyzed for statistical significance using Pearson’s chi-squared test with Yates’s correction for continuity.
Genotype distributions were checked for Hardy-Weinberg equilibrium using Fisher’s exact test. Genotype frequencies and their associations with disease risk were analyzed using SNPStats software (https://www.snpstats.net/start.htm; accessed on 31 May 2024). The log-additive model was considered in the genotype association analysis. Associations in the entire group of COVID-19 patients and controls were adjusted for covariates, which included variables showing differences in the general biological characteristics of the study groups.
Considering the significant role of epistatic interactions, as well as the joint influence of genetic and environmental factors on the risk of developing multifactorial diseases [20,21,22,23], we used the MB-MDR analysis for testing two-, three-, and four-level genotype combinations (G × G) and genotype-environment combinations (G × E). The empirical p-value (pperm) for each model was estimated using a permutation test, with models considered statistically significant if pperm < 0.05. All calculations were adjusted for covariates, and the most robust models (averaging 3–4 models per level) based on Wald statistics and significance levels were included in the study. Additionally, the MB-MDR method identified individual genotype combinations associated with the studied phenotypes (p < 0.05). Calculations were performed using the MB-MDR program for the R software environment (version 3.6.3).
The most significant G × G and G × E models were further analyzed using the MDR method, implemented in the MDR program (v3.0.2) (http://sourceforge.net/projects/mdr; accessed on 10 June 2024). The MDR method assessed interaction mechanisms (synergy, antagonism, additive interactions) and interaction strength (contribution of individual genes/environmental factors to trait entropy and interaction contributions). Results from the MDR analysis were visualized as graphs to illustrate these interactions.
The following bioinformatics resources were used to analyze the functional effects of GWAS SNPs:
  • GTEx Portal (http://www.gtexportal.org/; accessed on 4 June 2024): this tool was used to analyze the expression levels of the studied genes in whole blood, blood vessels, brain tissue, and heart. It also helped in analyzing the expression of quantitative trait loci (eQTLs) associated with these genes [24].
  • eQTLGen (https://www.eqtlgen.org/; accessed on 4 June 2024): This resource was used to examine the binding of GWAS SNPs to eQTLs in peripheral blood [25].
  • HaploReg (v4.2), a bioinformatics tool available at https://pubs.broadinstitute.org/mammals/haploreg/haploreg.php; accessed on 4 June 2024). This tool assessed the associations between GWAS SNPs and specific histone modifications that indicate promoters and enhancers. These modifications included acetylation of lysine residues at positions 27 and 9 of the histone H3 protein, as well as monomethylation at position 4 (H3K4me1) and tri-methylation at position 4 (H3K4me3) of the histone H3 protein. Additionally, HaploReg was used to investigate SNP positioning in DNase hypersensitive regions, regulatory motif sites, and binding locations for regulatory proteins [26].
  • atSNP Function Prediction (atSNP Search human version 1.0.0) (http://atsnp.biostat.wisc.edu/search; accessed on 3 June 2024): This online tool was utilized to evaluate the impact of GWAS SNPs on gene affinity to transcription factors (TFs) depending on the presence of reference or alternative alleles. TFs were included based on the degree of influence SNPs have on their interaction with DNA, calculated using a positional weight matrix [27].
  • Gene Ontology (http://geneontology.org/; accessed on 3 June 2024): This tool enabled the analysis of the joint involvement of TFs linked to the reference or SNP alleles in overrepresented biological processes directly related to the pathogenesis of cardiovascular diseases (CVDs) [28]. Biological functions controlled by transcription factors associated with GWAS SNPs were used as functional groups.
  • Cerebrovascular Disease Knowledge Portal (CDKP) (https://cd.hugeamp.org/; accessed on 4 June 2024) and Cardiovascular Disease Knowledge Portal (https://cvd.hugeamp.org/; accessed on 4 June 2024): These online tools, which combine and analyze results from genetic associations by the largest consortia studying cardio- and cerebrovascular diseases, were used for bioinformatic analyses of the associations of GWAS SNPs with atherosclerosis-associated diseases, intermediate phenotypes, and CVD risk factors such as total cholesterol, LDL, and BMI [29].

3. Results

3.1. Assosiations of GWAS-Significant SNPs with the Risk of CVDs in Severe COVID-19 Patients

Upon analyzing the associations between GWAS SNPs and CVD risk, we observed no correlation with AH but identified associations with CAD and CS (Table S2). In the overall analysis, SNP rs17713054 SLC6A20-LZTFL1 (risk allele A, OR = 2.14, 95% CI 1.06–4.36, p = 0.03) and rs12610495 DPP9 (risk allele G, OR = 1.69, 95% CI 1.02–2.81, p = 0.04) were found to increase the risk of CAD (Table 2). Additionally, rs61882275 ELF5 was associated with an increased risk of CS (risk allele A, OR = 1.98, 95% CI 1.14–3.45, p = 0.01). Notably, allele T rs7949972 ELF5 was associated with an elevated risk of both CAD (OR = 2.57, 95% CI 1.43–4.61, p = 0.0009) and CS (OR = 2.67, 95% CI 1.38–5.19, p = 0.003) (Table 2).

3.2. Gene–Gene and Gene–Environmental Interactions

3.2.1. Gene–Gene Interactions Associated with Coronary Artery Disease Risk in Patients with Severe COVID-19 (MB-MDR, MDR Modeling)

Using the MB-MDR approach, the nine most significant gene–gene interaction patterns associated with CAD in patients with severe COVID-19 were identified: 3 two-locus, 3 three-locus, and 3 four-locus (pperm ≤ 0.05) (Table 3). In total, the best models of G × G interactions included seven polymorphic loci: rs7949972 ELF5, rs17078346 SLC6A20-LZTFL1, rs12610495 DPP9, rs9636867 IFNAR2, rs61882275 ELF5, rs12585036 ATP11A, rs17713054 SLC6A20-LZTFL1; all were involved in 2 or more of the best models of gene–gene interactions. In the next step, we analyzed the interactions between these genetic variants and smoking using the multifactor dimensionality reduction (MDR) method (Figure 2).
The MDR method showed that, first, the genetic variants included in the best G × G models are characterized predominantly by antagonism and independent (additive) effects, with the exception of interactions between SNPs rs17078346 SLC6A20-LZTFL1 and rs9636867 IFNAR2, rs7949972 ELF5 and rs12585036 ATP11A, which exhibit additive (independent) effects in interaction with each other). Second, rs12610495 DPP9 is characterized by a maximum mono-effect (6.22% contribution to the entropy of CAD), exceeding the mono-effects of other SNPs (maximum indicators—2.33%), and exceeding the effects of intergenic interactions (maximum contribution of intergenic interactions to the entropy of the trait—2.59%). Thirdly, the mono-effects of all other (except for rs12610495) SNPs (contribution to entropy 0.05–2.33%) are comparable to the effects of intergenic interactions (0.03–2.59% contribution to entropy). Fourthly, the following combinations of genotypes of polymorphic gene variants have the strongest correlations with coronary artery disease in patients with severe COVID-19: rs7949972 ELF5 T/C × rs17078346 SLC6A20-LZTFL1 C/A (Beta = 0.24961; p = 0.00722); rs9636867 IFNAR2 A/G × rs12610495 DPP9 A/A (Beta = −0.2002; p = 0.008286); rs7949972 ELF5 T/C × rs12610495 DPP9 G/A (Beta = 0.18543; p = 0.01752); rs7949972 ELF5 T/C × rs618822758 ELF5 A/G × rs12610495 DPP9 G/A (Beta = 0.23456; p = 0.005411); rs9636867 IFNAR2 G/G × rs618822758 ELF5 A/G × rs17078346 SLC6A20-LZTFL1 C/A (Beta = 0.342103; p = 0.02595); rs9636867 IFNAR2 A/G × rs12610495 DPP9 G/A × rs12585036 ATP11A C/C (Beta = 0.39442; p = 0.00112); rs7949972 ELF5 T/C × rs9636867 IFNAR2 G/G × rs618822758 ELF5 A/G × rs17078346 SLC6A20-LZTFL1 C/A (Beta = 0.338995; p = 0.02709); rs9636867 IFNAR2 A/G × rs12610495 DPP9 G/A × rs12585036 ATP11A C/C × rs17713054 SLC6A20-LZTFL1 G/G (Beta = 0.3989; p = 0.005714); rs7949972 ELF5 C/C × rs9636867 IFNAR2 A/G × rs12610495 DPP9 G/G × rs17713054 SLC6A20-LZTFL1 G/G (Beta = −0.66382; p = 0.02712) (Table S3).

3.2.2. Gene–Environmental Interactions Associated with Coronary Artery Disease Risk in Severe COVID-19 Patients (MB-MDR, MDR Modeling)

Using the MB-MDR approach, the seven most significant gene-environment interaction patterns associated with severe COVID-19 were identified: 1 two-level, 3 three-level, and 3 four-level (Table 4). In total, the best models of G×E interactions included smoking in interaction with 6 SNPs, four of which—rs17078346 SLC6A20-LZTFL1, rs17713054 SLC6A20-LZTFL1, rs61882275 ELF5, rs9636867 IFNAR2—were involved in 2 or more of the most significant models of G × E interactions. It is noteworthy that all the best models of gene-environment interactions involved a single environmental risk factor: smoking. In the next step, we analyzed the interactions between these genetic variants and smoking using the multifactor dimensionality reduction (MDR) method (Figure 3).
First, MDR revealed that the mono-effect of smoking (0.66% contribution to entropy) is comparable to the mono-effects of SNPs included in the best gene-environment interaction models (0.34–1.70% contribution to entropy). Second, the effects of gene-environment interactions (0.09–0.53% contribution to entropy) are comparable to the mono-effect of smoking (0.66% entropy). Thirdly, smoking is characterized by multidirectional effects in interaction with genetic variants in the most significant G × E models: additive (independent effects in interaction with rs9636867 IFNAR2 and rs17713054 SLC6A20-LZTFL1); moderate synergism in interaction with rs17078346 SLC6A20-LZTFL1 and moderate antagonism in interaction with rs61882275 ELF5.
Fifth, the strongest associations with CAD in severe COVID-19 patients have the following gene–environmental interactions: rs12610495 DPP9 A/A × 0 (Beta = −0.22356; p = 0.000656); rs17078346 SLC6A20-LZTFL1 A/A × 0 (Beta = −0.21431; p = 0.000638); rs17713054 SLC6A20-LZTFL1 G/G × 0 (Beta = −0.21315; p = 0.000671); rs618822758 ELF5 A/G × rs17078346 SLC6A20-LZTFL1 C/A × 0 (Beta = 0.401734; p = 0.002264); rs7949972 ELF5 C/C × rs17713054 SLC6A20-LZTFL1 G/G × 0 (Beta = −0.18079; p = 0.02385); rs9636867 IFNAR2 A/G × rs618822758 ELF5 A/G × rs17078346 SLC6A20-LZTFL1 A/A × 1 (Beta = 0.58464; p = 0.01801); rs9636867 IFNAR2 A/G × rs618822758 ELF5 A/G × rs17713054 SLC6A20-LZTFL1 G/G × 1 (Beta = 0.584637; p = 0.01801) (Table S4).

3.2.3. Gene–Gene Interactions Associated with Cerebral Stroke Risk in Patients with Severe COVID-19 (MB-MDR, MDR Modeling)

Using the MB-MDR approach, nine of the most significant patterns of gene–gene interactions associated with cerebral stroke in patients with severe COVID-19 were identified: 2 two-locus, 1 three-locus, and 2 four-locus (pperm ≤ 0.05) (Table 5). In total, the best models of G × G interactions included seven polymorphic loci, three of which—rs12610495 DPP9, rs7949972 ELF5, rs9636867 IFNAR2—were involved in 2 or more best models of intergenic interactions. It should be noted that the combination rs7949972 ELF5 × rs9636867 IFNAR2 × rs12610495 DPP9 was the basis for the formation of all the most significant models of “high-order” intergenic interactions (three-locus and four-locus). In the next step, we analyzed the interactions between these genetic variants and smoking using the multifactor dimensionality reduction (MDR) method (Figure 4).
First, MDR showed that the genetic variants included in the best G × G models are characterized by multidirectional effects: synergism, antagonism, and independent (additive) effects. Second, the effects of intergenic interactions (contribution to the entropy of the trait—0.32–2.56%) are comparable to the mono-effects of SNPs (0.73–1.74% of the entropy of cerebral stroke). Thirdly, the following combinations of genotypes of polymorphic gene variants have the strongest associations with cerebral stroke in patients with severe COVID-19: rs17078346 SLC6A20-LZTFL1 A/A × rs12610495 DPP9 G/A (Beta = 0.29; p = 0.0002); rs12610495 DPP9 G/A × rs17713054 SLC6A20-LZTFL1 G/G (Beta = 0.26; p = 0.0005); rs7949972 ELF5 T/C × rs9636867 IFNAR2 A/G × rs12610495 DPP9 G/A (Beta = 0.4091; p = 0.0008); rs7949972 ELF5 T/T × rs9636867 IFNAR2 A/G × rs12610495 DPP9 G/A × rs143334143 CCHCR1 A/G (Beta = 1.8; p = 3.45 × 10−5); rs7949972 ELF5 T/T × rs9636867 IFNAR2 A/G × rs12610495 DPP9 G/A × rs11183780 CCHCR1 C/T (Beta = 1.8; p = 3.62 × 10−5) (Table S5).

3.2.4. Gene–Environmental Interactions Associated with Cerebral Stroke Risk in Severe COVID-19 Patients (MB-MDR, MDR Modeling)

Using the MB-MDR approach, the four most significant models of gene-environment interactions associated with cerebral stroke in severe COVID-19 patients were identified: 1 two-level, 1 three-level, and 2 four-level (Table 6). In total, the best models of G × E interactions included smoking and low consumption of fresh vegetables and fruit in interaction with 5 SNPs, four of which—rs12610495 DPP9, rs61882275 ELF5, rs12585036 ATP11A, rs11183780 CCHCR1, along with smoking and level of consumption of fresh vegetables and fruit—were involved in 2 or more of the most significant G × E interaction patterns. In the next step, we analyzed the interactions between these genetic variants and environmental factors using the multifactor dimensionality reduction (MDR) method (Figure 5).
First, MDR revealed that environmental risk factors such as smoking and the level of consumption of fresh vegetables and fruit are characterized by moderate and pronounced antagonism in interaction with all SNPs included in the 2 or more most significant gene-environment interactions. Second, smoking and the level of consumption of fresh vegetables and fruit interact antagonistically. Thirdly, all SNPs characterizing the best G × E models are characterized by antagonism in their interaction with each other, except for rs12610495 DPP9 and rs11183780 CCHCR1, which are characterized by moderate synergism in their interaction with each other. Fourth, the mono-effects of smoking and the level of consumption of fresh vegetables and fruit (1.30–1.58% contribution to entropy) are comparable to the mono-effects of SNPs (1.03%), except for rs11183780 CCHCR1, which is characterized by the least significant mono-effect (0.08% contribution to entropy). Fifth, the effects of gene-environment interactions (0.52–2.19%) exceed/are comparable to the mono-effects of SNPs in the best G × E models (0.08–1.03%). Sixth, the strongest associations with CS in severe COVID-19 patients have the following gene–environmental interactions: rs12610495 DPP9 G/A × 1 (Beta = 0.27; p = 0.004); rs618822758 ELF5 A/A × rs12585036 ATP11A C/C × 1 (Beta = 0.8; p = 4.36 × 10−6); rs618822758 ELF5 A/A × rs12610495 DPP9 G/A × rs11183780 CCHCR1 C/T × 1 (Beta = 1.75; p = 6.01 × 10−5); rs9636867 IFNAR2 A/G × rs12585036 ATP11A C/C × rs11183780 CCHCR1 C/T × 1 (Beta = 1.24; p = 6.29 × 10−5) (Table S6).

3.3. Functional Annotation of CVDs-Associated SNPs

3.3.1. Subsubsection

The SNP rs17713054 SLC6A20-LZTFL1 was found to reduce the expression levels of FLT1P1, CCR3, CCR1, SACM1L, CCR5, CCR2, RP11-24F11.2, and CXCR6 in blood, as well as CXCR6 in tibial tissue, while it was associated with an increase in the expression of CCR9 in whole blood (Table S7). SNP rs7949972 ELF5 was identified as decreasing the expression levels of CAT in whole blood, the heart (left ventricle, atrial appendage), and the tibial artery, with ABTB2 expression reduced in whole blood due to the influence of this SNP (Table S7). Additionally, rs61882275 ELF5 was linked to reduced expression of CAT in blood and the tibial artery, as well as ABTB2 in blood (Table S7). Regarding SNP rs12610495 DPP9, it was observed to elevate the expression levels of TNFAIP8L1 in whole blood while reducing the expression of DPP9 in the aorta, tibial artery, brain (cerebellum), and whole blood (Table S7).

3.3.2. Histone Modifications

Our study investigated histone modifications associated with CVD-related SNPs (Table 7).
SNP rs17713054 SLC6A20-LZTFL1 is within a DNA-binding region associated with histone H3 monomethylation at the 4th lysine residue (H3K4me1) in the aorta and heart. The effect of these histone tags is increased by the H3K27ac, marking enhancers in the aorta and heart.
SNP rs12610495 DPP9 is in a DNA-binding region associated with H3K4me1 in the heart and blood. Additionally, the impact of these histone modifications is further enhanced by the presence of H3K27ac in the heart.

3.3.3. Transcription Factors

The risk allele A of rs17713054 SLC6A20-LZTFL1 creates DNA-binding sites for 48 transcription factors (TFs) (Table S8). These TFs are implicated in transforming the growth factor beta receptor signaling pathway (GO:0007179; FDR = 4.94 × 10−2). On the other hand, the protective allele G of rs17713054 creates binding sites for 24 TFs, jointly involved in response to hypoxia (GO:0001666; FDR = 2.8 × 10−2), myoblast differentiation (GO:0045445; FDR = 2.56 × 10−3), and response to growth factor (GO:0070848; FDR = 1.79 × 10−3).
The protective allele A of rs12610495 DPP9 generates DNA-binding sites for 39 TFs (Table S9). These TFs participate in positive regulation of endothelial cell migration (GO:0010595; FDR = 0.04), positive regulation of angiogenesis (GO:0045766; FDR = 0.01), cardiac muscle tissue regeneration (GO:0061026; FDR = 0.002), cardiac right ventricle morphogenesis (GO:0003215; FDR = 0.03), and response to xenobiotic stimulus (GO:0009410; FDR = 0.03).
Regarding the risk allele C of rs7949972 ELF5, it generates DNA-binding regions for 31 TFs (Table S10), which are involved in two overrepresented biological processes: positive regulation of CD8-positive, alpha-beta T-cell differentiation (GO:0043378; FDR = 0.002) and negative regulation of CD4-positive, alpha-beta T-cell differentiation (GO:0043371; FDR = 0.03). The protective allele T rs7949972 ELF5 creates DNA-binding sites for 32 TFs, implicated in glial cell fate commitment (GO:0021781; FDR = 0.02) and regulation of neuron apoptotic process (GO:0043523; FDR = 0.04).
Lastly, the protective allele G rs61882275 ELF5 creates DNA-binding sites for 32 TFs, implicated in the regulation of neurogenesis (GO:0050767; FDR = 0.003) (Table S11).

3.3.4. Bioinformatic Analysis of the Relationship between GWAS SNPs and CVD-Related Phenotypes

According to the bioinformatic resources Cerebrovascular Disease Knowledge Portal (CDKP) and Cardiovascular Disease Knowledge Portal (CVDKP), which combines and analyzes the results of genetic associations of the largest consortia for the study of cardio- and cerebrovascular diseases, the GWAS SNPs associated with CAD and CS, are associated with several CVD-related intermediate phenotypes and (Table 8).

4. Discussion

Cardiovascular diseases are well-known major risk factors for severe COVID-19, contributing to worsened symptoms, higher risk of adverse outcomes, and elevated mortality rates [30,31]. Our findings corroborate this, demonstrating that the presence of underlying coronary artery disease (CAD) or cerebral stroke (CS) significantly increases the risk of mortality from COVID-19 (Table 1). Moreover, in the context of multisystem inflammatory syndrome (MIS-C), SARS-CoV-2 leads to multiorgan damage, including significant effects on the heart. The inflammatory changes observed in the heart during MIS-C differ from those caused by typical cardiotropic viral infections [32]. These differences are likely due to the virus binding to ACE2 receptors, excessive cytokine release, T-cell dysregulation, microvascular damage, and endothelial dysfunction [33]. Crucially, MIS-C almost always results in thromboembolic events, including potential coronary thrombosis and embolism in the CNS [34]. This has important implications for treatment and drug selection, emphasizing that myocarditis, while serious, may be less critical than other symptoms that require more urgent attention.
Here, we report that severe COVID-19-related GWAS SNPs—rs17713054 SLC6A20-LZTFL1, rs12610495 DPP9, and rs7949972 ELF5—are linked to the risk of CAD. Additionally, we found that rs61882275 ELF5 and rs7949972 ELF5 are associated with cerebral stroke risk. Notably, our study did not find an association between these GWAS loci and arterial hypertension, suggesting that these risk SNPs do not influence vascular tone but rather modify the risk of stroke and CAD, predominantly through pro-atherosclerosis mechanisms. Our bioinformatic analysis supports this hypothesis (Figure 6).
First, our analysis of transcription factors (TFs) indicates molecular mechanisms by which GWAS SNP rs17713054 SLC6A20-LZTFL1 potentially contributes to the development of CAD. The protective allele G of rs17713054 SLC6A20-LZTFL1 is involved in response to hypoxia and myoblast differentiation (Table S5). Conversely, the risk allele A rs17713054 is implicated in the transforming growth factor beta (TGFβ) receptor signaling pathway. The TGFβ family is crucial in regulating both normal and abnormal vascular functions, including angiogenesis (Table S5). Dysregulation of the TGFβ pathway can lead to vascular dysfunction and pathologies, including atherosclerosis and vascular calcification [35].
Additionally, allele A rs17713054 influences the expression of other genes through cis-eQTL effects, such as reducing the expression of FLT1P1 in blood, potentially disrupting vascular endothelial growth factor receptor 1 (VEGFR1) expression [36]. VEGFR1 is vital for angiogenesis and vascular permeability, acting as a decoy to sequester VEGF and prevent intracellular signaling initiation (Figure 6). VEGFR1 knockout mice exhibit higher mortality due to heart failure, cardiac hypertrophy, and cardiac dysfunction [37,38].
The SNP rs17713054 also affects the expression levels of several chemokine receptors (CCR1, CCR2, CCR3, CCR5, CCR9, and CXCR6), with dysregulated expression of these receptors linked to atherosclerosis, a major risk factor for CAD (Figure 6) [39,40,41]. Moreover, the rs17713054 downregulates SACM1L expression. Deficiency in SACM1L has been shown to block the autophagosome fusion with vacuoles/lysosomes, a critical step in autophagy [42]. Autophagy is essential for preventing atherosclerosis [43,44] and maintaining cardiac cellular homeostasis [45,46].
It is noteworthy that rs17713054 characterized the best G × G models of intergenic interactions, predominantly interacting antagonistically with other loci of GWAS-significant genes for severe COVID-19. It also determined the formation of the most significant models of gene-smoking interactions. SNP rs17713054 is in the SLC6A20-LZTFL1 intergenic region. Interestingly, smoking affects the expression of both of them: there is evidence in the literature that tobacco smoke pollution results in decreased expression of LZTFL1 mRNA and affects the expression of SLC6A20 mRNA [47]. This provides further support for the significant role of interactions between rs17713054 SLC6A20-LZTFL1 and smoking.
Second, SNP rs12610495 DDP9 was associated with an increased risk of CAD. Interestingly, when analyzing intergenic interactions, this genetic variant has the most pronounced mono-effect (6.22% contribution to CAD entropy), exceeding the mono-effects of other SNPs and the effects of intergenic interactions. SNP rs12610495 influences several key processes through its interaction with TFs. The protective allele A of rs12610495 DPP9 is involved in the positive regulation of endothelial cell migration, angiogenesis, cardiac muscle tissue regeneration, cardiac right ventricle morphogenesis, and response to xenobiotic stimuli (Figure 6, Table S6). These processes are crucial for heart health, as they are regenerative and restorative [48,49].
DPP9, a member of the dipeptidyl peptidase family, is known for its role in regulating inflammasomes and pyroptosis, implicating it in autoinflammatory diseases [50,51], including atherosclerosis [52,53]. SNP rs12610495 downregulates DPP9 expression in blood and arteries through cis-eQTL effects while upregulating TNFAIP8L1 (TNF-alpha-induced protein 8-like 1) in blood. The tumor necrosis factor (TNF)-alpha-induced protein 8 (TNFAIP8 or TIPE) family is important for maintaining immune homeostasis [54,55]. Notably, Tnfaip8l2 expression is significantly induced in mouse models of atherosclerosis, severe hypercholesterolemia, and obesity (Figure 6) [56]. These findings underscore the multifaceted role of rs12610495 DDP9 in CAD risk, highlighting its impact on endothelial function, inflammation, and immune regulation.
Thirdly, SNPs rs61882275 and rs7949972 in the ELF5 gene were associated with an increased risk of cerebral stroke, with rs7949972 also linked to a higher risk of CAD. It is important to note that polymorphic ELF5 loci were also involved in the most significant gene–gene interactions associated with CAD and CS (Table 3 and Table 5). A previous study found that rs7949972 ELF5 was associated with the comorbid conditions of bronchial asthma, arterial hypertension, and CAD [57]. Moreover, data from CDKP and CVDKP underscore the significance of rs7949972 ELF5 SNP in increasing the risk of CAD and stroke, as well as contributing to a worsened lipid profile by elevating levels of total cholesterol, LDL cholesterol, and non-HDL cholesterol. Additionally, rs61882275 ELF5 is associated with an increased risk of stroke, particularly TOAST, and otherwise undetermined.
ELF5, an epithelial-specific member of the Ets transcription factor family, regulates various cellular processes, including keratinocyte terminal differentiation, trophoblast differentiation, and epithelial–mesenchymal transformation in tumor cells [58]. Recent research has shown that increased ELF5 expression promotes angiogenesis following ischemia-induced cardiac injury [59].
Our bioinformatic analysis of ELF5 SNPs reveals key molecular mechanisms that may contribute to the development of CAD or CS. Transcription factors binding to the risk allele C of rs7949972 ELF5 are involved in the positive regulation of CD8-positive alpha-beta T-cell differentiation and the negative regulation of CD4-positive alpha-beta T-cell differentiation, indicating a role in immune regulation and inflammation processes. Additionally, ELF5 SNPs influence neuronal cell regulation: the protective allele T of rs7949972 ELF5 regulates glial cell fate commitment and the neuronal apoptotic process, while the protective allele G of rs61882275 ELF5 regulates neurogenesis (Figure 6, Tables S7 and S8).
Importantly, ELF5 SNPs downregulate the expression levels of catalase in blood, arteries, and heart via cis-eQTL effects. Oxidative stress can arise from the overproduction of reactive oxygen species (ROS) or inadequate antioxidant defenses [60,61]. Catalase (CAT), a mitochondrial enzyme, plays a crucial role in ROS metabolism and cellular defense against oxidative stress (Figure 6) [62]. Given that lipid peroxidation is a major predictor of CVD [63,64], the role of antioxidant enzymes like catalase in CVD pathogenesis is well-documented [65,66,67]. A large body of evidence shows that oxidative stress directly induces inflammatory cascades and accelerates the oxidation of LDL-C [68,69], leading to atherosclerotic plaque instability and the worsening of CAD [70,71,72].
Additionally, rs61882275 ELF5, when interacting with smoking, was implicated in the most significant gene-environment interactions associated with CAD. Previous studies have shown that tobacco smoke pollution decreases the expression of ELF5 mRNA [47]. This additional evidence of ELF5 downregulation due to smoking suggests a potential worsening of oxidative stress in patients who smoke and carry risk ELF5 alleles.
Thus, we further underscore the significant impact of ELF5 SNPs on cardiovascular health through their influence on oxidative stress and immune responses.

5. Conclusions

We are the first in the world to investigate the associations between GWAS-significant loci and the risk of CVD, including AH, CAD, and CS, in patients with severe COVID-19. Our study identified the most significant intergenic and gene-environment interactions and performed a comprehensive functional annotation of SNPs linked to CVD to analyze their involvement in the molecular mechanisms of these diseases. Our findings suggest that several GWAS loci, including rs17713054, rs7949972, and rs12610495, which were previously linked to severe COVID-19, are associated with an increased risk of CAD in hospitalized patients with severe COVID-19. Additionally, intergenic interactions involving rs9636867, rs7949972, rs17713054, rs12585036, rs12610495, rs17078346, and rs61882275, as well as gene-environment interactions of rs17713054, rs17078346, rs61882275, and rs9636867 with smoking, are also associated with this increased risk of CAD. Similarly, the GWAS loci rs7949972 and rs61882275, intergenic interactions of rs7949972, rs12610495, and rs9636867, and gene-environment interactions of rs61882275, rs12610495, rs12585036, and rs11183780 with smoking and low fresh fruit and vegetable intake, are linked to a higher risk of CS in hospitalized patients with severe COVID-19. Using bioinformatics tools, we established the high regulatory potential of the studied SNPs and identified mechanisms through which they can influence the risk of CAD and CS, primarily through their binding to transcription factors and expression of quantitative trait loci.

6. Study Limitations

First, the study faced limitations due to the small number of cases, as obtaining informed consent from severely ill hospitalized patients was challenging. This constraint may have reduced the statistical power, possibly leading to missed associations with the risk of CVD. Second, our research was confined to 10 GWAS-significant SNPs without exploring other genes implicated in the progression of severe COVID-19. Thirdly, we lacked data on crucial biochemical parameters for CVD, such as total cholesterol, triglyceride levels, lipoproteins, and history of CVDs, which prevented us from assessing the correlations between these parameters and the GWAS SNPs.

Supplementary Materials

The following supporting information can be downloaded at https://www.mdpi.com/article/10.3390/jmp5030026/s1, Table S1: The most significant gene–gene combinations associated with coronary heart disease in patients with severe COVID-19. Table S2: The most significant gene–environmental combinations associated with coronary heart disease in patients with severe COVID-19. Table S3: The most significant gene–gene combinations associated with cerebral stroke in patients with severe COVID-19. Table S4: The most significant gene–environmental combinations associated with cerebral stroke in patients with severe COVID-19. Table S5: Analysis of the effect of rs17713054 SLC6A20-LZTFL1 on the binding of DNA to transcription factors. Table S6: Analysis of the effect of rs12610495 DPP9 on the binding of DNA to transcription factors. Table S7: Analysis of the effect of rs7949972 ELF5 on the binding of DNA to transcription factors. Table S8: Analysis of the effect of rs61882275 ELF5 on the binding of DNA to transcription factors.

Author Contributions

Conceptualization, O.B.; methodology, O.B. and K.K.; validation, O.B.; formal analysis, A.L., A.D., E.K. and O.B.; investigation, A.L., A.D., M.B. and E.K.; resources, A.L. and O.B.; data curation, O.B.; writing—original draft preparation, A.L. and K.K.; writing—review and editing, O.B.; visualization, K.K. and M.B.; supervision, O.B.; project administration, O.B. All authors have read and agreed to the published version of the manuscript.

Funding

This research received no external funding.

Institutional Review Board Statement

The study was conducted according to the guidelines of the Declaration of Helsinki and was approved by the Ethical Review Committee of Kursk State Medical University, Russia (Protocol No. 1 from 11 January 2022). All the participants gave written informed consent before the enrollment in this study.

Informed Consent Statement

Informed consent was obtained from all subjects involved in the study.

Data Availability Statement

The data presented in this study are available upon request from the corresponding author.

Conflicts of Interest

The authors declare no conflicts of interest.

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Figure 1. The outline of the study.
Figure 1. The outline of the study.
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Figure 2. Graph reflecting the structure and power of the most significant G × G interactions of GWAS loci associated with CAD in severe COVID-19 patients. Notes: the color of the lines reflects the nature of the interaction: orange lines mean moderate synergism, brown means additive (independent) effects; blue and green represent strong and moderate antagonism, respectively; % reflects the strength and direction of the phenotypic effect of gene–gene interaction (% of entropy).
Figure 2. Graph reflecting the structure and power of the most significant G × G interactions of GWAS loci associated with CAD in severe COVID-19 patients. Notes: the color of the lines reflects the nature of the interaction: orange lines mean moderate synergism, brown means additive (independent) effects; blue and green represent strong and moderate antagonism, respectively; % reflects the strength and direction of the phenotypic effect of gene–gene interaction (% of entropy).
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Figure 3. Graph reflecting the structure and power of the most significant G × E interactions of GWAS loci associated with CAD in severe COVID-19 patients. Notes: the color of the lines reflects the nature of the interaction: orange and red mean moderate and strong synergism, green —moderate antagonism; brown means additive (independent) effects; % reflects the strength and direction of the phenotypic effect of gene–environmental interaction (% of entropy).
Figure 3. Graph reflecting the structure and power of the most significant G × E interactions of GWAS loci associated with CAD in severe COVID-19 patients. Notes: the color of the lines reflects the nature of the interaction: orange and red mean moderate and strong synergism, green —moderate antagonism; brown means additive (independent) effects; % reflects the strength and direction of the phenotypic effect of gene–environmental interaction (% of entropy).
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Figure 4. Graph reflecting the structure and power of the most significant G × G interactions of GWAS loci associated with CS in severe COVID-19 patients. Notes: the color of the lines reflects the nature of the interaction: red mean strong synergism, blue—pronounced antagonism; brown means additive (independent) effects; % reflects the strength and direction of the phenotypic effect of gene–gene interaction (% of entropy).
Figure 4. Graph reflecting the structure and power of the most significant G × G interactions of GWAS loci associated with CS in severe COVID-19 patients. Notes: the color of the lines reflects the nature of the interaction: red mean strong synergism, blue—pronounced antagonism; brown means additive (independent) effects; % reflects the strength and direction of the phenotypic effect of gene–gene interaction (% of entropy).
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Figure 5. Graph reflecting the structure and power of the most significant G×E interactions of GWAS loci associated with CS in severe COVID-19 patients. Notes: the color of the lines reflects the nature of the interaction: orange mean moderate synergism, green and blue—moderate and pronounced antagonism; % reflects the strength and direction of the phenotypic effect of gene–gene interaction (% of entropy).
Figure 5. Graph reflecting the structure and power of the most significant G×E interactions of GWAS loci associated with CS in severe COVID-19 patients. Notes: the color of the lines reflects the nature of the interaction: orange mean moderate synergism, green and blue—moderate and pronounced antagonism; % reflects the strength and direction of the phenotypic effect of gene–gene interaction (% of entropy).
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Figure 6. cis-eQTL effects and TFs-associated biological processes of GWAS SNPs linked to CVDs.
Figure 6. cis-eQTL effects and TFs-associated biological processes of GWAS SNPs linked to CVDs.
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Table 1. Baseline and clinical characteristics of the studied groups.
Table 1. Baseline and clinical characteristics of the studied groups.
Baseline and Clinical CharacteristicsCOVID-19 Patients without CVDCOVID-19 Patients with CVDp-Value
AH
n = 70n = 129
Age, Me [Q1; Q3]56 [45; 70]72 [65; 80]<0.001
BMI, Me [Q1; Q3]24.9 [22.5; 30]31.6 [27.4; 36.5]<0.001
Low physical activityYes, N (%)23 (32.9%)87 (67.4%)<0.001
No, N (%)47 (67.1%)42 (32.6%)
CAD
n = 115n = 84
Age, Me [Q1; Q3]61 [49; 71]76.5 [67.5; 83]<0.001
Low physical activityYes, N (%)49 (42.6%)61 (72.6%)<0.001
No, N (%)66 (57.4%)23 (27.4%)
Cerebral stroke
n = 167n = 32
Age, Me [Q1; Q3]67 [55; 77]75 [66.5; 83.5]<0.001
Low physical activityYes, N (%)86 (51.5%)24 (75%)<0.05
No, N (%)81 (48.5%)8 (25%)
Statistically significant differences are marked in bold.
Table 2. The significant associations between GWAS SNPs and CVDs in the entire group.
Table 2. The significant associations between GWAS SNPs and CVDs in the entire group.
Genetic VariantEffect AlleleOther
Allele		NOR
[95% CI] 1		p 2OR
[95% CI] 1		p 2OR
[95% CI] 1		p 2
AHCADCS
rs12610495
DPP9 		GA1831.84
[0.28–11.91]		0.521.69
[1.02–2.81]		0.041.41
[0.80–2.48]		0.24
rs61882275
ELF5		AG1852.64
[0.59–11.92]		0.181.51
[0.96–2.39]		0.0731.98
[1.14–3.45]		0.01
rs7949972
ELF5		TC1854.25
[0.39–46.54]		0.22.57
[1.43–4.61]		0.00092.67
[1.38–5.19]		0.003
All calculations were performed relative to the minor alleles (Effect allele) with adjustment for age and physical activity; 1—odds ratio and 95% confidence interval; 2—p-value; statistically significant differences are marked in bold.
Table 3. Gene–gene interactions associated with coronary artery disease in patients with severe COVID-19 (MB-MDR modeling).
Table 3. Gene–gene interactions associated with coronary artery disease in patients with severe COVID-19 (MB-MDR modeling).
Gene–Gene Interaction ModelsNHbeta HWHNLbeta LWLWmaxpperm
The best two-locus models of gene–gene interactions (for G × G models with pmin. < 0.001, 1000 permutations)
rs7949972 ELF5 × rs17078346 SLC6A20-LZTFL120.277813.341−0.14533.98013.340.003
rs9636867 IFNAR2 × rs12610495 DPP930.236213.131−0.20027.13213.130.006
rs7949972 ELF5 × rs12610495 DPP920.239711.712−0.205110.74711.710.017
The best three-locus models of gene–gene interactions (for G × G models with pmin. < 2 × 10−4, 1000 permutations)
rs7949972 ELF5 × rs61882275 ELF5 × rs12610495 DPP920.280714.632−0.21329.97114.630.005
rs9636867 IFNAR2 × rs61882275 ELF5 × rs17078346 SLC6A20-LZTFL140.443816.202−0.25967.12516.200.019
rs9636867IFNAR2 × rs12610495 DPP9 × rs12585036 ATP11A20.404915.092−0.26407.08915.090.028
The best four-locus models of gene–gene interactions (for G × G models with pmin. < 5 × 10−6, 1000 permutations)
rs7949972 ELF5 × rs9636867 IFNAR2 × rs61882275 ELF5 × rs17078346 SLC6A20-LZTFL160.466025.322−0.26337.35825.32< 0.001
rs9636867 IFNAR2 × rs12610495 DPP9 × rs12585036 ATP11A × rs17713054 SLC6A20-LZTFL140.424523.362−0.29076.63823.360.006
rs7949972 ELF5 × rs9636867 IFNAR2 × rs12610495 DPP9 × rs17713054 SLC6A20-LZTFL160.451122.593−0.30208.57322.590.017
Note: NH is the number of interacting high-risk genotypes, beta H—regression coefficient for high-risk interactions identified at the 2nd stage of analysis, WH—Wald statistics for high-risk interactions, NL—number of interacting low-risk genotypes, beta L—regression coefficient for low-risk interactions identified at the 2nd stage of analysis, WL—Wald statistics for low-risk interactions, Wmax—maximum values of the Wald statistics, pperm—permutational significance levels for models (all models are adjusted for age and physical activity); Loci included in 2 or more best G × G models are indicated in bold.
Table 4. Gene–environmental interactions associated with coronary artery disease in patients with severe COVID-19 (MB-MDR modeling).
Table 4. Gene–environmental interactions associated with coronary artery disease in patients with severe COVID-19 (MB-MDR modeling).
Gene–Gene Interaction ModelsNHbeta HWHNLbeta LWLWmaxpperm
The best two-order models of gene- interactions (for G × E models with pmin. < 0.001, 1000 permutations)
rs12610495 DPP9 × SMOKE 10.2256 6.458 1−0.223612.03812.04 0.003
rs17078346 SLC6A20-LZTFL1 × SMOKE 10.1599 4.122 1−0.214312.08512.09 0.004
rs17713054 SLC6A20-LZTFL1 × SMOKE 10.1641 4.642 1−0.213211.98811.99 0.005
The best three-order models of gene- interactions (for G × E models with pmin. < 0.0003, 1000 permutations)
rs61882275 ELF5 × rs17078346 SLC6A20-LZTFL1 × SMOKE 20.342014.9021−0.1674 4.22014.90 0.017
rs7949972 ELF5 × rs17713054 SLC6A20-LZTFL1 × SMOKE 20.2891 8.943 2−0.236414.47114.47 0.011
The best four-order models of gene- interactions (for G × E models with pmin. < 8 × 10−6, 1000 permutations)
rs9636867 IFNAR2 × rs61882275 ELF5 × rs17078346 SLC6A20-LZTFL1 × SMOKE 60.516625.77 2−0.24247.45125.77 0.004
rs9636867 IFNAR2 × rs61882275 ELF5 × rs17713054 SLC6A20-LZTFL1 × SMOKE 50.591022.24 2−0.24007.53922.24 0.013
Note: NH is the number of high-risk interactions, beta H—regression coefficient for high-risk interactions identified at the 2nd stage of analysis, WH—Wald statistics for high-risk interactions, NL—number of interacting low-risk interactions, beta L—regression coefficient for low-risk interactions identified at the 2nd stage of analysis, WL—Wald statistics for low-risk interactions, Wmax—maximum values of the Wald statistics, pperm—permutational significance levels for models (all models are adjusted for age); Loci included in 2 or more best G × E models are indicated in bold.
Table 5. Gene–gene interactions associated with cerebral stroke in patients with severe COVID-19 (MB-MDR modeling).
Table 5. Gene–gene interactions associated with cerebral stroke in patients with severe COVID-19 (MB-MDR modeling).
Gene–Gene Interaction ModelsNHbeta HWHNLbeta LWLWmaxpperm
The best two-locus models of gene–gene interactions (for G × G models with pmin. < 0.001, 1000 permutations)
rs17078346 SLC6A20-LZTFL1 × rs12610495 DPP910.291514.821−0.15214.81214.820.014
rs12610495 DPP9 × rs17713054 SLC6A20-LZTFL110.263812.591−0.15765.30012.590.046
The best three-locus models of gene–gene interactions (for G × G models with pmin. < 1 × 10−8, 1000 permutations)
rs7949972 ELF5 × rs9636867 IFNAR2 × rs12610495 DPP950.499138.480NANA38.480.004
The best four-locus models of gene–gene interactions (for G × G models with pmin. < 1 × 10−11, 1000 permutations)
rs7949972 ELF5 × rs9636867 IFNAR2 × rs12610495 DPP9 × rs143334143 CCHCR160.653460.640NANA60.640.005
rs7949972 ELF5 × rs9636867 IFNAR2 × rs12610495 DPP9 ×rs11183780 CCHCR150.637953.990NANA53.990.012
Note: NH is the number of interacting high-risk genotypes, beta H—regression coefficient for high-risk interactions identified at the 2nd stage of analysis, WH—Wald statistics for high-risk interactions, NL—number of interacting low-risk genotypes, beta L—regression coefficient for low-risk interactions identified at the 2nd stage of analysis, WL—Wald statistics for low-risk interactions, Wmax—maximum values of the Wald statistics, pperm—permutational significance levels for models (all models are adjusted for age and physical activity); Loci included in 2 or more best G × G models are indicated in bold.
Table 6. Gene–environmental interactions associated with cerebral stroke in patients with severe COVID-19 (MB-MDR modeling).
Table 6. Gene–environmental interactions associated with cerebral stroke in patients with severe COVID-19 (MB-MDR modeling).
Gene–Gene Interaction ModelsNHbeta HWHNLbeta LWLWmaxpperm
The best two-order models of gene- interactions (for G × E models with pmin. < 0.01, 1000 permutations)
rs12610495 DPP9 × SMOKE10.26718.4241−0.11622.8498.4240.047
The best three-order models of gene- interactions (for G × E models with pmin. < 1 × 10−4, 1000 permutations)
rs61882275 ELF5 × rs12585036 ATP11A × SMOKE10.764022.490NANA22.490.016
The best four-order models of gene- interactions (for G × E models with pmin. < 3 × 10−8, 1000 permutations)
rs61882275 ELF5 × rs12610495 DPP9 × rs11183780 CCHCR1 × VEGET21.732136.280NANA36.280.002
rs9636867 IFNAR2 × rs12585036 ATP11A × rs11183780 CCHCR1 × VEGET21.399735.310NANA35.310.002
Note: NH is the number of high-risk interactions, beta H—regression coefficient for high-risk interactions identified at the 2nd stage of analysis, WH—Wald statistics for high-risk interactions, NL—number of interacting low-risk interactions, beta L—regression coefficient for low-risk interactions identified at the 2nd stage of analysis, WL—Wald statistics for low-risk interactions, Wmax—maximum values of the Wald statistics, pperm—permutational significance levels for models (all models are adjusted for age); Loci included in 2 or more best G × E models are indicated in bold.
Table 7. The impact of GWAS SNPs on histone tags in various tissues.
Table 7. The impact of GWAS SNPs on histone tags in various tissues.
SNP (Ref/Alt Allele)Tissues MarksVessels—AortaHeartBlood
rs17713054 (G/A)
SLC6A20-LZTFL1 		H3K4me1EnhEnh-
H3K27acEnhEnh-
rs12610495 (A/G)
DPP9		H3K4me1-EnhEnh
H3K4me3---
H3K27ac-Enh-
H3K9ac---
H3K4me1—monomethylation at the 4th lysine residue of the histone H3 protein; H3K4me3—tri-methylation at the 4th lysine residue of the histone H3 protein; H3K9ac—the acetylation at the 9th lysine residues of the histone H3 protein; H3K27ac—acetylation of the lysine residues at N-terminal position 27 of the histone H3 protein; effect alleles are marked in bold. Enh—histone modification in the enhancer region.
Table 8. Results of aggregated bioinformatic analyses of associations between SNPs, cardio- and cerebrovascular diseases, and their intermediate phenotypes.
Table 8. Results of aggregated bioinformatic analyses of associations between SNPs, cardio- and cerebrovascular diseases, and their intermediate phenotypes.
No.SNPPhenotypep-ValueBeta (OR)Sample Size
1rs17713054
SLC6A20-LZTFL1
(G/A)		1 Long QT syndrome0.007OR▼0.7865673
21 Atrial fibrillation or flutter0.012OR▼0.9531130,776
31 LDL cholesterol0.018Beta▼−0.00482,783,500
41 Non-HDL cholesterol0.039Beta▼−0.0061,087,880
51 Triglycerides0.04Beta▼−0.00422,412,380
6rs7949972
ELF5
(C/T)		2 Coronary artery disease (CAD)0.012OR▲1.01111,250,150
72 TOAST other undetermined0.012OR▲1.0739,776
82 Pulse pressure0.029Beta▼−0.0031910,329
91 Total cholesterol0.0004Beta▲0.00192,664,070
101 LDL cholesterol0.0007Beta▲0.00283,137,790
111 Serum ApoB0.001Beta▲0.0059436,068
121 Non-HDL cholesterol0.0105Beta▲0.00391,085,390
131 Long QT syndrome0.02OR▼0.91165673
14rs61882275
ELF5
(G/A)		2 TOAST other undetermined0.035OR▲1.064939,604
15rs12610495
DPP9
(A/G)		1 Total cholesterol6.97 × 10−9Beta▼−0.00662,655,840
161 LDL cholesterol2.47 × 10−7Beta▼−0.00603,106,200
171 Non-HDL cholesterol2.53 × 10−6Beta▼−0.00821,026,040
181 Serum ApoB0.005Beta▼−0.006436,068
191 Hypertrophic cardiomyopathy0.0086OR▼0.917811,942
20
211 Left ventricular stroke volume0.014Beta▲0.015477,177
221 Long QT syndrome0.016OR▼0.8955673
1—data obtained using the bioinformatic resource Cardiovascular Disease Knowledge Portal (https://cvd.hugeamp.org/; accessed on 4 June 2024); 2—data obtained using the bioinformatic resource Cerebrovascular Disease Knowledge Portal (https://cd.hugeamp.org/; accessed on 4 June 2024); Effect alleles are marked in bold.
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Loktionov, A.; Kobzeva, K.; Dorofeeva, A.; Babkina, M.; Kolodezhnaya, E.; Bushueva, O. A Comprehensive Genetic and Bioinformatic Analysis Provides Evidence for the Engagement of COVID-19 GWAS-Significant Loci in the Molecular Mechanisms of Coronary Artery Disease and Stroke. J. Mol. Pathol. 2024, 5, 385-404. https://doi.org/10.3390/jmp5030026

AMA Style

Loktionov A, Kobzeva K, Dorofeeva A, Babkina M, Kolodezhnaya E, Bushueva O. A Comprehensive Genetic and Bioinformatic Analysis Provides Evidence for the Engagement of COVID-19 GWAS-Significant Loci in the Molecular Mechanisms of Coronary Artery Disease and Stroke. Journal of Molecular Pathology. 2024; 5(3):385-404. https://doi.org/10.3390/jmp5030026

Chicago/Turabian Style

Loktionov, Alexey, Ksenia Kobzeva, Anna Dorofeeva, Maryana Babkina, Elizaveta Kolodezhnaya, and Olga Bushueva. 2024. "A Comprehensive Genetic and Bioinformatic Analysis Provides Evidence for the Engagement of COVID-19 GWAS-Significant Loci in the Molecular Mechanisms of Coronary Artery Disease and Stroke" Journal of Molecular Pathology 5, no. 3: 385-404. https://doi.org/10.3390/jmp5030026

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