24 Apr 2015: Awata T, Yamashita H, Kurihara S, Morita-Ohkubo T, Miyashita Y, et al. (2015) Correction: A Genome-Wide Association Study for Diabetic Retinopathy in a Japanese Population: Potential Association with a Long Intergenic Non-Coding RNA. PLOS ONE 10(4): e0126789. https://doi.org/10.1371/journal.pone.0126789 View correction
Elucidation of the genetic susceptibility factors for diabetic retinopathy (DR) is important to gain insight into the pathogenesis of DR, and may help to define genetic risk factors for this condition. In the present study, we conducted a three-stage genome-wide association study (GWAS) to identify DR susceptibility loci in Japanese patients, which comprised a total of 837 type 2 diabetes patients with DR (cases) and 1,149 without DR (controls). From the stage 1 genome-wide scan of 446 subjects (205 cases and 241 controls) on 614,216 SNPs, 249 SNPs were selected for the stage 2 replication in 623 subjects (335 cases and 288 controls). Eight SNPs were further followed up in a stage 3 study of 297 cases and 620 controls. The top signal from the present association analysis was rs9362054 in an intron of RP1-90L14.1 showing borderline genome-wide significance (Pmet = 1.4×10−7, meta-analysis of stage 1 and stage 2, allele model). RP1-90L14.1 is a long intergenic non-coding RNA (lincRNA) adjacent to KIAA1009/QN1/CEP162 gene; CEP162 plays a critical role in ciliary transition zone formation before ciliogenesis. The present study raises the possibility that the dysregulation of ciliary-associated genes plays a role in susceptibility to DR.
Citation: Awata T, Yamashita H, Kurihara S, Morita-Ohkubo T, Miyashita Y, Katayama S, et al. (2014) A Genome-Wide Association Study for Diabetic Retinopathy in a Japanese Population: Potential Association with a Long Intergenic Non-Coding RNA. PLoS ONE9(11): e111715. https://doi.org/10.1371/journal.pone.0111715
Editor: Shiro Maeda, Graduate School of Medicine, University of the Ryukyus, Japan
Received: May 23, 2014; Accepted: September 30, 2014; Published: November 3, 2014
Copyright: © 2014 Awata et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Data Availability: The authors confirm that all data underlying the findings are fully available without restriction. All relevant data are within the paper and its Supporting Information files.
Funding: This study was supported in part by a Grant-in-Aid for the Support Project of Strategic Research Center in Private Universities to the Saitama Medical University Research Center for Genomic Medicine from MEXT of Japan, research grants from National Center for Global Health and Medicine, and “Database Integration Coordination Program” from JST (Japan Science and Technology Agency). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
Competing interests: AK is an employee of the Central Research Laboratory, Hitachi Ltd. There are no patents, products in development or marketed products to declare that might have a potential conflict with the present manuscript. This does not alter the authors’ adherence to all the PLoS ONE policies on sharing data and materials, as detailed online in the guide for authors.
Diabetic retinopathy (DR) remains the most common microvascular complication of diabetes, and is a major cause of visual impairment in adults worldwide . The multifactorial etiology of DR has been postulated, but is poorly understood. Several lines of evidence, such as ethnic differences ,  and familial clustering in both identical twins with type 2 diabetes (T2D)  and siblings with either type 1 diabetes ,  or T2D –, suggest that genetic factors have a role in the pathogenesis of DR, in addition to the duration of diabetes, hyperglycemia, hypertension and dyslipidemia , . Therefore, elucidation of the genetic susceptibility factors for DR is important to gain insight into the pathogenesis of DR, and may define genetic risk factors for this condition.
Initial genetic studies focused on a number of logical candidate genes for DR. Among them, the genes for vascular endothelial growth factor (VEGFA), aldose reductase (AKR1B1) and the advanced glycation end products receptor (AGER) have been the most extensively studied –; in particular, the SNP rs2010963 in the 5′-untranslated region of VEGF, which we first reported had a significant association with DR in 2002 , and AKR1B1 polymorphisms (the dinucleotide repeat microsatellite in the 5′ region and the SNP rs759853 in the promoter region) were confirmed by the meta-analyses performed by Qiu et al.  and Abhary et al. , respectively.
Subsequently, genome-wide linkage studies on Pima Indians ,  and Mexican-Americans  with T2D reported evidence for the linkage of DR to several chromosomal regions, but the susceptibility genes in these regions remain to be elucidated. Recently, several genome-wide association studies (GWAS) in Mexican-Americans , Chinese ,  and Caucasians  identified multiple susceptibility loci to DR; however, none of these loci reached the genome-wide significance level, and none has been replicated in other studies .
In the present study, subsequent to our candidate gene studies for DR in T2D , –, we conducted a GWAS to identify DR susceptibility loci in approximately two thousand Japanese patients with T2D.
Materials and Methods
The present study was approved by the Ethical Committee of Saitama Medical University (No. 124-III), the Ethical Committee of Saitama Social Insurance Hospital (No. 00-04), the Ethical Committee of Keio University (No. 13-70-3), the Ethical Committee of the National Center for Global Health and Medicine (No. 75), and the Ethical Committee of University of Tokyo (No. 2541). Written informed consent was obtained from all subjects and all samples were anonymized.
Genomic DNA Samples and Clinical Data
The present study applied a three-stage GWAS involving 1,986 Japanese unrelated T2D patients in total (Figure 1). The stage 1 samples for the discovery genome-wide scan were from 446 patients at Saitama Medical University Hospital (SMU); two samples were removed from the initial 448 patients as described below. The stage 2 samples for the replication study of 249 SNPs consisted of those from 623 patients at SMU, the Saitama Social Insurance Hospital (SSI) and Keio University Hospital (KU). The stage 3 samples for the replication study of eight SNPs were from 917 patients at the National Center for Global Health and Medicine Center Hospital (NCGM).
We recruited DNA samples from patients with type 2 diabetes who regularly visited the outpatient clinics (SSI and NCGM) or were admitted to the inpatient wards (SMU and KU) without any particular exclusion criteria. The clinical characteristics of the T2D patients in each group are shown in Table 1. In the present study, the subjects were selected independently from the clinical phenotypes, such as the duration of diabetes and the presence of DR. All patients received fundus examination by trained ophthalmologists, and the severity of DR was classified as none, non-proliferative (NPDR) or proliferative (PDR) based on the international clinical diabetic retinopathy/macular edema disease severity scale. We also collected the data regarding diabetic nephropathy (DN). In the present study, DN was diagnosed by the presence of overt proteinuria or macroalbuminuria (>300 mg/g creatinine).
SNP Genotyping and Quality Control
The initial number of samples used in the stage 1 analysis was 448 in total, consisting of 241 patients with DR (cases) and 207 without DR (controls). Genotyping was performed with Affymetrix GeneChip 6.0 microarrays interrogating 909,622 SNPs, according to the manufacturer’s instruction manual. Samples with a genotype call rate of <95%, and SNPs with a genotype call rate of <90%, with a minor allele frequency <0.05 or significant deviation (P<0.001) from Hardy-Weinberg equilibrium (HWE) were excluded. Further, population outliers were also excluded based on the principal component analysis (PCA) 1st and 2nd axis values calculated by the EIGENSTRAT software program . Among the stage 1 SNPs showing association of P<0.05 with DR under allele model, a total of 249 SNPs were selected for stage 2by the following four criteria (Table S1); (A) 110 SNPs were selected from the regions including the signals of P<10−3 for DR (allele model), (B) 86 SNP from the regions including the signals of P<10−3 under other DR-related phenotype (PDR or diabetic macular edema) and/or other model, (C) 29 SNPs with a P value between 10−3 and 10−2 (allele model) and (D) 29 SNPs with a P value between 10−3 and 10−2 (other DR-related phenotype and/or other model) were selected in their biological and/or bioinformatic potency in DR, respectively. For the criteria A and B, one or several SNPs were chosen from those in linkage disequilibrium for each genomic region. We performed cross-platform validation by genotyping 11 SNPs in all stage 1 samples that were run on the Affymetrix GeneChip 6.0 assay using the TaqMan SNP assay, and the concordance rate was 100%.
For the stage 2 replication study, SNP genotyping of a total of 623 patients from SMU, SSI and KU was carried out by the DigiTag2 assay. The DigiTag2 assay was reported to have high accuracy and reproducibility ; in addition, a very high concordance rate with the TaqMan SNP assay was also described . Among the 249 selected SNPs, we excluded 24 SNPs with a call rate <95%, and the overall call rate was 99.7%. Eight SNPs were selected for stage 3 based on the overall P<10−3 combining the stage 1 and stage 2 samples using an allele model. Finally, for the stage 3 replication study, the SNP genotyping of 917 patients from NCGM was carried out using the TaqMan SNP assay. All SNPs had a call rate >98%, and the overall call rate was 99.5%.
In the comparison of clinical features, continuous clinical data were compared using the unpaired Student’s t-test, and categorical clinical data were compared using Fisher’s exact test. For the stage 1 analysis, the genome-wide association analysis was performed using the PLINK software package (ver. 1.06) , R 2.9.0, and the Haploview 1.04 software program . For the stage 2 and 3 replication studies and the combined analyses of the overall samples, the association was tested using the SNPAlyze Ver. 8 Pro software program (Dynacom, Yokohama, Japan). The P values were calculated by genotype, allele, dominant, recessive and trend models for each SNP. For the eight selected SNPs from stage 1 and stage 2, logistic regression analysis was also performed to test the association of each SNP with DR, PDR and DN under the genotype (additive), dominant and recessive models adjusted for clinical features. StatView version 5 (SAS Institute, Cary, NC) was used for these tests. A combined meta-analysis was performed using a general variance-based method with a fixed effects model after testing for heterogeneity using Cochran's Q test with R 2.15.2. A total of 614,216 of the 909,622 genome-wide SNPs passed the QC criteria in stage 1. Therefore, the significance level for genome-wide association was considered to be P = 8.1×10−8 (0.05/614,216). The study power was estimated using the CaTS software program (http://www.sph.umich.edu/csg/abecasis/CaTS/) . The statistical power to detect associations among the present stage 1 (205 cases and 241 controls; 614,216 SNPs) and stage 2 (335 cases and 288 controls; 249 SNPs) was calculated to be 79% or 38%, respectively, based on a genome-wide significance level of P = 8.1×10−8, the additive genetic model, replication analysis and a genotype relative risk (GRR) of 1.4 or 1.3, assuming a risk allele frequency of 0.40 and disease (DR) prevalence of 50%. In addition, the power to detect stage 1+2 (540 cases and 529 controls; 614,216 SNPs) and stage 3 (297 cases and 620 controls; 8 SNPs) was calculated to be 99% or 85% based on the same conditions and a GRR of 1.4 or 1.3, respectively.
Case-Control Association Analysis
A total of 1,986 Japanese patients with type 2 diabetes of the five cohorts, all recruited from Saitama prefecture or the adjacent Tokyo Metropolitan area, were analyzed in the present study. Clinical features of the patients among the cohorts were shown in Table 1; lipid laboratory data were similar, moderate difference was observed in sex, age, duration of diabetes, and substantial difference was observed in HbA1c, presence of PDR and that of DN. Of note, higher HbA1c levels were observed in the SMU (stage 1 and stage 2) and KU (stage 2) patients recruited from the inpatient wards, compared to the SSI (stage 2) and (stage 3) NCGM patients, who were recruited from outpatient clinics.
In stage 1, the association analysis with DR using 614,216 genome-wide SNPs passed the QC criteria, and was originally carried out in 448 Japanese patients recruited from SMU, 207 of whom were DR-positive and 241 of whom were DR-negative. Two DR-positive patients were regarded as outliers by PCA, and were removed from the GWAS (Figure S1). The genomic inflation factor (λ) and mean chi-squared statistics calculated from the GWAS for these 446 patients were 1.006 and 1.003, respectively, both of which were very close to 1.0, suggesting that any observed associations were unlikely to be due to population stratification. The quantile-quantile plot showed apparent deviation from the expected values at the lower P values, suggesting that some associations were more significant than expected by chance (Figure 2). The GWAS for DR revealed seven loci (10 SNPs) at a value of P<10−5 with the best model (Figure 3 and Table S2). The top signals in the present study were at the level of 10−6, falling below a genome-wide threshold of P = 8.1×10−8 (0.05/614,216). The most significant SNP associated with DR was rs4538204, with a P value of 1.2×10−6 under the recessive model. This SNP is located in an intron of DPP10 on chromosome 2. The SNPs rs7625611, rs869494, rs4465961 and rs1497516, which were in strong linkage disequilibrium and located in an intergenic region between AC107622.1 and ZNF385D on chromosome 3, were also associated with DR (best P = 1.5×10−6, dominant model). The remaining five loci associated with DR were located on chromosomes 1, 3, 4, 18 and 19. Among them, the SNPs rs4516615, rs12640858 and rs8107333 are located in introns of FAM19A1, LDB2 and PCAT19, respectively.
P values of the Fisher's exact test under the allele model for SNPs in 446 patients were shown. The genomic inflation factor λ was 1.006.
P values of the Fisher's exact test under the allele model for SNPs in 446 patients were shown.
For the stage 2 replication, 249 SNPs selected from the stage 1 analysis were genotyped in three Japanese cohorts comprising a total of 623 T2D patients, consisting of 267 from SMU (independent cases from the first stage), 188 from SSI and 168 from KU. In an overall analysis combining the stage 1 and stage 2 samples, eight SNPs showed associations with a value of P<10−3 using the allele model; a meta-analysis combining the stage 1 and stage 2 data using general variance-based method with a fixed effects model showed similar P values with no evidence of heterogeneity (Table 2). The most strongly associated SNP, rs9362054 on chromosome 6, reached the genome-wide significance according to the overall analysis (P = 4.4×10−8), although the level of significance was just above that of genome-wide by the meta-analysis (Pmet = 1.4×10−7). This SNP is located in the RP1-90L14.1 gene (see the regional association plot below). The second most significant SNP associated with DR was rs10894267, with a Pmet value of 5.8×10−6 in an exon of RP11-890B15.2 on chromosome 11. The remaining six SNPs were weakly associated with DR, with Pmet values between 10−5 and 10−3; two of which were located in introns (GLIS1 and CD38) and four of which were located in intergenic regions.
These eight SNPs were selected for the stage 3 replication of genotyping in 917 T2D patients from the NCGM. As shown in Table 3, a meta-analysis combining all three stages revealed that the association of SNP rs9362054 with DR was the strongest, with a Pmet value of 1.7×10−5 using the allele model, although significant heterogeneity was observed for Phet = 5.6×10−3. The other seven SNPs all showed weak associations, with P values higher than 10−3 (Table 2).
The association of SNP rs9362054 was further evaluated by multiple logistic regression analysis using genotype, dominant and recessive models. After controlling for sex, the duration of diabetes and the HbA1c level, SNP rs9362054 was found to be strongly associated with DR according to a an overall analysis or meta-analysis (Table 3); controlling for hypertension and the lipid laboratory data was also performed with similar results (data not shown). The most significant signal was obtained in an overall analysis combining all stages in the recessive model (3.3×10−8) with the genome-wide significance level, while the meta-analysis of all stages showed a Pmet value of 1.3×10−6 with no evidence of heterogeneity (Phet = 3.8×10−1). Regarding the associations with PDR and DN as determined by multiple logistic regression analysis of all stages (Table 3), rs9362054 was not significantly associated with PDR under all models, but was significantly associated with DN with Pmet = 1.2×10−4 under the genotype model. Furthermore, according to a logistic regression analysis including the severity of DR (i.e. DR-negative, NPDR or PDR) as a variable, rs9362054 was significantly associated with DN (Pmet = 3.5×10−3 under the genotype model) independent of the severity of DR.
Regional Association Plot Around rs9362054
The most DR-associated SNP rs9362054 was located at 85Mb on chromosome 6. This SNP is in an intron of the RP1-90L14.1 gene between two protein-coding genes, KIAA1009 and TBX18. According to the UCSC human genome 19, RP1-90L14.1 is annotated as a long intergenic non-coding RNA (lincRNA)  by the GENECODE project. The regional plots for SNP rs9362054 demonstrated that the association signals with P<0.01 were confined to the RP1-90L14.1 gene region (Figure 4).
In the present study, we performed a three-stage GWAS for DR in Japanese T2D patients. The top SNP, rs9362054, was located in an intron of the RP1-90L14.1 gene; regional plots showed that the association signals around rs9362054 were confined to this gene region. RP1-90L14.1 encodes a lincRNA, a class of long non-coding RNAs (lncRNAs) ,  that are transcribed from intergenic regions; three transcripts were mapped to this gene as splice variants, and six non-coding exons were estimated to be present by the Ensembl project. The RP1-90L14.1 locus is widely conserved among vertebrates (Human, Rhesus, Mouse, Dog, Elephant, Opossum, Chicken, Xenopus tropicalis and Zebrafish) according to the UCSC Genome Browser. RP1-90L14.1 is also called lnc-KIAA1009-1 by LNCipedia 2.0 (a long non-coding RNA database) , because the nearest protein-coding gene on the same strand is KIAA1009; it is possible that RP1-90L14.1/lnc-KIAA1009-1 and KIAA1009 are functionally connected, since cis-regulation has been shown for many lncRNA genes .
Interestingly, KIAA1009, also known as QN1, was originally reported to be involved in cell cycle control during retinal development , and was found to be a microtubule-associated ATPase involved in cell division . Recently, it has been revealed that KIAA1009, renamed CEP162, plays a critical role in ciliary transition zone formation before ciliogenesis . Ciliopathies ,  comprise a group of disorders associated with dysfunction of a hair-like cellular organelle called a cilium; as cilia are a component of almost all vertebrate cells, cilial dysfunction can manifest as a constellation of features that include, characteristically, retinal degeneration, renal disease and cerebral anomalies; retinal degeneration is a common feature of ciliopathic disorders , , and may be associated with retinal cell death . In this regard, the strong signal for the RP1-90L14.1/lnc-KIAA1009-1 locus in the present study could be mediated through defective ciliogenesis due to KIAA1009/QN1/CEP162 dysregulation by some lnc-KIAA1009-1 variation(s). Although we should be cautious regarding this hypothesis, it was previously reported that transgenic rats with overexpression of a mutant cilia gene, polycystin-2, exhibited similar specific phenotypes to diabetic retinopathy, such as vasoregression and pericyte loss : furthermore, in the same transgenic rats, streptozotocin-induced chronic hyperglycemia was shown to ameliorate vasoregression and neuroglia cell loss , in agreement with the finding of a weaker association between RP1-90L14.1 and PDR than with DR (Table 3). In addition, it was noted that the previous GWAS for DR in Chinese patients  identified a signal in the locus of LRP-BBS5, and BBS5 is also known to be important in ciliogenesis .
In agreement with the frequent involvement of both retinopathy and renal disease in ciliopathies , , , rs9362054 in RP1-90L14.1 was also significantly associated with DN (Table 3). It should be noted that a significant association with DN was observed independently from the severity of DR, suggesting that rs9362054 may also contribute to the development of DN. To our knowledge, there has been no previous study reporting the association between DN and ciliary genes. Since the present study was not designed to identify DN susceptibility genes, further well-designed studies are required to verify the existence of any possible associations between the ciliary genes and DN.
In the present study, the stage 3 replications were mostly negative. However, possibly due to clinical heterogeneity (such as the duration of diabetes, metabolic control and medication(s) used for diabetes), the modest sample size and some bias, the replication of associations with DR appears to be difficult, as evidenced by the previous GWAS studies ; i.e. these studies have reported only borderline associations, and there were no obvious overlapping susceptibility loci between studies. Furthermore, in the present study, the potential susceptibility loci (Table S1 and Table 2) did not overlap.
One of the strengths of the present study is considered to be that the homogeneity of the subjects was expected because the participants were recruited exclusively from the Kanto district (Saitama prefecture or the adjacent Tokyo Metropolitan area), Japan. Furthermore, we took into account various clinical features, including the duration of diabetes and the HbA1c level, and the associations with PDR or DN were also evaluated in comparison to that with DR.
There are several limitations regarding the present study that should be kept in mind. First, the modest sample size of the stage 1 discovery cohort had a limited power to detect susceptibility SNPs; in fact, none reached the genome-wide significance level (P = 8.1×10−8). Consequently, the SNPs selected based on both provisional significance (P<10−3) and the viewpoint of the biological and/or bioinformatics potency in DR were proceeded for the stage 2 replication, and the two-stage analysis of stage 1 and stage 2 had sufficient power to detect susceptibility SNPs with relatively a high effect. Second, the documented HbA1c levels were collected at only one point, and were considerably different among the cohorts. Third, the patients with a short disease duration were not excluded from the control group, which may have reduced the power of the study, although the real duration of type 2 diabetes is considered to be longer than the clinically defined duration by several years. Finally, the stage 3 replication was carried out, although no associations of the stage 1+2 SNPs were confirmed despite the adequate power, as described in the statistical analysis section. In addition, under the predefined significance threshold of 1.6×10−4 (2×8.1×10−8/10−3) according to the replication-based method, the stage 3 analysis of rs9362054 had 85% power to detect association with the genome-wide significance using GDesignPlus  (StaGen Co., Ltd., Tokyo, Japan). Rather than the possibility of a false positive finding, we speculate that the substantial heterogeneity between the stage 3 and stage 1+2 studies (Table 2), as evidenced by the fact that the HbA1c levels were relatively low and the proportion of DR was the lowest among the cohorts in the stage 3 patients could have weakened the contribution of genetic factors to the development of DR. Recently, it has been demonstrated that a substantial proportion of the associations with borderline genome-wide significance are replicable . Therefore, further case-control studies of large well-characterized cohorts would be useful for validating the findings of the present study.
In conclusion, in the present GWAS in Japanese T2D patients, the most DR-associated locus, the lincRNA RP1-90L14.1, is intriguing, since the adjacent CEP162 gene plays an important role in ciliogenesis. Taking into account the findings in previous studies of the transgenic rats and the GWAS in Chinese, the present study raises the possibility that the dysregulation of ciliary-associated genes plays a role in susceptibility to DR.
Principal component analysis for the GWAS of diabetic retinopathy in stage 1. The results were shown along with Japanese (JPT) and Han-Chinese (CHB) individuals in the HapMap database. Two subjects were regarded as outliers, and were removed from the present study.
SNPs selected from stage 1 genome-wide scan. DR: diabetic retinopathy, PDR: proliferative diabetic retinopathy, DME: diabetic macular edema.
We thank Dr. Yutaka Nakachi for valuable comments, and Ms. Tomoko Hirata, Ms. Yoriko Mawatari and Ms. Ritsuko Doki for technical assistance.
Conceived and designed the experiments: TA YO. Performed the experiments: HY YM MK YO NN KT. Analyzed the data: TA MK NN KT AK. Contributed reagents/materials/analysis tools: HY S. Kurihara TM-O S. Katayama KM SY TM AS KY. Contributed to the writing of the manuscript: TA KY AK.
- 1. Stitt AW, Lois N, Medina RJ, Adamson P, Curtis TM (2013) Advances in our understanding of diabetic retinopathy. Clin Sci (Lond) 125: 1–17.
- 2. Emanuele N, Sacks J, Klein R, Reda D, Anderson R, et al. (2005) Ethnicity, race, and baseline retinopathy correlates in the veterans affairs diabetes trial. Diabetes Care 28: 1954–1958.
- 3. Zhang X, Saaddine JB, Chou CF, Cotch MF, Cheng YJ, et al. (2010) Prevalence of diabetic retinopathy in the United States, 2005–2008. JAMA 304: 649–656.
- 4. Leslie RD, Pyke DA (1982) Diabetic retinopathy in identical twins. Diabetes 31: 19–21.
- 5. Clustering of long-term complications in families with diabetes in the diabetes control and complications trial. The Diabetes Control and Complications Trial Research Group. Diabetes 46: 1829–1839.
- 6. Monti MC, Lonsdale JT, Montomoli C, Montross R, Schlag E, et al. (2007) Familial risk factors for microvascular complications and differential male-female risk in a large cohort of American families with type 1 diabetes. J Clin Endocrinol Metab 92: 4650–4655.
- 7. Rema M, Saravanan G, Deepa R, Mohan V (2002) Familial clustering of diabetic retinopathy in South Indian Type 2 diabetic patients. Diabet Med 19: 910–916.
- 8. Hallman DM, Huber JC Jr, Gonzalez VH, Klein BE, Klein R, et al. (2005) Familial aggregation of severity of diabetic retinopathy in Mexican Americans from Starr County, Texas. Diabetes Care 28: 1163–1168.
- 9. Zhang X, Gao Y, Zhou Z, Wang J, Zhou Q, et al. (2010) Familial clustering of diabetic retinopathy in Chongqing, China, type 2 diabetic patients. Eur J Ophthalmol 20: 911–918.
- 10. Frank RN (2004) Diabetic retinopathy. N Engl J Med 350: 48–58.
- 11. Abbate M, Cravedi P, Iliev I, Remuzzi G, Ruggenenti P (2011) Prevention and treatment of diabetic retinopathy: evidence from clinical trials and perspectives. Curr Diabetes Rev 7: 190–200.
- 12. Schwartz SE, Brantley MAJ, Flynn HWJ (2012) Genetics and diabetic retinopathy. Current Diabetes Reviews 9: 86–92.
- 13. Simo-Servat O, Hernandez C, Simo R (2013) Genetics in diabetic retinopathy: current concepts and new insights. Curr Genomics 14: 289–299.
- 14. Kuo JZ, Wong TY, Rotter JI (2014) Challenges in elucidating the genetics of diabetic retinopathy. JAMA Ophthalmol 132: 96–107.
- 15. Awata T, Inoue K, Kurihara S, Ohkubo T, Watanabe M, et al. (2002) A common polymorphism in the 5′-untranslated region of the VEGF gene is associated with diabetic retinopathy in type 2 diabetes. Diabetes 51: 1635–1639.
- 16. Qiu M, Xiong W, Liao H, Li F (2013) VEGF −634G>C polymorphism and diabetic retinopathy risk: a meta-analysis. Gene 518: 310–315.
- 17. Abhary S, Hewitt AW, Burdon KP, Craig JE (2009) A systematic meta-analysis of genetic association studies for diabetic retinopathy. Diabetes 58: 2137–2147.
- 18. Imperatore G, Hanson RL, Pettitt DJ, Kobes S, Bennett PH, et al. (1998) Sib-pair linkage analysis for susceptibility genes for microvascular complications among Pima Indians with type 2 diabetes. Pima Diabetes Genes Group. Diabetes 47: 821–830.
- 19. Looker HC, Nelson RG, Chew E, Klein R, Klein BE, et al. (2007) Genome-wide linkage analyses to identify Loci for diabetic retinopathy. Diabetes 56: 1160–1166.
- 20. Hallman DM, Boerwinkle E, Gonzalez VH, Klein BE, Klein R, et al. (2007) A genome-wide linkage scan for diabetic retinopathy susceptibility genes in Mexican Americans with type 2 diabetes from Starr County, Texas. Diabetes 56: 1167–1173.
- 21. Fu YP, Hallman DM, Gonzalez VH, Klein BE, Klein R, et al.. (2010) Identification of Diabetic Retinopathy Genes through a Genome-Wide Association Study among Mexican-Americans from Starr County, Texas. J Ophthalmol 2010.
- 22. Huang YC, Lin JM, Lin HJ, Chen CC, Chen SY, et al. (2011) Genome-wide association study of diabetic retinopathy in a Taiwanese population. Ophthalmology 118: 642–648.
- 23. Sheu WH, Kuo JZ, Lee IT, Hung YJ, Lee WJ, et al. (2013) Genome-wide association study in a Chinese population with diabetic retinopathy. Hum Mol Genet 22: 3165–3173.
- 24. Grassi MA, Tikhomirov A, Ramalingam S, Below JE, Cox NJ, et al. (2011) Genome-wide meta-analysis for severe diabetic retinopathy. Hum Mol Genet 20: 2472–2481.
- 25. Awata T, Kurihara S, Takata N, Neda T, Iizuka H, et al. (2005) Functional VEGF C-634G polymorphism is associated with development of diabetic macular edema and correlated with macular retinal thickness in type 2 diabetes. Biochem Biophys Res Commun 333: 679–685.
- 26. Awata T, Neda T, Iizuka H, Kurihara S, Ohkubo T, et al. (2004) Endothelial nitric oxide synthase gene is associated with diabetic macular edema in type 2 diabetes. Diabetes Care 27: 2184–2190.
- 27. Iizuka H, Awata T, Osaki M, Neda T, Kurihara S, et al. (2007) Promoter polymorphisms of the pigment epithelium-derived factor gene are associated with diabetic retinopathy. Biochem Biophys Res Commun 361: 421–426.
- 28. Price AL, Patterson NJ, Plenge RM, Weinblatt ME, Shadick NA, et al. (2006) Principal components analysis corrects for stratification in genome-wide association studies. Nat Genet 38: 904–909.
- 29. Nishida N, Tanabe T, Takasu M, Suyama A, Tokunaga K (2007) Further development of multiplex single nucleotide polymorphism typing method, the DigiTag2 assay. Anal Biochem 364: 78–85.
- 30. Awata T, Yamashita H, Kurihara S, Morita-Ohkubo T, Miyashita Y, et al. (2013) A low-frequency GLIS3 variant associated with resistance to Japanese type 1 diabetes. Biochem Biophys Res Commun 437: 521–525.
- 31. Purcell S, Neale B, Todd-Brown K, Thomas L, Ferreira MA, et al. (2007) PLINK: a tool set for whole-genome association and population-based linkage analyses. Am J Hum Genet 81: 559–575.
- 32. Barrett JC, Fry B, Maller J, Daly MJ (2005) Haploview: analysis and visualization of LD and haplotype maps. Bioinformatics 21: 263–265.
- 33. Skol AD, Scott LJ, Abecasis GR, Boehnke M (2006) Joint analysis is more efficient than replication-based analysis for two-stage genome-wide association studies. Nat Genet 38: 209–213.
- 34. Esteller M (2011) Non-coding RNAs in human disease. Nat Rev Genet 12: 861–874.
- 35. Fatica A, Bozzoni I (2014) Long non-coding RNAs: new players in cell differentiation and development. Nat Rev Genet 15: 7–21.
- 36. Volders PJ, Helsens K, Wang X, Menten B, Martens L, et al. (2013) LNCipedia: a database for annotated human lncRNA transcript sequences and structures. Nucleic Acids Res 41: D246–251.
- 37. Neron B, Marx M, Crisanti P (2001) Role of QN1 protein in cell proliferation arrest and differentiation during the neuroretina development. Mech Dev 102: 107–117.
- 38. Leon A, Omri B, Gely A, Klein C, Crisanti P (2006) QN1/KIAA1009: a new essential protein for chromosome segregation and mitotic spindle assembly. Oncogene 25: 1887–1895.
- 39. Wang WJ, Tay HG, Soni R, Perumal GS, Goll MG, et al. (2013) CEP162 is an axoneme-recognition protein promoting ciliary transition zone assembly at the cilia base. Nat Cell Biol 15: 591–601.
- 40. Waters AM, Beales PL (2011) Ciliopathies: an expanding disease spectrum. Pediatr Nephrol 26: 1039–1056.
- 41. Hildebrandt F, Benzing T, Katsanis N (2011) Ciliopathies. N Engl J Med 364: 1533–1543.
- 42. Estrada-Cuzcano A, Roepman R, Cremers FP, den Hollander AI, Mans DA (2012) Non-syndromic retinal ciliopathies: translating gene discovery into therapy. Hum Mol Genet 21: R111–124.
- 43. Wheway G, Parry DA, Johnson CA (2014) The role of primary cilia in the development and disease of the retina. Organogenesis 10: 1–17.
- 44. Feng Y, Wang Y, Stock O, Pfister F, Tanimoto N, et al. (2009) Vasoregression linked to neuronal damage in the rat with defect of polycystin-2. PLoS One 4: e7328.
- 45. Feng Y, Wang Y, Yang Z, Wu L, Hoffmann S, et al. (2014) Chronic hyperglycemia inhibits vasoregression in a transgenic model of retinal degeneration. Acta Diabetol 51: 211–218.
- 46. Arts HH, Knoers NV (2013) Current insights into renal ciliopathies: what can genetics teach us? Pediatr Nephrol 28: 863–874.
- 47. Hotta K, Kitamoto T, Kitamoto A, Mizusawa S, Matsuo T, et al. (2011) Association of variations in the FTO, SCG3 and MTMR9 genes with metabolic syndrome in a Japanese population. J Hum Genet 56: 647–651.
- 48. Panagiotou OA, Ioannidis JP, Genome-Wide Significance P (2012) What should the genome-wide significance threshold be? Empirical replication of borderline genetic associations. Int J Epidemiol 41: 273–286.