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Refractive error is the most common eye disorder worldwide
and is a prominent cause of blindness. Myopia affects over
30% of Western populations and up to 80% of Asians.
The CREAM consortium conducted genome-wide meta-analyses,
including 37,382 individuals from 27 studies of European
ancestry and 8,376 from 5 Asian cohorts. We identified ?6 new
loci for refractive error in individuals of European ancestry,
of which 8 were shared with Asians. Combined analysis
identified 8 additional associated loci. The new loci include
candidate genes with functions in neurotransmission (GRIA4),
ion transport (KCNQ5), retinoic acid metabolism (RDH5),
extracellular matrix remodeling (LAMA2 and BMP2) and eye
development (SIX6 and PRSS56). We also confirmed previously
reported associations with GJD2 and RASGRF1. Risk score
analysis using associated SNPs showed a tenfold increased risk
of myopia for individuals carrying the highest genetic load.
Our results, based on a large meta-analysis across independent
multiancestry studies, considerably advance understanding of
the mechanisms involved in refractive error and myopia.
Refractive error is the leading cause of visual impairment in the world1.
Myopia, or nearsightedness, in particular is associated with structural
changes of the eye, increasing the risk of severe complications, such as
macular degeneration, retinal detachment and glaucoma. The preva-
lence of myopia has been rising considerably
over the past few decades2, and it is estimated
that 2.5 billion people will be affected by myo-
pia within a decade3. Although several genetic
loci influencing refractive error have been
identified4–10, their contribution to pheno-
typic variance is small, and many more loci are
expected to explain its genetic architecture.
Here, the Consortium for Refractive Error
and Myopia (CREAM) presents results from
the largest international genome-wide meta-
analysis on refractive error, with data from
32 studies from Europe, the United States,
Australia and Asia. The meta-analysis was
performed in 3 stages. In the first stage, we
investigated the genome-wide association
study (GWAS) results of 37,382 individuals
from 27 populations of European ancestry (Supplementary Table 1
and Supplementary Note) using spherical equivalent as a continuous
outcome. In the second stage, we aimed to test the cross-ancestry trans-
ferability of the statistically significant associations from the first stage
in 8,376 individuals from 5 Asian cohorts (Supplementary Table 1 and
Supplementary Note). In the third stage, we performed a GWAS meta-
analysis on the combined populations (total n = 45,758). Subsequently,
we examined the influence of associated alleles on the risk of myopia in
a genetic risk score analysis, and, lastly, we evaluated gene expression
in ocular tissues and explored potential mechanisms by which newly
found loci might exert their effects on refractive development.
In stage 1, we analyzed ~2.5 million autosomal SNPs for which
data were obtained through whole-genome imputation of genotypes
to HapMap 2. The inflation factors (λGC) of the test statistics in indi-
vidual studies contributing to the meta-analysis ranged between 0.992
and 1.050, indicating excellent within-study control of population
substructure (Supplementary Table 2). Overall λ was 1.09, consistent
with a polygenic inheritance model for refractive error (quantile-
quantile plot; Supplementary Fig. 1). We did not perform a correc-
tion for λ, as it has been shown that, under polygenic inheritance,
substantial genomic inflation can be expected, even in the absence of
population structure and technical artifacts11. We identified 309 SNPs
that exceeded the conventional genome-wide significance threshold
of P = 5.0 × 10−8 in the European ancestry sample. These SNPs were
Genome-wide meta-analyses of multiancestry cohorts
identify multiple new susceptibility loci for refractive
error and myopia
A full list of authors and affiliations appears at the end of the paper.
Received 3 October 2012; accepted 16 January 2013; published online 10 February 2013; doi:10.1038/ng.2554
1011 12 13 1415 16 17 1819202122
Figure 1 Manhattan plot of the GWAS meta-analysis for refractive error in the combined analysis
(n = 45,758). The plot shows −log10-transformed P values for all SNPs. The upper horizontal line
represents the genome-wide significance threshold of P < 5.0 × 10−8; the lower line indicates P value
of 1 × 10−5. Previously reported genes are shown in gray. The RBFOX1 gene is also known as A2BP1.
© 2013 Nature America, Inc. All rights reserved.
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clustered in 18 distinct genomic regions across 14 chromosomes
(Fig. 1 and Table 1). In stage 2, we investigated the 18 best-associated
SNPs in the Asian population: 10 showed evidence of association
(Table 1). The most significant association in both ancestry groups
was at a previously identified locus on chromosome 15q14 in the
proximity of the GJD2 gene (encoding the connexin 36 gap-junction
protein; rs524952; Pcombined = 1.44 × 10−15)4,12. The locus near the
RASGRF1 gene (encoding Ras protein–specific guanine nucleotide–
releasing factor 1) was also replicated in the meta-analysis (rs4778879;
Pcombined = 4.25 × 10−11)9. The remaining 16 loci associated at genome-
wide significance had not previously been reported in association
with refractive error. Those loci that did not show significant asso-
ciation in the smaller sized Asian population mostly had a similar
effect size and direction of effect as in the European ancestry sample.
In stage 3, we identified eight additional loci with associations
that exceeded genome-wide significance in the combined analysis
(Table 2). Regional and forest plots of the associated loci are provided
in Supplementary Figures 2 and 3, respectively.
Genotype distributions of the risk alleles were evaluated in
Rotterdam Studies 1–3 (n = 9,307). The clinical usefulness for the
prediction of risk of myopia was evaluated by a weighted genetic risk
score analysis based on the aggregate of effects (β regression coeffi-
cients) of individual SNPs derived from the meta-analysis, using the
middle risk category as a reference. Risk scores ranged from a mean
risk score of 1.88 (95% confidence interval (CI) = 1.86–1.89) in the
lowest risk score category to 3.63 (95% CI = 3.61–3.65) in the highest
risk score category. Having the lowest or the highest genetic risk score
was associated with an odds ratio (OR) of 0.38 (95% CI = 0.18–0.77)
and an OR of 10.97 (95% CI = 3.73–31.25) of myopia, respectively
(Fig. 2). The predictive value (area under the receiver operating
characteristic curve, AUC) of myopia versus hyperopia was 0.67
(95% CI = 0.65–0.69), a relatively high value for genetic factors in a
complex trait13,14. The genetic variants explained 3.4% of the pheno-
typic variation in refractive error in the Rotterdam Study.
We examined the expression of genes harboring a genetic asso-
ciation signal by measuring the levels of RNA in various eye tissues
and found most of these genes expressed in the eye (Supplementary
Table 3). Expression data for the PRSS56, LOC100506035 and SHISA6
genes were not available; all other genes were expressed in the
retina. Subsequently, we assessed the areas with associated SNPs
for acetylation at histone H3 lysine 27 (H3K27ac) modifications15
and HaploReg16 annotations for marks of active regulatory elements
(Supplementary Fig. 4 and Supplementary Table 4). We found that
many associated loci contained these elements, and alteration of regu-
latory function is therefore a potential mechanism.
The widely accepted model for myopia development is that eye
growth is triggered by a visually evoked signaling cascade, which
originates from the sensory retina, traverses the retinal pigment
epithelium (RPE) and choroid and terminates in the sclera, where
active extracellular matrix (ECM) remodeling results in a relative
elongation of the eye17. Many of the genes in or near the identified
loci can be linked to biological processes that drive this cascade.
Neurotransmission in the retina is a necessary mechanism for eye
growth regulation; the most significantly associated gene GJD2 has
a role in this process. This gene forms a gap junction between neu-
ronal cells in the retina, enabling the intercellular exchange of small
molecules and ions. The other previously reported gene RASGRF1 is a
nuclear exchange factor that promotes the exchange of GTP for GDP
on Ras family GTPases and is involved in the synaptic transmission
of photoreceptor responses18,19. Both GJD2 and RASGRF1 knock-
out mice show retinal photoreception defects18,20. One of the newly
table 1 Genome-wide significant associations with refractive error in the european ancestry population with results in the Asian population and combined analysis
Stage 1 (n = 37,382)
Stage 2 (n = 8,376)
Combined (n = 45,758)
6.29 × 10–11
5.00 × 10–3
3.05 × 10–12
2.38 × 10–9
1.60 × 10–2
7.86 × 10–11
1.28 × 10–10
6.09 × 10–1
5.15 × 10–11
4.36 × 10–8
2.29 × 10–1
2.14 × 10–8
1.19 × 10–8
5.32 × 10–1
1.25 × 10–6
1.13 × 10–11
1.92 × 10–2
1.79 × 10–12
1.52 × 10–8
2.81 × 10–1
1.82 × 10–8
9.22 × 10–10
4.00 × 10–3
3.99 × 10–12
3.04 × 10–9
4.23 × 10–2
3.69 × 10–10
1.02 × 10–8
2.72 × 10–1
4.15 × 10–8
1.25 × 10–11
2.84 × 10–1
1.03 × 10–11
7.23 × 10–12
7.34 × 10–3
2.06 × 10–13
3.45 × 10–8
2.70 × 10–2
5.92 × 10–9
4.28 × 10–10
3.00 × 10–2
4.44 × 10–12
5.90 × 10–9
5.84 × 10–1
2.11 × 10–8
4.24 × 10–8
4.63 × 10–1
5.10 × 10–8
1.11 × 10–13
1.00 × 10–3
1.44 × 10–15
1.27 × 10–9
1.50 × 10–2
4.25 × 10–11
3.04 × 10–10
2.49 × 10–1
9.66 × 10–11
3.21 × 10–9
7.64 × 10–1
2.79 × 10–8
4.39 × 10–8
8.27 × 10–1
1.85 × 10–7
Summary of SNPs that showed genome-wide significant (P < 5 × 10−8) association with spherical equivalent (SE) in subjects of European ancestry (stage 1), with results of replication in Asians (stage 2) and combined analysis
(stage 3). We tested for heterogeneous effects between the Asian and European ancestry samples, for which P values are shown. Nearest gene, reference NCBI build 37; A1, reference allele; A2, other allele, MAF, average minor allele frequency; β, effect size on spherical
equivalent in diopters based on allele A1.
aPreviously reported genes.
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identified genes, GRIA4 (encoding glutamate receptor, ionotropic,
AMPA 4; rs11601239; Pcombined = 5.92 × 10−9), also has a potential
function in this pathway. This gene encodes a glutamate-gated ion
channel that mediates fast synaptic excitatory neurotransmission21, is
present in various retinal cells22 and has been shown to be critical for
light signaling in the retina23 and emmetropization24. Another gene
involved in synaptic transmission is RBFOX1 (encoding RNA-binding
protein, fox-1 homolog; also known as A2BP1; rs17648524; Pcombined =
5.64 × 10−10), encoding an RNA-binding splicing regulator that
modulates membrane excitability25.
We identified for the first time a number of candidate genes
involved in ion transport, channel activity and the maintenance of
membrane potential. KCNQ5 (encoding a member of the potassium
voltage-gated channel KQT-like subfamily; rs7744813; Pcombined =
4.18 × 10−9), participates in the transport of potassium ions from
the retina to the choroid and may contribute to voltage-gated potas-
sium ion channels in the photoreceptors and retinal neurons associ-
ated with myopia26,27. CD55 (encoding a decay-accelerating factor
for complement; rs1652333; Pcombined = 3.05 × 10−12) is known to
elevate cytosolic calcium ion concentration. Other ion channel genes
that were associated include CACNA1D (encoding a voltage-sensitive
calcium channel regulator; rs14165; Pcombined = 2.14 × 10–8), KCNJ2
(encoding a regulator of potassium ion transport; rs4793501; Pcombined =
2.79 × 10–8), CHRNG (encoding a nicotinic cholinergic receptor;
rs1881492; Pcombined = 2.15 × 10–11) and MYO1D (encoding a putative
binder of calmodulin; rs17183295; Pcombined = 9.66 × 10–11), which
mediates calcium ion sensitivity to KCNQ5 ion channels.
Retinoic acid is synthesized in the retina, is highly expressed in
the choroid and has been implicated in eye growth in experimental
myopia models28–30. RDH5 (encoding retinol dehydrogenase 5;
rs3138144; Pcombined = 4.44 × 10–12), a new refractive error suscep-
tibility gene is involved in the recycling of 11-cis-retinal in the
visual cycle31. Mutations in RDH5 cause congenital stationary
night blindness (MIM 136880), a disease associated with myopia.
Other genes involved in retinoic acid metabolism are RORB
(encoding RAR-related orphan receptor; rs7042950; Pcombined =
4.15 × 10–8) and CYP26A1 (encoding a member of the cytochrome
P450 superfamily; rs10882165; Pcombined = 1.03 × 10–11), genes that
showed significant associations in the European ancestry studies.
Notably, retinoic acid contributes to ECM remodeling by regulating
ECM remodeling of the sclera is the pathological hallmark of
myopia development. LAMA2 (encoding laminin α2; rs12205363;
Pcombined = 1.79 × 10−12) is the most prominent gene in this respect.
The LAMA2 protein forms a subunit of the heterotrimer laminins,
which are essential components of basement membranes, stabiliz-
ing cellular structures and facilitating cell migration32. Two genes
encoding bone morphogenetic proteins (BMP2: rs235770; Pcombined =
1.57 × 10−8 and BMP3: rs1960445; Pstage 1 = 1.19 × 10–8; Pcombined =
1.25 × 10–6) also have a role in the ECM architecture. They are
members of the transforming growth factor (TGF)-β superfamily,
regulate the growth and differentiation of mesenchymal cells and may
orchestrate the organization of other connective tissues than bone,
such as sclera. Notably, BMP2 shows expression in RPE in animal
models of myopia33.
Genes involved in eye development appeared as a separate
entity among the gene functions. SIX6 (encoding SIX homeobox 6;
rs1254319; Pcombined = 1.00 × 10−8) has been linked to anophthalmia
and glaucoma34,35, PRSS56 (encoding protease serine 56, rs1656404;
Pcombined = 7.86 × 10−11) has been linked to microphthalmia36–38,
CHD7 (encoding chromodomain helicase DNA-binding protein 7;
rs4237036; Pcombined = 1.82 × 10–8) has been linked to CHARGE
syndrome, a congenital condition with severe eye structural defects,
and ZIC2 (encoding a member of the ZIC family of C2H2-type zinc-
finger proteins; rs8000973; Pcombined = 5.10 × 10–8) has been linked
to brain development, including visual perception. For the remaining
new associated loci, a mechanism in the pathogenesis of myopia is
not immediately clear. Results from Ingenuity and the Protein Link
Evaluator39 (Supplementary Fig. 5) map the subcellular location of
all associated gene products and show their inter-relationships. Direct
connections between genes were infrequent, suggesting molecular
table 2 Additional genome-wide significant associations from the combined meta-analysis (n = 45,758)
number SNPChromosomePosition Nearest geneA1/A2
SEP value MAF
SEP valueP value
Combined (n = 45,758)Stage 1 (n = 37,382)Stage 2 (n = 8,376) Heterogeneity
0.017 1.09 × 10–8
0.019 4.18 × 10–9
0.021 7.26 × 10–9
0.017 5.47 × 10–9
0.015 1.00 × 10–8
0.019 5.64 × 10–100.36 –0.116 0.022 7.48 × 10–80.14 –0.140 0.058 1.60 × 10–2
0.015 7.29 × 10–110.36 –0.101 0.019 7.51 × 10–80.45 –0.097 0.034 4.00 × 10–3
0.016 1.57 × 10–8
0.39 –0.088 0.017 1.34 × 10–70.33 –0.087 0.050 8.20 × 10–2
0.25 –0.097 0.020 1.37 × 10–60.50 –0.105 0.035 3.06 × 10–3
0.41 0.114 0.021 6.80 × 10–80.33
0.25 –0.125 0.023 6.92 × 10–80.07 –0.136 0.091 1.35 × 10–1
0.27 0.104 0.019 5.46 × 10–80.36
0.32 –0.088 0.017 2.03 × 10–70.34 –0.087 0.036 1.57 × 10–2
0.1120.094 0.046 4.30 × 10–2
0.099 0.080 0.052 1.23 × 10–1
Summary of SNPs that showed genome-wide significant (P < 5 × 10−8) association with spherical equivalent in the combined analysis (stage 3), with results in subjects of European ancestry (stage 1) and Asians
(stage 2). We tested for heterogeneous effects between the two ancestry groups, for which P values are shown. Nearest gene, reference NCBI build 37. The RBFOX1 gene is also known as A2BP1.
Genetic risk score
OR of myopia
OR of myopia
Figure 2 Genetic risk score for myopia. Distribution of subjects from
Rotterdam Study 1–3 (n = 9,307) with myopia (SE ≤ −3 diopters (D)),
emmetropia (SE ≥ −1.5 D and ≤ 1.5 D) and hyperopia (SE ≥ 3 D) as a function
of the genetic risk score. This score is based on the regression coefficients
and allele dosages of the associated SNPs for all 26 loci identified in the
meta-analysis. Mean OR of myopia was calculated per risk category, using the
middle risk score category (risk score of 2.50–2.75) as a reference.
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ADVANCE ONLINE PUBLICATION Nature GeNetics
disease heterogeneity or functional redundancy in the pathobiological
events involved in the development of refractive error and myopia.
In summary, we identified 24 new loci associated with refractive
error through a large-scale meta-analysis of GWAS from interna-
tional multiancestry studies. The substantial overlap in genetic loci
for refractive error between individuals of European ancestry and
Asians provides evidence for shared genetic risk factors between the
populations. The tenfold increased risk of myopia for those carrying
the highest number of risk alleles shows the clinical significance of our
findings. Further elucidation of the mechanisms by which these loci
affect eye growth carries the potential to improve the visual outcome
of this common trait.
URLs. R, http://www.r-project.org/; LocusZoom, http://csg.sph.
umich.edu/locuszoom/; Ingenuity, http://www.ingenuity.com/.
Methods and any associated references are available in the online
version of the paper.
Accession codes. Data on RPE gene expression have been deposited at
the Gene Expression Omnibus (GEO) under accession GSE20191.
Note: Supplementary information is available in the online version of the paper.
We gratefully thank the invaluable contributions of all study participants, their
relatives and staff at the recruitment centers. Complete funding information and
acknowledgments by study can be found in the Supplementary Note.
V.J.M.V., P.G.H., R.W., C.J.H., C.C.W.K., A.W.H., D.A.M., T.L.Y. and C.M.v.D.
performed analyses and drafted the manuscript. C.C.W.K., D.S., C.J.H., J.E.B.-W.,
S.-M.S., C.M.v.D., A.H., D.A.M., S.M., A.D.P., V.V., C.W., P.N.B., T.-Y.W., J.S.R.,
T.L.Y., K.O., O. Pärssinen, S.P.Y., J.A.G., A. Metspalu, M.P., S.K.I. and N.P. jointly
conceived the project and supervised the work. J.E.B.W., S.-M.S., D.A.M., T.L.Y.,
C.J.H., C.C.W.K., D.S., J.E.B.-W., C.M.v.D., R.W., P.G.H., V.J.M.V., K.O., Y.-Y.T., T.-Y.W.,
P.N.B., V.V., N.A., B.A.O., A.H., J.R.V., F.R., A.G.U., N.P., C.M., A. Mirshahi, T.Z.,
B.F., J.F.W., Z.V., O. Polasek, A.F.W., C.H., I.R., S.K.I., E.C., J.H.L., R.P.I., S.J., M.S.,
J.J.W., P.M., I.C., J.S.R., P.M.C., C.E.P., G.W.M., A. Mishra, W.A., F.M., M.P., L.C.K.,
T.D.S., E.Y.-D., A.N., O.R., C.-C.K., T.M., A.D., R.T.O., Y.Z., J.L., R.L., P.C., V.A.B.,
W.-T.T., E.V., T.A., E.-S.T., A. Metspalu, T.H., R.K., B.E.K.K., J.E.C., K.P.B., L.J.C.,
C.P.P., D.W.H.H., S.P.Y., J.W., O. Pärssinen, J.B.J., L.X., H.S.W., S.M.H., A.D.P., M.K.,
T.L., K.-M.M., C.L.S., C.W., N.J.T., D.M.E., B.S.P., J.P.K., G.M., G.H.S.B., M.K.I.,
X.Z., C.-Y.C., A.W.H., S.M., R.H., J.A.G. and Q.F. were responsible for study-
specific data. G.H.S.B., V.J.M.V., Q.F. and J.A.G. were involved in the genetic risk
score analysis. T.L.Y., A.A.B.B., T.G.M.F.G. and F.H. performed the data expression
experiments. A.A.B.B., T.G.M.F.G., A.M. and S.M. were involved in pathway
analyses. J.E.B.-W., S.-M.S., D.A.M., T.L.Y., K.O., T.-Y.W., P.N.B., T.G.M.F.G., S.K.I.,
E.C., J.J.W., A.J.M.H.V., C.-C.K., B.E.K.K., S.P.Y., C.W., N.J.T., G.H.S.B., M.K.I.,
A.W.H. and J.A.G. critically reviewed the manuscript.
comPetIng FInAncIAl InteRests
The authors declare no competing financial interests.
Published online at http://www.nature.com/doifinder/10.1038/ng.2554.
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Virginie J m Verhoeven1,2,68, Pirro g Hysi3,68, Robert wojciechowski4,5,68, Qiao Fan6,68, Jeremy A guggenheim7,68,
René Höhn8,68, stuart macgregor9, Alex w Hewitt10,11, Abhishek nag3, ching-Yu cheng6,12,13,
ekaterina Yonova-doing3, Xin Zhou6, m kamran Ikram6,12,13, gabriëlle H s Buitendijk1,2, george mcmahon14,
John P kemp14, Beate st Pourcain15, claire l simpson4, kari-matti mäkelä16, terho lehtimäki16, mika kähönen17,
Andrew d Paterson18, s mohsen Hosseini18, Hoi suen wong18, liang Xu19, Jost B Jonas20, olavi Pärssinen21,22,23,
Juho wedenoja24, shea Ping Yip25, daniel w H Ho7,26, chi Pui Pang26, li Jia chen27, kathryn P Burdon28,
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chiea-chuen khor6,12,31,32, e-shyong tai6,33,34, tin Aung12,13, eranga Vithana13, wan-ting tay13,
Veluchamy A Barathi12,13,34, consortium for Refractive error and myopia (cReAm)35, Peng chen6, Ruoying li6,
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trial/epidemiology of diabetes Interventions and complications (dcct/edIc) Research group35,
david m evans14, nicholas J timpson14, Annemieke J m H Verkerk38, thomas meitinger39, olli Raitakari40,41,
Felicia Hawthorne42, tim d spector3, lennart c karssen2, mario Pirastu43, Federico murgia43, wei Ang44,
wellcome trust case control consortium 2 (wtccc2)35, Aniket mishra9, grant w montgomery45,
craig e Pennell44, Phillippa m cumberland46,47, Ioana cotlarciuc48, Paul mitchell49, Jie Jin wang10,49,
maria schache10, sarayut Janmahasathian50, Robert P Igo Jr50, Jonathan H lass50,51, emily chew52,
sudha k Iyengar50,51,53, the Fuchs’ genetics multi-center study group35, theo g m F gorgels54, Igor Rudan55,
caroline Hayward56, Alan F wright56, ozren Polasek57, Zoran Vatavuk58, James F wilson55, Brian Fleck59,
tanja Zeller60, Alireza mirshahi8, christian müller60, André g Uitterlinden2,37,61, Fernando Rivadeneira2,38,61,
Johannes R Vingerling1,2, Albert Hofman2,61, Ben A oostra62, najaf Amin2, Arthur A B Bergen54,63,64, Yik-Ying teo6,65,
Jugnoo s Rahi45,47,66, Veronique Vitart56, cathy williams15, Paul n Baird10, tien-Yin wong6,12,13, konrad oexle39,
norbert Pfeiffer8, david A mackey10,11, terri l Young42, cornelia m van duijn2, seang-mei saw6,12,13,34,69,
Joan e Bailey-wilson4,69, dwight stambolian67,69, caroline c klaver1,2,69 & christopher J Hammond3,69
1Department of Ophthalmology, Erasmus Medical Center, Rotterdam, The Netherlands. 2Department of Epidemiology, Erasmus Medical Center, Rotterdam,
The Netherlands. 3Department of Twin Research and Genetic Epidemiology, King’s College London School of Medicine, London, UK. 4Inherited Disease Research
Branch, National Human Genome Research Institute, US National Institutes of Health, Baltimore, Maryland, USA. 5Department of Epidemiology, Johns Hopkins
Bloomberg School of Public Health, Baltimore, Maryland, USA. 6Saw Swee Hock School of Public Health, National University Health Systems, National University of
Singapore, Singapore. 7Centre for Myopia Research, School of Optometry, The Hong Kong Polytechnic University, Hong Kong. 8Department of Ophthalmology, University
Medical Center Mainz, Mainz, Germany. 9Department of Statistical Genetics, Queensland Institute of Medical Research, Herston, Brisbane, Queensland, Australia.
10Centre for Eye Research Australia (CERA), University of Melbourne, Royal Victorian Eye and Ear Hospital, Melbourne, Victoria, Australia. 11Centre for Ophthalmology
and Visual Science, Lions Eye Institute, University of Western Australia, Perth, Western Australia, Australia. 12Department of Ophthalmology, National University Health
Systems, National University of Singapore, Singapore. 13Singapore Eye Research Institute, Singapore National Eye Centre, Singapore. 14Medical Research Council
Centre for Causal Analyses in Translational Epidemiology, School of Social and Community Medicine, University of Bristol, Bristol, UK. 15School of Social and
Community Medicine, University of Bristol, Bristol, UK. 16Department of Clinical Chemistry, Fimlab Laboratories and School of Medicine, University of Tampere,
Tampere, Finland. 17Department of Clinical Physiology, Tampere University Hospital and School of Medicine, University of Tampere, Tampere, Finland. 18Program in
Genetics and Genome Biology, Hospital for Sick Children and University of Toronto, Toronto, Ontario, Canada. 19Beijing Institute of Ophthalmology, Beijing Tongren
Hospital, Capital Medical University, Beijing, China. 20Department of Ophthalmology, Medical Faculty Mannheim, Ruprecht-Karls-University Heidelberg, Mannheim,
Germany. 21Department of Health Sciences, University of Jyväskylä, Jyväskylä, Finland. 22Gerontology Research Center, University of Jyväskylä, Jyväskylä, Finland.
23Department of Ophthalmology, Central Hospital of Central Finland, Jyväskylä, Finland. 24Department of Public Health, Hjelt Institute, University of Helsinki, Helsinki,
Finland. 25Department of Health Technology and Informatics, The Hong Kong Polytechnic University, Hong Kong. 26Department of Ophthalmology and Visual Sciences,
The Chinese University of Hong Kong, Hong Kong Eye Hospital, Kowloon, Hong Kong. 27Department of Ophthalmology and Visual Sciences, The Chinese University of
Hong Kong, Prince of Wales Hospital, Shatin, Hong Kong. 28Department of Ophthalmology, Flinders University, Adelaide, South Australia, Australia. 29Department of
Ophthalmology and Visual Sciences, University of Wisconsin School of Medicine and Public Health, Madison, Wisconsin, USA. 30Estonian Genome Center, University of
Tartu, Tartu, Estonia. 31Department of Pediatrics, National University of Singapore, Singapore. 32Division of Human Genetics, Genome Institute of Singapore, Singapore.
33Department of Medicine, National University of Singapore, Singapore. 34Duke–National University of Singapore Graduate Medical School, Singapore. 35A full list of
members appears in the supplementary Note. 36Institute of Epidemiology I, Helmholtz Zentrum München–German Research Center for Environmental Health,
Neuherberg, Germany. 37Institute of Epidemiology II, Helmholtz Zentrum München–German Research Center for Environmental Health, Neuherberg, Germany.
38Department of Internal Medicine, Erasmus Medical Center, Rotterdam, The Netherlands. 39Institute of Human Genetics, Technical University Munich, Munich,
Germany. 40Research Centre of Applied and Preventive Medicine, University of Turku, Turku, Finland. 41Department of Clinical Physiology and Nuclear Medicine, Turku
University Hospital, Turku, Finland. 42Department of Pediatric Ophthalmology, Duke Eye Center For Human Genetics, Durham, North Carolina, USA. 43Institute of
Population Genetics, National Research Council, Sassari, Italy. 44School of Women’s and Infants’ Health, University of Western Australia, Perth, Western Australia,
Australia. 45Department of Molecular Epidemiology, Queensland Institute of Medical Research, Herston, Brisbane, Queensland, Australia. 46Medical Research Council
Centre of Epidemiology for Child Health, Institute of Child Health, University College London, London, UK. 47Ulverscroft Vision Research Group, University College
London, London, UK. 48Imperial College Cerebrovascular Research Unit (ICCRU), Division of Brain Sciences, Department of Medicine, Imperial College London,
London, UK. 49Department of Ophthalmology, Centre for Vision Research, Westmead Millennium Institute, University of Sydney, Sydney, New South Wales, Australia.
50Department of Epidemiology and Biostatistics, Case Western Reserve University, Cleveland, Ohio, USA. 51Department of Ophthalmology and Visual Sciences, Case
Western Reserve University and University Hospitals Eye Institute, Cleveland, Ohio, USA. 52National Eye Institute, US National Institutes of Health, Bethesda, Maryland,
USA. 53Department of Genetics, Case Western Reserve University, Cleveland, Ohio, USA. 54Department of Clinical and Molecular Ophthalmogenetics, Netherlands
Institute of Neurosciences (NIN; an Institute of the Royal Netherlands Academy of Arts and Sciences (KNAW), Amsterdam, The Netherlands. 55Centre for Population
Health Sciences, University of Edinburgh, Edinburgh, UK. 56Medical Research Council Human Genetics Unit, Institute of Genetics and Molecular Medicine, University
of Edinburgh, Edinburgh, UK. 57Faculty of Medicine, University of Split, Split, Croatia. 58Department of Ophthalmology, Sisters of Mercy University Hospital, Zagreb,
Croatia. 59Princess Alexandra Eye Pavilion, Edinburgh, UK. 60Clinic for General and Interventional Cardiology, University Heart Center Hamburg, Hamburg, Germany.
61Netherlands Consortium for Healthy Ageing, Netherlands Genomics Initiative, The Hague, The Netherlands. 62Department of Clinical Genetics, Erasmus Medical
Center, Rotterdam, The Netherlands. 63Department of Clinical Genetics, Academic Medical Center, Amsterdam, The Netherlands. 64Department of Ophthalmology,
Academic Medical Center, Amsterdam, The Netherlands. 65Department of Statistics and Applied Probability, National University of Singapore, Singapore. 66Institute of
Ophthalmology, Moorfields Eye Hospital, London, UK. 67Department of Ophthalmology, University of Pennsylvania, Philadelphia, Pennsylvania, USA. 68These authors
contributed equally to this work. 69These authors jointly directed this work. Correspondence should be addressed to C.C.W.K. (email@example.com).
© 2013 Nature America, Inc. All rights reserved.
Study design. We performed a meta-analysis on directly genotyped and
imputed SNPs from individuals of European ancestry in 27 studies, with a
total of 37,382 individuals. Subsequently, we evaluated significantly associated
SNPs in 8,376 subjects of Asian origin from 5 different studies and performed
a meta-analysis on all studies combined.
Subjects and phenotyping. All studies participating in this meta-analysis
are part of CREAM. All studies had a population-based design and had a
similar protocol for phenotyping (Supplementary Table 1). Eligible
participants underwent a complete ophthalmological examination, including
a non-dilated measurement of refractive error for both eyes. Exclusion criteria
were all conditions that could alter refraction, such as cataract surgery, laser
refractive procedures, retinal detachment surgery, keratoconus or ocular or
systemic syndromes. Inclusion criteria included age of 25 years and over and
data on refractive error and genotype.
The meta-analysis of stage 1 was based on 27 studies of European ances-
try: 1958 British Birth Cohort, ALSPAC, ANZRAG, AREDS1a1b, AREDS1c,
CROATIA-Korcula, CROATIA-Split, CROATIA-Vis, EGCUT, FECD, TEST/
BATS, FITSA, Framingham, GHS 1, GHS 2, KORA, ORCADES, TwinsUK,
WESDR, YFS, ERF, DCCT, BMES, RS1, RS2, RS3 and OGP Talana. Stage 2
comprised 5 Asian studies: Beijing Eye Study, SCES, SIMES, SINDI and SP2.
Information on general methods, demographics and phenotyping and geno-
typing methods of the study cohorts can be found in Supplementary Table 1
and the Supplementary Note. All studies were performed with the approval
of their local medical ethics committee, and written informed consent was
obtained from all participants in accordance with the Declaration of Helsinki.
Genotyping and imputation. Information on genotyping in each cohort, the
particular platforms used to perform genotyping and the methods of imputa-
tion can be found in more detail in Supplementary Table 5. To produce con-
sistent data sets and enable meta-analysis of studies across different genotyping
platforms, the studies performed genomic imputation on available HapMap
Phase 2 genotypes with MACH40 or IMPUTE41, using the appropriate ancestry
groups as templates.
Each study applied stringent quality control procedures before imputation,
including MAF cutoffs, Hardy-Weinberg equilibrium (P > 1 × 10−7), genotypic
success rate (>95%), mendelian inconsistencies, exclusion of individuals with
more than 5% shared ancestry (exception made for family-based cohorts in
which due adjustment for family relationship was made) and removal of all
individuals whose ancestry as determined through genetic analysis did not
match the prevailing ancestry group of the corresponding cohort. SNPs with
low imputation quality were filtered using metrics specific to the imputation
method and thresholds used in previous GWAS analyses. Hence, imputation
quality criteria varied slightly between studies, and low-confidence imputed
SNPs were omitted in the meta-analysis for individual studies.
Statistical analysis. Spherical equivalent was calculated according to the
standard formula (SE = sphere + 1/2 cylinder), and the mean value from
two eyes was used for analysis. When data from only one eye was available,
the spherical equivalent of this eye was used.
Each cohort performed association analyses in which the spherical equiva-
lent was the dependent variable and genotypes (number of alleles in each of
the HapMap 2 loci) were the independent variables. Analyses in all cases also
adjusted for sex and age at the time of phenotype measurement. In family-
based cohorts, a score test–based association test was used to adjust for within-
family relatedness (Supplementary Note)42,43. Study-specific λ estimates are
shown in Supplementary Table 2.
All study effect estimates were corrected using genomic control and were
oriented to the positive strand of the NCBI Build 36 reference sequence of
the human genome, which was the genomic build on which most available
genotyping platforms were based. Coordinates and further annotations for the
SNPs were converted into Build 37, the most recent version of the available
builds at the time of writing.
Meta-analyses used effect size estimations (β regression coefficients) and
standard errors from individual cohorts’ summary statistics. Random effects
were assumed for all the meta-analyses that were performed using GWAMA44.
We tested for heterogeneous effects between the two ancestry groups using
METAL45 for Linux. For the purpose of these analyses, we defined significance
as equal to or better than the conventional multiple-testing genome-wide
thresholds of association (P < 5.0 × 10−8) for stage 1 and nominally significant
probabilities (P < 0.05) for stage 2. Manhattan, regional and forest plots were
made using R (see URLs) and LocusZoom (see URLs)46.
For the Rotterdam Study 1–3, a weighted genetic risk score per individual
was calculated using the regression coefficients from the GWAS meta-analysis
model for the association of SNPs within the associated 26 loci (Tables 1 and 2;
for each locus, only one SNP was included in the analysis) and the individual
allele dosages per genotype to evaluate the relationships between myopia
(SE ≤ −3 D), emmetropia (–1.5 D ≤ SE ≤ 1.5 D) and hyperopia (SE ≥ 3 D).
The weighted risk scores were categorized, and mean ORs per risk score
category were calculated for subjects with myopia versus hyperopia, using the
middle risk score category as a reference. Subsequently, AUCs were calculated
for myopia versus emmetropia and myopia versus hyperopia. Lastly, the pro-
portion of variance of spherical equivalent explained by the identified SNPs
was calculated. For these analyses, we used SPSS version 20.0.0.
Gene expression data in human eye tissue. Independently designed, collected
and reported human ocular tissue array data from two different sources, as
well as literature reviews, were used to verify evidence of expression of the
RPE, photoreceptors and choroid. Human gene expression data for RPE,
photoreceptors and choroid were obtained essentially as described47, and the
data set has been deposited in NCBI’s Gene Expression Omnibus48 (accession
GSE20191). In short, postmortem eye bulbs (RPE was obtained from six donor
eyes, choroid was obtained from three donor eyes and photoreceptors were
obtained from three donor eyes), provided by the Corneabank Amsterdam,
were rapidly frozen using liquid nitrogen. Donors were between 63 and
78 years old and had no known history of eye pathology. Cryosections were
cut from the macula, and histology was used to confirm a normal histological
appearance. RPE, photoreceptor and choroidal cells were isolated from macular
sections using the Laser Microdissection System (PALM). Total RNA was
isolated, and the mRNA component was amplified, labeled and hybridized to
a 44K microarray (Agilent Technologies)49. At least three to six microarrays
were performed per tissue. Sample isolation, procedures and expression micro-
array analysis were carried out according to MIAMI guidelines. To bring order
in the level of expression, we sorted all the genes represented on the 44K
microarray by increasing expression, and we calculated the corresponding
percentiles (Supplementary Table 3a).
Sclera, cornea and optic nerve. We assessed expression of the associated
genes in sclera, cornea and optic nerve tissue in an additional data set (data
not shown). Adult eyes were obtained from the North Carolina Eye Bank
(Winston-Salem, North Carolina). All whole globes were immersed in
RNALater (Qiagen) within 6.5 h of collection, shipped overnight on ice and
dissected on the day of arrival. The retina, choroid and sclera tissues were
isolated at the posterior pole using a circular, double-embedded technique
using round 7-mm and 5-mm biopsy punches. To reduce contamination of the
retina to the other ocular tissue samples, the second biopsy punch of 5 mm was
used in the center of the 7-mm punch after retinal removal. RNA samples (with
quality control of RNA concentration and 260/280 nm ratios performed using
Nanodrop; Invitrogen) were hybridized to whole-genome microaray Illumina
HumanHT-12 v4 Expression BeadChips (with over 25,000 genes and 48,000
probes) in 2 batches. The first batch was hybridized to adult RPE, choroid and
sclera RNA samples (n = 6). The second batch of newer chips with additional
probes was hybridized to adult optic nerve and cornea samples (n = 6). The
data were exported from Illumina GenomeStudio and were log2 transformed.
Sample outliers were determined by principal-component analyses using the
Hoteling’s T2 test50 (at 95% confidence interval) and removed from further
analyses. Data intensity was normalized by quantile normalization followed by
multichip averaging51 to reduce chip effects. For each tissue type, the probes
with signal intensities below background levels and those with the lowest (5%)
signal intensities (detection P < 0.10) were excluded. Evidence of expression for
the remaining probes was defined by detection P < 0.05. Probes with detection
P < 0.10 or > 0.05 required additional tissue expression support from EyeSAGE
or literature reports (Supplementary Table 3b).
© 2013 Nature America, Inc. All rights reserved. Download full-text
Search for regulatory elements. We used the ‘Integrated Regulation from
ENCODE’ track in the UCSC Genome Browser to look at H3K27ac modi-
fication as a mark of active regulatory elements. Numbers of H3K27ac
modifications were counted between the associated top SNP from a locus and
the nearest gene and within the nearest gene itself. We also used HaploReg16
annotations to look for other signs of regulatory activity at the site of the
associated SNP itself, such as enhancer histone marks, DNase hypersensitivity
sites, binding proteins and motifs changed.
Pathway analyses. We used two different programs for pathway analysis:
Ingenuity (see URLs), version August 2012, application build 172788, content
version 14197757) and the Disease Association Protein-Protein Link Evaluator
Subcellular localization assignment and functional annotation of myopia-
associated disease genes as well as molecular pathway analysis were carried
out using the Ingenuity knowledge database (IPA). The candidate myopia-
causing genes discovered in this study were entered into IPA. We used the
‘IPA toggle subcellular layout’ function to show the subcellular location
(extracellular, plasma membrane, cytoplasm, nucleus or unknown) of the
proteins corresponding to these genes, yielding a first glance at which signal-
ing molecules and pathways are involved in myopia. Subsequently, we used
the IPA ‘connect’ function to discover potential direct or indirect functional
relationships or molecular pathways in between these entries. This yielded
unexpectedly few hits, which suggests molecular disease heterogeneity and/
or functional redundancy in the pathobiological events leading to myopia.
Next, we used the IPA ‘overlay’ function to annotate the myopia candidate
genes with their involvement in ‘functions and diseases’, ‘canonical pathways’
and a range of custom-made gene lists from previous studies, including
photoreceptor-, RPE- and choroid-specific transcripts (ref. 52 and data not
shown). Lastly, we used DAPPLE39 to look for physical connections between
proteins encoded by disease-related genes from associated regions.
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