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. Author manuscript; available in PMC: 2016 Apr 22.
Published in final edited form as: JAMA Ophthalmol. 2015 Apr;133(4):406–412. doi: 10.1001/jamaophthalmol.2014.5627

Genetic and environmental factors in conjunctival UV autofluorescence

Seyhan Yazar 1,*, Gabriel Cuellar-Partida 1,*, Charlotte M McKnight 1, Piriya Quach-Thanissorn 1, Jenny A Mountain 1, Minas T Coroneo 1, Craig E Pennell 1, Alex W Hewitt 1, Stuart MacGregor 1, David A Mackey 1
PMCID: PMC4840232  NIHMSID: NIHMS778427  PMID: 25590795

Abstract

Importance

Conjunctival ultraviolet autofluorescence (CUVAF) has excellent potential as an objective biomarker of sun exposure. However, much variation in CUVAF is observed and the relative contribution of genes and environment to this variation has not yet been identified.

Objective

CUVAF photography was developed to detect and characterise pre-clinical sunlight-induced ocular damage. Ocular sun exposure has been related to cases of pterygia and also recently negatively correlated with myopia. We investigated sources of variation in CUVAF in relation to its potential clinical relevance.

Design

Cross-sectional analysis of three population-based cohort studies: Twins Eye Study in Tasmania, Brisbane Adolescent Twin Study and Western Australian Pregnancy Cohort (Raine) Study.

Setting

General community.

Participants

295 Australian families from the Tasmanian and Brisbane twin studies and 661 participants from the 20-year follow-up of the Raine Study. Only individuals with available genotype data were included.

Methods

We compared the CUVAF levels in three cohorts and performed a classical twin study to partition variation in CUVAF. We also conducted a genome-wide association analysis to identify specific genetic variants associated with CUVAF.

Main Outcome Measure(s)

The total area of CUVAF, heritability of CUVAF and single nucleotide polymorphisms (SNPs) associated with CUVAF from genome-wide association study.

Results

Within twin cohorts, individuals living closer to the equator (27.47° S) had higher levels of CUVAF compared to individuals from southern regions (42.88° S) (median of 45.2vs 28.7 mm2) (p<0.001). The additive genetic component explained 37% (95% confidence interval [CI], 22%–50%) of the variation in CUVAF while 50% (95%CI; 29%–71%) was due to the common environment. The SNP rs1060043 located approximately 800bp away from the SLC1A5 gene, a member of the solute carrier family 1, had a genome-wide significant association with a p-value of 3.2 × 10−8. Gene-based analysis did not improve our power to detect association with other genes.

Conclusion

Our findings confirm that while there is a large environmental component to CUVAF (= sun exposure), genes also play a significant role. We identified a SNP (rs1060043) as being significantly associated with CUVAF; replication of this finding in future studies is warranted.

INTRODUCTION

Excessive sun exposure particularly ultraviolet-light (UV) increases the risk of many ocular diseases including pterygium1, cortical cataract2, ocular surface squamous neoplasia3, climatic droplet keratopathy4 and eyelid malignancy.5 Despite early work suggesting sun exposure has a role in the pathogenesis of age-related macular degeneration6 and ocular melanoma7, these associations remain inconclusive. In recent years, a considerable number of epidemiological studies have reported that increased time spent outdoors is associated with lower rates of myopia in children, suggesting that sunlight brightness or UV-light may have a beneficial effect.8 These conflicting reports on effects of sun exposure require a better understanding of mechanisms underlying ocular sun damage and related eye diseases.

A challenge of studying ophthalmohelioses9 (sun-related ocular diseases) is the difficulty of assessing sun exposure. The usual method of determining an individual’s sun exposure is by self-reported questionnaire which is subject to recall errors. Often questions are designed to assess whole-body sun exposure rather than ocular sun exposure, thus accuracy of these measures in ocular diseases is arbitrary. Conjunctival ultraviolet autofluorescence (CUVAF) photography was developed to detect precursors of ocular sun damage using a technique similar to UV fluorescence in the detection of UV exposure-related dermatologic diseases.10 Previous studies have reported an association of CUVAF with the presence of pterygia11 and shown increasing total area of CUVAF is associated with increasing prevalence of pterygium.12 Time spent outdoors correlates highly with the level of CUVAF.8 This suggests CUVAF could be regarded as an objective measure of sun damage corresponding to amount of time spent outdoors and could help characterize local sun exposure.

Multiple biological mechanisms have been proposed to explain the cause of detected CUVAF in other tissues. These include alterations of collagen cross-linking or changes in cell metabolites such as reduced nicotinamide adenine dinucleotide (NADH) or derivatives of amino acids like tryptophan13.

CUVAF can be an ideal biomarker of ophthalmohelioses once its characteristics are defined better. In this current study, our main aim was to determine whether there is a genetic predisposition to variation in CUVAF identified in the three Australian cohorts. However, given that sun exposure is highly dependent on geographical location, the effect of latitudinal differences on CUVAF distribution was investigated. Following this analysis the contribution of genes to CUVAF variation was explored through a classical twin study and a genome-wide association study (GWAS).

METHODOLOGY

Participants

This study included two twin and one singleton cohorts each with Northern European ancestry from Australia. Twin pairs were identified from two existing cohorts, the Twin Eye Study in Tasmania (TEST) and the Brisbane Adolescent Twin Study (BATS). Methodologies of these studies were described in detail previously.14,15 In brief, a total of 487 twin pairs (200 monozygotic [MZ], 287 dizygotic [DZ]) were recruited in the TEST through several overlapping methods, including utilization of national twin registry and existing state-wide studies. A total of 2443 individuals who were enrolled into BATS were invited to participate into the twin eye study. Among the 1199 individuals agreed to participate, there were 185 MZ and 278 DZ twin pairs. The Western Australian Pregnancy (Raine) Cohort is an ongoing longitudinal birth cohort of 2868 individuals whose mothers were initially recruited to evaluate prenatal ultrasound.16,17 Their offspring were subsequently assessed in detail during childhood (1, 2,3,5,8 and 10 years) and adolescence (14 and 17 years). At the 20-year cohort follow-up, 1344 participants underwent an ocular examination.18 Comparison between the individuals who did and did not participate in the 20-year follow-up has been presented previously.19

Ethics Approval

This study was conducted in accordance with the Declaration of Helsinki and informed consent was obtained from all adult participants and parents of minors. Approval for this study was obtained from the Human Research Ethics Committees of the University of Tasmania, Royal Victorian Eye and Ear Hospital, QIMR Berghofer Medical Research Institute, Princess Margaret Hospital and the University of Western Australia.

Quantitative analysis of CUVAF

A camera system developed by Coroneo and colleagues11,20 was used to take CUVAF images for each participant. The camera system included a height adjustable table equipped with subject head-rest, camera positioning assembly, digital single-lens reflex camera (Nikon D100 (Nikon, Melville, New York, USA)), 105 mm f/2.8 Micro Nikkor (Nikkor, Melville, New York, USA) lens, and filtered electronic flash. Both nasal and temporal regions of both eyes were photographed at 0.94 magnification in total darkness. All images were saved in RGB format at the D100 settings of JPEG Fine (1:4 compression) and large resolution (3,000 2,000 pixels). The area of fluorescence in millimetres squared (mm2) for each photograph was determined using Adobe Photoshop CS4 Extend (Adobe Systems Inc., San Jose, California, USA). Reliability of CUVAF as a biomarker of sunlight exposure has been validated previously.21

Questionnaire

As part of the Raine Study 20-year examination, participants were asked to complete questionnaires regarding their socio-economic status, medical history and sun exposure. In relation to sun exposure, participants were asked to estimate time spent outdoors, with four possible responses to the question “In the summer, when not working at your job or at school, what part of the day do you spend outside?” Responses were ‘none’, ‘< ¼ of the day, approximately half of the day’ and ‘> ¾ of the day’. ‘None’ and ‘< ¼ of the day’ groups were combined due to low numbers in the ‘none’ category. Only socio-economic status and medical history questionnaires were available for TEST and BATS cohorts.

Study analysis was divided into three main components. These included: (1) comparison of CUVAF levels between TEST and BATS cohorts to identify effect of latitude; (2) a classical twin study using TEST and BATS cohorts to estimate heritability of CUVAF; (3) a meta GWAS study of CUVAF to identify common variants associated with this measurement by pooling data from all three cohorts.

Analytical Approach for Classical Twin Study

The classical twin model based on the multivariable linear structural equation was applied using OpenMx package in the statistical software R version 2.15.1 (R Foundation for Statistical Computing; http://www.r-project.org/). This model assumes the phenotypic variation observed between the MZ and DZ twins are due to variation in additive genetic (A), common environmental (C), and unique environmental (E) effects.

To determine the heritability of CUVAF, deterioration in the model fit was assessed by dropping each component in a hierarchical order from the full model. Each of the nested sub-models was then compared to the full model by chi-squared tests. The Akaike information criterion (AIC) was used to determine the best fitting model in which variation was explained by as a few parameters as possible. Before model fitting analyses, CUVAF was adjusted for age and gender.

Genotyping and quality control

TEST and BATS participants were genotyped using the Illumina Human 660W-Quad bead chip. A total of 1903 individuals from the Raine Study (some did not participate in the eye study) were genotyped in two different batches: 1593 individuals were genotyped in 2009 using the Human 660W-Quad bead chip and a further 310 individuals were genotyped in 2012 using the Illumina Human-OmniExpress bead chip.

As part of quality control (QC), the data were filtered by single nucleotide polymorphism (SNP) call rate <0.95, a Hardy-Weinberg equilibrium (HWE) p-value< 10−6 and a minor allele frequency (MAF) >0.01. To exclude population outliers, a principal component analysis (PCA) was carried out using SNPs with genotyping rate >0.98. Identical SNPs with the 1000 Genome panel were identified for the PCA analysis. All the samples beyond six standard deviations from PC1 and PC2 of 1000 Genomes British population were excluded. Individuals with identity-by-descent (IBD) estimate > 0.24 with another participants were also removed from the analysis.

Genotype imputation

TEST and BATS cohorts were imputed against the August 4, 2010 version of the publicly released 1000 Genomes Project European genotyping using MACH.22 Likewise, Raine Study was imputed against the November 23, 2010 version of the 1000 Genome Project European genotyping using MACH. We applied a minimum passing threshold of 0.3 on the Rsq metric for each SNP as the recommended practice with MACH and a MAF>0.01.

Genome-wide Association (GWA) Studies of CUVAF

GWAS of twin cohorts and the Raine Study were conducted separately. 7,773,124 SNPs (439,454 genotyped) associations of 295 families from the TEST and BATS cohorts were carried out using MERLIN23 with addition of age, sex and latitude as covariates in a linear model. For the Raine Study, a linear regression model in R with a PLINK interface24 was used to determine associations between 9,131,795 SNPs (561,216 genotyped) and CUVAF. In this cohort, reported time spent outdoors had a correlation with CUVAF (r=0.19 p<0.001). Hence, it was included as a covariate along with age and gender for 661 individuals who remained in the analysis. Inverse variance weighted meta-analysis with common SNPs imputed in both cohorts (n = 5,003,381) was conducted using METAL.25 Gene-based analysis was performed using Versatile Gene-based Association Study (VEGAS)26 with the combined SNP p-values of the RAINE and TEST/BATS analyses as input along and the default parameters.

RESULTS

After QC, 590 participants of 295 families from TEST/BATS and a total of 661 unrelated participants from the Raine Study had complete data available and were included in this current study. Characteristics of these three groups are displayed in Table 1. The age range varied between the cohorts, with the mean (range) age being 12 (5–51), 19 (13–28) and 20 (18–22) years in the TEST, BATS and Raine Study respectively. While there were more female (55% and 57%) participants in the TEST and BATS, more male participants (52%) participated in the Raine Study. Gender and age were correlated to CUVAF, correlation coefficient (r) being −0.09 (p=0.001) and 0.07(p=0.013) respectively in the pool of three cohorts.

Table 1.

Demographic characteristics of Conjunctival UV autofluorescence (CUVAF) study participants.

TEST BATS Raine Study
Number of participants 146 444 661
Number of families 73 222 661
Mean age in years (range) 12 (5–51) 19(13–28) 20 (18–22)
Number of MZ vs DZ twins 26/47 124/98 -
Gender (%females) 55% 57% 48%
Median CUVAF (IQR) 28.7 (15.0,42.3) 45.4 (26.7,68.5) 44.2 (20.3,69.8)
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Effect of latitude in distribution of CUVAF

CUVAF levels of two twin cohorts were compared based on their geographical locations. Of the 590 individuals, 146 were from Tasmania (Hobart latitude =42.88° S) and 444 from Queensland (Brisbane latitude = 27.47° S). The median CUVAF was higher in individuals from Queensland (45.41 mm2, interquartile range [IQR]: 26.77, 68.50) compared to individuals from Tasmania (28.74mm2, IQR: 15.01, 42.34)(p<0.001). To ensure that this difference was not present due to confounding effect of a difference in age and gender distribution within the two twin cohorts, we adjusted CUVAF for age and gender prior to comparison. The difference remained, with median CUVAF being 43.36 mm2 (IQR: 26.54, 66.69) in individuals from Queensland and 30.90 mm2 (IQR: 18.96, 47.31) in individuals from Tasmania (p<0.001). Moreover, a similar difference was present when the analysis restricted to younger twin pairs (10–20 years old) (BATS: 47.43 mm2 [IQR: 27.92, 66.4] vs TEST: 37.53 mm2 [IQR: 23.64, 48.53]; p=0.006).

CUVAF heritability

Of the 295 twins pairs included in the analysis, 150 (50.8%) were MZ twins. The pairwise correlation coefficient of CUVAF was 0.88 for MZ twins and 0.70 for DZ twins. The slightly higher correlation of MZ twins suggests a stronger common environmental contribution for the phenotype variance, compared with the genetic contribution under a classical twin model. This observation was confirmed by univariate model fitting. The best-fit model was an additive genetic, common environment and unique environment (ACE) model adjusted by age and gender. With this model, we estimated the variation explained by the additive genetic component to be 0.37 (95% confidence interval [CI], 0.22–0.56) while the common environment component explained 0.5 (95%CI, 0.29–0.71) of the variability of the trait.

Genome-wide association (GWA)

A genome-wide significant locus rs1060043 at (p=3.193×10−8) and suggestive loci are shown in Figure 1 and summarized in Table 2. The effect size of the CUVAF increasing allele was 11.34 mm2 per copy. Figure 2 shows the region around the rs1060043 locus. The top ten CUVAF-associated genes obtained from the gene-based test using VEGAS and SNP meta-analysed p-value estimates are displayed in Table 3.

Figure 1. Manhattan plot of the meta-analysis association p-values for conjunctival UV autofluorescence (CUVAF).

Figure 1

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SNPs based on chromosomal position vs logarithm of the p-values. Red line denotes the genome-wide significance (p<5×10−8). SNPs above the blue line represent the suggestive loci.

Table 2.

Top five loci associated with conjunctival UV autofluorescence (CUVAF).

TEST/BATS Raine Study Meta-analysis
SNP CHR Closest Locus A1/A2 Effect SE p-value Effect SE p-value Effect SE p-value
rs1060043 19 SLC1A5 A/G 7.32 2.70 0.006 16.71 3.13 1.37×10−7 11.34 2.05 3.19×10−8
rs1558253 17 SPAG9/NME1 T/G −20.89 3.89 8.47×10−8 −8.92 5.38 0.097 −16.78 3.16 1.09×10−7
rs990320 3 C3orf58 T/C −6.97 2.02 0.00058 −7.13 1.90 0.00019 −7.06 1.39 3.64×10−7
rs7309814 12 HDAC7 C/G 16.89 4.56 0.00021 12.91 3.54 0.00062 13.97 2.80 6.19×10−7
rs1213 9 MSANTD3 T/C −34.68 10.91 0.0014 −35.53 9.27 0.00014 −35.18 7.07 6.51×10−7
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Figure 2. Association of variants at the SLC1A5 locus.

Figure 2

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P values (−log10) of SNP association with conjunctival UV autofluoresnce in the meta-analysis are plotted against their positions at the SLC1A5 locus. SNPs are colored to display their linkage disequilibrium (LD) with rs1060043.

Table 3.

VEGAS pathway analysis results for the ten most significant genes associated with conjunctival UV autofluorescence (CUVAF).

Chromosome Gene Number of SNPs Start Position Stop Position Test Statistic p-value Best-SNP SNP p-value
3 IQCF3 32 51837608 51839916 260.975 7.80×10−5 rs9836804 6.77×10−6
8 PXMP3 110 78055048 78075079 518.863 7.90×10−5 rs7008266 8.26×10−6
10 ARMETL1 81 14901256 14919989 354.384 1.37×10−4 rs2688849 1.02×10−5
14 TRMT5 62 60507919 60517535 285.471 1.77×10−4 rs10129952 5.68×10−3
9 FANCC 152 96901156 97119812 943.041 1.78×10−4 rs4647558 3.57×10−5
10 HSPA14 73 14920266 14953746 339.968 1.79×10−4 rs2688849 1.02×10−5
3 C3orf58 105 145173602 145193895 1003.223 2.18×10−4 rs1075113 3.37×10−6
16 SNX20 72 49264386 49272667 455.891 2.60×10−4 rs6500327 4.40×10−5
10 SLIT1 277 98747784 98935673 1073.202 2.65×10−4 rs2636813 1.13×10−5
1 CD1A 63 156490550 156494682 433.266 4.28×10−4 rs614164 2.91×10−4
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DISCUSSION

A strong relationship between CUVAF and sun-related ocular damage has been reported previously12,21 suggesting that it could serve as a useful biomarker of ophthalmohelioses. In this study, we investigated the genetic characteristics of CUVAF. Given the possible confounding effect of geographical location of CUVAF, we initially explored the levels of CUVAF over two geographical regions defined by latitude in two ethnically homogeneous, European ancestry twin cohorts and identified lower amounts of CUVAF in individuals from lower ambient UVR region (Tasmania). Although previous studies report individuals from a higher ambient UVR region (Brisbane) spent less time outdoors compared to other regions of Australia including Tasmania, it must be noted that the intensity of UV exposure in Tasmania is lower.27 The finding of higher CUVAF levels in Brisbane is consistent with previous work by Wlodarczyk et al. who reported Queensland as having double the pterygium surgical rate per 100,000 when compared to Tasmania.28 Thus, pterygium may well be a sensitive indicator of UV exposure, since the cornea focuses peripheral incident light approximately twenty fold onto the usual limbal location of pterygia.9

We assessed heritability of CUVAF and have shown additive genetic effect is responsible for up to 37% of the variance of detected CUVAF amounts indicating genes are a significant contributor to variation in CUVAF. This present finding corroborates earlier evidence showing that the tendency to develop pterygium may be inherited.29–31 Interestingly, Hecht31 identified eleven early onset pterygium cases in two generations resident mainly in the Midwestern USA, without known extreme environmental insult, and suggested a genetic-environmental model for pterygium two decades ago. There is also increased susceptibility to pterygium development in genetic conditions in which there are abnormal DNA repair mechanisms9,32 such as xeroderma pigmentosum33, porphyria cutanea tarda34, polymorphous light eruption and possibly Cockayne syndrome35.

To further understand the genetic contribution to development of CUVAF, we conducted a GWAS in both twin cohorts and the Raine Study. The meta-analysis of GWAS allowed the identification of a significant association of rs1060043, which is located 800bp upstream of the solute carrier Family 1 (Neutral Amino Acid Transporter), Member 5 (SLC1A5) gene on 19q13. SLC1A5 is a peptide transporter gene expressed in retinal Muller cells and also serves as an effluxer of D-serine agonist in NMDA receptor sites.36 Many of the genes that belong to SLC1 gene family and SLC families have been detected in human cornea, rabbit cornea and corneal epithelium cells (SLC1A4, SLC6A14, SLC7A5).37–39 Variants in SLC45A2 and SLC24A4 influence pigmentation traits including iris color.40 The particular SNP identified in this study gives rise to a synonymous codon that is highly conserved in zebrafish and among multiple mammalian species including rhesus monkeys, chimpanzees, cattle and dogs suggesting that this gene has a critical function in mammals. The only locus in the best VEGAS pathway result was C3orf58. This gene and none of the other genes identified in the gene-based analysis had an ocular function.

The present study was designed to investigate whether genetic and environmental factors play a role in the development of CUVAF. This investigation had three important results. Firstly, individuals living in areas with higher UV radiation are more likely to have increased CUVAF. Secondly, although CUVAF was primarily caused by environmental factors, genetic factors also play a role in its development. Finally, a susceptibility locus related to CUVAF was detected. Although the study successfully demonstrated these findings, certain limitations in terms of its design and sample size must be acknowledged. For example, the environment of older twins varies, possibly due to relocation, compared to young twin pairs growing up together. Therefore inclusion of older adult twin pairs may have caused a selection bias when comparing the role of environment in presentation of CUVAF. On the other hand, when the analysis was restricted to younger twins, the effect of latitude on CUVAF remained the same. Thus this indicated that the effect of older individuals was minimal on representation of the young twin pairs in the current study. Moreover, a common limitation of single GWASs is being underpowered. Both our twins and singleton discovery cohorts were very limited in sample size that resulted in detection of inconsistent signals in individual cohort analysis. This issue was overcome by performing a meta-analysis which resulted more reliable outcomes. Overall, the current findings add to a growing body of literature contributing to the understanding of CUVAF development. Further research investigating the role of genetics and the environment would assist in identifying individuals who are predisposed to ocular sun damage to recommend personalised health messages.

Acknowledgments

Funding/Support: The cohorts in this study were supported by the University of Western Australia (UWA), The Telethon Kids Institute, Raine Medical Research Foundation, Women’s and Infants’ Research Foundation, The Faculty of Medicine UWA, Curtin University, Edith Cowan University, Australian National Health and Medical Research Council Project Grants 1021105 and 350415, LEI, the Australian Foundation for the Prevention of Blindness, Alcon Research Institute, R01EY018246, Centre for Inherited Diseases Research, the Clifford Craig Medical Research Trust, Ophthalmic Research Institute of Australia (ORIA), American Health Assistance Foundation, Peggy and Leslie Cranbourne Foundation, Foundation for Children, Jack Brockhoff Foundation, the Pfizer Australia Senior Research Fellowship, and the Victorian Government of Australia. SM acknowledges Australian Research Council Future Fellowship and Australian National Health and Medical Research Council Career Development Fellowship support.

Footnotes

Conflict of Interest: The authors have no proprietary or commercial interest in any materials or methods discussed in this article.

Author Contributions: Professor David Mackey and Dr Stuart MacGregor had full access to all of the data in the study and take responsibility for the integrity of the data and the accuracy of the data analysis.

Study concept and design: Yazar, Cuellar-Partida, McKnight, Hewitt, MacGregor, Mackey

Acquisition, analysis, or interpretation of data: Yazar, Cuellar-Partida, McKnight, Quach-Thanissorn, Mountain, Pennell, Coroneo, Hewitt, MacGregor, Mackey

Drafting of the manuscript: Yazar, Cuellar-Partida, Stuart MacGregor, Mackey

Critical revision of the manuscript for important intellectual content: Yazar, Cuellar-Partida, McKnight, Quach-Thanissorn, Mountain, Hewitt, Pennell, Coroneo, MacGregor, Mackey

Statistical analysis: Cuellar-Partida, Stuart MacGregor

Obtained funding: Hewitt, Pennell, MacGregor, Pennell

Administrative, technical, or material support: Yazar, McKnight, Quach-Thanissorn, Mountain, Coroneo, Mackey

Study supervision: Mackey, Hewitt, MacGregor

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