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Genome-wide association study of high-altitude pulmonary edema in a Han Chinese population

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Oncotarget. 2017; 8:31568-31580. https://doi.org/10.18632/oncotarget.16362

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Xun Li, Tianbo Jin, Mingxia Zhang, Hua Yang, Xuewen Huang, Xiaobo Zhou, Wenchao Huang, Lipeng Qin, Longli Kang, Ming Fan and Suzhi Li _

Abstract

Xun Li1,*, Tianbo Jin2,3,*, Mingxia Zhang3, Hua Yang3, Xuewen Huang1, Xiaobo Zhou1, Wenchao Huang1, Lipeng Qin1, Longli Kang2, Ming Fan4, Suzhi Li1

1Center of Altitude Disease, General Hospital of Tibet Military Area Command, Lhasa 850003, China

2Key Laboratory of High Altitude Environment and Genes Related to Diseases of Tibet Autonomous Region, School of Medicine, Xizang Minzu University, Xianyang, Shaanxi 712082, China

3School of Life Sciences, Northwest University, Xi’an, Shaanxi 710069, China

4Department of Brain Protection and Plasticity, Institute of Basic Medical Sciences, Beijing 100850, China

*These authors contributed equally to this work and are joint first authors

Correspondence to:

Ming Fan, email: fanming1973@126.com

Suzhi Li, email: suzhilixizang@163.com

Keywords: high-altitude pulmonary edema (HAPE), single nucleotide polymorphisms (SNPs), genome wide association analysis (GWAS), susceptibility gene

Received: October 20, 2016     Accepted: February 27, 2017     Published: March 18, 2017

ABSTRACT

A two-stage genome-wide association study (GWAS) was performed to identify and analyze genes and single nucleotide polymorphisms (SNPs) associated with high-altitude pulmonary edema (HAPE) in a Han Chinese patient population. In the first stage, DNA samples from 68 patients with recurrent HAPE were scanned using Affymetrix SNP Array 6.0 Chips, and allele frequencies were compared to those of 84 HapMap CHB samples to identify candidate SNPs. In the second stage, the 77 identified candidate SNPs were examined in an independent cohort of samples from 199 HAPE patients and 304 controls. Associations between SNPs and HAPE risk were tested using various genetic models. Of the 77 original SNPs, 7 were found to be associated with HAPE susceptibility in the second stage of the study. GO and pathway enrichment analysis of the 7 SNPs revealed 5 adjacent genes involved in various processes, including regulation of nucleoside diphosphate metabolism, thyroid hormone catabolism, and low-density lipoprotein receptor activity. These results suggest the identified SNPs and genes may contribute to the physiopathology of HAPE.


Genome-wide association study of high-altitude pulmonary edema in a Han Chinese population | Li | Oncotarget

INTRODUCTION

High altitude pulmonary edema (HAPE) is a non-cardiogenic form of pulmonary edema that develops in unacclimatized healthy individuals at altitudes above 2500–3000 m [1]. It is a potentially fatal medical condition and the most common cause of death among high-altitude illnesses [2]. However, the pathogenesis of HAPE remains poorly understood. Previous studies suggest that uneven hypoxic pulmonary vasoconstriction, pulmonary capillary damage, and increased pulmonary artery pressure play important roles in the pathogenesis of HAPE [3, 4].

HAPE is caused by the interaction of both genetic and environmental risk factors. Previous studies have shown that family history and race influence individual susceptibilities to HAPE [5]. Some people are susceptible to high-altitude pulmonary edema, whereas others are resistant to this condition [6, 7]. The prevalence of HAPE in the Han Chinese population in Tibet, which is about 0.4%~2% [8] and differs depending on age, gender, and occupation, is higher than that observed in native Tibetans. Rate of ascent, altitude reached, pre-acclimatization, and individual susceptibility are the major factors that contribute to high-altitude maladies [9]. In addition, patients who have previously developed HAPE are more likely to experience recurrence, which suggests the presence of a constitutional, and possibly a genetic, component in its etiology [10].

Several recent studies have examined the genetic basis of HAPE, focusing mainly on genetic polymorphisms in the beta2-adrenergic receptor [11], vascular endothelial growth factor [12], the renin angiotensin system [13], and pulmonary surfactant proteins A1 and A2 [14] in subjects susceptible to HAPE. Polymorphisms within these genes may explain individual variation in hypoxic responses and perhaps indicate susceptibility to high-altitude disease. However, the precise role of these genes in HAPE pathogenesis remains unclear.

To identify genetic variants across the whole genome that are specifically related to HAPE risk, we conducted a two-stage GWAS analysis in 68 patients with recurrent HAPE and in 84 HapMap CHB populations as references. We further evaluated potential associations with HAPE risk in a replication cohort with a total of 199 HAPE patients and 304 healthy controls from a Han Chinese population. While previous GWAS studies were based on case-control samples only, here we examined a large number of cases to identify genes that might be related to HAPE susceptibility.

RESULTS

A total of 571 subjects, including 267 HAPE patients (246 males, 21 females; mean age 32.6 ± 10.7) and 304 controls (290 males, 14 females; mean age 36.2 ± 4.5), were examined in this analysis. Age distribution differed between the patient and control groups (p < 0.05). Participant characteristics are listed in Table 1.

Table 1: Basic characteristics of cases and controls in this study

Variables

Case N (%)

Control N (%)

p-value

Age (years)

32.6 ± 10.7

36.2 ± 4.5

< 0.005a

Sex

> 0.005b

Male

246 (92.0%)

290 (95.4%

Female

21 (8.0%)

14 (4.6%)

Total

267

304

aP values were calculated from two-sided chi-square tests.

bP values were calculated by Student t tests.

We first scanned DNA samples from 68 patients with recurrent HAPE using Affymetrix Genome-Wide Human SNP Array 6.0 Chips. After filtering with standard quality-control procedures, 502,689 SNPs with an overall call rate of 99.92% were qualified for further GWAS analysis. To identify SNPs that might be associated with the risk of HAPE, we compared SNP allele frequencies in the 68 patients to those of the 84 HapMap CHB controls and found that frequencies differed for 77 SNPs. Information regarding these 77 SNPs and their associated genes is shown in Table 2. A Manhattan plot was generated for the SNPs in patients with recurrent HAPE under the allelic and genotypic model (Figure 1). MDS and QQ-plot revealed that there was no obvious population stratification in this experiment (Figure 2 and Figure 3).

Table 2: Basic information of the significantly different SNPs between 68 recurrent HAPE cases and 84 Hapmap CHB subjects in the first stage

SNP ID

Chromosome

Gene (s)

Alleles

MAF

Position

Band

Role

Aa/B

Case

Control

rs4908427

1

CAMTA1

G/A

0.059

0.054

6976226

1p36.31

Intron

rs9661274

1

G/A

0.059

0.060

30149249

1p35.3

rs17484974

1

C/T

0.132

0.054

39186794

1p34.3

rs12406517

1

PPAP2B

G/C

0.110

0.072

56974278

1p32.2

Intron

rs1694212

1

T/C

0.132

0.113

61480000

1p31.3

rs10789097

1

C/G

0.066

0.071

62119978

1p31.3

rs17188846

1

KCNH1

C/G

0.184

0.067

211261821

1q32.2

Intron

rs2577156

1

EPRS

C/A

0.051

0.077

220190845

1q41

Intron

rs3008613

1

MIA3

G/A

0.110

0.079

222795769

1q41

Intron

rs4491711

2

RASGRP3

G/A

0.103

0.066

33776743

2p22.3

Intron

rs11125567

2

CCDC88A

A/G

0.081

0.143

55627913

2p16.1

Intron

rs11898268

2

C/A

0.000

0.071

154622125

2q23.3

rs10167840

2

T/G

0.140

0.077

199241493

2q33.1

rs7612512

3

G/C

0.140

0.157

3412838

3p26.2

rs1846594

3

C/T

0.213

0.196

112916203

3q13.2

rs11924340

3

A/G

0.060

0.110

145325196

3q24

rs12504325

4

C4orf6

A/G

0.103

0.083

5537184

4p16.2

Downstream

rs17598758

4

G/T

0.110

0.089

20190068

4p15.31

rs7677143

4

C/T

0.147

0.220

117082198

4q26

rs6535838

4

A/C

0.199

0.101

153023402

4q31.3

rs7688505

4

T/A

0.110

0.110

185828318

4q35.1

rs41417552

5

CMBL

G/A

0.110

0.083

10305452

5p15.2

Intron

rs2161592

5

A/G

0.162

0.085

50772554

5q11.2

rs3777207

5

ELL2

A/G

0.118

0.084

95231115

5q15

Intron

rs6595114

5

C/T

0.118

0.101

117676709

5q23.1

rs2193963

5

C/T

0.096

0.089

121596196

5q23.2

rs17652561

5

SLC6A7

A/G

0.162

0.185

149584197

5q32

Intron (boundary)

rs2937582

5

A/G

0.434

0.080

166465008

5q34

rs2984100

6

C/G

0.125

0.143

8592499

6p24.3

rs7762263

6

T/C

0.110

0.066

11975250

6p24.1

rs4715938

6

G/C

0.103

0.113

14944857

6p23

rs725050

6

C/T

0.162

0.196

89267376

6q15

rs1419722

7

EIF3B

C/T

0.142

0.107

2413258

7p22.3

Intron

rs10178082

7

T/A

0.199

0.157

10706912

7p21.3

rs4947936

7

C/A

0.103

0.133

50906752

7p12.1

rs12226072

7

A/T

0.294

0.339

96443614

7q21.3

rs2956956

8

DLGAP2

C/T

0.066

0.083

1553118

8p23.3

Intron

rs2980508

8

SGK223

C/T

0.096

0.106

8171732

8p23.1

Downstream

rs310282

8

C/A

0.132

0.125

23614369

8p21.2

rs4573320

8

C/T

0.343

0.446

65128758

8q12.3

rs1568828

8

PREX2

A/G

0.081

0.101

69122128

8q13.2

Intron

rs1006698

9

KCNV2

T/G

0.206

0.232

2725283

9p24.2

Intron

rs1011531

9

A/G

0.110

0.114

13755192

9p23

rs13289064

9

C/G

0.228

0.179

16897685

9p22.2

rs10984811

9

ANP32B

C/A

0.149

0.173

100784050

9q22.33

Downstream

rs12554842

9

COL5A1

T/C

0.081

0.071

137573407

9q34.3

Intron

rs11593009

10

T/A

0.051

0.065

31974946

10p11.22

rs12243354

10

TET1

A/G

0.125

0.131

70411536

10q21.3

Intron (boundary)

rs7923700

10

GRID1

G/A

0.162

0.190

87843290

10q23.1

Intron

rs2239153

12

VWF

C/T

0.338

0.399

6186667

12p13.31

Intron

rs7303062

12

A/G

0.074

0.084

22990450

12p12.1

rs10879780

12

T/G

0.235

0.226

74837984

12q21.1

rs1316571

13

T/C

0.081

0.095

68320718

13q21.32

rs9550256

13

FAM70B

A/T

0.265

0.220

114494675

13q34

Intron

rs17435983

14

A/G

0.169

0.101

27860597

14q12

rs8007744

14

G/A

0.265

0.262

28329396

14q12

rs17777329

14

G/A

0.081

0.060

101934762

14q32.31

rs4787426

16

IL4R

G/T

0.059

0.065

27384731

16p12.1

Downstream

rs1075355

16

VAT1L

C/G

0.147

0.107

77874149

16q23.1

Intron

rs12931468

16

ATP2C2

G/C

0.074

0.054

84495301

16q24.1

Intron (boundary)

rs8067836

17

LASP1

G/T

0.081

0.071

37081707

17q12

Downstream

rs16955841

17

HLF

G/A

0.105

0.107

53364146

17q22

Intron

rs12450240

17

NARF

T/G

0.265

0.235

80423712

17q25.3

Intron

rs9961715

18

DLGAP1

C/T

0.029

0.054

3824312

18p11.31

Intron

rs12606093

18

KIAA0427

C/A

0.044

0.065

46077295

18q21.1

Intron

rs6074799

20

MACROD2

G/C

0.110

0.101

14771472

20p12.1

Intron

rs9617661

22

TUBA8

G/T

0.029

0.060

18595352

22q11.21

Intron

rs5758913

22

C/T

0.154

0.161

43148259

22q13.2

Notes: A/B stands for minor/major alleles on the entire sample frequencies.

Manhattan plot for the whole SNPs in recurrent HAPE subjects of Chinese Han decent.

Figure 1: Manhattan plot for the whole SNPs in recurrent HAPE subjects of Chinese Han decent. Chromosomes are shown in alternate colors. (A) Allelic model; (B) Genotypic model.

Multidimensional scaling approach (MDS) analysis for the first stage.

Figure 2: Multidimensional scaling approach (MDS) analysis for the first stage.

QQ plot for the whole SNPs for the first stage.

Figure 3: QQ plot for the whole SNPs for the first stage.

Of the 77 SNPs, 68 were qualified after Sequenom MassARRAY Assay Design. In a second experiment, we confirmed the results of the first experiment by genotyping the 68 SNPs in 199 HAPE patients and 304 controls of Han Chinese descent. Table 3 summarizes the characteristics of the tested SNPs and their predicted associations with HAPE risk in crude analysis. Three SNPs (rs17484974, rs725050, and rs10178082) were excluded at the 5% p-value for Hardy-Weinberg equilibrium (HWE). A χ2 test revealed that two SNPs, rs10789097 (OR = 1.825; 95% CI= 1.062–3.135, p = 0.027), and rs17777329 (OR = 1.800; 95% CI = 1.083–2.991, p = 0.022) were associated with an increased risk of HAPE (Table 3).

Table 3: Allele frequencies in cases and controls and odds ratio estimates for HAPE for the replication stage

SNP ID

Gene (s)

Alleles

MAF

HWE

ORs

95% CI

p-value

Aa/B

Case

Control

p-value

rs4908427

CAMTA1

G/A

0.035

0.048

1

0.728

0.380

1.395

0.337

rs9661274

G/A

0.065

0.067

0.3787

0.967

0.581

1.607

0.896

rs17484974

C/T

0.111

0.112

7.851E-47#

0.987

0.660

1.476

0.949

rs12406517

PPAP2B

G/C

0.055

0.054

0.2131

1.020

0.585

1.776

0.946

rs1694212

T/C

0.139

0.133

0.8025

1.049

0.726

1.517

0.798

rs10789097

C/G

0.075

0.043

1

1.825

1.062

3.135

0.027*

rs17188846

KCNH1

C/G

0.139

0.130

0.198

1.080

0.746

1.564

0.683

rs2577156

EPRS

C/A

0.076

0.077

0.08963

0.978

0.607

1.576

0.928

rs3008613

MIA3

G/A

0.093

0.105

0.552

0.871

0.569

1.334

0.526

rs4491711

RASGRP3

G/A

0.063

0.097

1

0.624

0.384

1.014

0.055

rs11125567

CCDC88A

A/G

0.111

0.127

1

0.857

0.578

1.271

0.443

rs11898268

C/A

0.003

0.000

1

-

-

-

-

rs10167840

T/G

0.088

0.095

1

0.914

0.589

1.420

0.690

rs7612512

G/C

0.151

0.172

1

0.857

0.606

1.212

0.382

rs1846594

C/T

0.193

0.232

0.6287

0.794

0.582

1.085

0.148

rs11924340

A/G

0.111

0.086

0.4683

1.329

0.871

2.029

0.186

rs12504325

C4orf6

A/G

0.083

0.090

0.4886

0.909

0.579

1.428

0.679

rs17598758

G/T

0.101

0.105

1

0.950

0.626

1.441

0.808

rs7677143

C/T

0.143

0.191

1

0.709

0.502

1.002

0.051

rs6535838

A/C

0.133

0.137

0.4691

0.972

0.671

1.407

0.879

rs7688505

T/A

0.156

0.140

0.8133

1.135

0.796

1.619

0.483

rs41417552

CMBL

G/A

0.169

0.127

0.4419

1.404

0.985

2.003

0.060

rs2161592

A/G

0.108

0.102

0.7525

1.067

0.707

1.609

0.758

rs3777207

ELL2

A/G

0.108

0.107

0.5511

1.012

0.673

1.521

0.955

rs6595114

C/T

0.118

0.097

0.5042

1.246

0.830

1.870

0.288

rs2193963

C/T

0.106

0.095

1

1.130

0.742

1.721

0.568

rs17652561

SLC6A7

A/G

0.145

0.151

0.6541

0.949

0.663

1.357

0.773

rs2937582

A/G

0.439

0.434

0.6413

1.021

0.791

1.318

0.871

rs2984100

C/G

0.184

0.156

0.2747

1.220

0.873

1.707

0.244

rs7762263

T/C

0.111

0.123

0.594

0.883

0.595

1.312

0.539

rs4715938

G/C

0.161

0.155

0.3873

1.048

0.741

1.481

0.792

rs725050

C/T

0.249

0.243

0.04164#

1.029

0.767

1.381

0.849

rs1419722

EIF3B

C/T

0.143

0.149

0.648

0.958

0.669

1.372

0.816

rs10178082

T/A

0.141

0.161

0.0001595#

0.852

0.597

1.216

0.378

rs4947936

C/A

0.163

0.150

0.6502

1.109

0.784

1.569

0.559

rs12226072

A/T

0.317

0.340

0.7994

0.897

0.685

1.175

0.431

rs2956956

DLGAP2

C/T

0.078

0.092

0.7288

0.833

0.527

1.317

0.433

rs2980508

SGK223

C/T

0.146

0.135

1

1.097

0.763

1.576

0.619

rs310282

C/A

0.096

0.135

0.3231

0.678

0.451

1.019

0.061

rs4573320

C/T

0.279

0.299

0.8912

0.907

0.685

1.200

0.493

rs1568828

PREX2

A/G

0.108

0.109

0.3729

0.995

0.662

1.494

0.980

rs1006698

KCNV2

T/G

0.216

0.263

0.2387

0.772

0.572

1.041

0.089

rs1011531

A/G

0.118

0.120

1

0.981

0.664

1.450

0.925

rs13289064

C/G

0.231

0.183

1

1.346

0.986

1.837

0.060

rs10984811

ANP32B

C/A

0.178

0.148

0.6479

1.250

0.889

1.757

0.199

rs12554842

COL5A1

T/C

0.095

0.109

0.2273

0.867

0.569

1.320

0.505

rs11593009

T/A

0.078

0.076

0.6833

1.032

0.642

1.658

0.896

rs12243354

TET1

A/G

0.138

0.137

0.809

1.014

0.703

1.464

0.940

rs7923700

GRID1

G/A

0.116

0.117

0.399

0.988

0.666

1.467

0.954

rs2239153

VWF

C/T

0.415

0.428

0.4145

0.948

0.734

1.224

0.682

rs7303062

A/G

0.050

0.061

0.6129

0.817

0.467

1.428

0.477

rs10879780

T/G

0.193

0.192

0.5762

1.007

0.731

1.389

0.964

rs1316571

T/C

0.078

0.079

0.102

0.985

0.616

1.577

0.951

rs9550256

FAM70B

A/T

0.234

0.261

0.232

0.865

0.644

1.161

0.333

rs17435983

A/G

0.143

0.128

0.7988

1.136

0.786

1.640

0.497

rs8007744

G/A

0.261

0.267

0.7692

0.973

0.729

1.299

0.852

rs17777329

G/A

0.085

0.049

0.5287

1.800

1.083

2.991

0.022*

rs4787426

IL4R

G/T

0.083

0.066

1

1.284

0.795

2.073

0.306

rs1075355

VAT1L

C/G

0.131

0.092

0.1553

1.481

0.992

2.212

0.053

rs12931468

ATP2C2

G/C

0.055

0.044

1

1.259

0.707

2.244

0.434

rs8067836

LASP1

G/T

0.111

0.082

0.7057

1.387

0.906

2.125

0.131

rs16955841

HLF

G/A

0.133

0.109

0.1143

1.255

0.830

1.898

0.282

rs12450240

NARF

T/G

0.242

0.281

0.8872

0.818

0.612

1.093

0.174

rs9961715

DLGAP1

C/T

0.055

0.041

0.4021

1.364

0.758

2.455

0.298

rs12606093

KIAA0427

C/A

0.063

0.066

0.3739

0.952

0.568

1.595

0.851

rs6074799

MACROD2

G/C

0.113

0.140

0.2313

0.784

0.533

1.154

0.216

rs9617661

TUBA8

G/T

0.050

0.033

1

1.556

0.826

2.930

0.168

rs5758913

C/T

0.156

0.151

0.6541

1.035

0.729

1.469

0.848

Notes: a Minor allele; *p value ≤ 0.05 indicates statistical significance; #site with HWE p ≤ 0.05 is excluded;

Abbreviations: HWE, Hardy-Weinberg Equilibrium; MAF, minor allele frequency; SNP, single nucleotide polymorphism; ORs, odds ratios; CI, confidence interval.

Associations between the SNPs and HAPE risk were tested under five different genetic models (co-dominant, dominant, co-dominant, recessive, and log-additive). Seven SNPs were associated with HAPE susceptibility. The rs41417552 SNP was associated with an increased risk of HAPE based on the results of the co-dominant (OR = 1.58; 95% CI = 1.04–2.40, p = 0.057 for the “A/G” genotype), dominant (OR = 1.62; 95% CI = 1.07–2.44, p = 0.022 for the “A/G-G/G” genotype), over-dominant (OR = 1.87; 95% CI = 1.06–3.27, p = 0.03 for the “A/G” genotype), and log-additive (OR = 1.59; 95% CI = 1.09–2.32, p = 0.017) models. The rs10984811 SNP increased HAPE risk in both the co-dominant (OR = 3.95; 95% CI = 1.33–11.73, p = 0.032 for the “C/C” genotype) and recessive (OR= 3.97; 95% CI = 1.34–11.75, p = 0.0089 for the “C/C” genotype) models. The rs17777329 SNP was also associated with an increased risk of HAPE in the co-dominant (OR = 1.88; 95% CI = 1.07–3.30, p = 0.051), dominant (OR = 1.95; 95% CI = 1.12–3.37, p = 0.018), over-dominant (OR = 1.87; 95% CI = 1.06–3.27, p = 0.03), and log-additive (OR = 1.89; 95% CI = 1.13–3.16, p = 0.015) models. The rs1075355 SNP was associated with increased HAPE risk in the co-dominant (OR = 1.65; 95% CI = 1.04–2.62, p = 0.093) and over-dominant (OR = 1.66; 95% CI = 1.04–2.63, p = 0.032) models. Additionally, the rs12226072 (OR = 0.58; 95% CI = 0.40–0.86, p = 0.0053) and rs6074799 (OR = 0.59; 95% CI = 0.37–0.93, p = 0.02) SNPs were associated with a decreased risk of HAPE in the over-dominant model, and the rs7677143 SNP was associated with a decreased risk of HAPE in the log-additive model (OR = 0.69; 95% CI = 0.48–0.99, p = 0.039) (Table 4).

Table 4: Logistic regression analysis of the associations between SNPs and HAPE risk

SNP

Model

Genotype

Controls

Cases

OR (95 % CI)a

P-valuea

AIC

BIC

rs7677143

Co-dominant

T/T

199 (65.5%)

145 (72.9%)

1

0.11

661.6

682.7

T/C

94 (30.9%)

51 (25.6%)

0.72 (0.48–1.09)

C/C

11 (3.6%)

3 (1.5%)

0.38 (0.10–1.40)

Dominant

T/T

199 (65.5%)

145 (72.9%)

1

0.062

660.6

677.5

T/C-C/C

105 (34.5%)

54 (27.1%)

0.69 (0.46–1.02)

Recessive

T/T-T/C

293 (96.4%)

196 (98.5%)

1

0.16

662.1

678.9

C/C

11 (3.6%)

3 (1.5%)

0.41 (0.11–1.53)

Over-dominant

T/T-C/C

210 (69.1%)

148 (74.4%)

1

0.16

662.1

678.9

T/C

94 (30.9%)

51 (25.6%)

0.75 (0.50–1.12)

Log-additive

0.69 (0.48–0.99)

0.039

659.8

676.7

rs12226072

Co-dominant

T/T

131 (43.1%)

103 (51.8%)

1

0.017

657.9

679

A/T

139 (45.7%)

66 (33.2%)

0.61 (0.41–0.90)

A/A

34 (11.2%)

30 (15.1%)

1.19 (0.67–2.09)

Dominant

T/T

131 (43.1%)

103 (51.8%)

1

0.077

660.9

677.8

A/T-A/A

173 (56.9%)

96 (48.2%)

0.72 (0.50–1.04)

Recessive

T/T-A/T

270 (88.8%)

169 (84.9%)

1

0.16

662

678.9

A/A

34 (11.2%)

30 (15.1%)

1.48 (0.86–2.54)

Over-dominant

T/T-A/A

165 (54.3%)

133 (66.8%)

1

0.0053

656.3

673.2

A/T

139 (45.7%)

66 (33.2%)

0.58 (0.40–0.86)

Log-additive

0.92 (0.71–1.20)

0.56

663.7

680.6

rs6074799

Co-dominant

C/C

222 (73%)

159 (79.9%)

1

0.03

659

680.1

C/G

79 (26%)

35 (17.6%)

0.60 (0.38–0.95)

G/G

3 (1%)

5 (2.5%)

2.57 (0.59–11.13)

Dominant

C/C

222 (73%)

159 (79.9%)

1

0.068

660.7

677.6

C/G-G/G

82 (27%)

40 (20.1%)

0.67 (0.43–1.04)

Recessive

C/C-C/G

301 (99%)

194 (97.5%)

1

0.15

662

678.9

G/G

3 (1%)

5 (2.5%)

2.88 (0.67–12.40)

Over-dominant

C/C-G/G

225 (74%)

164 (82.4%)

1

0.02

658.7

675.6

C/G

79 (26%)

35 (17.6%)

0.59 (0.37–0.93)

Log-additive

0.78 (0.52–1.15)

0.21

662.5

679.4

rs41417552

Co-dominant

A/A

230 (75.7%)

135 (68.2%)

1

0.057

658.3

679.4

A/G

71 (23.4%)

59 (29.8%)

1.58 (1.04–2.40)

G/G

3 (1%)

4 (2%)

2.68 (0.58–12.38)

Dominant

A/A

230 (75.7%)

135 (68.2%)

1

0.022

656.7

673.6

A/G-G/G

74 (24.3%)

63 (31.8%)

1.62 (1.07–2.44)

Recessive

A/A-A/G

301 (99%)

194 (98%)

1

0.27

660.8

677.7

G/G

3 (1%)

4 (2%)

2.35 (0.51–10.80)

Over-dominant

A/A-G/G

233 (76.6%)

139 (70.2%)

1

0.042

657.9

674.8

A/G

71 (23.4%)

59 (29.8%)

1.54 (1.02–2.34)

Log-additive

1.59 (1.09–2.32)

0.017

656.3

673.2

rs10984811

Co-dominant

A/A

219 (72%)

139 (69.8%)

1

0.032

659.2

680.3

C/A

80 (26.3%)

49 (24.6%)

0.97 (0.64–1.49)

C/C

5 (1.6%)

11 (5.5%)

3.95 (1.33–11.73)

Dominant

A/A

219 (72%)

139 (69.8%)

1

0.52

663.7

680.5

C/A-C/C

85 (28%)

60 (30.1%)

1.14 (0.76–1.70)

Recessive

A/A-C/A

299 (98.4%)

188 (94.5%)

1

0.0089

657.2

674.1

C/C

5 (1.6%)

11 (5.5%)

3.97 (1.34–11.75)

Over-dominant

A/A-C/C

224 (73.7%)

150 (75.4%)

1

0.68

663.9

680.8

C/A

80 (26.3%)

49 (24.6%)

0.92 (0.60–1.39)

Log-additive

1.28 (0.91–1.80)

0.16

662

678.9

rs17777329

Co-dominant

A/A

275 (90.5%)

167 (83.9%)

1

0.051

660.1

681.2

A/G

28 (9.2%)

30 (15.1%)

1.88 (1.07–3.30)

G/G

1 (0.3%)

2 (1%)

3.69 (0.33–41.57)

Dominant

A/A

275 (90.5%)

167 (83.9%)

1

0.018

658.4

675.3

A/G-G/G

29 (9.5%)

32 (16.1%)

1.95 (1.12–3.37)

Recessive

A/A-A/G

303 (99.7%)

197 (99%)

1

0.3

663

679.9

G/G

1 (0.3%)

2 (1%)

3.40 (0.30–38.26)

Over-dominant

A/A-G/G

276 (90.8%)

169 (84.9%)

1

0.03

659.3

676.2

A/G

28 (9.2%)

30 (15.1%)

1.87 (1.06–3.27)

Log-additive

1.89 (1.13–3.16)

0.015

658.1

675

rs1075355

Co-dominant

G/G

253 (83.2%)

149 (74.9%)

1

0.093

661.3

682.4

G/C

46 (15.1%)

48 (24.1%)

1.65 (1.04–2.62)

C/C

5 (1.6%)

2 (1%)

0.71 (0.13–3.85)

Dominant

G/G

253 (83.2%)

149 (74.9%)

1

0.052

660.3

677.2

G/C-C/C

51 (16.8%)

50 (25.1%)

1.56 (1.00–2.45)

Recessive

G/G-G/C

299 (98.4%)

197 (99%)

1

0.6

663.8

680.7

C/C

5 (1.6%)

2 (1%)

0.64 (0.12–3.51)

Over-dominant

G/G-C/C

258 (84.9%)

151 (75.9%)

1

0.032

659.5

676.4

G/C

46 (15.1%)

48 (24.1%)

1.66 (1.04–2.63)

Log-additive

1.40 (0.93–2.11)

0.1

661.4

678.3

Notes: aAdjusted for age and sex. *P-value ≤ 0.05 indicates statistical significance.

Abbreviations: SNP, single nucleotide polymorphism; OR, odds ratio; CI, confidence interval; AIC, Akaike’s Information Criterion; BIC, Bayesian Information Criterion.

To identify genes that might be involved in HAPE invasion, we also performed gene annotation and functional classification for the 7 significant loci we identified in the replication study. GO and KEGG pathway enrichment analyses identified 5 potential candidate genes located within ± 500kb of these SNPs (Table 5). These genes were mainly involved in cellular tight junctions, oxidation and reduction, extracellular matrix metabolism, pulmonary development, and pulmonary smooth muscular tension adjustment.

Table 5: Go and pathway analysis of the top genes of GWAS

Function

p-value

Adjusted
p-value

Genes

zinc ion binding

7.99E-07

1.60E-06

ADAMTS18;VAT1L

protein binding

1.59E-05

1.06E-05

INADL;KCNV2

thyroxine 5-deiodinase activity

7.56E-05

3.36E-05

DIO3

very-low-density lipoprotein receptor activity

2.27E-04

6.52E-05

VLDLR

thyroxine 5’-deiodinase activity

2.27E-04

6.52E-05

DIO3

metal ion binding

2.45E-04

6.52E-05

ADAMTS18

low density lipoprotein receptor activity

8.31E-04

1.45E-04

VLDLR

peptidase activity

8.69E-04

1.45E-04

ADAMTS18

oxidoreductase activity

0.001153

1.58E-04

WWOX

selenium binding

0.002265

2.01E-04

DIO3

ATP binding

0.004538

3.70E-04

CCT5

voltage-gated potassium channel activity

0.007231

5.26E-04

KCNV2

metalloendopeptidase activity

0.007756

5.35E-04

ADAMTS18

unfolded protein binding

0.008356

5.52E-04

CCT5

potassium ion binding

0.00948

6.12E-04

KCNV2

nucleotide binding

0.010312

6.34E-04

CCT5

manganese ion binding

0.011276

6.63E-04

NUDT7

coenzyme binding

0.0115

6.67E-04

WWOX

hydrolase activity

0.012359

6.83E-04

NUDT7

voltage-gated ion channel activity

0.013964

7.35E-04

KCNV2

protein dimerization activity

0.031265

0.001374

WWOX

magnesium ion binding

0.032144

0.001398

NUDT7

hydrolase activity, acting on acid anhydrides, in phosphorus-containing anhydrides

0.053236

0.002117

NUDT7

calcium ion binding

0.06698

0.002528

VLDLR

receptor activity

0.121001

0.00436

VLDLR

Pathways

p-value

Adjusted
p-value

Genes

1,4-Dichlorobenzene degradation

0.000371

0.000742

CMBL

gamma-Hexachlorocyclohexane degradation

0.006661

0.001665

CMBL

Maturity onset diabetes of the young

0.008871

0.001971

IAPP

Tight junction

0.049301

0.002641

INADL

Wnt signaling pathway

0.054948

0.002641

PPP2R5C

DISCUSSION

In this study, we conducted a two-stage GWAS analysis to investigate the genetic factors associated with HAPE in a Han Chinese population. Seven loci, including four susceptibility loci and three protective loci, were found to be associated with HAPE in this analysis. Gene annotation and functional classification of these loci revealed that five of the candidate genes are potentially involved in the pathogenesis of HAPE. To the best of our knowledge, this is one of the largest studies to explore the genetic factors underlying the development of HAPE in a Han Chinese population.

The rs10789097 locus contained no annotated genes, and the gene nearest to it was INADL, which encodes inactivation no-after potential (INAD) protein, also known as protein associated with Lin seven 1 (Pals1) -associated tight junction protein (PATJ). INAD contains multiple PDZ domains, which are protein-protein interaction modules that typically bind to short peptide sequences at the carboxyl terminus of target proteins. Proteins containing multiple PDZ domains often bind to different transmembrane and intracellular proteins and play central roles as organizers of multimeric complexes [15]. PATJ is a polarity protein and plays a complex role in the maintenance of epithelial polarity [16]. Considering that stress failure in pulmonary capillaries is an important contributor to HAPE pathogenesis, we speculate that the INADL gene may also impact the pathogenesis of HAPE.

The KCNV2 gene, which encodes the Kcnv2 protein, belongs to a group of potassium channel modulatory subunits that are electrically silent and cannot form functional homotetramers. These silent subunits form heterotetramers that modulate the properties of other subunits, increasing the functional diversity of channel subfamilies [17]. Voltage-gated K+ (KV) channel activity in pulmonary artery smooth muscle cells (PASMC) is important for the control of apoptosis and proliferation as well as the regulation of membrane potential and pulmonary vascular tone [18]. A previous study demonstrated that KNCV2 contributes to susceptibility to and was considered a genetic modifier of epilepsy [17]. However, the role of KNCV2 in HAPE remains unknown, and additional studies are needed.

The rs1075355 SNP had the strongest association in this study. It is located in the intron of the VAT1L gene and encodes a vesicle amine transport 1 homologue; its cellular localization and functions have not yet been researched. An association study suggested that a locus on chromosome 16q23-24 (including VAT1L) affected HDLC levels in two independent French-Canadian populations [19]. Additionally, a genome-wide association study of the rate of cognitive decline in Alzheimer’s disease indicated that rs9934540 genetic variants in the VAT1L gene intron were positively associated with the development of Alzheimer’s disease [20]. Two different genes, ADAMTS18 and WWOX, are adjacent to the rs1075355 SNP.

ADAMTS18 is a member of the ADAMTS protease family, which is comprised of complex secreted enzymes containing a reprolysin-type prometalloprotease domain attached to an ancillary domain with a highly-conserved structure including at least one thrombospondin type 1 repeat. Known functions of ADAMTS proteases include processing procollagens and von Willebrand factor and catabolism of aggrecan, versican, and brevican. ADAMTS also play important roles in connective tissue organization, coagulation, inflammation, arthritis, angiogenesis, and cell migration [21, 22] and are regulated by the Tissue Inhibitor of Metalloproteinase 3 Gene (TIMP3). Furthermore, Kobayashi et al.’s study in a Japanese population demonstrated that TIMP3 was associated with susceptibility to HAPE [23]. TIMPs play a crucial role in the physiological turnover of the extracellular matrix (ECM) by tightly regulating matrix metalloproteinase (MMP) activity [24]. TIMP3 is the only TIMP that binds tightly to the ECM, and the balance between MMPs and TIMPs plays an important role in maintaining the integrity of healthy tissues. Disturbances of the TIMP/MMP system are implicated in various pathologic conditions in lungs, including pulmonary inflammation, edema, emphysema, and fibrosis, where loss of ECM integrity is a principal feature [25]. Our findings together with those of previous studies demonstrate that the balance between MMPs and TIMPs plays an important role in HAPE pathogenesis.

The human WWOX gene encodes a putative tumor suppressor WW domain-containing oxidoreductase WOX1 (also known as WWOX or FOR). High frequencies of loss of heterozygosity (LOH) in this gene have been observed in prostate, lung, breast, and other cancers [27]. A recent genome-wide association analysis identified WWOX as one of the loci associated with forced vital capacity (FVC), a spirometric measure of pulmonary function used to diagnose and monitor lung diseases [27]. These findings indicate that the WWOX gene may be involved in lung development and the pathogenesis of restrictive lung disease; future studies are needed to determine whether WWOX is similarly associated with HAPE pathogenesis.

Although the statistical power of the present study was sufficient, some limitations should be considered when interpreting these results. First, the patient sample sizes were relatively small, and the association between the identified polymorphisms and HAPE susceptibility should be confirmed in future studies with larger sample sizes. Secondly, the mechanisms by which the potential candidate genes contribute to the pathogenesis of HAPE remain unclear, and functional studies of these candidate genes are needed. In conclusion, our study provides new evidence regarding the pathogenesis of HAPE in the Han Chinese population. Although the genetic factors that contribute to the development of HAPE remain largely unknown, we identified candidate genes that contribute to HAPE susceptibility. However, polymorphisms in these genes should be examined further before definitive conclusions regarding their role in HAPE pathogenesis can be made.

MATERIALS AND METHODS

Study populations

In this two-stage case-control study, we evaluated associations between genetic variants across the human genome and the risk of HAPE. All participants included in the study were from the Han Chinese population. Study subjects for both GWAS scan of HAPE and the replication phase of the experiment were selected according to detailed inclusion and exclusion criteria. Briefly, patients who lived on the Tibet Plateau and were diagnosed with HAPE were recruited from the General Hospital of Tibet Military Region. Control subjects were Han Chinese immigrants living in Lhasa, Tibet, and their medical histories and physical examinations confirmed that they were in good health. Demographic information was collected through interviews using a standard questionnaire. Ultimately, 267 HAPE cases (89 recurrent HAPE cases; mean age 32.6 ± 10.7 years) and 304 controls (mean age 36.2 ± 4.5 years) were selected for the study. Two mL of venous blood were collected from each individual into tubes containing 2% EDTA-K2, centrifuged, and stored at –80°C until analysis. DNA was extracted from whole blood samples using the QIAamp® DNA Blood Mini kit (Qiagen), and DNA concentrations were measured using a NanoDrop 2000. Informed consent was obtained from all subjects, and the Human Ethics Committee of our institute approved the investigation.

Study design

For the GWAS scan experiment, we scanned DNA samples from 68 patients with recurrent HAPE using Affymetrix SNP Array 6.0 Chips. The allele frequencies of the 68 patients were then compared to those of 84 HapMap CHB subjects to identify significant differences in SNP frequencies. In the replication experiment, associations between the SNPs identified in the GWAS scan and risk of HAPE where examined in 199 HAPE patients and 304 unrelated healthy controls. Furthermore, to identify candidate genes that might underlie HAPE susceptibility, we conducted Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis for the genes involved in the associated genetic loci.

Quality control (QC) in GWAS

A total of 906,660 SNPs were genotyped in 68 patients with recurrent HAPE during the GWAS experiment using Affymetrix Genome-Wide Human SNP Array 6.0 Chips as described previously [28]. A systematic quality control (QC) procedure was applied to both SNPs and samples prior to the association analysis. SNPs were excluded if they (i) did not map onto autosomal chromosomes; (ii) had a call rate of less than 95%; (iii) had a minor allele frequency (MAF) less than 0.05; or (iv) deviated from Hardy-Weinberg equilibrium (p < 0.001). Sixty-eight HAPE cases and 84 controls with 502,689 SNPs remained after QC.

SNP selection and genotyping in the replication study

After genome-wide association analysis, we compared the allele frequencies of the 502,689 SNPs in the 68 recurrent HAPE cases to those in the 84 HapMap CHB controls using a chi-squared (χ2) test. Allele frequencies differed significantly between HAPE cases and controls for 77 SNPs. In the replication study, these 77 SNPs were genotyped in 199 HAPE patients and 304 normal controls. SNPs that were significantly associated with HAPE risk (p < 0.05) in the replication study were selected for GO and KEGG pathway enrichment analyses. Genotyping was performed using Sequenom MassARRAY Assay Design 3.0 Software [29] with a genotype success rate greater than 97.3%.

Statistical analysis

SPSS 17.0 statistical software was used for statistical analysis. An exact test was used to test the departure of each SNP frequency from Hardy–Weinberg equilibrium (HWE) in control subjects. Differences in SNP genotype distribution between HAPE patients and controls were compared using a χ2 test [30]. Odds ratios (ORs) and 95% confidence intervals (CIs) were determined using unconditional logistic regression analysis with adjustments for age and gender [31]. All p values presented in this study are two-sided; p < 0.05 indicated a statistically significant difference.

Associations between SNPs and HAPE risk were tested using various genetic models (co-dominant, dominant, over-dominant, recessive, and log-additive) and analyzed using SNP Stats software (obtained from http://bioinfo.iconcologia.net, Catalan Institute of Oncology, Barcelona, Spain). To reduce population stratification, a multidimensional scaling approach (MDS) was used and a QQ-plot was generated using PLINK software (version 1.07) (http://www.cog-genomics.org/plink2/) [32]. R software (version 2.11.1) was used for statistical analysis and to generate plots, including Manhattan plots. GO analysis were performed using Bingo software [33], and pathway enrichment analyses were performed using Mas 3.0 software (http://bioinfo.capitalbio.com/mas3/).

Authors’ contributions

Not applicable.

ACKNOWLEDGMENTS

We are grateful to all of the patients and other individuals who made this work possible. We would also like to thank the clinicians and hospital staff who contributed to data collection for this study.

CONFLICTS OF INTEREST

The authors declare that they have no competing interests.

FUNDING

This work was supported by the Science and Technology Project of Tibet Autonomous Region (2009Z-3) and by a general financial grant from the China Postdoctoral Science Foundation (2012M512186).

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