Genome-wide association analyses identify multiple loci associated with central corneal thickness and keratoconus.

TL;DR: A meta-analysis on >20,000 individuals in European and Asian populations that identified 16 new loci associated with CCT at genome-wide significance showed that 2 CCT-associated loci conferred relatively large risks for keratoconus in 2 cohorts with 874 cases and 6,085 controls.

Abstract: Central corneal thickness (CCT) is associated with eye conditions including keratoconus and glaucoma. We performed a meta-analysis on >20,000 individuals in European and Asian populations that identified 16 new loci associated with CCT at genome-wide significance (P < 5 × 10(-8)). We further showed that 2 CCT-associated loci, FOXO1 and FNDC3B, conferred relatively large risks for keratoconus in 2 cohorts with 874 cases and 6,085 controls (rs2721051 near FOXO1 had odds ratio (OR) = 1.62, 95% confidence interval (CI) = 1.4-1.88, P = 2.7 × 10(-10), and rs4894535 in FNDC3B had OR = 1.47, 95% CI = 1.29-1.68, P = 4.9 × 10(-9)). FNDC3B was also associated with primary open-angle glaucoma (P = 5.6 × 10(-4); tested in 3 cohorts with 2,979 cases and 7,399 controls). Further analyses implicate the collagen and extracellular matrix pathways in the regulation of CCT.

Summary

Introduction

• 46A list of members is provided in the supplementary Note.
• 56These authors contributed equally to this work.

COMPETING FINANCIAL INTERESTS

• The authors declare no competing financial interests.
• Reprints and permissions information is available online at http://www.nature.com/reprints/index.html.
• NIH Public Access Author Manuscript Nat Genet.
• Author manuscript; available in PMC 2014 February 01.

Meta-analysis of CCT from >20,000 samples

• The authors collected 13 GWAS on CCT, totaling over 20,000 individuals (Supplementary Table 1).
• Author manuscript; available in PMC 2014 February 01.
• The authors tested the CCT-associated loci identified from the general population in these two sets.
• Removing all of the genome-wide significant genes and repeating the pathway analysis reduced the significance of the collagen pathway, as expected, but the pathway remained nominally significant (empirical P = 0.005), suggesting that more of the remaining collagen pathway genes also underlie variation in CCT.
• As reduced CCT is associated with POAG5 and progressive corneal thinning is observed in keratoconus17, the authors hypothesized that, for the effect directions to be consistent in the epidemiological sense, the CCT-reducing allele would also be the keratoconus or POAG risk allele.

DISCUSSION

• Previous GWAS of CCT have identified 11 loci, 5 of which were found in studies of individuals with European ancestry7,8 and 6 of which came from studies of 3 Asian populations6,9.
• The loci associated with CCT in European and Asian populations (Table 2) together explained 8.3% of additive variance in Europeans and 7% in Asians.
• Author manuscript; available in PMC 2014 February 01.
• The effect of these SNPs on keratoconus risk is large, and further evaluation of the clinical relevance of these SNPs is merited.
• Finally, using three different methods of pathway analysis, the authors showed that CCT-associated loci converge on collagen and ECM pathways (and their related pathways).

Samples

• Each cohort was approved by a research ethics committee, and all participants gave informed consent.
• Additional covariates that were controlled in the analysis, for example, principal components, study site or CCT measurement, were variable across individual studies.
• Author manuscript; available in PMC 2014 February 01.
• The authors also applied the recent approach of approximate conditional and joint multiple-SNP analysis28 to the set 1 meta-analysis results.
• The authors used HapMap JPT and CHB data as an approximate reference to identify proxies for the variants that were not genotyped or imputed in any of the Asian populations.

Meta-analysis of set 1 and set 2

• The authors performed a meta-analysis on the normal samples with European and Asian ancestry using Fisher’s method (Table 2).
• The authors also performed this meta-analysis using the inverse-variance weighting method—this method is potentially more powerful than Fisher’s method but is inappropriate if the trait distribution or allele frequencies vary across studies.
• Testing CCT-associated loci in the clinical cohorts (set 3: 1,936 POAG cases with European ancestry and set 4: 198 normal-tension glaucoma cases with Asian ancestry).
• The diagnostic criteria for POAG are provided in the Supplementary Note.
• Author manuscript; available in PMC 2014 February 01.

Pathway analysis

• The authors used three pathway analysis approaches, an extension of the VEGAS32 gene-based test to pathway analysis (VEGAS-Pathway), MAGENTA33 and GRAIL35.
• Because the set 1 samples were all of European descent, the authors used the HapMap 2 CEU population as the reference to estimate patterns of LD.
• The gene-based results for meta-analysis of European samples were presented for the genes of interest, including known CCT-associated loci, newly identified loci and their neighboring genes (Supplementary Table 5).
• Pathway P values were computed by summing χ2 test statistics derived from VEGAS P values.
• To ensure that clusters of genes did not adversely affect results, within each pathway, gene sets were pruned such that each gene was >500 kb away from all other genes in the pathway.

Polygenic modeling

• The authors used genotyped data in the target sets, retaining only SNPs with clear, non-ambiguous strand coding.
• The polygenic profile score for each individual in the target sets was calculated as the summation of the SNP genotypes in different P-value bins defined by the CCT meta-analysis, weighted by the CCT effects.
• When the target set included keratoconus or POAG cases and controls, logistic regression was used to assess association between the disease trait and the polygenic profile score.
• Analogously, when testing CCT in POAG cases, linear regression was used.

Full text

Edinburgh Research Explorer
Genome-wide association analyses identify multiple loci
associated with central corneal thickness and keratoconus
Citation for published version:
NEIGHBOR Consortium, Lu, Y, Vitart, V, Burdon, KP, Khor, CC, Bykhovskaya, Y, Mirshahi, A, Hewitt, AW,
Koehn, D, Hysi, PG, Ramdas, WD, Zeller, T, Vithana, EN, Cornes, BK, Tay, W-T, Tai, ES, Cheng, C-Y, Liu,
J, Foo, J-N, Saw, SM, Thorleifsson, G, Stefansson, K, Dimasi, DP, Mills, RA, Mountain, J, Ang, W, Hoehn,
R, Verhoeven, VJM, Grus, F, Wolfs, R, Castagne, R, Lackner, KJ, Springelkamp, H, Yang, J, Jonasson, F,
Leung, DYL, Chen, LJ, Tham, CCY, Rudan, I, Vatavuk, Z, Hayward, C, Gibson, J, Cree, AJ, Macleod, A,
Ennis, S, Polasek, O, Campbell, H, Wilson, JF, Viswanathan, AC, Fleck, B & Wright, AF 2013, 'Genome-
wide association analyses identify multiple loci associated with central corneal thickness and keratoconus',
Nature Genetics, vol. 45, no. 2, pp. 155-163. https://doi.org/10.1038/ng.2506
Digital Object Identifier (DOI):
10.1038/ng.2506
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Genome-wide association analyses identify multiple loci
associated with central corneal thickness and keratoconus
Yi Lu
1,56
, Veronique Vitart
2,56
, Kathryn P Burdon
3,56
, Chiea Chuen Khor
4,5,6,7,56
, Yelena
Bykhovskaya
8
, Alireza Mirshahi
9
, Alex W Hewitt
10,11
, Demelza Koehn
12
, Pirro G Hysi
13
,
Wishal D Ramdas
14,15
, Tanja Zeller
16
, Eranga N Vithana
4,5
, Belinda K Cornes
4
, Wan-Ting
Tay
4
, E Shyong Tai
6,17
, Ching-Yu Cheng
4,5,6,18
, Jianjun Liu
6,7
, Jia-Nee Foo
7
, Seang Mei
Saw
6
, Gudmar Thorleifsson
19
, Kari Stefansson
19,20
, David P Dimasi
3
, Richard A Mills
3
,
Jenny Mountain
21
, Wei Ang
22
, René Hoehn
9
, Virginie J M Verhoeven
14,15
, Franz Grus
9
,
Roger Wolfs
14,15
, Raphaële Castagne
23
, Karl J Lackner
24
, Henriët Springelkamp
14,15
, Jian
Yang
25
, Fridbert Jonasson
20,26
, Dexter Y L Leung
27
, Li J Chen
27
, Clement C Y Tham
27
, Igor
Rudan
28,29
, Zoran Vatavuk
30
, Caroline Hayward
2
, Jane Gibson
31
, Angela J Cree
32
, Alex
MacLeod
33
, Sarah Ennis
31
, Ozren Polasek
29,34
, Harry Campbell
28
, James F Wilson
28
,
Ananth C Viswanathan
35
, Brian Fleck
36
, Xiaohui Li
37
, David Siscovick
38
, Kent D Taylor
37
,
Jerome I Rotter
37
, Seyhan Yazar
11
, Megan Ulmer
39
, Jun Li
40
, Brian L Yaspan
41
, Ayse B
Ozel
40
, Julia E Richards
42
, Sayoko E Moroi
42
, Jonathan L Haines
41
, Jae H Kang
43
, Louis R
Pasquale
43,44
, R Rand Allingham
45
, Allison Ashley-Koch
39
, NEIGHBOR Consortium
46
, Paul
Mitchell
47
, Jie Jin Wang
47
, Alan F Wright
2
, Craig Pennell
22
, Timothy D Spector
13
, Terri L
Young
48,49
, Caroline C W Klaver
14,15
, Nicholas G Martin
50
, Grant W Montgomery
51
, Michael
G Anderson
12,52
, Tin Aung
4,5,53
, Colin E Willoughby
54
, Janey L Wiggs
44,57
, Chi P Pang
27,57
,
Unnur Thorsteinsdottir
19,20,57
, Andrew J Lotery
32,33,56
, Christopher J Hammond
13,57
,
© 2013 Nature America, Inc. All rights reserved.
Correspondence should be addressed to S.M. (stuart.macgregor@qimr.edu.au) or T.Y.W. (tien_yin_wong@nuhs.edu.sg).
46
A list of members is provided in the supplementary Note.
56
These authors contributed equally to this work.
57
These authors jointly directed this work.
AUTHOR CONTRIBUTIONS
S.M., V.V., D.A.M., T.Y.W. and Y.L. conceived and designed the study, and liaised with the International Glaucoma Genetics
Consortium for this project. Y.L. performed the primary analyses. S.M., J.Y., M.U., X.L., C.C.K., E.N.V., T.A., K.P.B., G.T., F.J.,
V.V., O.P., D.Y.L.L., L.J.C., C.C.Y.T., R.C., D.K., W.A., W.D.R., V.J.M.V., H.S., J.G., A.J.C., A. MacLeod, S.E., P.G.H., Y.B. and
X.L. contributed to analysis. S.M. and Y.L. performed pathway analysis. J.E.C., P.M., U.T., A.F.W., N.P., C.P.P., M.G.A., J.L.W.,
M.A.H., L.R.P., C.E.W., N.G.M., D.A.M., C.M.v.D., T.Y.W., A.J.L., C.J.H. and Y.S.R. were the overseeing principal investigators of
the individual studies. J.E.C., K.P.B., D.P.D., R.A.M., G.T., K.S., F.J., U.T., A.F.W., V.V., I.R., Z.V., C.H., O.P., H.C., J.F.W., B.F.,
N.P., A. Mirshahi, T.Z., R.H., F.G., R.C., K.J.L., C.P.P., D.Y.L.L., L.J.C., C.C.Y.T., M.G.A., D.K., J.L.W., L.R.P., M.U., J. Liu,
B.L.Y., A.B.O., J.E.R., S.E.M., J.L.H., J.H.K., L.R.P., R.R.A., A.A.-K., J.L.W., M.A.H., N.G.M., Y.L., G.W.M., S.M., D.A.M.,
A.W.H., J.M., W.A., S.Y., C.P., T.L.Y., W.D.R., V.J.M.V., R.W., H.S., C.C.W.K., C.M.v.D., C.C.K., E.N.V., B.K.C., W.-T.T.,
E.S.T., C.-Y.C., J.-N.F., J. Li, S.M.S., T.A., T.Y.W., J.G., A.J.C., A. MacLeod, S.E., A.J.L., P.G.H., T.D.S., T.L.Y. and C.J.H.
contributed reagents or methods to the genotyping, phenotyping and data analysis of corneal thickness data sets. J.E.C., K.P.B.,
D.P.D., R.A.M., C.P.P., D.Y.L.L., L.J.C., C.C.Y.T., J.L.W., L.R.P., M.U., J. Li, B.L.Y., A.B.O., J.E.R., S.E.M., J.L.H., J.H.K., L.R.P.,
R.R.A., A.A.-K., J.L.W., M.A.H., C.E.W., A.J.L., J.G., A.J.C., A. MacLeod, S.E., Y.S.R., Y.B., X.L., D.S., K.D.T., J.J.W., A.C.V.
and J.I.R. contributed reagents or the genotyping, phenotyping and data analysis of the glaucoma, and keratoconus samples. Y.L. and
S.M. wrote the first draft of this manuscript. K.P.B., V.V., C.C.K., Y.B., A. Mirshahi, A.W.H., D.K., P.G.H., W.D.R., J.L.W.,
C.M.v.D., Y.S.R., D.A.M., J.E.C. and T.Y.W. provided critical comments for manuscript revision. All authors reviewed the final
manuscript.
COMPETING FINANCIAL INTERESTS
The authors declare no competing financial interests.
Reprints and permissions information is available online at http://www.nature.com/reprints/index.html.
URLs. GHS Express, http://genecanvas.ecgene.net/uploads/ForReview/; Gene Ontology, http://www.geneontology.org/; SNAP,
http://www.broadinstitute.org/mpg/snap/ldsearchpw.php; MAGENTA, http://www.broadinstitute.org/mpg/magenta/; GRAIL, http://
www.broadinstitute.org/mpg/grail/.
Note: Supplementary information is available in the online version of the paper.
NIH Public Access
Author Manuscript
Nat Genet
. Author manuscript; available in PMC 2014 February 01.
Published in final edited form as:
Nat Genet
. 2013 February ; 45(2): 155–163. doi:10.1038/ng.2506.
NIH-PA Author Manuscript NIH-PA Author Manuscript NIH-PA Author Manuscript

Cornelia M van Duijn
15,57
, Michael A Hauser
39,57
, Yaron S Rabinowitz
8,55,57
, Norbert
Pfeiffer
9,57
, David A Mackey
10,11,57
, Jamie E Craig
3,57
, Stuart Macgregor
1,57
, and Tien Y
Wong
6,45,57
1
Queensland Institute of Medical Research, Statistical Genetics, Herston, Brisbane, Queensland,
Australia
2
Medical Research Council (MRC) Human Genetics Unit, Institute of Genetics and
Molecular Medicine, University of Edinburgh, Edinburgh, UK
3
Department of Ophthalmology,
Flinders University, Flinders Medical Centre, Adelaide, South Australia, Australia
4
Singapore Eye
Research Institute, Singapore
5
Department of Ophthalmology, Yong Loo Lin School of Medicine,
National University of Singapore, Singapore
6
Saw Swee Hock School of Public Health, National
University of Singapore, Singapore
7
Human Genetics, Genome Institute of Singapore, Agency for
Science, Technology and Research (A*STAR), Singapore
8
Regenerative Medicine Institute,
Ophthalmology Research, Department of Surgery, Cedars-Sinai Medical Center, Division of
Surgical Research, Los Angeles, California, USA
9
Department of Ophthalmology, University
Medical Center Mainz, Mainz, Germany
10
Centre for Eye Research Australia, University of
Melbourne, Royal Victorian Eye and Ear Hospital, Melbourne, Victoria, Australia
11
Lions Eye
Institute, Centre for Ophthalmology and Visual Science, University of Western Australia, Perth,
Western Australia, Australia
12
Department of Molecular Physiology and Biophysics, University of
Iowa, Iowa City, Iowa, USA
13
Department of Twin Research and Genetic Epidemiology, King’s
College London School of Medicine, St Thomas’ Hospital, London, UK
14
Department of
Ophthalmology, Erasmus Medical Center, Rotterdam, The Netherlands
15
Department of
Epidemiology, Erasmus Medical Center, Rotterdam, The Netherlands
16
University Heart Center
Hamburg, Clinic for General and Interventional Cardiology, Hamburg, Germany
17
Department of
Medicine, Yong Loo Lin School of Medicine, National University of Singapore, Singapore
18
Centre
for Quantitative Medicine, Office of Clinical Sciences, Duke-National University of SIngapore
Graduate Medical School, Singapore
19
deCODE genetics, Reykjavik, Iceland
20
Faculty of
Medicine, University of Iceland, Reykjavik, Iceland
21
Telethon Institute for Child Health Research,
Centre for Child Health Research, University of Western Australia, Perth, Western Australia,
Australia
22
School of Women’s and Infants’ Health, University of Western Australia, Perth,
Western Australia, Australia
23
Institut de Santé et de la Recherche Médicale (INSERM) Unité
Mixte de Recherche de Santé (UMRS) 937, Pierre and Marie Curie University and Medical
School, Paris, France
24
Clinical Chemistry and Laboratory Medicine, University Medical Center
Mainz, Mainz, Germany
25
University of Queensland Diamantina Institute, The University of
Queensland, Princess Alexandra Hospital, Brisbane, Queensland, Australia
26
Department of
Ophthalmology, Landspitali National University Hospital, Reykjavik, Iceland
27
Department of
Ophthalmology & Visual Sciences, The Chinese University of Hong Kong, Hong Kong Eye
Hospital, Hong Kong
28
Centre for Population Health Sciences, University of Edinburgh,
Edinburgh, UK
29
Croatian Centre for Global Health, University of Split Medical School, Split,
Croatia
30
Department of Ophthalmology, Hospital Sestre Milosrdnice, Zagreb, Croatia
31
Genetic
Epidemiology and Genomic Informatics Group, Human Genetics, Faculty of Medicine, University
of Southampton, Southampton General Hospital, Southampton, UK
32
Clinical Neurosciences
Research Grouping, Clinical and Experimental Sciences, Faculty of Medicine, University of
Southampton, Southampton General Hospital, Southampton, UK
33
Southampton Eye Unit,
Southampton General Hospital, Southampton, UK
34
Department of Public Health, University of
Split, Split, Croatia
35
National Institute for Health Research (NIHR) Biomedical Research Centre,
Moorfields Eye Hospital National Health Service (NHS) Foundation Trust and University College
London (UCL) Institute of Ophthalmology, London, UK
36
Princess Alexandra Eye Pavilion,
Edinburgh, UK
37
Medical Genetics Institute, Cedars-Sinai Medical Center, Los Angeles,
California, USA
38
Cardiovascular Health Research Unit, Departments of Medicine, University of
Washington, Seattle, Washington, USA
39
Department of Medicine, Duke University, Durham,
North Carolina, USA
40
Department of Human Genetics, University of Michigan, Ann Arbor,
Michigan, USA
41
Vanderbilt University School of Medicine, Center for Human Genetics Research,
Lu et al.
Page 2
Nat Genet
. Author manuscript; available in PMC 2014 February 01.
NIH-PA Author Manuscript NIH-PA Author Manuscript NIH-PA Author Manuscript

Nashville, Tennessee, USA
42
Department of Ophthalmology and Visual Sciences, University of
Michigan, Ann Arbor, Michigan, USA
43
Brigham and Women’s Hospital, Channing Division of
Network Medicine, Boston, Massachusetts, USA
44
Department of Ophthalmology, Harvard
Medical School, Massachusetts Eye and Ear Infirmary, Boston, Massachusetts, USA
45
Department of Ophthalmology, Duke University School of Medicine, Durham, North Carolina,
USA
47
Centre for Vision Research, Department of Ophthalmology and Westmead Millennium
Institute, University of Sydney, Westmead, New South Wales, Australia
48
Center for Human
Genetics, Duke University Medical Center, Durham, North Carolina, USA
49
Duke-National
University of Singapore, Singapore
50
Queensland Institute of Medical Research, Genetic
Epidemiology, Herston, Brisbane, Queensland, Australia
51
Queensland Institute of Medical
Research, Molecular Epidemiology, Herston, Brisbane, Queensland, Australia
52
Department of
Ophthamology and Visual Sciences, University of Iowa, Iowa City, Iowa, USA
53
Singapore
National Eye Centre, Singapore
54
Centre for Vision and Vascular Science, Queen’s University
Belfast, Belfast, UK
55
Cornea Genetic Eye Institute, Cedars-Sinai Medical Center, Los Angeles,
California, USA
Abstract
Central corneal thickness (CCT) is associated with eye conditions including keratoconus and
glaucoma. We performed a meta-analysis on >20,000 individuals in European and Asian
populations that identified 16 new loci associated with CCT at genome-wide significance (
P
< 5 ×
10
−8
). We further showed that 2 CCT-associated loci,
FOXO1
and
FNDC3B
, conferred relatively
large risks for keratoconus in 2 cohorts with 874 cases and 6,085 controls (rs2721051 near
FOXO1
had odds ratio (OR) = 1.62, 95% confidence interval (CI) = 1.4–1.88,
P
= 2.7 × 10
−10
,
and rs4894535 in
FNDC3B
had OR = 1.47, 95% CI = 1.29–1.68,
P
= 4.9 × 10
−9
).
FNDC3B
was
also associated with primary open-angle glaucoma (
P
= 5.6 × 10
−4
; tested in 3 cohorts with 2,979
cases and 7,399 controls). Further analyses implicate the collagen and extracellular matrix
pathways in the regulation of CCT.
Human ocular biometric parameters comprise a set of highly heritable and often correlated
quantitative traits. One notable example is CCT, which has an estimated heritability of up to
95% (ref. 1). Whereas extreme corneal thinning is a dramatic clinical feature for rare
congenital connective tissue disorders, including brittle cornea syndrome (BCS) and several
types of osteogenesis imperfecta
2,3
, mildly reduced CCT is involved in more common and
late-onset eye diseases. It is a hallmark of keratoconus and a risk factor for primary open-
angle glaucoma (POAG) in individuals with ocular hypertension
4,5
. Previous genome-wide
association studies (GWAS) conducted on both European and Asian populations have
identified 11 CCT-associated loci
6–9
. Among these loci, mutations in
ZNF469
(refs. 10–12),
COL5A1
(ref. 13) and
COL8A2
(refs. 14,15) are known to cause rare disorders of BCS,
Ehlers-Danlos syndrome (EDS) and corneal dystrophy, respectively. However, none was
found to be associated with common eye diseases.
Keratoconus is a common corneal ectasia, affecting 1 in 2,000 in the general population
16
. It
is a progressive eye disease characterized by thinning and asymmetrical conical protrusion
of the cornea, which causes variable and severe visual impairment. Owing to the limited
availability of medical treatments, keratoconus is one of the leading causes of corneal
transplantation worldwide
17
. Two GWAS have been conducted on susceptibility for
keratoconus, and these studies suggested some new genetic associations, but neither study
reported genome-wide significant loci
18,19
. POAG is the most common form of glaucoma,
which is the second leading cause of blindness worldwide
20
. Several risk loci for POAG
have been identified through early linkage and candidate gene studies
21,22
and recent
Lu et al.
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. Author manuscript; available in PMC 2014 February 01.
NIH-PA Author Manuscript NIH-PA Author Manuscript NIH-PA Author Manuscript

GWAS
23–27
. In both diseases, affected individuals have reduced CCT relative to the general
population.
Dissecting the genetics of key risk factors may provide insights into associated disease
etiology. Therefore, we conducted a large meta-analysis of GWAS on CCT from over
20,000 individuals, including individuals of European and Asian descent who were affected
or unaffected with glaucoma to identify new CCT-associated loci. To evaluate potential
clinical relevance, we tested the identified CCT-associated loci in 2 keratoconus
susceptibility studies (totaling 874 keratoconus cases and 6,085 controls) and 3 studies on
POAG risk (totaling 2,979 POAG cases and 7,399 controls). The overall study design is
shown in Supplementary Figure 1.
RESULTS
Meta-analysis of CCT from >20,000 samples
We collected 13 GWAS on CCT, totaling over 20,000 individuals (Supplementary Table 1).
Because of differences in the sample attributes, we performed meta-analyses of CCT within
each subset, including 13,057 individuals with European ancestry who were unaffected with
eye disease in set 1, 6,963 individuals with Asian ancestry who were unaffected with eye
disease in set 2, 1,936 POAG cases with European ancestry in set 3 and 198 normal tension
glaucoma (NTG, a common form of open-angle glaucoma) cases with Asian ancestry in set
4. All samples were genotyped on commercially available genotyping arrays. The majority
of studies in the first two sets were imputed according to the phased haplotypes of HapMap
reference samples, whereas few studies in the other two sets had imputation data available
by the time of this study.
We separated the twin studies (Australian BATS and TEST twin studies and UK twin
studies) from the first set of samples with European ancestry. Because family studies are less
affected by potential population stratification, these twin studies were analyzed as a direct
replication of association results from the discovery set consisting of all the remaining set 1
samples. All loci from the discovery set were found in the twin studies with similar effect
sizes and with at least nominal replication (Table 1). This result, together with a low
genomic control parameter (
λ
= 1.05; Supplementary Fig. 2), suggested that population
stratification had little effect on the meta-analysis. We then performed meta-analysis on the
results from the discovery set and the twin studies, reported as combined set 1. This
combined set identified 13 loci that were associated with CCT at genome-wide significance
in the populations of European descent (Table 1, Supplementary Fig. 3a–m and
Supplementary Table 2). Those included five known loci,
COL5A1
,
AVGR8
(reported here
as
FGF9
-
SGCG
),
FOXO1
,
AKAP13
and
ZNF469
, in European populations
7,8
, one locus,
LRRK1
-
CHSY1
, previously identified in Asian populations
6
but unknown in European
populations and seven new loci (Fig. 1 and Table 1).
Using a recent approach of approximate conditional and joint multiple-SNP analysis
28
, we
found that two loci harbored multiple independent variants that were associated with CCT
(Table 1). One example was the
COL5A1
region. Previous studies based on European and
Asian samples both suggested the presence of two independent signals in this region
8,9
. In
the current meta-analysis, we observed that the two variants rs3118520 and rs7044529,
which were 126 kb apart with linkage disequilibrium (LD)
r
2
< 0.01 in our reference
samples from the Queensland Institute of Medical Research (QIMR) cohort
28,29
, represented
two independent LD blocks. The first LD block was located upstream of
COL5A1
(
RXRA
-
COL5A1
), and the second was in an intron of
COL5A1
. Similarly, the locus at
LRRK1
-
CHSY1
harbored three independent associations that could be captured by rs2034809,
rs930847 and rs752092. Because the trait-increasing alleles of rs2034809 and rs930847
Lu et al.
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Nat Genet
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Frequently asked questions

Q1. What are the contributions mentioned in the paper "Genome-wide association analyses identify multiple loci associated with central corneal thickness and keratoconus" ?

The authors performed a meta-analysis on > 20,000 individuals in European and Asian populations that identified 16 new loci associated with CCT at genome-wide significance ( P < 5 × 10−8 ). The authors further showed that 2 CCT-associated loci, FOXO1 and FNDC3B, conferred relatively large risks for keratoconus in 2 cohorts with 874 cases and 6,085 controls ( rs2721051 near FOXO1 had odds ratio ( OR ) = 1. 62, 95 % confidence interval ( CI ) = 1. 4–1. 88, P = 2. 7 × 10−10, and rs4894535 in FNDC3B had OR = 1. 47, 95 % CI = 1. 29–1. 68, P = 4. 9 × 10−9 ). Dissecting the genetics of key risk factors may provide insights into associated disease etiology. Therefore, the authors conducted a large meta-analysis of GWAS on CCT from over 20,000 individuals, including individuals of European and Asian descent who were affected or unaffected with glaucoma to identify new CCT-associated loci. Because of differences in the sample attributes, the authors performed meta-analyses of CCT within each subset, including 13,057 individuals with European ancestry who were unaffected with eye disease in set 1, 6,963 individuals with Asian ancestry who were unaffected with eye disease in set 2, 1,936 POAG cases with European ancestry in set 3 and 198 normal tension glaucoma ( NTG, a common form of open-angle glaucoma ) cases with Asian ancestry in set 4. The majority of studies in the first two sets were imputed according to the phased haplotypes of HapMap reference samples, whereas few studies in the other two sets had imputation data available by the time of this study. The authors then performed meta-analysis on the results from the discovery set and the twin studies, reported as combined set 1. In the current meta-analysis, the authors observed that the two variants rs3118520 and rs7044529, which were 126 kb apart with linkage disequilibrium ( LD ) r2 < 0. 01 in their reference samples from the Queensland Institute of Medical Research ( QIMR ) cohort28,29, represented two independent LD blocks. Therefore, the authors performed another meta-analysis using Fisher ’ s method to combine association P values from GWAS of European samples and Asian samples. This meta-analysis showed that the known CCT-associated loci in the Asian populations, including COL8A2, FAM46A-IBTK, C7orf42 and 9p23 ( reported here as MPDZ-NF1B ), replicated in European samples and further identified ten new loci associated with CCT, including COL4A3, which had been suggested in a set of Croatian samples but had not reached genome-wide significance8 ( Fig. 2a, b and Table 2 ). Owing to phenotypic differences and potential confounding factors, for example, the fact that individuals with glaucoma take intraocular pressure–lowering medication, which has been shown to be associated with corneal thinning31, the authors conducted separate meta-analyses of CCT in the clinical cohorts. In set 4, most of the loci were found to have effect directions consistent with those from the other sets, but, owing to the very limited power in this study, only one locus showed nominal association ( Supplementary Table 4 ). Despite the slight phenotypic differences, the authors have shown that there is substantial overlap between the genetic variants that account for CCT variation in the general population and the variants found in individuals with glaucoma. To investigate whether the CCT-associated loci were enriched in functional units in terms of genes and pathways, the authors performed a gene-based analysis using the Versatile Gene–based Lu et al. N IH PA Athor M anscript N IH PA Athor M anscript N IH PA Athor M anscript Association Study ( VEGAS ) 32 and a further pathway analysis using a new tool extended from VEGAS ( VEGAS-Pathway ). The most significant gene was the CWC27-ADAMTS6 locus ( the gene-based P values for CWC27 and ADAMTS6 were < 1 × 10−6 ), followed by COL5A1 ( P value of 3 × 10−6 ) ( Supplementary Table 5 ). Notably, two pathways that were among the top—face morphogenesis and head morphogenesis—although not significant after correction for multiple testing, contained the gene PDGFRA, which has been reported to associate with human corneal curvature in Asian populations34. The authors also considered each locus singly, but none was significantly connected with the remaining loci ( GRAIL Ptext > 0. 05 for all genes ). In a more recent study, mutations in PRDM5 ( 4q27 ) were identified in two affected families with BCS who were known to be negative for mutations in the ZNF469 gene36. Similarly, the authors examined the CCT-associated loci for a role in POAG risk in 3 clinical cohorts ( 2,979 cases and 7,399 controls in total ): advanced POAG cases and controls from the Australian and New Zealand Registry of Advanced Glaucoma ( ANZRAG ) 24,37 and Lu et al. To further establish genes from the identified loci as candidates for influencing CCT, the authors examined publicly available microarray gene expression data from the corneas of two human donor samples and three strains of inbred mice40,41 ( Supplementary Table 13 ). Further analyses implicate the collagen and extracellular matrix pathways in the regulation of CCT. Two GWAS have been conducted on susceptibility for keratoconus, and these studies suggested some new genetic associations, but neither study reported genome-wide significant loci18,19. To evaluate potential clinical relevance, the authors tested the identified CCT-associated loci in 2 keratoconus susceptibility studies ( totaling 874 keratoconus cases and 6,085 controls ) and 3 studies on POAG risk ( totaling 2,979 POAG cases and 7,399 controls ). Because family studies are less affected by potential population stratification, these twin studies were analyzed as a direct replication of association results from the discovery set consisting of all the remaining set 1 samples. This result, together with a low genomic control parameter ( λ = 1. 05 ; Supplementary Fig. 2 ), suggested that population stratification had little effect on the meta-analysis. Previous studies based on European and Asian samples both suggested the presence of two independent signals in this region8,9. Furthermore, 11 of 15 lead SNPs ( or their proxies ) were at least nominally significant at P < 0. 05 ( Supplementary Table 3 ). These observations suggest that most of the CCT-associated loci identified from populations of European descent are shared in Asian populations. This suggests that similar pathway ( s ) regulate CCT regardless of eye disease status. Removing all of the genome-wide significant genes and repeating the pathway analysis reduced the significance of the collagen pathway, as expected, but the pathway remained nominally significant ( empirical P = 0. 005 ), suggesting that more of the remaining collagen pathway genes also underlie variation in CCT. The top two pathways suggested from this gene set enrichment approach were ECM structural constituents conferring tensile strength ( GO 0030020 ) and the collagen pathway ( Supplementary Table Lu et al. Applying polygenic modeling to the 222 keratoconus cases and 3,324 controls, the authors found that the aggregate effects of loci with more modest effect on CCT did not clearly enhance the prediction of keratoconus risk over and above the risk conferred by the six significantly associated loci ( Supplementary Fig. 4b ). Performing meta-analysis of these results, the authors found that FNDC3B was also significantly associated with POAG risk ( P = 5. 6 × 10−4 ) ( Table 3 ). Furthermore, polygenic modeling on these three sets showed limited evidence for CCT aggregate effects predicting POAG risk ( Supplementary Fig. 4c ). For lead SNPs with two nearby genes, presence-absence of corneal expression suggested prioritization among the two candidates in two loci ( expressed in CWC27, not expressed in ADAMTS6 ; expressed in PTGDS, not expressed in LCN12 ). The authors also queried the publicly available expression quantitative trait locus ( eQTL ) database GHS Express46 to determine potential SNP function in terms of transcript regulation. 

Q2. What conditions were used to test the expression of a single product?

PCR conditions involved incubation at 95 °C for 3 min and 40 cycles of 95 °C for 10 s, 55 °C for 10 s and 72 °C for 30 s. PCR products were subjected to melting curve analysis to ensure that only a single product was amplified. 

Q3. Why were these studies analyzed as a direct replication of the discovery set?

Because family studies are less affected by potential population stratification, these twin studies were analyzed as a direct replication of association results from the discovery set consisting of all the remaining set 1 samples. 

Q4. How did the authors combine the association results in each study?

The authors applied inverse variance–based meta-analysis, using METAL50 to combine individual association results in each of the four sample sets. 

Q5. Why did the authors conduct separate meta-analyses of CCT in the clinical cohorts?

Owing to phenotypic differences and potential confounding factors, for example, the fact that individuals with glaucoma take intraocular pressure–lowering medication, which has been shown to be associated with corneal thinning31, the authors conducted separate meta-analyses of CCT in the clinical cohorts. 

Q6. What were the parameters used to ensure no inflation in association P values?

The quantile-quantile plot and the genomic control parameters from individual studies were also examined to assure no obvious inflation in association P values owing to residual population stratification or other confounding factors at each site. 

Q7. What was the effect of the CCT-reducing allele at FNDC3B?

At FNDC3B, the CCTreducing allele resulted in elevated keratoconus risk (OR = 1.47, 95% CI = 1.29–1.68), but it lowered POAG risk (OR = 0.83, 95% CI = 0.74–0.92). 

Q8. How many loci are not known in European populations?

The eight loci not known in European populations (including multiple signals at the LRRK1-CHSY1 locus) explained an additional 3.5% of additive variance, adding up to 7.5% of the total variance explained in European populations. 

Q9. How many studies were available by the time of this study?

The majority of studies in the first two sets were imputed according to the phased haplotypes of HapMap reference samples, whereas few studies in the other two sets had imputation data available by the time of this study. 

Q10. What were the top three key words showing functional connection between the 27 loci?

Text as a knowledge base, the top key words showing functional connection between the 27 loci included collagen, syndrome, cornea, mutation and mitochondrial. 

Q11. What method was used to evaluate the effect size of the inverse-variance weighting method?

The authors also performed this meta-analysis using the inverse-variance weighting method—this method is potentially more powerful than Fisher’s method but is inappropriate if the trait distribution or allele frequencies vary across studies. 

Q12. How many SNPs were associated with keratoconus?

The authors found that, despite the modest effect on CCT, 11 SNPs showed nominal association with keratoconus, with 6 significant after correction for multiple testing.