Key Points
Question
Which genetic loci associate with chronic central serous chorioretinopathy (CSC) when using an unbiased genome-wide approach?
Findings
In this case-control genome-wide association study, a locus containing multiple variants in the CFH gene was associated with cCSC. Additionally, pathway analysis implicated the complement system and based on genetic data, components of the complement system were predicted to have an association with altered expression in patients with cCSC.
Meaning
This study of patients with cCSC demonstrates the potential role of the CFH gene and the complement system in the causative mechanism of cCSC.
This case-control genome-wide association study assesses new genetic loci and pathways associated with chronic central serous chorioretinopathy and to predict the association of genetic variants with gene expression in patients with this disease.
Abstract
Importance
To date, several targeted genetic studies on chronic central serous chorioretinopathy (cCSC) have been performed; however, unbiased genome-wide studies into the genetics of cCSC have not been reported. To discover new genetic loci associated with cCSC and to better understand the causative mechanism of this disease, we performed a genome-wide association study (GWAS) on patients with cCSC.
Objective
To discover new genetic loci and pathways associated with cCSC and to predict the association of genetic variants with gene expression in patients with cCSC.
Design, Setting, and Participants
This case-control GWAS was completed in the general community, 3 referral university medical centers, and outpatient care on Europeans individuals with cCSC and population-based control participants. Genotype data was collected from May 2013 to August 2017, and data analysis occurred from August 2017 to November 2017.
Main Outcomes and Measures
Associations of single-nucleotide polymorphisms, haplotypes, genetic pathways, and predicted gene expression with cCSC.
Results
A total of 521 patients with cCSC (median age, 51 years; interquartile range [IQR], 44-59 years; 420 [80.6%] male) and 3577 European population-based control participants (median age, 52 years; IQR, 37-71 years; 1630 [45.6%] male) were included. One locus on chromosome 1 at the complement factor H (CFH) gene reached genome-wide significance and was associated with an increased risk of cCSC (rs1329428; odds ratio [OR], 1.57 [95% CI, 1.38-1.80]; P = 3.12 × 10−11). The CFH haplotypes H1 and H3 were protective for cCSC (H1: OR, 0.64 [95% CI, 0.53-0.77]; P = 2.18 × 10−6; H3: OR, 0.54 [95% CI, 0.42-0.70]; P = 2.49 × 10−6), whereas haplotypes H2, H4, H5, and the aggregate of rare CFH haplotypes conferred increased risk (H2: OR, 1.57 [95% CI, 1.30-1.89]; P = 2.18 × 10−6; H4: OR, 1.43 [95% CI, 1.13-1.80]; P = 2.49 × 10−3; H5: OR, 1.80 [95% CI, 1.36-2.39]; P = 4.61 × 10−5; rare haplotypes: OR, 1.99 [95% CI, 1.43-2.77]; P = 4.59 × 10−5). Pathway analyses showed involvement of the complement cascade and alternative open reading frame (ARF) pathway in cCSC. Using PrediXcan, we identified changes in predicted expression of complement genes CFH, complement factor H related 1 (CFHR1), complement factor related 4 (CFHR4), and membrane cofactor protein (MCP/CD46). Additionally, the potassium sodium-activated channel subfamily T member 2 (KCNT2) and tumor necrosis factor receptor superfamily member 10a (TNFRSF10A) genes were differentially expressed in patients with cCSC.
Conclusions and Relevance
In this GWAS on cCSC, we identified a locus on chromosome 1 at the CFH gene that was significantly associated with cCSC, and we report protective and risk-conferring haplotypes in this gene. Pathway analyses were enriched for complement genes, and gene expression analysis suggests a role for CFH, CFHR1, CFHR4, CD46, KCNT2, and TNFRSF10A in the disease. Taken together, these results underscore the potential importance of the complement pathway in the causative mechanisms of cCSC.
Introduction
Central serous chorioretinopathy (CSC) is characterized by subretinal fluid accumulation between the neuroretina and the retinal pigment epithelium (RPE).1,2,3 Patients usually present with clinical symptoms of metamorphopsia and central vision impairment.4,5 Although acute CSC can show spontaneous resolution within 3 months, patients with chronic CSC (cCSC) have a prolonged presence of fluid with progressive loss of vision, permanent RPE alterations, and a reduced quality of life.4 Generally, CSC occurs more frequently in males (9.9 per 100 000) than females (1.7 per 100 000) and often presents at an age at which patients are still professionally active.6
The exact causative mechanism of cCSC is unknown, but clinical and experimental studies have implicated dysfunction of the RPE and hyperpermeability of the choroid in CSC.4 The disease has been associated with the use of corticosteroids, stress, and type A personality. Reports of familial occurrences of cCSC support a genetic component for the disease.1,3,4,7,8,9,10,11 Genetic studies on cCSC have been limited to candidate gene approaches; several associations have been reported with single-nucleotide polymorphisms (SNPs) in the genes age-related macular degeneration susceptibility 2 (ARMS2), cadherin 5 (CDH5), complement factor H (CFH), and the nuclear receptor subfamily 3 group C member 2 (NR3C2), as well as in copy number variations in the complement factor 4B (C4B) gene.12,13,14,15,16
Unbiased genome-wide association studies (GWAS) have identified hundreds of genomic loci implicated in complex diseases, such as age-related macular degeneration (AMD), myopia, and glaucoma.17 Such approaches have shed light on the genetic architecture of these diseases; however, establishing functional links between identified loci and disease remains challenging. Besides having direct effects on protein function or structure, SNPs can also influence the expression of nearby (cis) or distal (trans) genes. Such regulatory genetic variants are called expression quantitative trait loci (eQTL).18 Several eQTL databases, linking genotype information to tissue expression of genes, have been established. One of the largest of these projects is the GTEx project, in which expression profiles of 44 tissues of 449 donors have been collected in version 6.18 These eQTL databases offer functional information on SNPs identified in GWAS, contribute to a better understanding of the studied disease trait, and can be used to predict expression in genotyped samples.19
In this study, we performed a GWAS involving European patients with cCSC and population control participants to identify new cCSC disease loci and to increase our knowledge on the causative mechanisms of this disease. Additionally, we performed pathway analyses to discover new pathways implicated in cCSC. Using publicly available eQTL data, we aimed to identify new candidate genes predicted to be associated with differential expression in patients with cCSC compared with control participants.
Methods
Study Participants
Genomic DNA was extracted from blood using standard procedures. In total, 546 European patients with cCSC recruited from the outpatient clinics of the Radboud university medical centre (Radboudumc), Nijmegen, the Netherlands, University Hospital of Cologne (UHC), Cologne, Germany, and Leiden University Medical Center (LUMC), Leiden, the Netherlands, were included. Grading of all patients was performed by an experienced retinal specialist (C.J.F.B.), and was based on extensive ophthalmological examination including fundoscopy, spectral-domain optical coherence tomography, fluorescein angiography, and indocyanine green angiography. Diagnosis of cCSC was based on subgroups 1 and 2, which have been previously described.13 Briefly, patients that were included in this study showed the presence of serous fluid on optical coherence tomography in 1 or both eyes, either bilateral (subgroup 1) or unilateral (subgroup 2) RPE irregularities with 1 or more hot spots of leakage on fluorescein angiography, and corresponding hyperfluorescence on indocyanine green angiography.13 Patients diagnosed with acute CSC as recognized by a focal leakage spot (ink blot) or a smokestack pattern on fluorescein angiography, with less than 1 disc diameter of adjacent atrophic RPE alterations, and/or a duration of disease of less than 3 months were excluded from the study. Patients in whom evidence of another explanatory diagnosis or complication was present (subgroup 3), such as polypoidal choroidal vasculopathy, choroidal neovascularization, drusen, or other signs of AMD, were also excluded from this study.
This study was carried out in accordance with the tenets of the Declaration of Helsinki and was approved by the local ethics committees of the Radboudumc, LUMC, and UHC. Written informed consent was obtained for all participants involved in the study.
Genotyping data of controls was obtained from the Nijmegen Biomedical Study (NBS), a population-based survey conducted by the Department for Health Evidence and the Department of Laboratory Medicine of the Radboudumc. (eMethods in the Supplement; http://www.nijmegenbiomedischestudie.nl/).20 In this population-based study, no ophthalmologic grading was performed. Only control participants for which genotyping was available on the OmniExpress platform (n = 3654) were included in this study. Genotype data was collected from May 2013 to August 2017, and data analysis occurred from August 2017 to November 2017.
The mean age of patients with cCSC and control participants was compared with a Mann-Whitney U test. The sex distribution was compared with a χ2 test using SPSS version 22 (IBM). All values of P < .05 were deemed significant.
Genome-Wide Association and Haplotype Analyses
Genotyping was performed with OmniExpress-12 or OmniExpress-24 chips, and data were imputed with the Haplotype Reference Consortium release 1.1.2016. After stringent quality control (eMethods in the Supplement), 521 patients with cCSC and 3577 NBS control participants were included in the analysis, carrying 11 261 291 autosomal SNPs and 265 428 X-chromosomal SNPs. Single-variant association analysis was performed using the Firth bias-corrected likelihood ratio test, implemented in EPACTS (version 3.2.6, https://genome.sph.umich.edu/wiki/EPACTS; University of Michigan),21,22 correcting for sex and the first 2 components of ancestry analysis, to correct for potential population stratification (eMethods in the Supplement). To assess the potential role of confounding by AMD, the analysis was repeated in patients and controls younger than 51 years.
Haplotype analysis, combining multiple variants in the CFH gene, was performed with the haplo.stats (version 1.7.7) package23 in R (R Foundation for Statistical Computing).24 The SNPs described by Hageman et al22 and the CFH SNPs that were previously associated with cCSC were used as input.12,13 Haplotypes with a frequency greater than 1% were analyzed individually, while rare haplotypes (with a frequency less than 1%) were aggregated. Haplotype association with cCSC was performed using the haplo.glm command in R, correcting for sex and the first 2 principal components of ancestry analysis. The analysis was performed using the most common protective haplotype (H1) and cCSC risk–carrying haplotype (H2) as references. The frequency of the haplotypes in patients with cCSC and control participants was obtained using the haplo.cc command in R.
Pathway and PrediXcan Analysis
We used GWAS summary statistics to perform competitive gene-set analysis to identify pathways associated with cCSC using 2 different programs: MAGMA (version 1.06, https://ctg.cncr.nl/software/magma) and VEGASv2 (version 2, https://vegas2.qimrberghofer.edu.au/vegas2v2).25,26 Additionally, PrediXcan (Hakyimlab; https://github.com/hakyim/PrediXcan) was used to predict gene expression levels based on publicly available eQTL data of GTEx.27 PrediXcan was performed on all 44 provided GTEx tissues using genetic variants in 17 742 genes. We selected the genes that were associated in at least 1 tissue after Bonferroni correction for multiple testing (P < .05/17742 genes = 2.21 × 10−6) and were nominally associated (P < .05) in at least 50% of the tissues they were expressed in. (The eMethods in the Supplement provide further clarification.) This study had sufficient power to detect common variants with OR greater than 1.5 and had more than 80% power to detect the lead variant (eMethods and eFigure 1 in the Supplement).
Results
Genome-Wide Association Analysis
A total of 546 patients with cCSC were recruited from Radboudumc (n = 319), UHC (n = 74), and LUMC (n = 153). In addition, 3654 European population-based control participants were included. After quality control (eFigures 2 and 3 in the Supplement), a total of 521 European patients with cCSC (median age, 51 years; interquartile range [IQR], 44-59 years; 420 [80.6%] male) and 3577 control participants (median age, 52 years; IQR, 37-71 years; 1630 [45.6%] male; eTable 1 in the Supplement) were analyzed for 11 261 291 autosomal and 265 428 X-chromosomal SNPs. Because of the significant difference in sex between patients with cCSC and control participants (eTable 1 in the Supplement; P = 1.78 × 10−50), sex was included as a covariate in the analysis along with 2 principal components of the ancestry analysis.
The GWAS identified 20 SNPs that reached genome-wide significance (using a cutoff value for significance set at P < 5.0 × 10−8) that all resided at 1 locus on chromosome 1 in the CFH gene (lead variant: rs1329428; odds ratio [OR], 1.57 [95% CI, 1.38-1.80]; P = 3.12 × 10−11; Figure; Table 1; eFigure 4, eFigure 5, and eTable 2 in the Supplement). Additionally, 6 suggestive signals (P < 1 × 10−6) were found on chromosome 1 (OR, 0.64 [95% CI, 0.54-0.77]; P = 6.69 × 10−7), chromosome 8 (OR, 42.25 [95% CI, 9.55-186.89]; P = 1.33 × 10−7), chromosome 9 (OR, 8.36 [95% CI, 3.67-19.06]; P = 6.28 × 10−7), chromosome 15 (OR, 206.11 [95% CI, 4.50-9436.68]; P = 7.84 × 10−7), chromosome 17 (OR, 5.25 [95% CI, 2.94-9.48]; P = 1.51 × 10−7), and chromosome 20 (OR, 0.67 [95% CI, 0.57-0.79]; P = 7.14 × 10−7; Figure; Table 1). Conditioned analysis of the lead SNP on chromosome 1 did not reveal any other independent signal in the CFH gene (eFigure 5 in the Supplement) but did retain the suggestive signal at the CD46 gene on chromosome 1, indicating that the CD46 signal is independent of the CFH association (unconditioned and conditioned data are shown in eFigure 5 in the Supplement). Stratified analysis on patients and controls younger than 51 years identified associations with the same 20 SNPs in the CFH gene, but ORs were higher for the lead variant than in the complete cohort (OR, 1.89 [95% CI, 1.56-2.32]; P = 1.23 × 10−10; eTable 2 in the Supplement).
Figure. Manhattan Plot of Genome-Wide Association Study on 521 Patients With Chronic Central Serous Chorioretinopathy and 3577 Population Control Participants.
Genome-wide association analysis was performed correcting for sex and 2 principal components. The genome-wide significant signals are depicted in red (significant with cutoff value set at P < 5 × 10−8), whereas the suggestive variants are depicted in blue (significant with cutoff value set at P < 1 × 10−6).
Table 1. Top Hits in Chronic Central Serous Chorioretinopathy Genome-Wide Association Study.
| Top Single-Nucleotide Polymorphism | Chr | Base Pair (hg19) | Major Allele/ Minor Allele | Gene | Minor Allele Frequency in Patients With cCSC (n = 521) |
Minor Allele Frequency in Control Participants (n = 3577) |
Odds Ratio (95% CI) | P Value |
|---|---|---|---|---|---|---|---|---|
| rs1329428a | 1 | 196 702 810 | C/T | CFH | 0.543 | 0.434 | 1.57 (1.38-1.80) | 3.12 × 10−11 |
| rs561753392 | 8 | 68 131 981 | C/T | ARFGEF1 | 0.0070 | 0.0005 | 42.25 (9.55-186.89) |
1.33 × 10−7 |
| rs76766498 | 17 | 65 418 162 | A/G | PITPNC1 | 0.021 | 0.005 | 5.25 (2.94-9.48) | 1.51 × 10−7 |
| rs118083394 | 9 | 123 367 552 | C/T | MEGF6 | 0.014 | 0.002 | 8.36 (3.67-19.06) | 6.28 × 10−7 |
| rs4844392 | 1 | 207 991 209 | C/G | mir-29b-2/ CD46 | 0.159 | 0.230 | 0.64 (0.54-0.77) | 6.69 × 10−7 |
| rs2379120 | 20 | 61 030 580 | T/A | Upstream GATA5 | 0.218 | 0.300 | 0.67 (0.57-0.79) | 7.14 × 10−7 |
| rs541395042 | 15 | 61 175 324 | C/T | RORA | 0.006 | 0.00 | 206.11 (4.50-9436.68) |
7.84 × 10−7 |
Abbreviations: ARFGEF1, adenosine diphosphate ribosylation factor guanine nucleotide exchange factor 1; CD46, cluster of differentiation 46; CFH, complement factor H; chr, chromosome; GATA5, GATA-5 binding protein 5; hg, human genome; MEGF6, multiple epidermal growth factor like domains 6; mir-29b-2, microribonucleic acid 29b-2; PITPNC1, phosphatidylinositol transfer protein cytoplasmic 1; RORA, retinoic acid receptor-related orphan receptor alpha.
This is a genome-wide significant hit.
Haplotype Analysis
To further characterize the association at the CFH gene, haplotype analysis was performed. A logistic model corrected for sex and 2 principal components was performed using either the most common cCSC protective (H1) haplotype or the most common cCSC risk–conferring (H2) haplotype as reference (Table 2). The H2, H4, and H5 haplotypes, all containing the minor allele of the lead variant, were associated with an increased risk of cCSC (H2: OR, 1.57 [95% CI, 1.30-1.89]; P = 2.18 × 10−6; H4: OR, 1.43 [95% CI, 1.13-1.80]; P = 2.49 × 10−3; H5: OR, 1.80 [95% CI, 1.36-2.39]; P = 4.61 × 10−5). The H1 and H3 haplotypes, containing the major allele of the lead variant, were protective (H1: OR, 0.64 [95% CI, 0.53-0.77]; P = 2.18 × 10−6; H3: OR, 0.54 [95% CI, 0.42-0.70]; P = 2.49 × 10−6). Additionally, the aggregate of rare haplotypes also increased the risk of cCSC (OR, 1.99 [95% CI, 1.43-2.77]; P = 4.59 × 10−5). All associations were significant after Bonferroni correction for multiple testing for 10 tests (using a cutoff value of P < .005).
Table 2. Haplotype Analysis of the Complement Factor H Gene.
| Haplotype | rs3753394 | rs529825 | rs800292 | rs3766404 | rs1061170 | rs203674 | rs3753396 | rs2284664 | rs1329428a | rs1065489 | Frequency in Controls | Frequency in Patients With cCSC | Per CFH-H1b | Per CFH-H2c | ||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Odds Ratio (95% CI) | P Value, Correctedd,e | Odds Ratio (95% CI) | P Value, Correctedd,e | |||||||||||||
| H1 | C | G | G | T | C | G | A | C | C | G | 0.339 | 0.273 | 1 [Reference] |
1 [Reference] |
0.64 (0.53-0.77) |
2.18 × 10−6f |
| H2 | C | A | A | T | T | T | A | T | T | G | 0.221 | 0.274 | 1.57 (1.30-1.89) |
2.18 × 10−6f | 1 [Reference] | 1 [Reference] |
| H3 | T | G | G | T | T | T | G | C | C | T | 0.135 | 0.094 | 0.85 (0.66-1.09) |
.20 | 0.54 (0.42-0.70) |
2.49 × 10−6f |
| H4 | C | G | G | C | T | T | A | C | T | G | 0.115 | 0.128 | 1.43 (1.13-1.80) |
2.49 × 10−3f | 0.91 (0.72-1.15) |
.44 |
| H5 | T | G | G | T | T | T | A | C | T | G | 0.052 | 0.078 | 1.80 (1.36-2.39) |
4.61 × 10−5f | 1.15 (0.86-1.53) |
.34 |
| H6 | T | G | G | T | T | G | A | C | C | G | 0.030 | 0.025 | 1.17 (0.75-1.81) |
.49 | 0.74 (0.48-1.16) |
.19 |
| H7 | T | G | G | T | C | G | A | C | C | G | 0.027 | 0.018 | 0.82 (0.48-1.42) |
.49 | 0.53 (0.30-0.91) |
.02 |
| H8 | T | G | G | C | T | T | A | C | T | G | 0.022 | 0.027 | 1.58 (1.00-2.48) |
.048 | 1.01 (0.64-1.59) |
.97 |
| H9 | C | A | A | T | T | T | G | C | C | T | 0.017 | 0.017 | 1.25 (0.73-2.17) |
.42 | 0.80 (0.46-1.38) |
.42 |
| Rare | NAg | NAg | NAg | NAg | NAg | NAg | NAg | NAg | NAg | NAg | 0.042 | 0.066 | 1.99 (1.43-2.77) |
4.59 × 10−5f | 1.27 (0.91-1.77) |
.16 |
Abbreviations: cCSC, chronic central serous chorioretinopathy; NA, not applicable.
Results shown in the rs1329428 column are top hits for the genome-wide association study findings.
Analysis performed with the most common protective haplotype CFH-H1 set as reference.
Analysis performed with the most common risk-conferring haplotype CFH-H2 set as reference.
Bonferroni correction for multiple testing for 10 haplotypes with significance set at P < .05/10 = .005.
Analysis was performed correcting for sex and the first 2 principal components of ancestry analysis.
Values are significant based on a cutoff level of P < .005.
The data on rare haplotypes includes an accumulation of alleles that could be either minor or major.
Gene Set/Pathway Analysis
Using the GWAS summary statistics, we performed competitive gene-set analysis using 2 programs. MAGMA identified 3 pathways that were associated with cCSC after correction for multiple testing (Table 3; genes in eTable 3 in the Supplement). Two of the pathways are implicated in the complement cascade, and 1 is the ARF pathway. We identified the same reactome pathways of the complement cascade using VEGAS2. However, these findings were not significant after correction for multiple testing (using a cutoff value of P < 1 × 10−5; Table 3; genes in eTable 4 in the Supplement).
Table 3. Competitive Gene-Set Analysis of Multi-Marker Analysis of Genomic Annotation (MAGMA) and Versatile Gene-Based Association Study 2 (VEGAS2).
| Gene | Pathway Name | Genes, No. | β (SE) | P Value | |
|---|---|---|---|---|---|
| Nominal | Empirical, Corrected | ||||
| MAGMA | Reactome regulation of complement cascade | 13 | 1.290 (0.265) | 5.49 × 10−7 | 8.90 × 10−4a |
| Reactome complement cascade | 29 | 0.786 (0.179) | 5.56 × 10−6 | 7.37 × 10−3a | |
| Biocarta ARF pathway | 17 | 0.848 (0.194) | 6.49 × 10−6 | 8.65 × 10−3a | |
| VEGAS2 | Reactome: RHSA-977606 regulation of complement cascade | 13 | NA | 1.03 × 10−7 | 1.60 × 10−5 |
| Reactome: R-HSA-166658 complement cascade | 22 | NA | 6.09 × 10−7 | 4.20 × 10−5 | |
Abbreviations: ARF, alternative open reading frame; NA, not annotated.
Significant after correction for multiple testing; the MAGMA nominal significance threshold is P < 4.12 × 10−5, and the VEGAS2 empirical significance threshold is P < 1 × 10−5.
PrediXcan Analysis
For each individual, we predicted the association of genotypes with expression levels of 17 742 genes in 44 different GTEx (version 6) tissues using PrediXcan. The predicted gene expression of genes at the CFH locus (CFH, CFHR1, CFHR4, and KCNT2) was different between patients with cCSC and control participants after correction for multiple testing, using a cutoff value for significance of P < 2.82 × 10−6(for example, CFH in subcutaneous adipose tissue: OR, 1.09 [95% CI, 1.05-1.13]; P = 1.17 × 10−6; CFHR1 in subcutaneous adipose tissue: OR, 1.15 [95% CI, 1.10-1.21]; P = 1.85 × 10−8; CFHR4 in liver: OR, 0.91 [95% CI, 0.85-0.98]; P = 1.29 × 10−6; KCNT2 in gastroesophageal junction: OR, 3.70 [95% CI, 2.27-6.04]; P = 1.62 × 10−7; full results in Table 4). Additionally, we observed altered predicted expression of the CD46 and TNFRSF10A genes (results of all tissues in eTable 5 in the Supplement).
Table 4. PrediXcan Results, Genes Differentially Expressed Between Patients With Chronic Central Serous Chorioretinopathy and Control Participants.
| Gene | Tissues With Expression, No. | Tissues With Differential Expression, No. (%)a | Tissuesb | Odds Ratio (95% CI) | P Value |
|---|---|---|---|---|---|
| CFHR4 | 1 | 1 (100) | Liver | 0.91 (0.85-0.98) | 1.29 × 10−6 |
| CD46 | 25 | 21 (84) | Esophagus muscularis | 1.05 (1.03-1.07) | 1.79 × 10−6 |
| Suprapubic skin, not sun-exposed | 1.32 (1.18-1.48) | 2.26 × 10−6 | |||
| Atrial appendage of heart | 1.16 (1.09-1.24) | 2.39 × 10−6 | |||
| Sigmoid colon | 1.07 (1.04-1.09) | 2.57 × 10−6 | |||
| TNFRSF10A | 18 | 15 (83) | Transformed fibroblasts | 0.96 (0.94-0.98) | 2.41 × 10−6 |
| Adrenal gland | 0.95 (0.93-0.97) | 2.60 × 10−6 | |||
| CFHR1 | 12 | 9 (75) | Hypothalamus | 0.78 (0.72-0.85) | 1.73 × 10−8 |
| Subcutaneous adipose tissue | 1.15 (1.1-1.21) | 1.85 × 10−8 | |||
| Liver | 0.95 (0.93-0.97) | 1.87 × 10−7 | |||
| Transformed fibroblasts | 0.79 (0.71-0.87) | 1.41 × 10−6 | |||
| KCNT2 | 5 | 3 (60) | Gastroesophageal junction of esophagus | 3.70 (2.27-6.04) | 1.62 × 10−7 |
| Tibial nerve | 1.13 (1.08-1.18) | 4.99 × 10−7 | |||
| CFH | 5 | 3 (60) | Subcutaneous adipose tissue | 1.09 (1.05-1.13) | 1.17 × 10−6 |
| Tibial nerve | 1.10 (1.06-1.14) | 1.63 × 10−6 |
Abbreviations: CD46, cluster of differentiation 46; CFH, complement factor H; CFHR1/4, complement factor H related 1/4; GTEx, genotype-tissue expression; KCNT2, potassium sodium-activated channel subfamily T member 2; TNFRSF10A, tumor necrosis factor receptor superfamily member 10a.
The cutoff value for significance in differential expression was P < .05; only those genes that showed expression differences in more than 50% of their expressed tissues are depicted.
This section used a cutoff value for significance set at P < 2.81 × 10−6.
Discussion
In this unbiased genome-wide association study of cCSC, we identified a locus for cCSC on chromosome 1, at the CFH gene, that had previously been described in 2 targeted candidate gene studies.12,13 We discovered protective and risk haplotypes in CFH and found evidence for involvement of rare CFH haplotypes in the disease. Moreover, using gene-set analysis and publicly available expression databases, we uncovered additional evidence for the altered regulation of the complement system in cCSC and identified novel candidate genes and pathways implicated in this disease.
So far, to our knowledge, the association of cCSC with the CFH gene is the only genetic association for cCSC that has been replicated in multiple independent studies.12,13,28 The effect size of the lead variant (rs1329428) in CFH in this unbiased study was higher compared with the previous targeted study,13 but lower compared with the study by Miki et al12 (withs ORs in the 3 studies 1.57, 1.47, and 1.79, respectively). Stratified analysis of patients younger than 51 years to exclude potential confounding with AMD showed the same significant 20 SNPs with similar direction of effect, confirming the cCSC-specific association. Additionally, haplotype analysis of the CFH locus confirmed a previously reported protective effect of CFH-H313 and identified associations with the protective CFH-H1 and risk-carrying CFH-H2, CFH-H4, and CFH-H5 haplotypes. The risk associated with the CFH-H5 haplotype was higher than the risk caused by the single lead variant alone and higher to our knowledge than any previously reported SNP in CFH, with an OR of 1.80 for CFH-H5 compared with an OR of 1.57 for rs1329428. Interestingly, an aggregate of all haplotypes with a frequency of less than 1% also showed a higher risk for cCSC (OR, 1.99). Higher ORs of CFH-H5 and the rare haplotype aggregate suggest that other (rare) variants in these haplotypes might play a role in cCSC.
The factor H protein, encoded by the CFH gene, is able to block the formation of C3-convertases and therefore is an important regulator of the complement system.29 The CFH gene has been widely studied for its role in AMD, and, interestingly, variants that confer risk for this condition are protective for cCSC and vice versa.13 These genetic associations imply that these 2 diseases might have an opposite disease mechanism, with an overactivation of the complement system in AMD29 and a reduced activity of the complement system in cCSC.
In the current study, we further substantiated the involvement of the complement system in cCSC. Pathway analysis with MAGMA and VEGAS2 showed associations of 2 gene sets of the complement system. We used PrediXcan to predict the expression of genes based on the genotype information of patients with cCSC and control participants. Unfortunately, no eye-specific or retina-specific eQTL database is publicly available, and these tissues are not yet implemented in GTEx nor in any other large eQTL data set. Therefore, as an indicator for general differences, we used all GTEx tissues and observed that the expression of 4 complement genes (CFH, CFHR1, CFHR4, and CD46) was predicted to be different in patients with cCSC compared with control participants. All GTEx tissues that express CFH showed an upregulation of CFH, suggesting that complement system activity may be reduced in cCSC (Table 4). Depending on the tissue, CFHR1 showed upregulation or downregulation, indicating that regulation of this gene might be tissue specific; meanwhile, CFHR4 was consistently downregulated (Table 4). The factor H-associated (FHR) proteins, encoded by the CFHR genes, show sequence similarities to factor H and can compete with factor H for C3b binding and in this manner can influence complement activation.30 Deletions of CFHR1 have been found to be protective for AMD and CFHR4 deletions have been implicated in atypical hemolytic uremic syndrome,30 but the genes have not previously been associated with cCSC, to our knowledge.
Interestingly, we also observed an association with another complement gene independent of the CFH locus: CD46 was identified as one of the subthreshold hits in the GWAS (Table 1), and PrediXcan predicted upregulation of CD46 expression (Table 4). The CD46 gene encodes the membrane cofactor protein (MCP/CD46), a complement inhibitor that blocks all pathways of the complement cascade through inactivation of C3b and C4b.31,32 In light of the clinical characteristics of cCSC, in which patients present with hyperpermeability of the choroid and with fluid leakage through the retinal pigment epithelium, the involvement of CD46 is particularly interesting. It is an important regulator in the maintenance of epithelial cell barrier integrity through interaction with, for example, cadherins and integrins.32,33 Previous studies have shown that the activation of CD46 leads to a decrease in transepithelial resistance and loss of tight junctions in intestinal cells and a decreased membrane adhesion in RPE cells.32,33 The predicted increased CD46 expression in patients with cCSC therefore suggests a downregulation of complement activity, similar to that observed for factor H, and in addition destabilization of the integrity of the RPE, one of the main hallmarks of CSC. Together, both the complement and epithelial regulatory function of CD46 make it an interesting protein for further study in cCSC.
Taken together, the associations and predicted altered expression of CD46, CFH, CFHR1, and CFHR4 with cCSC imply a reduced activity of the complement system in cCSC. To date and to our knowledge, limited information is available on the actual activity of the complement system in patients with cCSC. One study did not find obvious systemic alterations in blood complement components of patients with cCSC, but this study had limited power, and CD46, FHR1, and FHR4 levels were not measured.34 Combined with previously described associations of CFH and C4B12,13,14 and the associations described here, a central role for the complement system in the disease mechanism of cCSC emerges. Larger studies measuring systemic complement regulators and activation products in patients with cCSC (specifically FH, FHR1, FHR4, C4B, and CD46) are warranted.
Besides consolidating the involvement of the complement system, we also identified a new cCSC candidate pathway and 2 new cCSC candidate genes. MAGMA identified an association between cCSC and the ARF pathway. This pathway is involved in ribosomal biogenesis, and activation of the pathway leads to termination of ribosomal RNA production and cell cycle arrest.35 In addition, PrediXcan showed decreased expression of TNFRSF10A and upregulation of KCNT2. Involvement of TNFRSF10A in cCSC was suggested previously in our candidate gene study of the main AMD loci. We showed a protective effect for a TNFRSF10A variant, which was also observed in this study (OR, 0.74 [95% CI, 0.64-0.85]; P = 1.47 × 10−5; data not shown) and the largest AMD GWAS (OR, 0.90; P = 4.5 × 10−11).13,36 The KCNT2 gene encodes a potassium sodium-activated outward rectifier channel (KCNT2) with an unknown function. The ARF pathway and TNFRSF10A and KCNT2 genes are interesting candidates for cCSC, but their exact role in the eye remains to be elucidated.
Limitations
To our knowledge, this study includes the largest reported cohort of European patients with cCSC; however, even larger sample sizes are necessary to find associations with rare variants or variants with a low effect size. Additionally, in this study, only individuals of European descent were included, and therefore genetic associations in other races/ethnicities still remain to be discovered. Replication of this study by centers inside or outside of Europe will be necessary to increase sample size, determine population specific associations, and replicate the suggestive signals observed in this study.
The use of PrediXcan on the GTEx data allows for prediction of expression levels in 44 tissues, excluding the eye. Although we only regarded those genes that showed differential expression in at least 50% of tissues in which they were expressed, this does not necessarily mean that this also applies to expression in the retina. Likewise, retina-specific genes might have been missed because of tissue-specific expression. A retina-specific eQTL database would be necessary to determine these associations, but to date, such a database has not been available.
Conclusions
In this study, we describe a GWAS for cCSC and confirmed the association of genetic variants in CFH with the disease. Additionally, we identified CFHR1, CFHR4, CD46, KCNT2, and TNFRSF10A as cCSC candidate genes because of their genetic associations and predicted altered expression. With CFH, CFHR1, CFHR4, and CD46 being important regulators of the complement cascade, this study strengthens the involvement of the complement system in cCSC. Further work on the expression of the proteins encoded by these genes is warranted. Additionally, the use of next-generation sequencing techniques, such as exome sequencing, will enable the identification of (rare) coding variants influencing protein function of these genes and might provide more insight in the causative mechanisms of cCSC.
eMethods.
eFigure 1. Power of the cCSC GWAS
eFigure 2. Principal component analysis (PCA) of the cCSC patients and controls
eFigure 3. Kinship analysis of cCSC patients and controls
eFigure 4. Conditioned analysis
eFigure 5. Quantile-quantile plot of the cCSC GWAS
eTable 1. Demographic characteristics of the GWAS cohort
eTable 2. Genome-wide significant hits in the CFH gene
eTable 3. Gene contents of the significant MAGMA gene-sets
eTable 4. Gene contents of the VEGAS2 most significant gene-sets
eTable 5. PrediXcan results of genes significant after Bonferroni correction for multiple testing.
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Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.
Supplementary Materials
eMethods.
eFigure 1. Power of the cCSC GWAS
eFigure 2. Principal component analysis (PCA) of the cCSC patients and controls
eFigure 3. Kinship analysis of cCSC patients and controls
eFigure 4. Conditioned analysis
eFigure 5. Quantile-quantile plot of the cCSC GWAS
eTable 1. Demographic characteristics of the GWAS cohort
eTable 2. Genome-wide significant hits in the CFH gene
eTable 3. Gene contents of the significant MAGMA gene-sets
eTable 4. Gene contents of the VEGAS2 most significant gene-sets
eTable 5. PrediXcan results of genes significant after Bonferroni correction for multiple testing.