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Diabetologia

, Volume 58, Issue 6, pp 1231–1238 | Cite as

Common variants in or near ZNRF1, COLEC12, SCYL1BP1 and API5 are associated with diabetic retinopathy in Chinese patients with type 2 diabetes

  • Danfeng Peng
  • Jie Wang
  • Rong Zhang
  • Feng Jiang
  • Shanshan Tang
  • Miao Chen
  • Jing Yan
  • Xue Sun
  • Shiyun Wang
  • Tao Wang
  • Dandan Yan
  • Yuqian Bao
  • Cheng HuEmail author
  • Weiping JiaEmail author
Article

Abstract

Aims/hypothesis

Three recent genome-wide association studies (GWAS) identified several single-nucleotide polymorphisms (SNPs) with modest effects on diabetic retinopathy in Mexican-American and white patients with diabetes. This study aimed to evaluate the effects of these variants on diabetic retinopathy in Chinese patients with type 2 diabetes.

Methods

A total of 1,972 patients with type 2 diabetes were recruited to this study, including 819 patients with diabetic retinopathy and 1,153 patients with diabetes of ≥5 years duration but without retinopathy. Forty SNPs associated with diabetic retinopathy in three GWAS were genotyped. Fundus photography was performed to diagnose and classify diabetic retinopathy.

Results

rs17684886 in ZNRF1 and rs599019 near COLEC12 were associated with diabetic retinopathy (OR 0.812, p = 0.0039 and OR 0.835, p = 0.0116, respectively) and with the severity of diabetic retinopathy (p = 0.0365 and p = 0.0252, respectively, for trend analysis). Sub-analysis in patients with diabetic retinopathy revealed that rs6427247 near SCYL1BP1 (also known as GORAB) and rs899036 near API5 were associated with severe diabetic retinopathy (OR 1.368, p = 0.0333 and OR 0.340, p = 0.0005, respectively). The associations between rs6427247 and rs899036 and severe diabetic retinopathy became more evident after a meta-analysis of published GWAS data (OR 1.577, p = 2.01 × 10−4 for rs6427247; OR 0.330, p = 5.84 × 10−7 for rs899036).

Conclusions/interpretation

We determined that rs17684886 and rs599019 are associated with diabetic retinopathy and that rs6427247 and rs899036 are associated with severe diabetic retinopathy in Chinese patients with type 2 diabetes.

Keywords

Diabetic retinopathy Polymorphisms Type 2 diabetes 

Abbreviations

AER

Albumin excretion rate

EDIC

Epidemiology of Diabetes Intervention and Control Trial

GoKinD

Genetics of Kidney in Diabetes

GWAS

Genome-wide association study

NPDR

Non-proliferative diabetic retinopathy

PDR

Proliferative diabetic retinopathy

SNP

Single-nucleotide polymorphism

WESDR

Wisconsin Epidemiologic Study of Diabetic Retinopathy

Introduction

Diabetes mellitus, particularly type 2 diabetes, has reached epidemic proportions in China and other countries. As a major chronic microvascular complication of diabetes, diabetic retinopathy is the leading cause of vision loss among working-age adults around the world [1]. The increasing prevalence of diabetes has led to an increase in the number of patients suffering from diabetic retinopathy, which represents a heavy public health burden. A meta-analysis has indicated that the global prevalence of diabetic retinopathy among patients with diabetes is 34.6% [2]. There are approximately 93 million people with diabetic retinopathy, 17 million with proliferative diabetic retinopathy (PDR) and 28 million with vision-threatening diabetic retinopathy.

Diabetic retinopathy is a multifactorial disease. Epidemiological and prospective studies have demonstrated that the duration of diabetes, poor glycaemic control and blood pressure are major risk factors for diabetic retinopathy [3, 4, 5]. Some patients with intensive glycaemic control and a shorter duration of diabetes may still develop diabetic retinopathy, whereas some are spared despite poor glycaemic control and a longer duration of diabetes [6]. These phenomena may be explained by the genetic contributions to diabetic retinopathy. Studies have suggested a genetic influence on the development of diabetic retinopathy [7]. One study found the same degree of severity in high concordance in twins with diabetes [8]. A familial aggregation of diabetic retinopathy has been observed across different ethnicities. Siblings and relatives of patients with diabetic retinopathy have a significantly increased risk of diabetic retinopathy compared with siblings and relatives of patients with diabetes but without diabetic retinopathy [9, 10, 11]. This trend is even more pronounced in families exhibiting more severe diabetic retinopathy [12, 13, 14]. However, the study of the genetics of diabetic retinopathy is still in its infancy, and attempts to identify susceptible loci have been unsuccessful.

Most of the genetic research into diabetic retinopathy has involved a candidate gene approach. A significant number of genes and genetic variants has been proposed through this approach (e.g. AKR1B1, VEGFA, ACE and AGER) [15]. However, individual studies have frequently yielded inconsistent and even conflicting findings. Since 2005, genome-wide association studies (GWAS) have been widely used for complex diseases, including diabetic retinopathy but, to date, no locus for diabetic retinopathy from GWAS reaches conventional significance criteria. In 2010, a GWAS among Mexican-American families identified several single-nucleotide polymorphisms (SNPs) and genes associated with severe diabetic retinopathy at a p value of less than 0.0001 [16]. Another genome-wide meta-analysis using Genetics of Kidney in Diabetes (GoKinD) and Epidemiology of Diabetes Intervention and Control Trial (EDIC) samples identified several SNPs at close to a genome-wide level for severe diabetic retinopathy (rs476141, p = 1.2 × 10−7; rs10521145, p = 3.4 × 10−6) [17]. A replication analysis for severe diabetic retinopathy was conducted in a cohort of diabetic individuals from the Wisconsin Epidemiologic Study of Diabetic Retinopathy (WESDR) [18]. The top two associations observed were for rs4865047 (p = 2.06 × 10−5) and rs1902491 (p = 2.81 × 10−5). Because the effects of these loci on diabetic retinopathy in the Chinese population are unknown, our study attempted to replicate the associations observed in these three studies in a cohort of Chinese patients with type 2 diabetes in Shanghai.

Methods

Participants

This study involved 1,972 patients with type 2 diabetes recruited from the Shanghai Diabetic Complications Study [19] and Shanghai Diabetes Institute Inpatient Database of Shanghai Jiao Tong University Affiliated Sixth People’s Hospital [20, 21]. All participants were unrelated patients with type 2 diabetes meeting the 1999 WHO criteria. Of these patients, 819 were diagnosed with diabetic retinopathy and 1,153 had diabetes for longer than 5 years but without diabetic retinopathy and were considered as cases and controls for diabetic retinopathy, respectively. This study was approved by the institutional review board of Shanghai Jiao Tong University Affiliated Sixth People’s Hospital, and written informed consent was obtained from each participant.

Clinical measurements

Fundus photography of all participants was performed according to a standardised protocol at the Department of Ophthalmology, Shanghai Jiao Tong University Affiliated Sixth People’s Hospital. Both eyes of each patient were photographed with a 45° 6.3 megapixel digital nonmydriatic camera (Canon CR6-45NM; Lake Success, NY, USA). Retinopathy was graded according to the International Classification of Diabetic Retinopathy [22] as follows: mild non-proliferative diabetic retinopathy (NPDR), moderate NPDR, severe NPDR or PDR. The more severely affected eye of each patient was used to classify their retinopathy status. The 24 h albumin excretion rate (AER) was used to assess nephropathy. AER was measured on three consecutive days, and the mean value was recorded for each patient. Patients with AER ≥ 30 mg/24 h were diagnosed with diabetic nephropathy. To evaluate glycaemic control the HbA1c level was measured by high-performance liquid chromatography (Variant II; Bio-Rad, Hercules, CA, USA). Blood pressure and lipid profile data were also collected for each participant.

SNP selection, genotyping and quality control

A total of 40 SNPs, including 21 SNPs reported in the Mexican-American GWAS (rs10501943, rs10519765, rs1197310, rs1445754, rs2300782, rs6427247, rs699549, rs763970, rs899036, rs599019, rs1106412, rs1033465, rs11583330, rs11635920, rs11812882, rs11927173, rs17083119, rs3014267, rs3098241, rs6726798 and rs6909083), 17 SNPs reported in the GoKinD and EDIC GWAS (rs476141, rs10521145, rs17670074, rs227455, rs238252, rs737141, rs11074904, rs11647881, rs11736136, rs151227, rs151320, rs17063155, rs17684886, rs238250, rs4941432, rs9888035 and rs11871508) and 2 SNPs reported in the WESDR replication study (rs4865047 and rs1902491), were genotyped in all participants. Genotyping was performed by primer extension of multiplex products with detection by matrix-assisted laser desorption ionisation–time of flight mass spectroscopy using a MassARRAY Compact Analyzer (Sequenom, San Diego, CA, USA). rs11871508 and rs6909083 failed during the assay design and genotyping, respectively. The genotyping data underwent a series of quality control checks and cleaned data were used in further statistical analyses. The call rate for each SNP was more than 96%. The concordance rate based on 100 duplicates was greater than 99% for all SNPs. Eighty-three individuals were excluded due to sample call rate <80%. The Hardy–Weinberg equilibrium test was performed before the statistical analysis (a two-tailed p value <0.01 was considered statistically significant), and rs238250 was excluded (p = 0.004). Another 12 SNPs (rs11583330, rs6726798, rs1106412, rs11812882, rs17063155, rs238252, rs151227, rs151320, rs10521145, rs11647881, rs11074904 and rs737141) were rare in our population (minor allele frequency <0.0015) and were excluded from statistical analyses.

Statistical analysis

The allelic frequencies were compared between patients with or without diabetic retinopathy using a χ 2 test in PLINK (v1.07; http://pngu.mgh.harvard.edu/~purcell/plink/) [23], and ORs with 95% CIs are presented. Genotype distributions between patients with or without diabetic retinopathy were compared using logistic regression under an additive model with adjustment of confounding factors. The effects of SNPs on the levels of retinopathy severity were analysed by trend analysis. Combined ORs from different studies were calculated by Comprehensive Meta Analysis (v2.2.057; Englewood, NJ, USA) with a fixed- or random-effect model after testing for heterogeneity. The test for homogeneity was assessed by the Cochran Q test. Correction for multiple testing was performed using PLINK through 10,000 permutation tests and empirical p values are presented. The statistical analyses were performed using SAS 9.3 (SAS institute, Cary, NC, USA) unless specified otherwise. A two-tailed p value <0.05 was considered statistically significant.

On the basis of an estimated effect size of genetic loci for diabetic retinopathy (~1.25), our samples had >85% power to detect an effect SNP with minor allele frequency of 0.3 and >75% power to detect an effect SNP with minor allele frequency of 0.2 at a level of significance of 0.05.

Results

The clinical characteristics of the samples that passed genotype quality control are shown in Table 1. Compared with patients without diabetic retinopathy, patients with diabetic retinopathy were younger, diagnosed with diabetes at earlier age, and had higher HbA1c and blood pressure and higher prevalence of nephropathy.
Table 1

Clinical characteristics of the samples

Characteristic

Patients with diabetic retinopathy

Patients without retinopathy

p value

Samples (n)

789

1,110

 

Mild NPDR

490

  

Moderate NPDR

136

  

Severe NPDR

118

  

PDR

45

  

Sex, male/female (n)

370/419

494/616

 

Age (years)

62.18 ± 11.03

64.06 ± 10.95

0.0001

BMI (kg/m2)

24.58 ± 7.46

24.40 ± 3.50

0.51

Age at diagnosis of diabetes (years)

51.40 ± 10.86

53.32 ± 10.65

<0.0001

Duration of diabetes (years)

10 (5–15)

10 (7–13)

0.79

HbA1c (%)

9.11 ± 2.27

8.36 ± 2.06

<0.0001

HbA1c (mmol/mol)

76.11 ± 24.90

67.91 ± 22.42

<0.0001

Systolic blood pressure (mmHg)

137.74 ± 19.14

134.53 ± 17.66

0.0002

Diastolic blood pressure (mmHg)

81.34 ± 9.71

80.30 ± 9.30

0.0282

Percentage with nephropathy (%)

37.4

26.6

<0.0001

The data are n, mean ± SD or median (interquartile range)

We first analysed the association between SNPs and the risk of diabetic retinopathy. As shown in Table 2, rs1197310, rs4865047, rs17684886 and rs599019 were nominally associated with diabetic retinopathy (OR 1.155, 95% CI 1.013, 1.316, p = 0.0309 for the rs1197310 T allele, OR 0.854, 95% CI 0.735, 0.991, p = 0.0379 for the rs4865047 T allele, OR 0.829, 95% CI 0.729, 0.944, p = 0.0046 for the rs17684886 A allele and OR 0.832, 95% CI 0.729, 0.950, p = 0.0065 for rs599019 C allele). After adjusting for confounding factors, including the duration of diabetes, HbA1c level, systolic and diastolic blood pressure, BMI and nephropathy, rs17684886 and rs599019 still exhibited significant association with diabetic retinopathy (OR 0.812, 95% CI 0.705, 0.935, p = 0.0039, empirical p = 0.0783 for rs17684886 and OR 0.835, 95% CI 0.725, 0.961, p = 0.0116, empirical p = 0.24 for rs599019). We then analysed the effects of rs17684886 or rs599019 on the severity of diabetic retinopathy in all samples. As shown in Table 3, both SNPs were associated with the level of diabetic retinopathy, with the risk allele more frequent in patients with more severe diabetic retinopathy (p = 0.0365 for rs17684886 and p = 0.0252 for rs599019 for trend analysis).
Table 2

Associations of SNPs with diabetic retinopathy

Chromosome

SNP

Position (Build 38)

Nearest gene

Minor/major allele

Risk allele

Minor allele frequency

Allelic OR (95% CI)

p value (empirical p valuea)

Genotypic OR (95% CI)b

p value (empirical p valuea)b

Cases

Controls

1

rs6427247

170411339

SCYL1BP1 (GORAB)

G/A

A

0.310

0.312

0.994 (0.864, 1.143)

0.93

0.974 (0.837, 1.134)

0.74

1

rs1033465

173018590

TNFSF18

T/A

T

0.022

0.018

1.170 (0.739, 1.853)

0.50

1.319 (0.813, 2.139)

0.26

1

rs3014267

227365216

CDC42BPA

G/A

A

0.238

0.249

0.939 (0.808, 1.092)

0.42

0.922 (0.784, 1.085)

0.33

1

rs476141

244013122

LOC339529

A/C

A

0.214

0.210

1.027 (0.877, 1.203)

0.74

1.000 (0.844, 1.185)

1.00

2

rs699549

4657673

LINC01249

T/C

C

0.246

0.257

0.945 (0.813, 1.097)

0.46

0.928 (0.790, 1.091)

0.37

2

rs763970

137878563

HNMT

A/C

A

0.260

0.254

1.033 (0.890, 1.198)

0.67

1.062 (0.905, 1.245)

0.46

3

rs11927173

23183703

UBE2E2

C/T

T

0.186

0.192

0.962 (0.815, 1.134)

0.64

0.985 (0.825, 1.176)

0.87

3

rs1197310

133409380

BFSP2

T/A

T

0.508

0.472

1.155 (1.013, 1.316)

0.0309 (0.53)

1.086 (0.945, 1.247)

0.24

4

rs4865047

55955640

CEP135

T/C

C

0.238

0.268

0.854 (0.735, 0.991)

0.0379 (0.61)

0.909 (0.772, 1.072)

0.26

4

rs11736136

82097121

LOC101928987

G/A

G

0.066

0.060

1.110 (0.851, 1.449)

0.44

1.145 (0.855, 1.534)

0.36

4

rs1902491

155134181

NPY2R

G/T

T

0.154

0.157

0.979 (0.819, 1.170)

0.81

1.020 (0.842, 1.237)

0.84

5

rs1445754

84279813

EDIL3

A/T

A

0.056

0.052

1.100 (0.828, 1.463)

0.51

1.119 (0.823, 1.522)

0.47

5

rs2300782

111453087

CAMK4

T/C

T

0.476

0.469

1.030 (0.905, 1.173)

0.66

1.018 (0.886, 1.169)

0.81

6

rs17083119

121080964

TBC1D32

G/A

A

0.096

0.098

0.978 (0.786, 1.216)

0.84

0.974 (0.772, 1.230)

0.83

6

rs227455

165064562

C6orf118

C/T

T

0.466

0.476

0.963 (0.846, 1.096)

0.57

0.971 (0.845, 1.117)

0.68

8

rs3098241

103413076

SLC25A32

G/A

G

0.366

0.343

1.108 (0.968, 1.268)

0.14

1.126 (0.970, 1.307)

0.12

10

rs17670074

19416454

MALRD1

C/A

A

0.367

0.372

0.982 (0.859, 1.123)

0.79

1.086 (0.937, 1.260)

0.27

10

rs9888035

19426089

MALRD1

C/T

C

0.010

0.007

1.500 (0.739, 3.042)

0.26

1.484 (0.705, 3.124)

0.30

11

rs899036

41661360

API5

C/A

A

0.102

0.105

0.968 (0.783, 1.196)

0.76

0.936 (0.743, 1.180)

0.58

11

rs10501943

100076267

CNTN5

C/T

C

0.041

0.039

1.045 (0.751, 1.454)

0.79

0.828 (0.571, 1.199)

0.32

13

rs4941432

42524211

TNFSF11

A/G

G

0.171

0.189

0.888 (0.750, 1.052)

0.17

0.868 (0.725, 1.040)

0.13

15

rs10519765

32913223

FMN1

A/G

G

0.080

0.083

0.968 (0.764, 1.225)

0.78

0.905 (0.698, 1.172)

0.45

15

rs11635920

32920456

FMN1

A/T

T

0.081

0.082

0.989 (0.781, 1.251)

0.92

0.928 (0.717, 1.201)

0.57

16

rs17684886

75052977

ZNRF1

A/T

T

0.460

0.507

0.829 (0.729, 0.944)

0.0046 (0.11)

0.812 (0.705, 0.935)

0.0039 (0.0783)

18

rs599019

294495

COLEC12

C/A

A

0.385

0.430

0.832 (0.729, 0.950)

0.0065 (0.15)

0.835 (0.725, 0.961)

0.0116 (0.24)

The OR with 95% CI shown is for the minor allele

aEmpirical p values are based on 10,000 permutations

bAdjusted for duration of diabetes, HbA1c, systolic blood pressure, diastolic blood pressure, BMI and nephropathy under an additive model

Table 3

Distribution of rs17684886 and rs599019 among patients with different severities of diabetic retinopathy

SNP

Minor allele

Patients without retinopathy (n = 1,110)

Mild NPDR (n = 490)

Moderate NPDR (n = 136)

Severe NPDR (n = 118)

PDR (n = 45)

p value for trend analysis

MAF

Genotype count 11/12/22a

MAF

Genotype count 11/12/22a

MAF

Genotype count 11/12/22a

MAF

Genotype count 11/12/22a

MAF

Genotype count 11/12/22a

rs17684886

A

0.493

285/555/270

0.542

101/242/142

0.544

26/72/38

0.517

26/62/30

0.556

12/16/17

0.0365

rs599019

C

0.430

363/517/210

0.388

194/200/86

0.396

48/67/20

0.343

52/51/15

0.443

14/21/9

0.0252

a11, major allele homozygotes; 12, heterozygotes; 22, minor allele homozygotes

MAF, minor allele frequency

Because the 40 SNPs were reported to be possibly associated with severe diabetic retinopathy, we examined this relationship in patients with diabetic retinopathy. For severe diabetic retinopathy analysis, patients with mild NPDR (n = 485) were regarded as controls and patients with severe NPDR or PDR were regarded as cases (n = 163). As shown in Table 4, rs2300782 and rs899036 were significantly associated with severe diabetic retinopathy (OR 1.380, 95% CI 1.073, 1.777, p = 0.0121 for the rs2300782 T allele and OR 0.448, 95% CI 0.268, 0.749, p = 0.0017 for the rs899036 C allele). rs6427247 and rs10501943 were marginally associated with severe diabetic retinopathy (p = 0.0525 for rs6427247 and p = 0.0516 for rs10501943). After adjusting for the duration of diabetes, HbA1c level, systolic and diastolic blood pressure, BMI and nephropathy, rs6427247 and rs899036 were still associated with severe diabetic retinopathy (OR 1.368, 95% CI 1.025, 1.825, p = 0.0333 for rs6427247 and OR 0.340, 95% CI 0.185, 0.624, p = 0.0005 for rs899036). The association between rs899036 and severe diabetic retinopathy remained significant even after adjusting for multiple comparisons (empirical p = 0.0093).
Table 4

Associations of SNPs with severe diabetic retinopathy

Chromosome

SNP

Position (Build 38)

Nearest gene

Minor/major allele

Risk allele

Minor allele frequency

Allelic OR (95% CI)

p value (empirical p valuea)

Genotypic OR (95% CI)b

p value (empirical p valuea)b

Severe NPDR or PDR

Mild NPDR

1

rs6427247

170411339

SCYL1BP1 (GORAB)

G/A

G

0.362

0.304

1.298 (0.997, 1.690)

0.0525

1.368 (1.025, 1.825)

0.0333 (0.55)

1

rs1033465

173018590

TNFSF18

T/A

T

0.025

0.021

1.195 (0.521, 2.740)

0.67

1.313 (0.558, 3.090)

0.53

1

rs3014267

227365216

CDC42BPA

G/A

G

0.255

0.237

1.103 (0.825, 1.474)

0.51

1.197 (0.873, 1.641)

0.27

1

rs476141

244013122

LOC339529

A/C

C

0.194

0.220

0.856 (0.625, 1.172)

0.33

0.912 (0.643, 1.293)

0.61

2

rs699549

4657673

LINC01249

T/C

C

0.219

0.252

0.833 (0.616, 1.125)

0.23

0.734 (0.526, 1.024)

0.07

2

rs763970

137878563

HNMT

A/C

C

0.258

0.268

0.947 (0.711, 1.261)

0.71

1.027 (0.757, 1.393)

0.86

3

rs11927173

23183703

UBE2E2

C/T

T

0.169

0.194

0.844 (0.607, 1.175)

0.32

0.858 (0.607, 1.214)

0.39

3

rs1197310

133409380

BFSP2

A/T

A

0.509

0.487

1.092 (0.848, 1.405)

0.50

1.192 (0.899, 1.580)

0.22

4

rs4865047

55955640

CEP135

T/C

T

0.250

0.241

1.052 (0.786, 1.407)

0.74

0.956 (0.688, 1.329)

0.79

4

rs11736136

82097121

LOC101928987

G/A

A

0.047

0.072

0.635 (0.358, 1.127)

0.12

0.641 (0.326, 1.259)

0.20

4

rs1902491

155134181

NPY2R

G/T

T

0.150

0.152

0.990 (0.697, 1.407)

0.96

0.997 (0.678, 1.467)

0.99

5

rs1445754

84279813

EDIL3

A/T

T

0.043

0.060

0.706 (0.388, 1.283)

0.25

0.691 (0.360, 1.324)

0.26

5

rs2300782

111453087

CAMK4

T/C

T

0.537

0.457

1.380 (1.073, 1.777)

0.0121 (0.26)

1.081 (0.817, 1.432)

0.58

6

rs17083119

121080964

TBC1D32

G/A

A

0.096

0.101

0.939 (0.614, 1.437)

0.77

1.055 (0.658, 1.691)

0.83

6

rs227455

165064562

C6orf118

C/T

T

0.454

0.470

0.938 (0.729, 1.207)

0.62

0.996 (0.754, 1.315)

0.98

8

rs3098241

103413076

SLC25A32

G/A

A

0.371

0.378

0.970 (0.748, 1.257)

0.82

1.028 (0.752, 1.404)

0.86

10

rs17670074

19416454

MALRD1

C/A

C

0.387

0.374

1.055 (0.815, 1.365)

0.69

1.051 (0.786, 1.407)

0.74

10

rs9888035

19426089

MALRD1

C/T

T

0.003

0.010

0.292 (0.037, 2.292)

0.21

0.300 (0.036, 2.475)

0.26

11

rs899036

41661360

API5

C/A

A

0.055

0.116

0.448 (0.268, 0.749)

0.0017 (0.0454)

0.340 (0.185, 0.624)

0.0005 (0.0093)

11

rs10501943

100076267

CNTN5

C/T

C

0.059

0.034

1.765 (0.989, 3.149)

0.0516

1.803 (0.866, 3.751)

0.12

13

rs4941432

42524211

TNFSF11

A/G

A

0.203

0.168

1.257 (0.914, 1.728)

0.16

1.277 (0.904, 1.804)

0.17

15

rs10519765

32913223

FMN1

A/G

A

0.092

0.079

1.175 (0.756, 1.828)

0.47

1.249 (0.744, 2.096)

0.40

15

rs11635920

32920456

FMN1

A/T

A

0.089

0.079

1.132 (0.724, 1.770)

0.59

1.264 (0.752, 2.125)

0.38

16

rs17684886

75052977

ZNRF1

A/T

A

0.472

0.458

1.061 (0.825, 1.364)

0.65

0.958 (0.723, 1.268)

0.76

18

rs599019

294495

COLEC12

C/A

A

0.370

0.388

0.930 (0.717, 1.206)

0.58

0.942 (0.713, 1.244)

0.67

The OR with 95% CI shown is for the minor allele

aEmpirical P values are based on 10,000 permutations

bAdjusted for duration of diabetes, HbA1c, systolic blood pressure, diastolic blood pressure, BMI and nephropathy under an additive model

We also performed a meta-analysis with the fixed-effect model combining our data and the published GWAS data for rs6427247 and rs899036 [16]. As shown in Table 5, the associations of these two SNPs with severe diabetic retinopathy became more evident. rs899036 showed an association with severe diabetic retinopathy that approached genome-wide significance (OR 0.330, 95% CI 0.214, 0.510, p = 5.84 × 10−7).
Table 5

Meta-analysis of associations between SNPs and severe diabetic retinopathy

SNP

Chinese population

Mexican-American population

Meta-analysis

Risk allele

OR (95% CI)

p value

Risk allele

OR (95% CI)

p value

Risk allele

OR (95% CI)

p value

Q value

rs6427247

G

1.577 (1.240, 2.004)

0.0333

G

2.17 (1.41, 3.35)

4.56 × 10−4

G

1.577 (1.240, 2.004)

2.01 × 10−4

0.08

rs899036

A

0.330 (0.214, 0.510)

0.0005

A

0.32 (0.17, 0.59)

2.52 × 10−4

A

0.330 (0.214, 0.510)

5.84 × 10−7

0.89

The OR with 95% CI shown is for the minor allele with adjustment for confounding factors

The Q value is calculated by the Cochran Q test to assess homogeneity

Discussion

In this study, we analysed the effects of 40 SNPs reported by GWAS on diabetic retinopathy in Chinese patients with type 2 diabetes. We determined that rs17684886 and rs599019 were associated with diabetic retinopathy in Chinese patients with type 2 diabetes after adjusting for confounding factors, with the minor allele conferring a lower risk of diabetic retinopathy (OR 0.812, p = 0.0039 for rs17684886 and OR 0.835, p = 0.0116 for rs599019). In our sub-analysis, we detected associations between rs6427247 and rs899036 and severe diabetic retinopathy after adjustment for confounding factors (OR 1.368, p = 0.0333 for rs6427247 and OR 0.340, p = 0.0005 for rs899036). However, we cannot fully exclude the possibility that the association between rs17684886 or rs599019 and diabetic retinopathy was a false-positive finding because only a trend association or no association was observed after adjusting for multiple comparisons (empirical p = 0.0783 for rs17684886 and empirical p = 0.24 for rs599019). Nevertheless, we also determined that these two SNPs were significantly associated with the severity of diabetic retinopathy (p = 0.0365 for rs17684886 and p = 0.0252 for rs599019 for trend analysis), further supporting a role for them in diabetic retinopathy and limiting the possibility of a false positive. Besides, risk alleles of rs17684886 and rs59919 for diabetic retinopathy are the same as those reported for severe diabetic retinopathy in previous GWAS [16, 17] and the association of rs6427247 and rs899019 with severe diabetic retinopathy in our study is in agreement with the results of previous GWAS [16].

The four variants associated with diabetic retinopathy or severe diabetic retinopathy in our study are located in the noncoding regions—rs17684886 is an intron SNP of ZNRF1 and the other three SNPs are located in intergenic regions: rs599019 is located downstream of COLEC12; rs6427247 is located upstream of SCYL1BP1 (also known as GORAB) and rs899036 is located upstream of API5. To date, 43% of all trait-associated SNPs identified in GWAS studies are located in intergenic regions [24]. However, the intergenic SNPs may have an impact on neighbouring gene function. ZNRF1 encodes an E3 ubiquitin-protein ligase that plays a role in neural-cell differentiation. It has been reported to interact with tubulin, promote Wallerian degeneration and regulate Na+/K+ ATPase [25, 26, 27]. COLEC12 encodes a member of the C-lectin family. This protein is an endothelial cell surface receptor that displays several functions associated with host defence [28, 29]. SCYL1BP1 encodes SCY1-like 1-binding protein 1, a regulator of the p53 pathway with tumour-suppressive function [30, 31, 32]. API5 encodes the protein apoptosis inhibitor 5, which is reported to be a suppressor of apoptosis and to play a key role in tumour progression [33, 34]. However, the relationships of these SNPs and other genes are largely unknown. Thus, additional studies are needed to identify the causal loci and genes and to elucidate the underlying mechanism of diabetic retinopathy.

This study has a number of limitations. First, the small sample size was insufficient to identify SNPs with smaller effects on diabetic retinopathy. Second, we did not adjust for lifestyle factors, such as alcohol consumption and cigarette smoking, as confounding factors. Whether there is an interaction between lifestyle and these genetic variants on diabetic retinopathy remains unknown. Third, we could not entirely exclude population stratification in patients with and without diabetic retinopathy as a potential source of bias and incorrect inferences in genotype–disease association. The effect of population stratification may be minimal in the current study, however, as all the participants were recruited from the same geographic area with the same ancestry. Hence, studies with a larger sample size are needed to further replicate the associations identified in our study of a Chinese population.

In summary, we determined that rs17684886 in ZNRF1 and rs599019 near COLEC12 were associated with the risk of diabetic retinopathy and that rs6427247 near SCYL1BP1 and rs899036 near API5 were associated with the risk of severe diabetic retinopathy in Chinese patients with type 2 diabetes. Additional studies are needed to replicate this finding.

Notes

Acknowledgements

The authors are grateful to the patients who participated in this research and gratefully acknowledge the skilful technical support of the nursing and medical staff at the Shanghai Clinical Centre for Diabetes and Department of Endocrinology and Metabolism.

Funding

This work was supported by grants from the national 973 programme (2011CB504001), the National Science Foundation of China (81200582, 81322010 and 81170735), the national 863 programme (2012AA02A509), the Shanghai Talent Development Grant (2012041) and the Excellent Young Medical Expert of Shanghai (XYQ2011041).

Duality of interest

The authors declare that there is no duality of interest associated with this manuscript.

Contribution statement

DP and JW performed the majority of the analyses and drafted the manuscript. RZ, FJ and ST participated in the design of the study. MC, JY, XS, SW, TW and DY participated in the data analysis. YB provided helpful comments on study design and data analysis. CH and WJ conceived the study, participated in its design and helped to draft the manuscript. All authors contributed to the drafting or critical revision of the manuscript for important intellectual content. All authors read and approved the final manuscript. CH and WJ are the guarantors of this work.

References

  1. 1.
    Klein BE (2007) Overview of epidemiologic studies of diabetic retinopathy. Ophthalmic Epidemiol 14:179–183CrossRefPubMedGoogle Scholar
  2. 2.
    Yau JW, Rogers SL, Kawasaki R et al (2012) Global prevalence and major risk factors of diabetic retinopathy. Diabetes Care 35:556–564CrossRefPubMedCentralPubMedGoogle Scholar
  3. 3.
    Stratton IM, Adler AI, Neil HA et al (2000) Association of glycaemia with macrovascular and microvascular complications of type 2 diabetes (UKPDS 35): prospective observational study. BMJ 321:405–412CrossRefPubMedCentralPubMedGoogle Scholar
  4. 4.
    Tapp RJ, Shaw JE, Harper CA et al (2003) The prevalence of and factors associated with diabetic retinopathy in the Australian population. Diabetes Care 26:1731–1737CrossRefPubMedGoogle Scholar
  5. 5.
    Zhang X, Saaddine JB, Chou CF et al (2010) Prevalence of diabetic retinopathy in the United States, 2005-2008. JAMA 304:649–656CrossRefPubMedCentralPubMedGoogle Scholar
  6. 6.
    Sun JK, Keenan HA, Cavallerano JD et al (2011) Protection from retinopathy and other complications in patients with type 1 diabetes of extreme duration: the Joslin 50-Year Medalist Study. Diabetes Care 34:968–974CrossRefPubMedCentralPubMedGoogle Scholar
  7. 7.
    Liew G, Klein R, Wong TY (2009) The role of genetics in susceptibility to diabetic retinopathy. Int Ophthalmol Clin 49:35–52CrossRefPubMedCentralPubMedGoogle Scholar
  8. 8.
    Leslie RD, Pyke DA (1982) Diabetic retinopathy in identical twins. Diabetes 31:19–21CrossRefPubMedGoogle Scholar
  9. 9.
    The Diabetes Control and Complications Trial Research Group (1997) Clustering of long-term complications in families with diabetes in the diabetes control and complications trial. Diabetes 46:1829–1839CrossRefGoogle Scholar
  10. 10.
    Zhang X, Gao Y, Zhou Z, Wang J, Zhou Q, Li Q (2010) Familial clustering of diabetic retinopathy in Chongqing, China, type 2 diabetic patients. Eur J Ophthalmol 20:911–918PubMedGoogle Scholar
  11. 11.
    Rema M, Saravanan G, Deepa R, Mohan V (2002) Familial clustering of diabetic retinopathy in South Indian type 2 diabetic patients. Diabet Med 19:910–916CrossRefPubMedGoogle Scholar
  12. 12.
    Looker HC, Nelson RG, Chew E et al (2007) Genome-wide linkage analyses to identify Loci for diabetic retinopathy. Diabetes 56:1160–1166CrossRefPubMedGoogle Scholar
  13. 13.
    Hietala K, Forsblom C, Summanen P, Groop PH (2008) Heritability of proliferative diabetic retinopathy. Diabetes 57:2176–2180CrossRefPubMedCentralPubMedGoogle Scholar
  14. 14.
    Arar NH, Freedman BI, Adler SG et al (2008) Heritability of the severity of diabetic retinopathy: the FIND-Eye study. Invest Ophthalmol Vis Sci 49:3839–3845CrossRefPubMedCentralPubMedGoogle Scholar
  15. 15.
    Ng DP (2010) Human genetics of diabetic retinopathy: current perspectives. J Ophthalmol 2010:172593CrossRefPubMedCentralPubMedGoogle Scholar
  16. 16.
    Fu YP, Hallman DM, Gonzalez VH et al (2010) Identification of diabetic retinopathy genes through a genome-wide association study among Mexican-Americans from Starr County, Texas. J Ophthalmol 2010:861291CrossRefPubMedCentralPubMedGoogle Scholar
  17. 17.
    Grassi MA, Tikhomirov A, Ramalingam S, Below JE, Cox NJ, Nicolae DL (2011) Genome-wide meta-analysis for severe diabetic retinopathy. Hum Mol Genet 20:2472–2481CrossRefPubMedCentralPubMedGoogle Scholar
  18. 18.
    Grassi MA, Tikhomirov A, Ramalingam S et al (2012) Replication analysis for severe diabetic retinopathy. Invest Ophthalmol Vis Sci 53:2377–2381CrossRefPubMedCentralPubMedGoogle Scholar
  19. 19.
    Jia W, Gao X, Pang C et al (2009) Prevalence and risk factors of albuminuria and chronic kidney disease in Chinese population with type 2 diabetes and impaired glucose regulation: Shanghai diabetic complications study (SHDCS). Nephrol Dial Transplant 24:3724–3731CrossRefPubMedGoogle Scholar
  20. 20.
    Hu C, Zhang R, Wang C et al (2010) Effects of GCK, GCKR, G6PC2 and MTNR1B variants on glucose metabolism and insulin secretion. PLoS One 5:e11761CrossRefPubMedCentralPubMedGoogle Scholar
  21. 21.
    Hu C, Wang C, Zhang R et al (2010) Association of genetic variants of NOS1AP with type 2 diabetes in a Chinese population. Diabetologia 53:290–298CrossRefPubMedGoogle Scholar
  22. 22.
    Wilkinson CP, Ferris FL 3rd, Klein RE et al (2003) Proposed international clinical diabetic retinopathy and diabetic macular edema disease severity scales. Ophthalmology 110:1677–1682CrossRefPubMedGoogle Scholar
  23. 23.
    Purcell S, Neale B, Todd-Brown K et al (2007) PLINK: a toolset for whole-genome association and population-based linkage analysis. Am J Hum Genet 81:559–575CrossRefPubMedCentralPubMedGoogle Scholar
  24. 24.
    Hindorff LA, Sethupathy P, Junkins HA et al (2009) Potential etiologic and functional implications of genome-wide association loci for human diseases and traits. Proc Natl Acad Sci U S A 106:9362–9367CrossRefPubMedCentralPubMedGoogle Scholar
  25. 25.
    Yoshida K, Watanabe M, Hatakeyama S (2009) ZNRF1 interacts with tubulin and regulates cell morphogenesis. Biochem Biophys Res Commun 389:506–511CrossRefPubMedGoogle Scholar
  26. 26.
    Wakatsuki S, Saitoh F, Araki T (2011) ZNRF1 promotes Wallerian degeneration by degrading AKT to induce GSK3B-dependent CRMP2 phosphorylation. Nat Cell Biol 13:1415–1423CrossRefPubMedGoogle Scholar
  27. 27.
    Hoxhaj G, Najafov A, Toth R, Campbell DG, Prescott AR, MacKintosh C (2012) ZNRF2 is released from membranes by growth factors and, together with ZNRF1, regulates the Na+/K+ATPase. J Cell Sci 125:4662–4675CrossRefPubMedCentralPubMedGoogle Scholar
  28. 28.
    Nakamura K, Funakoshi H, Miyamoto K, Tokunaga F, Nakamura T (2001) Molecular cloning and functional characterization of a human scavenger receptor with C-type lectin (SRCL), a novel member of a scavenger receptor family. Biochem Biophys Res Commun 280:1028–1035CrossRefPubMedGoogle Scholar
  29. 29.
    Jang S, Ohtani K, Fukuoh A et al (2009) Scavenger receptor collectin placenta 1 (CL-P1) predominantly mediates zymosan phagocytosis by human vascular endothelial cells. J Biol Chem 284:3956–3965CrossRefPubMedGoogle Scholar
  30. 30.
    Yan J, Di Y, Shi H, Rao H, Huo K (2010) Overexpression of SCYL1-BP1 stabilizes functional p53 by suppressing MDM2-mediated ubiquitination. FEBS Lett 584:4319–4324CrossRefPubMedCentralPubMedGoogle Scholar
  31. 31.
    Hu L, Liu M, Chen L et al (2012) SCYL1 binding protein 1 promotes the ubiquitin-dependent degradation of Pirh2 and has tumor-suppressive function in the development of hepatocellular carcinoma. Carcinogenesis 33:1581–1588CrossRefPubMedGoogle Scholar
  32. 32.
    Yang ZP, Xie YH, Ling DY et al (2014) SCYL1BP1 has tumor-suppressive functions in human lung squamous carcinoma cells by regulating degradation of MDM2. Asian Pac J Cancer Prev 15:7467–7471CrossRefPubMedGoogle Scholar
  33. 33.
    Morris EJ, Michaud WA, Ji JY, Moon NS, Rocco JW, Dyson NJ (2006) Functional identification of Api5 as a suppressor of E2F-dependent apoptosis in vivo. PLoS Genet 2:e196CrossRefPubMedCentralPubMedGoogle Scholar
  34. 34.
    Cho H, Chung JY, Song KH et al (2014) Apoptosis inhibitor-5 overexpression is associated with tumor progression and poor prognosis in patients with cervical cancer. BMC Cancer 14:545CrossRefPubMedCentralPubMedGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2015

Authors and Affiliations

  • Danfeng Peng
    • 1
  • Jie Wang
    • 1
  • Rong Zhang
    • 1
  • Feng Jiang
    • 1
  • Shanshan Tang
    • 1
  • Miao Chen
    • 1
  • Jing Yan
    • 1
  • Xue Sun
    • 1
  • Shiyun Wang
    • 1
  • Tao Wang
    • 1
  • Dandan Yan
    • 1
  • Yuqian Bao
    • 1
  • Cheng Hu
    • 1
    • 2
    Email author
  • Weiping Jia
    • 1
    Email author
  1. 1.Shanghai Diabetes Institute, Shanghai Key Laboratory of Diabetes Mellitus, Shanghai Clinical Center for DiabetesShanghai Jiao Tong University Affiliated Sixth People’s HospitalShanghaiPeople’s Republic of China
  2. 2.Institute for Metabolic DiseasesShanghai Jiao Tong University Affiliated Sixth People’s Hospital South CampusShanghaiPeople’s Republic of China

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