Populations

The nine GWASs meta-analyzed in the discovery GWAS portion of this study included subjects aged 35–84 years from the Cooperative Health Research in the Region of Augsburg Study (KORA F3, Southern Germany), subjects aged 55–80 from the Age-related Eye Study (AREDS), unrelated subjects aged 28–84 from the Framingham Eye Study (FES), subjects aged 46-86 from the Multi-Ethnic Study of Atherosclerosis (MESA) study, and subjects aged 18–88 from the Ogliastra Genetic Park-Talana (OGP-Talana) study in Sardinia, subjects aged 55 and older from the Rotterdam Eye Study I, subjects aged 55 and older from the Rotterdam Eye Study II, subjects aged 45 and older from the Rotterdam Eye Study III, and subjects aged 18–86 from the ERF study, resulting in a total sample size of 16,830 individuals for the myopia analyses and 14,981 for the hyperopia analyses. All individuals were of European ancestry. This study involved meta-analysis of aggregate statistics from multiple studies. Approval was obtained by the local ethics committees for all studies, all studies were conducted according to the principles expressed in the Declaration of Helsinki and informed consent was obtained from the study participants at all study sites.

Study design

GWAS analyses of genotype data imputed to HapMap-II were performed for the traits myopia and hyperopia (adjusted for age at examination, sex and years of education) in 9 studies: the Age-Related Eye Disease Study (AREDS), the “Kooperative Gesundheitsforschung in der Region Augsburg” (KORA, “Cooperative Health Research in the Region of Augsburg”), the Framingham Eye Study (FES), the Ogliastra Genetic Park – Talana (OGP-Talana) study, the Multiethnic Study of Atherosclerosis (MESA) and the Rotterdam Eye Studies RSI, RSII, RSIII and the Erasmus Rucphen Family Study (ERF). The results from these analyses were then combined into a discovery meta-analysis GWAS of each trait. Fixed effects meta-analyses were performed with METAL [22] using p values and the effective sample size for each population. METAL calculates a genomic control value [23] for each population and then adjusts each population's results using the corresponding λ value. The discovery meta-analysis genome-wide significance threshold was taken to be 5×10⁻⁸.

In an attempt to replicate our discovery meta-analysis results and to increase the power of the analyses using our discovery dataset, we obtained association results from 8 other studies, the Blue Mountains Eye Study (BMES), CROATIA-Split, CROATIA-Vis Island, CROATIA-Korcula studies, the Diabetes Control and Complications Trial (DCCT), and the Orkney Complex Disease Study (ORCADES) (Supplemental Methods), just for 30 genomic regions that contained SNPs with association p-values less than 1×10⁻⁵ to either myopia (11 regions) or hyperopia (14 regions) or both (5 regions) in our discovery meta-analysis (the previously well-replicated association region on chromosome 15q14 was excluded). These studies all performed association of SNPs in these regions with myopia and hyperopia (adjusted for age at examination, sex, years of education when available and up to three principal components when there was significant evidence of population stratification in the data). A replication meta-analysis was performed using the same methods as above on association results in the novel genome-wide significant region for the hyperopia trait in these 8 additional datasets. An additional meta-analysis was then performed in these 30 regions combining results from the discovery datasets and these 8 additional studies. All 8 of these additional datasets were part of the Verhoeven et al. study of mean spherical equivalent. This additional analysis and these datasets are described in Materials S1–S3.

Quality control of discovery datasets

Rotterdam eye studies I,II and III

Subjects with cataracts and history of cataract or refractive surgery were excluded from the study. DNA was extracted from blood leucocytes according to standard procedures. Genotyping of SNPs was performed using the Illumina Infinium II HumanHap550 chip v3.0 array (RS-I); the HumanHap550 Duo Arrays and the Illumina Human610-Quad Arrays (RS-II), and the Illumina Human 610 Quad Arrays (RS-III). Samples with low call rate (<97.5%), with excess autosomal heterozygosity (>0.336), or with sex-mismatch were excluded, as were outliers identified by the identity-by-state clustering analysis (outliers were defined as being>3 s.d. from population mean or having identity-by-state probabilities>97%). GWAS analyses were performed using GRIMP.

Erasmus rucphen family study

Subjects with cataracts and history of cataract or refractive surgery were excluded from the study. DNA was genotyped on one of four different platforms (Illumina 6k, Illumina 318K, Illumina 370K and Affymetrix 250K). Samples with low call rate (<97.5%), with excess autosomal heterozygosity (>0.336), or with sex-mismatch were excluded, as were outliers identified by the identity-by-state clustering analysis (outliers were defined as being>3 s.d. from population mean or having identity-by-state probabilities>97%). GWAS analyses were performed using the ProbABEL package from the ABEL set. A lambda correction was performed to adjust for cryptic relationship.

Genotype imputation of data

To produce a consensus set of genotypes for imputing to the HapMap-II, AREDS and KORA high quality SNPs were filtered to those present on HapMap-II. Imputation to the HapMap-II reference panel (CEU population release 22, NCBI build 36) was performed in MACH [22], [25] in 2 stages. Stage one was the model parameter estimation stage which used a random sample of 300 individuals from each population, using the greedy option which only uses the reference haplotypes (supplied here from the HapMap) and 100 Markov Chain iterations. Stage two is the actual imputation stage and uses the model parameters estimated in stage one to speed up the imputation of the genotypes. After imputation, the remaining high quality genotyped SNPs were merged back in with the SNPs from the imputation procedure for the AREDS and KORA data. For the FES data, genotype imputation to the HapMap-II reference panel (CEU population release 22, NCBI build 36) was carried out in a two-step process using the Markov Chain Haplotyping (MACH version 1.0.16.a) software. First, crossover and error-rate maps were built using 400 unrelated individuals (200 male and 200 female) sampled from FHS subjects. Second, genotype imputations of approximately 2.5 million autosomal HapMap-II SNPs were carried out on the entire FHS dataset using parameters estimated from step 1. For MESA, IMPUTE version 2.1.0 was used to perform imputation for the Caucasian participants (chromosomes 1-22) using HapMap Phase I and II - CEU as the reference panel (release #24 - NCBI Build 36 (dbSNP b126)). For OGP-Talana, using the phase II CEU HapMap individuals (release 22, NCBI build 36) as reference panel for imputation, genotypes were imputed for nearly 2.5 million SNPs using MACH. SNPs imputed with Rsq <0.3 were excluded. For RSI,II and III and ERF, a set of genotyped input SNPs with call rate>98%, with minor allele frequency>0.01, and with Hardy-Weinberg P value>10⁻⁶ was used for imputation. We used the Markov Chain Haplotyping (MACH) package version 1.0.15 software (Rotterdam, The Netherlands; imputed to plus strand of NCBI build 36, HapMap release #22) for the analyses. For each imputed SNP, a reliability of imputation was estimated as the ratio of the empirically observed dosage variance to the expected binomial dosage variance (O/E ratio).

Data analysis

Genetic association was estimated by fitting a logistic regression model separately to the traits myopia and hyperopia. To create the dichotomous traits, we calculated mean spherical equivalent (MSE) as the average of spherical equivalent (SE) of refraction between the two eyes, or the single SE value for persons with only a single SE measurement. For myopia, cases were defined as MSE <−1D, controls>0D and individuals between 0D and −1D coded as unknown. For hyperopia, cases were defined as MSE>+1D, controls <0D and individuals between 0D and +1D coded as unknown. A general additive genetic model was used to code the SNP effect (i.e. SNPs were coded according to the number of minor alleles [0,1,2] for each person); covariates included age; sex; and years of education. For AREDS, KORA and FES, this was accomplished using the PLINK (version 1.07) statistical software (http://pngu.mgh.harvard.edu/~purcell/plink) [26]. For AREDS analyses, the first three principal components (eigenvectors) of the EIGENSTRAT analysis were also included along with the covariates listed above. For MESA, these association tests were performed by SNPTEST v2.52. For OGP-Talana, all regression models were run using the ProbABEL package from the ABEL set of programs which adjusts jointly for cryptic relationship and population stratification. For RSI, II and III and ERF, we used genomic control [23] to obtain optimal and unbiased results and applied the inverse variance method of each effect size estimated for both autosomal SNPs that were genotyped and imputed in both cohorts.

Association analyses were performed for both traits and a genome-wide meta-analysis was performed on the 9 populations and 8 replication data sets (Blue Mountains Eye Study, Croatia Vis Island Study, Croatia Korcula Study, Diabetes Control and Complications Trial, Orkney Complex Disease Study, UK Twins Study, 1958 British Birth Cohort, Wisconsin Epidemiologic Study of Diabetic Retinopathy). Details of the genome-wide analyses of the individual discovery datasets and the replication analyses are shown in the supplemental methods and results including QQ-plots and Manhattan plots for each of the discovery cohorts in Figures S1-S9. Figure S10 is a flowchart showing the workflow of the entire study.

SNP selection for replication

Thirty genomic regions that contained SNPs with association p-values less than 1×10⁻⁵ to either myopia (11) or hyperopia (14) or both (5) in our discovery meta-analysis (excluding the 15q14 region) were chosen for replication or further study in the 8 additional datasets. We analyzed all SNPs within a 500 kb window centered on the most significant SNP in each region from the discovery meta-analysis.

For the comparison of our discovery meta-analysis results with the myopia age at onset loci from the Kiefer et al. [18] study, a list of strongly associated variants that were genome-wide significant (p≤5×10⁻⁸) or suggestive (p<1×10⁻⁶) in Kiefer et al. [18] was selected. We analyzed all SNPs within a 500 kb window centered on these replication SNPs in our data.

Calculation of effective number of tests and replication significance thresholds

It has become increasingly clear that only attempting to replicate the exact SNPs found to be genome-wide significant in a discovery GWAS can produce a failure to replicate due to underlying differences in linkage disequilibrium (LD) and allele frequencies [27], [28], even in populations self-identified as having the same ethnicity. Ioannidis et al. [29] have shown that restricting replication efforts to only a few of the most significant SNPs from an associated region leads to less robust information for those loci. The resulting failure to replicate may be because those selected SNP(s) are not necessarily more informative or closer to the causal variant than other SNPs in the region. Several approaches to this problem have been proposed, including incorporating linkage information [30], pathway-based association [31] and other methods which use multiple SNPs in the analysis [32], [33], [34], [35], [36], [37], [38], [39]. A linkage disequilibrium (LD) based binning strategy, proposed by Christoferou [39] may prove to be the most useful. However, the issues of handling SNPs which map to more than one gene due to overlapping reading frames and the correlations between genes and derivative gene scores still need to be resolved. Until that problem has a solution, it may be more powerful to study a dense panel of SNPs from each associated region, and utilize imputation to the latest version of 1000 Genomes data to provide additional genotypes to harmonize available SNPs across studies even when genotyped on different platforms. Here we selected all SNPs that were within a specified window of the original SNP and used the method of Ramos et al. [40] to model the LD structure in one of the replication populations to calculate the effective number of independent tests being performed across all of our replication regions. Traditional methods of correcting for multiple comparisons, such as the widely used Bonferroni correction considering all SNPs tested, are notoriously conservative because they do not take intermarker correlation fully into account but treat all the tests as independent. By using the effective number of independent tests in a Bonferroni correction, Type I error is still controlled and power is improved. Various approaches to calculating the effective number of independent tests when using such a regional replication strategy have been proposed since many of the SNPs in such a region are in LD with each other and do not represent independent tests [41], [42], [43], [44], [45], although many of these approaches are still overly conservative. The Ramos et al. [40] approach properly accounts for SNP interdependence, allows computation of the effective number of independent tests for very large numbers of highly correlated SNPs and is less computationally intensive than permutation-based methods. We used the method of Ramos et al. [40] to calculate the number of effective tests (N_eff) in all the replication regions and divided α by this effective number of tests to calculate the significance threshold separately in the AREDS, KORA and Framingham datasets. The Ramos method calculates N_eff by first estimating the KxK covariance matrix for the K SNPs in the replication regions using the genotype data. Then the covariance matrix is spectrally decomposed to calculate the eigenvalues. The effective number of tests is then estimated using the relationshipin which λ_k is the kth eigenvalue of the K×K covariance matrix for the K SNPs [46]. The Bonferroni-corrected significance threshold is then calculated as α/N_eff.

The markers in each region are very densely spaced, with high levels of LD between markers in each block. The calculations from the AREDS data gave the largest effective number of tests and thus the most conservative Bonferroni-corrected significance threshold; thus this was chosen as our significance threshold for our replication studies. However, the Bonferroni-corrected thresholds derived by applying this method to the KORA and Framingham data were only slightly less conservative than the threshold derived from the AREDS data.

Myopia

Results of the discovery meta-analysis (Figure 1, Table S4) shows one genome-wide significant marker corresponding to a previously identified myopia age at onset [18] and refractive error [19] locus on 8q12 (rs10113215, p = 1.25×10⁻⁸). We also observed association to the well-replicated locus on 15q14 (near GJD2) that was close to genome-wide significant (rs1370156, p = 2.29×10⁻⁷). No attempt was made to replicate the chromosome 15q14 region since it has been well replicated. SNPs in the 8q12 replication region did not reach the replication threshold (for rs10113215, replication p = 0.02; top replication p-value in the region was p = 0.0022 for rs6995115). For the discovery meta-analysis suggestive regions, one of the selected SNPs achieved the replication threshold for myopia (rs4326350 on 8p23, p = 6.1×10⁻⁵). However, it should be remembered that this region did not exhibit genome-wide significant association in the discovery meta-analysis (replication p-values in Table S6).

In addition to the 8q12 locus, 10 other myopia age at onset regions from the Kiefer et al. study [18] showed significant evidence of replication in our discovery meta-analysis (Table 2). Eight of these loci have also been reported as associated with MSE by Verhoeven et al. [19]. However, two of the regions we replicated were not reported significantly associated with MSE by Verhoeven et al. [19]. On chromosome 3p26, rs2587916 reached the replication threshold in our discovery meta-analysis (p = 2.79×10⁻⁵). This SNP is 256 bp away from the SNP reported in this region by Kiefer et al. [18], rs1843303 (which had p = 6.32×10⁻⁴ in our data, Table 2). These two SNPs exhibit strong linkage disequilibrium with an R² of 0.963 and a D′ of 1 in our data. The most significant SNP at the second locus on chromosome 6 is the same SNP as reported by Kiefer et al. [18], rs7744813 (p = 6.07×10⁻⁶, Table 2).

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Table 2. Results of the replication of regions significantly associated with myopia age at onset by Kiefer et al. [18] showing meta-analysis association results for each chosen SNP with myopia in our data.

https://doi.org/10.1371/journal.pone.0107110.t002

Due to the high genomic control values for OGP-Talana and ERF (Table 1), we examined QQ plots of only the common SNPs (MAF>0.2) to see if this made an improvement, since all the associated SNPs reported here have high MAFs. In OGP-Talana this improved the QQ plots (Figure S9) but it made no difference for ERF. Therefore, we dropped ERF from the analysis and re-examined the results (Figure 3). For most loci this made minimal difference to the p values. However, for 3 loci there was a considerable difference. The genome-wide significant result for myopia on chromosome 8 was no longer genome-wide significant (p = 8.8×10⁻⁷), although it still remained well below our replication significance threshold. The loci on 2q37 and 3p26 were no longer below our replication threshold.

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Figure 3. Q-Q and Manhattan Plots for the myopia analysis excluding the ERF cohort a) Q–Q plot for association between all SNPs analyzed and myopia in the meta-analysis excluding the ERF cohort.

Each dot represents an observed statistic (defined as -log10 P) versus the corresponding expected statistic. The red line corresponds to the null distribution. b) Manhattan plot for association between all SNPs analyzed and myopia in the meta-analysis excluding the ERF cohort. Each dot represents an observed statistic (defined as -log10 P). The darker gray line corresponds to the genome-wide significance threshold and the lighter gray line represents the suggestive threshold.

https://doi.org/10.1371/journal.pone.0107110.g003

Hyperopia

Meta-analysis results showed two genome-wide significant associations with hyperopia (Figure 2, Table S5). These regions overlapped with loci on 15q14 (rs11073060, p = 9.11×10⁻¹¹) and 8q12 (rs10089517,p = 1.82×10⁻¹¹) previously reported for MSE in Verhoeven et al. [19] and for myopia age at onset in Kiefer et al. [18]. No attempt was made to replicate the 15q14 locus since it has been well replicated for MSE. None of the SNPs selected to attempt replication of the discovery meta-analysis genome-wide significant association with hyperopia on chromosome 8q12 achieved the replication threshold (rs10089517, p = 0.08; top replication p-value in the region was 0.014 at rs11778476) (Table S7). In addition, for the discovery meta-analysis suggestive regions, one SNP achieved the replication threshold for hyperopia (rs12660628 on 6q21, p = 7.7×10⁻⁵). However, it should be remembered that this region did not exhibit genome-wide significant association in the discovery meta-analysis (replication p-values in Table S7).

In addition to the 15q14 and 8q12 loci, 10 other regions (Table 3) that were genome-wide significant in the Kiefer et al. [18] analysis of myopia age at onset exhibited p values for association with hyperopia that met our “replication” threshold for these regions. Given this is a different but related trait, this finding is interesting. Five of these regions have been replicated using myopia as the trait in our data here (three of which were also found to be significantly associated with MSE by Verhoeven et al. [19]). Verhoeven et al. [19] also found that 1 more of these 10 regions (Table 3) showed significant association with MSE. Of the remaining 4 regions from Table 3 the most significant of these 4 SNPs was rs1371993 (p = 1.13×10⁻⁵), a SNP on chromosome 4, 35Kb from the SNP reported by Kiefer et al. [18] for myopia age at onset (rs1031004, not available in our data).

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Table 3. Results of the hyperopia analyses in the regions that were significantly associated with myopia age at onset by Kiefer et al. [18] showing meta-analysis association results for each chosen SNP.

https://doi.org/10.1371/journal.pone.0107110.t003

Due to the high genomic control values for OGP-Talana and ERF (Table 1), we examined QQ plots of only the common SNPs (MAF>0.2) to see if this made an improvement, since all the SNPs reported here have high MAFs. In OGP-Talana this improved the QQ plots (Figure S9) but it made no difference for ERF. Therefore, we dropped ERF from the analysis and re-examined the results (Figure 4). For all loci this made minimal difference to the p values and did not change the conclusions.

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Figure 4. Q-Q and Manhattan Plots for the hyperopia analysis excluding the ERF cohort a) Q-Q plot for association between all SNPs analyzed and hyperopia in the meta-analysis excluding the ERF cohort.

Each dot represents an observed statistic (defined as -log10 P) versus the corresponding expected statistic. The red line corresponds to the null distribution. b) Manhattan plot for association between all SNPs analyzed and hyperopia in the meta-analysis excluding the ERF cohort. Each dot represents an observed statistic (defined as -log10 P). The darker gray line corresponds to the genome-wide significance threshold and the lighter gray line represents the suggestive threshold.

https://doi.org/10.1371/journal.pone.0107110.g004

Contributors

The Framingham Heart Study data were obtained from the NIH repository dbGaP (accession numbers phs000007/HMB-IRB-MDS and phs000007/HMB-IRB-NPU-MDS). This manuscript was not prepared in collaboration with investigators of the Framingham Heart Study and does not necessarily reflect the opinions or views of the Framingham Heart Study, Boston University, or NHLBI. The following persons and institutions participated in the DCCT/EDIC Study Research Group: Study Chairmen - S. Genuth, D.M. Nathan, B. Zinman (vice-chair), O. Crofford (past); Albert Einstein College of Medicine - J. Crandall, M. Reid, J. Brown-Friday, S. Engel, J. Sheindlin, H. Martinez (past), H. Shamoon (past), H. Engel (past), M. Phillips Case Western Reserve University - R. Gubitosi-Klug, L. Mayer, S. Pendegast, H. Zegarra, D. Miller, L. Singerman, S. Smith-Brewer, M. Novak, J. Quin (past), W. Dahms (deceased), Saul Genuth (past), M. Palmert (past); Cornell University Medical Center - D. Brillon, M.E. Lackaye, S. Kiss, R. Chan, V. Reppucci (past), T. Lee (past), M. Heinemann (past) Henry Ford Health System - F. Whitehouse, D. Kruger, J. K. Jones, M. McLellan (past), J.D. Carey, E. Angus, A. Thomas, A. Galprin (past); International Diabetes Center - R. Bergenstal, M. Johnson, M. Spencer (past), K. Morgan, D. Etzwiler (deceased), D. Kendall (past) Joslin Diabetes Center - Lloyd Paul Aiello, E. Golden, A. Jacobson (past), R. Beaser, O. Ganda, O. Hamdy, H. Wolpert, G. Sharuk, P. Arrigg, D. Schlossman, J. Rosenzwieg (past), L. Rand (past); Massachusetts General Hospital - D.M. Nathan, M. Larkin, M. Ong, J. Godine, E. Cagliero, P. Lou, K. Folino, S. Fritz (past), S. Crowell (past), K. Hansen (past), C. Gauthier-Kelly (past); Mayo Foundation - J. Service, G. Ziegler Medical University of South Carolina - L. Luttrell, S. Caulder, M. Lopes-Virella (past), J. Colwell (past), J. Soule (past), J. Fernandes, K. Hermayer, S. Kwon, M. Brabham (past), A. Blevins, J. Parker, D. Lee (past), N. Patel, C. Pittman, P. Lindsey (past), M. Bracey (past), K. Lee, M. Nutaitis, A. Farr (past), S. Elsing (past), T. Thompson (past), J. Selby (past), T. Lyons (past), S. Yacoub-Wasef (past), M. Szpiech (past), D. Wood (past), R. Mayfield (past); Northwestern University - M. Molitch, B. Schaefer, L. Jampol, A. Lyon, M. Gill, Z. Strugula, L. Kaminski, R. Mirza, E. Simjanoski, D. Ryan; University of California, San Diego - O. Kolterman, G. Lorenzi, M. Goldbaum University of Iowa - W. Sivitz, M. Bayless; University of Maryland School of Medicine - D. Counts, S. Johnsonbaugh, M. Hebdon (past), P. Salemi, R. Liss, T. Donner (past), J. Gordon (past), R. Hemady (past), A. Kowarski (past), D. Ostrowski (past) S. Steidl (past), B. Jones (past); University of Michigan - W.H. Herman, C.L. Martin, R. Pop-Busui, A. Sarma, J. Albers, E. Feldman, K. Kim, S. Elner, G. Comer, T. Gardner, R. Hackel, R. Prusak, L. Goings, A. Smith, J. Gothrup, P. Titus, J. Lee, M. Brandle, L. Prosser, D.A. Greene (past), M.J. Stevens (past), A. K. Vine (past); University of Minnesota - J. Bantle, N. Wimmergren, A. Cochrane, T. Olsen (past), E. Steuer (past), P Rath (past), B. Rogness (past); University of Missouri - D. Hainsworth, D. Goldstein, S. Hitt, J. Giangiacomo; University of New Mexico - D.S. Schade, J.L. Canady, J.E. Chapin, L.H. Ketai C; University of Pennsylvania – S. Braunstein, P.A. Bourne, S. Schwartz (past), A. Brucker, B.J. Maschak-Carey (past), L. Baker (deceased); University of Pittsburgh - T. Orchard, N. Silvers, C. Ryan, T. Songer, B. Doft, S. Olson, R.L. Bergren, L. Lobes, P. Paczan Rath, D. Becker, D. Rubinstein, P.W. Conrad, S. Yalamanchi, A. Drash (past); University of South Florida - A. Morrison, M.L. Bernal, J. Vaccaro-Kish (past), J. Malone, P.R. Pavan, N. Grove, M.N. Iyer, A.F. Burrows, E.A. Tanaka (past), R. Gstalder (past); University of Tennessee - S. Dagogo-Jack, C. Wigley, H. Ricks, A. Kitabchi, M. B. Murphy (past), S. Moser (past), D. Meyer (past), A. Iannacone (past), E. Chaum, S. Yoser (past), M. Bryer-Ash (past), S. Schussler (past), H. Lambeth (past); The University of Texas Southwestern Medical Center at Dallas - P. Raskin, S. Strowig; University of Toronto - B. Zinman, A. Barnie, R. Devenyi, M. Mandelcorn, M. Brent, S. Rogers, A. Gordon; University of Washington - J. Palmer, S. Catton, J. Brunzell, H. Wessells, I. H. de Boer, J. Hokanson, J. Purnell, J. Ginsberg, J. Kinyoun, S. Deeb, M. Weiss, G. Meekins, J. Distad, L. Van Ottingham (past); University of Western Ontario - J. Dupre, J. Harth, D. Nicolle, M. Driscoll, J. Mahon, C. Canny; Vanderbilt University - M. May, J. Lipps, A. Agarwal, T. Adkins, L. Survant, R. L. Pate, G. E. Munn, R. Lorenz (past), S. Feman (past); Washington University, St. Louis - N. White, L. Levandoski, I. Boniuk, G. Grand, M. Thomas, D. D. Joseph, K. Blinder, G. Shah, Boniuk (past), Burgess (past), J. Santiago (deceased); Yale University School of Medicine - W. Tamborlane, P. Gatcomb, K. Stoessel, K. Taylor (past)J. Goldstein (past), S. Novella (past), H. Mojibian (past), D. Cornfeld (past); Clinical Coordinating Center (Case Western Reserve University) - R. Gubitosi-Klug, J. Quin, P. Gaston, M. Palmert (past), R. Trail (past), W. Dahms (deceased); Data Coordinating Center (The George Washington University, The Biostatistics Center) - J. Lachin, P. Cleary, J. Backlund, W. Sun, B. Braffett, K. Klumpp, K. Chan (past), L. Diminick, D. Rosenberg (past), B. Petty (past), A. Determan (past), D. Kenny (past), B. Rutledge (past), Naji Younes (past), Williams (past), L. Dews, M. Hawkins; National Institute of Diabetes and Digestive and Kidney Disease Program Office - C. Cowie, J. Fradkin, C. Siebert (past), R. Eastman (past); Central Fundus Photograph Reading Center (University of Wisconsin) - R. Danis, S. Gangaputra, S. Neill, M. Davis (past), L. Hubbard (past), H. Wabers, M. Burger, J. Dingledine, V. Gama, R. Sussman; Central Biochemistry Laboratory (University of Minnesota) - M. Steffes, J. Bucksa, M. Nowicki, B. Chavers; Central Carotid Ultrasound Unit (New England Medical Center) - D. O′Leary, J. Polak, A. Harrington, L. Funk (past); Central ECG Reading Unit (University of Minnesota) – R. Crow (past), B. Gloeb (past), S. Thomas (past), C. O′Donnell (past); Central ECG Reading Unit (Wake Forest University) – E. Soliman, Z.M. Zhang, R. Prineas (past), C. Campbell; Central Neuropsychological Coding Unit – C. Ryan, D. Sandstrom, T. Williams, M. Geckle, E. Cupelli, F. Thoma, B. Burzuk, T. Woodfill; Central ANS Reading Unit (Mayo Clinic) – P. Low, C. Sommer, K. Nickander; Computed Tomography Reading Center (Harbor UCLA Research and Education Institute) – M. Budoff, R. Detrano (past), N. Wong, M. Fox, L. Kim (past), R. Oudiz; Johns Hopkins Medical Institutions – J. Lima, D. Bluemke, E. Turkbey, R. J. van der Geest, C. Liu, A. Malayeri, A. Jain, C. Miao (past), H. Chahal (past), R. Jarboe (past); External Evaluation Committee– G. Weir (Chairman), M. Espeland, B. Klein, T Manolio, L. Rand, D. Singer, M. Stern, A.E. Boulton, C. Clark (past), R. D′Agostino (past); Molecular Risk Factors Program Project (Medical University of South Carolina) - M. Lopes-Virella, W.T. Garvey (past), T.J. Lyons, A. Jenkins, R. Klein, G. Virella, A. Jaffa, Rickey Carter, D. Lackland (past), M. Brabham (past), D. McGee (past), D. Zheng (past), R. K. Mayfield (past); Genetic Studies Group (Hospital for Sick Children) - A. Paterson, A. Boright, S. Bull, L. Sun, S. Scherer (past), B. Zinman (past); SCOUT (Veralight) – J. Maynard; Epigenetics (Beckman Research Institute of City of Hope Medical Center) – R. Natarajan, F. Miao, L. Zhang, Z. Chen; Editor, EDIC Publications - D.M. Nathan. For the current paper, the Andrew D. Paterson serves as the DCCT/EDIC lead author and contact person and is a named co-author of this paper.

We thank numerous clinicians, clinical staff, patients, and their families for their participation in and dedication to the project; the Ogliastra population and all the individuals who participated in this study. We are very grateful to the municipal administrators for their collaboration to the project and for economic and logistic support. We are extremely appreciative of the support and wisdom provided by Dr. Hemin Chin of the National Eye Institute. The Center for Inherited Disease Research, fully funded through a federal contract (HHSN268200782096C) from National Institutes of Health to The Johns Hopkins University, performed genotyping of the AREDS and KORA cohorts. Clinical data and DNA from the DCCT/EDIC study will be made available through the National Institute of Diabetes and Digestive and Kidney Diseases repository at https://www.niddkrepository.org/niddk/home.do. This manuscript was not prepared under the auspices of and does not represent analyses or conclusions of the NIDDK Central Repositories, or the NIH. Rotterdam Study and ERF thank Ada Hooghart, Corina Brussee, Riet Bernaerts-Biskop, Patricia van Hilten, Pascal Arp, Jeanette Vergeer, Marijn Verkerk and Sander Bervoets.