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Meta-analysis of gene–environment-wide association scans accounting for education level identifies additional loci for refractive error

  • Nature Communications volume 7, Article number: 11008 (2016)
  • doi:10.1038/ncomms11008
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Abstract

Myopia is the most common human eye disorder and it results from complex genetic and environmental causes. The rapidly increasing prevalence of myopia poses a major public health challenge. Here, the CREAM consortium performs a joint meta-analysis to test single-nucleotide polymorphism (SNP) main effects and SNP × education interaction effects on refractive error in 40,036 adults from 25 studies of European ancestry and 10,315 adults from 9 studies of Asian ancestry. In European ancestry individuals, we identify six novel loci (FAM150B-ACP1, LINC00340, FBN1, DIS3L-MAP2K1, ARID2-SNAT1 and SLC14A2) associated with refractive error. In Asian populations, three genome-wide significant loci AREG, GABRR1 and PDE10A also exhibit strong interactions with education (P<8.5 × 10−5), whereas the interactions are less evident in Europeans. The discovery of these loci represents an important advance in understanding how gene and environment interactions contribute to the heterogeneity of myopia.

Introduction

Myopia, or nearsightedness, has rapidly emerged as a global health concern in the last three decades1. It is one of the leading causes of visual impairment and is associated with potentially blinding ocular complications including myopic maculopathy, glaucoma, cataract and retinal detachment2. Evidence from family and twin studies strongly supports the heritability of myopia3. Estimates for the heritability of the quantitative trait refractive error have been reported to be as high as 90% (ref. 4). On the other hand, the rapid upsurge of myopia in the last few decades in many parts of the world is likely to be a consequence of lifestyle changes such as the increasing educational intensity, in particular in urban East Asia5,6, and potentially gene and environment (G × E) interactions.

Major attempts undertaken in genome-wide association studies (GWAS) to elucidate the genetic determination of myopia and refractive error have recently led to the discovery of >30 distinct susceptibility loci7,8. Nevertheless, collectively these genetic variants are estimated to explain <12% of phenotypic variance in refractive errror7,8. As myopia is a result of the combination of genetic and environmental factors, interplay between genes and environment may account for a substantial proportion of the phenotypic variance. In recent times, we showed interactions between education and genetic risk score of myopia derived from 26 known GWAS single-nucleotide polymorphisms (SNPs) in the Rotterdam Study9; the combined effect of genetic predisposition and education on the risk of myopia was substantially greater than anticipated from a simple sum of these two factors. At the gene level, some genes such as SHISA6-DNAH9 have been shown to interact with education level and exhibit strong genetic effects for myopia among Asians with at least higher secondary education10. In the current study, we demonstrate that new genetic effects implicated in myopia development could be uncovered by studying interactions between genetic variants and education level.

In the context of the aetiology of refractive errors, education attainment is generally considered a surrogate measure for accumulated near work activity1. When viewing near objects, the eye generates extra optical power through the process of accommodation to focus the image on the retinal plane, to maintain clear vision11. There is an accommodative lag (less accommodation produced than needed) in many myopes, resulting in a hyperopic defocus on the retina for near work, which has long been proposed to promote eye growth1,12, but whether this occurs before or after the onset of myopia in humans is less clear. The retina has a central role in the mechanism linking such visual input with eye growth and refractive development13. Several neurotransmitters or molecules have been implicated in this process by animal studies including dopamine, acetylcholine, vasoactive intestinal peptide, GABA (γ-aminobutyric acid) and glucagon14,15. However, an organized framework for the retinal signalling mechanisms underlying refractive error development under various environmental conditions remains to be elucidated.

Factoring in environmental exposures may enhance power for the detection of genes, especially in circumstances where a genetic locus has a differential effect conditional on specific environment exposures16. Gene–environment-wide interaction studies (GEWIS) using a joint meta-analysis (JMA) approach on SNP main effects and SNP × environment interactions have recently been described17,18. This approach has successfully identified six novel loci associated with fasting insulin and glucose accounting for interactions with body mass index18. It also led to the identification of two novel loci for pulmonary function that did not emerge from analyses based on the genetic main effects alone19. The well-documented effects of educational attainment on myopia and refractive error make the proposed interaction an excellent analytical candidate for the GEWIS.

The availability of large-scale GWAS spherical equivalent data sets from the Consortium for Refractive Error And Myopia (CREAM) makes G × E interaction analyses feasible. To identify additional genetic variants for refractive error, we performed GEWIS-based analyses on 40,036 adults of European ancestry from 25 studies and 10,315 adults of Asian ancestry from 9 studies. We identified nine new loci using the JMA approach, where three loci exhibited G × E interaction on refractive error in Asians, including the GABAC receptor subunit ρ1 gene GABRR1.

Results

Educational level and its main effects on spherical equivalent

The baseline characteristics of 50,351 participants from 34 studies in our meta-analysis are shown in Table 1. A total of 40,036 participants were of European descent and 10,315 were of Asian descent; the age of the participants ranged from 20 to 99 years. We grouped individuals into two educational categories: a higher education group that included individuals who completed higher secondary or university education and a lower education group comprising those with lower secondary education or below (see Methods). Among Europeans, the proportions of participants in the higher education group ranged from 16.5% (FITSA20 and OGP Talana21) to 94.4% (AREDS22) with an average of 50.7% (Supplementary Table 1). In Asians, the proportions of individuals in the higher education group ranged from 6.7% (SiMES23) to 75.9% (Nagahama24) with an average of 30.0%. Across all studies, individuals in the higher education group had a spherical equivalent refractive error that was on average 0.59 dioptres (D) more myopic, or less hyperopic, compared with those in the lower education group (β=−0.59 D; 95% confidence interval (CI): −0.64 to −0.55 D). High education level was associated with a twofold more myopic spherical equivalent in individuals of Asian as compared with European ancestry (Asians: β=−1.09 D, 95% CI: −1.20 to −0.98 D; Europeans: β=−0.49 D, 95% CI: −0.54 to −0.44 D; Fig. 1). Among Asian studies, we also observed heterogeneity of education effects for refractive error. The education effects on spherical equivalent in Singapore Chinese were significantly larger than that in other Asian studies (Singapore Chinese: β=−1.75 D, 95% CI: −1.92 to −1.58 D; other Asian cohorts: β=−0.60 D, 95% CI: −0.75 to −0.46 D).

Table 1: Characteristics of study participants.
Figure 1: Forest plot of education main effects on spherical equivalent across studies.
Figure 1

The β-coefficient represents the differences of dioptres in refractive error comparing individuals in higher education group versus lower education group in Europeans (n=40,036), Asians (n=10,315) and all studies (n=50,351). The studies are sorted by effect size of education on spherical equivalent within Europeans and Asians studies, respectively.

GEWIS in Europeans

After stringent quality control (QC) filtering, 6 million SNPs in each study were eligible for the genome-wide JMA test (Supplementary Table 2). The JMA for SNP main effects and SNP × education interactions in 40,036 European Ancestry individuals showed an association with spherical equivalent at 12 previously implicated loci (Fig. 2a, Supplementary Table 3 and Supplementary Fig 1). We also identified four previously unreported loci associated with spherical equivalent achieving genome-wide significance (PJMA<5.0 × 10−8; Phet≥0.086; Table 2): FAM150B-ACP1, LINC00340, FBN1 and DIS3L-MAP2K1. The significant association for JMA testing at these loci in Europeans was primarily driven by SNP effects in both the lower and higher education strata (4.40 × 10−8Pmain≤1.35 × 10−6 and 7.61 × 10−11Pmain≤1.75 × 10−6, respectively). SNP × education interaction was not significant (Pint≥0.208). The estimated effect sizes of SNP effects on spherical equivalent were highly similar across education strata.

Figure 2: Manhattan plots of −log10(P) for the JMA on SNP main effects and SNP × education effects on spherical equivalent in (a) European ancestry populations and (b) Asian populations.
Figure 2

The horizontal red line indicates the genome-wide significance level of PJMA<5 × 10−8. The horizontal blue line indicates the suggestive significance level of PJMA<1 × 10−5. Novel loci reaching genome-wide significance are labelled in red and known loci are in grey.

Table 2: Six genetic loci associated with spherical equivalent from the JMA in the European populations and combined analyses.

GEWIS in Asians

The JMA for spherical equivalent in 10,315 individuals from the Asians cohorts identified genome-wide significant association for three genes: AREG, GABRR1 and PDE10A (PJMA<5.0 × 10−8; Table 3 and Fig. 2b). SNP × education interaction effects associated with spherical equivalent were observed at all three loci, with genetic effects significantly larger within participants who had a higher level of education compared with those with a lower education level: AREG (rs12511037, βint=−0.89±0.14 D, Pint=6.87 × 10−11), GABRR1 (rs13215566, βint=−0.56±0.14 D, Pint=8.48 × 10−5) and PDE10A (rs12206610, βint=−0.72±0.13 D, Pint=2.32 × 10−8). The genotype and phenotype associations were highly significant in the higher education stratum (main genetic effects, 1.97 × 10−10Pmain≤8.16 × 10−8) but were considerably weaker in the lower education stratum (0.008≤Pmain≤0.243). There was no evidence of inter-study heterogeneity at index SNPs within AREG, GABRR1 or PDE10A loci (Q-test: Phet≥0.122).

Table 3: Three genetic loci associated with spherical equivalent with a significant SNP × education interaction in Asians and results in European populations.

GABRR1 and PDE10A index SNPs were not associated with spherical equivalent in European samples, for either the JMA test, SNP main effect or SNP × education interaction (Table 3). AREG SNP rs12511037 was excluded in the meta-analysis of European studies after QC filtering; hence, a proxy SNP, rs1246413, in linkage disequilibrium (LD) with rs12511037 in Asians (r2=0.97) was tested but not associated with spherical equivalent (PJMA=0.527; Pint=0.176). The meta-regression including study-level characteristics as covariates in the model confirmed the heterogeneity between populations of European and Asian ancestry (GABRR1: P=0.006; PDE10A: P=0.0419; Supplementary Table 4). For PDE10A, besides ethnicity, average spherical equivalent of each study also explained the inter-study heterogeneity for the interaction effects (P=0.025).

We examined whether the underlying assumption of G × E independence held at these three G × E interaction loci. We performed a meta-analysis of logistic regression analysis for education level on AREG SNP rs12511037, GABRR1 SNP rs13215566 and PDE10A SNP rs12296610, adjusting for age, gender and population stratification in Asian cohorts (n=10,315). Our analysis did not reveal any significant associations between these loci and education level (P≥0.102, Phet≥0.170; Supplementary Table 5). Furthermore, the three loci were not associated with educational attainment in a large meta-analysis of GWAS recently conducted in European cohorts25. Thus, our G × E results are unlikely to be biased due to dependence between gene and education.

We also evaluated the association for spherical equivalent in Asian cohorts for four loci identified from European populations. Two of them showed significant associations in the JMA test: FAM150B-ACP1 (PJMA=0.031) and DIS3L-MAP2K1 (PJMA=0.0042; Table 2). The SNP effect sizes in lower and higher education strata in Asians were similar at FAM150B-ACP1. The signal at the DIS3L-MAP2K1 locus was mainly driven by SNP × education interaction in Asians (Pint=7.95 × 10−4), whereas the interaction effect was not statistically significant in Europeans (Pint=0.208).

Combined GEWIS of all cohorts

We subsequently conducted a JMA in the combined data including both the European and Asian participants of all 34 studies. This analysis revealed two additional SNPs: ARID2-SNAT1 (PJMA=4.38 × 10−8) and SLC14A2 (PJMA=2.54 × 10−8). Both loci showed suggestive association with spherical equivalent in European cohorts, with the same direction of effect and similar effect sizes in Asian cohorts (Table 2). We also detected genome-wide significant associations with spherical equivalent for 17 known loci8 identified in our previous CREAM GWAS (Supplementary Table 3). The regional plots of the identified novel loci are presented in Supplementary Fig 2.

Gene and education interactions for GWAS known loci

We also evaluated the interactions between education and previously reported genetic association with spherical equivalent at 39 loci identified from recent two large GWAS studies7,8. Two SNP × education interactions were nominally significant (Supplementary Table 6): TJP2 in Europeans (rs11145488, Pint=6.91 × 10−3) and SHISA6-DNAH9 in Asians (rs2969180, Pint=4.02 × 10−3). In general, the index SNPs tested at 39 loci had larger SNP × education interaction effect on spherical equivalent in Asians versus Europeans (meta-regression P for fold changes<0.001; Supplementary Fig. 3). For 20 SNPs with the same direction of the interaction effect, the magnitudes of interaction effects were fourfold larger on average in Asians than in Europeans (P=0.003).

Gene and near work interactions for three identified loci

High education levels may reflect an estimator for the greater accumulative effect of near work26,27. We thus examined whether there was evidence for SNP × near work interactions associated with spherical equivalent at the three loci (AREG, GABRR1 and PDE10A) in paediatric cohorts (SCORM28, Guangzhou Twins29 and ALSPAC30; combined n=5,835; Supplementary Table 7). Tentative support for a SNP × near work interaction was observed for PDE10A (rs12206610, Pint=0.032, Phet=0.658), with the stronger genetic effect in children spending more hours on reading, writing or compute use. Weaker support for an interaction was noted at GABRR1 (rs13215566, Pint=0.309, Phet=0.655), although the direction of meta-analysed interaction effect was largely consistent across paediatric studies with that observed in adults. We did not observe the interaction at AREG (rs12511037, Pint=0.795, Phet=0.062).

Gene expression in human tissues

Using the Ocular Tissue Database31, we examined the expression of the associated genes in 20 normal human donor eyes. The majority of genes identified were expressed in human retina, sclera, choroid or retinal pigment epithelium (RPE) (Supplementary Table 8). Among these genes, GABRR1, ACP1 and SNAT1 had the highest expression in the retina. The Probe Logarithmic Intensity Error-normalized messenger RNA expression levels in the retina ranged from 121.66 to 236.69. Of note, MAP2K1 was widely expressed in the retina, sclera and choroid/RPE.

Discussion

This study represents the most comprehensive genome-wide scan of gene and education interactions to date, for refractive error. Here we identified novel genetic loci associated with refractive error by testing the joint contribution of SNP main effects and SNP × education effects in large multi-ethnic populations. Three loci (AREG, GABRR1 and PDE10A) showed strong interactions with education in populations of Asian descent, with larger genetic effects within participants who had a higher level of education compared with those with a lower education level; no interactions achieved statistical significance in Europeans for top JMA associations or known myopic loci. Apart from confirming known associations at 17 previous published loci, we identified six new loci (FAM150B-ACP1, LINC00340, FBN1, DIS3L-MAP2K1, ARID2-SNAT1 and SLC14A2) significantly associated with spherical equivalent using the combined multi-racial cohort.

A recent meta-analysis of GWAS in multi-ethnic populations comprises 32 studies (n=45,756) from CREAM and a large GWAS in Europeans (n=45,771) have reported a total of 39 genetic loci associated with refractive phenotypes7,8. The current genome-wide meta-analysis included 5,000 more subjects than the previous GWAS of main effects. We identified nine additional novel loci using the JMA approach. These loci can be placed within the biological context of the visually evoked signalling cascade that begins in the retina and mediates sclera remodelling32. The newly identified genes are involved in retinal neurotransmission (GABRR1 and SNAT1), extracellular matrix remodelling (FBN1, MAP2K1 and AREG), circadian rhythm (PDE10A) and platelet-derived growth factor receptor signalling (ACP1) (Supplementary Table 9). Network analysis revealed that most of the novel genes may tend to be co-expressed and co-localized with the known myopia susceptibility genes through multiple biological networks such as LAMA2, GJD2, RASGRF1, BMP3, RDH5, ZMAT4, RBFOX1, RDH5 and so on (Supplementary Fig. 4). Our data, in line with previous findings, substantiate the assumption of heterogeneity in the molecular mechanisms involved in refractive error and myopia.

Among the novel loci, GABRR1 on chromosome 6q15 (53 kb), encoding GABAC receptor subunit ρ1, is an interesting functional candidate suggestive of a role in myopia development. Modulation of synaptic plasticity via GABA-mediated inhibition would be well placed to alter the ‘gain’ of the visually guided feedback system controlling refractive development33. The lead SNP rs13215566 in GABRR1, together with seven SNPs within the LD block (r2≥0.8), are intronic, potentially affecting regulatory motifs (such as zfp128 and gcm1) that may influence transcriptional regulation (Supplementary Table 10). The variant rs13215029, in LD (r2=1) with rs13215566, is associated with cis-acting expression of GABRR1 (P=2.3 × 10−4) in skin tissues (Supplementary Table 9)34. A recent pharmacological study provided evidence that retinal dopaminergic and GABAergic neurotransmitters play a substantial role in the modulation of refractive development in form-deprivation myopia35,36,37. Stone et al.35 have reported that antagonists to GABA-A, -B and -C receptors inhibited form-deprivation myopia in chicks, with greatest effect in the equatorial dimension. GABA receptors, expressed in bipolar and ganglion neuron cells, also interact with dopamine pathways in the retina36. A recent proteomics study determined that levels of GABA transporter-1 were significantly reduced in myopic murine retina after atropine treatment, implying that GABA signalling is involved in the anti-myopic effects of atropine37. Altogether, these data suggest that GABAC receptor ρ1 may regulate the development of myopia through functional feedback from RPE to neuron cells in the retina. Further studies are needed to investigate the effect of genetic deletion of GABRR1 on refractive eye development and the role of the GABAergic pathway in myopia development using gene knockout mice. Therefore, our result in humans is in line with animal experiments, supporting the notion that the GABAergic neurotransmitter signalling pathway in the retina could be a potential factor in the progression of myopia.

SNP rs10889855 on chromosome 6 is an intronic variant within the ARID2 gene (AT-rich interactive domain 2) and 500 kb downstream of SNAT1 (solute carrier family 38, member, alias SLC38A1). SNAT1 is a transporter of glutamine, a precursor of GABA38. It is also highly expressed in human retina. In our previous meta-analysis in CREAM8, we identified variants in another glutamate receptor gene GRIA4 (encoding glutamate receptor, ionotropic); altogether, current evidence supports the notion that retinal neurotransmitters GABA and glutamine may be involved in the refractive development.

The strongest association signal for gene and environment interactions was from rs12511037, located 14 kb downstream the AREG gene (amphiregulin). AREG is a ligand of the epidermal growth factor receptor promoting the growth of normal epithelial cells, which is critical for cell differentiation and proliferation such as regrowth of the wounded cornea39. A link has been found between the muscarinic acetylcholine receptors, dominant in myopia progression, and the epidermal growth factor receptor in muscarinic system40,41.

Another novel association, rs16949788 on chromosome 15, derives from a region that spans DIS3L and MAP2K1. MAP2K1 encodes mitogen-activated protein kinase 1, which binds to muscarinic receptors during proliferation42 and inhibits the proliferation of human scleral fibroblasts exposed to all-trans retinoic acid43. All-trans retinoic acid is a modulator of ocular growth, inhibiting the proliferation of human scleral fibroblasts44.

FBN1 (Fibrillin 1), a member of the fibrillin family, encodes a large extracellular matrix glycoprotein. Mutations in FBN1 cause Marfan’s syndrome, a disorder of connective tissue affecting the ocular, skeletal and cardiovascular systems45. As a candidate gene for myopia, attempts to study its association with myopia previously produced inconclusive results46,47, probably owing, in part, to underpowered studies with insufficient sample sizes. Using data from a large multi-ethnic population, our results support the role of FBN1 in myopia development.

The genome-wide significant SNPs from the JMA approach did not exhibit any interactions with education in Europeans, in contrast to the significant interactive effect among Asians. In particular, the G × E interactions at AREG, GABRR1 and PDE10A were only evident in Asian populations. There are a number of possible reasons for the observed differences. First, the variation of LD patterns and joint effects of genetic variants might affect the transferability of G × E signals across populations. Similar LD patterns were seen at GABRR1 and PDE10A regions across populations whereas a long stretch of LD flanking AREG was present only in Asian populations (Supplementary Fig. 5). As the true causal variants transferrable across populations are probably not implicated in our study, the identified novel myopia risk loci provide a much-needed starting point for follow-up and functional downstream analyses. Second, we used education level in adults as a surrogate measure of the underlying risk factors for influencing refractive development. Ideally, near work intensity would be measured prospectively in children before the onset of myopia. Studies included in our analyses do not have additional data on relevant childhood exposures. Thus, the best available surrogate measure of cumulative near work exposure in adult cohorts is educational level. We nevertheless believe that education is a highly reliable proxy for the relevant exposures underlying refractive development and is universally associated with refractive error in our study. Third, the differences of G × E interactions in Asian versus Europeans may reflect quantitative differences in near work intensity during childhood. For example, 6- and 7- year-old children in England and Australia reported less near work activity outside of school (1.0–2.3 h per day)48,49 compared with children in Singapore and China (2.7–3.5 h per day)50,51. A similar trend was observed in older children48,52,53,54,55. We thus speculate that the total exposure to near work activity may be greater in East Asians compared with European-derived populations with the same levels of education; hence, G × E interaction estimates would tend to be inflated in Asian populations compared with European groups. Fourth, other environmental factors such as outdoor activities could also interact with genes. East Asian children tend to have less exposure to outdoor activities compared with their European peers56. However, the majority of adult cohorts did not report time outdoors and thus could not be accounted for in the current study. Finally, the population mean of refractive error is less myopic in Europeans (0.10 D) versus Asians (−0.60 D). Of note, for the previously known myopia loci, the magnitudes of interaction effects were fourfold larger on average in Asians than in Europeans (Supplementary Fig. 2). The impact of G × E interactions may be seen at certain severity levels of myopia.

The risk alleles of rs12511037 in AREG, rs1321556 in GABRR1 and rs12206610 in PDE10A had no or weak influence on myopic shift in the lower education group compared with the higher education group. This suggests that the hereditary predisposition to myopia could be latent for the risk allele carriers, if they are less exposed to the myopiagenic environment associated with high-level education. A lack of strong SNP × near work associations in children might be due to the inadequate statistical power in paediatric cohorts of relatively small sample sizes, or the possibility that environmental risk exposures other than near work might underlie the SNP × education interaction seen in the adult Asian samples.

In summary, we identified nine novel loci associated with refractive error in a large multi-ethnic cohort study by GEWIS approach. Our data provide evidence that specific genetic variants interact with education, to influence refractive development, and further support a role for GABA neurotransmitter signalling in myopia development. These findings provide promising candidate genes for follow-up work and may lead to new genetic targets for therapeutic interventions on myopia.

Methods

Study participants

Thirty-four studies from members of CREAM, comprising 40,036 individuals of European ancestry from 25 studies and 10,315 individuals of Asian ancestry from 9 studies, were made available for this analysis (Table 1 and Supplementary Note 1). Individuals aged <20 years were excluded and so were those who had undergone cataract surgery, laser or other intra-ocular procedures that could alter refraction. Many of these studies were also included in the previous CREAM GWAS on spherical equivalent8. All studies adhered to the tenets of the Declaration of Helsinki and were approved by their local research ethics committees. The exact names of the Institutional Research Board committees can be found under Supplementary Note 2. All participants provided a signed consent form before the start of the study.

Phenotyping and education levels

All participants underwent ophthalmological examinations (Supplementary Table 1). Non-cycloplegic refraction was measured by autorefraction and/or subjective refraction. Spherical equivalent was calculated as the sphere power plus half of the cylinder power for each eye. The mean spherical equivalent of the right and left eyes was used as a quantitative outcome. When data from only one eye was available, the spherical equivalent of that eye was used. For each study, the participants reported the highest level of education achieved or the years of schooling through a self-reported questionnaire, or in an interview.

We dichotomized education for all participants in each study. The higher education group consisted of those who had achieved the highest educational level of A-levels, high school (higher secondary education), vocational training (for example, diploma), university degree or those with ≥12 years spent in formal education (beginning from first grade). Those who had achieved the highest educational level of O-level, middle school (lower secondary education) or those with <12 years of formal education were classified into the lower education group. If both number of formal study years and education levels were available in the cohort, we classified participants based on years of formal education. For the four cohorts of relatively young European participants (BATS, DCCT, RAINE and WESDR; total n=2,349), almost all of them had completed 12 or more years of schooling. We thus chose to categorize individuals with tertiary or university education as the higher education group in these studies. Sensitivity analysis excluding these four cohorts did not appreciably change our meta-analysis results.

Genotyping and imputation

Detailed information on the genotyping platforms and QC procedures for each study is provided in Supplementary Table 2 and Supplementary Note 1. Each study applied stringent QC filters for GWAS. In general, duplicate DNA samples, individuals with low call rate (<95%), gender mismatch or ethnic outliers were excluded. SNPs were excluded if low genotyping call rate (>5% missingness), monomorphic SNPs, with minor allele frequency (MAF) <1% or in Hardy–Weinberg disequilibrium (P<10−6). After QC filtering, the array genotypes of each study were imputed using the 1000 Genomes Project data as reference panels (build 37, phase 1 release, March 2012) with the software Minimac57 or IMPUTE2 (ref. 58). Approximately six million SNPs that passed imputation quality thresholds (MACH: r2>0.5 or IMPUTE info score >0.5) and with MAF ≥5% were eligible for the meta-analysis (Supplementary Table 2).

Statistical models

For each study, a linear regression model for each genotyped or imputed SNP was constructed with the mean spherical equivalent as the outcome. We assumed an additive genetic model where the number of risk alleles is an ordinal variable (0, 1 and 2) for directly genotyped SNPs or a continuous variable of allele dosage probability ranging from 0 to 2 for imputed SNPs. The primary analytic model included SNP, education and SNP × education interaction term, as well as age and sex as covariates. Additional adjustments for the top principal components of genomic marker variations were performed in individual studies when applicable (that is, when there was evidence of population stratification).

We used the following additive genetic model to test for a joint effect of SNP (βSNP) and SNP × education interaction (βSNP × education) on mean spherical equivalent:

where Y is the mean spherical equivalent and education is a dichotomous variable (0=lower education group and 1=higher education group); cov is a set of covariates such as age, sex and first top five principal components when applicable. For family-based studies, the kinship matrix was estimated empirically from the SNP data and included as a random effect in the generalized mixed model59. To test an effect of SNP × education interaction, we assessed βSNP × education from equation (1).

The linear regression analyses in each study were conducted with Quickes or ProbABEL for the unrelated samples and MixABEL for family-based data. The command ‘robust’ was used in the above software to calculate the robust (‘sandwich’, Huber-White) s.e. of βSNP and βSNP × education, and error covariance of β, to correct the potential inflation of false positive rate for the interaction P-value60.

In addition, each study also tested the main effect of education on spherical equivalent by adjusting for age and gender using the linear regression model:

where the definition of each variable is the same as in equation (1). We performed meta-analysis of the education effects on mean spherical equivalent in Europeans, Asians (Singapore Chinese versus others) and combined data using a fixed-effect model with inverse-variance weighting (R package ‘meta’).

GEWIS join meta-analyses

We adopted the JMA approach17,61, to simultaneously test both SNP main effects and SNP × education interactions for spherical equivalent with a fixed-effect model, using SNP and SNP × education regression coefficients (βSNP and βSNP × education, respectively) and a β’s covariance matrix from each study. A Wald’s statistic, following a χ2-distribution with two degrees of freedom, was used to test the joint significance of the βSNP and βSNP × education. The JMA was performed with METAL62, as previously described by Manning et al.61. A Cochran’s Q-test was used to assess heterogeneity of the β-coefficients across studies for the SNP and interaction effects. To test for interaction between the SNP and education, we conducted a secondary meta-analysis of the SNP × education interaction effects for spherical equivalent (βSNP × education, one degree of freedom), with a fixed-effects model using inverse-variance weighting in METAL; this is a traditional meta-analysis to investigate SNP × education interactions per se. Effects and s.e. of the SNP effect on spherical equivalent in the lower education group (βSNP) and higher education group (βSNP+βSNP × education) were derived from the JMA output61. We used the P-value of 5 × 10−8 as a significant threshold for JMA test. For the SNP and SNP × education effects for the identified top loci, the P-value threshold for significance was set at 0.0055=0.05/9 (9 index SNPs underlying analyses).

We performed a meta-regression to explore sources of heterogeneity in our meta-analysis for three loci showing G × E interactions (R package ‘metafor’). Meta-regression included the following study-specific variables as covariates: study sample size, proportion of individuals in the higher education group, average spherical equivalent, education main effects on spherical equivalent (higher education level versus lower), ethnicity (Asian versus European), study design (independent samples versus family-based studies), study year and average age. Meta-regression was also conducted to test the fold changes of the interaction β-coefficients in Asians versus Europeans for the 39 known myopia loci.

The study-specific genomic control inflation factors λgc for the joint test for SNP and interaction terms ranged from 1.009 to 1.125 with an average of 1.019 (Supplementary Table 2), calculated by the ratio of the observed median χ2 divided by the expected median of the 2df χ2-distribution (1.382). Genomic control correction was applied to each individual study63. For studies of small sample sizes (n<500) with λgc >1.05, we further, before starting the meta-analysis, excluded SNPs showing significant joint P-value <1 × 10−5 but neither the SNP nor SNP × education effects supported such an association. Quantile–quantile plots showed only modest inflation of the test statistics in the JMA test (Europeans: λgc=1.081; Asians: λgc=1.053; Combined: λgc=1.092; Supplementary Fig. 1), similar to previous GEWIS studies with comparable sample sizes18,19. We excluded a small number of markers in the meta-analysis with Phet<0.0001. The λgc for the SNP × education interaction term in the individual studies ranged from 1.01 to 1.08, indicating little evidence of test statistic inflation on SNP × education effect for each study.

Annotation of genetic variants and gene expression in humans

The coordinates and variant identifiers are reported on the NCBI B37 (hg19) genome build and annotated using UCSC Genome Browser64. We identified variants within each of the LD blocks (r2≥0.8) in European and Asian populations of the 1000 Genomes Project (100 kb flanking the top SNP at each locus), to apply functional annotations of transcription regulation using HaploReg65 and Encyclopedia of DNA Elements66 data. We also generated funcational association and co-expression network using GeneMANIA67, to determine whether the disease-related genes identified in this study and previous GWAS7,8 are functionally connected.

To assess gene expression in human tissues, we examined the Ocular Tissue Database and the EyeSAGE database31,68. The estimated gene and exome-level abundances are available online. Normalization of gene expression used the Probe Logarithmic Intensity Error method with genomic control-background correction31. Relationships between genotype and cis regulation of gene expression levels (Supplementary Table 9) were assessed using expression quantitative trait locus associations in multiple human tissues from UK samples34, as well as gene expression profiles obtained from GTExPortal database69.

Additional information

How to cite this article: Fan, Q. et al. Meta-analysis of gene–environment-wide association scans accounting for education level identifies additional loci for refractive error. Nat. Commun. 7:11008 doi: 10.1038/ncomms11008 (2016).

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Acknowledgements

We gratefully thank all the participants or volunteers who participated in the studies. A full list of funding information and acknowledgements by study can be found in the Supplementary Note 2.

Author information

Author notes

    • Qiao Fan
    • , Virginie J. M. Verhoeven
    • , Robert Wojciechowski
    • , Veluchamy A. Barathi
    •  & Pirro G. Hysi

    These authors contributed equally to this work.

    • Ching-Yu Cheng
    • , Christopher J. Hammond
    • , Caroline C. W. Klaver
    •  & Seang-Mei Saw

    These authors jointly supervised this work.

Affiliations

  1. Singapore Eye Research Institute, Singapore National Eye Centre, Singapore 169856, Singapore

    • Qiao Fan
    • , Veluchamy A. Barathi
    • , M. Kamran Ikram
    • , Charumathi Sabanayagam
    • , Preeti Gupta
    • , Vincent Tan
    • , Lei Zhou
    • , Candice E. H. Ho
    • , Roger W. Beuerman
    • , Rosalynn Siantar
    • , Eranga Vithana
    • , Chiea-Chuen Khor
    • , Tin Aung
    • , Tien-Yin Wong
    • , Ching-Yu Cheng
    •  & Seang-Mei Saw
  2. Duke-NUS Medical School, Singapore 169857, Singapore

    • Qiao Fan
    • , Veluchamy A. Barathi
    • , M. Kamran Ikram
    • , Charumathi Sabanayagam
    • , Roger W. Beuerman
    • , E-Shyong Tai
    • , Eranga Vithana
    • , Tien-Yin Wong
    • , Ching-Yu Cheng
    •  & Seang-Mei Saw
  3. Department of Ophthalmology, Erasmus Medical Center, 3000 CA Rotterdam, The Netherlands

    • Virginie J. M. Verhoeven
    • , Johannes R. Vingerling
    • , Caroline C. W. Klaver
    • , Jan Willem L. Tideman
    •  & Milly Tedja
  4. Department of Epidemiology, Erasmus Medical Center, 3000 CA Rotterdam, The Netherlands

    • Virginie J. M. Verhoeven
    • , Elisabeth M. van Leeuwen
    • , Najaf Amin
    • , André G. Uitterlinden
    • , Fernando Rivadeneira
    • , Albert Hofman
    • , Cornelia M. van Duijn
    • , Caroline C. W. Klaver
    • , Jan Willem L. Tideman
    •  & Milly Tedja
  5. Computational and Statistical Genomics Branch, National Human Genome Research Institute, National Institutes of Health, Baltimore, Maryland 21224, USA

    • Robert Wojciechowski
    • , Claire L. Simpson
    • , Tae-Hwi Schwantes-An
    •  & Joan E. Bailey Wilson
  6. Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland 20205, USA

    • Robert Wojciechowski
  7. Department of Ophthalmology, National University Health Systems, National University of Singapore Singapore 119228, Singapore

    • Veluchamy A. Barathi
    • , Wan’e Lim
    • , Roger W. Beuerman
    • , Eranga Vithana
    • , Tin Aung
    • , Tien-Yin Wong
    • , Ching-Yu Cheng
    •  & Seang-Mei Saw
  8. Department of Twin Research and Genetic Epidemiology, King’s College London School of Medicine, London SE1 7EH, UK

    • Pirro G. Hysi
    • , Cristina Venturini
    • , Katie M. Williams
    •  & Christopher J. Hammond
  9. School of Optometry and Vision Sciences, Cardiff University, Cardiff CF24 4HQ, UK

    • Jeremy A. Guggenheim
  10. Department of Ophthalmology, University Medical Center Mainz, 55131 Mainz, Germany

    • René Höhn
    • , Alireza Mirshahi
    •  & Norbert Pfeiffer
  11. Department of Ophthalmology, Inselspital, University Hospital Bern, CH-3010 Bern, Switzerland

    • René Höhn
  12. Medical Research Council Human Genetics Unit, Institute of Genetics and Molecular Medicine, University of Edinburgh, Edinburgh EH4 2XU, Scotland

    • Veronique Vitart
    • , Jenifer E. Huffman
    • , Caroline Hayward
    •  & Alan F. Wright
  13. Department of Public Health and Primary Care, Institute of Public Health, University of Cambridge School of Clinical Medicine, Cambridge CB2 0SR, UK

    • Anthony P. Khawaja
    • , Robert N. Luben
    •  & Kay-Tee Khaw
  14. Department of Ophthalmology and Visual Sciences, Kyoto University Graduate School of Medicine, Kyoto 6068507, Japan

    • Kenji Yamashiro
    • , Masahiro Miyake
    •  & Nagahisa Yoshimura
  15. Program in Genetics and Genome Biology, The Hospital for Sick Children and Institute for Medical Sciences, University of Toronto, Toronto Ontario, Canada M5G 1X8

    • S Mohsen Hosseini
    • , Hoi-Suen Wong
    •  & Andrew D. Paterson
  16. Department of Clinical Chemistry, Fimlab Laboratories and School of Medicine, University of Tampere, Tampere 33520, Finland

    • Terho Lehtimäki
    •  & Ilkka Seppälä
  17. Statistical Genetics Laboratory, QIMR Berghofer Medical Research Institute, Herston, Brisbane, Queensland 4029, Australia

    • Yi Lu
    • , Puya Gharahkhani
    •  & Stuart MacGregor
  18. Estonian Genome Center, University of Tartu, Tartu 51010, Estonia

    • Toomas Haller
    • , Evelin Mihailov
    •  & Andres Metspalu
  19. Centre for Eye Research Australia (CERA), Royal Victorian Eye and Ear Hospital, University of Melbourne, Melbourne, Victoria 3002, Australia

    • Jing Xie
    • , Alex W. Hewitt
    • , Paul N. Baird
    • , Maria Schache
    •  & Seyhan Yazar
  20. Université de Bordeaux, ISPED (Institut de Santé Publique d'Épidémiologie et de Développement), Bordeaux 33000, France

    • Cécile Delcourt
    •  & Audrey Cougnard-Gregoire
  21. INSERM, U1219-Bordeaux Population Health Research Center, Bordeaux 33000, France

    • Cécile Delcourt
    •  & Audrey Cougnard-Gregoire
  22. Institute of Population Genetics, National Research Council, Sassari 07100, Italy

    • Mario Pirastu
    • , Laura Portas
    • , Simona Vaccargiu
    •  & Maurizio Fossarello
  23. Department of Public Health, University of Helsinki, Helsinki 00014, Finland

    • Juho Wedenoja
  24. Department of Ophthalmology, University of Helsinki and Helsinki University Hospital, Helsinki 00014, Finland

    • Juho Wedenoja
  25. UCL Institute of Ophthalmology, London SE1 7EH, UK

    • Cristina Venturini
  26. Menzies Research Institute Tasmania, University of Tasmania, Hobart, Tasmania 7000, Australia

    • Alex W. Hewitt
    •  & David A. Mackey
  27. Department of Statistical Science, School of Mathematics and Computational Science, Sun Yat-Sen University, Guangzhou 510275, China

    • Xiaobo Guo
  28. Institute of Medical Biostatistics, Epidemiology and Informatics, University Medical Center Mainz, 55131 Mainz, Germany

    • Johanna Mazur
    •  & Maria Blettner
  29. Department of Ophthalmology, King’s College London, London SE1 7EH, UK

    • Katie M. Williams
    •  & Christopher J. Hammond
  30. Faculty of Medicine, University of Split, Split 21000, Croatia

    • Ozren Polasek
  31. Usher Institute for Population Health Sciences and Informatics, University of Edinburgh, Teviot Place, Edinburgh EH8 9AG, Scotland

    • Harry Campbell
    • , Igor Rudan
    • , James F. Wilson
    •  & Peter K. Joshi
  32. Department of Ophthalmology, Sisters of Mercy University Hospital, Zagreb 10000, Croatia

    • Zoran Vatavuk
  33. MRC Integrative Epidemiology Unit (IEU), University of Bristol, Bristol BS8 2BN, UK

    • George McMahon
    • , Beate St Pourcain
    •  & David M. Evans
  34. School of Social and Community Medicine, University of Bristol, Bristol BS8 2BN, UK

    • George McMahon
    • , Beate St Pourcain
    • , David M. Evans
    •  & Cathy Williams
  35. Max Planck Institute for Psycholinguistics, Wundtlaan 1, 6525 XD Nijmegen, The Netherlands

    • Beate St Pourcain
  36. University of Queensland Diamantina Institute, Translational Research Institute, Brisbane, Queensland 4102, Australia

    • David M. Evans
  37. Department of Genetics, Genomics and Informatics, University of Tennessee Health Science Center, Memphis, Tennessee 38163, USA

    • Claire L. Simpson
  38. Department of Epidemiology and Biostatistics, Case Western Reserve University, Cleveland, Ohio 44106, USA

    • Robert P. Igo
  39. Dardenne Eye Hospital, Bonn-Bad Godesberg, 53177 Bonn, Germany

    • Alireza Mirshahi
  40. Inserm, U1167, Lille 59000, France

    • Céline Bellenguez
  41. Univ. Lille, U1167, Lille 59000, France

    • Céline Bellenguez
  42. Université Lille 2, Lille 59000, France

    • Céline Bellenguez
  43. Research Centre of Applied and Preventive Medicine, University of Turku, Turku 20520, Finland

    • Olli Raitakari
  44. Department of Clinical Physiology and Nuclear Medicine, Turku University Hospital, Turku 20520, Finland

    • Olli Raitakari
  45. Department of Clinical Physiology, Tampere University Hospital and School of Medicine, University of Tampere, Tampere 33520, Finland

    • Mika Kähönen
  46. Clinic for General and Interventional Cardiology, University Heart Center Hamburg, 20246 Hamburg, Germany

    • Tanja Zeller
  47. Institute of Human Genetics, Helmholtz Zentrum München, 85764 Neuherberg, Germany

    • Thomas Meitinger
  48. Institute of Human Genetics, Klinikum rechts der Isar, Technische Universität München, 81675 Munich, Germany

    • Thomas Meitinger
    •  & Konrad Oexle
  49. Institute of Genetic Epidemiology, Helmholtz Zentrum München—German Research Center for Environmental Health, 85764 Neuherberg, Germany

    • Janina S. Ried
    •  & Christian Gieger
  50. Department of Internal Medicine, Erasmus Medical Center, 3000 CA Rotterdam, The Netherlands

    • André G. Uitterlinden
    •  & Fernando Rivadeneira
  51. Netherlands Consortium for Healthy Ageing, Netherlands Genomics Initiative, 2518 AD Hague, The Netherlands

    • André G. Uitterlinden
    • , Fernando Rivadeneira
    •  & Albert Hofman
  52. Beijing Institute of Ophthalmology, Beijing Tongren Hospital, Capital Medical University, Beijing 100044, China

    • Ya Xing Wang
    • , Jost B. Jonas
    •  & Liang Xu
  53. Saw Swee Hock School of Public Health, National University of Singapore and National University Health Systems, Singapore 117549, Singapore

    • Xu Wang
    • , Eileen Tai-Hui Boh
    • , E-Shyong Tai
    • , Chiea-Chuen Khor
    • , Yik-Ying Teo
    • , Tien-Yin Wong
    •  & Seang-Mei Saw
  54. National Healthcare Group Eye Institute, Tan Tock Seng Hospital, Singapore 308433, Singapore

    • Rosalynn Siantar
  55. Department of Medicine, National University of Singapore, Singapore 119228, Singapore

    • E-Shyong Tai
  56. Division of Human Genetics, Genome Institute of Singapore, Singapore 138672, Singapore

    • Chiea-Chuen Khor
    •  & Yik-Ying Teo
  57. Division of Genetics and Epidemiology, UCL Institute of Ophthalmology, London EC1V 9EL, UK

    • Paul J. Foster
  58. NIHR Biomedical Research Centre, Moorfields Eye Hospital NHS Foundation Trust and UCL Institute of Ophthalmology, London EC1V 2PD, UK

    • Paul J. Foster
  59. Department of Ophthalmology and Visual Sciences, University of Wisconsin School of Medicine and Public Health, Madison, Wisconsin 53726, USA

    • Barbara E. K. Klein
    •  & Ronald Klein
  60. Department of Ophthalmology, Centre for Vision Research, Westmead Institute for Medical Research, University of Sydney, Sydney, New South Wales 2145, Australia

    • Paul Mitchell
    •  & Jie Jin Wang
  61. Department of Ophthalmology and Visual Sciences, School of Medicine and Public Health, University of Wisconsin, Madison, Wisconsin 53705, USA

    • Terri L. Young
  62. State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-Sen University, Guangzhou 510060, China

    • Mingguang He
  63. Department of Ophthalmology, Central Hospital of Central Finland, Jyväskylä 40620, Finland

    • Olavi Pärssinen
  64. Gerontology Research Center and Department of Health Sciences, University of Jyväskylä, Jyväskylä 40014, Finland

    • Olavi Pärssinen
  65. Medical Faculty Mannheim, Department of Ophthalmology, Ruprecht-Karls-University Heidelberg, 69115 Mannheim, Germany

    • Jost B. Jonas
  66. Department of Statistics and Applied Probability, National University of Singapore, Singapore 117546, Singapore

    • Yik-Ying Teo
  67. Centre for Ophthalmology and Visual Science, Lions Eye Institute, University of Western Australia, Perth, Western Australia 6009, Australia

    • David A. Mackey
    •  & Seyhan Yazar
  68. Department of Ophthalmology, University of Pennsylvania, Philadelphia, Pennsylvania 19104, USA

    • Dwight Stambolian
  69. Medical Research Council Centre of Epidemiology for Child Health, Institute of Child Health, University College London, London WC1E 6BT, UK

    • Jugnoo S. Rahi
  70. Institute of Ophthalmology, Moorfields Eye Hospital, London EC1V 2PD, UK

    • Jugnoo S. Rahi
  71. Ulverscroft Vision Research Group, University College London, London WC1E 6BT, UK

    • Jugnoo S. Rahi
  72. Université de Bordeaux, 33400 Talence, France

    • Jean-François Korobelnik
  73. 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, 33076 Bordeaux, France

    • Jean-François Korobelnik
  74. MRC Integrative Epidemiology Unit (IEU), The University of Bristol, Bristol BS8 2BN, UK

    • John P. Kemp
    • , Nicholas J. Timpson
    •  & George Davey Smith
  75. Department of Ophthalmology, Flinders University, Adelaide, South Australia 5001, Australia

    • Jamie E. Craig
    • , Kathryn P. Burdon
    •  & Rhys D. Fogarty
  76. Department of Epidemiology and Biostatistics, CaseWestern Reserve University, Cleveland, Ohio 44106, USA

    • Sudha K. Iyengar
    • , Sarayut Janmahasatian
    • , Jeremy R. Fondran
    •  & Jonathan H. Lass
  77. Department of Ophthalmology and Visual Sciences, Case Western Reserve University and University Hospitals Eye Institute, Cleveland, Ohio 44106, USA

    • Sudha K. Iyengar
    •  & Jonathan H. Lass
  78. Department of Genetics, Case Western Reserve University, Cleveland, Ohio 44106, USA

    • Sudha K. Iyengar
  79. National Eye Institute, National Institutes of Health, Bethesda, Maryland 20892, USA

    • Emily Chew
  80. Genetic Epidemiology Laboratory, QIMR Berghofer Medical Research Institute, Herston, Brisbane, Queensland 4029, Australia

    • Nicholas G. Martin
  81. Suraj Eye Institute, Nagpur, Maharashtra 440001, India

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    •  & Songhomitra Panda-Jonas
  82. Department of Pediatric Ophthalmology, Duke Eye Center For Human Genetics, Durham, North Carolina 27710, USA

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  83. MRC Epidemiology Unit, Institute of Metabolic Sciences, University of Cambridge, Cambridge CB2 1TN, UK

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  84. MRC Epidemiology Unit, Institute of Metabolic Science, Addenbrooke's Hospital, Cambridge CB2 0QQ, UK

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  85. Gerontology Research Center, University of Jyväskylä, Jyväskylä Finland

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  86. Department of Public Health, University of Helsinki, Helsinki 00014, Finland

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  87. Institute for Molecular Medicine, University of Helsinki, Helsinki 00014, Finland

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  88. Department of Mental Health and Alcohol Abuse Services, National Institute for Health and Welfare, Helsinki 00271, Finland

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  89. Department of Ophthalmology and Visual Sciences, Hong Kong Eye Hospital, The Chinese University of Hong Kong, Kowloon, Hong Kong

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    • Xiaohui Li
  96. Department of Health Technology and Informatics, The Hong Kong Polytechnic University, Hong Kong, Hong Kong

    • Shea Ping Yip
    •  & Maurice K.H. Yap
  97. Institute of Molecular Genetics, National Research Council, Pavia 27100, Italy

    • Ginevra Biino
  98. Princess Alexandra Eye Pavilion, Edinburgh EH3 9HA, UK

    • Brian Fleck
  99. Department of Ophthalmology and Visual Sciences, Moran Eye Center, University of Utah, Salt Lake City, Utah 84132, USA

    • Margaret M. Deangelis
  100. Department of Ophthalmology and Visual Sciences, John Moran Eye Center, University of Utah, Salt Lake City, Utah 84132, USA

    • Margaux Morrison
  101. Departments of Medicine (Biomedical Genetics), Ophthalmology, Neurology, Epidemiology and Biostatistics, Boston University Schools of Medicine and Public Health, Boston, Massachusetts 02118, USA

    • Lindsay Farrer
  102. School of ophthalmology and optometry, Wenzhou Medical University, Wenzhou 325035, China

    • Xiangtian Zhou
    •  & Wei Chen
  103. Department of Ophthalmology, Yokohama City University School of Medicine, Yokohama, Kanagawa 236-0027, Japan

    • Nobuhisa Mizuki
    •  & Akira Meguro
  104. Department of Clinical Chemistry, Fimlab Laboratories and School of Medicine, University of Tampere, Tampere 33014, Finland.

    • Kari Matti Mäkelä

Consortia

  1. Consortium for Refractive Error and Myopia (CREAM)

    A full list of consortium members appears at the end of the paper.

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Contributions

Q.F., V.J.M.V., R.W., C.-Y.C, C.J.H., C.C.W.K. and S.-M.S. conceived the project. Q.F., V.J.M.V., R.W., V.A.B., P.G.H., J.A.G., R.H., V.V., A.P.K., S.M.H., T.L., Y.L., T.H., J.X., C.D.,L.P., J.W.,P.G., C.V., M.M., A.W.H., X.G., J.M., R.P.I., C.L.S., C.B., A.D.P., J.E.B.-W., Y.-Y.T., X.W., X.W., E.T.-H.B., C.E.H.H. and W.L. performed analyses. J.E.H., K.M.W., K.Y., O.P., H.C., I.R., Z.V., J.F.W., P.K.J, G.M., B.S.P., D.M.E., T.-H.S.-A., A.M., A.C.-G., M.B., O.R., M.K., I.S., T.Z., T.M., J.S.R., C.G., M.P., E.M.v.L., N.A., A.G.U., F.R., A.H., J.R.V., Y.X.W., M.K.I., C.S., P.G., V.T., L.Z., R.W.B., R.S., E.-S.T., E.V., E.M., C.-C.K., C.H., R.N.L., P.J.F., B.E.K.K., R.K., H.-S.W., P.M., A.M., T.A., T.L.Y., M.H., O.P., C.M.v.D., J.J.W., C.W., J.B.J.,Y.-Y.T., D.A.M., K.O., N.Y., N.P., T.-Y.W, P.N.B., D.S., C.-Y.C. and C.J.H. were responsible for collecting clinic data and performing genotyping in each study. Q.F., V.J.M.V., R.W., C.C.W.K. and S.-M.S. drafted the paper. P.G.H., V.A.B., J.A.G., V.V., R.H., A.P.K., A.D.P., S.M.H., T.Y., J.J.W., T.-Y.W., P.N.B., C.J.H. and C.-Y.C. critically reviewed the manuscript.

Competing interests

The authors declare no competing financial interests.

Corresponding authors

Correspondence to Caroline C. W. Klaver or Seang-Mei Saw.

Supplementary information

PDF files

  1. 1.

    Supplementary Information

    Supplementary Figures 1-5, Supplementary Tables 1-10, Supplementary Notes 1-2 and Supplementary References

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