Association of Genetic Variation With Keratoconus | Cornea | JAMA Ophthalmology | JAMA Network
[Skip to Navigation]
Sign In
Figure.  Manhattan Plot of Association Results in the Discovery Cohort
Manhattan Plot of Association Results in the Discovery Cohort

Results of logistic regression with the first 3 principal components as covariates (−log10P values) are plotted for each chromosome. The orange and black lines represent the genome-wide significance threshold of a P value of 5.00 × 10−8 and the threshold for follow-up of a P value of 1.00 × 10−6, respectively.

Table 1.  Demographics of the Australian Discovery Cohort and the 3 Replication Cohorts
Demographics of the Australian Discovery Cohort and the 3 Replication Cohorts
Table 2.  Lead Single-Nucleotide Polymorphisms at All 3 Loci With Significant Results in the Discovery Cohort
Lead Single-Nucleotide Polymorphisms at All 3 Loci With Significant Results in the Discovery Cohort
Table 3.  Replication and Analysis of Association Results at Lead SNPs
Replication and Analysis of Association Results at Lead SNPs
1.
Rabinowitz  YS.  Keratoconus.  Surv Ophthalmol. 1998;42(4):297-319. doi:10.1016/S0039-6257(97)00119-7PubMedGoogle ScholarCrossref
2.
Pearson  AR, Soneji  B, Sarvananthan  N, Sandford-Smith  JH.  Does ethnic origin influence the incidence or severity of keratoconus?  Eye (Lond). 2000;14(Pt 4):625-628. doi:10.1038/eye.2000.154PubMedGoogle ScholarCrossref
3.
Godefrooij  DA, de Wit  GA, Uiterwaal  CS, Imhof  SM, Wisse  RP.  Age-specific incidence and prevalence of keratoconus: a nationwide registration study.  Am J Ophthalmol. 2017;175:169-172. doi:10.1016/j.ajo.2016.12.015PubMedGoogle ScholarCrossref
4.
Zadnik  K, Barr  JT, Edrington  TB,  et al.  Baseline findings in the Collaborative Longitudinal Evaluation of Keratoconus (CLEK) Study.  Invest Ophthalmol Vis Sci. 1998;39(13):2537-2546.PubMedGoogle Scholar
5.
Wojcik  KA, Blasiak  J, Szaflik  J, Szaflik  JP.  Role of biochemical factors in the pathogenesis of keratoconus.  Acta Biochim Pol. 2014;61(1):55-62. doi:10.18388/abp.2014_1923PubMedGoogle ScholarCrossref
6.
Wang  Y, Rabinowitz  YS, Rotter  JI, Yang  H.  Genetic epidemiological study of keratoconus: evidence for major gene determination.  Am J Med Genet. 2000;93(5):403-409. doi:10.1002/1096-8628(20000828)93:5<403::AID-AJMG11>3.0.CO;2-APubMedGoogle ScholarCrossref
7.
Nowak  DM, Gajecka  M.  The genetics of keratoconus.  Middle East Afr J Ophthalmol. 2011;18(1):2-6. doi:10.4103/0974-9233.75876PubMedGoogle ScholarCrossref
8.
Abu-Amero  KK, Al-Muammar  AM, Kondkar  AA.  Genetics of keratoconus: where do we stand?  J Ophthalmol. 2014;2014:641708. doi:10.1155/2014/641708PubMedGoogle Scholar
9.
Burdon  KP, Vincent  AL.  Insights into keratoconus from a genetic perspective.  Clin Exp Optom. 2013;96(2):146-154. doi:10.1111/cxo.12024PubMedGoogle ScholarCrossref
10.
Czugala  M, Karolak  JA, Nowak  DM,  et al.  Novel mutation and three other sequence variants segregating with phenotype at keratoconus 13q32 susceptibility locus.  Eur J Hum Genet. 2012;20(4):389-397. doi:10.1038/ejhg.2011.203PubMedGoogle ScholarCrossref
11.
Bykhovskaya  Y, Li  X, Epifantseva  I,  et al.  Variation in the lysyl oxidase (LOX) gene is associated with keratoconus in family-based and case-control studies.  Invest Ophthalmol Vis Sci. 2012;53(7):4152-4157. doi:10.1167/iovs.11-9268PubMedGoogle ScholarCrossref
12.
Hughes  AE, Bradley  DT, Campbell  M,  et al.  Mutation altering the miR-184 seed region causes familial keratoconus with cataract.  Am J Hum Genet. 2011;89(5):628-633. doi:10.1016/j.ajhg.2011.09.014PubMedGoogle ScholarCrossref
13.
Lechner  J, Bae  HA, Guduric-Fuchs  J,  et al.  Mutational analysis of MIR184 in sporadic keratoconus and myopia.  Invest Ophthalmol Vis Sci. 2013;54(8):5266-5272. doi:10.1167/iovs.13-12035PubMedGoogle ScholarCrossref
14.
Burdon  KP, Macgregor  S, Bykhovskaya  Y,  et al.  Association of polymorphisms in the hepatocyte growth factor gene promoter with keratoconus.  Invest Ophthalmol Vis Sci. 2011;52(11):8514-8519. doi:10.1167/iovs.11-8261PubMedGoogle ScholarCrossref
15.
Sahebjada  S, Schache  M, Richardson  AJ, Snibson  G, Daniell  M, Baird  PN.  Association of the hepatocyte growth factor gene with keratoconus in an Australian population.  PLoS One. 2014;9(1):e84067. doi:10.1371/journal.pone.0084067PubMedGoogle Scholar
16.
Bae  HA, Mills  RA, Lindsay  RG,  et al.  Replication and meta-analysis of candidate loci identified variation at RAB3GAP1 associated with keratoconus.  Invest Ophthalmol Vis Sci. 2013;54(7):5132-5135. doi:10.1167/iovs.13-12377PubMedGoogle ScholarCrossref
17.
Li  X, Bykhovskaya  Y, Haritunians  T,  et al.  A genome-wide association study identifies a potential novel gene locus for keratoconus, one of the commonest causes for corneal transplantation in developed countries.  Hum Mol Genet. 2012;21(2):421-429. doi:10.1093/hmg/ddr460PubMedGoogle ScholarCrossref
18.
Lu  Y, Vitart  V, Burdon  KP,  et al; NEIGHBOR Consortium.  Genome-wide association analyses identify multiple loci associated with central corneal thickness and keratoconus.  Nat Genet. 2013;45(2):155-163. doi:10.1038/ng.2506PubMedGoogle ScholarCrossref
19.
Iglesias  AI, Mishra  A, Vitart  V,  et al; Blue Mountains Eye Study—GWAS group; NEIGHBORHOOD Consortium; Wellcome Trust Case Control Consortium 2 (WTCCC2).  Cross-ancestry genome-wide association analysis of corneal thickness strengthens link between complex and mendelian eye diseases.  Nat Commun. 2018;9(1):1864. doi:10.1038/s41467-018-03646-6PubMedGoogle ScholarCrossref
20.
Burdon  KP, Coster  DJ, Charlesworth  JC,  et al.  Apparent autosomal dominant keratoconus in a large Australian pedigree accounted for by digenic inheritance of two novel loci.  Hum Genet. 2008;124(4):379-386. doi:10.1007/s00439-008-0555-zPubMedGoogle ScholarCrossref
21.
Rabinowitz  YS.  Videokeratographic indices to aid in screening for keratoconus.  J Refract Surg. 1995;11(5):371-379.PubMedGoogle Scholar
22.
Fritsche  LG, Igl  W, Bailey  JN,  et al.  A large genome-wide association study of age-related macular degeneration highlights contributions of rare and common variants.  Nat Genet. 2016;48(2):134-143. doi:10.1038/ng.3448PubMedGoogle ScholarCrossref
23.
Burdon  KP, Mitchell  P, Lee  A,  et al.  Association of open-angle glaucoma loci with incident glaucoma in the Blue Mountains Eye Study.  Am J Ophthalmol. 2015;159(1):31-6.e1. doi:10.1016/j.ajo.2014.09.020PubMedGoogle ScholarCrossref
24.
Dimasi  DP, Burdon  KP, Hewitt  AW,  et al.  Genetic investigation into the endophenotypic status of central corneal thickness and optic disc parameters in relation to open-angle glaucoma.  Am J Ophthalmol. 2012;154(5):833-842.e2. doi:10.1016/j.ajo.2012.04.023PubMedGoogle ScholarCrossref
25.
Anderson  CA, Pettersson  FH, Clarke  GM, Cardon  LR, Morris  AP, Zondervan  KT.  Data quality control in genetic case-control association studies.  Nat Protoc. 2010;5(9):1564-1573. doi:10.1038/nprot.2010.116PubMedGoogle ScholarCrossref
26.
Price  AL, Patterson  NJ, Plenge  RM, Weinblatt  ME, Shadick  NA, Reich  D.  Principal components analysis corrects for stratification in genome-wide association studies.  Nat Genet. 2006;38(8):904-909. doi:10.1038/ng1847PubMedGoogle ScholarCrossref
27.
Loh  P-R, Danecek  P, Palamara  PF,  et al.  Reference-based phasing using the Haplotype Reference Consortium panel.  Nat Genet. 2016;48(11):1443-1448. doi:10.1038/ng.3679PubMedGoogle ScholarCrossref
28.
Auton  A, Brooks  LD, Durbin  RM,  et al; 1000 Genomes Project Consortium.  A global reference for human genetic variation.  Nature. 2015;526(7571):68-74. doi:10.1038/nature15393PubMedGoogle ScholarCrossref
29.
Das  S, Forer  L, Schönherr  S,  et al.  Next-generation genotype imputation service and methods.  Nat Genet. 2016;48(10):1284-1287. doi:10.1038/ng.3656PubMedGoogle ScholarCrossref
30.
Chang  CC, Chow  CC, Tellier  LCAM, Vattikuti  S, Purcell  SM, Lee  JJ.  Second-generation PLINK: rising to the challenge of larger and richer datasets.  Gigascience. 2015;4:7. doi:10.1186/s13742-015-0047-8PubMedGoogle ScholarCrossref
31.
Fried  LP, Borhani  NO, Enright  P,  et al.  The cardiovascular health study: design and rationale.  Ann Epidemiol. 1991;1(3):263-276. doi:10.1016/1047-2797(91)90005-WPubMedGoogle ScholarCrossref
32.
Psaty  BM, O’Donnell  CJ, Gudnason  V,  et al; CHARGE Consortium.  Cohorts for Heart and Aging Research in Genomic Epidemiology (CHARGE) Consortium: design of prospective meta-analyses of genome-wide association studies from 5 cohorts.  Circ Cardiovasc Genet. 2009;2(1):73-80. doi:10.1161/CIRCGENETICS.108.829747PubMedGoogle ScholarCrossref
33.
Willer  CJ, Li  Y, Abecasis  GR.  METAL: fast and efficient meta-analysis of genomewide association scans.  Bioinformatics. 2010;26(17):2190-2191. doi:10.1093/bioinformatics/btq340PubMedGoogle ScholarCrossref
34.
Ward  LD, Kellis  M.  HaploReg: a resource for exploring chromatin states, conservation, and regulatory motif alterations within sets of genetically linked variants.  Nucleic Acids Res. 2012;40(Database issue):D930-D934. doi:10.1093/nar/gkr917PubMedGoogle ScholarCrossref
35.
GTEx Consortium.  Human genomics, The Genotype-Tissue Expression (GTEx) pilot analysis: multitissue gene regulation in humans.  Science. 2015;348(6235):648-660. doi:10.1126/science.1262110PubMedGoogle ScholarCrossref
36.
Wagner  AH, Anand  VN, Wang  WH,  et al.  Exon-level expression profiling of ocular tissues.  Exp Eye Res. 2013;111:105-111. doi:10.1016/j.exer.2013.03.004PubMedGoogle ScholarCrossref
37.
You  J, Corley  SM, Wen  L,  et al.  RNA-Seq analysis and comparison of corneal epithelium in keratoconus and myopia patients.  Sci Rep. 2018;8(1):389. doi:10.1038/s41598-017-18480-xPubMedGoogle ScholarCrossref
38.
Birch-Machin  MA, Russell  EV, Latimer  JA.  Mitochondrial DNA damage as a biomarker for ultraviolet radiation exposure and oxidative stress.  Br J Dermatol. 2013;169(suppl 2):9-14. doi:10.1111/bjd.12207PubMedGoogle ScholarCrossref
39.
Afshari  NA, Igo  RP  Jr, Morris  NJ,  et al.  Genome-wide association study identifies three novel loci in Fuchs endothelial corneal dystrophy.  Nat Commun. 2017;8:14898. doi:10.1038/ncomms14898PubMedGoogle ScholarCrossref
40.
Jurkunas  U, Azar  DT.  Potential complications of ocular surgery in patients with coexistent keratoconus and Fuchs’ endothelial dystrophy.  Ophthalmology. 2006;113(12):2187-2197. doi:10.1016/j.ophtha.2006.06.036PubMedGoogle ScholarCrossref
41.
Vira  S, Abugo  U, Shih  CY,  et al.  Descemet stripping endothelial keratoplasty for the treatment of combined fuchs corneal endothelial dystrophy and keratoconus.  Cornea. 2014;33(1):1-5. doi:10.1097/ICO.0b013e3182a7389cPubMedGoogle ScholarCrossref
Limit 200 characters
Limit 25 characters
Conflicts of Interest Disclosure

Identify all potential conflicts of interest that might be relevant to your comment.

Conflicts of interest comprise financial interests, activities, and relationships within the past 3 years including but not limited to employment, affiliation, grants or funding, consultancies, honoraria or payment, speaker's bureaus, stock ownership or options, expert testimony, royalties, donation of medical equipment, or patents planned, pending, or issued.

Err on the side of full disclosure.

If you have no conflicts of interest, check "No potential conflicts of interest" in the box below. The information will be posted with your response.

Not all submitted comments are published. Please see our commenting policy for details.

Limit 140 characters
Limit 3600 characters or approximately 600 words
    Original Investigation
    December 19, 2019

    Association of Genetic Variation With Keratoconus

    Author Affiliations
    • 1Menzies Institute for Medical Research, University of Tasmania, Hobart, Tasmania, Australia
    • 2Centre for Eye Research Australia, Melbourne, Victoria, Australia
    • 3Department of Surgery (Ophthalmology), Royal Victorian Eye and Ear Hospital, University of Melbourne, Melbourne, Victoria, Australia
    • 4Department of Surgery, Cedars-Sinai Medical Center, Los Angeles, California
    • 5Cornea Genetic Eye Institute, Beverly Hills, California
    • 6Board of the Governors Regenerative Medicine Institute, Cedars-Sinai Medical Center, Los Angeles, California
    • 7Biomedical Sciences Research Institute, Ulster University, Coleraine, Northern Ireland, United Kingdom
    • 8Belfast Health and Social Care Trust, Belfast, Northern Ireland, United Kingdom
    • 9Vision Eye Institute, Melbourne, Victoria, Australia
    • 10School of Primary and Allied Health Care, Monash University, Melbourne, Victoria, Australia
    • 11Melbourne Stem Cell Centre, Melbourne, Victoria, Australia
    • 12QIMR Berghofer Medical Research Institute, Brisbane, Australia
    • 13Centre for Vision Research, Westmead Institute for Medical Research, University of Sydney, Sydney, New South Wales, Australia
    • 14Department of Ophthalmology, Flinders University, Adelaide, South Australia, Australia
    • 15Lions Eye Institute, University of Western Australia, Perth, Western Australia, Australia
    • 16Institute for Translational Genomics and Population Science, Los Angeles Biomedical Research Institute, Los Angeles, California
    • 17Department of Pediatrics, Harbor-UCLA Medical Center, Torrance, California
    • 18Cardiovascular Health Research Unit, Department of Medicine, University of Washington, Seattle
    JAMA Ophthalmol. 2020;138(2):174-181. doi:10.1001/jamaophthalmol.2019.5293
    Key Points

    Question  Which genetic loci are associated with keratoconus?

    Findings  In this case-control genome-wide association study of a discovery cohort and 3 independent replication cohorts, a locus containing multiple variants across 6 protein-coding genes on chromosome 11 was associated with keratoconus. Several of these genes are likely involved in apoptotic pathways.

    Meaning  This study of patients with keratoconus and control participants showed a potential role of genes involved in apoptotic pathways.

    Abstract

    Importance  Keratoconus is a condition in which the cornea progressively thins and protrudes in a conical shape, severely affecting refraction and vision. It is a major indication for corneal transplant. To discover new genetic loci associated with keratoconus and better understand the causative mechanism of this disease, we performed a genome-wide association study on patients with keratoconus.

    Objective  To identify genetic susceptibility regions for keratoconus in the human genome.

    Design, Setting, and Participants  This study was conducted with data from eye clinics in Australia, the United States, and Northern Ireland. The discovery cohort of individuals with keratoconus and control participants from Australia was genotyped using the Illumina HumanCoreExome single-nucleotide polymorphism array. After quality control and data cleaning, genotypes were imputed against the 1000 Genomes Project reference panel (phase III; version 5), and association analyses were completed using PLINK version 1.90. Single-nucleotide polymorphisms with P < 1.00 × 10−6 were assessed for replication in 3 additional cohorts. Control participants were drawn from the cohorts of the Blue Mountains Eye Study and a previous study of glaucoma. Replication cohorts were from a previous keratoconus genome-wide association study data set from the United States, a cohort of affected and control participants from Australia and Northern Ireland, and a case-control cohort from Victoria, Australia. Data were collected from January 2006 to March 2019.

    Main Outcomes and Measures  Associations between keratoconus and 6 252 612 genetic variants were estimated using logistic regression after adjusting for ancestry using the first 3 principal components.

    Results  The discovery cohort included 522 affected individuals and 655 control participants, while the replication cohorts included 818 affected individuals (222 from the United States, 331 from Australia and Northern Ireland, and 265 from Victoria, Australia) and 3858 control participants (2927 from the United States, 229 from Australia and Northern Ireland, and 702 from Victoria, Australia). Two novel loci reached genome-wide significance (defined as P < 5.00 × 10−8), with a P value of 7.46 × 10−9 at rs61876744 in patatin-like phospholipase domain–containing 2 gene (PNPLA2) on chromosome 11 and a P value of 6.35 × 10−12 at rs138380, 2.2 kb upstream of casein kinase I isoform epsilon gene (CSNK1E) on chromosome 22. One additional locus was identified with a P value less than 1.00 × 10−6 in mastermind-like transcriptional coactivator 2 (MAML2) on chromosome 11 (P = 3.91 × 10−7). The novel locus in PNPLA2 reached genome-wide significance in an analysis of all 4 cohorts (P = 2.45 × 10−8).

    Conclusions and Relevance  In this relatively large keratoconus genome-wide association study, we identified a genome-wide significant locus for keratoconus in the region of PNPLA2 on chromosome 11.

    Introduction

    Keratoconus is characterized by progressive thinning of the cornea, the clear tissue at the front of the eye. Asymmetrical bulging and conical protrusion of the cornea leads to extreme refractive error (myopia and irregular astigmatism), causing severe visual impairment.1 Keratoconus is relatively common, with a reported prevalence of around 55 per 100 000 individuals in white populations1 and up to 229 per 100 000 individuals in Asian populations.2 Because of recent advances in diagnostic imaging, it is now thought that the true incidence and prevalence of keratoconus may be 5 to 10 times higher than previously reported.3 The causative mechanism of keratoconus is poorly understood. Associations have been made with eye rubbing and atopy, but no direct connection has been established.4 Various biochemical pathways may be involved, including oxidative stress, apoptosis, and disruption to extracellular matrix turnover.5

    Although many cases of keratoconus present as sporadic, there is a well-recognized genetic component to the disease. The estimated prevalence in relatives of patients with keratoconus is 3.34% (95% CI, 3.22%-3.46%), which is 15 to 67 times higher than in the general population.6 In addition, more than 20 syndromes are associated with keratoconus, including Down syndrome, Leber congenital amaurosis, and several connective tissue disorders.7 Linkage studies have identified at least 16 loci for keratoconus8,9; however, the causative genes and variants have remained elusive. Common variants in the dedicator of cytokinesis 9 (DOCK9)10 and lysyl oxidase (LOX)11 genes have been implicated, as well as rare mutations in microRNA 184 (MIR184),12,13 although these loci have not been broadly replicated. Genome-wide association studies (GWAS) have implicated several loci. Variation in the promoter region of the hepatocyte growth factor (HGF) gene14,15 and upstream of the Rab3 guanosine triphosphatase–activating protein catalytic subunit (RAB3GAP1) gene16,17 have both been associated in multiple independent studies. Furthermore, a GWAS for central corneal thickness (CCT) identified loci that are also associated with keratoconus, including retinoid X receptor alpha–collagen alpha-1 (RXRACOL5A1), forkhead box protein O1 (FOXO1), and fibronectin type III domain containing 3B (FNDC3B),18 and more recently, a suggestive but nonsignificant association at the decorin (DCN) gene.19

    We present findings from a GWAS of 522 patients with keratoconus, a relatively large sample for this complex disease, plus control participants. We also sought association and independent replication in additional cohorts.

    Methods
    Study Design

    We report a study of 4 independent cohorts of white patients with keratoconus. The first case-control cohort used for the discovery phase included patients with keratoconus and control participants. All single-nucleotide polymorphisms (SNPs) with P values less than 1.00 × 10−6 were looked up in imputed genotypes from a previously published GWAS study of patients with keratoconus and control participants from the United States. In addition, SNPs were genotyped in an independent replication cohort of affected individuals and control participants from Australia and Northern Ireland and additional affected individuals and control participants from a Victoria, Australia, cohort.

    Ethics

    The protocol was approved by the Southern Adelaide Clinical Human Research Ethics Committee, and the Human Research Ethics Committee of the Royal Victorian Eye and Ear Hospital, and the Health and Medical Human Research Ethics Committee of the University of Tasmania. All participants gave written informed consent, and the study conformed to the tenets of the Declaration of Helsinki.

    Discovery Cohort

    Participants with keratoconus were ascertained through the eye clinic of Flinders Medical Centre, Adelaide, Australia; optometry and ophthalmology clinics in Adelaide and Melbourne, Australia; or an Australia-wide invitation to members of Keratoconus Australia, a community-based support group for patients. Clinical data were obtained from the participants’ eye care practitioners.

    The diagnosis of keratoconus was based on both clinical examination and videokeratography pattern analysis, as described previously.20 Clinical examinations included slitlamp biomicroscopy, cycloplegic retinoscopy, and fundus evaluation. Slitlamp biomicroscopy was used to identify stromal corneal thinning, Vogts striae, or a Fleischer ring. Retinoscopy examinations were performed on a fully dilated pupil to determine the presence or absence of retroillumination signs of keratoconus, such as the oil-droplet sign and scissoring of the red reflex. Videokeratography evaluation was performed on each eye using the Orbscan (Orbtek [Bausch & Lomb]). Patients were classified as having keratoconus if they had at least 1 clinical sign of keratoconus and a confirmatory videokeratography.21 A history of penetrating keratoplasty performed because of keratoconus was also sufficient for inclusion as an affected individual. Patients with syndromic forms of keratoconus were excluded, and if multiple individuals from the same family presented, only 1 was included in the study.

    Control participant data were obtained from the Australian cohort previously described in a GWAS for age-related macular degeneration (AMD) from the International AMD Genomics Consortium22; this cohort has been described in detail previously. For the current analysis, data from 676 Australian unaffected control participants (including 465 from the Blue Mountains Eye Study23 and healthy individuals previously recruited as control participants for a study of glaucoma24) were combined as control participants for keratoconus. Genetically related individuals and those who did not pass all sample quality-control procedures for the AMD GWAS were excluded. The DNA for affected individuals and control participants was extracted from whole blood using the QiaAMP DNA Maxi kit (Qiagen).

    Genotyping and Data Quality Control

    Individuals with keratoconus were genotyped for 551 839 variants with the HumanCoreExome array (HumanCoreExome-24v1-1_A [Illumina]). For the control participants, genotypes of 569 645 variants were generated with a customized Illumina HumanCoreExome array (called HumanCoreExome_Goncalo_15038949_A), as described previously.22

    Quality-control procedures were carried out according to the protocol described by Anderson et al,25 modified as follows. Reverse and ambiguous-strand SNPs were detected using snpflip (https://github.com/biocore-ntnu/snpflip; accessed March 24, 2017) and flipped or excluded. Only SNPs common to both arrays were included. Individuals with discordant sex information, a missing genotype rate greater than 0.05, or heterozygosity more than 3 SDs from the mean were excluded. Genetically related individuals were detected by calculating pairwise identity by descent, and the individual with the lower genotyping rate in any pair with identity by descent greater than 0.185 was removed. Ancestry outliers were identified by principal component analysis using EIGENSTRAT26 and removed. Markers were excluded if they had missing genotype rate greater than 3%, had significantly different missing data rates between affected individuals and control participants, had minor allele frequency less than 0.01, or deviated significantly (P < 10−5) from Hardy-Weinberg equilibrium. Following all exclusions and quality control, affected individuals and control participants were genotyped for 264 115 common-platform SNPs.

    Genomic Imputation and Association Analysis

    We phased autosomal genotype data using Eagle (version 2.3.5 [Broad Institute])27 and then imputed genotypes on the basis of the European super population of the 1000 Genomes Project reference panel (phase III; version 5)28 using Minimac3 (version 2.0.1 [Center for Statistical Genetics]).29 We excluded indels, SNPs within 5 base pairs of an indel, rare variants (minor allele frequency <0.01), and variants with poor imputation quality (R2 <0.8). This filtering yielded a total of 6 252 612 quality-controlled variants, including 250 964 genotyped variants. Association analyses were performed on the most-likely genotypes under a logistic regression model using PLINK (version 1.90 [Christopher Chang/Grail Inc])30 using the first 3 principal components as covariates. Any P value less than 5.00 × 10−8 was considered significant.

    Replication Cohorts and Analyses

    The US cohort has been previously described.17 Briefly, white individuals who were clinically affected by keratoconus were enrolled into the GWAS17 as a part of the longitudinal videokeratography and genetic study at the Cornea Genetic Eye Institute.6 After removing samples with poor genotyping quality, samples were included in the analysis.

    Control participants were obtained from the Cardiovascular Health Study (CHS), a population-based cohort study of risk factors for cardiovascular disease and stroke in adults 65 years or older, recruited at 4 field centers.31,32 The CHS was approved by the institutional review board at each recruitment site, and participants provided informed consent for the use of their genetic information. African American CHS participants were excluded from analysis because of an insufficient number of racially/ethnically matched affected individuals. The samples included in the analysis were therefore from self-reported white individuals. Participants did not have an eye examination to exclude keratoconus. Outliers detected by principal component analysis were excluded, and the analysis was adjusted for the top 3 principal components.

    IMPUTE version 2.3.0 (Marchini Research Group) was used to perform imputation of the genotyping data from 370K BeadChip arrays (Illumina) in patients with keratoconus and control participants from the CHS using 1000 Genomes Phase I data as the reference panel. All SNPs with P values less than 1.00 × 10−6 identified in the discovery analysis were looked up and extracted, with the exception of 4 SNPs at the casein kinase I isoform epsilon gene (CSNK1E) locus, which were not imputed in the US study. The SNPs with P values less than 1.00 × 10−6 in the discovery cohort were selected for genotyping in additional replication cohorts using the MassARRAY System (Agena Bioscience) by the Australian Genome Research Facility. The SNPs were chosen from each locus that were compatible with the assay design, with preference given to SNPs with the smallest P value in the discovery cohort. Twenty-seven SNPs were genotyped in additional affected individuals recruited under the same protocol as the discovery cohort, as well as in patients from Northern Ireland, as described previously.14 These individuals were compared with unaffected examined control participants, consisting of individuals from the Blue Mountains Eye Study not included in the discovery cohort and older individuals recruited from nursing homes in the Launceston area of Tasmania, Australia. All control participants underwent a thorough ocular examination, and keratoconus was excluded. These SNPs were also genotyped in a replication cohort from Melbourne, Victoria, Australia, consisting of affected individuals and examined control participants, as described previously.15

    Association was assessed in each cohort individually using logistic regression. Analysis of results from discovery and replication cohorts was performed using METAL version 2011-03-25 (University of Michigan Center for Statistical Genetics).33

    Functional Annotation of Associated Variants

    The lead SNP at the novel locus on chromosome 11, rs61876744, was queried in HaploReg version 4.1 (https://pubs.broadinstitute.org/mammals/haploreg/haploreg.php; accessed May 28, 2019),34 including data from the Genotype–Tissue Expression (GTex) pilot analysis.35 Genes in the associated region were assessed for ocular tissue expression using the Ocular Tissue Database (https://genome.uiowa.edu/otdb/)36 and differential expression between corneas from patients with keratoconus and myopia in a previously published study.37

    Results
    Cohort Characteristics

    The first case-control cohort included 522 participants with keratoconus (290 men [55.6%]; mean [SD] age, 45 [15.2] years) and 655 control participants (307 men [46.9%]; mean [SD] age, 65 [10.6] years), after exclusions were completed. Candidate P values (<1.00 × 10−6) were looked up in a previous GWAS cohort of 222 US patients with keratoconus (123 men [55.4%]; mean [SD] age, 44 [13.3] years) and 3324 US control participants (1149 men [39.3%]; mean [SD] age, 72 [5.4] years). Another 27 SNPs were genotyped in the Australia and Northern Ireland cohort, which included 331 affected individuals (186 from Australia and 175 from Northern Ireland; 203 men [61.3%]; mean [SD] age, 41 [15.9] years) and 229 control participants (84 from the Blue Mountains Study in Australia who were not included in the discovery cohort and 145 nursing home residents in Launceston, Tasmania, Australia; 84 men [36.7%]; mean [SD] age, 75 [11.5] years), and the cohort from Melbourne, Victoria, Australia, which included 265 affected individuals (159 men [60.0%]; mean [SD] age, 35 [14.9] years) and 702 control participants (268 men [38.2%]; mean [SD] age, 52 [15.2] years). The demographic data for each cohort are given in Table 1.

    Genome-Wide Association Testing of the Discovery Cohort

    Genome-wide association analysis was conducted in the discovery cohort (Figure; eFigure 1 in the Supplement). The genomic inflation factor was λ1000 = 1.023, and all included samples were from individuals of European ancestry (eFigure 2 in the Supplement). Two novel loci reached genome-wide significance (defined as P < 5.00 × 10−8), with a P value of 7.46 × 10−9 at rs61876744 in the patatin-like phospholipase domain–containing 2 gene (PNPLA2) on chromosome 11 and a P value of 6.35 × 10−12 at rs138380, 2.2 kb upstream of the CSNK1E gene, on chromosome 22. One additional locus was identified (P < 1.00 × 10−6) in the mastermind-like transcriptional coactivator 2 (MAML2) gene on chromosome 11 (rs10831500; P = 3.91 × 10−7). Association results for all SNPs with P values less than 1.00 × 10−6 are shown in eTable 1 in the Supplement. Locus-specific plots for all 3 loci are shown in eFigure 3 in the Supplement. The locus on chromosome 11 showing genome-wide significance included 25 SNPs with P values less than 1.00 × 10−6 spanning 6 protein-coding genes (cell cycle exit and neuronal differentiation 1 [CEND1], solute carrier family 25 member 22 [SLC25A22], proapoptotic nucleolar protein 1 [PANO1], P53-induced death domain protein 1 [PIDD1], 60S acidic ribosomal protein P2 [RPLP2], and PNPLA2) and multiple RNA genes.

    Because previously reported keratoconus risk loci were initially identified in GWAS for CCT, we assessed each of the loci reaching suggestive significance in the current analysis in the previously reported analysis for CCT. As shown in Table 2, only the MAML2 locus showed nominal association with CCT (in this analysis: minor allele frequency: affected individuals, 0.422; control participants, 0.330; P = 3.91 × 10−7; in the CCT analysis: P = .01). We also looked up the lead SNPs from the CCT analysis in the results from the discovery cohort (eTable 2 in the Supplement). The SNPs at the FNDC3B gene (in this analysis: minor allele frequency: affected individuals, 0.240; control participants, 0.193; P = 1.82 × 10−3; in the CCT analysis: P = 7.22 × 10−14), the multiple PDZ domain crumbs cell polarity complex component (MPDZ) gene (in this analysis: minor allele frequency: affected individuals, 0.231; control participants, 0.170; P = 7.81 × 10−3; in the CCT analysis: P = 3.01 × 10−4), and the mothers against decapentaplegic homolog 3 (SMAD3) gene (in this analysis: minor allele frequency: affected individuals, 0.172; control participants, 0.211; P = 8.03 × 10−3; in the CCT analysis: P = 6.40 × 10−9) genes showed nominal associations in this analysis.

    Association Testing of the Replication Cohorts

    All SNPs with P values less than 1.00 × 10−6 identified in the discovery cohort were analyzed in the previously generated GWAS data for the US cohort. Twenty-seven SNPs compatible with a single-assay design were genotyped in the Australia and Northern Ireland and Victoria, Australia, replication cohorts (Table 3; eTable 3 in the Supplement).

    Multiple SNPs in both novel loci at PNPLA2 and MAML2 showed association with P values less than .05 in the US cohort. None of the SNPs in the CSNK1E locus reached significance, although all showed the same direction of negative association as in the discovery cohort. None of the 3 loci reached significance in either the Australia and Northern Ireland or the Victoria, Australia, replication cohort, but most SNPs showed the same (varying) directions of association as in the discovery cohort. Minor allele frequencies for affected individuals and control participants in each cohort are given in eTable 3 in the Supplement.

    Analysis of Combined Data

    Analysis of the combined data from the discovery and all 3 replication cohorts found 12 SNPs at the novel PNPLA2 locus to be associated with keratoconus at genome-wide significance (P < 5.00 × 10−8). The MAML2 locus on chromosome 11 showed suggestive but nonsignificant association, with a P value of 3.83 × 10−6 at rs10831500. The CSNK1E locus on chromosome 22 reached a P value of 3.18 × 10−4 at rs138378. For all 3 loci, there were other SNPs with smaller P values in the combined analysis, but these did not include data for all replication cohorts (eTable 3 in the Supplement).

    Functional Annotations of Novel Associated Loci

    At the significant locus on chromosome 11, the lead SNP rs61876744 is located in the second intron of the PNPLA2 gene (NM_020376.3). The associated region extends for around 40 kbp, encompassing multiple transcripts. The PNPLA2 gene is highly expressed in all eye tissues assessed in the ocular tissue database, including the cornea, as are other protein-coding genes at this locus (eTable 4 in the Supplement). A recent study37 compared gene expression in corneal epithelium from patients with keratoconus and myopia and found that PNPLA2 and PIDD1 were differentially expressed, with a false-discovery rate (FDR) of less than 0.05 (PNPLA2: log2 fold change, −1.277; P = 1.38 × 10−4; FDR, 0.028; PIDD1: log2 fold change, −1.429; P = 1.19 × 10−3; FDR, 0.046; eTable 4 in the Supplement), while RPLP2 and CSNK1E are significant at an FDR less than 0.10 (RPLP2: log2 fold change, −0.659; P = 5.37 × 10−3; FDR, 0.078; CSNK1E: log2 fold change, −0.436; P = 5.30 × 10−3; FDR, 0.078).

    HaploReg identified 7 SNPs in strong linkage disequilibrium with the lead SNP (rs61876744), and all report an expression quantitative trait locus (eQTL) for an antisense RNA transcript AP006621 (multiple transcripts 1 through 8) in multiple tissues assessed in GTex (eTable 5 in the Supplement), in which the more common allele, C, is associated with increased transcript levels (P = 4.6 × 10−18 for AP006621.5 in sun-exposed skin; eFigure 4 in the Supplement). This RNA gene is not represented in the ocular tissue database. A similar finding is seen in GTex for the PIDD1 gene in sun-exposed skin (P = 1.5 × 10−16; eFigure 4 in the Supplement).

    Discussion

    This study has identified a candidate locus for keratoconus on chromosome 11 that shows replication in the US data and consistent direction of association in the other cohorts. The lead SNP is located in an intron of PNPLA2. This gene encodes patatin-like phospholipase domain-containing protein 2, which catalyzes the initial step in triglyceride hydrolysis. The relevance of this pathway to keratoconus is not obvious, but it is well known that the closest gene to an association signal is not necessarily the causative gene. At least 4 other protein-coding genes at this locus are also expressed in the cornea, and RNA-coding genes are also annotated in the region. There is a strong eQTL signal of the lead SNP rs61876744 for an antisense RNA gene, AP006621.8, which is located on the opposite strand to the protein-coding PNPLA2 gene. The antisense RNA AP006621 transcripts may have a role in regulating PNPLA2 or other genes at this locus and elsewhere. The minor allele at rs61876744, T, is associated with reduced risk of keratoconus and reduced expression of AP006621 in many tissues. This suggests that overexpression of AP006621 may destabilize corneal structures. Oxidative stress and apoptosis have been suggested as part of the pathogenesis of keratoconus5 and sun (or ultraviolet light) exposure is known to trigger oxidative stress and DNA-damage pathways.38 Several genes at this locus likely play a role in apoptotic pathways, including PPID and PANO1.

    The chromosome 11 locus overlaps with a previously reported (although not genome-wide significant) association signal for Fuchs endothelial corneal dystrophy (FECD).39 The lead SNP in the FECD GWAS is rs12223324 in the Parkinson disease 7 domain-containing protein 1 (PDDC1) gene, which is upstream of PNPLA2. This SNP does not reach significance in this keratoconus GWAS. It is unknown how this locus might lead to FECD, but the overlap of genetic association with keratoconus is intriguing, given that both diseases affect the cornea. Although rare, there are reports in the literature of patients with both FECD and keratoconus.40,41 The participants in the current study do not have FECD, and thus this disease does not account for the association observed here.

    Although we observed the strongest association in the discovery cohort at the CSNK1E locus, this result was not replicated. The signal appears to be driven by a single genotyped SNP that has influenced the imputation of a surrounding linkage disequilibrium block (eTable 1 and eFigure 3 in the Supplement). The signal at the MAML2 locus is supported by the US replication cohort. Further replication of these loci is required before any firm conclusions can be drawn.

    Previous GWAS for keratoconus (using a subset of cases involved in the current study) reported RAB3GAP117 and a region upstream of the HGF gene,14 although neither study reached genome-wide significance. The lead SNPs at these loci reached nonsignificant P values in the current discovery cohort, suggesting these loci are not major contributors in this better-powered study. Previously reported keratoconus-associated loci with genome-wide significance (RXRACOL5A1, FOXO1, and FNDC3B) are also associated with CCT. The current findings suggest that mechanisms other than susceptibility to a thinner cornea may also be at play in the genetic risk of keratoconus.

    Limitations

    All control cohorts used in this study had older mean ages than the case cohorts; thus, it will be important to assess these loci for age-associated effects in future studies. Batch effects are a potential problem in an analysis in which cases and controls are genotyped separately; however, the low inflation factor seen in this analysis offers reassurance that batch effects are unlikely to have a major influence.

    Conclusions

    In summary, this study has identified a locus for keratoconus on chromosome 11. The lead SNP is in an intron of the PNPLA2 gene and an eQTL for a long noncoding RNA, AP006621.8. We have also assessed loci near MAML2 and CSNK1E that require further replication. It is very likely that additional risk loci exist for keratoconus, and larger studies will be needed to identify them.

    Back to top
    Article Information

    Accepted for Publication: October 25, 2019.

    Corresponding Author: Bennet J. McComish, PhD, Menzies Institute for Medical Research, University of Tasmania, Private Bag 23, Hobart 7000, Tasmania, Australia (bennet.mccomish@utas.edu.au).

    Published Online: December 19, 2019. doi:10.1001/jamaophthalmol.2019.5293

    Author Contributions: Drs McComish and Burdon had full access to all of the data in the study and take responsibility for the integrity of the data and the accuracy of the data analysis.

    Concept and design: Bykhovskaya, Willoughby, Charlesworth, Mills, Rotter, Baird, Craig, Burdon.

    Acquisition, analysis, or interpretation of data: McComish, Sahebjada, Willoughby, Richardson, Tenen, Charlesworth, MacGregor, Mitchell, Lucas, Mackey, Li, Wang, Jensen, Rotter, Taylor, Hewitt, Rabinowitz, Baird, Craig, Burdon.

    Drafting of the manuscript: McComish, Charlesworth, Lucas, Li, Baird.

    Critical revision of the manuscript for important intellectual content: McComish, Sahebjada, Bykhovskaya, Willoughby, Richardson, Tenen, Charlesworth, MacGregor, Mitchell, Mills, Mackey, Wang, Jensen, Rotter, Taylor, Hewitt, Rabinowitz, Baird, Craig, Burdon.

    Statistical analysis: McComish, Sahebjada, Charlesworth, MacGregor, Li, Jensen, Rotter, Taylor, Baird, Burdon.

    Obtained funding: Mills, Mackey, Rotter, Hewitt, Rabinowitz, Baird, Craig, Burdon.

    Administrative, technical, or material support: Bykhovskaya, Willoughby, Richardson, MacGregor, Lucas, Mackey, Rotter, Hewitt, Baird.

    Supervision: Charlesworth, Mitchell, Rotter, Hewitt, Rabinowitz, Baird, Craig, Burdon.

    Conflict of Interest Disclosures: Dr Rotter reported grants from the National Institutes of Health during the conduct of the study. Dr Burdon reported grants from National Health and Medical Research Council during the conduct of the study. No other disclosures were reported.

    Funding/Support: The Genotype–Tissue Expression Project was supported by the Common Fund of the Office of the Director of the National Institutes of Health, and by National Cancer Institute, National Human Genome Research Institute, National Heart, Lung, and Blood Institute, National Institute on Drug Abuse, National Institute of Mental Health, and National Institute of Neurological Disorders and Stroke. The data used for the analyses described in the manuscript were obtained from the Genotype–Tissue Expression Portal on May 27, 2019. This study was supported by the Australian National Health and Medical Research Council (project grant GNT1104700) and Senior Research Fellowships (grant 1138585 [Dr Baird] and 1059954 [Dr Burdon]). The Centre for Eye Research Australia receives Operational Infrastructure Support from the Victorian Government. The discovery case cohort was funded by a National Health and Medical Research Council Centre for Research Excellence grant (1023911). Control genotype data for the discovery cohort were provided by the International AMD Genetics Consortium genotyped under the Center for Inherited Diseases Research Program (contract number HHSN268201200008I). The US replication cohort is supported in part by the National Eye Institute (grant R01 EY009052). The provision of genotyping data was supported in part by the National Center for Advancing Translational Sciences (Clinical Translational Science Institute grant UL1TR001881) and the National Institute of Diabetes and Digestive and Kidney Disease Diabetes Research Center (grant DK063491) to the Southern California Diabetes Endocrinology Research Center. The Cardiovascular Health Study (control cohort) was supported by the National Heart, Lung, and Blood Institute (grants HHSN268201200036C, HHSN268200800007C, HHSN268201800001C, N01HC55222, N01HC85079, N01HC85080, N01HC85081, N01HC85082, N01HC85083, N01HC85086, U01HL080295, and U01HL130114), with additional contribution from the National Institute of Neurological Disorders and Stroke. Additional support was provided by the National Institute on Aging (grant R01AG023629).

    Role of the Funder/Sponsor: The funders had no role in the design and conduct of the study; collection, management, analysis, and interpretation of the data; preparation, review, or approval of the manuscript; and decision to submit the manuscript for publication.

    Disclaimer: The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health.

    Additional Information: A full list of principal Cardiovascular Health Study investigators and institutions can be found at CHS–NHLBI.org.

    References
    1.
    Rabinowitz  YS.  Keratoconus.  Surv Ophthalmol. 1998;42(4):297-319. doi:10.1016/S0039-6257(97)00119-7PubMedGoogle ScholarCrossref
    2.
    Pearson  AR, Soneji  B, Sarvananthan  N, Sandford-Smith  JH.  Does ethnic origin influence the incidence or severity of keratoconus?  Eye (Lond). 2000;14(Pt 4):625-628. doi:10.1038/eye.2000.154PubMedGoogle ScholarCrossref
    3.
    Godefrooij  DA, de Wit  GA, Uiterwaal  CS, Imhof  SM, Wisse  RP.  Age-specific incidence and prevalence of keratoconus: a nationwide registration study.  Am J Ophthalmol. 2017;175:169-172. doi:10.1016/j.ajo.2016.12.015PubMedGoogle ScholarCrossref
    4.
    Zadnik  K, Barr  JT, Edrington  TB,  et al.  Baseline findings in the Collaborative Longitudinal Evaluation of Keratoconus (CLEK) Study.  Invest Ophthalmol Vis Sci. 1998;39(13):2537-2546.PubMedGoogle Scholar
    5.
    Wojcik  KA, Blasiak  J, Szaflik  J, Szaflik  JP.  Role of biochemical factors in the pathogenesis of keratoconus.  Acta Biochim Pol. 2014;61(1):55-62. doi:10.18388/abp.2014_1923PubMedGoogle ScholarCrossref
    6.
    Wang  Y, Rabinowitz  YS, Rotter  JI, Yang  H.  Genetic epidemiological study of keratoconus: evidence for major gene determination.  Am J Med Genet. 2000;93(5):403-409. doi:10.1002/1096-8628(20000828)93:5<403::AID-AJMG11>3.0.CO;2-APubMedGoogle ScholarCrossref
    7.
    Nowak  DM, Gajecka  M.  The genetics of keratoconus.  Middle East Afr J Ophthalmol. 2011;18(1):2-6. doi:10.4103/0974-9233.75876PubMedGoogle ScholarCrossref
    8.
    Abu-Amero  KK, Al-Muammar  AM, Kondkar  AA.  Genetics of keratoconus: where do we stand?  J Ophthalmol. 2014;2014:641708. doi:10.1155/2014/641708PubMedGoogle Scholar
    9.
    Burdon  KP, Vincent  AL.  Insights into keratoconus from a genetic perspective.  Clin Exp Optom. 2013;96(2):146-154. doi:10.1111/cxo.12024PubMedGoogle ScholarCrossref
    10.
    Czugala  M, Karolak  JA, Nowak  DM,  et al.  Novel mutation and three other sequence variants segregating with phenotype at keratoconus 13q32 susceptibility locus.  Eur J Hum Genet. 2012;20(4):389-397. doi:10.1038/ejhg.2011.203PubMedGoogle ScholarCrossref
    11.
    Bykhovskaya  Y, Li  X, Epifantseva  I,  et al.  Variation in the lysyl oxidase (LOX) gene is associated with keratoconus in family-based and case-control studies.  Invest Ophthalmol Vis Sci. 2012;53(7):4152-4157. doi:10.1167/iovs.11-9268PubMedGoogle ScholarCrossref
    12.
    Hughes  AE, Bradley  DT, Campbell  M,  et al.  Mutation altering the miR-184 seed region causes familial keratoconus with cataract.  Am J Hum Genet. 2011;89(5):628-633. doi:10.1016/j.ajhg.2011.09.014PubMedGoogle ScholarCrossref
    13.
    Lechner  J, Bae  HA, Guduric-Fuchs  J,  et al.  Mutational analysis of MIR184 in sporadic keratoconus and myopia.  Invest Ophthalmol Vis Sci. 2013;54(8):5266-5272. doi:10.1167/iovs.13-12035PubMedGoogle ScholarCrossref
    14.
    Burdon  KP, Macgregor  S, Bykhovskaya  Y,  et al.  Association of polymorphisms in the hepatocyte growth factor gene promoter with keratoconus.  Invest Ophthalmol Vis Sci. 2011;52(11):8514-8519. doi:10.1167/iovs.11-8261PubMedGoogle ScholarCrossref
    15.
    Sahebjada  S, Schache  M, Richardson  AJ, Snibson  G, Daniell  M, Baird  PN.  Association of the hepatocyte growth factor gene with keratoconus in an Australian population.  PLoS One. 2014;9(1):e84067. doi:10.1371/journal.pone.0084067PubMedGoogle Scholar
    16.
    Bae  HA, Mills  RA, Lindsay  RG,  et al.  Replication and meta-analysis of candidate loci identified variation at RAB3GAP1 associated with keratoconus.  Invest Ophthalmol Vis Sci. 2013;54(7):5132-5135. doi:10.1167/iovs.13-12377PubMedGoogle ScholarCrossref
    17.
    Li  X, Bykhovskaya  Y, Haritunians  T,  et al.  A genome-wide association study identifies a potential novel gene locus for keratoconus, one of the commonest causes for corneal transplantation in developed countries.  Hum Mol Genet. 2012;21(2):421-429. doi:10.1093/hmg/ddr460PubMedGoogle ScholarCrossref
    18.
    Lu  Y, Vitart  V, Burdon  KP,  et al; NEIGHBOR Consortium.  Genome-wide association analyses identify multiple loci associated with central corneal thickness and keratoconus.  Nat Genet. 2013;45(2):155-163. doi:10.1038/ng.2506PubMedGoogle ScholarCrossref
    19.
    Iglesias  AI, Mishra  A, Vitart  V,  et al; Blue Mountains Eye Study—GWAS group; NEIGHBORHOOD Consortium; Wellcome Trust Case Control Consortium 2 (WTCCC2).  Cross-ancestry genome-wide association analysis of corneal thickness strengthens link between complex and mendelian eye diseases.  Nat Commun. 2018;9(1):1864. doi:10.1038/s41467-018-03646-6PubMedGoogle ScholarCrossref
    20.
    Burdon  KP, Coster  DJ, Charlesworth  JC,  et al.  Apparent autosomal dominant keratoconus in a large Australian pedigree accounted for by digenic inheritance of two novel loci.  Hum Genet. 2008;124(4):379-386. doi:10.1007/s00439-008-0555-zPubMedGoogle ScholarCrossref
    21.
    Rabinowitz  YS.  Videokeratographic indices to aid in screening for keratoconus.  J Refract Surg. 1995;11(5):371-379.PubMedGoogle Scholar
    22.
    Fritsche  LG, Igl  W, Bailey  JN,  et al.  A large genome-wide association study of age-related macular degeneration highlights contributions of rare and common variants.  Nat Genet. 2016;48(2):134-143. doi:10.1038/ng.3448PubMedGoogle ScholarCrossref
    23.
    Burdon  KP, Mitchell  P, Lee  A,  et al.  Association of open-angle glaucoma loci with incident glaucoma in the Blue Mountains Eye Study.  Am J Ophthalmol. 2015;159(1):31-6.e1. doi:10.1016/j.ajo.2014.09.020PubMedGoogle ScholarCrossref
    24.
    Dimasi  DP, Burdon  KP, Hewitt  AW,  et al.  Genetic investigation into the endophenotypic status of central corneal thickness and optic disc parameters in relation to open-angle glaucoma.  Am J Ophthalmol. 2012;154(5):833-842.e2. doi:10.1016/j.ajo.2012.04.023PubMedGoogle ScholarCrossref
    25.
    Anderson  CA, Pettersson  FH, Clarke  GM, Cardon  LR, Morris  AP, Zondervan  KT.  Data quality control in genetic case-control association studies.  Nat Protoc. 2010;5(9):1564-1573. doi:10.1038/nprot.2010.116PubMedGoogle ScholarCrossref
    26.
    Price  AL, Patterson  NJ, Plenge  RM, Weinblatt  ME, Shadick  NA, Reich  D.  Principal components analysis corrects for stratification in genome-wide association studies.  Nat Genet. 2006;38(8):904-909. doi:10.1038/ng1847PubMedGoogle ScholarCrossref
    27.
    Loh  P-R, Danecek  P, Palamara  PF,  et al.  Reference-based phasing using the Haplotype Reference Consortium panel.  Nat Genet. 2016;48(11):1443-1448. doi:10.1038/ng.3679PubMedGoogle ScholarCrossref
    28.
    Auton  A, Brooks  LD, Durbin  RM,  et al; 1000 Genomes Project Consortium.  A global reference for human genetic variation.  Nature. 2015;526(7571):68-74. doi:10.1038/nature15393PubMedGoogle ScholarCrossref
    29.
    Das  S, Forer  L, Schönherr  S,  et al.  Next-generation genotype imputation service and methods.  Nat Genet. 2016;48(10):1284-1287. doi:10.1038/ng.3656PubMedGoogle ScholarCrossref
    30.
    Chang  CC, Chow  CC, Tellier  LCAM, Vattikuti  S, Purcell  SM, Lee  JJ.  Second-generation PLINK: rising to the challenge of larger and richer datasets.  Gigascience. 2015;4:7. doi:10.1186/s13742-015-0047-8PubMedGoogle ScholarCrossref
    31.
    Fried  LP, Borhani  NO, Enright  P,  et al.  The cardiovascular health study: design and rationale.  Ann Epidemiol. 1991;1(3):263-276. doi:10.1016/1047-2797(91)90005-WPubMedGoogle ScholarCrossref
    32.
    Psaty  BM, O’Donnell  CJ, Gudnason  V,  et al; CHARGE Consortium.  Cohorts for Heart and Aging Research in Genomic Epidemiology (CHARGE) Consortium: design of prospective meta-analyses of genome-wide association studies from 5 cohorts.  Circ Cardiovasc Genet. 2009;2(1):73-80. doi:10.1161/CIRCGENETICS.108.829747PubMedGoogle ScholarCrossref
    33.
    Willer  CJ, Li  Y, Abecasis  GR.  METAL: fast and efficient meta-analysis of genomewide association scans.  Bioinformatics. 2010;26(17):2190-2191. doi:10.1093/bioinformatics/btq340PubMedGoogle ScholarCrossref
    34.
    Ward  LD, Kellis  M.  HaploReg: a resource for exploring chromatin states, conservation, and regulatory motif alterations within sets of genetically linked variants.  Nucleic Acids Res. 2012;40(Database issue):D930-D934. doi:10.1093/nar/gkr917PubMedGoogle ScholarCrossref
    35.
    GTEx Consortium.  Human genomics, The Genotype-Tissue Expression (GTEx) pilot analysis: multitissue gene regulation in humans.  Science. 2015;348(6235):648-660. doi:10.1126/science.1262110PubMedGoogle ScholarCrossref
    36.
    Wagner  AH, Anand  VN, Wang  WH,  et al.  Exon-level expression profiling of ocular tissues.  Exp Eye Res. 2013;111:105-111. doi:10.1016/j.exer.2013.03.004PubMedGoogle ScholarCrossref
    37.
    You  J, Corley  SM, Wen  L,  et al.  RNA-Seq analysis and comparison of corneal epithelium in keratoconus and myopia patients.  Sci Rep. 2018;8(1):389. doi:10.1038/s41598-017-18480-xPubMedGoogle ScholarCrossref
    38.
    Birch-Machin  MA, Russell  EV, Latimer  JA.  Mitochondrial DNA damage as a biomarker for ultraviolet radiation exposure and oxidative stress.  Br J Dermatol. 2013;169(suppl 2):9-14. doi:10.1111/bjd.12207PubMedGoogle ScholarCrossref
    39.
    Afshari  NA, Igo  RP  Jr, Morris  NJ,  et al.  Genome-wide association study identifies three novel loci in Fuchs endothelial corneal dystrophy.  Nat Commun. 2017;8:14898. doi:10.1038/ncomms14898PubMedGoogle ScholarCrossref
    40.
    Jurkunas  U, Azar  DT.  Potential complications of ocular surgery in patients with coexistent keratoconus and Fuchs’ endothelial dystrophy.  Ophthalmology. 2006;113(12):2187-2197. doi:10.1016/j.ophtha.2006.06.036PubMedGoogle ScholarCrossref
    41.
    Vira  S, Abugo  U, Shih  CY,  et al.  Descemet stripping endothelial keratoplasty for the treatment of combined fuchs corneal endothelial dystrophy and keratoconus.  Cornea. 2014;33(1):1-5. doi:10.1097/ICO.0b013e3182a7389cPubMedGoogle ScholarCrossref
    ×