December 2009
Volume 50, Issue 12
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Biochemistry and Molecular Biology  |   December 2009
Common Sequence Variation in the VEGFA Gene Predicts Risk of Diabetic Retinopathy
Author Affiliations & Notes
  • Sotoodeh Abhary
    From the Departments of Ophthalmology and
  • Kathryn P. Burdon
    From the Departments of Ophthalmology and
  • Aanchal Gupta
    the South Australian Institute of Ophthalmology, Department of Ophthalmology and Visual Sciences, Adelaide University and Royal Adelaide Hospital, Adelaide, South Australia.
  • Stewart Lake
    From the Departments of Ophthalmology and
  • Dinesh Selva
    the South Australian Institute of Ophthalmology, Department of Ophthalmology and Visual Sciences, Adelaide University and Royal Adelaide Hospital, Adelaide, South Australia.
  • Nikolai Petrovsky
    Endocrinology, Flinders Medical Centre and Flinders University, Adelaide, South Australia; and
  • Jamie E. Craig
    From the Departments of Ophthalmology and
  • Corresponding author: Jamie E. Craig, Department of Ophthalmology, Flinders Medical Centre, Bedford Park 5042, Adelaide, South Australia, Australia; jamie.craig@flinders.edu.au
Investigative Ophthalmology & Visual Science December 2009, Vol.50, 5552-5558. doi:10.1167/iovs.09-3694
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      Sotoodeh Abhary, Kathryn P. Burdon, Aanchal Gupta, Stewart Lake, Dinesh Selva, Nikolai Petrovsky, Jamie E. Craig; Common Sequence Variation in the VEGFA Gene Predicts Risk of Diabetic Retinopathy. Invest. Ophthalmol. Vis. Sci. 2009;50(12):5552-5558. doi: 10.1167/iovs.09-3694.

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      © ARVO (1962-2015); The Authors (2016-present)

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Abstract

Purpose.: Vascular endothelial growth factor (VEGF) is a multifunctional cytokine that plays a role in angiogenesis and microvascular permeability. This study was conducted to determine whether common sequence variation in the VEGFA gene plays a role in the development of diabetic retinopathy (DR).

Method.: Five hundred fifty-four subjects with diabetes mellitus (DM) including 190 type 1 DM (T1DM) and 364 type 2 DM (T2DM) were recruited. The study group consisted of 235 participants without DR, 158 with nonproliferative DR (NPDR), 132 with proliferative DR (PDR), and 93 with clinically significant macular edema (CSME). Blinding DR was defined as severe NPDR, PDR, or CSME. Fifteen VEGFA tag single-nucleotide polymorphisms (SNPs) were genotyped in all subjects and tested for association with blinding DR.

Results.: Multiple tag SNPs in the VEGFA gene were associated with blinding DR. After controlling for sex, HbA1c, and duration of disease, in T1DM, the AA genotype of rs699946 (P = 0.007, odds ratio [OR], 4.1; 95% confidence interval [CI], 1.5–11.4) and the GG genotype of rs833068 (P = 0.017, OR, 3.1; 95% CI, 1.3–7.2) were most significantly associated. In T2DM, the C allele of rs3025021 (P = 0.002; OR, 3.8; 95% CI, 1.5–10.0) and the G allele of rs10434 (P = 0.002; OR, 2.6; 95% CI, 1.3–5.3) were most significantly associated with blinding DR. Haplotype analyses suggested an important role for the haplotype TCCGCG in blinding DR (P = 0.0004).

Conclusions.: Sequence variation in the VEGFA gene is associated with risk of developing blinding DR in T1DM and T2DM. Identifying specific genetic markers will allow for refined screening algorithms and earlier intervention in patients at highest risk.

Diabetes mellitus (DM) is a metabolic disorder that has reached epidemic proportions worldwide. The incidence of both type 1 (T1DM) and type 2 (T2DM) diabetes mellitus is predicted to rise substantially over the next few decades. 13 Globally, diabetic retinopathy (DR), a microvascular complication of DM, is a leading cause of blindness. 46 The pathogenesis of DR most likely is multifactorial in nature. Large longitudinal prospective studies have confirmed that prolonged hyperglycemia is the most important single determinant of risk. 7,8 The duration of DM has also been strongly associated with the development and severity of DR. 912 Several studies have shown that susceptibility to DR also has a heritable component, independent of glycemic control 1315 and duration of diabetes. 1316 Identification of the specific genetic risk factors for DR susceptibility is a priority for screening algorithms, developing new treatments, and improving outcomes. 
Diabetic microvascular changes in the retina lead to hypoxia, which stimulates production of VEGF, a multifunctional cytokine that promotes angiogenesis and is a potent mediator of microvascular permeability. 17 VEGF is believed to play a significant role in the development of DR by inducing hyperpermeability of retinal vessels, breakdown of the blood–retinal barrier and neovascularization. 1820 Complications can arise as a result of abnormal barrier function of new vessels, leading to intraretinal hemorrhage and exudation. New blood vessels have increased fragility leading to sudden severe loss of vision due to vitreous hemorrhage. 
Evidence for a role of VEGF as an angiogenic factor in proliferative DR (PDR) has been obtained in both in vitro studies and animal models, where VEGF levels have been reported to be elevated up to 30-fold in hypoxic retinas, 21,22 with a resultant increase in vascular permeability. 21,23 In ischemic retinas, VEGF inhibition has been shown to cause almost a 100% reduction in retinal neovascularization. 23 Experimental retinal ischemia in primates induced an elevation in aqueous VEGF levels and upregulation of VEGF mRNA. 24 Prevention of the blood retinal barrier breakdown has been reported with VEGF inhibition. 21  
VEGF levels in the vitreous of patients with PDR 2531 and macular edema 32 have been significantly elevated when compared to the vitreous of control subjects. This is also true in the vitreous of diabetic mice when compared with nondiabetic control. 33 VEGF protein expression has been shown to be influenced by genetic variation in the VEGFA gene. 3436  
The human VEGFA gene is located on chromosome 6, region p12. Twelve previous studies have examined the effects of more than 25 single-nucleotide polymorphisms (SNPs) in the VEGFA gene and the development of DR. 34,3747 Of interest, the VEGFA gene has had the largest number of individual SNPs examined in relation to DR when compared to any other published gene. 48 This study describes the association of multiple tag SNPs in the VEGFA gene with blinding DR in a large Australian cohort of individuals with T1DM and T2DM. 
Methods
Subjects were recruited from ophthalmology and endocrine clinics of three tertiary hospitals in metropolitan Adelaide, South Australia. Ethics approval was obtained from the Human Research Ethics Committees of each hospital. This study adhered to the tenets of the Declaration of Helsinki. In total, 554 patients with DM were recruited. This cohort consisted of 190 T1DM and 364 T2DM patients of Caucasian European descent. All participants were over 18 years of age and required to have either T1DM or T2DM of at least 5 years' duration, requiring oral hypoglycemic medication or insulin therapy for DM. 
Informed consent was obtained from all participants. Retinopathy status was graded according to the Early Treatment Diabetic Retinopathy Study criteria 49 by a trained ophthalmologist using slit lamp biomicroscopy after pupil dilation. Retinopathy status for the worse eye was used in the analyses. If a participant had received laser treatment for blinding complications such as severe nonproliferative diabetic retinopathy (NPDR), proliferative diabetic retinopathy (PDR), or clinically significant macular edema (CSME), the retinopathy status prior to laser treatment was used in the analyses. Individuals who had previously been treated with laser and were in a quiescent phase were not subjected to photographic or ocular coherence tomography (OCT) documentation. Individuals with active disease were documented with color photography, fluorescein angiography or OCT as clinically indicated for future reference, however the clinical grading was used for classification. Blinding retinopathy was defined as severe non proliferative diabetic retinopathy (NPDR), PDR, or clinically significant macular edema (CSME). A detailed questionnaire containing information regarding sex, age, ethnicity, age at diagnosis of diabetes, family diabetic history, co-existing risk factors, systemic complications of diabetes, ocular complications as a result of diabetic retinopathy, ocular history, smoking history, and alcohol intake was conducted. Blood pressure and body mass index (BMI) were measured. Renal function tests (serum creatinine, urine albumin, and albumin-creatinine ratio), blood cholesterol and HbA1c levels were obtained. Three recent HbA1c levels were averaged for each participant. For cases of blinding DR, HbA1c levels at the time of the ocular complication were used, and for control cases with DM, HbA1c levels immediately before recruitment were averaged. Patients were classified as hypertensive if they were on pharmacologic treatment for hypertension or they had a blood pressure reading greater than or equal to 140/90 mm Hg at the time of recruitment. Hypercholesterolemia was defined as total cholesterol equal or greater than 5.5 mM, or current use of lipid-lowering medication. Nephropathy was defined as the presence of microalbuminuria (30–300 mg/d) or macroalbuminuria (>300 mg/d). DNA was extracted from peripheral blood samples (QiaAmp Blood Maxi Kit; Qiagen, Valencia, CA). 
Using the tagger program implemented in Haploview 4.0 50 tag SNPs across the VEGFA gene, including the promoter region, were selected on the basis of linkage disequilibrium patterns observed in the Caucasian (CEU) samples genotyped as part of the International HapMap Project. 51 A study has shown that this population is a suitable surrogate for the selection of tag SNPs to be used in Australian samples with predominantly Northwest European descent. 52 Only SNPs with minor allele frequency greater than 5% in HapMap were considered. Fifteen tag SNPs (rs1547651, rs833058, rs833060, rs699947, rs3024987, rs833068, rs833070, rs2146323, rs3025007, rs3025020, rs3025021, rs3025030, rs3025035, and rs10434), which captured all alleles with an r 2 of at least 0.8 (mean, r 2 = 0.973), were genotyped in all individuals using primer extension reaction chemistry (iPLEX Gold; Sequenom, Herston, Queensland, Australia) on a mass spectrometer (Autoflex; Sequenom) at the Australian Genome Research Facility (Brisbane, Australia). 
Baseline characteristics of cases and controls were compared with the use of t-test for continuous variables and χ2 for discrete traits. SNP genotyping was checked for compliance with Hardy-Weinberg equilibrium with a χ2 test. Linkage disequilibrium between markers was calculated with Haploview 4.0. Genotypic associations were assessed in SNPstats. 53 Dominant and recessive models were considered with respect to the minor allele. Odds ratios were calculated (SPSS ver. 15.0; SPSS Inc., Chicago, IL). Haplotype associations were undertaken in Haplo Stats (ver. 1.2.1 54 ) in two blocks of linkage disequilibrium (Fig. 1). Block 1 consisted of the first nine SNPs and block 2 the remaining six SNPs. Multiple testing of individual SNPs was adjusted for using the SNP spectral decomposition (SNPSpD) method of Nyholt 56 modified by Li and Ji. 57 Haplotype tests were adjusted with a simple Bonferroni test for the number of haplotypes in the block examined. 
Results
Of the 554 participants recruited for the study, 281 had no DR and 273 had DR. Of the participants with DR, 215 were classified as having blinding DR, consisting of 23 participants with severe NPDR, 132 with PDR, and 93 with CSME. Some individuals fell into more than one group of DR, as CSME can co-occur with any of the other DR gradings. If either eye had CSME irrespective of other DR gradings, the patient was classified as having CSME. Fifty-five patients with CSME fell into this category, of whom 14 had co-existing severe NPDR and 41 had PDR. 
Subjects with T1DM and no DR had a significantly younger age, shorter disease duration, lower HbA1c levels and lower nephropathy and hypertension rates when compared to the T1DM cases with blinding DR. Subjects with T2DM and no DR were more likely to be female, have shorter disease duration, lower HbA1c levels, lower BMI readings and less nephropathy rates when compared with subjects with T2DM and blinding DR (Table 1). 
Table 1.
 
Clinical Characteristics of No DR Compared to Blinding DR in Type-1 and -2 Diabetes
Table 1.
 
Clinical Characteristics of No DR Compared to Blinding DR in Type-1 and -2 Diabetes
Clinical Characteristics T1 DM T2 DM
No DR (n = 94) Blinding DR (n = 76) P No DR (n = 187) Blinding DR (n = 139) P
Sex (female) 48 (51) 29 (45) 0.478 100 (53) 45 (35) 0.001
Age, y 38.4 ± 14.9 49.8 ± 15.5 <0.001 64.3 ± 14.5 64.0 ± 10.7 0.853
Disease duration, y 15.4 ± 9.1 30.9 ± 13.4 <0.001 12.9 ± 8.7 17.5 ± 8.6 <0.001
HbAIc, % 7.6 ± 2.5 8.8 ± 2.3 0.004 6.6 ± 2.9 7.5 ± 3.4 0.014
BMI, kg/m2 25.9 ± 7.1 25.9 ± 9.8 0.958 32.2 ± 9.1 29.6 ± 11.5 0.026
Hypercholesterolemia, % 33 (35) 32 (50) 0.062 120 (64) 81 (63) 0.802
Nephropathy, % 40 (15) 29 (45) <0.001 43 (23) 46 (36) 0.014
Smoker, % 44 (47) 35 (55) 0.331 99 (53) 69 (53) 0.924
Hypertension, % 37 (39) 46 (72) <0.001 153 (82) 107 (83) 0.796
All SNPs were in Hardy-Weinberg Equilibrium in all four groups. Genotype frequencies are given in Table 2 and are similar between the two types of diabetes. The linkage disequilibrium pattern between all SNPs was assessed and is presented in Figure 1. Two main blocks of linkage disequilibrium were observed, although these broke down into four formal blocks when the block definition of Gabriel et al. 55 was applied. 
Table 2.
 
Genotype Frequencies for Each SNP in No DR and Blinding DR by Type of Diabetes
Table 2.
 
Genotype Frequencies for Each SNP in No DR and Blinding DR by Type of Diabetes
SNP Genotype T1DM T2DM
No DR n (%) Blinding DR n (%) No DR n (%) Blinding DR n (%)
1 rs547651 AA 66 (70) 52 (68) 142 (77) 99 (71)
AT 22 (23) 22 (29) 37 (20) 37 (27)
TT 6 (6) 2 (3) 5 (3) 3 (2)
2 rs833058 CC 40 (43) 36 (47) 69 (38) 49 (35)
CT 35 (38) 29 (38) 82 (45) 74 (53)
TT 18 (19) 11 (14) 31 (17) 16 (12)
3 rs699946 AA 56 (60) 59 (79) 117 (64) 86 (62)
AG 33 (35) 14 (19) 56 (31) 50 (36)
GG 4 (4) 2 (3) 9 (5) 3 (2)
4 rs833060 GG 41 (44) 49 (65) 98 (54) 74 (53)
GT 46 (49) 22 (29) 69 (38) 51 (37)
TT 6 (6) 4 (5) 15 (8) 14 (10)
5 rs699947 AA 24 (26) 23 (31) 45 (25) 31 (23)
AC 43 (46) 35 (47) 91 (50) 74 (54)
CC 26 (28) 17 (23) 45 (25) 31 (23)
6 rs3024987 CC 75 (81) 55 (73) 139 (76) 108 (78)
CT 18 (19) 17 (23) 39 (21) 29 (21)
TT 0 (0) 3 (4) 4 (2) 2 (1)
7 rs833068 GG 33 (35) 44 (59) 88 (48) 61 (44)
GA 52 (56) 26 (35) 74 (41) 61 (44)
AA 8 (9) 5 (7) 20 (11) 17 (12)
8 rs833070 TT 24 (26) 24 (32) 45 (24) 33 (24)
TC 44 (47) 35 (46) 92 (50) 73 (53)
CC 26 (28) 17 (22) 47 (26) 33 (24)
9 rs2146323 CC 44 (47) 23 (31) 85 (47) 54 (39)
CA 35 (38) 44 (59) 78 (43) 69 (50)
AA 14 (15) 8 (11) 19 (10) 14 (10)
10 rs3025007 CC 26 (28) 19 (25) 44 (24) 37 (27)
CT 54 (57) 35 (46) 94 (51) 74 (530
TT 14 (15) 22 (29) 46 (25) 28 (20)
11 rs3025020 CC 45 (48) 35 (47) 94 (52) 55 (40)
CT 43 (46) 34 (46) 67 (37) 74 (53)
TT 5 (5) 5 (7) 21 (12) 10 (7)
12 rs3025021 CC 37 (39) 30 (39) 88 (48) 70 (50)
CT 45 (48) 38 (50) 70 (38) 63 (45)
TT 12 (13) 8 (11) 26 (14) 6 (4)
13 rs3025030 GG 71 (77) 51 (68) 131 (72) 94 (68)
GC 19 (21) 23 (31) 48 (26) 39 (28)
CC 2 (2) 1 (1) 3 (2) 5 (4)
14 rs3025035 CC 81 (87) 65 (87) 163 (90) 120 (86)
CT 12 (13) 9 (12) 19 (10) 17 (12)
TT 0 (0) 1 (1) 0 (0) 2 (1)
15 rs10434 GG 26 (28) 24 (32) 56 (31) 48 (35)
GA 44 (47) 37 (49) 83 (45) 76 (55)
AA 23 (25) 15 (20) 44 (24) 13 (9)
Figure 1.
 
Linkage disequilibrium between SNPs in and around the VEGFA gene, generated in Haploview. The D′ value for each pair of SNPs is given, multiplied by 100. A blank cell indicates D′ = 1.0. Block definitions of Gabriel et al. 55 were applied.
Figure 1.
 
Linkage disequilibrium between SNPs in and around the VEGFA gene, generated in Haploview. The D′ value for each pair of SNPs is given, multiplied by 100. A blank cell indicates D′ = 1.0. Block definitions of Gabriel et al. 55 were applied.
Type 1 Diabetes Mellitus
In the multivariate analyses, after controlling for sex, HbA1c, and duration of disease, SNP rs699946 was most significantly associated with blinding DR in T1DM under a dominant model (i.e., AC=CC genotypes, P = 0.007, Table 3). These genotypes were found less commonly in the blinding cases compared with those with no DR, thus the AA genotype at SNP rs699946 is associated with an increased risk of blinding DR (odds ratio [OR], 4.1; 95% confidence interval [CI], 1.5–11.4). The GG genotype of rs833068 was similarly associated (P = 0.017; OR, 3.1; 95% CI, 1.3–7.2) with blinding DR. Both SNPs remained significant after further adjustment for smoking, hypercholesterolemia, hypertension, BMI, and nephropathy (P = 0.006 and 0.047, respectively). The subsets of patients with severe NPDR, PDR, and CSME were also considered individually for these associated SNPs. Of interest, rs699946 was associated with CSME (P = 0.039; OR, 5.7; 95% CI, 1.1–29.3), while rs833068 was associated with both CSME (P = 0.017; OR, 5.1; 95% CI, 1.3–19.5) and PDR (P = 0.029; OR, 4.2; 95% CI, 1.2–15.1). These two SNPs are in tight linkage disequilibrium (D′ = 0.99; Fig. 1). rs3024987 was also found to be significantly associated with blinding DR in the recessive model (Table 3). However, only three participants with blinding DR and no participants with no DR carried this genotype and with numbers too small to draw conclusions, the importance of this result is unclear. 
Table 3.
 
Probabilities for Association of VEGFA Tag SNPs with Blinding DR in T1DM and T2DM
Table 3.
 
Probabilities for Association of VEGFA Tag SNPs with Blinding DR in T1DM and T2DM
SNP T1DM Unadjusted P T1DM Adjusted P * T2DM Unadjusted P T2DM Adjusted P *
Dominant Recessive Dominant Recessive Dominant Recessive Dominant Recessive
1 rs1547651 0.800 0.240 0.670 0.150 0.220 0.750 0.710 0.300
2 rs833058 0.570 0.400 0.530 0.410 0.620 0.160 0.300 0.160
3 rs699946 0.010 0.570 0.007 0.770 0.660 0.180 0.640 0.170
4 rs833060 0.006 0.760 0.260 0.420 0.910 0.570 0.660 0.330
5 rs699947 0.490 0.430 0.580 0.980 0.690 0.660 0.840 0.140
6 rs3024987 0.260 0.027 0.820 0.012 0.780 0.610 0.960 0.550
7 rs833068 0.003 0.640 0.017 0.620 0.430 0.730 0.280 0.470
8 rs833070 0.380 0.430 0.540 0.980 0.710 0.880 0.830 0.290
9 rs2146323 0.028 0.400 0.220 0.430 0.190 0.950 0.610 0.520
10 rs3025007 0.700 0.026 0.800 0.062 0.580 0.300 0.690 0.800
11 rs3025020 0.890 0.710 0.680 0.390 0.031 0.190 0.120 0.320
12 rs3025021 0.990 0.650 0.320 0.940 0.650 0.002 0.400 0.002
13 rs3025030 0.180 0.680 0.390 0.800 0.450 0.260 0.890 0.240
14 rs3025035 0.930 0.200 0.840 0.260 0.380 0.067 1.000 0.046
15 rs10434 0.610 0.440 0.260 0.660 0.400 0.001 0.990 0.002
Type 2 Diabetes Mellitus
In T2DM, rs3025021 and rs10434 were both significantly associated with blinding DR after controlling for sex, HbA1c and duration of disease (P = 0.002 and 0.002, respectively, Table 3) in the multivariate analyses. Again, the minor allele for each SNP appears to be protective for blinding DR with the recessive genotype being less frequent in cases (Table 2). Thus, the genotypes containing the major allele (i.e., CC=CT for rs3025021 and GG=GA for rs10434) appear to confer the greatest risk for blinding DR (OR, 3.8; 95% CI, 1.5–10.0; and OR, 2.6; 95% CI, 1.3–5.3, respectively). Both SNPs remained significant after adjustment for additional covariates including smoking, hypercholesterolemia, hypertension, BMI, and nephropathy (P = 0.019 and 0.015 respectively). In the subanalysis, rs3025021 was also associated with PDR (P = 0.024; OR, 5.8; 95% CI, 1.3–26.7) and rs10434 with both CSME (P = 0.027; OR, 2.9; 95% CI, 1.1–7.6) and PDR (P = 0.021; OR, 3.0; 95% CI, 1.2–7.8). 
When T1DM and T2DM were combined, rs3025021 (P = 0.014) and rs10434 (P = 0.009) remained significantly associated with blinding DR after controlling for sex, disease type, HbA1c, and duration of disease in the multivariate analyses. rs10434 was also significantly associated with CSME (P = 0.003) in the combined analyses. 
Haplotype analyses revealed no significant association with blinding DR in T1DM. In T2DM, after controlling for age, HbA1c, and duration of disease, haplotype 1 of block 1 (ATGGCCACC) was significantly associated with blinding DR (P = 0.039) and haplotype 1 of block 2 (TCCGCG) was most significantly associated with blinding DR under an additive model (P = 0.0004). This haplotype was also significantly associated with CSME (P = 0.0007). 
The SNPSpD method for multiple testing correction in SNP-association studies estimated 10 independent tests. After adjusting for these 10 tests in the multivariate analyses, rs3025021 (P = 0.02) and rs10434 (P = 0.01) were both significantly associated with blinding DR in T2DM. In the combined analyses, rs10434 also remained significantly associated with CSME (P = 0.03). Haplotype 1 in block 2 (TCCGCG) also remained significant in the analysis of T2DM patients (blinding DR P = 0.004 [Table 4], CSME P = 0.007). All other significant SNPs and haplotypes in the multivariate analyses became nonsignificant after correction for multiple SNP and haplotypes tested. 
Table 4.
 
Association of Haplotypes with Blinding DR in T1DM and T2DM
Table 4.
 
Association of Haplotypes with Blinding DR in T1DM and T2DM
Haplotype Frequency No DR Frequency Blinding DR Frequency P
T1DM
Block 1 1 2 3 4 5 6 7 8 9
    1 A T A G C T G C C 0.116 0.104 0.196 0.116
    2 A C A T C C A C C 0.124 0.119 0.152 0.453
    3 A T G T C C A C C 0.134 0.143 0.087 0.553
    4 A C A G A C G T C 0.152 0.166 0.065 0.131
    5 T C A G A C G T A 0.162 0.160 0.174 0.915
    6 A C A G A C G T A 0.192 0.181 0.261 0.173
Block 2 10 11 12 13 14 15
    1 T C C C C G 0.098 0.098 0.097 0.987
    2 T T C G C G 0.106 0.104 0.117 0.329
    3 T C T G C A 0.151 0.149 0.156 0.990
    4 C T C G C G 0.160 0.148 0.231 0.177
    5 C C T G C A 0.204 0.207 0.192 0.656
T2DM
Block 1 1 2 3 4 5 6 7 8 9
    1 A T G G C C A C C 0.039 0.037 0.047 0.039
    2 A T A G C C G C C 0.050 0.053 0.039 0.050
    3 A C A T C C A C C 0.111 0.100 0.156 0.111
    4 A T A G C T G C C 0.117 0.109 0.148 0.117
    5 T C A G A C G T A 0.131 0.138 0.109 0.131
    6 A C A G A C G T C 0.161 0.173 0.109 0.161
    7 A T G T C C A C C 0.161 0.166 0.140 0.161
    8 A C A G A C G T A 0.195 0.192 0.203 0.195
Block 2 10 11 12 13 14 15
    1 T C C G C G 0.041 0.034 0.095 4 × 10–4*
    2 C C C G C A 0.048 0.045 0.063 0.652
    3 C C C C C G 0.069 0.071 0.049 0.505
    4 T C C C C G 0.071 0.067 0.106 0.668
    5 T C C G C A 0.081 0.096 0.000 0.019
    6 T C T G C A 0.128 0.129 0.126 0.841
    7 T T C G C G 0.130 0.131 0.091 0.692
    8 C C T G C A 0.161 0.161 0.163 0.754
    9 C T C G C G 0.162 0.163 0.181 0.693
Discussion
VEGF is an important cytokine that plays a role in angiogenesis and mediates microvascular permeability, making polymorphisms in the VEGFA gene potential candidate contributors to the pathogenesis of DR. 17,33  
There is conflicting evidence for the association of VEGFA variants with DR development, with interstudy variability including participant ethnicity, study design, retinopathy grading scales, statistical analytical methods, and study power playing major roles. The most investigated VEGFA SNP has been rs2010963, located in the 5′ untranslated region of the gene, with most studies showing no significant association between the polymorphism and the presence of DR, regardless of ethnicity. 34,37,3943,45,46 Most of these studies examined this SNP in participants with T2DM, with Churchill et al., 39 being the only study to make this comparison in a combined DM cohort and no significant association was found. All studies were of a cross-sectional design, with the exception of two studies examining this SNP in a T1DM cohort. 37,42 Al-Kateb et al. 37 was the largest longitudinal study to investigate VEGFA SNPs and DR development, whereby 1369 Caucasian subjects were genotyped from the Diabetes Control and Complications Trial. No association of DR in T1DM with the rs2010963 polymorphism was found in this cohort. 37 However, after controlling for covariate risk factors, they found eight other SNPs that showed significant association (P < 0.05) with severe DR (an ETDRS level 53/<53 or scatter laser treatment 49 ), with rs3025021 having the most significant association (P = 0.0017). No associations of VEGFA SNPs with CSME were found. In another longitudinal study, however, Nakanishi and Watanabe 42 did not replicate the association of these SNPs with the progression of DR in 175 Japanese participants with T1DM, after controlling for associated risk factors (including HbA1c) in multivariate analyses. 
We investigated the association of 15 tag SNPs in the VEGFA gene with blinding DR in T1DM and T2DM. After adjustment for known clinical covariates, the SNPs rs699946 and rs833068 were most significantly associated with blinding DR in T1DM and rs3025021 and rs10434 in T2DM, although only the T2DM result survived correction for multiple testing. The rs10434 SNP was also significantly associated with CSME in combined DM after correction for multiple testing. To our knowledge, this is a novel finding. This study revealed the C allele of rs3025021 to be a risk allele for DR in T2DM. Although it was not found to be significantly associated with T1DM in our study, it was found to be the most significant SNP for severity of DR in the study of Al-Kateb et al., 37 where only T1DM patients were considered. The lack of association in T1DM in the present study may be due to the smaller cohort size or different genetic backgrounds between the two studies. rs3025021 remained significantly associated in the combined T1DM and T2DM analyses, providing additional evidence of its important role. It remains to be determined which variants are functionally related to the DR susceptibility. 
Haplotype analysis suggested an important role of the TCCGCG haplotype (consisting of SNPs rs3025007, rs3025020, rs3025021, rs3025030, rs3025035, and rs10434) in blinding DR and CSME in T2DM. This haplotype was not observed at >2% frequency in the smaller T1DM cohort. Although it is relatively rare (overall frequency of ∼4%), this haplotype contains the risk allele of the two individually associated SNPs (C for rs3025021 and G for rs10434). Several other haplotypes also contain these alleles, indicating that the true risk allele that is tagged by these SNPs is probably on the background of this particular haplotype, rather than being these SNPs themselves. To our knowledge, this is the first study to report this association. 
It is acknowledged that participants with DR in this study had the presence of increased risk factors (including longer diabetes duration, higher HbA1c levels and higher rates of nephropathy) when compared to those without DR. However, standard statistical measures to control for their effects on DR development were made in the multivariate analyses. 
VEGF protein expression has been shown to be influenced by SNPs in the VEGFA gene, particularly in the promoter region 3436 and VEGF protein levels have been shown to be elevated in PDR patients and the vitreous of subjects with diabetes. 2527,33 The associated SNPs in T1DM are in linkage disequilibrium with the promoter region of VEGFA and may well be tagging such SNPs, directly influencing protein expression and therefore vitreous concentrations of VEGF. It is unclear how SNPs in block 2 would affect the regulation of VEGF levels, although this is now the second report of significantly associated SNPs in this region, suggesting this is a true association. Further functional studies are necessary to examine the link between specific VEGFA SNPs and haplotypes in DR. 
In conclusion, several VEGFA SNPs are associated with increased risk of developing blinding DR in both T1DM and T2DM, independent of duration of diabetes and degree of glycemic control. Diabetic ocular screening is a major public health cost and a significant amount of eye care providers' time is devoted to the clinical screening of patients with DM who have no DR and who may never develop significant visual loss. Conversely, patients not uncommonly present with severe diabetic ocular complications without having attended for screening at recommended intervals. Identifying specific VEGFA genetic markers for high-risk of developing DR could allow for refined DR screening algorithms, earlier intervention, and development of novel treatments targeting VEGF action thereby reducing the morbidity associated with diabetic retinopathy. 
Footnotes
 Supported by a grant from the Ophthalmic Research Institute of Australia and the Flinders Medical Research Foundation. KPB is a Peter Doherty Fellow of the National Health and Medical Research Council of Australia (NHMRC) and JEC is an NHMRC Practitioner Fellow.
Footnotes
 Disclosure: S. Abhary, None; K.P. Burdon, None; A. Gupta, None; S. Lake, None; D. Selva, None; N. Petrovsky, None; J.E. Craig, None
Footnotes
 The publication costs of this article were defrayed in part by page charge payment. This article must therefore be marked “advertisement” in accordance with 18 U.S.C. §1734 solely to indicate this fact.
The authors thank participating patients and their ophthalmologists, research nurses, and laboratory assistants. 
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Figure 1.
 
Linkage disequilibrium between SNPs in and around the VEGFA gene, generated in Haploview. The D′ value for each pair of SNPs is given, multiplied by 100. A blank cell indicates D′ = 1.0. Block definitions of Gabriel et al. 55 were applied.
Figure 1.
 
Linkage disequilibrium between SNPs in and around the VEGFA gene, generated in Haploview. The D′ value for each pair of SNPs is given, multiplied by 100. A blank cell indicates D′ = 1.0. Block definitions of Gabriel et al. 55 were applied.
Table 1.
 
Clinical Characteristics of No DR Compared to Blinding DR in Type-1 and -2 Diabetes
Table 1.
 
Clinical Characteristics of No DR Compared to Blinding DR in Type-1 and -2 Diabetes
Clinical Characteristics T1 DM T2 DM
No DR (n = 94) Blinding DR (n = 76) P No DR (n = 187) Blinding DR (n = 139) P
Sex (female) 48 (51) 29 (45) 0.478 100 (53) 45 (35) 0.001
Age, y 38.4 ± 14.9 49.8 ± 15.5 <0.001 64.3 ± 14.5 64.0 ± 10.7 0.853
Disease duration, y 15.4 ± 9.1 30.9 ± 13.4 <0.001 12.9 ± 8.7 17.5 ± 8.6 <0.001
HbAIc, % 7.6 ± 2.5 8.8 ± 2.3 0.004 6.6 ± 2.9 7.5 ± 3.4 0.014
BMI, kg/m2 25.9 ± 7.1 25.9 ± 9.8 0.958 32.2 ± 9.1 29.6 ± 11.5 0.026
Hypercholesterolemia, % 33 (35) 32 (50) 0.062 120 (64) 81 (63) 0.802
Nephropathy, % 40 (15) 29 (45) <0.001 43 (23) 46 (36) 0.014
Smoker, % 44 (47) 35 (55) 0.331 99 (53) 69 (53) 0.924
Hypertension, % 37 (39) 46 (72) <0.001 153 (82) 107 (83) 0.796
Table 2.
 
Genotype Frequencies for Each SNP in No DR and Blinding DR by Type of Diabetes
Table 2.
 
Genotype Frequencies for Each SNP in No DR and Blinding DR by Type of Diabetes
SNP Genotype T1DM T2DM
No DR n (%) Blinding DR n (%) No DR n (%) Blinding DR n (%)
1 rs547651 AA 66 (70) 52 (68) 142 (77) 99 (71)
AT 22 (23) 22 (29) 37 (20) 37 (27)
TT 6 (6) 2 (3) 5 (3) 3 (2)
2 rs833058 CC 40 (43) 36 (47) 69 (38) 49 (35)
CT 35 (38) 29 (38) 82 (45) 74 (53)
TT 18 (19) 11 (14) 31 (17) 16 (12)
3 rs699946 AA 56 (60) 59 (79) 117 (64) 86 (62)
AG 33 (35) 14 (19) 56 (31) 50 (36)
GG 4 (4) 2 (3) 9 (5) 3 (2)
4 rs833060 GG 41 (44) 49 (65) 98 (54) 74 (53)
GT 46 (49) 22 (29) 69 (38) 51 (37)
TT 6 (6) 4 (5) 15 (8) 14 (10)
5 rs699947 AA 24 (26) 23 (31) 45 (25) 31 (23)
AC 43 (46) 35 (47) 91 (50) 74 (54)
CC 26 (28) 17 (23) 45 (25) 31 (23)
6 rs3024987 CC 75 (81) 55 (73) 139 (76) 108 (78)
CT 18 (19) 17 (23) 39 (21) 29 (21)
TT 0 (0) 3 (4) 4 (2) 2 (1)
7 rs833068 GG 33 (35) 44 (59) 88 (48) 61 (44)
GA 52 (56) 26 (35) 74 (41) 61 (44)
AA 8 (9) 5 (7) 20 (11) 17 (12)
8 rs833070 TT 24 (26) 24 (32) 45 (24) 33 (24)
TC 44 (47) 35 (46) 92 (50) 73 (53)
CC 26 (28) 17 (22) 47 (26) 33 (24)
9 rs2146323 CC 44 (47) 23 (31) 85 (47) 54 (39)
CA 35 (38) 44 (59) 78 (43) 69 (50)
AA 14 (15) 8 (11) 19 (10) 14 (10)
10 rs3025007 CC 26 (28) 19 (25) 44 (24) 37 (27)
CT 54 (57) 35 (46) 94 (51) 74 (530
TT 14 (15) 22 (29) 46 (25) 28 (20)
11 rs3025020 CC 45 (48) 35 (47) 94 (52) 55 (40)
CT 43 (46) 34 (46) 67 (37) 74 (53)
TT 5 (5) 5 (7) 21 (12) 10 (7)
12 rs3025021 CC 37 (39) 30 (39) 88 (48) 70 (50)
CT 45 (48) 38 (50) 70 (38) 63 (45)
TT 12 (13) 8 (11) 26 (14) 6 (4)
13 rs3025030 GG 71 (77) 51 (68) 131 (72) 94 (68)
GC 19 (21) 23 (31) 48 (26) 39 (28)
CC 2 (2) 1 (1) 3 (2) 5 (4)
14 rs3025035 CC 81 (87) 65 (87) 163 (90) 120 (86)
CT 12 (13) 9 (12) 19 (10) 17 (12)
TT 0 (0) 1 (1) 0 (0) 2 (1)
15 rs10434 GG 26 (28) 24 (32) 56 (31) 48 (35)
GA 44 (47) 37 (49) 83 (45) 76 (55)
AA 23 (25) 15 (20) 44 (24) 13 (9)
Table 3.
 
Probabilities for Association of VEGFA Tag SNPs with Blinding DR in T1DM and T2DM
Table 3.
 
Probabilities for Association of VEGFA Tag SNPs with Blinding DR in T1DM and T2DM
SNP T1DM Unadjusted P T1DM Adjusted P * T2DM Unadjusted P T2DM Adjusted P *
Dominant Recessive Dominant Recessive Dominant Recessive Dominant Recessive
1 rs1547651 0.800 0.240 0.670 0.150 0.220 0.750 0.710 0.300
2 rs833058 0.570 0.400 0.530 0.410 0.620 0.160 0.300 0.160
3 rs699946 0.010 0.570 0.007 0.770 0.660 0.180 0.640 0.170
4 rs833060 0.006 0.760 0.260 0.420 0.910 0.570 0.660 0.330
5 rs699947 0.490 0.430 0.580 0.980 0.690 0.660 0.840 0.140
6 rs3024987 0.260 0.027 0.820 0.012 0.780 0.610 0.960 0.550
7 rs833068 0.003 0.640 0.017 0.620 0.430 0.730 0.280 0.470
8 rs833070 0.380 0.430 0.540 0.980 0.710 0.880 0.830 0.290
9 rs2146323 0.028 0.400 0.220 0.430 0.190 0.950 0.610 0.520
10 rs3025007 0.700 0.026 0.800 0.062 0.580 0.300 0.690 0.800
11 rs3025020 0.890 0.710 0.680 0.390 0.031 0.190 0.120 0.320
12 rs3025021 0.990 0.650 0.320 0.940 0.650 0.002 0.400 0.002
13 rs3025030 0.180 0.680 0.390 0.800 0.450 0.260 0.890 0.240
14 rs3025035 0.930 0.200 0.840 0.260 0.380 0.067 1.000 0.046
15 rs10434 0.610 0.440 0.260 0.660 0.400 0.001 0.990 0.002
Table 4.
 
Association of Haplotypes with Blinding DR in T1DM and T2DM
Table 4.
 
Association of Haplotypes with Blinding DR in T1DM and T2DM
Haplotype Frequency No DR Frequency Blinding DR Frequency P
T1DM
Block 1 1 2 3 4 5 6 7 8 9
    1 A T A G C T G C C 0.116 0.104 0.196 0.116
    2 A C A T C C A C C 0.124 0.119 0.152 0.453
    3 A T G T C C A C C 0.134 0.143 0.087 0.553
    4 A C A G A C G T C 0.152 0.166 0.065 0.131
    5 T C A G A C G T A 0.162 0.160 0.174 0.915
    6 A C A G A C G T A 0.192 0.181 0.261 0.173
Block 2 10 11 12 13 14 15
    1 T C C C C G 0.098 0.098 0.097 0.987
    2 T T C G C G 0.106 0.104 0.117 0.329
    3 T C T G C A 0.151 0.149 0.156 0.990
    4 C T C G C G 0.160 0.148 0.231 0.177
    5 C C T G C A 0.204 0.207 0.192 0.656
T2DM
Block 1 1 2 3 4 5 6 7 8 9
    1 A T G G C C A C C 0.039 0.037 0.047 0.039
    2 A T A G C C G C C 0.050 0.053 0.039 0.050
    3 A C A T C C A C C 0.111 0.100 0.156 0.111
    4 A T A G C T G C C 0.117 0.109 0.148 0.117
    5 T C A G A C G T A 0.131 0.138 0.109 0.131
    6 A C A G A C G T C 0.161 0.173 0.109 0.161
    7 A T G T C C A C C 0.161 0.166 0.140 0.161
    8 A C A G A C G T A 0.195 0.192 0.203 0.195
Block 2 10 11 12 13 14 15
    1 T C C G C G 0.041 0.034 0.095 4 × 10–4*
    2 C C C G C A 0.048 0.045 0.063 0.652
    3 C C C C C G 0.069 0.071 0.049 0.505
    4 T C C C C G 0.071 0.067 0.106 0.668
    5 T C C G C A 0.081 0.096 0.000 0.019
    6 T C T G C A 0.128 0.129 0.126 0.841
    7 T T C G C G 0.130 0.131 0.091 0.692
    8 C C T G C A 0.161 0.161 0.163 0.754
    9 C T C G C G 0.162 0.163 0.181 0.693
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