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. 2018 Sep 17;9(5):383–404. doi: 10.3892/br.2018.1152

Identification of 26 novel loci that confer susceptibility to early-onset coronary artery disease in a Japanese population

Yoshiji Yamada 1,2,, Yoshiki Yasukochi 1,2, Kimihiko Kato 1,3, Mitsutoshi Oguri 1,4, Hideki Horibe 5, Tetsuo Fujimaki 6, Ichiro Takeuchi 2,7,8, Jun Sakuma 2,8,9
PMCID: PMC6201041  PMID: 30402224

Abstract

Early-onset coronary artery disease (CAD) has a strong genetic component. Although genome-wide association studies have identified various genes and loci significantly associated with CAD mainly in European populations, genetic variants that contribute toward susceptibility to this condition in Japanese patients remain to be definitively identified. In the present study, exome-wide association studies (EWASs) were performed to identify genetic variants that confer susceptibility to early-onset CAD in Japanese. A total of 7,256 individuals aged ≤65 years were enrolled in the present study. EWAS were conducted on 1,482 patients with CAD and 5,774 healthy controls. Genotyping of single nucleotide polymorphisms (SNPs) was performed using Illumina Human Exome-12 DNA Analysis BeadChip or Infinium Exome-24 BeadChip arrays. The association between allele frequencies for 31,465 SNPs that passed quality control and CAD was examined using Fisher's exact test. To compensate for multiple comparisons of allele frequencies with CAD, a false discovery rate (FDR) of <0.05 was applied for statistically significant associations. The association between allele frequencies for 31,465 SNPs and CAD, as determined by Fisher's exact test, demonstrated that 170 SNPs were significantly (FDR <0.05) associated with CAD. Multivariable logistic regression analysis with adjustment for age, sex, and the prevalence of hypertension, diabetes mellitus and dyslipidemia revealed that 162 SNPs were significantly (P<0.05) associated with CAD. A stepwise forward selection procedure was performed to examine the effects of genotypes for the 162 SNPs on CAD. The 54 SNPs were significant (P<0.05) and independent [coefficient of determination (R2), 0.0008 to 0.0297] determinants of CAD. These SNPs together accounted for 15.5% of the cause of CAD. Following examination of results from previous genome-wide association studies and linkage disequilibrium of the identified SNPs, 21 genes (RNF2, YEATS2, USP45, ITGB8, TNS3, FAM170B-AS1, PRKG1, BTRC, MKI67, STIM1, OR52E4, KIAA1551, MON2, PLUT, LINC00354, TRPM1, ADAT1, KRT27, LIPE, GFY and EIF3L) and five chromosomal regions (2p13, 4q31.2, 5q12, 13q34 and 20q13.2) that were significantly associated with CAD were newly identified in the present study. Gene ontology analysis demonstrated that various biological functions were predicted in the 18 genes identified in the present study. The network analysis revealed that the 18 genes had potential direct or indirect interactions with the 30 genes previously revealed to be associated with CAD or with the 228 genes identified in previous genome-wide association studies. The present study newly identified 26 loci that confer susceptibility to CAD. Determination of genotypes for the SNPs at these loci may prove informative for assessment of the genetic risk for CAD in Japanese patients.

Keywords: coronary artery disease, myocardial infarction, ischemic heart disease, genetics, exome-wide association study

Introduction

Coronary atherosclerosis is a chronic inflammatory vascular disease and is initiated as a result of endothelial damage and dysfunction, which lead to the accumulation and oxidation of low density lipoprotein (LDL)-cholesterol in the arterial wall (1,2) Monocytes migrate from the blood into the subendothelial intima and transform into macrophages, which then accumulate lipid particles (foam cells) to form the lipid core of atherosclerotic plaques (2,3). Inflammatory and thrombotic processes serve central roles in the formation of atherosclerotic lesions and subsequent plaque rupture, which lead toward acute coronary syndrome (2,3).

Coronary artery disease (CAD) and myocardial infarction (MI) are serious clinical conditions that remain the leading cause of mortality in the United States (4). Disease prevention is an important strategy for reducing the overall burden of CAD and MI, with the identification of biomarkers for disease risk being key for risk prediction and for potential intervention, in order to reduce the chance of future adverse coronary events. In addition to conventional risk factors for CAD, including hypertension, diabetes mellitus and dyslipidemia, the importance of genetic factors has been highlighted (57). Genes responsible for familial hypercholesterolemia and Tangier disease are prototypical examples of monogenic forms of CAD and MI with Mendelian inheritance (5,8). Familial hypercholesterolemia is an autosomal dominant disorder characterized by marked increases in the circulating concentrations of total cholesterol and LDL-cholesterol caused by mutations of the genes for LDL receptor (LDLR), apolipoprotein B (APOB), proprotein convertase subtilisin/kexin type 9 (PCSK9), cytochrome P450 family 7 subfamily A member 1 (CYP7A1) or LDL receptor adaptor protein 1 (LDLRAP1) (9,10). Tangier disease is an autosomal recessive disorder characterized by a decrease in the circulating concentration of high density lipoprotein (HDL)-cholesterol as a result of loss-of-function mutations in the ATP-binding cassette subfamily A member 1 gene (ABCA1) (1113). The etiology of common forms of CAD is multifactorial and includes genetic components, as well as environmental and lifestyle factors (58). The heritability of common forms of CAD has been estimated to be 40–60% on the basis of family and twin studies (6,7,14).

Genome-wide association studies (GWASs) in European-ancestry (1521), African American (22) or Han Chinese populations (23,24) have identified various genes and loci that confer susceptibility to CAD or MI. A meta-analysis of GWASs for CAD among European-ancestry populations, including low-frequency variants, identified 202 independent genetic variants at 129 loci with a false discovery rate (FDR) of <5% (25). These genetic variants together accounted for ~28% of the heritability of CAD, demonstrating that genetic susceptibility to this condition is largely determined by common variants with small effect sizes (6,25). A more recent meta-analysis for CAD in European-ancestry populations identified 304 independent genetic variants with an FDR of <5%, and these variants accounted for 21.2% of the heritability of CAD (26). In total, GWASs identified 163 loci associated with CAD at a genome-wide significance level and >300 possible loci for this condition with an FDR of <5% (7). Although several single nucleotide polymorphisms (SNPs) have been revealed to be significantly associated with MI in Japanese patients (27,28), genetic variants that contribute toward susceptibility to CAD and MI in Japanese patients remain to be definitively identified.

A study of monozygotic and dizygotic twins revealed that mortality from CAD at younger ages was significantly influenced by genetic factors in males and females, whereas the genetic effect was smaller at older ages (29,30). A family history of MI is also more apparent in individuals with early-onset MI than in those with late-onset MI, suggestive of a greater heritability in the former (31,32).

The present study included exome-wide association studies (EWASs) for CAD with the use of human exome array-based genotyping methods in order to identify genetic variants that confer susceptibility to this condition in Japanese patients. In order to increase the statistical power of the EWAS, patients with early-onset CAD were examined.

Materials and methods

Study subjects

In our previous EWAS, the median age of subjects with CAD was 69 years (33). Therefore, patients with an age of ≤65 years were defined as individuals with early-onset CAD in the present study. A total of 7,256 Japanese subjects aged ≤65 years [mean age, 51.7 years; age range, 18–65 years; males/females (%), 58.3/41.7; 1,482 with CAD, including 1,152 with MI, and 5,774 controls] were enrolled in the present study. The subjects were individuals who either visited outpatient clinics or were admitted to participating hospitals in Japan (Gifu Prefectural Tajimi Hospital, Tajimi; Gifu Prefectural General Medical Center, Gifu; Japanese Red Cross Nagoya First Hospital, Nagoya; Northern Mie Medical Center Inabe General Hospital, Inabe; and Hirosaki University Hospital and Hirosaki Stroke and Rehabilitation Center, Hirosaki, Japan) due to various symptoms or for an annual health check-up between October 2002 and March 2014, or who were community-dwelling individuals recruited to a population-based cohort study in Inabe between March 2010 and September 2014 (34).

The diagnosis of CAD was based on the detection of stenosis of >50% in any major coronary artery or in the left main trunk by coronary angiography. The diagnosis of MI was based on typical electrocardiographic changes and on increases in the serum activity of creatine kinase (MB isozyme) and in the serum concentration of troponin T. The diagnosis was confirmed by identification of the responsible stenosis in any of the major coronary arteries or in the left main trunk by coronary angiography. The control individuals had no history of MI, CAD, aortic aneurysm or peripheral artery disease; of ischemic or hemorrhagic stroke; or of other atherosclerotic, thrombotic, embolic or hemorrhagic disorders. Although certain control individuals had conventional risk factors for CAD, including hypertension, diabetes mellitus, dyslipidemia and CKD, they did not have any cardiovascular complications.

EWAS

Venous blood (5 or 7 ml) was collected into tubes containing 50 mmol/l ethylenediaminetetraacetic acid (disodium salt), peripheral blood leukocytes were isolated, and genomic DNA was extracted from these cells with the use of a DNA extraction kit (Genomix; Talent SRL, Trieste, Italy; or SMITEST EX-R&D; Medical & Biological Laboratories, Co., Ltd., Nagoya, Japan). The EWASs for CAD (1,482 cases and 5,774 controls) was performed with the use of a Human Exome-12 v1.2 DNA Analysis BeadChip or Infinium Exome-24 v1.0 BeadChip (Illumina, Inc., San Diego, CA, USA). These exome arrays include putative functional exonic variants selected from ~12,000 individual exome and whole-genome sequences. The exonic content consists of ~244,000 SNPs from European, African, Chinese and Hispanic individuals (35). SNPs contained in only one of the exome arrays (~2.6% of all SNPs) were excluded from analysis. Quality control was performed as follows (36): i) Genotyping data with a call rate of <97% were discarded, with the mean call rate for the remaining data being 99.9%; ii) gender specification was checked for each sample, and those for which gender phenotype in the clinical records was inconsistent with genetic sex were discarded; iii) duplicate samples and cryptic relatedness were checked by calculation of identity by descent, and all pairs of DNA samples exhibiting an identity by descent of >0.1875 were inspected and one sample from each pair was excluded; iv) the frequency of heterozygosity for SNPs was calculated for all samples, and those with extremely low or high heterozygosity (>3 standard deviations from the mean) were discarded; v) SNPs in sex chromosomes or mitochondrial DNA were excluded from the analysis, as were nonpolymorphic SNPs or SNPs with a minor allele frequency of <1.0%; vi) SNPs whose genotype distributions deviated significantly (P<0.01) from Hardy-Weinberg equilibrium in control individuals were discarded; and vii) genotype data were examined for population stratification by principal components analysis (37), and population outliers were excluded from the analysis. A total of 31,465 SNPs passed quality control for the EWASs of CAD and these SNPs were subjected to analyses.

Statistical analysis

For analysis of the characteristics of the study subjects, quantitative data were compared between subjects with CAD and controls using the unpaired Student's t-test. Categorical data were compared between the two groups using the Pearson's χ2 test. Allele frequencies were estimated by the gene counting method, and Fisher's exact test was applied to identify departure from the Hardy-Weinberg equilibrium. In the EWAS, the association between allele frequencies of each SNP and CAD was examined using the Fisher's exact test. The genomic inflation factor (λ) was 0.93. To compensate for multiple comparisons of genotypes with CAD, an FDR was applied for statistical significance of association (38). The significance level was set at an FDR of <0.05 for the EWAS. Multivariable logistic regression analysis was performed with CAD as a dependent variable and independent variables, including age, sex (0, female and 1, male), the prevalence of hypertension, diabetes mellitus, and dyslipidemia (0, no history of these conditions; 1, positive history), as well as the genotype of each SNP. Genotypes of the SNPs were assessed according to dominant [0, AA; 1, AB + BB (A, major allele; B, minor allele)] and recessive (0, AA + AB; 1, BB) genetic models, and the P-value, odds ratio and 95% confidence interval were calculated. A stepwise forward selection procedure was also performed to examine the effects of genotypes on CAD. The P-levels for inclusion in and exclusion from the model were 0.25 and 0.1, respectively. In the stepwise forward selection procedure, each genotype was examined according to a dominant or recessive model on the basis of statistical significance in the multivariable logistic regression analysis. The association between genotypes of SNPs and intermediate phenotypes of CAD was examined using the Pearson's χ2 test. With the exception of the initial EWAS by the Fisher's exact test (FDR <0.05), P<0.05 was considered to indicate a statistically significant difference. Statistical tests were performed using JMP Genomics version 9.0 software (SAS Institute, Inc., Cary, NC, USA).

Association between genes, chromosomal loci and SNPs identified in the present study and phenotypes previously reported by GWASs

The genes, chromosomal loci, and SNPs identified in the present study were compared with the cardiovascular disease-related phenotypes previously reported by GWASs available in the Genome-Wide Repository of Associations Between SNPs and Phenotypes (GRASP) Search database v. 2.0.0.0 (https://grasp.nhlbi.nih.gov/Search.aspx), developed by the Information Technology and Applications Center at the National Center for Biotechnology Information (National Heart, Lung, and Blood Institute, National Institutes of Health, Bethesda, MD, USA) (39,40).

Gene Ontology analysis

Biological functions of the genes were examined by the use of the Gene Ontology and GO Annotations databases (QuickGO version 2018; https://www.ebi.ac.uk/QuickGO/; European Bioinformatics Institute, European Molecular Biology Laboratory, Hinxton, Cambridgeshire, UK) (41,42).

Network analysis of gene-gene interactions

Network analyses were performed to predict functional gene-gene interactions by the use of GeneMANIA Cytoscape plugin (http://apps.cytoscape.org/apps/genemania; Donnelly Centre for Cellular and Biomolecular Research, University of Toronto, Toronto, Canada) (4345) using Cytoscape v3.4.0 software (http://www.cytoscape.org/; The Cytoscape Consortium, San Diego, CA, USA) (46). To begin with, the 30 genes (ACE, NOS3, CCL2, PON1, CD40LG, LOX, APOB, CRP, APOA1, LPA, ESR1, LDLR, APOC3, VEGFA, LTA, HMOX1, MMP3, APOA5, PCSK9, CDKN2B, TLR4, GNB3, PTGS2, NPPB, ABCG8, ESR2, CXCL12, MIA3, IRS1 and ABO) were selected from the DisGeNET database (http://www.disgenet.org/web/DisGeNET; Integrative Biomedical Informatics Group, Research Programme on Biomedical Informatics, Barcelona Biomedical Research Park, Barcelona, Spain) (47,48), according to the rank order of high scores in association with CAD. Next, the 234 genes previously identified by GWASs (7) were selected, among which six genes were not included in GeneMANIA database and had no interaction with other genes. Therefore, the 228 genes (SKI, PRDM16, FHL3, PCSK9, PPAP2B, SORT1, NGF, CASQ2, TDRKH, IL6R, ATP1B1, NME7, DDX59, CAMSAP2, LMOD1, HHAT, SERTAD4, DIEXF, MIA3, AGT, APOB, ABCG5, ABCG8, PRKCE, VAMP5, VAMP8, GGCX, ZEB2, FIGN, CALCRL, TFPI, WDR12, NBEAL1, FN1, TNS1, IRS1, KCNJ13, COL6A3, FGD5, ALS2CL, RTP3, CDC25A, SPINK8, MAP4, ZNF589, RHOA, ITGB5, DNAJC13, STAG1, MSL2, NCK1, PPP2R3A, MRAS, ARHGEF26, TIPARP, FNDC3B, RGS12, REST, NOA1, STBD1, PRDM8, FGF5, HNRNPD, UNC5C, MAD2L1, PDE5A, ZNF827, EDNRA, PALLD, SEMA5A, MAP3K1, LOX, SLC22A4, IL5, RAD50, ARHGAP26, FOXC1, PHACTR1, EDN1, HDGFL1, C2, ANKS1A, PI16, KCNK5, VEGFA, RAB23, FAM46A, CENPW, TCF21, PLEKHG1, LPA, PLG, MAD1L1, DAGLB, RAC1, KDELR2, TMEM106B, HDAC9, CCM2, BCAP29, GPR22, CFTR, ZC3HC1, KLHDC10, PARP12, TBXAS1, NOS3, NAT2, LPL, BMP1, ZFPM2, TRIB1, KLF4, SVEP1, DAB2IP, ABO, CDC123, KIAA1462, CXCL12, TSPAN14, FAM213A, LIPA, CYP17A1, CNNM2, NT5C2, SH3PXD2A, HTRA1, TRIM5, TRIM22, TRIM6, SWAP70, CTR9, ARNTL, HSD17B12, SIPA1, SERPINH1, ARHGAP42, PDGFD, APOA1, APOC3, APOA4, APOA5, C1S, PRPF31, HOXC4, LRP1, FGD6, SH2B3, KSR2, HNF1A, CCDC92, SCARB1, FLT1, N4BP2L2, PDS5B, COL4A1, COL4A2, MCF2L, CUL4A, ARID4A, PSMA3, TMED10, SERPINA1, HHIPL1, YY1, TRIP4, SMAD3, ADAMTS7, MFGE8, FURIN, FES, CETP, HP, CFDP1, BCAR1, PLCG2, CDH13, SMG6, PEMT, CORO6, BLMH, ANKRD13B, GIT1, SSH2, EFCAB5, COPRS, RAB11FIP4, DHX58, KAT2A, RAB5, NKIRAS2, DNAJC7, KCNH4, HCRT, GHDC, GOSR2, UBE2Z, GIP, BCAS3, PECAM1, DDX5, TEX2, ACAA2, RPL17, PMAIP1, MC4R, LDLR, SMARCA4, FCHO1, COLGALT1, ZNF507, HNRNPUL1, TGFB1, APOE, APOC1, PVRL2, COTL1, SNRPD2, PROCR, EIF6, ZHX3, PLCG1, PLTP, MMP9, ZNF831, BACH1, KCNE2 and ADORA2A) were applied to analysis.

Results

Characteristics of subjects

The characteristics of the 7,256 subjects enrolled in the present study are presented in Table I. The age, the frequency of males, and the prevalence of obesity, hypertension, diabetes mellitus (DM), dyslipidemia, chronic kidney disease (CKD) and hyperuricemia, as well as body mass index, systolic and diastolic blood pressure, fasting plasma glucose level, blood glycosylated hemoglobin (hemoglobin A1c) content, and the serum concentrations of triglycerides, creatinine, and uric acid were greater, whereas the serum concentration of HDL-cholesterol and estimated glomerular filtration rate were lower, in patients with CAD than in controls.

Table I.

Characteristics of control subjects and patients with coronary artery disease.

Characteristic Control Coronary artery disease P-value
No. subjects 5,774 1,482
Age, years   50.6±10.2 55.9±7.4 <0.0001
Sex, males/females, % 52.1/47.9 82.5/17.5 <0.0001
Smoking, % 42.5 43.0   0.7719
Obesity, % 31.0 43.0 <0.0001
Body mass index, kg/m2 23.2±3.5 24.5±3.5 <0.0001
Hypertension, % 31.7 70.0 <0.0001
Systolic BP, mmHg 121±18 139±27 <0.0001
Diastolic BP, mmHg   75±13   78±15 <0.0001
Diabetes mellitus, % 12.7 58.7 <0.0001
Fasting plasma glucose, mmol/l   5.66±1.78   7.55±3.39 <0.0001
Blood hemoglobin A1c, %   5.72±0.96   6.89±1.75 <0.0001
Dyslipidemia, % 56.9 84.1 <0.0001
Serum triglycerides, mmol/l   1.32±0.98   1.84±1.34 <0.0001
Serum HDL-cholesterol, mmol/l   1.65±0.45   1.20±0.36 <0.0001
Serum LDL-cholesterol, mmol/l   3.18±0.83   3.18±0.98   0.9770
Chronic kidney disease, % 10.3 29.4 <0.0001
Serum creatinine, µmol/l   69.8±61.0   95.5±119.3 <0.0001
eGFR, ml min−1 1.73 m−2   78.7±17.1   70.7±26.9 <0.0001
Hyperuricemia, % 15.2 25.5 <0.0001
Serum uric acid, µmol/l 321±89   353±102 <0.0001

Quantitative data represent the mean ± standard deviation and were compared between subjects with coronary artery disease and controls with the unpaired Student's t-test. Categorical data were compared between the two groups using Pearson's χ2 test. P<0.05 was considered to indicate a statistically significant difference. Obesity was defined as a body mass index of ≥25 kg/m2; hypertension as a systolic BP of ≥140 mmHg, diastolic BP of ≥90 mmHg, or the taking of anti-hypertensive medication; diabetes mellitus as a fasting plasma glucose level of ≥6.93 mmol/l, blood hemoglobin A1c content of ≥6.5%, or the taking of anti-diabetes medication; dyslipidemia as a serum triglyceride concentration of ≥1.65 mmol/l, serum HDL-cholesterol concentration of <1.04 mmol/l, serum LDL-cholesterol concentration of ≥3.64 mmol/l or the taking of anti-dyslipidemic medication; chronic kidney disease as an estimated glomerular filtration rate (eGFR) of <60 ml min−1 1.73 m−2; and hyperuricemia as a serum uric acid concentration of >416 µmol/l or the taking of uric acid-lowering medication. BP, blood pressure; HDL, high-density lipoprotein; LDL, low-density lipoprotein; eGFR, estimated glomerular filtration rate.

EWAS for CAD

The association between allele frequencies for 31,465 SNPs that passed quality control and CAD was examined using the Fisher's exact test, and the 170 SNPs were significantly (FDR <0.05) associated with CAD (Table II).

Table II.

170 SNPs significantly (FDR <0.5) associated with coronary artery disease in the exome-wide association study.

Gene SNP Nucleotide substitutiona Amino acid substitution Chromosome Position MAF, % Allele OR P-value, allele frequency FDR, allele frequency
PLCB2 rs200787930 C/T E1106K 15 40289298 1.2 0.03 1.24×10−29 1.56×10−26
MARCH1 rs61734696 G/T Q137K 4 164197303 1.2 0.03 2.09×10−29 2.54×10−26
VPS33B rs199921354 C/T R80Q 15 91013841 1.2 0.03 2.76×10−29 3.30×10−26
CXCL8 rs188378669 G/T E31* 4 73741568 1.2 0.03 3.15×10−29 3.70×10−26
TMOD4 rs115287176 G/A R277W 1 151170961 1.2 0.03 1.21×10−28 1.39×10−25
COL6A3 rs146092501 C/T E1386K 2 237371861 1.2 0.04 2.93×10−28 3.27×10−25
ZNF77 rs146879198 G/A R340* 19 2934109 1.2 0.04 2.92×10−28 3.27×10−25
ADGRL3 rs192210727 G/T R580I 4 61909615 1.3 0.10 2.92×10−23 3.06×10−20
OR52E4 rs11823828 T/G F227L 11 5884973 36.6 1.54 3.40×10−21 3.35×10−18
ALDH2 rs671 G/A E504K 12 111803962 27.6 1.41 4.12×10−15 3.78×10−12
ACAD10 rs11066015 G/A 12 111730205 27.5 1.41 4.92×10−15 4.45×10−12
BRAP rs3782886 A/G 12 111672685 29.3 1.37 4.38×10−13 3.71×10−10
HECTD4 rs11066280 T/A 12 112379979 29.0 1.37 6.94×10−13 5.73×10−10
HECTD4 rs2074356 C/T 12 112207597 25.4 1.36 1.21×10−11 9.78×10−9
NAA25 rs12231744 C/T R876K 12 112039251 35.1 0.77 1.68×10−9 1.24×10−6
GOSR2 rs1052586 T/C 17 46941097 48.7 0.79 3.94×10−8 2.61×10−5
ATXN2 rs7969300 T/C N248S 12 111555908 38.8 0.79 4.41×10−8 2.87×10−5
LILRB2 rs73055442 C/T R103H 19 54279838 1.6 44.10 2.00×10−7 1.20×10−4
rs12229654 T/G 12 110976657 22.5 1.28 2.09×10−7 1.24×10−4
LOC107987429 rs2844533 T/C 6 31383025 15.3 1.32 3.49×10−7 1.95×10−4
MTFR2 rs143974258 G/A R360* 6 136231355 3.3 0.05 6.66×10−7 3.60×10−4
PSORS1C1 rs3130559 C/T 6 31129524 44.2 0.82 1.51×10−6 7.74×10−4
rs2596548 G/T 6 31362769 5.4 1.51 1.83×10−6 9.21×10−4
EIF3L rs9466 T/C 22 37877742 21.6 1.28 1.96×10−6 9.77×10−4
LPGAT1 rs150552771 T/C K200E 1 211783358 5.0 7.14 2.26×10−6 0.0011
LAIR2 rs34429135 T/A F115Y 19 54508164 2.5 ND 2.70×10−6 0.0013
rs2523644 A/G 6 31374707 8.1 1.40 2.75×10−6 0.0013
rs10757278 A/G 9 22124478 49.5 0.83 2.92×10−6 0.0014
CCHCR1 rs130067 T/G E328D 6 31150734 33.2 0.81 3.10×10−6 0.0015
TCHP rs74416240 G/A 12 109904793 13.3 1.30 3.25×10−6 0.0015
rs1333049 G/C 9 22125504 49.4 1.20 3.95×10−6 0.0018
CDKN2B-AS1 rs4977574 A/G 9 22098575 47.1 1.21 4.18×10−6 0.0019
CDKN2B-AS1 rs2383207 G/A 9 22115960 33.7 0.81 4.86×10−6 0.0022
SLC16A1 rs1049434 T/A D490E 1 112913924 34.7 0.82 5.76×10−6 0.0025
GIT2 rs925368 T/C N389S 12 109953174 12.5 1.30 6.02×10−6 0.0026
rs1333048 A/C 9 22125348 49.6 1.20 6.46×10−6 0.0028
rs2523578 T/C 6 31360765 8.1 1.39 6.54×10−6 0.0028
rs404890 G/T 6 32231090 30.5 1.22 8.90×10−6 0.0037
APOE rs7412 C/T R176C 19 44908822 4.3 0.60 1.06×10−5 0.0043
CCHCR1 rs130071 G/A 6 31148433 5.1 1.52 1.06×10−5 0.0043
rs602633 C/A 1 109278889 7.6 0.69 1.15×10−5 0.0046
CELSR2 rs12740374 G/T 1 109274968 7.7 0.69 1.15×10−5 0.0046
MKI67 rs145121731 G/A S2722L 10 128102595 1.5 2.04 1.20×10−5 0.0047
CUBN rs78201384 C/T E304K 10 17111024 2.7 0.52 1.38×10−5 0.0054
PSORS1C3 rs887466 T/C 6 31175734 41.1 1.20 1.38×10−5 0.0054
PSORS1C1 rs3094663 G/A 6 31139310 30.9 1.20 1.40×10−5 0.0054
rs10853110 A/G 17 49241052 39.2 1.20 1.49×10−5 0.0057
WDR37 rs10794720 C/T 10 1110225 8.5 0.71 1.52×10−5 0.0057
CELSR2 rs629301 A/C 1 109275684 7.8 0.70 1.52×10−5 0.0057
SKIV2L rs592229 G/T 6 31962664 42.4 1.20 1.57×10−5 0.0058
rs12182351 T/C 6 32233930 29.8 1.22 1.59×10−5 0.0059
POU5F1 rs3130503 G/A 6 31169388 29.5 1.20 1.64×10−5 0.0060
PSORS1C3 rs1265155 T/C 6 31175917 41.1 1.19 1.68×10−5 0.0061
CELSR2 rs646776 A/G 1 109275908   7.7 0.70 1.70×10−5 0.0062
rs2596503 C/T 6 31353033 19.3 1.24 1.75×10−5 0.0063
TRPM1 rs2241493 T/C N54S 15 31070149 12.6 0.76 1.81×10−5 0.0065
CCDC141 rs13419085 T/C N1170S 2 178837710   1.8 0.46 1.92×10−5 0.0068
VARS2 rs9394021 A/G Q777R 6 30925350 44.9 0.84 1.98×10−5 0.0069
SFTA2 rs2286655 T/C 6 30931969 44.9 1.19 1.99×10−5 0.0069
rs3873334 T/C 6 30928370 44.9 1.19 1.98×10−5 0.0069
rs9261800 C/G 6 30408822   2.8 7.21 2.02×10−5 0.0069
TCF19 rs3130453 C/T 6 31157072 34.4 0.83 2.10×10−5 0.0072
C21orf59 rs76974938 C/T D67N 21 32609946   2.4 0.00 2.14×10−5 0.0073
DDR1 rs2239518 T/C 6 30897948 44.9 1.19 2.19×10−5 0.0074
CDSN rs3130984 C/T S143N 6 31117187 13.4 1.29 2.20×10−5 0.0074
rs197932 T/C 17 46896981 26.9 0.82 2.23×10−5 0.0075
CDSN rs3130981 C/T D527N 6 31116036 13.6 1.29 2.30×10−5 0.0075
MICB-DT rs3132469 C/T 6 31488790   5.3 1.46 2.41×10−5 0.0078
HLA-DQB1 rs1049056 C/A A6S 6 32666592 11.9 1.30 2.51×10−5 0.0081
DDR1 rs2239517 A/G 6 30897338 44.6 1.19 2.59×10−5 0.0083
CCHCR1 rs1265110 G/A 6 31151645 30.2 0.83 2.69×10−5 0.0085
CCDC63 rs10774610 T/C 12 110902439 23.7 1.22 2.76×10−5 0.0087
GTF2H4 rs2284176 C/T 6 30907845 44.6 1.19 2.80×10−5 0.0088
GTF2H4 rs3909130 G/A 6 30906388 44.6 1.19 2.84×10−5 0.0089
GTF2H4 rs916920 G/A 6 30909425 44.7 1.19 2.85×10−5 0.0089
rs1264569 A/G 6 30397543   4.6 1.49 2.98×10−5 0.0092
CACNA1D rs35874056 G/A G460S 3 53702798   2.0 25.00 3.09×10−5 0.0094
rs9468845 A/G 6 30901816 44.7 1.19 3.12×10−5 0.0094
DDR1 rs8408 C/T 6 30899889 44.7 1.19 3.11×10−5 0.0094
DDR1 rs7756521 C/T 6 30880476 44.7 1.19 3.10×10−5 0.0094
CDKN2B-AS1 rs1011970 G/T 9 22062135   5.6 1.41 3.15×10−5 0.0095
ADAT1 rs145161932 T/C R57G 16 75612670   1.4 0.39 3.29×10−5 0.0098
POU5F1 rs885950 T/G 6 31172375 34.0 0.83 3.28×10−5 0.0098
DDR1 rs4618569 A/G 6 30887474 44.7 1.19 3.41×10−5 0.0101
KRT13 rs146918776 A/G Y281H 17 41502993   1.5 1.94 3.51×10−5 0.0103
rs2523638 G/A 6 31376496 43.1 1.19 3.53×10−5 0.0103
PSRC1 rs599839 A/G 1 109279544   7.9 0.71 3.52×10−5 0.0103
rs9275141 G/T 6 32683340 26.4 1.21 3.62×10−5 0.0105
CCDC63 rs10849915 T/C 12 110895818 23.6 1.22 3.63×10−5 0.0105
HLA-DRA rs3177928 G/A 6 32444658   5.9 1.41 3.81×10−5 0.0108
OAS3 rs2072134 C/T 12 112971371 17.6 1.24 4.06×10−5 0.0114
USP45 rs41288947 C/G T521R 6 99446210 14.9 1.26 4.11×10−5 0.0115
CCHCR1 rs1265109 A/C 6 31151812 48.2 1.18 4.16×10−5 0.0116
LOC101929163 rs6930777 C/T 6 32383789   5.5 1.43 4.45×10−5 0.0122
rs7333181 G/A 13 111568950   2.5 0.54 4.45×10−5 0.0122
DDR1 rs1264323 T/C 6 30888130 38.8 1.19 4.48×10−5 0.0122
LINC00243 rs3094111 G/A 6 30820414 14.7 1.25 4.52×10−5 0.0123
rs10484561 T/G 6 32697643   5.9 1.41 4.55×10−5 0.0123
PSORS1C1 rs3130558 G/C 6 31129406 13.7 1.27 4.59×10−5 0.0124
HLA-DQB1 rs1049060 T/A S27T 6 32666529 28.8 1.20 4.92×10−5 0.0131
rs2844650 G/A 6 30934756   4.7 1.47 4.99×10−5 0.0131
DDR1 rs3132572 T/C 6 30893952   4.7 1.47 4.99×10−5 0.0131
CCHCR1 rs1265115 T/G 6 31149298 47.7 1.18 4.96×10−5 0.0131
CCHCR1 rs3094225 T/C 6 31145275 48.4 1.18 4.93×10−5 0.0131
LOC107987453 rs3129987 C/T 6 30798427 14.5 1.25 5.05×10−5 0.0132
DPCR1 rs2517451 A/G 6 30946974   4.7 1.47 5.11×10−5 0.0133
KIAA1551 rs10771894 A/G S352G 12 31982009 32.4 1.19 5.18×10−5 0.0134
rs13427905 C/T 2 71846585 18.5 0.80 5.22×10−5 0.0134
ABCA1 rs1883025 G/A 9 104902020 28.8 0.83 5.46×10−5 0.0139
SFTA2 rs2253705 G/A 6 30932317 18.0 1.23 5.60×10−5 0.0141
PLUT rs954750 G/A 13 27889801 48.3 1.18 5.86×10−5 0.0146
TCF19 rs1419881 T/C 6 31162816 48.1 1.18 6.37×10−5 0.0156
rs13209234 G/A 6 32448198   5.9 1.41 6.47×10−5 0.0158
PSORS1C1 rs1265100 T/C 6 31137533 32.2 0.83 6.55×10−5 0.0159
YEATS2 rs76174573 G/T C1232F 3 183804099   3.7 0.61 6.74×10−5 0.0162
ABO rs1053878 C/T P156L 9 133256264 22.8 1.20 6.78×10−5 0.0162
rs4014195 C/G 11 65739351 16.6 1.24 6.78×10−5 0.0162
SFTA2 s2253588 C/G 6 30931600 23.6 1.21 6.93×10−5 0.0165
CYP4F8 rs201166643 C/A R488S 19 15629257   1.1 ND 7.00×10−5 0.0165
NAXE rs7516274 C/G L19V 1 156591859   1.8 0.48 7.18×10−5 0.0169
rs10757283 T/C 9 22134173 33.8 0.84 7.25×10−5 0.0170
BTNL2 rs28362680 G/A A202V 6 32403039 39.7 0.85 7.40×10−5 0.0171
BTNL2 rs10947262 C/T 6 32405535 39.7 0.85 7.40×10−5 0.0171
KRT27 rs17558532 C/T A284T 17 40779624   3.6 0.62 7.71×10−5 0.0176
GTF2H4 rs3130780 G/T 6 30906531 18.0 1.23 7.71×10−5 0.0176
rs2532934 T/C 6 30926982 24.1 1.20 7.74×10−5 0.0176
VARS2 rs753725 G/A 6 30923094 24.1 1.20 7.68×10−5 0.0176
PLUT rs11619319 A/G 13 27913462 48.1 1.18 7.64×10−5 0.0176
rs3095273 C/T 6 29598592   5.5 1.41 8.16×10−5 0.0184
TNS1 rs918949 C/T V1590I 2 217809974 42.8 0.85 8.39×10−5 0.0188
LINC00243 rs3130785 C/T 6 30828961 14.6 1.24 8.37×10−5 0.0188
VARS2 rs2249464 C/T R309W 6 30920384 24.1 1.20 9.39×10−5 0.0207
rs3095345 A/G 6 30854636 17.9 1.22 9.37×10−5 0.0207
ITGB8 rs80015015 G/A C481Y 7 20401881   7.1 1.35 1.01×10−4 0.0220
VARS2 rs885905 C/T 6 30922654 23.4 1.20 1.07×10−4 0.0232
LIPE rs34052647 G/A R611C 19 42407617   5.5 1.39 1.16×10−4 0.0249
PHACTR1 rs9369640 A/C 6 12901209   9.1 0.74 1.30×10−4 0.0275
BTNL2 rs41417449 T/C M295V 6 32396234 23.0 0.83 1.35×10−4 0.0280
BTNL2 rs41441651 C/T D336N 6 32396111 23.0 0.83 1.35×10−4 0.0280
BTNL2 rs28362675 C/A E454* 6 32394744 23.0 0.83 1.35×10−4 0.0280
BTNL2 rs78587369 G/A T165I 6 32403150 23.0 0.83 1.35×10−4 0.0280
BTNL2 rs3763315 G/T 6 32408877 23.0 0.83 1.35×10−4 0.0280
BTNL2 rs2076528 T/G 6 32396417 23.0 0.83 1.35×10−4 0.0280
PRKG1 rs9414827 G/A 10 51137314 10.1 0.76 1.37×10−4 0.0282
rs6537384 T/G 4 145949613 28.8 1.19 1.43×10−4 0.0294
rs6067640 G/A 20 51092837 38.5 0.85 1.48×10−4 0.0302
rs10514995 A/G 5 66443611 48.7 1.16 1.51×10−4 0.0306
BTNL2 rs34423804 T/A V283D 6 32396269 23.0 0.83 1.63×10−4 0.0329
PHACTR1 rs9349379 G/A 6 12903725 34.2 0.85 1.69×10−4 0.0341
STIM1 rs116855870 A/G 11 4055527   1.1 1.93 1.71×10−4 0.0343
ZNF142 rs3821033 C/T A1313T 2 218642579 11.2 1.26 1.78×10−4 0.0355
LINC00354 rs4907518 G/A 13 111898209 45.6 0.85 1.82×10−4 0.0362
TNS3 rs11763932 G/A 7 47567880 42.0 0.85 1.91×10−4 0.0378
BTRC rs2270439 C/A P566H 10 101550817   3.5 0.63 1.94×10−4 0.0381
MIA3 rs2936051 A/G E881G 1 222629862 40.1 0.85 1.96×10−4 0.0384
rs6825911 C/T 4 110460482 45.9 0.86 2.01×10−4 0.0391
VNN1 rs2294757 G/A T26I 6 132713959 37.4 0.85 2.02×10−4 0.0393
ZNF860 rs140232911 C/T S161L 3 31989561 10.4 0.44 2.09×10−4 0.0406
rs838880 C/T 12 124777047 47.5 1.16 2.23×10−4 0.0430
MIA3 rs2936052 A/G K605R 1 222629034 34.4 0.85 2.26×10−4 0.0430
DTNBP1 rs2743868 G/A 6 15625577 31.6 1.18 2.26×10−4 0.0430
MON2 rs11174549 A/G I1385V 12 62565357   5.0 1.40 2.26×10−4 0.0430
rs507666 G/A 9 136149399 27.8 1.18 2.26×10−4 0.0430
FAM170B rs73302786 G/T D252E 10 49131709   3.5 1.47 2.36×10−4 0.0445
PSORS1C3 rs3131018 G/T 6 31175805 15.7 1.23 2.36×10−4 0.0445
PIEZO2 rs35033671 C/A C1148F 18 10759842 11.0 1.27 2.39×10−4 0.0448
SLC22A3 rs1810126 C/T 6 160451119 49.1 0.86 2.46×10−4 0.0460
PANK1 rs11185790 G/A 10 89612776 46.9 1.16 2.57×10−4 0.0481
GFY rs73053944 C/G T203S 19 49427038   2.9 1.51 2.58×10−4 0.0481
RNF2 rs1046592 A/G 1 185100429 33.9 0.85 2.63×10−4 0.0488

Allele frequencies were analyzed using Fisher's exact test.

a

Major allele/minor allele. SNP, single nucleotide polymorphisms; MAF, minor allele frequency; OR, odds ratio; FDR, false discovery rate; ND, not determined.

Multivariable logistic regression analysis of the association between SNPs and CAD

The association between the 170 SNPs identified in the EWAS for CAD and this condition was examined by multivariable logistic regression analysis with adjustment for age, sex and the prevalence of hypertension, diabetes mellitus and dyslipidemia (Table III). The 162 SNPs were significantly (P<0.05 in a dominant or recessive model) associated with CAD.

Table III.

162 SNPs associated with coronary artery disease as determined by multivariable logistic regression analysis.

Dominant model Recessive model


Gene SNP P-value OR 95% CI P-value OR 95% CI
PLCB2 rs200787930 C/T <0.0001 0.02 0.01–0.09
MARCH1 rs61734696 G/T <0.0001 0.02 0.01–0.10
VPS33B rs199921354 C/T <0.0001 0.02 0.01–0.09
CXCL8 rs188378669 G/T <0.0001 0.02 0.01–0.09
TMOD4 rs115287176 G/A <0.0001 0.02 0.01–0.10
COL6A3 rs146092501 C/T <0.0001 0.02 0.01–0.10
ZNF77 rs146879198 G/A <0.0001 0.02 0.01–0.10
ADGRL3 rs192210727 G/T <0.0001 0.07 0.03–0.16   0.9959
OR52E4 rs11823828 T/G <0.0001 1.66 1.41–1.97 <0.0001 2.44 2.01–2.97
ALDH2 rs671 G/A <0.0001 1.73 1.50–2.01 <0.0001 1.80 1.44–2.26
ACAD10 rs11066015 G/A <0.0001 1.73 1.49–2.01 <0.0001 1.79 1.42–2.25
BRAP rs3782886 A/G <0.0001 1.71 1.48–1.99 <0.0001 1.70 1.36–2.12
HECTD4 rs11066280 T/A <0.0001 1.73 1.49–2.01 <0.0001 1.73 1.38–2.17
HECTD4 rs2074356 C/T <0.0001 1.61 1.39–1.87 <0.0001 1.76 1.38–2.26
NAA25 rs12231744 C/T <0.0001 0.63 0.54–0.73 <0.0001 0.55 0.43–0.70
GOSR2 rs1052586 T/C   0.0003 0.73 0.62–0.87 <0.0001 0.64 0.53–0.77
ATXN2 rs7969300 T/C <0.0001 0.63 0.55–0.74 <0.0001 0.57 0.45–0.71
rs12229654 T/G <0.0001 1.46 1.26–1.69 <0.0001 1.72 1.31–2.25
LOC107987429 rs2844533 T/C <0.0001 1.36 1.17–1.59   0.8616
MTFR2 rs143974258 G/A   0.0014 0.04 0.01–0.28
PSORS1C1 rs3130559 C/T   0.0127 0.82 0.70–0.96   0.0629
rs2596548 G/T <0.0001 1.76 1.41–2.20   0.2047
EIF3L rs9466 T/C   0.0053 1.24 1.07–1.44   0.0199 1.47 1.06–2.04
LPGAT1 rs150552771 T/C   0.9970 <0.0001 2.20 1.83–2.64
rs2523644 A/G <0.0001 1.59 1.31–1.92   0.9088
rs10757278 A/G <0.0001 0.71 0.60–0.83   0.0023 0.77 0.65–0.91
CCHCR1 rs130067 T/G   0.0010 0.78 0.68–0.91   0.0183 0.73 0.57–0.95
TCHP rs74416240 G/A   0.0002 1.35 1.15–1.58   0.1725
rs1333049 G/C   0.0031 1.29 1.09–1.53 <0.0001 1.41 1.20–1.66
CDKN2B-AS1 rs4977574 A/G   0.0003 1.36 1.15–1.60 <0.0001 1.43 1.21–1.69
CDKN2B-AS1 rs2383207 G/A <0.0001 0.75 0.65–0.87   0.0171 0.75 0.59–0.95
SLC16A1 rs1049434 T/A   0.0106 0.83 0.71–0.96 <0.0001 0.57 0.45–0.73
GIT2 rs925368 T/C   0.0001 1.37 1.16–1.61   0.3189
rs1333048 A/C   0.0036 1.29 1.09–1.53 <0.0001 1.40 1.19–1.64
rs2523578 T/C <0.0001 1.55 1.27–1.88   0.8694
rs404890 G/T   0.0005 1.29 1.12–1.50   0.0160 1.35 1.06–1.72
APOE rs7412 C/T   0.0001 0.56 0.42–0.76   0.2259
CCHCR1 rs130071 G/A   0.0149 1.36 1.06–1.73   0.2668
rs602633 C/A   0.0001 0.64 0.51–0.80   0.1782
CELSR2 rs12740374 G/T <0.0001 0.63 0.51–0.79   0.1708
MKI67 rs145121731 G/A   0.0014 1.94 1.29–2.91   0.9957
CUBN rs78201384 C/T   0.0003 0.50 0.34–0.73   0.9959
PSORS1C3 rs887466 T/C   0.0013 1.29 1.11–1.52   0.1666
PSORS1C1 rs3094663 G/A <0.0001 1.41 1.21–1.63   0.6404
rs10853110 A/G   0.0026 1.26 1.09–1.47   0.0102 1.29 1.06–1.56
WDR37 rs10794720 C/T   0.0003 0.68 0.55–0.84   0.0734
CELSR2 rs629301 A/C   0.0002 0.65 0.52–0.82   0.1708
SKIV2L rs592229 G/T   0.0154 1.22 1.04–1.42   0.0138 1.26 1.05–1.51
rs12182351 T/C   0.0008 1.28 1.11–1.48   0.0138 1.37 1.07–1.75
POU5F1 rs3130503 G/A <0.0001 1.41 1.22–1.63   0.6308
PSORS1C3 rs1265155 T/C   0.0017 1.29 1.10–1.50   0.1666
CELSR2 rs646776 A/G   0.0002 0.65 0.52–0.82 0.2660
rs2596503 C/T   0.0204 1.19 1.03–1.38 0.1828
TRPM1 rs2241493 T/C   0.0002 0.71 0.60–0.85 0.0410 0.49 0.25–0.97
CCDC141 rs13419085 T/C   0.0005 0.43 0.27–0.69
VARS2 rs9394021 A/G   0.0261 0.82 0.69–0.98 0.0117 0.81 0.69–0.95
SFTA2 rs2286655 T/C   0.0097 1.24 1.05–1.45 0.0277 1.22 1.02–1.45
rs3873334 T/C   0.0117 1.23 1.05–1.44 0.0261 1.22 1.02–1.45
TCF19 rs3130453 C/T   0.0077 0.82 0.71–0.95 0.0024 0.68 0.53–0.87
DDR1 rs2239518 T/C   0.0103 1.23 1.05–1.45 0.0282 1.22 1.02–1.45
CDSN rs3130984 C/T <0.0001 1.39 1.18–1.64 0.1220
rs197932 T/C   0.0042 0.81 0.70–0.93 0.0348 0.73 0.54–0.98
CDSN rs3130981 C/T <0.0001 1.39 1.18–1.64 0.1226
MICB-DT rs3132469 C/T <0.0001 1.63 1.30–2.05 0.3960
HLA-DQB1 rs1049056 C/A   0.0050 1.28 1.08–1.52 0.1586
DDR1 rs2239517 A/G   0.0115 1.23 1.05–1.44 0.0326 1.21 1.02–1.44
CCHCR1 rs1265110 G/A   0.0096 0.83 0.71–0.95 0.0948
CCDC63 rs10774610 T/C   0.0006 1.29 1.12–1.49 0.0214 1.38 1.05–1.82
GTF2H4 rs2284176 C/T   0.0153 1.22 1.04–1.43 0.0306 1.21 1.02–1.45
GTF2H4 rs3909130 G/A   0.0138 1.22 1.04–1.43 0.0326 1.21 1.02–1.44
GTF2H4 rs916920 G/A   0.0139 1.22 1.04–1.43 0.0326 1.21 1.02–1.44
rs1264569 A/G   0.0004 1.54 1.21–1.97 0.5339
rs9468845 A/G   0.0141 1.22 1.04–1.43 0.0332 1.21 1.02–1.44
DDR1 rs8408 C/T   0.0141 1.22 1.04–1.43 0.0326 1.21 1.02–1.44
DDR1 rs7756521 C/T   0.0155 1.22 1.04–1.43 0.0303 1.21 1.02–1.45
CDKN2B-AS1 rs1011970 G/T   0.0047 1.36 1.10–1.69 0.2199
ADAT1 rs145161932 T/C   0.0104 0.48 0.27–0.84 0.9960
POU5F1 rs885950 T/G   0.0049 0.81 0.70–0.94 0.0129 0.73 0.57–0.94
DDR1 rs4618569 A/G   0.0141 1.22 1.04–1.43 0.0333 1.21 1.02–1.44
KRT13 rs146918776 A/G <0.0001 2.21 1.50–3.26
rs2523638 G/A   0.0197 1.20 1.03–1.41 0.1032
PSRC1 rs599839 A/G   0.0004 0.67 0.54–0.83 0.1023
rs9275141 G/T   0.0134 1.20 1.04–1.39 0.0065 1.43 1.11–1.85
CCDC63 rs10849915 T/C   0.0005 1.30 1.12–1.50 0.0458 1.33 1.01–1.76
HLA-DRA rs3177928 G/A <0.0001 1.58 1.27–1.96 0.7635
OAS3 rs2072134 C/T   0.0004 1.31 1.13–1.53 0.0268 1.51 1.05–2.16
USP45 rs41288947 C/G   0.0002 1.35 1.15–1.58 0.1360
CCHCR1 rs1265109 A/C   0.0007 1.35 1.14–1.61 0.0334 1.20 1.01–1.41
LOC101929163 rs6930777 C/T <0.0001 1.60 1.28–2.00 0.9815
rs7333181 G/A   0.0219 0.64 0.44–0.94 0.9969
DDR1 rs1264323 T/C   0.0240 1.19 1.02–1.39 0.0423 1.22 1.01–1.48
LINC00243 rs3094111 G/A   0.0061 1.24 1.06–1.45 0.5293
rs10484561 T/G <0.0001 1.59 1.28–1.98 0.7637
PSORS1C1 rs3130558 G/C <0.0001 1.39 1.18–1.64 0.2712
HLA-DQB1 rs1049060 T/A   0.0676 0.0077 1.38 1.09–1.75
rs2844650 G/A <0.0001 1.66 1.30–2.11 0.4729
DDR1 rs3132572 T/C <0.0001 1.66 1.30–2.11 0.4729
CCHCR1 rs1265115 T/G   0.0004 1.36 1.15–1.62 0.0443 1.19 1.00–1.40
CCHCR1 rs3094225 T/C <0.0001 1.46 1.23–1.74 0.5822
LOC107987453 rs3129987 C/T   0.0037 1.26 1.08–1.47 0.5667
DPCR1 rs2517451 A/G <0.0001 1.65 1.30–2.11 0.4729
CCHCR1 rs1265115 T/G   0.0004 1.36 1.15–1.62 0.0443 1.19 1.00–1.40
CCHCR1 rs3094225 T/C <0.0001 1.46 1.23–1.74 0.5822
LOC107987453 rs3129987 C/T   0.0037 1.26 1.08–1.47 0.5667
DPCR1 rs2517451 A/G <0.0001 1.65 1.30–2.11 0.4729
KIAA1551 rs10771894 A/G   0.1122 0.0085 1.35 1.08–1.69
rs13427905 C/T   0.0024 0.78 0.67–0.92 0.1397
ABCA1 rs1883025 G/A   0.0051 0.81 0.70–0.94 0.0080 0.68 0.51–0.90
SFTA2 rs2253705 G/A   0.0015 1.28 1.10–1.48 0.6682
PLUT rs954750 G/A   0.0409 1.19 1.01–1.41 0.0008 1.33 1.12–1.57
TCF19 rs1419881 T/C   0.0009 1.34 1.13–1.60 0.0487 1.18 1.00–1.39
rs13209234 G/A <0.0001 1.55 1.25–1.93 0.7617
PSORS1C1 rs1265100 T/C   0.0055 0.81 0.70–0.94 0.4135
YEATS2 rs76174573 G/T   0.0031 0.61 0.44–0.85 0.1756
ABO rs1053878 C/T   0.0033 1.25 1.08–1.44 0.1723
rs4014195 C/G   0.0083 1.23 1.05–1.43 0.2586
SFTA2 rs2253588 C/G   0.0021 1.26 1.09–1.45 0.7016
rs10757283 T/C   0.0079 0.82 0.71–0.95 0.0181 0.74 0.58–0.95
BTNL2 rs28362680 G/A   0.1143 0.0082 0.76 0.62–0.93
BTNL2 rs10947262 C/T   0.1143 0.0082 0.76 0.62–0.93
KRT27 rs17558532 C/T   0.0004 0.57 0.41–0.78 0.5002
GTF2H4 rs3130780 G/T   0.0022 1.26 1.09–1.47 0.6706
rs2532934 T/C   0.0023 1.25 1.08–1.45 0.5320
VARS2 rs753725 G/A   0.0021 1.26 1.09–1.45 0.5274
PLUT rs11619319 A/G   0.0482 1.18 1.00–1.40 0.0007 1.33 1.13–1.58
rs3095273 C/T   0.0045 1.38 1.11–1.73 0.1778
TNS1 rs918949 C/T   0.0028 0.79 0.68–0.92 0.1301
LINC00243 rs3130785 C/T   0.0060 1.24 1.06–1.45 0.5417
VARS2 rs2249464 C/T   0.0028 1.25 1.08–1.44 0.5320
rs3095345 A/G   0.0021 1.27 1.09–1.47 0.7469
ITGB8 rs80015015 G/A <0.0001 1.56 1.28–1.91 0.2511
VARS2 rs885905 C/T   0.0028 1.25 1.08–1.44 0.7687
LIPE rs34052647 G/A   0.0001 1.53 1.23–1.90 0.0074 3.70 1.42–9.65
PHACTR1 rs9369640 A/C   0.0025 0.73 0.60–0.90 0.1527
BTNL2 rs41417449 T/C   0.1641 0.0017 0.55 0.38–0.80
BTNL2 rs41441651 C/T   0.1586 0.0017 0.55 0.38–0.80
BTNL2 rs28362675 C/A   0.1584 0.0017 0.55 0.38–0.80
BTNL2 rs78587369 G/A   0.1590 0.0018 0.55 0.38–0.80
BTNL2 rs3763315 G/T   0.1637 0.0017 0.55 0.38–0.80
BTNL2 rs2076528 T/G   0.1524 0.0017 0.55 0.38–0.80
PRKG1 rs9414827 G/A   0.0003 0.70 0.58–0.85 0.0395 0.46 0.22–0.96
rs6537384 T/G   0.0191 1.19 1.03–1.38 0.1787
rs6067640 G/A   0.0323 0.85 0.73–0.99 0.0054 0.74 0.60–0.91
rs10514995 A/G   0.0467 1.19 1.00–1.42 0.0825
BTNL2 rs34423804 T/A   0.1675 0.0018 0.55 0.38–0.80
PHACTR1 rs9349379 G/A   0.0029 0.80 0.69–0.93 0.0820
STIM1 rs116855870 A/G   0.0133 1.76 1.13–2.76 0.9967
ZNF142 rs3821033 C/T   0.0015 1.32 1.11–1.57 0.3469
LINC00354 rs4907518 G/A   0.0138 0.82 0.70–0.96 0.0050 0.77 0.64–0.92
TNS3 rs11763932 G/A   0.0054 0.81 0.69–0.94 0.0018 0.73 0.60–0.89
BTRC rs2270439 C/A   0.0063 0.64 0.47–0.88 0.6622
MIA3 rs2936051 A/G   0.1239 0.0002 0.67 0.55–0.83
rs6825911 C/T   0.0020 0.78 0.67–0.91 0.0446 0.83 0.70–1.00
VNN1 rs2294757 G/A 0.2269 0.0334 0.79 0.63–0.98
ZNF860 rs140232911 C/T 0.0013 0.14 0.04–0.46
rs838880 C/T 0.0338 1.20 1.01–1.41 0.0308 1.20 1.02–1.43
MIA3 rs2936052 A/G 0.0927 0.0044 0.71 0.56–0.90
DTNBP1 rs2743868 G/A 0.0208 1.19 1.03–1.37 0.0941
MON2 rs11174549 A/G 0.0427 1.28 1.01–1.61 0.1917
rs507666 G/A 0.0098 1.21 1.05–1.40 0.1384
FAM170B rs73302786 G/T 0.0003 1.62 1.25–2.11 0.6351
PSORS1C3 rs3131018 G/T 0.0002 1.35 1.15–1.58 0.9209
PIEZO2 rs35033671 C/A 0.0035 1.30 1.09–1.54 0.1909
PANK1 rs11185790 G/A 0.0065 1.26 1.07–1.48 0.0457 1.19 1.00–1.41
GFY rs73053944 C/G 0.0011 1.60 1.21–2.13 0.0698
RNF2 rs1046592 A/G 0.0301 0.85 0.74–0.98 0.7186

Multivariable logistic regression analysis was performed with adjustment for age, sex, and the prevalence of hypertension, diabetes mellitus and dyslipidemia. P<0.05 was considered to indicate a statistically significant difference. SNP, single nucleotide polymorphism; OR, odds ratio; CI, confidence interval.

Stepwise forward selection procedure of the effects of SNPs on CAD

A stepwise forward selection procedure was performed to examine effects of genotypes for the 162 SNPs associated with CAD by multivariable logistic regression analysis on this condition (Table IV). The 54 SNPs were significant (P<0.05) and independent [coefficient of determination (R2), 0.0008 to 0.0297] determinants of CAD. These SNPs together accounted for 15.5% of the cause of CAD.

Table IV.

54 SNPs associated with coronary artery disease as determined by a stepwise forward selection procedure.

Gene SNP P-value R2 (individual) R2 (accumulated)
PLCB2 rs200787930 <0.0001 0.0297 0.0297
ALDH2 rs671 <0.0001 0.0061 0.0358
GOSR2 rs1052586 <0.0001 0.0053 0.0411
PSORS1C1 rs3094663 <0.0001 0.0052 0.0463
CCHCR1 rs130071 <0.0001 0.0059 0.0522
rs13427905 <0.0001 0.0047 0.0569
OR52E4 rs11823828 <0.0001 0.0043 0.0612
EIF3L rs9466 <0.0001 0.0042 0.0654
KIAA1551 rs10771894 <0.0001 0.0039 0.0693
CCDC141 rs13419085 <0.0001 0.0035 0.0728
MIA3 rs2936051   0.0001 0.0033 0.0761
rs602633   0.0001 0.0033 0.0794
KRT27 rs17558532   0.0001 0.0032 0.0826
TRPM1 rs2241493   0.0002 0.0030 0.0856
rs7333181   0.0002 0.0030 0.0886
ADAT1 rs145161932   0.0002 0.0029 0.0915
APOE rs7412   0.0003 0.0028 0.0943
YEATS2 rs76174573   0.0004 0.0026 0.0969
SLC16A1 rs1049434   0.0005 0.0025 0.0994
RNF2 rs1046592   0.0007 0.0025 0.1019
rs6825911   0.0006 0.0024 0.1043
ITGB8 rs80015015   0.0007 0.0024 0.1067
USP45 rs41288947   0.0007 0.0024 0.1091
PHACTR1 rs9369640   0.0007 0.0024 0.1115
rs1333048   0.0008 0.0024 0.1139
rs838880   0.0011 0.0022 0.1161
STIM1 rs116855870   0.0017 0.0021 0.1182
rs2523644   0.0016 0.0021 0.1203
MKI67 rs145121731 0.0020 0.0020 0.1223
FAM170B-AS1 rs73302786 0.0019 0.0020 0.1243
rs6067640 0.0022 0.0020 0.1263
GFY rs73053944 0.0024 0.0019 0.1282
WDR37 rs10794720 0.0033 0.0018 0.1300
SKIV2L rs592229 0.0037 0.0018 0.1318
rs6537384 0.0041 0.0017 0.1335
rs10757283 0.0058 0.0016 0.1351
CDKN2B-AS1 rs1011970 0.0110 0.0014 0.1365
PRKG1 rs9414827 0.0087 0.0014 0.1379
rs197932 0.0127 0.0013 0.1392
LINC00354 rs4907518 0.0125 0.0013 0.1405
LIPE rs34052647 0.0151 0.0013 0.1418
BTRC rs2270439 0.0143 0.0013 0.1431
TNS3 rs11763932 0.0163 0.0013 0.1444
TNS1 rs918949 0.0158 0.0012 0.1456
rs12229654 0.0184 0.0011 0.1467
rs4014195 0.0213 0.0011 0.1478
PANK1 rs11185790 0.0227 0.0011 0.1489
rs507666 0.0279 0.0010 0.1499
MON2 rs11174549 0.0359 0.0009 0.1508
HECTD4 rs2074356 0.0396 0.0009 0.1517
CUBN rs78201384 0.0484 0.0009 0.1526
PLUT rs954750 0.0497 0.0008 0.1534
ABCA1 rs1883025 0.0479 0.0008 0.1542
rs10514995 0.0493 0.0008 0.1550

SNP, single nucleotide polymorphisms; R2, coefficient of determination.

Association between SNPs associated with CAD and intermediate phenotypes

The association between the 54 SNPs associated with CAD and intermediate phenotypes of this condition, including hypertension, DM, hypertriglyceridemia, hypo-HDL-cholesterolemia, hyper-low density lipoprotein (LDL)-cholesterolemia, CKD, obesity, and hyperuricemia, was examined using Pearson's χ2 test (Table V).

Table V.

Association between SNPs associated with coronary artery disease and intermediate phenotypes.

Gene SNP Hypertension DM Hyper-TG Hypo-HDL Hyper-LDL CKD Obesity Hyperuricemia
PLCB2 rs200787930 <0.0001a   0.0004a 0.3432 <0.0001a <0.0001a <0.0001a   0.0405a   0.9639
ALDH2 rs671   0.0039a   0.0074a   0.0298a <0.0001a <0.0001a   0.0273a   0.0350a <0.0001a
GOSR2 rs1052586   0.3498   0.0167a 0.4457   0.2898   0.2638   0.6185 0.3670   0.4679
PSORS1C1 rs3094663   0.0069a   0.0670 0.0947   0.0020a   0.3869   0.1080 0.7345   0.5091
CCHCR1 rs130071   0.0865   0.0008a 0.2247   0.0143a   0.0141a   0.5894 0.8651   0.0423a
rs13427905   0.0149a   0.0149a 0.0524   0.0545   0.6318   0.9487 0.1197   0.0920
OR52E4 rs11823828   0.0024a <0.0001a   0.0265a   0.1186   0.4445   0.0027a 0.1141   0.2815
EIF3L rs9466   0.0008a   0.0204a   0.0054a   0.2905   0.0114a   0.2368 0.2435   0.0312a
KIAA1551 rs10771894   0.2439   0.0091a 0.9562   0.0343a   0.6934   0.3869 0.0974   0.5419
CCDC141 rs13419085   0.3387   0.1255 0.6537   0.1647   0.7447   0.2483 0.8101   0.7938
MIA3 rs2936051   0.4092   0.5246 0.9990   0.0475a   0.1222   0.6614 0.8787   0.1949
rs602633   0.4468   0.2375 0.7350   0.0005a   0.0021a   0.0842 0.6617   0.9338
KRT27 rs17558532   0.1706   0.2358 0.3643   0.3607   0.7663   0.0133a 0.2306   0.5325
TRPM1 rs2241493   0.3861   0.2332 0.7465   0.7106   0.0815   0.1387 0.5698   0.9502
rs7333181   0.2308   0.0487a   0.0379a   0.1185   0.2010   0.0795 0.2182   0.6544
ADAT1 rs145161932   0.4468   0.0160a 0.3611   0.3357   0.5412   0.7534 0.5836   0.1202
APOE rs7412   0.3680   0.9184 0.6322   0.1157 <0.0001a   0.6367 0.5319   0.2528
YEATS2 rs76174573   0.1305   0.0687   0.0380a   0.0606   0.8313   0.6458 0.6338   0.1706
SLC16A1 rs1049434 0.8319   0.0016a 0.1897   0.0686 0.9212 0.0646 0.8850   0.2672
RNF2 rs1046592   0.0007a   0.0140a 0.5319   0.0544 0.4098 0.7276 0.4643   0.1040
rs6825911   0.0317a 0.4070 0.5755   0.1068 0.1423 0.4050 0.2325   0.6717
ITGB8 rs80015015 0.4001 0.5178 0.4838   0.0075a 0.5169 0.3341 0.1339   0.2408
USP45 rs41288947 0.4383 0.1373 0.2641   0.0162a 0.6636 0.1341   0.0063a   0.0682
PHACTR1 rs9369640 0.1667 0.4673 0.8831   0.5247 0.8191 0.7417 0.6674   0.4133
rs1333048 0.2947   0.0251a 0.5799   0.0156a 0.3204 0.6650 0.5825   0.2450
rs838880 0.9565   0.0108a 0.3044   0.0045a 0.7818 0.7699 0.8126   0.2552
STIM1 rs116855870 0.1425 0.2455 0.6418   0.8631 0.7116 0.5285 0.7357   0.3365
rs2523644 0.4105 0.2101 0.6604   0.0773 0.0627   0.0449a 0.2968   0.2580
MKI67 rs145121731 0.0903 0.2528 0.2203   0.0138a 0.4030   0.0048a 0.2459   0.5059
FAM170B-AS1 rs73302786 0.2662 0.6366 0.2511   0.2687 0.8989 0.8203 0.2323   0.5404
rs6067640   0.0380a 0.2144 0.7990   0.0077a 0.6995 0.1809 0.6901   0.9347
GFY rs73053944   0.0145a 0.5731 0.5880   0.2471 0.8788 0.6316 0.4534   0.9116
WDR37 rs10794720 0.6272   0.0103a 0.9125   0.6954 0.6528 0.1352 0.7804   0.0458a
SKIV2L rs592229   0.0014a 0.0754 0.0752   0.0157a 0.7557 0.1230 0.3617   0.1727
rs6537384 0.3890 0.1107 0.2980   0.3818   0.0344a 0.0808 0.0620   0.7745
rs10757283 0.8792   0.0082a 0.9667   0.0420a 0.4745   0.0342a 0.8876   0.4636
CDKN2B-AS1 rs1011970   0.0443a 0.0628 0.6524   0.0834 0.5456 0.4293 0.6420   0.3237
PRKG1 rs9414827 0.6287 0.8365 0.6694   0.0424a 0.1488 0.6060   0.0291a   0.0549
rs197932 0.1918   0.0272a 0.5146   0.5673 0.2395 0.3102 0.7843   0.4625
LINC00354 rs4907518 0.2933 0.8434 0.3915   0.1285   0.0028a 0.6454 0.6846   0.7513
LIPE rs34052647 0.1525 0.7148 0.0040a   0.4199 0.0801 0.0879 0.0940   0.0081a
BTRC rs2270439 0.3191 0.9636 0.1684   0.1393 0.9515 0.2852 0.4689   0.5780
TNS3 rs11763932 0.4812 0.6129 0.9920   0.8857 0.1424 0.9598 0.9307   0.9591
TNS1 rs918949 0.1509 0.0510 0.3218   0.5993 0.7044 0.5484 0.6955   0.9461
rs12229654   0.0203a 0.3290 0.1080 <0.0001a   0.0171a 0.1167 0.2297 <0.0001a
rs4014195 0.1622   0.0445a   0.0270a   0.1811 0.7090 0.3732 0.5607   0.2233
PANK1 rs11185790 0.3638 0.4169 0.1750   0.2583 0.3889   0.0149a 0.6355   0.3282
rs507666 0.9872   0.0084a 0.7080   0.0370a   0.0129a 0.4210 0.9126   0.6992
MON2 rs11174549   0.0283a   0.0133a 0.8790   0.3587   0.0325a 0.2617 0.6406   0.9153
HECTD4 rs2074356   0.0285a 0.1092   0.0109a <0.0001a   0.0002a 0.0174 0.1786 <0.0001a
CUBN rs78201384 0.1429 0.9473 0.7525   0.0027a   0.0269a   0.0327a 0.9812   0.3888
PLUT rs954750 0.9214   0.0212a 0.6905   0.8004 0.8585   0.0264a 0.2382   0.8865
ABCA1 rs1883025 0.8085 0.2006   0.0134a   0.3092 0.0667 0.8891 0.4019   0.3377
rs10514995   0.0014a   0.0353a 0.2529   0.3708 0.7542 0.0855 0.5232   0.0365a

Data are P-values. The association between genotypes of each SNP and intermediate phenotypes was examined using Pearson's χ2 test. SNP, single nucleotide polymorphism; DM, diabetes mellitus; hyper-TG, hypertriglyceridemia; hypo-HDL, hypo-HDL-cholesterolemia; hyper-LDL, hyper-LDL-cholesterolemia; CKD, chronic kidney disease.

a

P<0.05 was considered to indicate a statistically significant difference.

The SNP rs671 of ALDH2 was significantly (P<0.05) associated with all the intermediate phenotypes; rs200787930 of PLCB2 and rs2074356 of HECTD4 to six of the eight phenotypes; rs9466 of EIF3L to five of the eight phenotypes; rs130071 of CCHCR1, rs11823828 of OR52E4 and rs12229654 to four of the eight phenotypes; rs11174549 of MON2, rs10514995, rs507666, rs10757283 and rs78201384 of CUBN to three of the eight phenotypes; rs1046592 of RNF2, rs13427905, rs3094663 of PSORS1C1, rs6067640, rs592229 of SKIV2L, rs4014195, rs7333181, rs838880, rs1333048, rs10771894 of KIAA1551, rs954750 of PLUT, rs10794720 of WDR37, rs34052647 of LIPE, rs602633, rs145121731 of MKI67, rs41288947 of USP45, and rs9414827 of PRKG1 to two of the eight phenotypes; and rs73053944 of GFY, rs6825911, rs1011970 of CDKN2B-AS1, rs1049434 of SLC16A1, rs145161932 of ADAT1, rs1052586 of GOSR2, rs197932, rs1883025 of ABCA1, rs76174573 of YEATS2, rs80015015 of ITGB8, rs2936051 of MIA3, rs7412 of APOE, rs4907518 of LINC00354, rs6537384, rs17558532 of KRT27, rs11185790 of PANK1, and rs2523644 to one of the eight phenotypes.

Linkage disequilibrium analyses

Linkage disequilibrium (LD) was examined among SNPs associated with CAD. There was significant LD among rs12229654 at 12q24.1, rs671 of ALDH2, and rs2074356 of HECTD4 [square of the correlation coefficient (r2), 0.564 to 0.882)].

Association between genes, chromosomal loci and SNPs identified in the present study and phenotypes previously reported by GWASs

The association between genes, chromosomal loci and SNPs identified in the present study and cardiovascular disease-related phenotypes previously reported by GWASs available in the GRASP Search database (Table VI). Chromosomal region 1p13.3, MIA3, PHACTR1, SKIV2L, CDKN2B-AS1, 9p21, ALDH2 and HECTD4 were previously revealed to be associated with CAD or MI. SLC16A1, PSORS1C1, CCHCR1, 6p21.3, ABCA1, 9q34.2, CUBN, PANK1, 12q24.1, 12q24.31, PLCB2 and APOE were previously associated with circulating concentrations of LDL-cholesterol, HDL-cholesterol, triglycerides or insulin, or type 1 diabetes mellitus. Chromosome 4q24, 17q21.3 and GOSR2 were previously associated with systolic or diastolic blood pressure or pulse pressure. CCDC141, TNS1, WDR37 and 11q13.1 were previously associated with cardiac, pulmonary or renal function. The remaining 21 genes (RNF2, YEATS2, USP45, ITGB8, TNS3, FAM170B-AS1, PRKG1, BTRC, MKI67, STIM1, OR52E4, KIAA1551, MON2, PLUT, LINC00354, TRPM1, ADAT1, KRT27, LIPE, GFY and EIF3L) and five chromosomal regions (2p13, 4q31.2, 5q12, 13q34 and 20q13.2) identified in the present study have not been revealed to be associated with CAD or cardiovascular disease-related phenotypes in previous GWASs.

Table VI.

Association between genes, chromosomal loci and SNPs associated with coronary artery disease in the present study and previously examined cardiovascular disease-related phenotypes.

Gene/chr. locus SNP Chr. Position Previously examined phenotypes
1p13.3 rs602633 1 109278889 CAD (23202125, 20032323), LDL-cholesterol (20686565, 23063622, 19060906, 21943158, 18193043, 18262040, 19913121, 21977987, 20339536), HDL-cholesterol (23063622, 20686565), total cholesterol (20686565, 23063622)
SLC16A1 rs1049434 1 112913924 HDL-cholesterol (23063622)
RNF2 rs1046592 1 185100429 None
MIA3 rs2936051 1 222629862 CAD (19198612, 21347282, 23364394, 21378990, 17554300, 22319020, 21966275), MI (19198609)
2p13 rs13427905 2 71846585 None
CCDC141 rs13419085 2 178837710 Heart rate (23583979, 20639392), left ventricular mass (19584346)
TNS1 rs918949 2 217809974 Lung function, forced expiratory volume in
1 second (20010834, 21946350, 23284291)
YEATS2 rs76174573 3 183804099 None
4q24 rs6825911 4 110460482 Systolic BP (21572416), diastolic BP (21572416)
4q31.2 rs6537384 4 145949613 None
5q12 rs10514995 5 66443611 None
PHACTR1 rs9369640 6 12901209 CAD (21378988, 23202125, 22745674, 21347282, 23364394, 21378990, 22751097, 22745674), MI (19198609, 21378990), ischemic stroke (22306652)
PSORS1C1 rs3094663 6 31139310 Type 1 diabetes (17554300, 17632545), triglycerides (20686565), total cholesterol (20686565)
CCHCR1 rs130071 6 31148433 Triglycerides (20686565)
6p21.3 rs2523644 6 31374707 Type 1 diabetes (17554300, 17632545), LDL-cholesterol (23063622, 20686565), triglycerides (23063622, 20686565), total cholesterol (23063622, 20686565)
SKIV2L rs592229 6 31962664 CAD (21971053), type 1 diabetes (17554300, 17632545), LDL-cholesterol (20686565), triglycerides (20686565), total cholesterol (20686565)
USP45 rs41288947 6 99446210 None
ITGB8 rs80015015 7 20401881 None
TNS3 rs11763932 7 47567880 None
CDKN2B-AS1 rs1011970 9 22062135 CAD (21347282), LDL-cholesterol (23063622), abdominal aortic aneurysm (20622881), type 2 diabetes (17463249)
9p21 rs1333048 9 22125348 CAD (23202125, 21606135, 19198612, 17634449, 20032323, 23364394), MI (17478679), intracranial aneurysm (22961961)
9p21 rs10757283 9 22134173 Type 2 diabetes (20581827)
ABCA1 rs1883025 9 104902020 HDL-cholesterol (20686565, 23505323, 23063622, 21909109, 19060911, 21347282, 19060906, 18193043, 18193044, 18193046, 22629316, 20864672, 21347282, 23726366), LDL-cholesterol (20686565), total cholesterol (20686565, 23063622, 20339536)
9q34.2 rs507666 9 136149399 Venous thrombosis (22675575), VLDL-cholesterol
small lipoprotein fraction concentration (19936222), LDL-cholesterol lipoprotein fraction concentration (19936222)
WDR37 rs10794720 10 1110225 Estimated glomerular filtration rate (20383146, 22479191), serum creatinine (20383146)
CUBN rs78201384 10 17111024 LDL-cholesterol (23063622), HDL-cholesterol (23063622), total cholesterol (23063622)
FAM170B-AS1 rs73302786 10 49131709 None
PRKG1 rs9414827 10 51137314 None
PANK1 rs11185790 10 89612776 Insulin concentration (19060910)
BTRC rs2270439 10 101550817 None
MKI67 rs145121731 10 128102595 None
STIM1 rs116855870 11 4055527 None
OR52E4 rs11823828 11 5884973 None
11q13.1 rs4014195 11 65739351 Serum urate (23263486), serum creatinine (20383146), estimated glomerular filtration rate (20383146)
KIAA1551 rs10771894 12 31982009 None
MON2 rs11174549 12 62565357 None
12q24.1 rs12229654 12 110976657 HDL-cholesterol (21909109)
ALDH2 rs671 12 111803962 CAD (21971053, 21572416, 23202125), MI (21971053), LDL-cholesterol (21572416, 20686565), HDL-cholesterol
(21572416, 21372407), total cholesterol (20686565), systolic BP
(21572416), diastolic BP (21572416, 21909115), serum creatinine
(22797727), estimated glomerular filtration rate (22797727), type 1 diabetes (17554300)
HECTD4 rs2074356 12 112207597 CAD (21971053, 21572416, 22751097, 19820697, 23364394, 23202125), MI (19820697), LDL-cholesterol (21572416, 20686565), HDL-cholesterol (21572416, 21909109, 22751097), total cholesterol
(20686565), systolic BP (21572416, 21909115), diastolic BP
(21572416, 21909115, 19862010, 19430479, 22751097), hypertension (21572416), serum creatinine (22797727), estimated
glomerular filtration rate (22797727), type 1 diabetes (18978792)
12q24.31 rs838880 12 124777047 HDL-cholesterol (20686565)
PLUT rs954750 13 27889801 None
13q34 rs7333181 13 111568950 None
LINC00354 rs4907518 13 111898209 None
TRPM1 rs2241493 15 31070149 None
PLCB2 rs200787930 15 40289298 Triglycerides (23063622)
ADAT1 rs145161932 16 75612670 None
KRT27 rs17558532 17 40779624 None
17q21.3 rs197932 17 46896981 Pulse pressure (21909110), systolic BP (21909110, 21909115)
GOSR2 rs1052586 17 46941097 Pulse pressure (21909110), systolic BP (21909110, 21909115)
LIPE rs34052647 19 42407617 None
APOE rs7412 19 44908822 LDL-cholesterol (23100282, 23063622, 20686565, 22629316, 19060911, 23067351, 23696881, 20838585), HDL-cholesterol
(21386085), triglycerides (23063622, 20686565, 22629316, 19060911, 21386085), total cholesterol (23063622, 20686565)
GFY rs73053944 19 49427038 None
20q13.2 rs6067640 20 51092837 None
EIF3L rs9466 22 37877742 None

Data were obtained from the GRASP Search database (https://grasp.nhlbi.nih.gov/Search.aspx) with a P-value of <1.0×10−6. Numbers in parentheses are PubMed IDs. SNP, single nucleotide polymorphisms; Chr., chromosome; HDL, high density lipoprotein; LDL, low density lipoprotein; CAD, coronary artery disease; MI, myocardial infarction; BP, blood pressure.

Gene Ontology analysis of genes identified in the present study

Biological functions of the 21 genes identified in the present study were estimated using the database of Gene Ontology and GO Annotations (QuickGO; Table VII). Given that FAM170B-AS1 is the gene for non-coding RNA, FAM170B was examined. Various biological functions were predicted in the 18 genes (RNF2, YEATS2, USP45, ITGB8, TNS3, FAM170B, PRKG1, BTRC, MKI67, STIM1, OR52E4, MON2, TRPM1, ADAT1, KRT27, LIPE, GFY and EIF3L), although those of KIAA1551, PLUT and LINC00354 were not. Gene ontology analysis revealed that ITGB8, PRKG1, STIM1 and LIPE may be involved in the development of CAD.

Table VII.

Gene ontology analysis of the 21 genes identified in the present study.

Gene Function Biological process
RNF2 Ubiquitin-protein transferase activity, chromatin binding, zinc ion binding, transferase activity, metal ion binding, ubiquitin protein ligase activity, RING-like zinc finger domain binding Histone H2A-K119 monoubiquitination, negative regulation of transcription by RNA polymerase II, regulation of DNA-templated transcription, germ cell development, negative regulation of DNA binding transcription factor activity, negative regulation of G0 to G1 transition
YEATS2 Modification-dependent protein binding, RNA polymerase II transcription factor activity, sequence-specific DNA binding Negative regulation of transcription by RNA polymerase II, histone H3 acetylation, negative regulation of DNA-templated transcription
USP45 Thiol-dependent ubiquitin-specific protease activity, cysteine-type peptidase activity, zinc ion binding, thiol-dependent ubiquitinyl hydrolase activity Protein deubiquitination, ubiquitin-dependent protein catabolic process, DNA repair, global genome nucleotide-excision repair
ITGB8 Extracellular matrix protein binding, signaling receptor binding Ganglioside metabolic process, cell adhesion, integrin-mediated signaling pathway, regulation of gene expression, positive regulation of angiogenesis, cartilage development, extracellular matrix organization, cell-matrix adhesion
TNS3 Protein binding, focal adhesion Positive regulation of cell proliferation, cell migration, lung alveolus development
FAM170B Protein binding, outer acrosomal membrane Positive regulation of acrosome reaction, regulation of fertilization
PRKG1 cGMP-dependent protein kinase activity, calcium channel regulator activity, nucleotide binding, ATP binding, transferase activity, cGMP binding, protein serine/threonine kinase activity, cGMP-dependent protein kinase activity Negative regulation of vascular smooth muscle cell proliferation and migration, neuron migration, cGMP-mediated signaling, dendrite development, forebrain development, relaxation of vascular smooth muscle, regulation of GTPase activity, negative regulation of platelet aggregation, actin cytoskeleton organization
BTRC Ubiquitin-protein transferase activity, ubiquitin protein ligase activity, β-catenin binding, protein phosphorylated amino acid binding, protein dimerization activity Protein polyubiquitination, ubiquitin-dependent protein catabolic process, regulation of circadian rhythm, regulation of canonical Wnt signaling pathway, protein dephosphorylation, mammary gland epithelial cell proliferation, regulation of I-κB kinase/NF-κB signaling, positive regulation of DNA-templated transcription, G2/M transition of mitotic cell cycle, negative regulation of DNA binding transcription factor activity, stress-activated MAPK cascade, interleukin-1-mediated signaling pathway
MKI67 RNA binding, DNA binding, ATP binding, protein binding Regulation of mitotic nuclear division, regulation of chromosome segregation and organization, cell proliferation
STIM1 Calcium channel regulator activity, calcium ion binding, microtubule plus-end binding, metal ion binding, protein binding Cellular calcium ion homeostasis, activation of store-operated calcium channel activity, regulation of calcium ion transport, positive regulation of angiogenesis, regulation of cardiac conduction
OR52E4 Olfactory receptor activity, G-protein coupled receptor activity Signal transduction, G-protein coupled receptor signaling pathway, detection of chemical stimulus involved in sensory perception of smell
KIAA1551 Uncharacterized
MON2 Protein binding, protein transport Golgi to endosome transport
PLUT Uncharacterized
LINC00354 Uncharacterized
TRPM1 Ion channel activity G-protein coupled glutamate receptor signaling pathway, ion transmembrane transport, protein tetramerization, cellular response to light stimulus
ADAT1 RNA binding, tRNA-specific adenosine deaminase activity, hydrolase activity, metal ion binding tRNA processing
KRT27 Structural molecule activity, intermediate filament Hair follicle morphogenesis, keratinization, cornification
LIPE Triglyceride lipase activity, serine hydrolase activity, protein kinase binding, hormone-sensitive lipase activity Protein phosphorylation, lipid metabolic process, steroid metabolic process, cholesterol metabolic process, triglyceride catabolic process, long-chain fatty acid catabolic process, diacylglycerol catabolic process
GFY Protein localization to non-motile cilium, non-motile cilium assembly Sensory perception of smell, response to stimulus
EIF3L Translation initiation factor activity, RNA binding, protein binding Translational initiation, viral translational termination-reinitiation

Data for predicted functions and biological processes of the genes were obtained from database of Gene Ontology and GO Annotations (QuickGO; http://www.ebi.ac.uk/QuickGO/).

Network analysis of newly identified genes

Network analysis of the 21 genes identified in the present study was performed using the GeneMANIA Cytoscape plugin with Cytoscape v3.4.0 software (Figs. 1 and 2). FAM170B was applied to the analysis instead of FAM170B-AS1. PLUT and LINC00354 were not included in the GeneMANIA database. GFY had no interaction with other genes. The network analysis revealed that the 18 genes identified in the present study had potential direct or indirect interactions with the 30 genes previously revealed to be associated with CAD (Fig. 1). Similar analysis revealed that complex networks were observed between the 18 genes identified in the present study and the 228 genes identified in previous GWASs (Fig. 2).

Figure 1.

Figure 1.

Network analysis of the 18 genes identified in the present study (closed red circle) was performed to predict functional gene-gene interactions by the use of GeneMANIA Cytoscape plugin (http://apps.cytoscape.org/apps/genemania) using Cytoscape v3.4.0 software (http://www.cytoscape.org/). The 30 genes (closed green circle) were selected from the DisGeNET database (http://www.disgenet.org/web/DisGeNET) according to the rank order of high scores in association with CAD and applied to analysis.

Figure 2.

Figure 2.

Network analysis of the 18 genes identified in the present study (closed red circle) was performed to predict functional gene-gene interactions by the use of GeneMANIA Cytoscape plugin (http://apps.cytoscape.org/apps/genemania) using Cytoscape v3.4.0 software (http://www.cytoscape.org/). The 228 genes previously identified by GWASs (closed green circle) were applied to analysis. Interactions between closed red circles or between closed red and green circles are shown with bold lines. Molecules shown in closed grey circles represent putative mediators of interactions between the genes.

Discussion

Despite recent advances in therapy for acute coronary syndrome, including coronary stent implantation (49), CAD remains the leading cause of mortality and is therefore a key public health problem (4). The identification of genetic variants that confer susceptibility to CAD is therefore clinically important for the prevention and management of this condition.

The EWAS was performed for patients with early-onset CAD, with genetic factors serving a greater role in such patients compared with those with late-onset CAD. The present study identified the 54 SNPs as significant and independent determinants of CAD. These SNPs together accounted for 15.5% of the cause of CAD. Among these loci, 21 genes (RNF2, YEATS2, USP45, ITGB8, TNS3, FAM170B-AS1, PRKG1, BTRC, MKI67, STIM1, OR52E4, KIAA1551, MON2, PLUT, LINC00354, TRPM1, ADAT1, KRT27, LIPE, GFY and EIF3L) and 5 chromosomal regions (2p13, 4q31.2, 5q12, 13q34 and 20q13.2) that confer susceptibility to CAD have been newly identified.

Among 26 SNPs identified, 14 SNPs were significantly associated with two to five of the eight intermediate phenotypes. The SNP rs9466 of EIF3L was associated with hypertension, DM, hypertriglyceridemia, hyper-LDL-cholesterolemia and hyperuricemia; rs11823828 of OR52E4 with hypertension, DM, hypertriglyceridemia, and CKD; rs11174549 of MON2 with hypertension, DM, hyper-LDL-cholesterolemia; rs10514995 at 5q12 with hypertension, DM and hyperuricemia; rs1046592 of RNF2 and rs13427905 at 2p13 with hypertension and DM; rs6067640 at 20q13.2 with hypertension and hypo-HDL-cholesterolemia; rs7333181 at 13q34 with DM and hypertriglyceridemia; rs10771894 of KIAA1551 with DM and hypo-HDL-cholesterolemia; rs954750 of PLUT with DM and CKD; rs34052647 of LIPE with hypertriglyceridemia and hyperuricemia; rs145121731 of MKI67 with hypo-HDL-cholesterolemia and CKD; rs41288947 of USP45 and rs9414827 of PRKG1 with hypo-HDL-cholesterolemia and obesity. The seven SNPs were significantly related to one of the eight intermediate phenotypes. The rs73053944 of GFY was associated with hypertension; rs145161932 of ADAT1 with DM; rs76174573 of YEATS2 with hypertriglyceridemia; rs80015015 of ITGB8 with hypo-HDL-cholesterolemia; rs4907518 of LINC00354 and rs6537384 at 4q31.2 with hyper-LDL-cholesterolemia; rs17558532 of KRT27 with CKD. Given that these intermediate phenotypes are risk factors for CAD (4), the association between these loci and CAD may be attributable, at least in part, to their effects on intermediate phenotypes. By contrast, five SNPs in TNS3, FAM170B-AS1, BTRC, STIM1 and TRPM1 were not associated with intermediate phenotypes. The underlying molecular mechanisms of the association between these loci and CAD remain to be elucidated.

Recent GWASs have identified potential biological pathways underlying the association between genetic loci and CAD, including metabolism of LDL-cholesterol, triglycerides and lipoprotein (a); insulin resistance; thrombosis; inflammation, cell adhesion and transendothelial migration; cellular proliferation, vascular remodeling and extracellular matrix metabolism; and vascular tone and nitric oxide signaling (50,51). Network analysis of functional gene-gene interactions may be informative to clarify biological process of CAD and to identify therapeutic targets for this condition (52). Therefore, the present study performed gene ontology and network analyses to predict biological processes of the identified genes and interactions between these genes and those previously revealed to be associated with CAD. Gene ontology analysis revealed that biological functions of ITGB8 (integrin-mediated signaling pathway), PRKG1 (relaxation of vascular smooth muscle), STIM1 (activation of store-operated calcium channel activity) and LIPE (cholesterol and triglyceride metabolism) may serve roles in the development of CAD. However, the roles of the remaining 17 genes in CAD remain unclear. The network analysis revealed that the 18 genes identified in the present study had direct or indirect interactions with the 30 genes selected from the DisGeNET database (47,48), as well as complex networks with 228 genes previously identified by the GWASs (7). However, the underlying molecular mechanisms of these interactions remain to be elucidated.

It was previously demonstrated that six SNPs were associated with CAD (P<0.01), as determined by multivariable logistic regression analysis with adjustment for covariates following an initial EWAS screening of allele frequencies among subjects with early-onset and late-onset forms of this condition (33). The associations between three of the six SNPs [rs202069030 (P=2.58×10−6), rs7188 (P=0.0098) and rs2271395 (P=0.0042)] and CAD were replicated (P<0.05) in the present study. These results suggested that genetic variants associated with CAD differ, in part, between early-onset and late-onset patients with this condition. We also examined nine SNPs associated with MI (P<0.01) in a previous study (33). Associations between five of the nine SNPs [rs202103723 (P=0.0033), rs188212047 (P=0.0034), rs1265110 (P=2.69×10−5), rs9258102 (P=0.0374) and rs439121 (P=0.0108)] and CAD (P<0.05) were identified in the present study.

There are several limitations to the present study: i) Given that the results were not replicated, their validation will be necessary in independent study populations or in other ethnic groups; ii) it is possible that SNPs identified in the present study are in LD with other genetic variants in the same gene or in other nearby genes that are actually responsible for the development of CAD; and iii) the functional relevance of identified SNPs to the pathogenesis of CAD remains to be elucidated.

In conclusion, the present study identified the 54 SNPs as significant and independent determinants of CAD. Among these loci, 21 genes (RNF2, YEATS2, USP45, ITGB8, TNS3, FAM170B-AS1, PRKG1, BTRC, MKI67, STIM1, OR52E4, KIAA1551, MON2, PLUT, LINC00354, TRPM1, ADAT1, KRT27, LIPE, GFY and EIF3L) and 5 chromosomal regions (2p13, 4q31.2, 5q12, 13q34 and 20q13.2) that confer susceptibility to CAD were newly identified in the present study. Determination of genotypes for the SNPs at these loci may prove informative for assessment of the genetic risk for CAD in Japanese patients.

Acknowledgements

Not applicable.

Funding

The present study was supported by CREST, Japan Science and Technology Agency, Kawaguchi, Japan (grant no. JPMJCR1302).

Availability of data and materials

All datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request.

Authors' contributions

YY contributed to the conception and design of the study; to acquisition, analysis and interpretation of the data; and to drafting of the manuscript. KK, MO, HH and TF all contributed to the acquisition of the data and to the revision of the manuscript. YY, IT and JS contributed to the analysis and interpretation of the data, as well as to the revision of the manuscript.

Ethics approval and consent to participate

The study protocol complied with the Declaration of Helsinki and was approved by the Committees on the Ethics of Human Research of Mie University Graduate School of Medicine, Hirosaki University Graduate School of Medicine, and participating hospitals (Gifu Prefectural Tajimi Hospital, Gifu Prefectural General Medical Center, Japanese Red Cross Nagoya First Hospital, Northern Mie Medical Center Inabe General Hospital, and Hirosaki Stroke and Rehabilitation Center). Written informed consent was obtained from all subjects.

Patient consent for publication

All authors approved submission of the final version of the article for publication.

Competing interests

The authors declare that they have no competing interests.

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Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Data Availability Statement

All datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request.


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