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Association of rs662799 in APOA5 with CAD in Chinese Han population

BMC Cardiovascular DisordersBMC series – open, inclusive and trusted201818:2

https://doi.org/10.1186/s12872-017-0735-7

Received: 7 February 2017

Accepted: 12 December 2017

Published: 8 January 2018

Abstract

Background

CAD (Coronary Artery Disease) is a complex disease that influenced by various environmental and genetic factors. Previous studies have found many single nucleotide polymorphisms (SNPs) associated with the risk of CAD occurrence. However, the results are inconsistent. In this study, we aim to investigate genetic etiology in Chinese Han population by analysis of 7 SNPs in lipid metabolism pathway that previously has been reported to be associated with CAD.

Methods

A total of 631 samples were used in this study, including 435 CAD cases and 196 normal healthy controls. SNP genotyping were conducted via multiplex PCR amplifying followed by NGS (next-generation sequencing).

Results

Rs662799 in APOA5 (Apolipoprotein A5) gene was associated with CAD in Chinese Han population (Odds-ratio = 1.374, P-value = 0.03). No significant association was observed between the rest of SNPs and CAD. Stratified association analysis revealed rs5882 was associated with CAD in non-hypertension group (Odds-ratio = 1.593, P-value = 0.023). Rs1800588 was associated with CAD in smoking group (Odds-ratio = 1.603, P-value = 0.035).

Conclusion

The minor allele of rs662799 was the risk factor of CAD occurrences in Chinese Han population.

Keywords

Coronary artery diseaseChinese Han populationCase-control studySingle nucleotide polymorphisms

Background

Coronary Artery Disease (CAD) is a common type of cardiovascular disease which is the leading cause of death worldwide [1]. In China, it was estimated that 700,000 people died from CAD each year [2]. Many environmental factors have been identified to be associated with CAD, including diabetes, hypertension, smoking, TG (Triglyceride), HDL-C (high-density lipoprotein cholesterol), LDL-C (low-density lipoprotein cholesterol), TC (Serum total cholesterol) [3]. Genetic variants also contribute to CAD risk [4, 5]. To date, Genome Wide Association Study (GWAS) studies have identified many SNPs to be associated with CAD among different populations [610], including SNPs located within or nearby lipid metabolism genes. Some of them were successfully replicated in different populations, but there are still inconsistent results in some researches.

In this study, we aim to investigate the association of 7 SNPs of lipid metabolism genes (LIPC rs1800588, LPL rs320, APOC3 rs5128, CETP rs5882, PON1 rs662, APOA5 rs662799, APOB rs693) with the risk of developing CAD in Chinese Han population.

Methods

Patients and controls

The participants in this study were recruited from Wuhan General Hospital of Guangzhou Military Region between 2010 and 2016. Individuals with incomplete information were excluded. A total of 435 CAD patients and 196 non-CAD controls were involved in the study. All the participants were unrelated Chinese Han individuals. This study was approved by the Medical Ethics Committee of Southern Medical University and compliant with the principles set forth by the Declaration of Helsinki Principles.

Data collection

Subjects were defined as smokers if they smoked more than 100 cigarettes in lifetime. Subjects were diagnosed hypertension as systolic blood pressure (SBP) >140 mmHg or diastolic blood pressure (DBP) >90 mmHg [11]. Diabetes mellitus was defined as either fasting plasma glucose levels of ≥7.0 mmol/L or plasma glucose levels of ≥11.1 mmol/L [12]. Total cholesterol, HDL cholesterol, LDL cholesterol, C-reactive protein (CRP) and triglyceride (TG) levels were measured according to standard laboratory methods in Southern Medical University. The diagnostic criteria for CAD cases were defined as followings: at least one of the major segments of coronary arteries (right coronary artery, left circumflex, or left anterior descending arteries) with more than or equal to 50% organic stenosis based on coronary angiography.

SNP genotyping

Blood samples (5 ml) were collected from participants. DNA extraction was conducted by TianGen DNA extraction kit (TianGen Ltd., Beijing, China) according to the manufacture instruction. DNA quality was analyzed by Electrophoresis and NanoDrop (NanoDrop Technologies, Houston, TX, USA) quantification. 20 ng DNA was used for PCR amplification. Chimeric specific primers were designed using Oligo 6.0, which contain target sequences and universal sequences. The product sizes of PCR reaction were between 107 and 160 bp, and the primer length was 37–38 bp, with melting temperature (Tm) 55–65 °C and the GC content between 20 and 80%. Multiplex PCR was conducted and followed by adaptor adding. Final products were purified by polyacrylamide gel electrophoresis (PAGE) and then sequenced on MiSeq platform (Illumina, USA).

Sequencing data analysis

Sequencing data were separated by index sequences using FASTX-toolkit as each sample group has a unique index sequence. Then, the index and adapter sequences were trimed out with cutadapt. And target sequences were mapped to human genome reference sequence (NCBI, dbSNP bulid 142) using BWA software. SNPs were called by samtools.

Statistical analysis

Continuous variables between cases and controls are calculated using Student’s t test and presented as mean ± SD. Categorical variables were calculated using chi-square test. Gene frequencies, allele frequencies, and differences in genotype and allele frequencies between different groups were also examined. Hardy-Weinberg equilibrium (HWE) test was performed by SHEsis [13]. P value >0.05 was considered in HWE. Allele distribution between cases and controls were analyzed using chi-square test. Odds ratios (ORs) and 95% confidence intervals (CIs) were calculated by logistic regression analysis after adjusting for age, hypertension, type 2 diabetes, TG, TC, HDL-C and LDL-C. P value <0.05 was considered as statistically significant. Association analysis were conducted by SPSS version 21.0 (SPSS Inc., Chicago, Illinois, USA).

Results

Clinical characteristics of cases and controls

In this study, 435 CAD patients and 196 controls were included. Clinical characteristics are summarized in Table 1. Samples in case group are more likely to have hypertension, diabetes and smoking. EF, HDLC and APOA1 levels are significantly lower in case group than in control group, while TG, APOA1, GLU and GHb levels are significantly higher in case group than in control group. These data indicate that male, EF, HDLC, TG, ApoA, APOA1, GLU, GHb, hypertension, diabetes and smoking are risk factors of developing CAD in this study.
Table 1

Baseline characteristics

 

Cases

Controls

P

Participant (n)

435

196

 

Gender (male, N (%))

144 (33.1%)

92 (46.9%)

0.001

Age (years)

60.8 ± 10.5

59.9 ± 10.4

0.322

EF

63.374 ± 8.058

64.757 ± 8.600

0.005

CRP

1.822 ± 2.891

1.137 ± 2.032

0.139

HDLC

1.116 ± 0.506

1.164 ± 0.303

0.006

LDLC

2.335 ± 0.778

2.242 ± 0.659

0.296

TC

4.379 ± 1.055

4.211 ± 0.896

0.102

TG

1.949 ± 1.835

1.5 ± 0.933

0.001

ApoA

222.737 ± 236.699

162.74 ± 218.465

0.001

APOA1

1.055 ± 0.213

1.155 ± 0.294

0.015

ApoB

0.784 ± 0.248

0.75 ± 0.203

0.609

GLU

6.046 ± 2.221

5.322 ± 1.375

0.001

GHb

5.924 ± 1.205

5.57 ± 0.903

0.007

Hypertension, yes, N (%)

282 (65.6%)

106 (54.1%)

0.006

Diabetes, yes, N (%)

123 (28.5%)

25 (12.8%)

0.000019

Smoker, yes, N (%)

206 (47.7%)

73 (37.6%)

0.019

Association with the risk of developing CAD

Seven SNPs were genotyped in 435 cases and 196 controls. Allele frequency distribution was shown in Table 2. All SNPs were accorded with HWE. In these 7 SNPs, rs5882 and rs662799 were associated with CAD (p ≤ 0.05), in which rs662799 was explored by Ye et al. of the association between coronary heart disease (CHD) and the APOA5 rs662799 polymorphism [14]. After adjustment of age, gender, smoking, diabetes and hypertension, only rs662799 was significantly associated with increasing risk of developing CAD (risk allele T, OR = 1.374, 95% CI = 1.032–1.83, P = 0.03) (Table 3). Minor (T) allele frequency of SNP rs662799 in control and case group were 33.9 and 38.2% respectively. The results remained significant when HDLC, LDLC, TC and TG were introduced into the model (data not shown). No significant differences were observed in other polymorphisms after adjustment.
Table 2

Hardy-Weinberg equilibrium

SNP

Genotype

Cases

Controls

Actual

Expected

χ2

P

Actual

Expected

χ2

P

rs1800588

CC

149

147.1273

0.163509

0.685946

78

78.67222

0.050014

0.823039

CT

178

181.7455

82

80.65556

TT

58

56.12727

20

20.67222

rs320

GG

12

11.31992

0.059714

0.806948

7

4.666667

1.68

0.194924

GT

107

108.3602

42

46.66667

TT

260

259.3199

119

116.6667

rs5128

CC

44

46

0.195652

0.658253

21

16.8342

2.076069

0.149625

CG

188

184

72

80.33161

GG

182

184

100

95.8342

rs5882

AA

116

120.1608

0.681031

0.409232

74

68.04188

3.21016

0.073182

AG

213

204.6784

80

91.91623

GG

83

87.1608

37

31.04188

rs662

AA

51

50.9484

0.000125

0.991075

26

30.48262

1.854151

0.173301

AG

186

186.1032

99

90.03476

GG

170

169.9484

62

66.48262

rs662799

AA

211

206.1553

1.235773

0.266287

111

108.7513

0.759702

0.383422

AG

170

179.6894

67

71.4974

GG

44

39.15529

14

11.7513

rs693

CC

349

348.889

0.015305

0.901541

147

147.8421

0.974012

0.323682

CT

35

35.22208

24

22.31579

TT

1

0.888961

0

0.842105

Table 3

association analysis

rs

Control

Case

P

OR

95%CI

FDR

Padj

OR

95%CI

rs1800588

0.661/0.339

0.618/0.382

0.163

1.205

0.927–1.566

0.228

0.104

1.257

0.954–1.656

rs320

0.833/0.167

0.827/0.173

0.803

1.045

0.741–1.472

0.803

0.963

0.991

0.693–1.418

rs5128

0.705/0.295

0.667/0.333

0.187

1.193

0.918–1.55

0.218

0.31

1.152

0.876–1.514

rs5882

0.597/0.403

0.536/0.464

0.048

1.281

1.002–1.638

0.168

0.128

1.222

0.944–1.581

rs662

0.596/0.404

0.646/0.354

0.098

0.809

0.629–1.04

0.229

0.123

0.813

0.624–1.058

rs662799

0.753/0.247

0.696/0.304

0.043

1.326

1.008–1.744

0.301

0.03

1.374

1.032–1.83

rs693

0.93/0.07

0.952/0.048

0.135

0.669

0.394–1.137

0.236

0.168

0.679

0.392–1.177

Stratified association analysis

Gender, smoking, diabetes and hypertension status were stratified for further investigation (Table 4). Rs662799 was associated with CAD in male and hypertension group. Rs5882 was associated with CAD in non-hypertension group. Rs1800588 was associated with CAD in smoking group.
Table 4

Stratified analysis

 

rs1800588

rs320

rs5128

rs5882

rs662

rs662799

rs693

 

Padj

OR

95% CI

Padj

OR

95% CI

Padj

OR

95% CI

Padj

OR

95% CI

Padj

OR

95% CI

Padj

OR

95% CI

Padj

OR

95% CI

Male

0.059

1.417

0.987–2.036

0.399

1.233

0.758–2.006

0.285

1.215

0.85–1.736

0.25

1.218

0.87–1.705

0.392

0.858

0.604–1.218

0.003

1.8

1.215–2.668

0.356

0.711

0.345–1.467

Female

0.835

1.047

0.68–1.61

0.294

0.747

0.433–1.288

0.742

1.075

0.7–1.651

0.32

1.227

0.819–1.839

0.146

0.739

0.492–1.111

0.936

0.982

0.637–1.514

0.316

0.646

0.275–1.519

Diabetes

0.961

0.982

0.472–2.042

0.447

1.484

0.536–4.106

0.686

1.154

0.576–2.309

0.105

1.738

0.891–3.394

0.591

0.831

0.422–1.635

0.226

1.576

0.754–3.291

0.091

0.323

0.087–1.197

No diabetes

0.073

1.313

0.975–1.767

0.695

0.926

0.629–1.363

0.374

1.145

0.849–1.543

0.362

1.14

0.86–1.51

0.155

0.811

0.608–1.082

0.067

1.34

0.98–1.832

0.392

0.77

0.423–1.401

Hypertension

0.206

1.269

0.877–1.835

0.313

1.28

0.792–2.069

0.158

1.297

0.903–1.863

0.929

0.985

0.7–1.385

0.429

0.869

0.613–1.232

0.004

1.792

1.199–2.679

0.233

0.615

0.277–1.366

No hypertension

0.24

1.285

0.846–1.954

0.214

0.7

0.399–1.228

0.969

0.992

0.649–1.516

0.023

1.593

1.067–2.379

0.14

0.736

0.49–1.106

0.992

1.002

0.657–1.529

0.515

0.775

0.36–1.668

Smoking

0.035

1.603

1.035–2.482

0.702

0.895

0.506–1.581

0.402

1.197

0.786–1.824

0.393

1.191

0.798–1.779

0.115

0.715

0.472–1.084

0.108

1.456

0.921–2.301

0.235

0.631

0.295–1.35

No smoking

0.625

1.094

0.763–1.57

0.811

1.058

0.667–1.677

0.511

1.13

0.785–1.626

0.273

1.209

0.861–1.699

0.451

0.876

0.621–1.236

0.123

1.339

0.924–1.941

0.552

0.783

0.35–1.753

Discussion

CAD was a complex disease that influenced by a combination of genetic and environmental factors, but so far the molecular mechanisms of CAD were only partially revealed [15]. Lipoprotein metabolism was associated with CAD susceptibility in general population. LDLC, TC, TG, HDLC were associated with CAD status in many studies.

Many genes involving in lipid and lipoprotein metabolism pathway had been revealed to be related with CAD. Notably, SNPs of some genes that involved in lipid metabolism were identified to be associated with CAD developing risk. Many studies investigate the relationship between polymorphisms in or near these genes and CAD development. However, the results were inconsistent in different races or populations. The present study was performed to investigate if the polymorphisms in lipid metabolism genes were associated with CAD occurrences.

A significant association of the SNP rs662799 in APOA5 genes with CAD was observed after adjustment of gender, age, smoking, diabetes, hypertension and lipid status. However, no association was found between other SNPs and CAD susceptibility.

Apoa5 played an important role in TG metabolism (synthesis and removal of TG) [16]. TG was also a risk factor for CAD. APOA5 gene expression level was associated with TG plasma concentration [16]. Rs662799 was a polymorphism located in the promoter region (−1131 T > C) that influences the expression level of APOA5 gene [17]. The allele frequency of rs662799 in HapMap database was 1.7, 13.3. 26.7 and 28.9% in European, African, Chinese and Japanese respectively.

In previous researches, rs662799 was associated with TG level [18, 19]. In some studies, no association was detected between rs662799 and CAD [20], possibly due to lack of power.

There are limitations to the study. Our analysis only includes the study of Chinese Han population, and the sample size is small. So, it might not apply to other populations.

Conclusion

In conclusion, we confirmed that rs662799 in APOA5 gene was significantly associated with CAD development. As the cohort size was limited, there may not be sufficient power to detect the effects of other SNPs. Besides of SNPs, rare mutations might also contribute to CAD development. Further studies with larger population and different technology were needed in order to provide more insights into the biological relevance of CAD.

Abbreviations

APOA5: 

Apolipoprotein A5

CAD: 

Coronary Artery Disease

DBP: 

Diastolic blood pressure

GWAS: 

Genome Wide Association Study

HDL-C: 

High-density lipoprotein cholesterol

HWE: 

Hardy-Weinberg equilibrium

LDL-C: 

Low-density lipoprotein cholesterol

NGS: 

Next-generation Sequencing

PAGE: 

Polyacrylamide gel electrophoresis

SBP: 

Systolic blood pressure

SNPs: 

Single Nucleotide Polymorphisms

TC: 

Serum total cholesterol

TG: 

Triglyceride

Declarations

Acknowledgements

We are grateful for all the members which participate in this study.

Funding

This work was supported by the Natural Science Foundation of Hubei Province, China (Grant No. 2014CFA066).

Availability of data and materials

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

Authors’ contributions

SFD designed the study, HC performed the experiments, and MZ, XYW, XL, YW, DCL analyzed the data and prepared the manuscript. All authors have read and approved the final version of this manuscript.

Ethics approval and consent to participate

This study was approved by the Medical Ethics Committee of Southern Medical University and compliant with the principles set forth by the Declaration of Helsinki Principles. Written informed consent was provided by all patients.

Consent for publication

Not applicable.

Competing interests

The authors declare that they have no competing interests.

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Authors’ Affiliations

(1)
Department of Cardiology, Southern Medical University, Guangzhou, China
(2)
Department of Cardiology, Wuhan General Hospital of Guangzhou Military Region, Wuhan City, China
(3)
Department of Cardiology, Inner Mongolia People’s Hospital, Huhehaote, China
(4)
Department of Cardiology, Inner Mongolia Medical University, Huhehaote, China

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Copyright

© The Author(s). 2018

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