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BMC Ophthalmology

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Associations of complement factor B and complement component 2 genotypes with subtypes of polypoidal choroidal vasculopathy

  • Koji Tanaka1,
  • Tomohiro Nakayama2Email author,
  • Ryusaburo Mori1,
  • Naoyuki Sato2,
  • Akiyuki Kawamura1 and
  • Mitsuko Yuzawa1
BMC Ophthalmology201414:83

https://doi.org/10.1186/1471-2415-14-83

Received: 10 March 2014

Accepted: 9 June 2014

Published: 25 June 2014

Abstract

Background

We previously reported on subtypes of polypoidal choroidal vasculopathy (PCV), and categorized PCV as polypoidal choroidal neovascularization (CNV) and typical PCV. The aim of this study was to clarify whether complement component 2 (C2) and complement factor B (CFB) genotypes are associated with subtypes of polypoidal choroidal vasculopathy, such as polypoidal CNV and typical PCV.

Methods

First, we categorized 677 patients into typical age-related macular degeneration (tAMD; 250 patients), PCV (376) and retinal angiomatous proliferation (RAP; 51). Second, we categorized 282 patients with PCV as having polypoidal CNV (84 patients) or typical PCV (198) based on indocyanine green angiographic findings. In total, 274 subjects without AMD, such as PCV and CNV, served as controls. A SNP (rs547154) in the C2 gene and three SNPs (rs541862, rs2072633, rs4151667) in the CFB gene were genotyped, and case–control studies were performed in subjects with these PCV subtypes.

Results

In tAMD, no SNPs were associated with allele distributions. In PCV, rs547154 and rs2072633 were associated with allele distributions. RAP was only associated with rs2072633. After logistic regression analysis with adjustment for confounding factors, tAMD, PCV and RAP were found to be associated with rs2072633.

As to PCV subtypes, there were significant differences in the distributions of rs547154, rs541862 and rs2072633 in the case–control studies for polypoidal CNV, but not between the typical PCV and control groups. Logistic regression analysis with adjustment for confounding factors showed the distributions of rs547154, rs541862 and rs2072633 to differ significantly between the controls and polypoidal CNV cases and that these SNPs were protective. The A/A genotype of rs2072633 was significantly more common in the polypoidal CNV than in the typical PCV group (p = 0.03), even with adjustment for polyp number and greatest linear dimension.

Conclusions

PCV might be genetically divisible into polypoidal CNV and typical PCV. The C2 and CFB gene variants were shown to be associated with polypoidal CNV. Typical PCV was not associated with variants in these genes.

Keywords

Subtypes of PCVC2CFBGenetic variants

Background

Age-related macular degeneration (AMD) is a leading cause of blindness in Western countries and its prevalence is increasing in Japan [1]. AMD is thought to be a heterogeneous multifactorial disease associated with several environmental factors and genetic variants. Hypertension [2] and cigarette smoking [3] are closely related to the development of AMD. Identification of AMD susceptibility genes might increase our ability to predict the risk of developing this disease. Complement factor H (CFH), age-related maculopathy susceptibility 2 (ARMS2) and high-temperature requirement factor A1 (HTRA1) have been shown to be associated with AMD in both Japanese and Caucasian patients [47]. In addition, complement component 2(C2) and complement factor B (CFB) known as activators of alternative complement cascades are reportedly related to AMD in Caucasians [8]. Both were reported to be protective genes against AMD development [9, 10]. Genetic studies of PCV have found no association between either C2 or CFB and PCV [11, 12]. Nakata et al. reported that, in the Japanese population, C2 and CFB are associated with both PCV and typical AMD (tAMD) [13].

Polypoidal choroidal vasculopathy (PCV), characterized by a branching vascular network with polypoidal lesions detectable by indocyanine green angiography (IA) [14], is included among the forms of exudative AMD in Japan [15]. Our group previously reported on subtypes of PCV, and categorized PCV as polypoidal choroidal neovascularization (CNV) and PCV in the narrow sense (also referred to as typical PCV) [16]. In the first type, both feeder and draining vessels are visible on IA and network vessels are numerous. This type is thought to be the representative form of CNV beneath the retinal pigment epithelium. In the second group, neither feeder nor draining vessels are detectable and the number of network vessels is small. This type is thought to represent an abnormality of the choroidal vasculature based on hyaline arteriosclerosis [17]. We also showed that there are differences in these two types classified according to IA and optic coherence tomography findings [18]. Genetically, we demonstrated an association between the ARMS2 gene and these two types of PCV [19]. There was a significant ARMS2 gene difference in case–control studies of polypoidal CNV, but no difference between the typical PCV and control groups. This observation suggests that PCV might be genetically divisible into polypoidal CNV and typical PCV.

The possibility of dividing PCV into two types has been raised by other investigators. Okubo et al. reported that PCV can be divided into two types; the small-short and large-long types, but the clinical features in their report differed from those described by our group [20]. Miki et al. recently advocated dividing PCV into polypoidal lesions with a clear branching vascular network and polypoidal lesions without such a vascular network [21]. After classifying PCV into two types based on IA findings, we conducted ARMS2 and CFH genotyping for our patients. The results were highly consistent with our report showing typical PCV to be unrelated to the ARMS2 gene.

The present study aimed to investigate whether there is an association between the C2 or the CFB gene and any of the subtypes of PCV. To our knowledge, this is the first study to examine associations of the C2 and CFB genes with PCV subtypes.

Methods

Participants

Six hundred and seventy-seven patients diagnosed as having AMD at Nihon University Surugadai Hospital in Tokyo were enrolled in this study between 2008 and 2010 (472 men, 205 women; mean age 72.11 years). We then categorized AMD as tAMD, PCV and RAP based on IA and color photograph. (tAMD; 187 men, 63 women, PCV; 266 men, 110 women, RAP;19 men, 32 women)

Furthermore, we also classified PCV patients into groups with two different types of PCV, polypoidal CNV and typical PCV. Two hundred and eighty-two (195 men, 87 women; mean age 70.0 ± 8.8 years) out of 376 patients were enrolled after classification based on whether or not both feeder and draining vessels were seen on IA (Figures 1 and 2). Due to unclear IA findings, we could not classify the remaining 94 patients. Eighty-four patients were diagnosed with polypoidal CNV, 198 with typical PCV. Polyp numbers and greatest linear dimension (GLD) were determined by IA at the first visit.
Figure 1

Typical PCV. Neither feeder nor draining vessels were visible in the early phase of indocyanine green angiography. The network is composed of a small number of vessels with a polypoidal lesion.

Figure 2

Polypoidal CNV. Both feeder and draining vessels were observed in the early phase of indocyanine green angiography. Large numbers of network vessels were seen to be fluorescing in an umbrella-like configuration. Several of the polypoidal lesions were dilatations of marginal tortuous vessels.

Information on hypertension, diabetes mellitus and smoking was obtained from medical histories collected for each patient. Smokers were defined as current or former smokers, whereas non-smokers were defined as subjects with no previous or current smoking history.

In total, 274 subjects free of AMD (110 men, 164 women; mean age 72.9 ± 7.4 years) served as controls. There were no remarkable findings on fundus examinations of the controls. Informed consent was obtained from each individual as per the protocol approved by the Human Studies Committee of Nihon University. This investigation was performed according to the guidelines of the Declaration of Helsinki.

Genotyping

DNA was extracted from peripheral blood leukocytes by the phenol and chloroform extraction method [22, 23]. Genotyping was performed using the TaqMan® SNP Genotyping Assay (Applied Biosystems Inc. Foster City, CA, USA). TaqMan® SNP Genotyping Assays were performed using the Taq amplification method [22, 23].

We targeted C2 rs547154(IVS10), and CFB rs541862, rs2072633(IVS17) and rs4151667(H9L), all of which were identified as having positive associations with AMD in prior studies [11, 13].

Plates were read on the SDS 7700 instrument with the end-point analysis mode of the SDS version 1.6.3 software package (Applied Biosystems). Genotypes were determined visually based on the dye-component fluorescent emission data depicted in the X-Y scatter-plot of the SDS software. Genotypes were also determined automatically by the signal processing algorithms of the software [22, 23].

Statistical analysis

Data are shown as means ± SD. Differences between the PCV subtype and control groups were assessed by analysis of variance (ANOVA) followed by Fisher’s protected least significant difference test. Hardy-Weinberg equilibrium was assessed by chi-squared analysis. The overall distribution of alleles was analyzed using 2 × 2 contingency tables. The distribution of the genotypes between patient groups and controls was tested using a 2-sided Fisher’s exact test and multiple logistic regression analysis. After Bonferroni correction, statistical significance was set at p < 0.0125.

Based on the genotype data of the genetic variations, linkage disequilibrium (LD) analyses and a haplotype-based case–control study were carried out using the expectation maximization algorithm with the SNPAlyze software program ver3.2 (Dynacom, Yokohama, Japan). |D’| values > 0.5 were used to assign SNP locations to one haplotype block. The frequency distribution of occurrence of the haplotypes was calculated by χ 2 analyses.

Results

The clinical features of AMD patients and the control group are shown in Table 1. Distributions of genotypes and alleles are shown in Table 2. Four variants were in Hardy-Weinberg equilibrium in the control group (data not shown, p > 0.05). There were significant differences in PCV the allele distributions of rs547154 (C2 gene) and rs2072633 (CFB gene) between the PCV group and the controls. The RAP allele distribution of rs2072633 differed significantly between the RAP group and the controls. The tAMD group showed no difference from the controls.
Table 1

Characteristics of study participants

 

Case

Control

 

Total AMD

P vs. control

Typical AMD

P vs. control

PCV

P vs. control

RAP

P vs. control

Subjects, n

677

 

250

 

376

 

51

 

274

Male/female

472/205

<0.0001*

187/63

<0.0001*

266/110

<0.0001*

19/32

0.757

110/164

Age

72.1(±8.7)

0.157

73.6(±7.5)

0.289

70.0(±8.9)

<0.0001*

80.9(±6.8)

<0.0001*

72.9(±7.4)

HT

39%

0.308

41%

0.658

38%

0.226

41%

0.878

43%

DM

11%

<0.0001*

14%

0.079

9%

0.081

6%

0.016*

20%

Smoker

35%

<0.0001*

37%

<0.0001*

36%

<0.0001*

16%

84%

18%

p-values reflect comparisons between each of the case groups and the control group, calculated using Fisher’s exact test.

*p < 0.05.

Table 2

Genotype and allele distributions in AMD patients and control group

    

Total

    

Total AMD patients

tAMD

PCV

RAP

Control

    

n

%

p-value

n

%

p-value

n

%

p-value

n

%

p-value

n

%

rs547154

Genotype

 

G/G

601

88.8%

0.024

221

88.4%

0.192

335

89.1%

0.053

45

88.2%

0.561

229

83.6%

   

T/G

74

10.9%

 

28

11.2%

 

40

10.6%

 

6

11.8%

 

41

15.0%

   

T/T

2

0.3%

 

1

0.4%

 

1

0.3%

 

0

0.0%

 

4

1.5%

  

Dominant model

G/G

601

88.8%

0.032

221

88.4%

0.132

335

89.1%

0.046

45

88.2%

0.530

229

83.6%

   

TG + TT

76

11.2%

 

29

11.6%

 

41

10.9%

 

6

11.8%

 

45

16.4%

  

Recessive model

TT

2

0.3%

0.061

1

0.4%

0.375

1

0.3%

0.168

0

0.0%

0.385

4

1.5%

 

Allele

 

G

1276

94.2%

0.004*

470

94.0%

0.026

710

94.4%

0.005*

96

94.1%

0.260

499

91.1%

   

T

78

5.8%

 

30

6.0%

 

42

5.6%

 

6

5.9%

 

49

8.9%

rs541862

Genotype

 

T/T

600

88.6%

0.054

221

88.4%

0.246

335

89.1%

0.078

44

86.3%

0.715

229

83.6%

   

T/C

75

11.1%

 

28

11.2%

 

40

10.6%

 

7

13.7%

 

42

15.3%

   

C/C

2

0.3%

 

1

0.4%

 

1

0.3%

 

0

00.0%

 

3

1.1%

  

Dominant model

T/T

600

88.6%

0.038

221

88.4%

0.132

335

89.1%

0.046

44

86.3%

0.835

229

83.6%

   

TC + CC

77

11.4%

 

29

11.6%

 

41

10.9%

 

7

13.7%

 

45

16.4%

  

Recessive model

C/C

2

0.3%

0.147

1

0.4%

0.625

1

0.3%

0.315

00.0%

0.453

3

1.1%

 
   

TC + TT

675

99.7%

 

249

99.6%

 

375

99.7%

51

 

100.0%

 

271

98.9%

 

Allele

 

T

1275

94.2%

0.025

470

94.0%

0.099

710

94.4%

0.027

95

93.1%

0.698

500

91.2%

   

C

79

5.8%

 

30

6.0%

 

42

5.6%

 

7

6.9%

 

48

8.8%

rs2072633

rs2072633

 

G/G

115

17.0%

0.0024*

48

19.2%

0.120

61

16.2%

0.0026*

6

11.8%

0.048

69

25.2%

   

G/A

323

47.7%

 

120

48.0%

 

178

47.3%

 

25

49.0%

 

134

48.9%

   

A/A

239

35.3%

 

82

32.8%

 

137

36.4%

 

20

39.2%

 

71

25.9%

  

Dominant model

G/G

115

17.0%

0.0038*

48

19.2%

0.101

61

16.2%

0.0048*

6

11.8%

0.037

69

25.2%

   

GA + AA

562

83.0%

 

202

80.8%

 

315

83.8%

 

45

88.2%

 

205

74.8%

  

Recessive model

A/A

239

35.3%

0.0051*

82

32.8%

0.083

137

36.4%

0.0045*

20

39.2%

0.052

71

25.9%

   

GA + GG

438

64.7%

 

168

67.2%

 

239

63.6%

 

31

60.8%

 

203

74.1%

 

Allele

 

G

553

40.8%

0.0005*

216

43.2%

0.037

300

39.9%

0.0005*

37

36.3%

0.013

272

49.6%

   

A

801

59.2%

 

284

56.8%

 

452

60.1%

 

65

63.7%

 

276

50.4%

rs4151667

Genotype

 

T/T

653

96.5%

0.386

241

96.4%

0.514

363

96.5%

0.412

49

96.1%

0.797

261

95.3%

   

A/T

24

3.5%

 

9

3.6%

 

13

3.5%

 

2

3.9%

 

13

4.7%

   

A/A

0

0.0%

 

0

0.0%

 

0

0.0%

 

0

0.0%

 

0

0.0%

  

Dominant mode

T/T

653

96.5%

0.459

241

96.4%

0.664

363

96.5%

0.424

49

96.1%

0.797

261

95.3%

   

AT + AA

24

3.5%

 

9

3.6%

 

13

3.5%

 

2

3.9%

 

13

4.7%

  

Recessive model

A/A

0

0.0%

-

0

0.0%

-

0

0.0%

-

0

0.0%

-

0

0.0%

   

AT + TT

677

100.0%

 

250

100.0%

 

376

100.0%

 

51

100.0%

 

274

100.0%

 

Allele

 

T

1330

98.2%

0.461

491

98.2%

0.667

739

98.3%

0.429

100

98.0%

0.779

535

97.6%

   

A

24

1.8%

 

9

1.8%

 

13

1.7%

 

2

2.0%

 

13

2.4%

AMD; age related macular degeneration tAMD; typical age related macular degeneration PCV; polypoidal choroidal vasculopathy RAP; retinal angiomatous proliferation.

P- values are for the comparison between cases and controls.

p- values for genotypes were calculated by Fisher’s exact test. (after Bonferroni correction *p < 0.0125).

The results of logistic regression analysis, with adjustment for confounding factors, including age, gender and risk factors, are shown in Table 3. This analysis was performed for the dominant or recessive genotype models showing significant results, as presented in Table 2. Susceptibility genotypes were those with high frequencies in patient groups in case–control studies. The rs2072633 distribution of the controls differed significantly from those of the tAMD, PCV and RAP groups. After Bonferroni correction, only PCV showed significant difference in this SNP.
Table 3

Logistic regression analysis with adjustment for confounding factors

  

Total AMD patients

tAMD

PCV

RAP

  

p-value vs. control

(Bonferroni correction)

OR

95% CI

p-value vs. control

(Bonferroni correction)

OR

95%CI

p-value vs. control

(Bonferroni correction)

OR

95% CI

p-value vs. control

(Bonferroni correction)

OR

95% CI

rs547154

dominant model

0.044*

0.176

0.62

0.39-0.99

0.186

0.744

  

0.103

0.412

  

0.626

1.000

  
 

recessive model

0.051

0.204

  

0.159

0.636

  

0.105

0.420

  

-

   

rs541862

dominant model

0.049*

0.196

0.63

0.39-0.99

0.169

0.676

  

0.119

0.476

  

0.877

1.000

  
 

recessive model

0.133

0.532

  

0.232

0.928

  

0.213

0.852

  

0.996

1.000

  

rs2072633

dominant model

0.0003*

0.001**

0.49

0.33-0.73

0.042*

0.168

0.59

0.36-0.99

0.001*

0.004**

  

0.048*

0.192

0.35

0.13-0.99

 

recessive model

0.031*

0.124

0.68

0.48-0.97

0.312

1

  

0.010*

0.04**

0.46

0.29-0.74

0.423

1.000

  

rs4151667

dominant model

0.273

1

  

0.952

1

  

0.270

1

0.59

0.40-0.88

0.769

1.000

  
 

recessive model

-

   

-

   

-

   

-

   

Logistic regression analysis was performed for each genotype with adjustment for confounding factors (age, gender, hypertension, diabetes mellitus and smoking).

PCV; polypoidal choroidal vasculopathy.

OR; odds ratios CI; confidence intervals.

p-values are for the comparisons between cases and controls.

p-values for genotypes were calculated using Fisher’s exact test. *p < 0.05B.

Bonferroni correction was performed for each of the genotypes. **p < 0.05.

Blanks indicate that there were no siginificant differences.

The clinical features of PCV patients and the control group are shown in Table 4. There were significant differences in polyp numbers and GLD, both of which were greater in polypoidal CNV group.
Table 4

Characteristics of PCV participants

 

Case

Control

 

Total PCV

P vs. control

Polypoidal CNV

P vs. control

P vs. typical PCV

Typical PCV

P vs. control

Subjects, n

282

 

84

  

198

 

274

Male/female

195/87

<0.0001*

63/21

<0.0001*

0.205

132/66

<0.0001*

110/164

Age(±SD)

70.0(±8.7)

<0.0001*

68.8(±8.9)

<0.0001*

0.130

70.5(±8.7)

<0.0001*

72.9(±7.4)

Hypertension

39%

0.390

38%

0.45

0.792

40%

0.509

43%

Diabetes

9%

<0.0001*

10%

0.032*

0.649

8%

<0.0001*

20%

Smoking

33%

<0.0001*

37%

<0.0001*

0.406

31%

0.001*

18%

Number of polyps

-

 

4.17

 

<0.0001*

1.95

 

-

GLD, mm

-

 

3.78

 

<0.0001*

2.78

 

-

p-values reflect comparisons between each of the case groups and the control group, calculated using Fisher’s exact test *p < 0.05.

PCV; polypoidal choroidal vasculopathy CNV; choroidal neovascularization GLD; greatest linear dimension SD; standard deviation.

Distributions of genotypes and alleles of the four variants are shown in Table 5. Four variants were in Hardy-Weinberg equilibrium in the control group (data not shown, p > 0.05). There were significant differences in all genotype models and allele distributions of rs547154 (C2 gene), rs541862 and rs2072633 (CFB gene), but not rs4151667, between the polypoidal CNV group and the controls. However, there were no significant differences in any genotype model or allele distribution for any of the SNPs between the typical PCV and control groups.
Table 5

Genotype and allele distributions in PCV patients and control group

   

Total PCV patients

Polypoidal CNV

Typical PCV

Control

   

Number

%

p-value

Number

%

p-value

Number

%

p-value

Number

%

rs547154

Genotype

G/G

255

90.4%

80

95%

175

88%

229

84%

   
  

T/G

26

9.2%

0.0400

4

5%

0.023

22

11%

0.276

41

15%

  

T/T

1

0.4%

 

0

0%

 

1

1%

 

4

1%

 

Dominant model

G/G

255

90.4%

0.016

80

95%

0.007*

175

88%

0.142

229

84%

  

TG + TT

27

9.6%

 

4

5%

 

23

12%

 

45

16%

 

Recessive model

TT

1

0.4%

0.168

0

0%

0.265

1

1%

0.317

4

1%

  

TG + GG

281

99.6%

 

84

100%

 

197

99%

 

270

99%

 

Allele

G

536

95.0%

0.009*

164

98%

0.004*

372

94%

0.110

499

91%

  

T

28

5.0%

 

4

2%

 

24

6%

 

49

9%

rs541862

Genotype

T/T

255

90.4%

 

80

95%

 

175

88%

 

229

84%

  

T/C

26

9.2%

0.049

4

5%

0.023

22

11%

0.318

42

15%

  

C/C

1

0.4%

 

0

0%

 

1

1%

 

3

1%

 

Dominant model

T/T

255

90.4%

0.016

80

95%

0.007*

175

88%

0.142

229

84%

  

TC + CC

27

9.6%

 

4

5%

 

23

12%

 

45

16%

 

Recessive model

C/C

1

0.4%

0.302

0

0%

0.336

1

1%

0.490

3

1%

  

TC + TT

281

99.6%

 

84

100%

 

197

99%

 

271

99%

 

Allele

T

536

95.0%

0.013

164

98%

0.004*

372

94%

0.137

500

91%

  

C

28

5.0%

 

4

2%

 

24

6%

 

48

9%

rs2072633

Genotype

G/G

50

17.7%

 

13

15%

 

37

19%

 

69

25%

  

G/A

131

46.5%

0.017

34

40%

0.005*

97

49%

0.149

134

49%

  

A/A

101

35.8%

 

37

44%

 

64

32%

 

71

26%

 

Dominant model

G/G

50

17.7%

0.032

13

15%

0.064

37

19%

0.095

69

25%

  

GA + AA

232

82.3%

 

71

85%

 

161

81%

 

205

75%

 

Recessive model

A/A

101

35.8%

0.012*

37

44%

0.002*

64

32%

0.128

71

26%

  

GA + GG

181

64.2%

 

47

56%

 

134

68%

 

203

74%

 

Allele

G

231

41.0%

0.004*

60

36%

0.002*

171

43%

0.055

272

50%

  

A

333

59.0%

 

108

64%

 

225

57%

 

276

50%

rs4151667

Genotype

T/T

273

96.8%

 

81

96%

 

192

97%

 

261

95%

  

A/T

9

3.2%

0.348

3

4%

0.649

6

3%

0.350

13

5%

  

A/A

0

0.0%

 

0

0%

 

0

0%

 

0

0%

 

Dominant model

T/T

273

96.8%

0.348

81

96%

0.649

192

97%

0.350

261

95%

  

AT + AA

9

3.2%

 

3

4%

 

6

3%

 

13

5%

 

Recessive model

A/A

0

0.0%

-

0

0%

-

0

0%

-

0

0%

  

AT + TT

282

100.0%

 

84

100%

 

198

100%

 

274

100%

 

Allele

T

555

98.4%

0.394

165

98%

0.775

390

98%

0.482

535

98%

  

A

9

1.6%

 

3

2%

 

6

2%

 

13

2%

PCV; polypoidal choroidal vasculopathy CNV; choroidal neovascularization.

p- values are for the comparison between cases and controls.

p- values for genotypes were calculated by Fisher’s exact test. (after Bonferroni correction *p < 0.0125).

The results of logistic regression analysis, with adjustment for confounding factors, including age, gender and risk factors, are shown in Tables 6 and 7. This analysis was performed for the dominant or recessive genotype models showing significant results, as presented in Table 5. Susceptibility genotypes were those with high frequencies in patient groups in case–control studies. The distributions of rs541862, rs547154 and rs2072633 differed significantly between the controls and the polypoidal CNV group. After Bonferroni correction, the distribution of rs2072633 remained significant only for polypoidal CNV, i.e. not for typical PCV. Logistic regression analysis was also performed to compare the polypoidal CNV and typical PCV groups. The only significant difference, after adjusting for confounding factors such as polyp numbers and GLD, was in rs2072633. After Bonferroni correction, no significant difference remained.
Table 6

Logistic regression analysis between cases and controls

  

Total PCV patients

Polypoidal CNV

Typical PCV

  

p-value vs. control

(Bonferroni correction)

OR

95% CI

p-value vs. Control

(Bonferroni correction)

OR

95% CI

p-value vs. Control

(Bonferroni correction)

OR

95% CI

rs547154

dominant model

0.018*

0.072

0.48

0.26-0.89

0.014*

0.056

0.22

0.05-0.86

0.139

0.556

  
 

recessive model

0.217

0.868

  

0.097

0.388

  

0.409

1.000

  

rs541862

dominant model

0.023*

0.092

0.49

0.26-0.91

0.015*

0.060

0.22

0.05-0.87

0.162

0.648

  
 

recessive model

0.417

1

  

0.131

0.524

  

0.646

1.000

  

rs2072633

dominant model

0.012*

0.048**

0.52

0.32-0.87

0.104

0.416

  

0.037*

0.148

0.55

0.32-0.96

 

recessive model

0.035*

0.140

0.63

0.41-0.96

0.009*

0.036**

0.40

0.20-0.79

0.199

0.796

  

rs4151667

dominant model

0.326

1

  

0.984

1

  

0.211

0.844

  
 

recessive model

-

-

  

-

-

  

-

   

Logistic regression analysis was performed for each genotype with adjustment for confounding factors (age, gender, hypertension, diabetes mellitus and smoking).

PCV; polypoidal choroidal vasculopathy.

OR; odds ratios CI; confidence intervals.

p-values are for comparisons between cases and controls.

p-values for genotypes were calculated using Fisher’s exact test. *p < 0.05.

Bonferroni correction was performed for each of the genotypes. **p < 0.05.

Blanks indicate that there were no siginificant differences.

Table 7

Logistic regression analysis between polypoidal CNV and typical PCV

  

Polypoidal CNV

p-value vs. typical PCV

(Bonferroni correction)

OR

95%CI

rs547154

dominant model

0.073

0.292

  
 

recessive model

0.392

1

  

rs541862

dominant model

0.073

0.292

  
 

recessive model

0.392

1

  

rs2072633

dominant model

0.720

1

  
 

recessive model

0.038*

0.152

2.09

1.04-4.22

rs4151667

dominant model

0.915

1

  
 

recessive model

-

-

  

Logistic regression analysis was performed for each genotype with adjustment for confounding factors (age, gender, hypertension, diabetes mellitus and smoking).

OR; odds ratios CI; confidence intervals GLD; greatest linear dimension.

p-values are for the comparisons between polypoidal CNV and typical PCV.

p-values for genotypes were calculated using Fisher’s exact test. *p < 0.05.

Bonferroni correction were performed for each genotypes. p < 0.05.

Blanks indicate that there were no siginificant differences.

LD was assessed for three SNPs in CFB, and the distribution of estimated haplotype frequencies is shown in Tables 8 and 9. The T-A-T(rs541862-rs2072633-rs4151667) and C-G-T haplotypes both showed strong associations in the polypoidal CNV, typical PCV and control groups. Furthermore, the T-A-A haplotype differed significantly between polypoidal CNV and typical PCV.
Table 8

Linkage disequilibrium map through 3 SNPs in CFB gene

 

rs541862

rs2072633

rs4151667

rs541862

-

0.929

0.278

rs2072633

-0.040

-

1

rs4151667

-0.001

0.012

-

The upper right shows the D’-value, the lower left the D-value.

Table 9

Haplotype association analysis in cases and controls

Polypoidal CNV vs. control

Haplotypes

%

  

rs541862

rs2072633

rs4151667

Polypoidal CNV

Control

Chi-Squ

p-value

T

A

T

63%

42%

22.177

<0.0001*

C

A

T

0%

9%

14.9366

0.0001*

T

G

T

35%

47%

7.9166

0.0049*

C

G

T

2%

0%

13.5581

0.0002*

T

G

A

0%

2%

3.9293

0.0475*

Typical PCV vs. control

Haplotypes

%

  

rs541862

rs2072633

rs4151667

Typical PCV

Control

Chi-Square

p-value

T

A

T

57%

42%

20.5614

<0.0001*

C

A

T

0%

9%

34.8144

<0.0001*

T

G

T

37%

47%

9.3704

0.0022*

C

G

T

6%

0%

33.5324

<0.0001*

Polypoidal CNV vs. typical PCV

Haplotypes

%

  

rs541862

rs2072633

rs4151667

Typical PCV

Polypoidal CNV

Chi-Squ

p-value

T

A

T

57%

63%

1.5687

0.2104

C

A

T

0%

0%

0

1

T

G

T

36%

33%

0.3302

0.5656

C

G

T

6%

2%

3.0395

0.0813

T

A

A

0%

2%

7.1092

0.0077*

C

A

A

0%

0%

0

1

T

G

A

1%

0%

2.1402

0.1435

C

G

A

0%

0%

0

1

*p-value > 0.05 calculated by chi-square analysis.

Discussion

ARMS2 genes, especially the rs10490924 of CFH and rs1061170, are both known as PCV susceptibility genes [24, 25]. On the other hand, our group previously reported that typical PCV did not correlate significantly with rs10490924 [19]. This result raised the possibility of two distinct genetic types of PCV. In the present study, the C2 gene and the CFB gene were also found to be associated with polypoidal CNV, in terms of both genotypes and allele distributions. No associations with typical PCV were detected. These results indicate the C2 and CFB genes to also be associated with PCV subtypes. Our group recently reported typical PCV to have the features of abnormal choroidal vessels and that polypoidal CNV also has features of neovascularization. The differences between tAMD and polypoidal CNV were that the latter had polypoidal lesion detectable by IA, while tAMD had no polypoidal lesion. Furthermore, polypoidal CNV is characterized by a larger GLD and more polyps than typical PCV [18]. As polypoidal CNV has neovascularization features, the ARMS2 gene might be highly associated with neovascularization. Though there are reports describing rs4151667 as being associated with AMD, the minor allele homozygous frequency was very low in all of these reports [9, 10]. In this study, the minor allele homozygous frequency of rs4151667 was zero, such that there was no difference between cases and controls.

Nakata et al. reported the C2 (rs547154) and CFB (rs541862) genes to be significantly associated with both tAMD and PCV in the Japanese population [13]. Nevertheless, rs2072633 (CFB gene) and rs4151672 (CFB gene) showed no correlations with either tAMD or PCV. In the present study, we showed rs2072633 to be significantly associated with PCV. This result indicates the CFB genes to be associated with PCV. Before Bonferroni correction, tAMD was also associated with rs547154 and rs2072633. We previously reported that polypoidal CNV resembles tAMD, while typical PCV clearly differs from CNV. Though not significant after Bonferroni correction, given the prior reports dividing PCV into two types, we can reasonably speculate that the C2 and CFB genes might be related to tAMD and polypoidal CNV but not to typical PCV. The present C2 and CFB gene results also are not inconsistent with this possibility. Since typical PCV was not associated with any of the SNPs tested, we can also speculate that typical PCV might differ genetically from AMD.

C2 and CFB functioned as activators of the complement cascade. CFB is localized to the choroidal vasculature and Bruch’s membrane [26]. Smailhodzic et al. reported AMD patients to show increased alternative pathway activation and elevated CFB levels [27]. Scholl et al. also showed plasma CFB to be significantly elevated in AMD patients [28]. For these reasons, AMD might be related to CFB.

Recently, Liu et al. reported the C2-CFB-RDBP-SKIV2L region of SNPs to be associated only with tAMD, not with PCV. They concluded that the mechanisms underlying the development of tAMD and PCV might be different [29]. Nakashizuka et al. reported histopathological characteristics of PCV [17]. In their report, areas of PCV showed little fibrosis or granulation as compared to those with CNV. This might indicate that typical PCV involves less inflammation than CNV. Since polypoidal CNV has AMD features, C2 and CFB might be related only to polypoidal CNV.

The results presented in Table 8 show that three of the SNPs in CFB were in LD block. Haplotypes T-A-T and T-G-T differed significantly between the PCV and control groups. Furthermore, T-A-T would confer a risk for PCV, while T-G-T would be protective against PCV development. We could reasonably draw the same conclusion for haplotypes C-A-T and C-G-T. These results indicate that rs2072633 might be one of the key SNPs favoring PCV development.

There has been controversy regarding the division of PCV into two subtypes. Tsujikawa et al. reported that if there is risk associated with being homozygous for the ARMS2 gene, it would be the larger GLD in PCV [30]. Their report described two types of PCV, with larger GLD and smaller GLD. The aforementioned report by Miki and colleagues presented results very similar to ours, indicating the ARMS2 gene to have no association with typical PCV [21]. These two reports also support the assumption that the ARMS2 gene is unrelated to PCV [17, 18]. While IA findings of polypoidal CNV appeared to be consistent with CNV, the histopathological and IA features of typical PCV showed choroidal vasculature abnormalities. These observations suggested polypoidal CNV to be genetically and histopathologically close to tAMD, a representative form of CNV. Furthermore, typical PCV showed no association with CNV.

The small sample size with only one genotype is the major limitation of this study. Further study is clearly needed.

Conclusion

The present study is the first to examine the associations between variants in the C2 and CFB genes and PCV subtypes. We found the C2 and CFB genes to possibly be genetic markers for polypoidal CNV. Furthermore, these variants showed no associations with typical PCV. These results suggest polypoidal CNV to have a genetic background different from that of typical PCV. Further studies are needed to examine the effects of various treatments on PCV subtypes.

Abbreviations

PCV: 

Polypoidal choroidal vasculopathy

CNV: 

Choroidal neovascularization

C2: 

Complement component 2

CFB: 

Complement factor B

AMD: 

Age-related macular degeneration

CFH: 

Complement factor H

ARMS2: 

Age-related maculopathy susceptibility 2

HTRA1: 

High-temperature requirement factor A1

tAMD: 

Typical AMD

IA: 

Indocyanine green angiography

GLD: 

Greatest linear dimension.

Declarations

Acknowledgments

We would like to thank all patients who participated in this study. This work was funded in part by the Research Committee on Chorioretinal Degenerations and Optic Atrophy, and by The Ministry of Health and Welfare of Japan (Mitsuko Yuzawa).

Authors’ Affiliations

(1)
Department of Ophthalmology, Nihon University School of Medicine
(2)
Department of Pathology and Microbiology, Nihon University School of Medicine

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  31. Pre-publication history

    1. The pre-publication history for this paper can be accessed here:http://www.biomedcentral.com/1471-2415/14/83/prepub

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