Interaction between XRCC 1 gene polymorphisms and diabetes on susceptibility to primary open-angle glaucoma

Abstract

Our study was designed to investigate the relationship of the POAG (primary open angle glaucoma) risk with the XRCC1 SNPs (single nucleotide polymorphisms) and other gene–diabetes interactions and haplotype combinations. HWE (Hardy–Weinberg equilibrium) test and GMDR for the best haplotype interaction of four SNPs in XRCC1 gene and diabetes were assessed. Logistic regression analysis revealed that rs25487-A and rs861539-C allele, with adjusted ORs (95% CI) of 1.60 (1.19–2.02) and 1.62 (1.20–2.08), respectively, were the associated risk factors with increased POAG. GMDR test indicated that testing accuracy of two-locus model (including rs25487 and T2DM) was 62.11% (P < 0.01). Therefore, regardless of how the data are partitioned, the highest cross-validation consistency across the multidimensional model is showed by this best model. The analysis indicated that T2DM inﬂuenced the POAG risk depending on the genotypes at rs25487. Pairwise LD analysis suggested that the haplotype G-G was the most common in the POAG patients (49.45%) and in the controls (55.78%), respectively. The results showed that haplotype A-A significantly correlated with a higher POAG risk. The re25487-A and rs861539-c and the interaction of between rs25487 and T2DM and the haplotype A-A were all associated with higher POAG risk.

Impact statement

Some studies have suggested that diabetes and XRCC gene may be risk factors for glaucoma; however, no studies have focused on the interaction between the XRCC gene and T2DM with respect to POAG risk. Therefore, the present study evaluated the initiative gene–environment interactions in POAG.

Keywords

Primary open angle glaucoma X-ray cross-complementing group single nucleotide polymorphisms interaction haplotype diabetes

Introduction

Glaucoma-caused irreversible blindness represents a major public health problem worldwide,¹ in which POAG (primary open-angle glaucoma) is one type of the most common form.² Some risk factors including ocular hypertension,^3,4 age, a first-degree relative with glaucoma, pigment dispersion, pseudoexfoliation, myopia, and genetic factors^5–7 have been previously reported.

NER (nucleotide excision repair) and BER (base excision repair) can repair the oxidative DNA damage.^8,9 BER is the critical pathway for repair of oxidative DNA damage.¹⁰ Several SNPs belong to DNA repair genes were reported, among which polymorphisms of XRCC (X-ray cross-complementing group) genes may have phenotypic significance.^11,12 To date, the molecular mechanism underlying POAG remains largely unknown. Generally, the pathologic mechanism underlying POAG is considered to be a complex process that is mainly caused by the combined action of environmental and genetic factors.¹³ Some studies have suggested that one of the potential risk factors for glaucoma is diabetes;^14,15 however, no studies have focused on the XRCC–T2DM interaction with respect to POAG risk. Therefore, the present study evaluated the interactions between the XRCC SNPs (single nucleotide polymorphisms) and environment in POAG.

Materials and methods

Subjects

This study collected 1233 participants, including 410 with POAG and 823 controls, who were hospitalized in the Second Hospital of Jilin University. The diagnosis of POAG was determined by a gynecologist based on the following: (1) no other causes; (2) gonioscopic grading III or IV of open iridocorneal angle; and (3) glaucoma hemi-field test showing glaucomatous visual field change detected by the Humphrey automated perimeter. The IOP measured by applanation tonometry before management was > 21 mmHg. Age-matched (±3 years) control subjects, in 1:2 ratio to patients, did not have glaucoma, the family history of glaucoma, other major eye diseases (except mild cataracts and refractive errors), tumors, diabetes, cardiovascular diseases and neurologic diseases. All of the recruited subjects were Han Chinese with no genetic relationships to one other. At recruitment, all subjects signed the written informed consent.

SNPs selection and genotyping

NCBI SNPs-database was used for SNPs selection: (1) the minor allele frequency reported in HapMap was > 5%; (2) reported associations existed with glaucoma or related risk factors, but were not well known; and (3) not included in a published genome-wide association study; 3 mL EDTA-treated blood samples were obtained from all of the participants for genomic DNA extraction according to the instructions of DNA Blood Mini Kit (Qiagen, Hilden, Germany) and the gDNA was kept at −20°C until use. The genotyping for the selected four SNPs was conducted using PCR-based restriction fragment length polymorphism. The primers used in this study are shown in Table 1. The amplification conditions were: initial denaturation step at 95°C for 5 min, followed by 30 cycles at 94°C for 0.5 min, 60°C for 0.5 min, 72°C for 1 min, and 10 min final extension at 72°C.

Table 1.

Description and primer sequences designed for sequencing four SNPs.

SNPs	Chromosome	Functional consequence	Major/minor alleles	Primer (5′→3′)
ERCC1-280 (Arg > His)rs25489	19:43552260	Missense	G/A	Forward: 5′-CAGTGGTGCTAACCTAATC-3′Reverse: 5′-AGTA-GTCTGCTGGCTCTGG-3′
ERCC1-399 (Arg > Gln) rs25487	19:43551574	Missense	G/A	Forward: 5′- CAGTGGTGCTAACCTAATC-3′Reverse: 5′-AGTAGTCTGCTGGCTCTGGG-3′
ERCC2-Lys751Glnrs13181	19:45351661	Downstream variant 500B, missense, nc transcript variant	A/C	Forward: 5′-GCCCGCTCTGGATTATACG-3′Reverse: 5′-CTATCATCTCCTGGCCCCC-3′
XRCC3-241 (Thr > Met)rs861539	14:103699416	Intron variant, missense	T/C	Forward: 5′-GGTCGAGTGACAGTCCAAAC-3′Reverse: 5′-TGCAACGGC TGAGGGTCTT-3′

SNPs: single nucleotide polymorphisms.

Statistical analysis

A Chi-squared test was performed for difference analysis between groups on percentages calculated for categorical variables, and the t-test was used for the continuous variables with normal distribution (means ± standard deviations). HWE (Hardy–Weinberg equilibrium) for SNPs in controls was analyzed by SNPstats. Logistic regression calculated the ORs (95% CI) for associations between the four XCRR SNPs and POAG risk. The ORs were adjusted for age, gender, BMI, smoking, and alcohol consumption. The best combination of SNP–T2DM interactions was assessed by GMDR (generalized multifactor dimensionality reduction). Haplotype analysis was performed using PHASE 2.0 (University of Manchester, Manchester, UK). All P values reported in the results section were two-tailed, and the P values obtained from logistic regression were corrected for multiple testing.

Results

A comparison of case and control groups with respect to demographic characteristics is shown in Table 2. There were total of 1233 participants including 410 patients with POAG and 823 control participants. Their average age was 65.9 ± 13.8 years. No significant change existed in the parameters of interest, including gender, age, BMI, and hypertension between the two groups (both P > 0.05). In contrast, the percentage of participants who smoked cigarettes, consumed alcohol, T2DM and intraocular pressure in the POAG was higher than in controls.

Table 2.

Comparison for different demographic characteristics in case and control groups.

Variables	Case group(n = 410)	Control group(n = 823)	P values
Males, n (%)	207 (50.5)	405 (49.2)	0.672
Age (year), (Means ± SD)	66.8 ± 14.2	65.5 ± 15.3	0.150
Smokers, n (%)	127 (31.0)	208 (25.3)	0.034
Alcohol drinkers, n (%)	157 (38.3)	254 (30.9)	0.009
BMI (kg/m²), (Means ± SD)	22.4 ± 9.5	23.1 ± 9.3	0.217
T2DM, n (%)	118 (28.8)	124 (15.1)	<0.001
Hypertension, n (%)	131 (32.0)	226 (27.5)	0.101
Intraocular pressure (mmHg), (Means ± SD)	27.6 ± 6.8	14.3 ± 4.1	<0.001
Family history of glaucoma, n (%)	50 (12.2)	–

SD: standard deviation; BMI: body mass index; T2DM: type 2 diabetes.

In the current study, the genotype frequencies in the control were distributed accordingly to HWE. The frequency of the rs25487-A allele was 30.1% in cases and 19.9% in controls. The frequency of the rs861539-C allele was 31.7% in cases and 20.8% in controls, which was also a statistically significant difference. The rs25487-A and rs861539-C were associated with a higher POAG risk (adjusted ORs [95% CI], 1.60 [1.19–2.02] and 1.62 [1.20–2.08], respectively; Table 3).

Table 3.

Association analysis for four target SNPs within XRCC gene and POAG risk.

SNPs	Genotypes or alleles	Frequencies n (%)		OR (95%CI)^a	P values	HWE test for controls
SNPs	Genotypes or alleles	Controls (n=823)	Cases (n=410)	OR (95%CI)^a	P values	HWE test for controls
ERCC1-280 (Arg>His), rs25489
	GG genotype	474 (57.6)	215 (52.4)	1.00 (ref)		0.086
	GA genotype	289 (35.1)	155 (37.8)	1.24 (0.81–1.83)	0.426
	AA genotype	60 (7.3)	40 (9.8)	1.57 (0.75–2.40)	0.628
	G allele	1237 (75.2)	585 (71.3)	1.00 (ref)
	A allele	409 (24.8)	235 (28.7)	1.26 (0.79–1.99)	0.578
ERCC1-399 (Arg>Gln), rs25487
	GG genotype	535 (65.0)	204 (49.8)	1.00 (ref)		0.110
	GA genotype	248 (30.1)	165 (40.2)	1.49 (1.15–1.87)	0.002
	AA genotype	40 (4.9)	41 (10.0)	2.02 (1.31–2.76)	<0.001
	G allele	1318 (80.1)	573 (69.9)	1.00 (ref)
	A allele	328 (19.9)	247 (30.1)	1.60 (1.19–2.02)	<0.001
ERCC2-Lys751Gln, rs13181
	AA genotype	483 (58.7)	215 (52.4)	1.00 (ref)		0.306
	AC genotype	288 (35.0)	160 (39.0)	1.23 (0.95–1.61)	0.331
	CC genotype	52 (6.3)	35 (8.5)	1.35 (0.87–1.86)	0.485
	A allele	1254 (76.2)	590 (72.0)	1.00 (ref)
	C allele	392 (23.8)	230 (28.0)	1.27 (0.93–1.66)	0.386
XRCC3-241 (Thr>Met), rs861539
	TT genotype	524 (63.7)	196 (47.8)	1.00 (ref)		0.081
	TC genotype	255 (31.0)	168 (41.0)	1.49 (1.25–1.87)	<0.001
	CC genotype	44 (5.3)	46 (11.2)	2.10 (1.41–2.86)	<0.001
	T allele	1303 (79.2)	560 (68.3)	1.00 (ref)
	C allele	343 (20.8)	260 (31.7)	1.62 (1.20–2.08)	<0.001

Adjusted for age, gender, BMI, smoking and alcohol drinking.

P < 0.00417 (Bonferroni correction threshold).

POAG: primary open angle glaucoma; SNPs: single nucleotide polymorphisms; OR: odds ratio; HWE: Hardy–Weinberg equilibrium.

The comparison of CVC (cross-validation consistency) and the TA (test accuracy) between gene–gene and gene–T2DM model was determined by GMDR analysis (Table 4). The TA for two-locus model rs25487 and T2DM was 62.11% at the P < 0.01 level indicating rs25487 and T2DM model had the highest CVC independent of how the data were divided, thus providing evidence of rs25487 gene–T2DM interaction. The T2DM related to POAG risk was affected by the rs25487 function.

Table 4.

GMDR analysis for the best interaction combination models.

Locus no.	Best combination	Cross-validation consistency	Testing accuracy	P values*
Gene–gene interactions
2	rs25489 × rs25487	9/10	0.5399	0.0547
3	rs25489 × rs25487× rs13181	9/10	0.4958	0.1719
4	rs25489 × rs25487 × rs13181 × rs861539	7/10	0.4958	0.3770
Gene–T2DM interactions
2	rs25487× T2DM	9/10	0.6011	0.0010
3	rs25489 × rs25487× T2DM	7/10	0.5399	0.3770
4	rs25489 × rs25487× rs13181× T2DM	6/10	0.4958	0.4258
5	rs25489 × rs25487 × rs13181 × rs861539× T2DM	3/10	0.4958	0.9893

*Adjusted for age, gender, BMI, alcohol drinking and smoking.

Pairwise LD value between rs25489 and rs25487 within the ERCC1 gene was > 0.75. Therefore, SHEsis software was used for haplotype analysis for rs25489 and rs25487. Among all samples, the G-G haplotype was 49.45% in the POAG patients and 55.78% in the controls, respectively. The A-A haplotype was also significantly related to the increased risk of POAG (Table 5).

Table 5.

Haplotype analysis on association of XRCC1 gene and POAG risk.

Haplotypes (rs25489 and rs25487)	Frequencies		OR (95% CI)	P values*
Haplotypes (rs25489 and rs25487)	Case group	Control group	OR (95% CI)	P values*
G-G	0.4945	0.5578	1.00	–
G-A	0.2347	0.2283	1.33 (0.71–2.01)	0.460
A-G	0.1981	0.1811	1.23 (0.67–1.95)	0.542
A-A	0.0727	0.0328	1.68 (1.06–2.31)	<0.001

*Adjusted for gender, age, smoking, alcohol drinking and BMI.

POAG: primary open angle glaucoma.

Discussion

In the current study, we have shown that the rs25487-A and rs861539-C alleles are associated with an increased risk of POAG and could be a risk factor for developing POAG; however, there was no relationship between rs25489 and rs13181 and POAG. It is well known that some gene polymorphisms were related to a lower DNA repair capacity and oxidative stress in the eye has been thought as a risk factor in the pathogenesis of cataracts,¹⁶ age-related macular degeneration,¹⁷ glaucoma,¹⁸ and POAG.¹⁹ Reports involving the association between XRCC gene polymorphisms and POAG risk are limited and the results are inconsistent. It has been suggested that XPD codon 751 and XRCC1 codon 399 polymorphisms are not related to POAG risk;²⁰ however, the sample sizes of the cases and controls were not large enough to detect any true differences between the groups. An association has been reported polymorphisms between rs25487 (XRCC1) and rs13181 (XPD). Szaflik et al.²¹ concluded that the XRCC1-399Arg/Gln its 399Gln allele is the potential risk factors for development of POAG. Moreover, Szaflik et al.²¹ suggested that XRCC1-399 Arg/Gln may be associated with progression of POAG. Based on a study²² involving a Polish population, it was suggested that the XRCC1-399Arg/Gln mutation may act as a predictive risk factor of POAG. The difference between these studies and the study conducted by Güven et al.²⁰ may reflect population group differences. In addition, the sample size in the Güven et al.²⁰ study was relatively smaller than the previous studies and the current study.

Generally, the pathologic mechanism underlying POAG is a complex process that is caused mainly by the interaction between genetic and environmental factors.¹³ Some studies have showed that diabetes may be a risk factor for glaucoma;^14,15 however, there are few reports on the interaction between T2DM and the XRCC gene. In the study, we found that the rate for T2DM was higher in cases than in controls, our interaction analysis between the XRCC gene and T2DM indicated significant interaction between rs25487 and T2DM, which provided evidence of XRCC 1 gene–T2DM interaction effects. The analysis indicated that T2DM inﬂuenced the risk for POAG depending on the genotypes at rs25487. The present study is the first report attempting to elucidate the possible impact of an XRCC gene–T2DM interaction on POAG risk. Our haplotype analysis for rs1799782 and rs13181 showed the D′ value was > 0.75, indicating that the T-C haplotype was significantly associated with increased POAG risk. Pairwise LD analysis was also conducted for SNPs within substantial LD. We showed that the G-G haplotype existed most frequently in the two groups: the G-G was 49.45% in the POAG patients and 55.78% in the controls, respectively. As well, the results indicated that the haplotype A-A was significantly related to an increased risk for POAG.

The limitations of this study were: First, we studied a relatively smaller sample size, although statistical analysis requirements were met. Second, the cases were all POAG patients; other types of glaucoma patients should be studied in the future to explore the impact of this gene on different subtypes of glaucoma.

In conclusion, we showed that the rs25487-A and rs861539-C alleles, as well as the interaction between rs25487 and T2DM and haplotype A-A are accompanied by a significantly increased risk of POAG.

Footnotes

Authors’ contributions

Yanyan Wang: wrote the manuscript, conceived and designed the experiments. Chenguang Wang, and Shounan Qi: cellular experiment operation. Zaoxia Liu: data processing and statistical analysis. Guanfang Su and Yajuan Zheng: experimental guidance and data verification.

ACKNOWLEDGEMENTS

We appreciate the cooperation of the families and individuals who cooperated in this study.

DECLARATION OF CONFLICTING INTERESTS

The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

FUNDING

The author(s) received no financial support for the research, authorship, and/or publication of this article.

References

Actis

Dall’Orto

Penna

Brogliatti

Rolle

An internal medicine perspective review of risk factors for assessing and progression of primary open angle glaucoma. Minerva Med 2013; 104:471–85

Quigley

Broman

AT.

The number of people with glaucoma worldwide in 2010 and 2020. Br J Ophthalmol 2006; 90:262–7

Coleman

Miglior

Risk factors for glaucoma onset and progression. Surv Ophthalmol 2008; 53:S3–S10

Leske

Heijl

Hyman

Bengtsson

Komaroff

Factors for progression and glaucoma treatment: the early manifest glaucoma trial. Curr Opin Ophthalmol 2004; 15:102–6

Boland

Quigley

HA.

Risk factors and open-angle glaucoma: classification and application. J Glaucoma 2007; 16:406–18

Marcus

de Vries

Junoy Montolio

Jansonius

NM.

Myopia as a risk factor for open-angle glaucoma: a systematic review and meta-analysis. Ophthalmology 2011; 118:1989–94.e2

Chen

Hughes

Chen

Qian

Cao

Wang

Sun

Genetic variants associated with different risks for high tension glaucoma and normal tension glaucoma in a Chinese population. Invest Ophthalmol Vis Sci 2015; 56:2595–600

Frosina

Overexpression of enzymes that repair endogenous damage to DNA. Eur J Biochem 2000; 267:2135–49

Wood

Mitchell

Sgouros

Lindahl

Human DNA repair genes. Science 2001; 291:1284–9

10.

Christmann

Tomicic

Roos

Kaina

Mechanisms of human DNA repair: an update. Toxicology 2003; 193:3–34

11.

Clarkson

Wood

RD.

Polymorphisms in the human XPD (ERCC2) gene, DNA repair capacity and cancer susceptibility: an appraisal. DNA Repair 2005; 4:1068–74

12.

Manuguerra

Saletta

Karagas

Berwick

Veglia

Vineis

Matullo

XRCC3 and XPD/ERCC2 single nucleotide polymorphisms and the risk of cancer: a HuGE review. Am J Epidemiol 2006; 164:297–302

13.

Wiggs

JL.

The cell and molecular biology of complex forms of glaucoma: updates on genetic, environmental, and epigenetic risk factors. Invest Ophthalmol Vis Sci 2012; 53:2467–9

14.

Bonovas

Peponis

Filioussi

Diabetes mellitus as a risk factor for primary open-angle glaucoma: a meta-analysis. Diabet Med 2004; 21:609–14

15.

Zhao

Cho

Kim

Friedman

Guallar

Diabetes, glucose metabolism, and glaucoma: the 2005–2008 national health and nutrition examination survey. PLoS One 2014; 9:e112460

16.

Truscott

RJ.

Age-related nuclear cataract oxidation is the key. Exp Eye Res 2005; 80:709–25

17.

Yildirim

Ateş

Tamer

Muşlu

Ercan

Atik

Kanik

Changes in antioxidant enzyme activity and malondi aldehyde level in patients with age-related macular degeneration. Ophthalmologica 2004; 218:202–6

18.

Aslan

Cort

Yucel

Oxidative nitrative stress markers in glaucoma. Free Radic Biol Med 2008; 45:367–76

19.

Majsterek

Malinowska

Stanczyk

Kowalski

Blaszczyk

Kurowska

Kaminska

Szaflik

JP.

Elevation of oxidative stress markers in pathogenesis of primary open-angle. Exp Mol Pathol 2011; 90:231–7

20.

Güven

Unal

Batar

Eroğlu

Devarnoğlu

Tamçelik

Uçar

Sarici

Polymorphisms of DNA repair genes XRCC1 and XPD and risk of primary open angle glaucoma (POAG). Mol Vis 2007; 13:12–7

21.

Szaflik

Cuchra

Przybylowska-Sygut

Dziki

Kurowska

Gacek

Drzewoski

Szaflik

Majsterek

Association of the 399Arg/Gln XRCC1, the 194 Arg/Trp XRCC1, the 326Ser/Cys OGG1, and the 324Gln/His MUTYH gene polymorphisms with clinical parameters and the risk for development of primary open-angle glaucoma. Mutat Res 2013; 753:12–22

22.

Cuchra

Markiewicz

Mucha

Pytel

Szymanek

Szemraj

Szaflik

Majsterek

The role of base excision repair in the development of primary open angle glaucoma in the Polish population. Mutat Res 2015; 778:26–40