Volume 10 Supplement 2

From genes to systems genomics: human genomics

Open Access

Association of six CpG-SNPs in the inflammation-related genes with coronary heart disease

  • Xiaomin Chen1,
  • Xiaoying Chen3,
  • Yan Xu3,
  • William Yang2,
  • Nan Wu1, 3,
  • Huadan Ye3,
  • Jack Y. Yang5,
  • Qingxiao Hong3,
  • Yanfei Xin4,
  • Mary Qu Yang5,
  • Youping Deng6, 7 and
  • Shiwei Duan3Email author
Human Genomics201610(Suppl 2):21

https://doi.org/10.1186/s40246-016-0067-1

Published: 25 July 2016

Abstract

Background

Chronic inflammation has been widely considered to be the major risk factor of coronary heart disease (CHD). The goal of our study was to explore the possible association with CHD for inflammation-related single nucleotide polymorphisms (SNPs) involved in cytosine-phosphate-guanine (CpG) dinucleotides. A total of 784 CHD patients and 739 non-CHD controls were recruited from Zhejiang Province, China. Using the Sequenom MassARRAY platform, we measured the genotypes of six inflammation-related CpG-SNPs, including IL1B rs16944, IL1R2 rs2071008, PLA2G7 rs9395208, FAM5C rs12732361, CD40 rs1800686, and CD36 rs2065666). Allele and genotype frequencies were compared between CHD and non-CHD individuals using the CLUMP22 software with 10,000 Monte Carlo simulations.

Results

Allelic tests showed that PLA2G7 rs9395208 and CD40 rs1800686 were significantly associated with CHD. Moreover, IL1B rs16944, PLA2G7 rs9395208, and CD40 rs1800686 were shown to be associated with CHD under the dominant model. Further gender-based subgroup tests showed that one SNP (CD40 rs1800686) and two SNPs (FAM5C rs12732361 and CD36 rs2065666) were associated with CHD in females and males, respectively. And the age-based subgroup tests indicated that PLA2G7 rs9395208, IL1B rs16944, and CD40 rs1800686 were associated with CHD among individuals younger than 55, younger than 65, and over 65, respectively.

Conclusions

In conclusion, all the six inflammation-related CpG-SNPs (rs16944, rs2071008, rs12732361, rs2065666, rs9395208, and rs1800686) were associated with CHD in the combined or subgroup tests, suggesting an important role of inflammation in the risk of CHD.

Keywords

Coronary heart disease Inflammation Promoter CpG-SNP Polymorphism

Background

Coronary heart disease (CHD) is considered to be the leading cause of mortality and morbidity in the elderly [1]. CHD has emerged as a serious burden to human health [2]. Both genetic and environmental factors were shown to play an important role in the development of CHD [3]. Environmental factors, including smoking and dietary changes, were found to be associated with CHD [4]. Heritable factors were estimated to contribute to 30–60 % of the variation in the risk of CHD [3]. In addition, the incidence of CHD was generally higher in men than in women regardless of their menopause type [5]. Therefore, further exploration of the interactive mechanism between genes and environment is much more significant and helpful for specific diagnosis [6].

Increasing amount of evidence has indicated that inflammation is responsible for CHD [7, 8]. Many inflammation-related genes are found to be associated with risk of CHD [9, 10]. Interleukin 1, beta gene (IL1B) encodes the cytokine which exerts a wide range of inflammatory activities [11]. Genetic association between IL1B and CHD has been previously found [12]. Interleukin 1 receptor, type II (IL1R2) encodes a cytokine receptor that belongs to the interleukin 1 receptor family [13]. Notably, IL1R2 is recognized as a general factor in inflammatory response [14]. Phospholipase A2, group VII (PLA2G7) gene encodes a kind of secreted enzyme [15], and patients with high PLA2G7 expression was found to have an increased risk of cardiovascular diseases [16]. Gene polymorphisms in family with sequence similarity 5, member C (FAM5C) gene were shown to be associated with an increased risk of acute myocardial infarction [17], and elevated FAM5C levels were found in atherosclerotic plaques and coronary artery endothelium [18]. CD40 encodes a cell surface receptor that plays a pivotal role in macrophage activation and parasite immunity [19], and CD40 could up-regulate prime macrophages through a proinflammatory program in recent studies [19]. CD36 encodes a platelet receptor glycoprotein involved in different biological processes such as inflammation, atherosclerosis, and platelet activation [20]. The association between monocyte/macrophage CD36 and atherosclerosis has been found in the previous studies [21].

Single nucleotide polymorphisms (SNPs) can change biological properties of the encoded protein and affect gene expression levels in an allele-specific manner (24450106). Due to the mutability of cytosine-phosphate-guanine (CpG) dinucleotides, CpG-SNPs, as an important class of cis-regulatory polymorphisms, connect genetic variation to the individual variability of the epigenome [22]. CpG-SNPs in the promoter regions had been found to be associated with multiple diseases, including type 2 diabetes [23], breast cancer [24], schizophrenia [25], and epithelial ovarian cancer [26]. In light of previous studies, we aimed to evaluate whether the six inflammation-related gene CpG-SNPs could contribute to the risk of CHD.

Results and discussion

Genotypic and allelic tests were performed for a total of six CpG-SNPs between CHD and non-CHD individuals (Table 1). According to the most recent human assembly, hg38/GRCh38, five CpG-SNPs (FAM5C rs12732361, IL1R2 rs2071008, IL1B rs16944, CD40 rs1800686, and CD36 rs2065666) were located upstream of the corresponding transcription start sites (47, 178, 387, 508, and 659 bp, respectively). The remaining one (PLA2G7 rs9395208) was located in the 111 bp downstream of transcription start sites.
Table 1

The list of genotyping primers for six CpG-SNPs

Gene

SNP

Primers

Sequence (5′ to 3′)

IL1B

rs16944

1st PCR primer

ACGTTGGATGAGAGGCTCCTGCAATTGACA

  

2nd primer

ACGTTGGATGCTGTCTGTATTGAGGGTGTG

  

Extend primer

GGGGTGGGTGCTGTTCTCTGCCTC

IL1R2

rs2071008

1st PCR primer

ACGTTGGATGGAAAAATCCATGCAGCCTCC

  

2nd primer

ACGTTGGATGTGGTGGCTGACTTTCCAAGG

  

Extend primer

TGGGAAGAAGCAAGCACCCC

PLA2G7

rs9395208

1st PCR primer

ACGTTGGATGTGGACCCGCGGTTAACTTAG

  

2nd primer

ACGTTGGATGATCAGGTCTGCGGAAAGGAG

  

Extend primer

GCATTGCCTGGCTCT

FAM5C

rs12732361

1st PCR primer

ACGTTGGATGTTACACAGAGAGCCACGAAC

  

2nd primer

ACGTTGGATGAGGATCACCACGAATCACCC

  

Extend primer

GAAACCCCCACCATTCCCCA

CD40

rs1800686

1st PCR primer

ACGTTGGATGATGGATGGGAAGTTGAGACG

  

2nd primer

ACGTTGGATGCCCAACTCAGAATTTCGCTC

  

Extend primer

GTCGCTTTCAAAGGAAATTCCCT

CD36

rs2065666

1st PCR primer

ACGTTGGATGCTCTGAAGATATAATGACAAG

  

2nd primer

ACGTTGGATGCAGTTTCTCTGTTCACTTCG

  

Extend primer

CGTTCACTTCGTTTTAGTATAGAATTA

As shown in Table 2, the genotype distributions of all the six SNPs in the non-CHD controls met Hardy-Weinberg equilibrium (HWE). Our results showed that PLA2G7 rs9395208 and CD40 rs1800686 were significantly associated with CHD on the allele level. The frequency of allele rs9395208-G was significantly lower in the case group than in the control (84.7 versus 87.3 %; P = 0.04, OR = 0.806, 95 % CI = 0.657–0.991). Meanwhile, a higher rs1800686-G frequency showed in the cases than in controls (69.4 versus 65.4 %; P = 0.02; OR = 1.198, 95 % CI = 1.029–0.394). No significant difference was observed on the genotype level for all the CpG-SNPs (P > 0.05).
Table 2

Comparisons of genotype and allele frequencies between cases and controls

SNP

Groups

Genotype (counts)

χ 2

P (df = 2)

HWE P valuea

Allele (counts)

χ 2

P (df = 1)

OR (95 % CI)

IL1B rs16944

 

GG

AG

AA

   

G

A

   
 

Cases

220

380

176

  

0.627

820

732

   
 

Controls

173

379

180

4.387

0.112

0.335

725

739

3.310

0.069

1.142 (0.990–1.317)

IL1R2 rs2071008

 

GG

GT

TT

   

G

T

   
 

Cases

434

285

65

  

0.064

1153

415

   
 

Controls

417

275

46

2.382

0.304

0.941

1109

367

1.020

0.313

0.919 (0.781–1.082)

PLA2G7 rs9395208

 

GG

GC

CC

   

G

C

   
 

Cases

565

198

21

  

0.468

1328

240

   
 

Controls

568

154

17

4.603

0.100

0.095

1290

188

4.210

0.040

0.806 (0.657–0.991)

FAM5C rs12732361

 

GG

AG

AA

   

G

A

   
 

Cases

492

242

49

  

0.011

1226

340

   
 

Controls

431

254

52

3.022

0.221

0.088

1116

358

2.850

0.091

1.157 (0.977–1.370)

CD40 rs1800686

 

GG

AG

AA

   

G

A

   
 

Cases

378

332

74

  

0.929

1088

480

   
 

Controls

317

333

89

5.411

0.067

0.914

967

511

5.440

0.020

1.198 (1.029–1.394)

CD36 rs2065666

 

GG

GC

CC

   

G

C

   
 

Cases

439

288

56

  

0.356

1166

400

   
 

Controls

411

282

43

1.24

0.538

0.555

1104

368

0.120

0.729

0.972 (0.825–1.145)

a HWE Hardy-Weinberg equilibrium; P value <0.05 was considered a departure from HWE

Gender disparities widely existed in the prevalence of CHD [27], and female CHD patients aged 40 or older have 10 % greater risk of death than the males [27]. Hence, we performed a gender-based subgroup analysis to detect the difference both in genotype and allele frequencies (Table 3). It showed significant associations of FAM5C rs12732361 and CD36 rs2065666 with CHD in males on the allele level (P = 0.03, OR = 1.266, 95 % CI = 1.022–1.568; P = 0.04, OR = 0.806, 95 % CI = 0.652–0.996, respectively). Meanwhile, CD40 rs1800686 was shown to be significantly associated with CHD in females (P = 0.02, OR = 1.347, 95 % CI = 1.043–1.740 by allele).
Table 3

A list of SNPs associated with CHD in the subgroup tests

Gene

SNP

Model or subgroup

Group

Genotype or allele (counts)

χ 2

P (df)

OR (95 % CI)

IL1B

rs16944

Dominant

Cases

GG/GA + AA (220/556)

4.350

0.037 (1)

1.279 (1.015–1.611)

   

Controls

GG/GA + AA (173/559)

   

IL1B

rs16944

≤55 years of age

Cases

G/A (236/236)

6.190

0.013 (1)

1.422 (1.077–1.877)

   

Controls

G/A (292/248)

   

IL1B

rs16944

55–65 years of age

Cases

GG/GA/AA (79/134/57)

6.147

0.046 (2)

 
   

Controls

GG/GA/AA (53/145/66)

   

IL1B

rs16944

55–65 years of age

Cases

G/A (292/248)

4.560

0.033 (1)

1.299 (1.022–1.653)

   

Cases

G/A (251/277)

   

IL1R2

rs2071008

≤55 years of age

Controls

G/T (254/106)

3.860

0.049 (1)

0.733 (0.538–1.000)

   

Cases

G/T (366/112)

   

PLA2G7

rs9395208

Dominant

Cases

GG/GC + CC (565/219)

4.590

0.032 (1)

0.777 (0.616–0.979)

   

Controls

GG/GC + CC (568/171)

   

PLA2G7

rs9395208

≤55 years of age

Cases

G/C (295/65)

5.750

0.016 (1)

0.627 (0.427–0.920)

   

Controls

G/C (420/58)

   

FAM5C

rs12732361

Male

Cases

G/C (849/225)

4.660

0.031 (1)

1.266 (1.022–1.568)

   

Controls

G/C (629/211)

   

CD40

rs1800686

Female

Cases

G/A (354/138)

5.230

0.022 (1)

1.347 (1.043–1.740)

   

Controls

G/A (417/219)

   

CD40

rs1800686

Dominant

Cases

GG/GA + AA (378/406)

4.340

0.037 (1)

1.239 (1.013–1.517)

   

Controls

GG/GA + AA (317/422)

   

CD40

rs1800686

≥65 years of age

Cases

GG/GA/AA (163/146/24)

9.437

0.009 (2)

 
   

Controls

GG/GA/AA (90/109/32)

   

CD40

rs1800686

≥65 years of age

Cases

G/A (472/194)

8.600

0.0034 (1)

1.456 (1.132–1.874)

   

Controls

G/A (289/173)

   

CD36

rs2065666

Male

Cases

G/C (790/286)

4.000

0.046 (1)

0.806 (0.652–0.996)

   

Controls

G/C (641/187)

   

Age is another well-known risk factor in the development and prognosis of CHD [28], and there were over 70 % of coronary-related deaths occurred in the people older than 70 in North America and Western Europe [28]. Therefore, we further evaluated the genotype and allele frequencies in different age subgroups (Table 3). For the individuals younger than 55, the frequencies of both rs16944-G and rs2071008-G alleles were significantly lower in case group than in control group (P = 0.01, OR = 1.422, 95 % CI = 1.077–1.877; P = 0.04, OR = 0.733, 95 % CI = 0.538–1.000, respectively). For the individuals with age between 55 and 65 years old, rs16944 was associated with CHD on both genotype and allele levels (χ 2 = 6.15, P = 0.04 by genotype; P = 0.03, OR = 1.299, 95 % CI = 1.022–1.653 by allele). Among the individuals older than 55, rs1800686 was also shown to be associated with CHD on both genotype and allele levels (genotype: χ 2 = 9.44, P = 0.009; allele: P = 0.003; OR = 1.456, 95 % CI = 1.132–1.8740).

Additionally, we have conducted a comparison under the dominant and recessive inheritance models between cases and controls (Table 3). In the dominant model, significant associations among rs16944, rs9395208 and rs1800686 with CHD were observed. The rs16944-A and rs1800686-A alleles were risk factors for CHD (rs16944: P = 0.03, OR = 1.279, 95 % CI = 1.015–1.611; rs1800686: P = 0.04, OR = 1.239, 95 % CI = 1.013–1.517). The rs9395208-C allele was a protective factor for CHD (P = 0.03, OR = 0.777, 95 % CI = 0.616–0.979). Meanwhile, all the six CpG-SNPs showed no significant association with CHD in the recessive model.

Besides, a post hoc power analysis showed that our association study had strong power (93.9–94.1 %) to detect significant association of the six CpG-SNPs under an OR of 1.3.

The present study performed a comprehensive analysis of association between six CpG-SNPs (IL1B rs16944, IL1R2 rs2071008, PLA2G7 rs9395208, FAM5C rs12732361, CD40 rs1800686, and CD36 rs2065666) and CHD. At the allelic level, CD40 rs1800686-G was found to be a risk factor, while PLA2G7 rs9395208-G was found to be a protective factor. Moreover, all the CpG-SNPs were significantly associated with the risk of CHD in the combined or subgroup analyses.

Inflammation represents an important feature in the process of atherosclerosis, which can form, destabilize, and rupture atherosclerotic plaques, finally causing CHD [29]. Over the years, researchers have found some inflammatory factors related to the pathogenesis and prognosis of CHD, such as CLOCK SNP rs4580704 [30], ICAM-1 SNP rs281432 [31], and NFKB1 SNP rs28362491 [32]. Some of these inflammation-related genes were expected to be drug targets for the control and treatment of CHD. Meanwhile, we hypothesized that hereditable methylation could be associated with CHD. Because of low minor allele frequency or weak haplotype associations, genome-wide searches for genetic risk factors for CHD have in general not investigated the CpG-SNPs. Our previous work [33] indicated that CpG-SNPs of the thrombotic pathway genes contributed to the risk of CHD, which suggested a clue for investigating the contributions of the inflammation-related CpG-SNPs to the susceptibility to CHD.

PLA2G7 gene encodes a secreted enzyme whose activity is associated with CHD [34]. PLA2G7 functions as a biomarker of plaque inflammation and stability [35]. Several PLA2G7 SNPs (e.g., rs7756935, rs1805017, and rs13210554) have been reported on the susceptibility to CHD [35, 36]. Here, we discovered a significant association of PLA2G7 CpG-SNP rs9395208 with CHD, even in the individuals aged ≤55, providing additional evidence of age dimorphism in the risk of CHD. Additionally, Jiang et al. [15] have reported that the correlation between PLA2G7 methylation and CHD risk in females is independent of age, smoking, diabetes, and hypertension, which indicated a close relationship between PLA2G7 and hereditable methylation in the pathogenic mechanism of CHD.

The contribution of CD40 rs1800686 is another main finding in the current study. CD40 is considered to determine T cell responses to antigen presentation and B cells immunoglobulin isotype switching, which plays a key role in the inflammatory and prothrombotic processes by bonding with CD40 ligand (CD40L) in atherosclerosis [37]. Previous study provided evidence of association of CD40 rs1883832 with an overall increased risk of CHD in Chinese population [38]. In the current study, a similar result was drawn that CD40 rs1800686-A allele contributed to CHD risk. Age and gender are considered as two independent risk factors in CHD [28, 39]. Subsequent subgroup analysis identified age and gender differences existed in the rs1800686, which revealed an age- and gender-based mechanism on the genetic variations.

As a scavenger receptor, CD36 is not only involved in the metabolism of lipids but also plays an important role in the adhesion of negatively charged macromolecules [40]. CD36 is widely expressed in cells and tissues including microvascular endothelial cells, monocytes, and macrophages [41]. Previous study implied that increased CD36 expression level could reflect the severity of coronary artery atherosclerosis [42]. Several CD36 SNPs (e.g., rs5956, rs3173798, and rs3211892) have been reported to be associated with CHD [42]. Here, we detected a significant protection role of CD36 CpG-SNP rs2065666 G carries in CHD male patients. Our data suggested a possible molecular mechanism through which SNP could influence a phenotype.

Interleukins (IL-1B and IL1R2) were shown to play a role in the inflammatory response, and they lead to the development of atherosclerotic plaques [43, 44]. Recently, gender dimorphism was observed in the association of IL-1B polymorphism with CHD [45]. We find no significant difference on IL-1B rs16944 allele or genotype level by gender subgroup analysis. However, age-based subgroup tests showed that IL-1B rs16944-G was a risk factor of CHD in younger population, while IL1R2 rs2071008-G was a protective factor. Further validation study or functional analysis of these variants is needed in the future.

Increased expression of FAM5C may be induced by inflammatory stimuli [17]. This case-control study observed significant associations between FAM5C rs12732361 polymorphisms and CHD only in males. Due to a lack of CHD study on FAM5C, more investigations are needed to validate our results.

There are also limitations in the current study. The findings of our study are limited to a small population with documented CHD and cannot be generalized to the population at large. Although we found positive results in the current study, the power effect might be reduced by strict multiple adjustment or subgroup analyses. Therefore, larger sample size and other ethnic populations are required to be investigated.

Conclusions

In conclusion, our case-control study suggested that the six inflammation-related CpG-SNPs were significantly associated with the risk of CHD in the combined or the subgroup samples. Moreover, our results also revealed that IL1B rs16944, PLA2G7 rs9395208, and CD40 rs1800686 had a significant contribution to the risk of CHD under the dominant model.

Methods

Sample collection

A total of 784 CHD patients and 739 healthy controls were collected from Ningbo First Hospital of Ningbo University between May 2008 and April 2015 in Zhejiang province, China. CHD patients and non-CHD controls were defined as described before [33, 46]. Patients with congenital heart disease, cardiomyopathy, liver disease, and renal disease were excluded. Blood samples were collected in 3.2 % citrate sodium-treated tubes and processed in the central clinical laboratory of the hospital. All the participants provided written informed consent form under a protocol approved by the Medical Ethics Committees of Ningbo First Hospital and Ningbo University.

SNP selection and genotyping

The selected CpG-SNPs are on the promoter of inflammation-related genes. Meanwhile, the minor allele frequencies of the selected CpG-SNPs are over 10 % in HapMap HCB population. CpG-SNPs with design problems or failed assays were excluded. Finally, six CpG-SNPs in inflammation-related genes were included in the current study. DNA extraction and quantification were described as previously [33, 46]. The polymerase chain reaction (PCR) amplification was performed on the GeneAmp® PCR System 9700 (Applied Biosystems, Foster City, CA, USA), and the genotyping was performed on the MassARRAY iPLEX® assay platform (Sequenom, San Diego, CA, USA). The sequences of the amplification and extension primers for the six CpG-SNPs were shown in Table 1.

Statistical analyses

Genotype and allele frequencies of the polymorphisms between cases and controls were calculated by the CLUMP22 software with 10,000 Monte Carlo simulations [47]. The distribution of Hardy-Weinberg equilibrium (HWE) was tested using the Arlequin program (version 3.5, Bern, Switzerland), and P > 0.05 was considered to be in HWE. Power analysis was performed using the Power and Sample Size Calculation software (v3.0.43). A two-tailed P < 0.05 was considered statistically significant.

Declarations

Declarations

The research and publication was supported by the grants from the National Natural Science Foundation of China (31100919, 81371469), Zhejiang Provincial Natural Science Foundation (LR13H020003), Ningbo City Medical Science and Technology projects (2014A20), and K. C. Wong Magna Fund in Ningbo University. This article has been published as part of Human Genomics volume 10 Supplement 2, 2016: from genes to systems genomics: human genomics. The full contents of the supplement are available online at http://humgenomics.biomedcentral.com/articles/supplements/volume-10-supplement-2.

Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.

Authors’ Affiliations

(1)
Cardiovascular Center of Ningbo First Hospital, Ningbo University
(2)
Texas Advanced Computing Center, University of Texas at Austin
(3)
School of Medicine, Ningbo University
(4)
Center of Safety Evaluation, Zhejiang Academy of Medical Sciences
(5)
MidSouth Bioinformatics Center, Department of Information Science, George Washington Donaghey College of Engineering and Information Science, and Joint Bioinformatics Graduate Program, University of Arkansas at Little Rock and University of Arkansas for Medical Sciences
(6)
Medical College, Wuhan University of Science and Technology
(7)
Department of Internal Medicine and Biochemistry, Rush University Medical Center

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