Article | Open

Childhood gene-environment interactions and age-dependent effects of genetic variants associated with refractive error and myopia: The CREAM Consortium

  • Scientific Reports 6, Article number: 25853 (2016)
  • doi:10.1038/srep25853
  • Download Citation
Published online:


Myopia, currently at epidemic levels in East Asia, is a leading cause of untreatable visual impairment. Genome-wide association studies (GWAS) in adults have identified 39 loci associated with refractive error and myopia. Here, the age-of-onset of association between genetic variants at these 39 loci and refractive error was investigated in 5200 children assessed longitudinally across ages 7–15 years, along with gene-environment interactions involving the major environmental risk-factors, nearwork and time outdoors. Specific variants could be categorized as showing evidence of: (a) early-onset effects remaining stable through childhood, (b) early-onset effects that progressed further with increasing age, or (c) onset later in childhood (N = 10, 5 and 11 variants, respectively). A genetic risk score (GRS) for all 39 variants explained 0.6% (P = 6.6E–08) and 2.3% (P = 6.9E–21) of the variance in refractive error at ages 7 and 15, respectively, supporting increased effects from these genetic variants at older ages. Replication in multi-ancestry samples (combined N = 5599) yielded evidence of childhood onset for 6 of 12 variants present in both Asians and Europeans. There was no indication that variant or GRS effects altered depending on time outdoors, however 5 variants showed nominal evidence of interactions with nearwork (top variant, rs7829127 in ZMAT4; P = 6.3E–04).


The refractive errors myopia and hyperopia are common visual disorders that typically require correction with spectacles, contact lenses, or refractive eye surgery. Myopia – particularly with increasing severity – is a leading cause of irreversible visual impairment and blindness due primarily to stretching and thinning of the ocular tissues within the posterior segment of the eye. These changes are associated with an increased risk of retinal detachment, chorioretinal atrophy, choroidal neovascularisation, myopic maculopathy, glaucoma and cataract1,2. Myopia is rare in infancy, usually developing during school age or in early adulthood3. For current generations of young adults, approximately 30–40% of individuals in Western countries4,5 and 80% of those in urban areas of East Asia6,7 have myopia.

Genome-wide association studies (GWAS) in primarily population-based samples8,9,10,11,12,13,14 and next-generation sequencing (NGS) studies of carefully selected high myopia pedigrees harbouring extremely rare, high penetrance disease-causing mutations15,16,17,18,19,20 have improved our understanding of the genetics of refractive error and myopia. To date at least 39 distinct loci harbouring common genetic variants showing genome-wide significant association with refractive error have been identified through GWAS. For the genetic variants that contribute most to the burden of myopia in the general population (i.e. the GWAS-identified variants) it is not yet known whether the variants act during very early life, childhood, or in adulthood. This is an important question given that knowledge of the time and mode of action of the causal variants at the associated loci is necessary for detecting children at-risk of myopia (who would benefit most from treatment intervention), and would aid the design of new therapies capable of halting myopia progression.

For environmental risk factors to which most children are exposed, inter-individual differences in genetic susceptibility may account for some of the phenotypic variance21. Exposure to nearwork, i.e. reading and other tasks requiring prolonged near vision, has long been proposed as an environmental risk factor for myopia to which children are ubiquitously exposed during their schooling. The total duration of reading, the period of continuous reading, the reading distance between the text and the eyes, and variation in nearwork exposure outside of the school day have each been shown to be associated with refractive error or myopia progression22,23. The other most strongly implicated environmental risk factor for myopia is insufficient time spent outdoors24,25,26, and it has been suggested that time spent outdoors and time spent performing nearwork activities together underlie the robust association between myopia and educational achievement2,27. Gene-environment (GxE) interactions – which in this project we define as marker-phenotype associations whose effects differ statistically depending on whether individuals have been exposed to a high vs. low level of an environmental risk factor – may contribute extensively to variation in disease susceptibility28. Given the recent identification of gene-environment interactions involving nearwork or level of education, a key question in myopia research currently is whether GxE interactions contribute to the rising prevalence of myopia and to the higher incidence rate observed in young Asian populations as compared to their European counterparts.

We carried out analyses of pediatric/adolescent cohorts collaborating in the Consortium for Refractive Error And Myopia (CREAM) to investigate whether the top index variants at the 39 loci previously identified in GWAS meta-analyses of adults have early-onset effects manifest during childhood. We also tested for evidence of GxE interactions involving either nearwork or time spent outdoors. A single large cohort with longitudinal measurements of refractive error over much of childhood was used for the primary analyses. Meta-analyses of cross-sectional samples were then used to test for replication.


Participants and phenotypes

All participants were aged <25 years-old and none had been included in the earlier CREAM meta-analysis of refractive error9, which only included individuals >25 years of age. Details of the participant recruitment and phenotypic assessment are presented in the Supplementary Information. The study was conducted in accordance with the Declaration of Helsinki, and all participants provided informed consent. The experimental protocols for the study were approved by the respective ethical review boards at host institutions, as follows. ALSPAC, the ALSPAC Ethics and Law Committee and the Local Research Ethics Committees; BATS, the Human Research Ethics Committee at the QIMR Berghofer Medical Research Institute; GZT, the Ethics Review Board of the Zhongshan Ophthalmic Center of Sun Yat-Sen University; RAINE, the Human Research Ethics Committee at the University of Western Australia; SCORM and STARS, the Institutional Review Boards of the Singapore Eye Research Institute, Singapore General Hospital, National University of Singapore, and the National Healthcare Group, Singapore; TEDS, the Institute of Psychiatry ethics committee; TEST, the Royal Victorian Eye and Ear Hospital, the University of Tasmania, and the Australian Twin Registry; WESDR, the Health Sciences Institution Review Board of the University of Wisconsin, Madison.

Participants underwent cycloplegic autorefraction (RAINE, TEST, BATS, GZT, SCORM, STARS) or non-cycloplegic autorefraction (ALSPAC) or subjective refraction (TEDS, WESDR) and the spherical equivalent refractive error averaged between the two eyes was calculated. Parental questionnaires that included items on time spent engaged in nearwork outside of school, and time spent in outdoor activities were used to classify children as spending a high or low amount of time performing nearwork (Table S6) or outdoors (Table S7) each day. Classification was done within each cohort separately, using a median split (“low” group, exposure below median level; “high” group, exposure above median level).

Genetic analysis

DNA samples obtained from blood or saliva were genotyped using either an Illumina or Affymetrix high-density single nucleotide polymorphism (SNP) array, and genotypes at untyped markers were imputed using the 1000-Genomes Project reference panel (see Table S5 for details). Stringent quality control procedures (e.g. imputation quality r2 or info score >0.5) were applied to each cohort separately (Supplementary Information). 39 SNPs that showed genome-wide significant association with refractive error in the general adult population in two previous GWAS analyses8,9 were selected for evaluation (Table S1).

Cross-sectional models and meta-analyses

For each of the 8 cross-sectional cohorts separately, single SNP tests of association with refractive error were conducted using the following linear regression model:

where yi is the spherical equivalent refractive error of the ith participant, of age ai and sex si and with gi their risk allele dosage on the scale 0–2 for the test SNP, and εi the residual. Regression coefficients are indicated as βAge, βSex, and βSNP for the model parameters age, sex and SNP genotype, respectively. Additional G x E interaction models were tested for samples with information available on environmental exposures, nearwork or time outdoors (both exposures coded: 0 = low, 1 = high). For the ith participant, using ni to denote nearwork and ti for time outdoors:

Results from the individual cohorts were meta-analyzed in 5599 individuals comprising 5 cohorts of European ancestry (BATS, RAINE, TEDS, TEST, WESDR; N = 3,143; Table 1) and 3 cohorts of Asian ancestry (GZT, SCORM, STARS; N = 2,456; Table 1) using a weighted inverse-variance, fixed effects model29. A random effects model was used if Cochran’s Q-test for heterogeneity yielded a P-value below 0.05.

Table 1: Demographics of study samples.

Longitudinal study (ALSPAC)

Refractive error was included in the clinical assessments for ages 7, 10, 11, 12 and 15 years in ALSPAC children30. Linear mixed models for refractive trajectory were fit as described30 using the nlme package in R31 for individuals (N = 5,200; Table 1) who underwent at least 3 refractive assessments and whose genotype data passed quality control filters (as described in the Supplementary Information). Briefly, SNP dosage, age and higher-order age terms (age2 and age3) were modelled as fixed effects while for each child, the difference from the average refractive error at baseline and the linear rate of change in refractive error were modelled as individual-level random effects, using an autoregressive correlation structure. To examine GxE interactions, initially, 3-way interaction models were tested that included the interaction between SNP, change-from-baseline in age, and environmental exposures (nearwork or time outdoors). If the P-value for the 3-way interaction was >0.05 then models including only 2-way interactions were tested.

Quanto32 was used to gauge the power to detect main and interaction effects in the ALSPAC cohort. These calculations assumed a minor allele frequency (MAF) of 0.25, a sample size of 4461 (corresponding to 5,200 minus 739 participants with missing information about time spent performing nearwork), a binary exposure affecting 39% of the cohort (equivalent to that for high vs. low nearwork exposure in ALSPAC) and a refractive error distribution with a mean of zero and a standard deviation of 1.50 D. The estimated power would be conservative given that a linear mixed model analysis will have greater power than a linear model analysis.

Genetic risk score for all 39 SNPs

A genetic risk score was computed by summing the dosage of risk alleles for all 39 SNPs. In individuals of Asian ancestry only 31 of the 39 SNPs were polymorphic (MAF > 0.05) and therefore contributed to the genetic risk score calculation. The frequency distribution of genetic risk score in each sample was normally distributed with a mean of 36 (95% C.I. 29 to 42) alleles in Europeans and 40 (95% C.I. 37 to 42) alleles in Asians. To calculate the variance in refractive error explained by the genetic risk score at a specific age for participants in the ALSPAC cohort, refractive error at age 7.5 years (or at age 15 years) was regressed on genetic risk score using a linear model. The covariates age and sex were not associated with refractive error when included in the age 7.5 or the age 15 year model, and their inclusion did not improve the fit of either model (note that being a birth cohort, the age range was narrow). Hence these covariates were omitted. The variance explained by the genetic risk score was therefore taken as the R2 value for a model that included the genetic risk score as the only predictor variable.

Pathway analysis

The genes (Table 2) implicated in having early-onset effects (N = 10 genes) or later-onset effects (N = 11 genes) in the ALSPAC discovery sample were evaluated using PANTHER Version 10.0 (release date May 15, 2015)33 and DAVID Version 6.7 (release date 27 Jan, 2010)34 to identify potential functional pathways.

Table 2: Age-of-onset of SNP associations with refractive error in the discovery cohort (ALSPAC).


Early-onset and later-onset effects in childhood

Nine cohorts of children/adolescents were studied (Table 1). The largest of these, ALSPAC (N = 5,200), which had longitudinal data for refractive error, was used for discovery analyses, and 8 cross-sectional cohorts were used for validation. The discovery cohort had ~80% power to detect an association for a SNP with an effect size of 0.1 D and MAF of 0.25.

Of the 39 SNPs examined, 16 showed evidence of onset in childhood (Table 2 and Table S2). Early-onset associations already manifest at 7.5 years of age were present for 10 SNPs (P = 4.8E–02 to P = 5.3E–03). Later-onset associations that emerged between the ages of 7.5 and 15 were noted for 11 SNPs (P = 4.9E–02 to 8.8E–04 for SNP x Age interaction). Five SNPs showed a main effect at baseline as well as later progressive effects. Examples of SNPs showing evidence of early-onset and later-onset effects are presented in Fig. 1 for early-onset CHRNG SNP rs1881492, later-onset A2BP1 (also known as RBFOX1) rs17648524, and PRSS56 rs1656404 with both effects. For all associated SNPs the “direction of effect” was the same as in the original GWAS8,9.

Figure 1: SNPs associated with early-onset and later-onset effects on refractive development during childhood.
Figure 1

Analyses were carried out using data from longitudinal eye examinations in 5,200 ALSPAC participants. Each panel shows how refractive error trajectory varied with SNP genotype, for 4 different SNPs: rs1881492, rs17648524, rs1656404 and rs2155413. The lines in each panel show the refractive error trajectories predicted by the best-fit linear mixed model (LMM) for participants carrying the number of risk alleles indicated (0, 1 or 2). The SNPs in panels (a,c) showed an association with refractive error at baseline, i.e. evidence of early onset in childhood. The SNPs in panels (b,c) showed an age-dependent interaction with refractive error over later childhood. The SNP in panel (d) did not show evidence of effects during childhood.

The genetic risk score was very strongly associated with refractive error both at 7.5 years of age (β = −0.018 D, 95% CI −0.012 to −0.024, P = 2.2E–9) and with increasing age (β = −0.003 D/yr, 95% CI −0.002 to −0.004, P = 5.8E–14). By the age of 15 years, the model suggested that the 39 SNPs together would produce a more than 1.0 D difference in refractive error between participants carrying the lowest and highest number of risk alleles observed (Fig. 2). At age 7.5 years the genetic risk score explained 0.6% of the variation in refractive error (N = 4,566; P = 6.6E–08); at age 15 years the corresponding figure was 2.3% (N = 3,666; P = 6.9E–21).

Figure 2: Association between a genetic risk score for 39 SNPs and refractive error trajectories in ALSPAC participants.
Figure 2

The genetic risk score was calculated as the sum of the number of risk alleles (0–2) carried by an individual at each of the 39 myopia-susceptibility SNPs. The coloured lines show the trajectories for children carrying the number of risk alleles indicated, as predicted by the best-fit linear mixed model.

For validation we tested the genetic risk score and 12 of the 16 above SNPs (4 were nearly monomorphic in Asians) in the 8 multi-ethnic cross-sectional study cohorts (combined N = 5,599; Table 1). The average age of the participants varied from 6.6 years-old in the STARS cohort to 20.0 years-old in RAINE. The genetic risk score and 4 SNPs – rs7744813 (KCNQ5), rs7837791 (TOX), rs8000973 (ZIC2) and rs17648524 (A2BP1) – were associated with refractive error (P < 0.05; Table 3). All 4 SNPs had the expected direction of effect and none exhibited evidence of between-cohort heterogeneity. Interestingly, 3 of the 4 SNPs had evidence of both early-onset and later progressive effects in the discovery cohort. Meta-analysis summary plots for the genetic risk score and the individual SNPs tested for replication are presented in Figure S1. There was suggestive evidence that SNPs had larger effect sizes in Asian than in European ancestry participants (Figure S2).

Table 3: Replication meta-analysis results for SNP main effects.

Tests in the Discovery Cohort for SNP x SNP interactions for all 741 possible pairs of the 39 SNPs revealed no evidence for interactions exceeding that expected by chance (not shown).

Interactions with time engaged in nearwork

Two types of interactions between SNP genotype and nearwork exposure were evaluated in the ALSPAC discovery cohort: An interaction already present at the baseline age of 7.5 years-old (a 2-way SNP x nearwork interaction) and an interaction that developed progressively during later childhood (a 3-way, SNP x nearwork x age-from-baseline interaction). For a SNP with a risk allele frequency of 0.25, and ignoring the repeated measures nature of the data, the analysis of ALSPAC participants had >90% power to detect an interaction effect of 0.25 D at α = 0.05 (and >50% power at α = 1.28E–3, corresponding to a Bonferroni correction for testing all 39 SNPs).

Nominal support for 3-way SNP x nearwork x age-from-baseline interactions was observed for 4 markers (Fig. 3a–d): rs17428076 upstream of DLX1 (P = 0.049), rs7829127 within ZMAT4 (P = 6.3E–04), rs7084402 upstream of BICC1 (P = 0.043) and rs17648524 within A2BP1 (P = 2.3E–03). In models that considered just 2-way interactions at baseline, only rs1254319 upstream of SIX6 showed nominal evidence of an interaction (P = 0.042; Fig. 3e). Of these 5 interactions, only that involving rs7829127 (ZMAT4) survived correction for multiple testing (corrected P = 0.025). Consistent with the limited evidence for individual SNP x nearwork interactions, no evidence of interaction between the genetic risk score and ALSPAC children’s level of nearwork was observed (2-way interaction, P = 0.20; 3-way interaction, P = 0.086).

Figure 3: Refractive error trajectories in ALSPAC participants for SNPs showing evidence of an interaction with nearwork or time outdoors.
Figure 3

Levels of nearwork activity and time spent outdoors were assessed at 8–9 years of age and classified as high or low (above or below the median level). Panels (a–d) show how refractive error trajectories varied depending on nearwork level and the number of risk alleles (0–2) carried for 4 different markers that showed SNP x nearwork x age-from-baseline (3-way) interactions. Panel (e) Refractive trajectories for the only marker to show a SNP x nearwork (2-way) interaction at baseline age. Panel (f ) Refractive trajectories for the only marker to show a SNP x time outdoors x age-from-baseline (3-way) interaction. The coloured lines show the trajectories predicted by the best-fit linear mixed model for children carrying the number of copies of the risk allele indicated in the legend.

Four of the cross-sectional study cohorts, 1 of European ancestry (TEDS) and 3 of Asian ancestry (GZT, SCORM and STARS), had information available regarding the time participants spent engaged in nearwork (Table S6), allowing tests for replication. In the meta-analysis of all 4 replication studies (Table S3) none of the SNPs that showed nominal evidence of an interaction with nearwork in the ALSPAC discovery cohort showed evidence of replication (all P > 0.16). Likewise, the genetic risk score did not show evidence of an interaction with nearwork in the cross-sectional cohorts (P = 0.49).

Interactions with time spent outdoors

In the discovery cohort, only rs13091182 within ZBTB38 showed nominal evidence of a 3-way interaction involving time outdoors (uncorrected P = 0.028; corrected P > 0.05; Fig. 3f). Surprisingly, the risk allele of rs13091182 was associated with slower progression towards myopia (or less hyperopia) in general and with faster progression towards myopia in children who spent more time outdoors, suggesting a potentially false-positive result. There was no evidence for 2-way SNP x time outdoors interactions (uncorrected P > 0.20 for all 39 SNPs). Similarly, for the genetic risk score, there was no indication of an interaction with time spent outdoors (2-way interaction, P = 0.16; 3-way interaction, P = 0.49).

Five of the cross-sectional samples had information available on the time participants spent outdoors (TEDS, RAINE, GZT, SCORM and STARS). The single SNP, rs13091182, showing evidence of an interaction with time outdoors in the discovery cohort showed no evidence of replication (indeed, none of the 31 SNPs with MAF > 0.05 in both ancestry groups showed evidence of an interaction with time outdoors; all P > 0.17; Table S4). Similarly, the genetic risk score did not show evidence of an interaction with time spent outdoors in the replication cohorts.

Pathway analysis

Pathway analysis identified a single functional pathway for the set of 10 genes (Table 2) implicated in having early-onset effects, namely “hedgehog signalling” (Panther P = 0.043; key genes ZIC2 and BMP4). The set of 11 genes implicated in having later-onset effects did not show enrichment for specific pathways.


Early-onset and later-onset SNP effects

Sixteen SNPs showed evidence of effects in childhood in ALSPAC participants (Table 2); 10 SNPs had early-onset effects manifest by age 7.5 years, 11 SNPs had later-onset effects, and 5 SNP had early-onset effects that progressed further during later childhood. For the 12 of these 16 SNPs available in the cross-sectional cohorts, 4 showed evidence of replication (Table 3). There was suggestive evidence that SNP effect sizes were approximately 2 times larger in Asian as compared to European ancestry children/adolescents (Figure S2). A genetic risk score that captured the effects of all 39 GWAS-identified variants confirmed the involvement of genetic influences acting at an early age (7.5 years) and then increasing further in magnitude across later childhood.

We sought to discover whether the early-onset and later-onset variants clustered according to functional pathway (for example, if GWAS SNPs A and B are causal variants that affect the expression levels of genes X and Y, respectively, and X acts downstream of Y to regulate refractive development, then one might expect the onset age for SNPs A and B to coincide). However, as summarised in Table 4, SNPs associated with early-onset or later-onset effects did not clearly cluster according to the known function(s) of the genes implicated in mediating the SNPs’ effects. Pathway analysis confirmed this impression, with only a single functional pathway being identified. Potential reasons for this lack of functional clustering are, first, that many genes in the genome have diverse functions, which are sometimes poorly understood. For instance, during development of the human visual system, an ion channel may play a vital role during early embryonic development of the retina, be a necessary component of the visual cycle, and yet also contribute to neuronal plasticity. Second, precisely which gene or genes mediate the effect of a specific GWAS-identified SNP is not known with certainty for any of the refractive error GWAS SNPs identified to date: While the nearest gene to a GWAS SNP is usually considered the most likely to be involved, this does not always hold true35.

Table 4: Summary of findings.

The 39 SNPs examined were identified in adult GWAS meta-analyses with sample sizes of approximately 45,000 individuals, and all had small effects (typically 0.1 D per copy of the risk allele). The ALSPAC longitudinal cohort (N = 5,200) had ~80% power to detect an association for a SNP with an effect size of 0.1 D and MAF of 0.25 (but note that the true power would likely have been lower because: refractive development would not be complete by 15 years of age, our models tested primarily for yearly effects rather than cumulative effects, and the “winner’s curse” phenomenon36, i.e. the over-estimation of effect sizes in the original GWAS investigations). Therefore, a likely reason why some of the 39 SNPs we studied failed to show childhood-onset associations in the longitudinal cohort is limited statistical power. Thus, we cannot conclude that the SNPs that did not show observable childhood-onset associations have an age-of-onset beyond 15 years-old even though they might well do: much larger studies will be required to definitively address this issue. Similarly, the limited concordance between the longitudinal and cross-sectional studies was also likely due to limited statistical power, although 8 of the 12 SNPs tested for replication showed the expected direction of effect (Table 3).

Interactions with environmental exposures

In general there was scant evidence for GxE interactions, especially for SNP x time spent outdoors effects. Given the expected power of >90% to detect interaction effects with a magnitude 0.25 D or more, this argues against SNP x nearwork or SNP x time outdoors interactions of this size being present for the majority of variants studied, rather than lack of statistical power precluding their discovery.

In the ALSPAC longitudinal analysis the gene-environment interaction between ZMAT4 SNP rs7829127 genotype and nearwork survived correction for multiple-testing (Pcorr = 0.025). Although this interaction was not replicated in the cross-sectional meta-analyses, variants at this locus have previously been reported to show an interaction with the duration of education in a meta-analysis of 5 studies from Singapore (SNP x education interaction = −0.42 D, 95% C.I. −0.15 to −0.69, P = 0.002)37. We did not explore interactions between SNPs and years of education, since in several cohorts the participants were still students. The functional role of ZMAT4 is not known.

Why might GxE interactions involving these 39 SNPs be so scarce? First, differences in environmental risk exposures were not considered in the original GWAS investigations carried out by CREAM9 and 23andMe8. Thus, SNPs with strong interaction effects but no main effects may not have been detected using those GWAS designs. Second, the age range and ethnic diversity of the original GWAS discovery samples were highly varied. Given the substantial increase in the prevalence of myopia in the past few decades, which strongly implicates a major role for environmental risk factors, it seems almost certain that the individuals studied in the CREAM and 23andMe GWAS meta-analyses would have grown up in environments with a wide range of risk exposure profiles depending on the participants’ years of birth: young (recently born) individuals would have been exposed to a much more myopiagenic environment than older (more distantly born) adults. Therefore, a variant that increases the risk of myopia only in children who perform excessive nearwork may have shown an (apparent) main effect association with refractive error in a GWAS carried out in a young adult cohort, in which participants were ubiquitously exposed to high nearwork during childhood. However, this same variant may not have shown an association with refractive error in a GWAS on an older cohort, due to the lower nearwork exposure during childhood of the older individuals. Thus, support for the association of such a variant in the CREAM and 23andMe GWAS samples may have been diluted rather than strengthened during the meta-analysis of younger and older cohorts.

Separate from tests for gene-environment interactions, time spent outdoors itself was not associated with myopia in 3 of the 5 cross-sectional studies (GZT, STARS, and TEDS) and the association was of borderline significance in another (TEDS). This lack of an association with time outdoors implies that detecting a SNP x time outdoors interaction would also have been challenging, even after meta-analysis of data from all 5 cohorts.

Interestingly, a large-effect GxE interaction predisoposing children to myopia was identified recently, involving a rare variant at the APLP2 gene locus and time spent reading38. APLP2 was implicated in myopia development through studies in an animal model39, which – given the statistical challenge of identifying GxE interaction effects in human populations – suggests that combining findings from animal models and human studies could be a fruitful future approach.

We reasoned that correction for multiple testing was not appropriate when examining the age-of-onset of the 39 SNPs investigated, because of compelling existing evidence that by adulthood these SNPs truly are associated with refractive error. That is, our analyses sought to discover whether or not each SNP had an effect during childhood, not whether a group of candidate SNPs were associated with refractive error per se. By contrast, in view of very limited evidence for interactions with environmental exposures for most of the SNPs examined, correction for multiple testing was considered appropriate when evaluating SNP x nearwork and SNP x time outdoors interactions: In these analyses, a large number of independent hypothesis tests were carried out, with little or no prior knowledge that an interaction must be present at some age.

Limitations of the present work

The present work had a number of other limitations. The cross-sectional samples were not matched for age, which prevented us from testing for “early” and “later” onset effects in the replication stage. The level of exposure to nearwork and time outdoors also varied across samples, which meant that imprecisely-matched interaction effects were meta-analysed, potentially reducing statistical power. We chose to categorise time spent performing nearwork and time spent outdoors relative to the median activity level in each study sample because the measurement scales used in the various studies were not standardised (precluding the use of an absolute measure). If in reality these environmental risk factors exert their influence non-linearly – for instance if spending more than a certain threshold number of hours per day outdoors is needed to protect against myopia development – then our approach may have poorly captured the effects of the environmental exposures. For the combined meta-analysis of European and Asian cross-sectional studies, we assumed that each lead SNP tagged the underlying causal variant(s) equally well in European and Asian ancestry individuals, which is an oversimplification. Finally, we chose to examine only a simple, binary GxE model, whereas more complex scenarios may exist40,41,42.


Specific myopia-predisposing SNPs were found to differ in the age at which they had their effects, and whether or not these effects got progressively stronger during later childhood. Thus, SNPs implicating the genes CHRNG, CACNA1D, LAMA2, CYP26A1 and BMP4 were associated with early onset changes in refractive error that did not progress further, while SNPs close to PRSS56, KCNQ5, TOX, ZIC2 and SHISA6 showed early-onset effects that became greater still at older ages. Effects that only appeared in later childhood – after the age of 7.5 years – implicated the genes CD55, CHD7, RORB, KCNMA1, A2BP1 and GJD2. Gene-environment interactions involving nearwork or time outdoors were rare or absent for the vast majority of the GWAS-identified SNPs, and indeed a genetic risk score that demonstrated very convincing association with early-onset (P = 2.2E–9) and later progressive (P = 5.8E–14) changes in refractive error appeared to act independently of the time children spent in these activities. However, one robust interaction between rs7829127 in ZMAT4 and time spent performing nearwork (nominal P = 6.3E–04, corrected P = 0.025) was observed, replicating a previously-identified interaction involving rs7829127 and years of education37,43,44.

Additional Information

How to cite this article: Fan, Q. et al. Childhood gene-environment interactions and age-dependent effects of genetic variants associated with refractive error and myopia: The CREAM Consortium. Sci. Rep. 6, 25853; doi: 10.1038/srep25853 (2016).


  1. 1.

    , , & Myopia and associated pathological complications. Ophthalmic Physiol. Opt. 25, 381–391 (2005).

  2. 2.

    , & Myopia. Lancet 379, 1739–1748 (2012).

  3. 3.

    , & Adult-onset myopia - The Genes in Myopia (GEM) twin study. Invest. Ophthalmol. Vis. Sci. 49, 3324–3327 (2008).

  4. 4.

    , , , & Prevalence of refractive error in the United States, 1999–2004. Arch. Ophthalmol. 126, 1111–1119 (2008).

  5. 5.

    et al. Prevalence of refractive error in Europe: the European Eye Epidemiology (E3) Consortium. Eur. J. Epidemiol. 30, 305–315 (2015).

  6. 6.

    et al. Increases in the prevalence of reduced visual acuity and myopia in Chinese children in Guangzhou over the past 20 years. Eye 27, 1353–1358, 10.1038/eye.2013.194 (2013).

  7. 7.

    , , & Prevalence of myopia among Hong Kong Chinese schoolchildren: changes over two decades. Ophthalmic Physiol. Opt. 32, 17–24 (2012).

  8. 8.

    et al. Genome-wide analysis points to roles for extracellular matrix remodeling, the visual cycle, and neuronal development in myopia. PLos Genet. 9, e1003299, 10.1371/journal.pgen.1003299 (2013).

  9. 9.

    et al. Genome-wide meta-analyses of multiancestry cohorts identify multiple new susceptibility loci for refractive error and myopia. Nat. Genet. 45, 314–318 (2013).

  10. 10.

    et al. Nine loci for ocular axial length identified through genome-wide association studies, including shared loci with refractive error. Am. J. Hum. Genet. 93, 264–277, 10.1016/j.ajhg.2013.06.016 (2013).

  11. 11.

    et al. Genome-wide association study identifies ZFHX1B as a susceptibility locus for severe myopia. Hum. Mol. Genet. 22, 5288–5294., 10.1093/hmg/ddt385 (2013).

  12. 12.

    et al. A Genome-wide association study reveals association between common variants in an intergenic region of 4q25 and high-grade myopia in the Chinese Han population. Hum. Mol. Genet. 20, 2861–2868 (2011).

  13. 13.

    et al. A genome-wide meta-analysis identifies two novel loci associated with high myopia in the Han Chinese population. Hum. Mol. Genet. 22, 2325–2333., 10.1093/hmg/ddt066 (2013).

  14. 14.

    et al. Meta-analysis of genome-wide association studies in 5 cohorts reveals common variants in RBFOX1, a regulator of tissue-specific splicing, associated with refractive error. Hum. Mol. Genet. 22, 2754–2764, 10.1093/hmg/ddt116 (2013).

  15. 15.

    et al. Mutations in LRPAP1 are associated with severe myopia in humans. Am. J. Hum. Genet. 93, 313–320, 10.1016/j.ajhg.2013.06.002 (2013).

  16. 16.

    et al. SLC39A5 mutations interfering with the BMP/TGF-β pathway in non-syndromic high myopia. J. Med. Genet. 51, 518–525 (2014).

  17. 17.

    et al. Homozygous loss-of-function mutation of the LEPREL1 gene causes severe non-syndromic high myopia with early-onset cataract. Clin. Genet. 86, 575–579, 10.1111/cge.12309 (2014).

  18. 18.

    et al. Exome Sequencing Identifies ZNF644 Mutations in High Myopia. PLos Genet. 7, e1002084 (2011).

  19. 19.

    et al. Mutations in SCO2 are associated with autosomal-dominant high-grade myopia. Am. J. Hum. Genet. 92, 820–826 (2013).

  20. 20.

    et al. Exome sequencing reveals CCDC111 mutation associated with high myopia. Hum. Genet. 132, 913–921, 10.1007/s00439-013-1303-6 (2013).

  21. 21.

    et al. Selective breeding for susceptibility to myopia reveals a gene-environment interaction. Invest. Ophthalmol. Vis. Sci. 52, 4003–4011, 10.1167/iovs.10-7044 (2011).

  22. 22.

    et al. Near Work Related Parameters and Myopia in Chinese Children: the Anyang Childhood Eye Study. Plos ONE 10, e0134514 (2015).

  23. 23.

    Nearwork and myopia. Lancet 356, 1456–1457 (2000).

  24. 24.

    et al. Time outdoors, visual activity, and myopia progression in juvenile-onset myopes. Invest. Ophthalmol. Vis. Sci. 53, 7169–7175, 10.1167/iovs.11-8336 (2012).

  25. 25.

    et al. Time outdoors and physical activity as predictors of incident myopia in childhood: A prospective cohort study. Invest. Ophthalmol. Vis. Sci. 53, 2856–2865 (2012).

  26. 26.

    et al. Time Outdoors and Myopia Progression Over 2 Years in Chinese Children: The Anyang Childhood Eye Study. Invest. Ophthalmol. Vis. Sci. 56, 4734–4740 (2015).

  27. 27.

    & How genetic is school myopia? Prog. Retin. Eye Res. 24, 1–38. (2005).

  28. 28.

    & Gene-environment interactions in human disease: nuisance or opportunity? Trends Genet. 27, 107–115, 10.1016/j.tig.2010.12.004 (2011).

  29. 29.

    & GWAMA: software for genome-wide association meta-analysis. BMC Bioinformatics 11, 288, 10.1186/1471-2105-11-288 (2010).

  30. 30.

    et al. Does vitamin D mediate the protective effects of time outdoors on myopia? Findings from a prospective birth cohort. Invest. Ophthalmol. Vis. Sci. 55, 8550–8558, 10.1167/iovs.14-15839 (2014).

  31. 31.

    & Mixed Effects Models in S and S-Plus. (Springer, 2000).

  32. 32.

    Sample size requirements for matched case-control studies of gene-environment interaction. Statistics Med. 21, 35–50 (2002).

  33. 33.

    et al. The PANTHER database of protein families, subfamilies, functions and pathways. Nucleic Acids Res. 33, D284–D288 (2005).

  34. 34.

    , & Systematic and integrative analysis of large gene lists using DAVID bioinformatics resources. Nat Protoc 4, 44–57 (2009).

  35. 35.

    et al. Genetic and epigenetic fine mapping of causal autoimmune disease variants. Nature 518, 337–343, 10.1038/nature13835 (2015).

  36. 36.

    & Statistical power and significance testing in large-scale genetic studies. Nat. Rev. Genet. 15, 335–346 (2014).

  37. 37.

    et al. Education influences the association between genetic variants and refractive error: A meta-analysis of five Singapore studies. Hum. Mol. Genet. 23, 546–554, 10.1093/hmg/ddt431 (2014).

  38. 38.

    et al. APLP2 Regulates Refractive Error and Myopia Development in Mice and Humans. PLos Genet. 11, e1005432, 10.1371/journal.pgen.1005432 (2015).

  39. 39.

    , , , & Form deprivation modulates retinal neurogenesis in primate experimental myopia. Proc. Natl. Acad. Sci. USA 103, 4681–4686, 10.1073/pnas.0600589103 (2006).

  40. 40.

    et al. Gene-gene and gene-environment interactions detected by transcriptome sequence analysis in twins. Nat Genet 47, 88–91, 10.1038/ng.3162 (2015).

  41. 41.

    & Gene-environment dependence creates spurious gene-environment interaction. Am. J. Hum. Genet. 95, 301–307, 10.1016/j.ajhg.2014.07.014 (2014).

  42. 42.

    et al. Genome-wide gene-environment study identifies glutamate receptor gene GRIN2A as a Parkinson’s Disease modifier gene via interaction with coffee. PLos Genet. 7, e1002237 (2011).

  43. 43.

    et al. Education influences the role of genetics in myopia. Eur. J. Epidemiol. 28, 973–980, 10.1007/s10654-013-9856-1 (2013).

  44. 44.

    et al. Meta-analysis of gene-environment-wide association scans accounting for education level identifies additional loci for refractive error. Nat. Commun. 7, 10.1038/ncomms11008 (2016).

Download references


ALSPAC. We are extremely grateful to all the families who took part in this study, the midwives for their help in recruiting them, and the whole ALSPAC team, which includes interviewers, computer and laboratory technicians, clerical workers, research scientists, volunteers, managers, receptionists and nurses. The UK Medical Research Council and the Wellcome Trust (Grant ref: 102215/2/13/2) and the University of Bristol provide core support for ALSPAC. This publication is the work of the authors and JAG and CW will serve as guarantors for the contents of this paper. This research was specifically funded by grant MC_UU_12013/3&4 from the UK Medical Research Council and grant Z0GM from the Hong Kong Polytechnic University. GWAS data were generated by Sample Logistics and Genotyping Facilities at the Wellcome Trust Sanger Institute and LabCorp (Laboratory Corportation of America) using support from 23andMe.

BATS and TEST. The Australian Twin Registry is supported by an Australian National Health and Medical Research Council (NHMRC) Enabling Grant (2004– 2009). Part of the genotyping was funded by an NHMRC Medical Genomics Grant. Genotyping for the remainder was performed by the National Institutes of Health (NIH)/National Eye Institute (NEI) grant RO1EY018246 and a Center for Inherited Diseases Research (CIDR) genotyping project grant both awarded to Terri L. Young (PI), and we are grateful to Dr Camilla Day and staff. Ophthalmic examination of the Twin cohorts was funded by the Clifford Craig Medical Research Trust, Ophthalmic Research Institute of Australia (ORIA), American Health Assistance Foundation (AHAF), Peggy and Leslie Cranbourne Foundation, Foundation for Children, NHMRC Project Grant 350415 (2005–2007), Jack Brockhoff Foundation and the Pfizer Australia Senior Research Fellowship (DAM). We also would like to acknowledge that CERA receives Operational Infrastructure Support from the Victorian Government. The Berghofer QIMR authors thank the twins eye study investigators; Nicholas G Martin, Scott D. Gordon, Anjali K. Henders, Sarah E. Medland, Brian McEvoy, Dale R. Nyholt, Margaret J. Wright, Megan J. Campbell, and Anthony Caracella for their assistance in processing the Australian genotyping data. We are also grateful for Jane MacKinnon, Shayne Brown, Sandra Staffieri, Olivia Bigault, Colleen Wilkinson, Julie Barbour, Byoung Sung Chu, Jonathan Ruddle Paul Sanfilippo, Cong Sun, Justin Sherwin, Robert Macmillan, Rachael Adams, Robyn Troutbeck, Ya Ling Ma, Christine Chen and Amy Cohn In addition, we appreciate the assistance in recruiting twins from Thanuja Gunasekera, Allison McKenzie, Anne-Louise Ponsonby, Terry Dwyer, James Dilger, Palma Ragno, Jenny Boadle, Kim Dorrell, Shyamali Dharmage, John Hopper

GZT. We are extremely grateful to all the twins and their parents, and the whole Guangzhou Twins team which includes research scientists, interviewers, statistician, laboratory technicians, clerical workers, volunteers, managers, receptionists, nurses and graduate students. The Guangzhou Twin Eye study is supported by National Science Foundation for Distinguished Young Scholars (81125007), Fundamental Research Funds for the Central Universities, Fundamental Research Funds for the State Key Laboratory, NSFC(11401600), the Fundamental Research Funds for the Central Universities (15lgpy07) and the free application projects from the SYSU-CMU Shunde International Joint Research Institute.

RAINE. We are grateful to all the study participants. We also thank the Raine Study and Lions Eye Institute (LEI) research staff for cohort coordination and data collection. The core management of the Raine Study is funded by The University of Western Australia (UWA), The Telethon Institute for Child Health Research, Raine Medical Research Foundation, UWA Faculty of Medicine, Dentistry and Health Sciences, Women’s and Infant’s Research Foundation and Curtin University. Genotyping was funded by Australian National Health and Medical Research Council (NHMRC) project grant 1021105. Support for the REHS was provided by LEI, the Australian Foundation for the Prevention of Blindness and Ophthalmic Research Institute of Australia (ORIA).

SCORM: We wish to express our gratitude to all the participants and patients who volunteered to take part in this study. The SCORM GWAS study was supported by the Singapore BioMedical Research Council (BMRC). Additional support was provided by the Singapore Tissue Network. We acknowledge the Genome Institute of Singapore for genotyping all the samples collected from SCORM.

STARS: The Strabismus, Amblyopia, and Refractive Error Study of Preschool Children (STARS) was supported by a NMRC grant (1176/2008). We acknowledge the Genome Institute of Singapore for genotyping all the samples collected from STARS.

TEDS. We gratefully acknowledge the ongoing contribution of the participants in the Twins Early Development Study (TEDS) and their families. TEDS is supported by a program grant to Robert Plomin from the UK Medical Research Council [G0901245; previously G0500079], with additional support from the US National Institutes of Health [HD044454 and HD059215]. RP is supported by a Medical Research Council Research Professorship award [G19/2] and a European Research Council Advanced Investigator award [295366]. KMW is supported by a Medical Research Council Clinical Research Training Fellowship. EK is supported by an Institute of Psychiatry, Psychology and Neuroscience Excellence Studentship.

WESDR. The WESDR was supported by grant R01-EY016379 from the National Eye Institute, National Institutes of Health and an unrestricted grant from Research to Prevent Blindness, New York, NY.

JEBW is supported by the Intramural Research Program of the National Human Genome Research Institute, NIH. TLY is supported by Research to Prevent Blindness, Inc., and NIH NEI R01EY014685.

Author information

Author notes

    • Qiao Fan
    • , Xiaobo Guo
    •  & J. Willem L. Tideman

    These authors contributed equally to this work.

    • Christopher J. Hammond
    • , David A. Mackey
    • , Mingguang He
    • , Seang-Mei Saw
    • , Cathy Williams
    •  & Jeremy A. Guggenheim

    These authors jointly supervised this work.


  1. Centre for Quantitative Medicine, Duke-NUS Medial School, Singapore

    • Qiao Fan
  2. Department of Statistical Science, School of Mathematics & Computational Science, Sun Yat-Sen University, Guangzhou, China

    • Xiaobo Guo
  3. State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangzhou, China

    • Xiaobo Guo
    • , Xiaohu Ding
    •  & Mingguang He
  4. Department of Epidemiology, Erasmus Medical Center, Rotterdam, The Netherlands

    • J. Willem L. Tideman
    • , Caroline C. W. Klaver
    • , Cornelia M. van Duijn
    • , Milly S. Tedja
    •  & Virginie J. M. Verhoeven
  5. Department of Ophthalmology, Erasmus Medical Center, Rotterdam, The Netherlands

    • J. Willem L. Tideman
    • , Caroline C. W. Klaver
    • , Milly S. Tedja
    •  & Virginie J. M. Verhoeven
  6. Department of Ophthalmology, King’s College London, St Thomas’ Hospital campus, London, UK

    • Katie M. Williams
    •  & Christopher J. Hammond
  7. Department of Twin Research and Genetic Epidemiology, King’s College London School of Medicine, London, UK

    • Katie M. Williams
    • , Pirro G. Hysi
    •  & Christopher J. Hammond
  8. Centre for Ophthalmology and Visual Science, Lions Eye Institute, University of Western Australia, Perth, Australia

    • Seyhan Yazar
    • , David A. Mackey
    •  & Alex W. Hewitt
  9. Genetics and Genome Biology Program, Hospital for Sick Children and University of Toronto, Toronto, Ontario, Canada

    • S. Mohsen Hosseini
    •  & Andrew D. Paterson
  10. MRC Integrative Epidemiology Unit (IEU) at the University of Bristol, Bristol, UK

    • Laura D. Howe
    • , Beaté St Pourcain
    • , David M. Evans
    • , Nicholas J. Timpson
    • , George McMahon
    • , George Davey Smith
    •  & John P. Kemp
  11. School of Social and Community Medicine, University of Bristol, Bristol, UK

    • Laura D. Howe
    •  & Cathy Williams
  12. Max Planck Institute for Psycholinguistics, Wundtlaan 1, 6525 XD Nijmegen, The Netherlands

    • Beaté St Pourcain
  13. University of Queensland Diamantina Institute, Translational Research Institute, Brisbane, Queensland, Australia

    • David M. Evans
  14. MRC Social, Genetic and Developmental Psychiatry Centre, Institute of Psychiatry, Psychology & Neuroscience, King’s College London, London, UK

    • Eva Krapohl
    •  & Robert Plomin
  15. Beijing Institute of Ophthalmology, Beijing Tongren Hospital, Capital Medical University, Beijing, China

    • Ya Xing Wang
    • , Jost B. Jonas
    •  & Liang Xu
  16. Beijing Ophthalmology and Visual Science Key Lab, Beijing, China

    • Ya Xing Wang
    •  & Jost B. Jonas
  17. Department of Ophthalmology, Medical Faculty Mannheim, Ruprecht-Karls-University Heidelberg, Mannheim, Germany

    • Jost B. Jonas
  18. Centre for Eye Research Australia (CERA), University of Melbourne, Royal Victorian Eye and Ear Hospital, Melbourne, Victoria, Australia

    • Paul Nigel Baird
    • , Jie Jin Wang
    • , David A. Mackey
    • , Mingguang He
    • , Alex W. Hewitt
    • , Jing Xie
    •  & Maria Schache
  19. Centre for Vision Research, Department of Ophthalmology and Westmead Institute for Medical Research, University of Sydney, Sydney, Australia

    • Jie Jin Wang
    •  & Paul Mitchell
  20. Department of Ophthalmology, National University Health Systems, National University of Singapore, Singapore

    • Ching-Yu Cheng
    • , Tien-Yin Wong
    • , Chiea-Chuen Khor
    • , Jiemin Liao
    • , M. Kamran Ikram
    • , Peng Chen
    •  & Tin Aung
  21. Singapore Eye Research Institute, Singapore National Eye Centre, Singapore, Singapore

    • Ching-Yu Cheng
    • , Tien-Yin Wong
    • , Seang-Mei Saw
    • , Amutha Barathi Veluchamy
    • , Tin Aung
    •  & Wanting Zhao
  22. Department of Statistics and Applied Probability, National University of Singapore, Singapore, Singapore

    • Yik-Ying Teo
  23. Saw Swee Hock School of Public Health, National University Health Systems, National University of Singapore, Singapore, Singapore

    • Yik-Ying Teo
    • , Seang-Mei Saw
    • , Chiea-Chuen Khor
    • , E-Shyong Tai
    •  & M. Kamran Ikram
  24. Computational and Statistical Genomics Branch, National Human Genome Research Institute, National Institutes of Health, Baltimore, MD, USA

    • Robert Wojciechowski
    • , Joan E. Bailey-Wilson
    •  & Claire L. Simpson
  25. Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland, USA

    • Robert Wojciechowski
  26. Wilmer Eye Institute, Johns Hopkins School of Medicine, Baltimore, MD

    • Robert Wojciechowski
  27. Ophthalmology and Visual Sciences, University of Wisconsin-Madison, Madison, WI, USA

    • Terri L. Young
    • , Barbara E. Klein
    •  & Ronald Klein
  28. Duke-National University of Singapore Graduate Medical School, Singapore, Singapore

    • Terri L. Young
    • , Amutha Barathi Veluchamy
    •  & E-Shyong Tai
  29. Department of Health Sciences and Gerontology Research Center, University of Jyväskylä, Jyväskylä, Finland

    • Olavi Pärssinen
    •  & Taina Rantanen
  30. Department of Ophthalmology, Central Hospital of Central Finland, Jyväskylä, Finland

    • Olavi Pärssinen
  31. Institute of Human Genetics, Technical University Munich, Munich, Germany

    • Konrad Oexle
    •  & Thomas Meitinger
  32. Department of Ophthalmology, University Medical Center Mainz, Mainz, Germany

    • Norbert Pfeiffer
    •  & René Höhn
  33. School of Optometry & Vision Sciences, Cardiff University, Cardiff, UK

    • Jeremy A. Guggenheim
  34. Department of Ophthalmology, Yokohama City University School of Medicine, Yokohama, Kanagawa, Japan.

    • Akira Meguro
    •  & Nobuhisa Mizuki
  35. Medical Research Council Human Genetics Unit, Institute of Genetics and Molecular Medicine, University of Edinburgh, Edinburgh, UK.

    • Alan F. Wright
    • , Caroline Hayward
    •  & Veronique Vitart
  36. Department of Ophthalmology and Visual Sciences, The Chinese University of Hong Kong Hong Kong Eye Hospital, Kowloon, Hong Kong.

    • Alvin L. Young
    • , Chi Pui Pang
    • , Li Jia Chen
    • , Pancy O. Tam
    •  & Vishal Jhanji
  37. Estonian Genome Center, University of Tartu, Tartu, Estonia.

    • Andres Metspalu
    • , Evelin Mihailov
    •  & Toomas Haller
  38. Institute of Genetic Epidemiology, Helmholtz Zentrum München—German Research Center for Environmental Health, Neuherberg, Germany.

    • Angela Döring
    • , Christian Gieger
    •  & Janina S. Ried
  39. Department of Public Health and Primary Care, Institute of Public Health, University of Cambridge School of Clinical Medicine, Cambridge, UK.

    • Anthony P. Khawaja
    • , Kay-Tee Khaw
    •  & Robert N. Luben
  40. Princess Alexandra Eye Pavilion, Edinburgh, UK.

    • Brian Fleck
  41. Université de Bordeaux, Bordeaux, France.

    • Cécile Delcourt
    •  & Jean-François Korobelnik
  42. INSERM (Institut National de la Santé Et de la Recherche Médicale), ISPED (Institut de Santé Publique d’Épidémiologie et de Développement), Centre INSERM U897- Epidemiologie-Biostatistique, Bordeaux, France.

    • Cécile Delcourt
    •  & Jean-François Korobelnik
  43. Division of Human Genetics, Genome Institute of Singapore, Singapore, Singapore.

    • Chiea-Chuen Khor
  44. Department of Ophthalmology and Visual Sciences, University of Utah, Moran Eye Center, Salt Lake City, USA.

    • Chiea-Chuen Khor
  45. Department of Ophthalmology, University of Pennsylvania, Philadelphia, Pennsylvania, USA.

    • Dwight Stambolian
  46. Department of Genetics, Case Western Reserve University, Cleveland, Ohio, USA.

    • Emily Chew
    •  & Sudha K. Iyengar
  47. Department of Medicine, National University of Singapore, Singapore, Singapore.

    • E-Shyong Tai
  48. Institute of Molecular Genetics, National Research Council, Pavia, Italy.

    • Ginevra Biino
  49. Centre for Global Health Research, The Usher Institute for Population Health Sciences and Informatics, University of Edinburgh, Scotland, UK.

    • Harry Campbell
    • , Igor Rudan
    • , James F. Wilson
    •  & Ozren Polasek
  50. Department of Clinical Chemistry, Fimlab laboratories and School of Medicine, University of Tampere, Tampere, Finland.

    • Ilkka Seppälä
    • , Kari-Matti Mäkelä
    •  & Terho Lehtimäki
  51. Department of Public Health, University of Helsinki, Helsinki, Finland.

    • Jaakko Kaprio
    •  & Juho Wedenoja
  52. Department of Ophthalmology, Flinders University, Adelaide, Australia.

    • Jamie E. Craig
    • , Kathryn P. Burdon
    •  & Rhys D. Fogarty
  53. Department of Epidemiology and Biostatistics, Case Western Reserve University, Cleveland, Ohio, USA.

    • Jeremy R. Fondran
    • , Jonathan H. Lass
    • , Robert P. Igo Jr
    • , Sarayut Janmahasatian
    •  & Sudha K. Iyengar
  54. MRC Epidemiology Unit, Institute of Metabolic Sciences, University of Cambridge, Cambridge, UK.

    • Jing Hua Zhao
    •  & Nick J. Wareham
  55. Department of Ophthalmology and Visual Sciences, Case Western Reserve University and University Hospitals Eye Institute, Cleveland, Ohio, USA.

    • Jonathan H. Lass
    •  & Sudha K. Iyengar
  56. Institute of Child Health, University College London, London, UK.

    • Jugnoo S. Rahi
    •  & Phillippa Cumberland
  57. Institute of Ophthalmology, Moorfields Eye Hospital, London, UK.

    • Jugnoo S. Rahi
  58. Ulverscroft Vision Research Group, University College London, London, UK.

    • Jugnoo S. Rahi
    •  & Phillippa Cumberland
  59. Department of Ophthalmology, Helsinki University Central Hospital, Helsinki, Finland.

    • Juho Wedenoja
  60. Department of Ophthalmology and Visual Sciences, Kyoto University Graduate School of Medicine, Kyoto, Japan.

    • Kenji Yamashiro
    • , Masahiro Miyake
    •  & Nagahisa Yoshimura
  61. Department of Ophthalmology and Visual Sciences, John Moran Eye Center, University of Utah, Salt Lake City, Utah, USA.

    • Lindsay Farrer
    • , Margaret M. Deangelis
    •  & Margaux Morrison
  62. Institute of Genetic and Biomedic Research, National Research Council, Cagliari, Italy.

    • Mario Pirastu
    •  & Simona Vaccargiu
  63. Centre for Myopia Research, School of Optometry, The Hong Kong Polytechnic University, Hong Kong, Hong Kong.

    • Maurice K. H. Yap
  64. University Hospital ‘San Giovanni di Dio’, Cagliari, Italy.

    • Maurizio Fossarello
  65. Department of Clinical Physiology, Tampere University Hospital and School of Medicine, University of Tampere, Tampere, Finland.

    • Mika Kähönen
  66. Genetic Epidemiology Laboratory, QIMR Berghofer Medical Research Institute, Herston, Brisbane, Queensland, Australia.

    • Nicholas G. Martin
  67. Research Centre of Applied and Preventive Medicine, University of Turku, Turku, Finland.

    • Olli Raitakari
  68. Department of Clinical Physiology and Nuclear Medicine, Turku University Hospital, Turku, Finland.

    • Olli Raitakari
  69. Faculty of Medicine, University of Split, Split, Croatia.

    • Ozren Polasek
  70. Division of Genetics and Epidemiology, UCL Institute of Ophthalmology, London, UK.

    • Paul J. Foster
  71. NIHR Biomedical Research Centre, Moorfields Eye Hospital NHS Foundation Trust and UCL Institute of Ophthalmology, London, UK.

    • Paul J. Foster
  72. Statistical Genetics Laboratory, QIMR Berghofer Medical Research Institute, Herston, Brisbane, Queensland, Australia.

    • Puya Gharahkhani
    • , Stuart MacGregor
    •  & Yi Lu
  73. Department of Health Technology and Informatics, The Hong Kong Polytechnic University, Hong Kong, Hong Kong.

    • Shea Ping Yip
  74. Department of Pediatric Ophthalmology, Duke Eye Center For Human Genetics, Durham, North Carolina, USA.

    • Sheng Feng
  75. Suraj Eye Institute, Nagpur, Maharashtra, India.

    • Songhomitra Panda-Jonas
    •  & Vinay Nangia
  76. Gerontology Research Center, University of Jyväskylä, Jyväskylä, Finland.

    • Taina Rantanen
  77. Department of Radiology, Southwest Hospital, The Third Military Medical University, Chongqing, China.

    • Wei Chen
    •  & Xiangtian Zhou
  78. Department of Ophthalmology, Sisters of Mercy University Hospital, Zagreb, roatia.

    • Zoran Vatavuk


  1. The CREAM Consortium


  1. Search for Qiao Fan in:

  2. Search for Xiaobo Guo in:

  3. Search for J. Willem L. Tideman in:

  4. Search for Katie M. Williams in:

  5. Search for Seyhan Yazar in:

  6. Search for S. Mohsen Hosseini in:

  7. Search for Laura D. Howe in:

  8. Search for Beaté St Pourcain in:

  9. Search for David M. Evans in:

  10. Search for Nicholas J. Timpson in:

  11. Search for George McMahon in:

  12. Search for Pirro G. Hysi in:

  13. Search for Eva Krapohl in:

  14. Search for Ya Xing Wang in:

  15. Search for Jost B. Jonas in:

  16. Search for Paul Nigel Baird in:

  17. Search for Jie Jin Wang in:

  18. Search for Ching-Yu Cheng in:

  19. Search for Yik-Ying Teo in:

  20. Search for Tien-Yin Wong in:

  21. Search for Xiaohu Ding in:

  22. Search for Robert Wojciechowski in:

  23. Search for Terri L. Young in:

  24. Search for Olavi Pärssinen in:

  25. Search for Konrad Oexle in:

  26. Search for Norbert Pfeiffer in:

  27. Search for Joan E. Bailey-Wilson in:

  28. Search for Andrew D. Paterson in:

  29. Search for Caroline C. W. Klaver in:

  30. Search for Robert Plomin in:

  31. Search for Christopher J. Hammond in:

  32. Search for David A. Mackey in:

  33. Search for Mingguang He in:

  34. Search for Seang-Mei Saw in:

  35. Search for Cathy Williams in:

  36. Search for Jeremy A. Guggenheim in:


Conceived and designed the research: A.D.P., C.C.W.K., C.W., D.A.M., J.A.G., J.E.B., K.O., N.P., O.P., R.W., S.M.S. and X.D. Analyzed and interpreted the data: E.K., J.B.J., J.W.L.T., K.M.W., M.H., Q.F., S.Y., X.G. and Y.X.W. Obtained the funding: J.J.W., P.N.B., S.M.S., T.Y.W. and T.L.Y. Supervised research personnel: C.C.W.K., C.J.H., C.Y.C., D.A.M., E.K., M.H., P.G.H., R.P., T.Y.W. and Y.Y.T. Drafted the manuscript: J.A.G., J.W.L.T. and Q.F. Made critical revision of the manuscript for important intellectual content: B.S.P., C.J.H., C.W., D.M.E., G.M., J.B.J., J.J.W., K.M.W., L.D.H., N.J.T., P.G.H., P.N.B., R.P., T.L.Y. and Y.X.W. Reviewed the manuscript: CREAM consortium members.

Competing interests

The authors declare no competing financial interests.

Corresponding author

Correspondence to Jeremy A. Guggenheim.

Supplementary information


By submitting a comment you agree to abide by our Terms and Community Guidelines. If you find something abusive or that does not comply with our terms or guidelines please flag it as inappropriate.

Creative Commons BYThis work is licensed under a Creative Commons Attribution 4.0 International License. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in the credit line; if the material is not included under the Creative Commons license, users will need to obtain permission from the license holder to reproduce the material. To view a copy of this license, visit