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

  • Authors:
    • Yoshiji Yamada
    • Yoshiki Yasukochi
    • Kimihiko Kato
    • Mitsutoshi Oguri
    • Hideki Horibe
    • Tetsuo Fujimaki
    • Ichiro Takeuchi
    • Jun Sakuma

  • View Affiliations

  • Published online on: September 17, 2018     https://doi.org/10.3892/br.2018.1152
  • Pages: 383-404

  • Copyright: © Yamada et al. This is an open access article distributed under the terms of Creative Commons Attribution License.

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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.

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 (5–7). 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) (11–13). The etiology of common forms of CAD is multifactorial and includes genetic components, as well as environmental and lifestyle factors (5–8). 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 (15–21), 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) (43–45) 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.

Table I.

Characteristics of control subjects and patients with coronary artery disease.

CharacteristicControlCoronary artery diseaseP-value
No. subjects5,7741,482
Age, years   50.6±10.255.9±7.4<0.0001
Sex, males/females, %52.1/47.982.5/17.5<0.0001
Smoking, %42.543.0   0.7719
Obesity, %31.043.0<0.0001
Body mass index, kg/m223.2±3.524.5±3.5<0.0001
Hypertension, %31.770.0<0.0001
Systolic BP, mmHg121±18139±27<0.0001
Diastolic BP, mmHg   75±13   78±15<0.0001
Diabetes mellitus, %12.758.7<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, %56.984.1<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, %10.329.4<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, %15.225.5<0.0001
Serum uric acid, µmol/l321±89   353±102<0.0001

[i] 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.

Table II.

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

GeneSNPNucleotide substitutionaAmino acid substitutionChromosomePositionMAF, %Allele ORP-value, allele frequencyFDR, allele frequency
PLCB2rs200787930C/TE1106K15402892981.20.03 1.24×10−29 1.56×10−26
MARCH1rs61734696G/TQ137K41641973031.20.03 2.09×10−29 2.54×10−26
VPS33Brs199921354C/TR80Q15910138411.20.03 2.76×10−29 3.30×10−26
CXCL8rs188378669G/TE31*4737415681.20.03 3.15×10−29 3.70×10−26
TMOD4rs115287176G/AR277W11511709611.20.03 1.21×10−28 1.39×10−25
COL6A3rs146092501C/TE1386K22373718611.20.04 2.93×10−28 3.27×10−25
ZNF77rs146879198G/AR340*1929341091.20.04 2.92×10−28 3.27×10−25
ADGRL3rs192210727G/TR580I4619096151.30.10 2.92×10−23 3.06×10−20
OR52E4rs11823828T/GF227L11588497336.61.54 3.40×10−21 3.35×10−18
ALDH2rs671G/AE504K1211180396227.61.41 4.12×10−15 3.78×10−12
ACAD10rs11066015G/A 1211173020527.51.41 4.92×10−15 4.45×10−12
BRAPrs3782886A/G 1211167268529.31.37 4.38×10−13 3.71×10−10
HECTD4rs11066280T/A 1211237997929.01.37 6.94×10−13 5.73×10−10
HECTD4rs2074356C/T 1211220759725.41.36 1.21×10−11 9.78×10−9
NAA25rs12231744C/TR876K1211203925135.10.77 1.68×10−9 1.24×10−6
GOSR2rs1052586T/C 174694109748.70.79 3.94×10−8 2.61×10−5
ATXN2rs7969300T/CN248S1211155590838.80.79 4.41×10−8 2.87×10−5
LILRB2rs73055442C/TR103H19542798381.644.10 2.00×10−7 1.20×10−4
rs12229654T/G 1211097665722.51.28 2.09×10−7 1.24×10−4
LOC107987429rs2844533T/C 63138302515.31.32 3.49×10−7 1.95×10−4
MTFR2rs143974258G/AR360*61362313553.30.05 6.66×10−7 3.60×10−4
PSORS1C1rs3130559C/T 63112952444.20.82 1.51×10−6 7.74×10−4
rs2596548G/T 6313627695.41.51 1.83×10−6 9.21×10−4
EIF3Lrs9466T/C 223787774221.61.28 1.96×10−6 9.77×10−4
LPGAT1rs150552771T/CK200E12117833585.07.14 2.26×10−60.0011
LAIR2rs34429135T/AF115Y19545081642.5ND 2.70×10−60.0013
rs2523644A/G 6313747078.11.40 2.75×10−60.0013
rs10757278A/G 92212447849.50.83 2.92×10−60.0014
CCHCR1rs130067T/GE328D63115073433.20.81 3.10×10−60.0015
TCHPrs74416240G/A 1210990479313.31.30 3.25×10−60.0015
rs1333049G/C 92212550449.41.20 3.95×10−60.0018
CDKN2B-AS1rs4977574A/G 92209857547.11.21 4.18×10−60.0019
CDKN2B-AS1rs2383207G/A 92211596033.70.81 4.86×10−60.0022
SLC16A1rs1049434T/AD490E111291392434.70.82 5.76×10−60.0025
GIT2rs925368T/CN389S1210995317412.51.30 6.02×10−60.0026
rs1333048A/C 92212534849.61.20 6.46×10−60.0028
rs2523578T/C 6313607658.11.39 6.54×10−60.0028
rs404890G/T 63223109030.51.22 8.90×10−60.0037
APOErs7412C/TR176C19449088224.30.60 1.06×10−50.0043
CCHCR1rs130071G/A 6311484335.11.52 1.06×10−50.0043
rs602633C/A 11092788897.60.69 1.15×10−50.0046
CELSR2rs12740374G/T 11092749687.70.69 1.15×10−50.0046
MKI67rs145121731G/AS2722L101281025951.52.04 1.20×10−50.0047
CUBNrs78201384C/TE304K10171110242.70.52 1.38×10−50.0054
PSORS1C3rs887466T/C 63117573441.11.20 1.38×10−50.0054
PSORS1C1rs3094663G/A 63113931030.91.20 1.40×10−50.0054
rs10853110A/G 174924105239.21.20 1.49×10−50.0057
WDR37rs10794720C/T 1011102258.50.71 1.52×10−50.0057
CELSR2rs629301A/C 11092756847.80.70 1.52×10−50.0057
SKIV2Lrs592229G/T 63196266442.41.20 1.57×10−50.0058
rs12182351T/C 63223393029.81.22 1.59×10−50.0059
POU5F1rs3130503G/A 63116938829.51.20 1.64×10−50.0060
PSORS1C3rs1265155T/C 63117591741.11.19 1.68×10−50.0061
CELSR2rs646776A/G 1109275908   7.70.70 1.70×10−50.0062
rs2596503C/T 63135303319.31.24 1.75×10−50.0063
TRPM1rs2241493T/CN54S153107014912.60.76 1.81×10−50.0065
CCDC141rs13419085T/CN1170S2178837710   1.80.46 1.92×10−50.0068
VARS2rs9394021A/GQ777R63092535044.90.84 1.98×10−50.0069
SFTA2rs2286655T/C 63093196944.91.19 1.99×10−50.0069
rs3873334T/C 63092837044.91.19 1.98×10−50.0069
rs9261800C/G 630408822   2.87.21 2.02×10−50.0069
TCF19rs3130453C/T 63115707234.40.83 2.10×10−50.0072
C21orf59rs76974938C/TD67N2132609946   2.40.00 2.14×10−50.0073
DDR1rs2239518T/C 63089794844.91.19 2.19×10−50.0074
CDSNrs3130984C/TS143N63111718713.41.29 2.20×10−50.0074
rs197932T/C 174689698126.90.82 2.23×10−50.0075
CDSNrs3130981C/TD527N63111603613.61.29 2.30×10−50.0075
MICB-DTrs3132469C/T 631488790   5.31.46 2.41×10−50.0078
HLA-DQB1rs1049056C/AA6S63266659211.91.30 2.51×10−50.0081
DDR1rs2239517A/G 63089733844.61.19 2.59×10−50.0083
CCHCR1rs1265110G/A 63115164530.20.83 2.69×10−50.0085
CCDC63rs10774610T/C 1211090243923.71.22 2.76×10−50.0087
GTF2H4rs2284176C/T 63090784544.61.19 2.80×10−50.0088
GTF2H4rs3909130G/A 63090638844.61.19 2.84×10−50.0089
GTF2H4rs916920G/A 63090942544.71.19 2.85×10−50.0089
rs1264569A/G 630397543   4.61.49 2.98×10−50.0092
CACNA1Drs35874056G/AG460S353702798   2.025.00 3.09×10−50.0094
rs9468845A/G 63090181644.71.19 3.12×10−50.0094
DDR1rs8408C/T 63089988944.71.19 3.11×10−50.0094
DDR1rs7756521C/T 63088047644.71.19 3.10×10−50.0094
CDKN2B-AS1rs1011970G/T 922062135   5.61.41 3.15×10−50.0095
ADAT1rs145161932T/CR57G1675612670   1.40.39 3.29×10−50.0098
POU5F1rs885950T/G 63117237534.00.83 3.28×10−50.0098
DDR1rs4618569A/G 63088747444.71.19 3.41×10−50.0101
KRT13rs146918776A/GY281H1741502993   1.51.94 3.51×10−50.0103
rs2523638G/A 63137649643.11.19 3.53×10−50.0103
PSRC1rs599839A/G 1109279544   7.90.71 3.52×10−50.0103
rs9275141G/T 63268334026.41.21 3.62×10−50.0105
CCDC63rs10849915T/C 1211089581823.61.22 3.63×10−50.0105
HLA-DRArs3177928G/A 632444658   5.91.41 3.81×10−50.0108
OAS3rs2072134C/T 1211297137117.61.24 4.06×10−50.0114
USP45rs41288947C/GT521R69944621014.91.26 4.11×10−50.0115
CCHCR1rs1265109A/C 63115181248.21.18 4.16×10−50.0116
LOC101929163rs6930777C/T 632383789   5.51.43 4.45×10−50.0122
rs7333181G/A 13111568950   2.50.54 4.45×10−50.0122
DDR1rs1264323T/C 63088813038.81.19 4.48×10−50.0122
LINC00243rs3094111G/A 63082041414.71.25 4.52×10−50.0123
rs10484561T/G 632697643   5.91.41 4.55×10−50.0123
PSORS1C1rs3130558G/C 63112940613.71.27 4.59×10−50.0124
HLA-DQB1rs1049060T/AS27T63266652928.81.20 4.92×10−50.0131
rs2844650G/A 630934756   4.71.47 4.99×10−50.0131
DDR1rs3132572T/C 630893952   4.71.47 4.99×10−50.0131
CCHCR1rs1265115T/G 63114929847.71.18 4.96×10−50.0131
CCHCR1rs3094225T/C 63114527548.41.18 4.93×10−50.0131
LOC107987453rs3129987C/T 63079842714.51.25 5.05×10−50.0132
DPCR1rs2517451A/G 630946974   4.71.47 5.11×10−50.0133
KIAA1551rs10771894A/GS352G123198200932.41.19 5.18×10−50.0134
rs13427905C/T 27184658518.50.80 5.22×10−50.0134
ABCA1rs1883025G/A 910490202028.80.83 5.46×10−50.0139
SFTA2rs2253705G/A 63093231718.01.23 5.60×10−50.0141
PLUTrs954750G/A 132788980148.31.18 5.86×10−50.0146
TCF19rs1419881T/C 63116281648.11.18 6.37×10−50.0156
rs13209234G/A 632448198   5.91.41 6.47×10−50.0158
PSORS1C1rs1265100T/C 63113753332.20.83 6.55×10−50.0159
YEATS2rs76174573G/TC1232F3183804099   3.70.61 6.74×10−50.0162
ABOrs1053878C/TP156L913325626422.81.20 6.78×10−50.0162
rs4014195C/G 116573935116.61.24 6.78×10−50.0162
SFTA2s2253588C/G 63093160023.61.21 6.93×10−50.0165
CYP4F8rs201166643C/AR488S1915629257   1.1ND 7.00×10−50.0165
NAXErs7516274C/GL19V1156591859   1.80.48 7.18×10−50.0169
rs10757283T/C 92213417333.80.84 7.25×10−50.0170
BTNL2rs28362680G/AA202V63240303939.70.85 7.40×10−50.0171
BTNL2rs10947262C/T 63240553539.70.85 7.40×10−50.0171
KRT27rs17558532C/TA284T1740779624   3.60.62 7.71×10−50.0176
GTF2H4rs3130780G/T 63090653118.01.23 7.71×10−50.0176
rs2532934T/C 63092698224.11.20 7.74×10−50.0176
VARS2rs753725G/A 63092309424.11.20 7.68×10−50.0176
PLUTrs11619319A/G 132791346248.11.18 7.64×10−50.0176
rs3095273C/T 629598592   5.51.41 8.16×10−50.0184
TNS1rs918949C/TV1590I221780997442.80.85 8.39×10−50.0188
LINC00243rs3130785C/T 63082896114.61.24 8.37×10−50.0188
VARS2rs2249464C/TR309W63092038424.11.20 9.39×10−50.0207
rs3095345A/G 63085463617.91.22 9.37×10−50.0207
ITGB8rs80015015G/AC481Y720401881   7.11.35 1.01×10−40.0220
VARS2rs885905C/T 63092265423.41.20 1.07×10−40.0232
LIPErs34052647G/AR611C1942407617   5.51.39 1.16×10−40.0249
PHACTR1rs9369640A/C 612901209   9.10.74 1.30×10−40.0275
BTNL2rs41417449T/CM295V63239623423.00.83 1.35×10−40.0280
BTNL2rs41441651C/TD336N63239611123.00.83 1.35×10−40.0280
BTNL2rs28362675C/AE454*63239474423.00.83 1.35×10−40.0280
BTNL2rs78587369G/AT165I63240315023.00.83 1.35×10−40.0280
BTNL2rs3763315G/T 63240887723.00.83 1.35×10−40.0280
BTNL2rs2076528T/G 63239641723.00.83 1.35×10−40.0280
PRKG1rs9414827G/A 105113731410.10.76 1.37×10−40.0282
rs6537384T/G 414594961328.81.19 1.43×10−40.0294
rs6067640G/A 205109283738.50.85 1.48×10−40.0302
rs10514995A/G 56644361148.71.16 1.51×10−40.0306
BTNL2rs34423804T/AV283D63239626923.00.83 1.63×10−40.0329
PHACTR1rs9349379G/A 61290372534.20.85 1.69×10−40.0341
STIM1rs116855870A/G 114055527   1.11.93 1.71×10−40.0343
ZNF142rs3821033C/TA1313T221864257911.21.26 1.78×10−40.0355
LINC00354rs4907518G/A 1311189820945.60.85 1.82×10−40.0362
TNS3rs11763932G/A 74756788042.00.85 1.91×10−40.0378
BTRCrs2270439C/AP566H10101550817   3.50.63 1.94×10−40.0381
MIA3rs2936051A/GE881G122262986240.10.85 1.96×10−40.0384
rs6825911C/T 411046048245.90.86 2.01×10−40.0391
VNN1rs2294757G/AT26I613271395937.40.85 2.02×10−40.0393
ZNF860rs140232911C/TS161L33198956110.40.44 2.09×10−40.0406
rs838880C/T 1212477704747.51.16 2.23×10−40.0430
MIA3rs2936052A/GK605R122262903434.40.85 2.26×10−40.0430
DTNBP1rs2743868G/A 61562557731.61.18 2.26×10−40.0430
MON2rs11174549A/GI1385V1262565357   5.01.40 2.26×10−40.0430
rs507666G/A 913614939927.81.18 2.26×10−40.0430
FAM170Brs73302786G/TD252E1049131709   3.51.47 2.36×10−40.0445
PSORS1C3rs3131018G/T 63117580515.71.23 2.36×10−40.0445
PIEZO2rs35033671C/AC1148F181075984211.01.27 2.39×10−40.0448
SLC22A3rs1810126C/T 616045111949.10.86 2.46×10−40.0460
PANK1rs11185790G/A 108961277646.91.16 2.57×10−40.0481
GFYrs73053944C/GT203S1949427038   2.91.51 2.58×10−40.0481
RNF2rs1046592A/G 118510042933.90.85 2.63×10−40.0488

{ label (or @symbol) needed for fn[@id='tfn2-br-0-0-1152'] } 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.

Table III.

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

Dominant modelRecessive model
GeneSNP P-valueOR95% CIP-valueOR95% CI
PLCB2rs200787930C/T<0.00010.020.01–0.09
MARCH1rs61734696G/T<0.00010.020.01–0.10
VPS33Brs199921354C/T<0.00010.020.01–0.09
CXCL8rs188378669G/T<0.00010.020.01–0.09
TMOD4rs115287176G/A<0.00010.020.01–0.10
COL6A3rs146092501C/T<0.00010.020.01–0.10
ZNF77rs146879198G/A<0.00010.020.01–0.10
ADGRL3rs192210727G/T<0.00010.070.03–0.16   0.9959
OR52E4rs11823828T/G<0.00011.661.41–1.97<0.00012.442.01–2.97
ALDH2rs671G/A<0.00011.731.50–2.01<0.00011.801.44–2.26
ACAD10rs11066015G/A<0.00011.731.49–2.01<0.00011.791.42–2.25
BRAPrs3782886A/G<0.00011.711.48–1.99<0.00011.701.36–2.12
HECTD4rs11066280T/A<0.00011.731.49–2.01<0.00011.731.38–2.17
HECTD4rs2074356C/T<0.00011.611.39–1.87<0.00011.761.38–2.26
NAA25rs12231744C/T<0.00010.630.54–0.73<0.00010.550.43–0.70
GOSR2rs1052586T/C   0.00030.730.62–0.87<0.00010.640.53–0.77
ATXN2rs7969300T/C<0.00010.630.55–0.74<0.00010.570.45–0.71
rs12229654T/G<0.00011.461.26–1.69<0.00011.721.31–2.25
LOC107987429rs2844533T/C<0.00011.361.17–1.59   0.8616
MTFR2rs143974258G/A   0.00140.040.01–0.28
PSORS1C1rs3130559C/T   0.01270.820.70–0.96   0.0629
rs2596548G/T<0.00011.761.41–2.20   0.2047
EIF3Lrs9466T/C   0.00531.241.07–1.44   0.01991.471.06–2.04
LPGAT1rs150552771T/C   0.9970 <0.00012.201.83–2.64
rs2523644A/G<0.00011.591.31–1.92   0.9088
rs10757278A/G<0.00010.710.60–0.83   0.00230.770.65–0.91
CCHCR1rs130067T/G   0.00100.780.68–0.91   0.01830.730.57–0.95
TCHPrs74416240G/A   0.00021.351.15–1.58   0.1725
rs1333049G/C   0.00311.291.09–1.53<0.00011.411.20–1.66
CDKN2B-AS1rs4977574A/G   0.00031.361.15–1.60<0.00011.431.21–1.69
CDKN2B-AS1rs2383207G/A<0.00010.750.65–0.87   0.01710.750.59–0.95
SLC16A1rs1049434T/A   0.01060.830.71–0.96<0.00010.570.45–0.73
GIT2rs925368T/C   0.00011.371.16–1.61   0.3189
rs1333048A/C   0.00361.291.09–1.53<0.00011.401.19–1.64
rs2523578T/C<0.00011.551.27–1.88   0.8694
rs404890G/T   0.00051.291.12–1.50   0.01601.351.06–1.72
APOErs7412C/T   0.00010.560.42–0.76   0.2259
CCHCR1rs130071G/A   0.01491.361.06–1.73   0.2668
rs602633C/A   0.00010.640.51–0.80   0.1782
CELSR2rs12740374G/T<0.00010.630.51–0.79   0.1708
MKI67rs145121731G/A   0.00141.941.29–2.91   0.9957
CUBNrs78201384C/T   0.00030.500.34–0.73   0.9959
PSORS1C3rs887466T/C   0.00131.291.11–1.52   0.1666
PSORS1C1rs3094663G/A<0.00011.411.21–1.63   0.6404
rs10853110A/G   0.00261.261.09–1.47   0.01021.291.06–1.56
WDR37rs10794720C/T   0.00030.680.55–0.84   0.0734
CELSR2rs629301A/C   0.00020.650.52–0.82   0.1708
SKIV2Lrs592229G/T   0.01541.221.04–1.42   0.01381.261.05–1.51
rs12182351T/C   0.00081.281.11–1.48   0.01381.371.07–1.75
POU5F1rs3130503G/A<0.00011.411.22–1.63   0.6308
PSORS1C3rs1265155T/C   0.00171.291.10–1.50   0.1666
CELSR2rs646776A/G   0.00020.650.52–0.820.2660
rs2596503C/T   0.02041.191.03–1.380.1828
TRPM1rs2241493T/C   0.00020.710.60–0.850.04100.490.25–0.97
CCDC141rs13419085T/C   0.00050.430.27–0.69
VARS2rs9394021A/G   0.02610.820.69–0.980.01170.810.69–0.95
SFTA2rs2286655T/C   0.00971.241.05–1.450.02771.221.02–1.45
rs3873334T/C   0.01171.231.05–1.440.02611.221.02–1.45
TCF19rs3130453C/T   0.00770.820.71–0.950.00240.680.53–0.87
DDR1rs2239518T/C   0.01031.231.05–1.450.02821.221.02–1.45
CDSNrs3130984C/T<0.00011.391.18–1.640.1220
rs197932T/C   0.00420.810.70–0.930.03480.730.54–0.98
CDSNrs3130981C/T<0.00011.391.18–1.640.1226
MICB-DTrs3132469C/T<0.00011.631.30–2.050.3960
HLA-DQB1rs1049056C/A   0.00501.281.08–1.520.1586
DDR1rs2239517A/G   0.01151.231.05–1.440.03261.211.02–1.44
CCHCR1rs1265110G/A   0.00960.830.71–0.950.0948
CCDC63rs10774610T/C   0.00061.291.12–1.490.02141.381.05–1.82
GTF2H4rs2284176C/T   0.01531.221.04–1.430.03061.211.02–1.45
GTF2H4rs3909130G/A   0.01381.221.04–1.430.03261.211.02–1.44
GTF2H4rs916920G/A   0.01391.221.04–1.430.03261.211.02–1.44
rs1264569A/G   0.00041.541.21–1.970.5339
rs9468845A/G   0.01411.221.04–1.430.03321.211.02–1.44
DDR1rs8408C/T   0.01411.221.04–1.430.03261.211.02–1.44
DDR1rs7756521C/T   0.01551.221.04–1.430.03031.211.02–1.45
CDKN2B-AS1rs1011970G/T   0.00471.361.10–1.690.2199
ADAT1rs145161932T/C   0.01040.480.27–0.840.9960
POU5F1rs885950T/G   0.00490.810.70–0.940.01290.730.57–0.94
DDR1rs4618569A/G   0.01411.221.04–1.430.03331.211.02–1.44
KRT13rs146918776A/G<0.00012.211.50–3.26
rs2523638G/A   0.01971.201.03–1.410.1032
PSRC1rs599839A/G   0.00040.670.54–0.830.1023
rs9275141G/T   0.01341.201.04–1.390.00651.431.11–1.85
CCDC63rs10849915T/C   0.00051.301.12–1.500.04581.331.01–1.76
HLA-DRArs3177928G/A<0.00011.581.27–1.960.7635
OAS3rs2072134C/T   0.00041.311.13–1.530.02681.511.05–2.16
USP45rs41288947C/G   0.00021.351.15–1.580.1360
CCHCR1rs1265109A/C   0.00071.351.14–1.610.03341.201.01–1.41
LOC101929163rs6930777C/T<0.00011.601.28–2.000.9815
rs7333181G/A   0.02190.640.44–0.940.9969
DDR1rs1264323T/C   0.02401.191.02–1.390.04231.221.01–1.48
LINC00243rs3094111G/A   0.00611.241.06–1.450.5293
rs10484561T/G<0.00011.591.28–1.980.7637
PSORS1C1rs3130558G/C<0.00011.391.18–1.640.2712
HLA-DQB1rs1049060T/A   0.0676 0.00771.381.09–1.75
rs2844650G/A<0.00011.661.30–2.110.4729
DDR1rs3132572T/C<0.00011.661.30–2.110.4729
CCHCR1rs1265115T/G   0.00041.361.15–1.620.04431.191.00–1.40
CCHCR1rs3094225T/C<0.00011.461.23–1.740.5822
LOC107987453rs3129987C/T   0.00371.261.08–1.470.5667
DPCR1rs2517451A/G<0.00011.651.30–2.110.4729
CCHCR1rs1265115T/G   0.00041.361.15–1.620.04431.191.00–1.40
CCHCR1rs3094225T/C<0.00011.461.23–1.740.5822
LOC107987453rs3129987C/T   0.00371.261.08–1.470.5667
DPCR1rs2517451A/G<0.00011.651.30–2.110.4729
KIAA1551rs10771894A/G   0.1122 0.00851.351.08–1.69
rs13427905C/T   0.00240.780.67–0.920.1397
ABCA1rs1883025G/A   0.00510.810.70–0.940.00800.680.51–0.90
SFTA2rs2253705G/A   0.00151.281.10–1.480.6682
PLUTrs954750G/A   0.04091.191.01–1.410.00081.331.12–1.57
TCF19rs1419881T/C   0.00091.341.13–1.600.04871.181.00–1.39
rs13209234G/A<0.00011.551.25–1.930.7617
PSORS1C1rs1265100T/C   0.00550.810.70–0.940.4135
YEATS2rs76174573G/T   0.00310.610.44–0.850.1756
ABOrs1053878C/T   0.00331.251.08–1.440.1723
rs4014195C/G   0.00831.231.05–1.430.2586
SFTA2rs2253588C/G   0.00211.261.09–1.450.7016
rs10757283T/C   0.00790.820.71–0.950.01810.740.58–0.95
BTNL2rs28362680G/A   0.1143 0.00820.760.62–0.93
BTNL2rs10947262C/T   0.1143 0.00820.760.62–0.93
KRT27rs17558532C/T   0.00040.570.41–0.780.5002
GTF2H4rs3130780G/T   0.00221.261.09–1.470.6706
rs2532934T/C   0.00231.251.08–1.450.5320
VARS2rs753725G/A   0.00211.261.09–1.450.5274
PLUTrs11619319A/G   0.04821.181.00–1.400.00071.331.13–1.58
rs3095273C/T   0.00451.381.11–1.730.1778
TNS1rs918949C/T   0.00280.790.68–0.920.1301
LINC00243rs3130785C/T   0.00601.241.06–1.450.5417
VARS2rs2249464C/T   0.00281.251.08–1.440.5320
rs3095345A/G   0.00211.271.09–1.470.7469
ITGB8rs80015015G/A<0.00011.561.28–1.910.2511
VARS2rs885905C/T   0.00281.251.08–1.440.7687
LIPErs34052647G/A   0.00011.531.23–1.900.00743.701.42–9.65
PHACTR1rs9369640A/C   0.00250.730.60–0.900.1527
BTNL2rs41417449T/C   0.1641 0.00170.550.38–0.80
BTNL2rs41441651C/T   0.1586 0.00170.550.38–0.80
BTNL2rs28362675C/A   0.1584 0.00170.550.38–0.80
BTNL2rs78587369G/A   0.1590 0.00180.550.38–0.80
BTNL2rs3763315G/T   0.1637 0.00170.550.38–0.80
BTNL2rs2076528T/G   0.1524 0.00170.550.38–0.80
PRKG1rs9414827G/A   0.00030.700.58–0.850.03950.460.22–0.96
rs6537384T/G   0.01911.191.03–1.380.1787
rs6067640G/A   0.03230.850.73–0.990.00540.740.60–0.91
rs10514995A/G   0.04671.191.00–1.420.0825
BTNL2rs34423804T/A   0.1675 0.00180.550.38–0.80
PHACTR1rs9349379G/A   0.00290.800.69–0.930.0820
STIM1rs116855870A/G   0.01331.761.13–2.760.9967
ZNF142rs3821033C/T   0.00151.321.11–1.570.3469
LINC00354rs4907518G/A   0.01380.820.70–0.960.00500.770.64–0.92
TNS3rs11763932G/A   0.00540.810.69–0.940.00180.730.60–0.89
BTRCrs2270439C/A   0.00630.640.47–0.880.6622
MIA3rs2936051A/G   0.1239 0.00020.670.55–0.83
rs6825911C/T   0.00200.780.67–0.910.04460.830.70–1.00
VNN1rs2294757G/A0.2269 0.03340.790.63–0.98
ZNF860rs140232911C/T0.00130.140.04–0.46
rs838880C/T0.03381.201.01–1.410.03081.201.02–1.43
MIA3rs2936052A/G0.0927 0.00440.710.56–0.90
DTNBP1rs2743868G/A0.02081.191.03–1.370.0941
MON2rs11174549A/G0.04271.281.01–1.610.1917
rs507666G/A0.00981.211.05–1.400.1384
FAM170Brs73302786G/T0.00031.621.25–2.110.6351
PSORS1C3rs3131018G/T0.00021.351.15–1.580.9209
PIEZO2rs35033671C/A0.00351.301.09–1.540.1909
PANK1rs11185790G/A0.00651.261.07–1.480.04571.191.00–1.41
GFYrs73053944C/G0.00111.601.21–2.130.0698
RNF2rs1046592A/G0.03010.850.74–0.980.7186

[i] 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.

Table IV.

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

GeneSNPP-valueR2 (individual)R2 (accumulated)
PLCB2rs200787930<0.00010.02970.0297
ALDH2rs671<0.00010.00610.0358
GOSR2rs1052586<0.00010.00530.0411
PSORS1C1rs3094663<0.00010.00520.0463
CCHCR1rs130071<0.00010.00590.0522
rs13427905<0.00010.00470.0569
OR52E4rs11823828<0.00010.00430.0612
EIF3Lrs9466<0.00010.00420.0654
KIAA1551rs10771894<0.00010.00390.0693
CCDC141rs13419085<0.00010.00350.0728
MIA3rs2936051   0.00010.00330.0761
rs602633   0.00010.00330.0794
KRT27rs17558532   0.00010.00320.0826
TRPM1rs2241493   0.00020.00300.0856
rs7333181   0.00020.00300.0886
ADAT1rs145161932   0.00020.00290.0915
APOErs7412   0.00030.00280.0943
YEATS2rs76174573   0.00040.00260.0969
SLC16A1rs1049434   0.00050.00250.0994
RNF2rs1046592   0.00070.00250.1019
rs6825911   0.00060.00240.1043
ITGB8rs80015015   0.00070.00240.1067
USP45rs41288947   0.00070.00240.1091
PHACTR1rs9369640   0.00070.00240.1115
rs1333048   0.00080.00240.1139
rs838880   0.00110.00220.1161
STIM1rs116855870   0.00170.00210.1182
rs2523644   0.00160.00210.1203
MKI67rs1451217310.00200.00200.1223
FAM170B-AS1rs733027860.00190.00200.1243
rs60676400.00220.00200.1263
GFYrs730539440.00240.00190.1282
WDR37rs107947200.00330.00180.1300
SKIV2Lrs5922290.00370.00180.1318
rs65373840.00410.00170.1335
rs107572830.00580.00160.1351
CDKN2B-AS1rs10119700.01100.00140.1365
PRKG1rs94148270.00870.00140.1379
rs1979320.01270.00130.1392
LINC00354rs49075180.01250.00130.1405
LIPErs340526470.01510.00130.1418
BTRCrs22704390.01430.00130.1431
TNS3rs117639320.01630.00130.1444
TNS1rs9189490.01580.00120.1456
rs122296540.01840.00110.1467
rs40141950.02130.00110.1478
PANK1rs111857900.02270.00110.1489
rs5076660.02790.00100.1499
MON2rs111745490.03590.00090.1508
HECTD4rs20743560.03960.00090.1517
CUBNrs782013840.04840.00090.1526
PLUTrs9547500.04970.00080.1534
ABCA1rs18830250.04790.00080.1542
rs105149950.04930.00080.1550

[i] 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.

Table V.

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

GeneSNPHypertensionDMHyper-TGHypo-HDLHyper-LDLCKDObesityHyperuricemia
PLCB2rs200787930 <0.0001a   0.0004a0.3432 <0.0001a <0.0001a <0.0001a   0.0405a   0.9639
ALDH2rs671   0.0039a   0.0074a   0.0298a <0.0001a <0.0001a   0.0273a   0.0350a <0.0001a
GOSR2rs1052586   0.3498   0.0167a0.4457   0.2898   0.2638   0.61850.3670   0.4679
PSORS1C1rs3094663   0.0069a   0.06700.0947   0.0020a   0.3869   0.10800.7345   0.5091
CCHCR1rs130071   0.0865   0.0008a0.2247   0.0143a   0.0141a   0.58940.8651   0.0423a
rs13427905   0.0149a   0.0149a0.0524   0.0545   0.6318   0.94870.1197   0.0920
OR52E4rs11823828   0.0024a <0.0001a   0.0265a   0.1186   0.4445   0.0027a0.1141   0.2815
EIF3Lrs9466   0.0008a   0.0204a   0.0054a   0.2905   0.0114a   0.23680.2435   0.0312a
KIAA1551rs10771894   0.2439   0.0091a0.9562   0.0343a   0.6934   0.38690.0974   0.5419
CCDC141rs13419085   0.3387   0.12550.6537   0.1647   0.7447   0.24830.8101   0.7938
MIA3rs2936051   0.4092   0.52460.9990   0.0475a   0.1222   0.66140.8787   0.1949
rs602633   0.4468   0.23750.7350   0.0005a   0.0021a   0.08420.6617   0.9338
KRT27rs17558532   0.1706   0.23580.3643   0.3607   0.7663   0.0133a0.2306   0.5325
TRPM1rs2241493   0.3861   0.23320.7465   0.7106   0.0815   0.13870.5698   0.9502
rs7333181   0.2308   0.0487a   0.0379a   0.1185   0.2010   0.07950.2182   0.6544
ADAT1rs145161932   0.4468   0.0160a0.3611   0.3357   0.5412   0.75340.5836   0.1202
APOErs7412   0.3680   0.91840.6322   0.1157 <0.0001a   0.63670.5319   0.2528
YEATS2rs76174573   0.1305   0.0687   0.0380a   0.0606   0.8313   0.64580.6338   0.1706
SLC16A1rs10494340.8319   0.0016a0.1897   0.06860.92120.06460.8850   0.2672
RNF2rs1046592   0.0007a   0.0140a0.5319   0.05440.40980.72760.4643   0.1040
rs6825911   0.0317a0.40700.5755   0.10680.14230.40500.2325   0.6717
ITGB8rs800150150.40010.51780.4838   0.0075a0.51690.33410.1339   0.2408
USP45rs412889470.43830.13730.2641   0.0162a0.66360.1341   0.0063a   0.0682
PHACTR1rs93696400.16670.46730.8831   0.52470.81910.74170.6674   0.4133
rs13330480.2947   0.0251a0.5799   0.0156a0.32040.66500.5825   0.2450
rs8388800.9565   0.0108a0.3044   0.0045a0.78180.76990.8126   0.2552
STIM1rs1168558700.14250.24550.6418   0.86310.71160.52850.7357   0.3365
rs25236440.41050.21010.6604   0.07730.0627   0.0449a0.2968   0.2580
MKI67rs1451217310.09030.25280.2203   0.0138a0.4030   0.0048a0.2459   0.5059
FAM170B-AS1rs733027860.26620.63660.2511   0.26870.89890.82030.2323   0.5404
rs6067640   0.0380a0.21440.7990   0.0077a0.69950.18090.6901   0.9347
GFYrs73053944   0.0145a0.57310.5880   0.24710.87880.63160.4534   0.9116
WDR37rs107947200.6272   0.0103a0.9125   0.69540.65280.13520.7804   0.0458a
SKIV2Lrs592229   0.0014a0.07540.0752   0.0157a0.75570.12300.3617   0.1727
rs65373840.38900.11070.2980   0.3818   0.0344a0.08080.0620   0.7745
rs107572830.8792   0.0082a0.9667   0.0420a0.4745   0.0342a0.8876   0.4636
CDKN2B-AS1rs1011970   0.0443a0.06280.6524   0.08340.54560.42930.6420   0.3237
PRKG1rs94148270.62870.83650.6694   0.0424a0.14880.6060   0.0291a   0.0549
rs1979320.1918   0.0272a0.5146   0.56730.23950.31020.7843   0.4625
LINC00354rs49075180.29330.84340.3915   0.1285   0.0028a0.64540.6846   0.7513
LIPErs340526470.15250.71480.0040a   0.41990.08010.08790.0940   0.0081a
BTRCrs22704390.31910.96360.1684   0.13930.95150.28520.4689   0.5780
TNS3rs117639320.48120.61290.9920   0.88570.14240.95980.9307   0.9591
TNS1rs9189490.15090.05100.3218   0.59930.70440.54840.6955   0.9461
rs12229654   0.0203a0.32900.1080 <0.0001a   0.0171a0.11670.2297 <0.0001a
rs40141950.1622   0.0445a   0.0270a   0.18110.70900.37320.5607   0.2233
PANK1rs111857900.36380.41690.1750   0.25830.3889   0.0149a0.6355   0.3282
rs5076660.9872   0.0084a0.7080   0.0370a   0.0129a0.42100.9126   0.6992
MON2rs11174549   0.0283a   0.0133a0.8790   0.3587   0.0325a0.26170.6406   0.9153
HECTD4rs2074356   0.0285a0.1092   0.0109a <0.0001a   0.0002a0.01740.1786 <0.0001a
CUBNrs782013840.14290.94730.7525   0.0027a   0.0269a   0.0327a0.9812   0.3888
PLUTrs9547500.9214   0.0212a0.6905   0.80040.8585   0.0264a0.2382   0.8865
ABCA1rs18830250.80850.2006   0.0134a   0.30920.06670.88910.4019   0.3377
rs10514995   0.0014a   0.0353a0.2529   0.37080.75420.08550.5232   0.0365a

{ label (or @symbol) needed for fn[@id='tfn6-br-0-0-1152'] } 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.

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. locusSNPChr.PositionPreviously examined phenotypes
1p13.3rs6026331109278889CAD (23202125, 20032323), LDL-cholesterol (20686565, 23063622, 19060906, 21943158, 18193043, 18262040, 19913121, 21977987, 20339536), HDL-cholesterol (23063622, 20686565), total cholesterol (20686565, 23063622)
SLC16A1rs10494341112913924HDL-cholesterol (23063622)
RNF2rs10465921185100429None
MIA3rs29360511222629862CAD (19198612, 21347282, 23364394, 21378990, 17554300, 22319020, 21966275), MI (19198609)
2p13rs13427905271846585None
CCDC141rs134190852178837710Heart rate (23583979, 20639392), left ventricular mass (19584346)
TNS1rs9189492217809974Lung function, forced expiratory volume in
1 second (20010834, 21946350, 23284291)
YEATS2rs761745733183804099None
4q24rs68259114110460482Systolic BP (21572416), diastolic BP (21572416)
4q31.2rs65373844145949613None
5q12rs10514995566443611None
PHACTR1rs9369640612901209CAD (21378988, 23202125, 22745674, 21347282, 23364394, 21378990, 22751097, 22745674), MI (19198609, 21378990), ischemic stroke (22306652)
PSORS1C1rs3094663631139310Type 1 diabetes (17554300, 17632545), triglycerides (20686565), total cholesterol (20686565)
CCHCR1rs130071631148433Triglycerides (20686565)
6p21.3rs2523644631374707Type 1 diabetes (17554300, 17632545), LDL-cholesterol (23063622, 20686565), triglycerides (23063622, 20686565), total cholesterol (23063622, 20686565)
SKIV2Lrs592229631962664CAD (21971053), type 1 diabetes (17554300, 17632545), LDL-cholesterol (20686565), triglycerides (20686565), total cholesterol (20686565)
USP45rs41288947699446210None
ITGB8rs80015015720401881None
TNS3rs11763932747567880None
CDKN2B-AS1rs1011970922062135CAD (21347282), LDL-cholesterol (23063622), abdominal aortic aneurysm (20622881), type 2 diabetes (17463249)
9p21rs1333048922125348CAD (23202125, 21606135, 19198612, 17634449, 20032323, 23364394), MI (17478679), intracranial aneurysm (22961961)
9p21rs10757283922134173Type 2 diabetes (20581827)
ABCA1rs18830259104902020HDL-cholesterol (20686565, 23505323, 23063622, 21909109, 19060911, 21347282, 19060906, 18193043, 18193044, 18193046, 22629316, 20864672, 21347282, 23726366), LDL-cholesterol (20686565), total cholesterol (20686565, 23063622, 20339536)
9q34.2rs5076669136149399Venous thrombosis (22675575), VLDL-cholesterol
small lipoprotein fraction concentration (19936222), LDL-cholesterol lipoprotein fraction concentration (19936222)
WDR37rs10794720101110225Estimated glomerular filtration rate (20383146, 22479191), serum creatinine (20383146)
CUBNrs782013841017111024LDL-cholesterol (23063622), HDL-cholesterol (23063622), total cholesterol (23063622)
FAM170B-AS1rs733027861049131709None
PRKG1rs94148271051137314None
PANK1rs111857901089612776Insulin concentration (19060910)
BTRCrs227043910101550817None
MKI67rs14512173110128102595None
STIM1rs116855870114055527None
OR52E4rs11823828115884973None
11q13.1rs40141951165739351Serum urate (23263486), serum creatinine (20383146), estimated glomerular filtration rate (20383146)
KIAA1551rs107718941231982009None
MON2rs111745491262565357None
12q24.1rs1222965412110976657HDL-cholesterol (21909109)
ALDH2rs67112111803962CAD (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)
HECTD4rs207435612112207597CAD (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)
12q24.31rs83888012124777047HDL-cholesterol (20686565)
PLUTrs9547501327889801None
13q34rs733318113111568950None
LINC00354rs490751813111898209None
TRPM1rs22414931531070149None
PLCB2rs2007879301540289298Triglycerides (23063622)
ADAT1rs1451619321675612670None
KRT27rs175585321740779624None
17q21.3rs1979321746896981Pulse pressure (21909110), systolic BP (21909110, 21909115)
GOSR2rs10525861746941097Pulse pressure (21909110), systolic BP (21909110, 21909115)
LIPErs340526471942407617None
APOErs74121944908822LDL-cholesterol (23100282, 23063622, 20686565, 22629316, 19060911, 23067351, 23696881, 20838585), HDL-cholesterol
(21386085), triglycerides (23063622, 20686565, 22629316, 19060911, 21386085), total cholesterol (23063622, 20686565)
GFYrs730539441949427038None
20q13.2rs60676402051092837None
EIF3Lrs94662237877742None

[i] 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.

Table VII.

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

GeneFunctionBiological process
RNF2Ubiquitin-protein transferase activity, chromatin binding, zinc ion binding, transferase activity, metal ion binding, ubiquitin protein ligase activity, RING-like zinc finger domain bindingHistone 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 bindingNegative regulation of transcription by RNA polymerase II, histone H3 acetylation, negative regulation of DNA-templated transcription
USP45Thiol-dependent ubiquitin-specific protease activity, cysteine-type peptidase activity, zinc ion binding, thiol-dependent ubiquitinyl hydrolase activityProtein deubiquitination, ubiquitin-dependent protein catabolic process, DNA repair, global genome nucleotide-excision repair
ITGB8Extracellular matrix protein binding, signaling receptor bindingGanglioside metabolic process, cell adhesion, integrin-mediated signaling pathway, regulation of gene expression, positive regulation of angiogenesis, cartilage development, extracellular matrix organization, cell-matrix adhesion
TNS3Protein binding, focal adhesionPositive regulation of cell proliferation, cell migration, lung alveolus development
FAM170BProtein binding, outer acrosomal membranePositive regulation of acrosome reaction, regulation of fertilization
PRKG1cGMP-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 activityNegative 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
BTRCUbiquitin-protein transferase activity, ubiquitin protein ligase activity, β-catenin binding, protein phosphorylated amino acid binding, protein dimerization activityProtein 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
MKI67RNA binding, DNA binding, ATP binding, protein bindingRegulation of mitotic nuclear division, regulation of chromosome segregation and organization, cell proliferation
STIM1Calcium channel regulator activity, calcium ion binding, microtubule plus-end binding, metal ion binding, protein bindingCellular calcium ion homeostasis, activation of store-operated calcium channel activity, regulation of calcium ion transport, positive regulation of angiogenesis, regulation of cardiac conduction
OR52E4Olfactory receptor activity, G-protein coupled receptor activitySignal transduction, G-protein coupled receptor signaling pathway, detection of chemical stimulus involved in sensory perception of smell
KIAA1551 Uncharacterized
MON2Protein binding, protein transportGolgi to endosome transport
PLUT Uncharacterized
LINC00354 Uncharacterized
TRPM1Ion channel activityG-protein coupled glutamate receptor signaling pathway, ion transmembrane transport, protein tetramerization, cellular response to light stimulus
ADAT1RNA binding, tRNA-specific adenosine deaminase activity, hydrolase activity, metal ion bindingtRNA processing
KRT27Structural molecule activity, intermediate filamentHair follicle morphogenesis, keratinization, cornification
LIPETriglyceride lipase activity, serine hydrolase activity, protein kinase binding, hormone-sensitive lipase activityProtein phosphorylation, lipid metabolic process, steroid metabolic process, cholesterol metabolic process, triglyceride catabolic process, long-chain fatty acid catabolic process, diacylglycerol catabolic process
GFYProtein localization to non-motile cilium, non-motile cilium assemblySensory perception of smell, response to stimulus
EIF3LTranslation initiation factor activity, RNA binding, protein bindingTranslational initiation, viral translational termination-reinitiation

[i] 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).

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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Yamada Y, Yasukochi Y, Kato K, Oguri M, Horibe H, Fujimaki T, Takeuchi I and Sakuma J: Identification of 26 novel loci that confer susceptibility to early‑onset coronary artery disease in a Japanese population. Biomed Rep 9: 383-404, 2018
APA
Yamada, Y., Yasukochi, Y., Kato, K., Oguri, M., Horibe, H., Fujimaki, T. ... Sakuma, J. (2018). Identification of 26 novel loci that confer susceptibility to early‑onset coronary artery disease in a Japanese population. Biomedical Reports, 9, 383-404. https://doi.org/10.3892/br.2018.1152
MLA
Yamada, Y., Yasukochi, Y., Kato, K., Oguri, M., Horibe, H., Fujimaki, T., Takeuchi, I., Sakuma, J."Identification of 26 novel loci that confer susceptibility to early‑onset coronary artery disease in a Japanese population". Biomedical Reports 9.5 (2018): 383-404.
Chicago
Yamada, Y., Yasukochi, Y., Kato, K., Oguri, M., Horibe, H., Fujimaki, T., Takeuchi, I., Sakuma, J."Identification of 26 novel loci that confer susceptibility to early‑onset coronary artery disease in a Japanese population". Biomedical Reports 9, no. 5 (2018): 383-404. https://doi.org/10.3892/br.2018.1152