Latest Development on Genetics of Common Retinal Diseases : The Asia-Pacific Journal of Ophthalmology

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Latest Development on Genetics of Common Retinal Diseases

Chen, Li Jia PhD*,†,‡; Chen, Zhen Ji MMed*; Pang, Chi Pui DPhil*,‡,§

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Asia-Pacific Journal of Ophthalmology 12(2):p 228-251, March/April 2023. | DOI: 10.1097/APO.0000000000000592



Neovascular age-related macular degeneration (nAMD), polypoidal choroidal vasculopathy (PCV), and central serous choroid retinopathy (CSCR) are common and complex retinal diseases that involve choroidopathy and neovascularization. They mainly affect the macular region, and their occurrences are pan-ethnic. Prevalence of nAMD is similar in white and Asian populations, but PCV is much more prevalent in Asians than other populations including whites.1,2 There are overlaps in clinical features of nAMD and PCV. Questions remain whether they are independent disease entities or subtypes of the same disease. Both PCV and nAMD can have subretinal exudation, hemorrhage, and scarring (nAMD, Figs. 1A, B; PCV, Figs. 1C, D), with consequential loss of central vision. PCV has been estimated to contribute to 22%–54% of age-related macular degeneration (AMD) prevalence in Asians, compared with that of <10% in whites.3 Another major macular disease, central serous chorioretinopathy (CSCR), also overlaps with PCV and nAMD in clinical presentations, especially in microvasculature of the retina (Figs. 1E, F).4 Overall CSCR is a less severe and less progressive disease. It tends to happen in young adulthood and mid-age, whereas nAMD and PCV affect more in elderly people older than 60 years. Their responses to treatment are different, with PCV more responsive to combined anti-vascular endothelial growth factor (VEGF) and photodynamic therapy (PDT) and nAMD to anti-VEGF monotherapy. In contrast, acute CSCR usually resolves spontaneously over a few months, whereas chronic CSCR, with a disease duration >3 months, usually requires treatment, such as half-dose PDT. However, once secondary choroidal neovascularization occurs, chronic CSCR patients should be treated with anti-VEGF. AMD and related diseases are major causes of irreversible blindness, especially in Asia. Its prevalence is increasing.1,5 There is a need to differentiate nAMD, PCV, and CSCR for the most appropriate treatment to prevent irreversible visual deterioration. Delineation of their genetic architectures should throw light on the differential molecular mechanisms of these diseases. In recent years there have been active investigations in their molecular genetics.

Clinical images of neovascular age-related macular degeneration [(A) fundus photo; (B) optical coherence tomography image], polypoidal choroidal vasculopathy [(C) fundus photo; (D) optical coherence tomography image] and central serous choroid retinopathy [(E) < fundus photo; (F) optical coherence tomography image]. White arrow indicates subretinal hemorrhage in neovascular age-related macular degeneration; blue arrow indicates subretinal exudate, and green arrow indicates a polyp in polypoidal choroidal vasculopathy; yellow arrow indicates the subretinal fluid in central serous choroid retinopathy; red arrow indicates the subretinal fluid in neovascular age-related macular degeneration, polypoidal choroidal vasculopathy, and central serous choroid retinopathy. Red “*” indicates the pigment epithelial detachment in neovascular age-related macular degeneration and polypoidal choroidal vasculopathy; black “*” indicates the subretinal exudate inneovascular age-related macular degeneration and polypoidal choroidal vasculopathy; and white “*” indicates the intraretinal fluid in neovascular age-related macular degeneration.


Early evidence for genetic influences on the development of AMD was provided by familial aggregation and twins studies. First-degree relatives of patients with late AMD have been found to develop AMD at an increased rate at a relatively young age,6,7 and significantly higher concordance of AMD was found in monozygotic than in dizygotic twins,8 suggesting a role of genetic susceptibility in the disease risk. Later, familial linkage studies confirmed the genetic involvement in AMD. The first AMD locus was mapped to chromosome 1q25–q31 by genome-wide linkage analysis in a pedigree of AMD.9 It was later confirmed as the locus that harbors an important AMD-associated gene, complement factor H (CFH), which mainly involved in the complement pathway and alternative pathway.10,11 This finding laid the foundation for future genetic analysis of AMD.

In contrast to AMD, family aggregation and twins studies on PCV have been less reported. In 2 brothers of West Indian origin, bilateral PCV was confirmed based on clinical features and indocyanine green angiography.12 In a cohort of monozygotic twins with typical PCV, concordances in disease progression and response to treatment between the twins were identified.13 Thus the role of genetic factors in affecting the occurrence of PCV and its clinical manifestations was evident. However, no linkage locus has been reported for PCV from familial linkage analysis. This is likely due to the late disease onset and rarity of the large pedigree of PCV that are eligible for linkage analysis. The complex and overlapping clinical features of PCV and nAMD also result in phenotypic diversities among patients and thus difficulties in linkage analysis.

In CSCR, aggregation of the disease in core families14 and high frequency (around 50%) of affected subjects among the relatives of CSCR patients15,16 have suggested the involvement of genetic factors in its etiology. So far, no linkage locus has been reported for CSCR by familial linage analysis, likely due to the rarity of large disease pedigrees. Instead, an association study, which involves the comparison of certain genetic variants between unrelated patients and control subjects, is a useful strategy for identifying genetic loci for CSCR.


With the advent and advancement of next-generation sequencing platforms, the whole genome can be sequenced efficiently. The large data sets of genomic variants can be analyzed with different bioinformatics and biostatistics programs. Hence, a whole tier of genetic technologies and approaches has been formed, mainly including the genotyping of single, multiple, or genome-wide single-nucleotide polymorphisms (SNPs) or microsatellite markers; sequencing of single or multiple amplicons of a gene, a targeted chromosomal region (targeted sequencing), the whole exome [whole-exome sequencing (WES)], or the whole genome [whole genome sequencing (WGS)]; and determination of copy number variants and large structural variants in specific chromosomal regions or the whole genome.

The selection of strategies, that is, the combination of technologies and approaches, for genetic analysis of a targeted disease, is affected by multiple factors. On the clinical side, factors to be considered include the clinical homogeneity of the disease (eg, heterogeneities in clinical manifestations and existence of clinical subtypes), age of disease onset (eg, early or late onset), sex predilection (eg, female or male prominent), prevalence (eg, common or rare), apparent inheritance pattern (Mendelian or sporadic), complexity of endophenotypes (eg, ocular parameters that are relevant to the disease manifestations and risks), systemic associations (eg, neurological stress and cortical steroid intake in CSCR), and involvement of risks from life styles and/or environmental exposure (eg, smoking for AMD and PCV). While at the technology side, what are considered include the aims and scale of the intended study (eg, hypothesis-based or hypothesis-free), availability of equipment, manpower and expertise, and the budget (Fig. 2).

Strategies and technologies used in the gene mapping for retinal diseases of Mendelian inheritance (left panel) and multifactorial etiology (right panel). Apart from the genetic factors, inherent and environmental risk factors also play a role in the both forms of retinal diseases, with more in the multifactorial form.

In genetic studies, strategies combining different approaches may be adopted, as reflected in variable study designs and consequently different outputs. For example, for multigeneration large pedigrees of disease following Mendelian inheritance, linkage analysis using genome-wide microsatellite markers or SNPs can be used to map the linkage locus. This is usually followed by a series of candidate gene sequencing studies to identify the responsible gene(s) and variant(s) in this locus. However, the loci identified by traditional linkage analysis usually contain a large number of genes, mostly hundreds. Pinpointing the causal genes is difficult. In recent years, WES and WGS with combined linkage analysis and regional deep sequencing in large pedigrees followed by pipeline filtering strategies have been conducted to identify the disease-causing or associated variants with unprecedented efficiency.

In contrast, for sporadic diseases, which are mostly multifactorial in etiology, case-control association analysis is an appropriate strategy, where allelic/genotypic distributions of the gene variants of interest are compared between cases and controls. Significant differences in the distributions suggest a positive association. With information from the HapMap project17 and the availability of array-based high-throughput genotyping platforms, genome-wide association studies [genome-wide association studies (GWAS)] had become possible. The first GWAS on eye disease, published in 2005, identified a strong association of the Y402H (rs1061170) polymorphism in the CFH gene with advanced AMD in a white cohort.18 Since then, GWAS—a hypothesis-free approach—had become very prominent and efficient in identifying novel disease-associated genes/loci and variants. This has resulted in a new understanding of the disease pathways and mechanisms. These variants are usually common SNPs with a minor allele frequency (MAF) >1% and confer an odds ratio (OR) typically within the range of 1.1–2.0.19 Therefore, the contributions of individual variants in the disease heritability and pathogenesis are small. Moreover, the initial hits of association signals from GWAS are usually located in intronic or intergenic regions, of which the functions are not well-known. Also, whether the gene upstream or downstream to the intergenic SNP is responsible for the disease needs to be confirmed by candidate gene analyses. With the development of WES, exome-wide association studies (EWAS) have become an important hypothesis-free approach to identify disease-associated coding exonic variants, which are more likely to affect gene function. These variants can be common or rare. The first WGS study in AMD was published in 2013, and a disease-associated, rare nonsynonymous SNP in Complement C3 (C3) was identified.20

In genomic studies, newly identified candidate genes are usually directly sequenced in a larger cohort of patients and controls to further estimate their contributions to the population. In candidate gene studies, one or more genes can be sequenced to identify disease-causing or associated variants, and one or more SNPs are compared in case and control cohorts to determine the association profiles. There are strategies to select the candidate genes, such as (1) genes that are located in the linkage loci of the targeted disease, (2) genes that have been linked to another disease with similar clinical features, (3) genes with functional roles in a potential disease pathway, (4) genes identified in previous GWAS, EWAS, WES, and/or WGS as putatively disease-associated or disease-causing, (5) genes, which are associated with the disease in the initial cohort, and (6) genes, which are associated with the disease based on a limited number of SNPs, to be fine-mapped using more markers with full coverage of the genes.

Candidate gene analysis as a major hypothesis-based approach has more specific targets and advantages, including (1) addressing specific questions about one or more genes on the disease of interest in specific study cohorts, (2) requiring a less stringent significance threshold (eg, P value) to achieve a significant association, thereby requiring smaller sample sizes, (3) useful and required for validating and fine-mapping the initial association signals from GWAS and EWAS, (4) needed for validating the initially-identified disease-causing genes and depict the mutation spectra and frequencies, and (5) requiring shorter study period, less labor and laboratory consumptions, and lower budget. Therefore, the candidate gene approach is an important complementary strategy to hypothesis-free approaches in depicting the genetic landscape of eye diseases.


Molecular Genetics of Neovascular Age-Related Macular Degeneration

Combing different genetic strategies have led to the identification of a large number of genes and variants for common retinal diseases, especially nAMD, PCV, and CSCR (Table 1). So far, the mapping for AMD-associated genes has been the most fruitful. Some genes identified for inherited macular dystrophies were proposed to be candidate genes for AMD. One example is the Adenosine triphosphate binding cassette subfamily A member 4 (ABCA4, also known as ABCR) gene for autosomal recessive Stargardt type 1 macular dystrophy. By screening the ABCR gene in 167 unrelated AMD patients and 220 controls, Allikmets et al21 identified 13 AMD-associated alterations in 26 patients (16%) and 1 control (0.45%). In a cohort of 1176 AMD patients and 1444 controls, 2 variants, G1961E and D2177N, were identified in 40 patients (around 3.4%) and 13 control subjects (around 0.95%, P < 0.0001), further supporting ABCR as a susceptibility gene for AMD.22 However, a lack of association between ABCR and AMD was also reported.23,24 Similarly, both positive25 and negative26 associations with AMD had been reported for the ELOVL Fatty Acid Elongase 4 (ELOVL4) gene of autosomal dominant Stargardt type 3 macular dystrophy.

TABLE 1 - Genes Associated With AMD, PCV and/or CSCR
No. Gene/loci Representative variants Effect allele Effect Population Method Effect Population Method Effect Population Method
Common variants shared by AMD, PCV and CSCR
 1 *CFH rs1061170 C + Multipopulations GWAS1 + EuropeanAmerican Candidate gene study2 Multipopulations Candidate gene study3
rs1329428 C + Multipopulations GWAS1 + Chinese Candidate gene study4 Japanese Candidate gene study5
rs800292 G + Chinese GWAS6 + Chinese Candidate gene study4 Japanese Candidate gene study5
rs3753394 T + Chinese Candidate gene study7 + Chinese Candidate gene study4 Japanese Candidate gene study5
 2 *ARMS2-HTRA1 rs10490924 T + German Candidate gene study8 + Japanese Candidate gene study9 Dutch Candidate gene study10
rs11200638 A + Chinese GWAS6 + Japanese Candidate gene study9 /
rs3750848 G + Multipopulations Candidate gene study11 + Japanese Candidate gene study12 /
 3 *TNFRSF10A rs13278062 T + Japanese GWAS13 + Japanese GWAS13 + Multipopulations GWAS14
 4 *COL4A3 rs11884770 T Multipopulations GWAS15 / /
Multiple rare variants in gene-based analysis / NA Multipopulations Candidate gene study16 /
rs4276018 G / / Japanese Candidate gene study17
 5 *B3GALTL rs9542236 C + Multipopulations GWAS18 / /
Multiple rare variants in gene-based analysis / NA Multipopulations Candidate gene study16 /
rs9564692 T / / Japanese Candidate gene study17
 6 *LIPC rs10468017 T Multipopulations GWAS19 / /
rs1532085 A / + Chinese Candidate gene study20 /e
rs2043085 A / / + Chinese Candidate gene study21
Common variants shared by AMD and PCV
 7 *ADAMTS9 rs6795735 T + Multipopulations GWAS18 / /
rs6775974 C / + Multipopulations Candidate gene study16 /
 8 *TGFBR1 rs334353 T + Multipopulations GWAS18 / /
rs44466 C / + Multipopulations Candidate gene study18 /
 9 *VEGFA rs4711751 T + Multipopulations GWAS22 / /
rs833069 G / + Korean Candidate gene study23 /
 10 *CETP rs3764261 A + Multipopulations GWAS22 + Japanese Candidate gene study24 /
rs5882 G / + Chinese Candidate gene study25 /
 11 *RDBP-CFB rs522162 G American GWAS26 / /
rs3880457 C / Japanese Candidate gene study27 /
 12 *C2-CFB-SKIV2L rs401775 T + Multipopulations Candidate gene study16 + Multipopulations Candidate gene study16 /
 13 *ANGPT2 rs4455855 A + Multipopulations Candidate gene study28 + Multipopulations Candidate gene study28 /
rs13269021 G + Multipopulations Candidate gene study28 + Multipopulations Candidate gene study28 /
 14 *TIE2 rs625767 C Multipopulations Candidate gene study29 Multipopulations Candidate gene study29 /
 15 *ABCG1 rs225396 T + Multipopulations Candidate gene study30 + Multipopulations Candidate gene study30 /
 16 FPR1 rs867229 T Chinese Candidate gene study31 / /
rs78488639 A + Chinese Candidate gene study31 + Chinese Candidate gene study31 /
 17 GDF6 rs6982567 A + EuropeanAmerican Candidate gene study32 + Chinese Candidate gene study33 /
 18 ELN rs2301995 C + Japanese Candidate gene study34 Japanese Candidate gene study35 /
 19 *SKIV2L rs438999 C Irish Candidate gene study36 / /
rs429608 A Chinese Candidate gene study37 Japanese Candidate gene study38 /
rs2075702 C / Japanese Candidate gene study27 /
 20 *C3 rs2230199 G + English Candidate gene study39 / /
rs2241394 C Japanese Candidate gene study40 Japanese Candidate gene study12 /
 21 *C2 rs9332739 C European American Candidate gene study41 / /
rs547154 T European American Candidate gene study41 Japanese Candidate gene study42 /
 22 *CFB L9H A + European American Candidate gene study41 / /
R32Q A + European American Candidate gene study41 / /
rs541862 C Japanese Candidate gene study42 Japanese Candidate gene study42 /
Common variants associated with CSCR
 23 *GATA5 rs6061548 T / / + Japanese GWAS14
 24 *VIPR2 rs3793217 G / / + Japanese GWAS43
 25 SLC7A5 rs11865049 A / / + Japanese GWAS44
 26 MCUB-PLA2G12A-CFI rs4698775 G / / + Chinese Candidate gene study21
 27 NR3C2 rs2070951 G / / + Multipopulations Candidate gene study45
 28 CDH5 rs7499886 A / / + Multipopulations Candidate gene study46
Common variants associated with PCV
 29 HERPUD1 rs2217332 A / Chinese Candidate gene study47 /
 30 9p21 rs10757278 A / + Chinese Candidate gene study48 /
Common variants associated with AMD
 31 *RLBP1 rs3825991 A + Multipopulations GWAS49 / /
 32 CFHR4 rs6685931 C + Multipopulations GWAS50 / /
 33 *ATF7IP2 rs28368872 A + Multipopulations GWAS51 / /
 34 *TNR rs58978565 CAGAGT + Multipopulations GWAS51 / /
 35 STON-GTF2A1L/LHCGR/FSHR rs4482537 C + Multipopulations GWAS52 / /
 36 CD46 1:207980901 G + Multipopulations GWAS53 / /
 37 C20orf85 rs201459901 TA Multipopulations GWAS15 / /
 38 CNN2 rs67538026 T Multipopulations GWAS15 / /
 39 NPLOC4-TSPAN10 rs6565597 T + Multipopulations GWAS15 / /
 40 TMEM97/VTN rs11080055 A Multipopulations GWAS15 / /
 41 *CTRB2-CTRB1 rs72802342 A Multipopulations GWAS15 / /
 42 ACAD10 rs61941274 A + Multipopulations GWAS15 / /
 43 ARHGAP21 rs12357257 A + Multipopulations GWAS15 / /
 44 MIR6130-RORB rs10781182 T + Multipopulations GWAS15 / /
 45 TRPM3 rs71507014 G + Multipopulations GWAS15 / /
 46 *KMT2E-SRPK2 rs1142 T + Multipopulations GWAS15 / /
 47 PILRB-PILRA rs7803454 T + Multipopulations GWAS15 / /
 48 PRLR-SPEF2 rs114092250 A Multipopulations GWAS15 / /
 49 MMP20 rs10895322 G + Japanese GWAS54 / /
 50 *FGD6 rs10507047 G Multipopulations EWAS55 / /
 51 *SLC44A4 rs12661281 T + Multipopulations EWAS55 / /
 52 *C6orf223 rs2295334 A Multipopulations EWAS55 / /
 53 RAD51B rs8017304 A + Multipopulations GWAS18 / /
 54 *SLC16A8 rs8135665 T + Multipopulations GWAS18 / /
 55 IER3-DDR1 rs3130783 A + Multipopulations GWAS18 / /
 56 *COL8A1-FILIP1L rs13081855 T + Multipopulations GWAS18 / /
 57 SOD2 rs2842992 A Multipopulations GWAS56 / /
 58 MBP rs1789110 A Multipopulations GWAS56 / /
 59 PECI rs582301 A + Multipopulations GWAS56 / /
 60 C8orf42 rs722782 A Multipopulations GWAS56 / /
 61 TRIB1 rs35691538 A + Multipopulations GWAS56 / /
 62 BRUNOL4 rs8091635 T + Multipopulations GWAS56 / /
 63 TRA2A rs10262213 T Multipopulations GWAS56 / /
 64 ADAM19 rs59795197 T Multipopulations GWAS56 / /
 65 *SERPINB8-CDH7 rs17073641 C + European American GWAS26 / /
 66 NOTCH4 rs2071277 C + English GWAS57 / /
 67 TNXB-FKBPL rs12153855/rs9391734 C + English GWAS57 / /
 68 FRK/COL10A1 rs1999930 T Multipopulations GWAS22 / /
 69 *TIMP3 rs9621532 C Multipopulations GWAS22 / /
 70 *REST-C4orf14-POLR2B-IGFBP7 rs1713985 G + Japanese GWAS13 / /
 71 RREB1 rs11755724 A Multipopulations GWAS19 / /
 72 *CFI rs2285714 T + Multipopulations GWAS58 / /
 73 MYRIP rs2679798 G Multipopulations GWAS59 / /
 74 CTRB1 rs8056814 A Multipopulations WES60 / /
 75 *UBE3D rs7739323 T Multipopulations WES61 / /
rs141853578 T + Chinese Candidate gene study62 / /
 76 SIRT1 rs7895833 G + Lithuanian Candidate gene study63 / /
 77 TR rs8177178 T + Chinese Candidate gene study62 / /
 78 RP1L1 rs3924612 G + Lithuanian Candidate gene study64 / /
 79 PRPH2 rs3818086 A + Polish Candidate gene study65 / /
 80 TRF1 rs10107605 C + Lithuanian Candidate gene study66 / /
 81 mTOR rs2295080 G + Finnish Candidate gene study67 / /
 82 IL1RL1 rs1041973 A + Lithuanian Candidate gene study68 / /
 83 IL1RAP rs4624606 A + Lithuanian Candidate gene study68 / /
 84 CYP2J2 rs890293 T Lithuanian Candidate gene study69 / /
 85 TYR rs621313 A Multipopulations Candidate gene study53 / /
 86 PVRL2 rs6857 T Multipopulations Candidate gene study53 / /
 87 LOXL1 rs1048661 T + Chinese Candidate gene study70 / /
 88 KCTD10 rs56209061 A Lithuanian Candidate gene study71 / /
 89 MIR146A rs2910164 G + Italian Candidate gene study72 / /
 90 MIR27A rs11671784 A + Italian Candidate gene study72 / /
 91 SLCO1B1 rs4149056 C + Lithuanian Candidate gene study73 / /
 92 FILIP1L rs144351944 G + Chinese Candidate gene study74 / /
 93 BBX rs189132250 G + Chinese Candidate gene study74 / /
 94 SGCD rs931798 A + Mexican Candidate gene study75 / /
 95 RAGE rs1800624 T Lithuanian Candidate gene study76 / /
rs1800625 G + Lithuanian Candidate gene study76 / /
 96 NRTN-FUT6-C3 rs12019136 G + Multipopulations Candidate gene study16 / /
 97 MT2A rs28366003 G + Spanish Candidate gene study77 / /
 98 TOMM40 rs2075650 G + Multipopulations Candidate gene study78 / /
 99 *PGF rs2268615 G + Chinese Candidate gene study79 / /
 100 *VEGFR1 rs9554322 C + Chinese Candidate gene study80 / /
 101 TLR2 R753Q A + Istanbul Candidate gene study81 / /
 102 MMP-2 -1306 C>T C + Lithuanian Candidate gene study82 / /
 103 *DAPL1 rs17810398 T + Multipopulations Candidate gene study83 / /
 104 IL17 rs2275913 A + Chinese Candidate gene study84 / /
 105 ADIPOQ rs822396 G + Chinese Candidate gene study85 / /
 106 FBN2 rs154001 C + American Candidate gene study86 / /
 107 SMUG1 c.-31A>G A + Polish Candidate gene study87 / /
g.4235T>C T + Polish Candidate gene study87 / /
 108 COL1A2 rs42524 G Chinese Candidate gene study32 / /
 109 GSTP1 rs1695 G + Chinese Candidate gene study88 / /
 110 IRP1 rs17483548 G + Polish Candidate gene study89 / /
rs867469 G + Polish Candidate gene study89 / /
 111 ADIPOR1 rs10753929 T + Finnish Candidate gene study90 / /
 112 HMOX1 19G>C G Polish Candidate gene study91 / /
 113 HMOX2 −42+1444A>G A Polish Candidate gene study91 / /
 114 *C9 R95X NA Japanese Candidate gene study92 / /
 115 Gas6 834+7G>A A Hungarian Candidate gene study93 / /
 116 CCR2 rs4586 T + Indian Candidate gene study94 / /
 117 CCL2 rs1799865 T + Indian Candidate gene study94 / /
 118 KDR rs2071559 T + Italian Candidate gene study95 / /
 119 IGF1R rs2872060 G + Multipopulations Candidate gene study96 / /
 120 CFD rs3826945 C + Multipopulations Candidate gene study97 / /
 121 FADS1-3 rs174547 T + Multipopulations Candidate gene study98 / /
 122 ABCA1 rs1883025 C + Multipopulations Candidate gene study98 / /
 123 TLR3 rs3775291 T European American Candidate gene study99 / /
 124 TFR2 −576 A + Polish Candidate gene study100 / /
 125 ABCA4 rs3112831 A + Spanish Candidate gene study101 / /
 126 FGF2 rs6820411 A + Spanish Candidate gene study101 / /
 127 *ELOVL4 M299V NA American Candidate gene study102 / /
 128 MTHFR rs1801133 C + Japanese Candidate gene study34 / /
 129 GSTM1 Null genotype NA + Turkish Candidate gene study103 / /
 130 TNF-a −1031 C + Chinese Candidate gene study104 / /
 131 RORA rs12900948 G + European American Candidate gene study105 / /
 132 XPD751 K751Q A + Turkish Candidate gene study106 / /
 133 SERPING1 rs1005510 A + American Candidate gene study107 / /
rs2511989 G + American Candidate gene study107 / /
 134 *NOS2A rs8072199 T + American Candidate gene study108 / /
 135 SCARB1 rs5888 T + Multipopulations Candidate gene study109 / /
 136 CD36 rs3173798 C Japanese Candidate gene study110 / /
rs3211883 A Japanese Candidate gene study110 / /
 137 TNMD rs1155974 T + Finnish Candidate gene study111 / /
 138 *mtDNA A4917G G + American Candidate gene study112 / /
 139 *IL8 −251 A + English Candidate gene study113 / /
rs2227306 T + Italian Candidate gene study114 / /
rs2227543 T + Chinese Candidate gene study62 / /
 140 PEDF rs1136287 T + Chinese Candidate gene study115 / /
 141 VEGF 674 C + English Candidate gene study116 / /
−460 C + Polish Candidate gene study117 / /
−634 C + Polish Candidate gene study117 / /
 142 ERCC6 −6530C>G G + American Candidate gene study118 / /
 143 CFHR3-CFHR1 Deletion NA Multipopulations Candidate gene study119 / /
 144 CFHR1 CFHR1*A Allotype + Spanish Candidate gene study120 / /
 145 LRP6 hCV345771 T + American Candidate gene study121 / /
 146 TLR4 rs4986790 G + American Candidate gene study122 / /
 147 PLEKHA1 rs4146894 T + German Candidate gene study8 / /
 148 HLA B*4001 NA English Candidate gene study123 / /
DRB1*1301 NA English Candidate gene study123 / /
Cw*0701 NA + English Candidate gene study123 / /
 149 *MMP9 CA allele >=22 repeats + Italian Candidate gene study124 / /
 150 CX3CR1 V249I NA + American Candidate gene study125 / /
 151 CST3 rs1064039 A + Multipopulations Candidate gene study126 / /
 152 ACE Alu insertion Alu+ + American Candidate gene study127 / /
 153 *APOE NA ε2 + Dutch Candidate gene study128 / /
Rare variants associated with CSCR
 154 CRH rs562792458 c.154_155insCGC / / + Chinese Candidate gene study129
 155 PTPRB 4145C>T T / / + Dutch WES130
 156 SGK1 M32V G / / + Turkish Candidate gene study131
 157 *PIGZ Multiple rare variants in gene-based analysis / / NA Dutch Exome sequencing study132
 158 *RSAD1 Multiple rare variants in gene-based analysis / / NA Dutch Exome sequencing study132
 159 *DUOX1 Multiple rare variants in gene-based analysis / / NA Dutch Exome sequencing study132
 160 *LAMB3 Multiple rare variants in gene-based analysis / / NA Dutch Exome sequencing study132
 161 C4B 3 copies NA / / Multi-populations Candidate gene study3
Rare variants associated with PCV
 162 *FGD6 K329R G / + Chinese WES133 /
 163 *IGFN1 6196A>G G / + Chinese Candidate gene study134 /
Rare variants associated with AMD
 164 *SLC16A8 214+1G>C C + Multipopulations GWAS15 / /
 165 *TIMP3 S38C NA + Multipopulations GWAS15 / /
 166 *CETP D442G NA + Multipopulations EWAS55 / /
 167 *UBE3D rs7739323 T Multipopulations WES61 / /
 168 TACC2 rs112188313 A + Multipopulations WES135 / /
 169 PHF12 rs148347485 G + Multipopulations WES135 / /
 170 RXFP2 rs121918303 C + Multi-populations WES135 / /
 171 PUS7 rs139058270 C + Multipopulations WES135 / /
 172 SPEF2 rs80010329 G + Multipopulations WES135 / /
 173 BCAR1 rs74024754 C + Multipopulations WES135 / /
 174 PLEKHA1 S177N NA + Jewish WES136 / /
 175 *COL8A1 V58A NA + Multipopulations WES137 / /
 176 PELI3 A307V NA Multipopulations WES60 / /
 177 *CFI V412M NA + Israelis WES138 / /
 178 HMCN1 4162delC NA + Israelis WES138 / /
 179 *C9 P167S NA + Multipopulations WES139 / /
 180 C3 K155Q NA + Multipopulations WES139 / /
 181 FBN2 E1144K NA + American WES86 / /
 182 C4A Increased copy number NA Multipopulations Candidate gene study119 / /
 183 P2X7 G150R NA + Tunisian Jewish Candidate gene study141 / /
 184 P2X4 Y315C NA + Tunisian Jewish Candidate gene study141 / /
 185 CNP147 <2 copies NA Multipopulations Candidate gene study142 / /
 186 CFH R1210C NA + Multipopulations Candidate gene study143 / /
 187 FBLN5 V60L NA + American Candidate gene study144 / /
 188 HEMICENTIN-1 Q5345R NA + American Candidate gene study145 / /
 189 VMD2 K149stop* NA + Multipopulations Candidate gene study146 / /
 190 *ABCR E471K NA + American Candidate gene study147 / /
AMD indicates age-related macular degeneration; CSCR, central serous choroid retinopathy; EWAS, exome-wide association study; GWAS, genome-wide association study; NA, data not available from the original report; PCV, polypoidal choroidal vasculopathy; WES, whole-exome sequencing.
“+” indicates an increased risk; “−” , an decreased risk; “/” , no previous report; “*” , the genes mentioned in the text.
Please refer to (Supplementary Digital Content 1 for the reference list of studies included in this table.

With the completion of the International HapMap Project and the advancement of genotyping platforms, regional fine-mapping using haplotype-tagging SNP and GWAS became a feasible and effective approach for mapping new AMD genes. In 2005, a GWAS reported a strong association of advanced AMD with the CFH Y402H polymorphism in whites.18 The association was confirmed by another 2 studies using regional mapping27,28 and subsequently replicated in multiple white populations. The Y402H polymorphism was also associated with the progression of AMD.29 However, the MAF of Y402H is low in East Asians with no association with AMD.30,31 Instead, another coding variant in CFH, I62V (rs800292), is a major associated SNP with nAMD in Asians,31–33 showing allelic diversities in the association profiles among different ethnicities. Subsequently, other genes in the complement pathway have been associated with AMD, including Complement C2 (C2),34Complement Factor B (CFB),34C3,35 and Complement Factor I (CFI),36 initially in whites. C2/CFB and C3 were also independently related to progression from early/intermediate to advanced AMD.37 However, in a Chinese cohort, the SKI2 subunit of superkiller complex (SKIC2, also known as SKIV2L) gene, rather than C2 or CFB, was the gene variant associated with nAMD in the C2-CFB-negative elongation factor complex member E (NELFE, also known as RDBP)-SKIV2L locus.38 Also, C339 and CFI40 were not associated with nAMD in other Chinese cohorts, suggesting ethnic diversities.

Subsequent GWAS on AMD have been reported in different populations. In Chinese, a HtrA serine peptidase 1 (HTRA1) promoter polymorphism, rs11200638, was found to be strongly associated with nAMD in a GWAS.41 This association was confirmed in whites and other populations.42,43 In a GWAS of nAMD in Japanese, 2 susceptibility loci were identified: Tumor necrosis factor receptor superfamily member 10a (TNFRSF10A)-LOC389641 and RE1 silencing transcription factor (REST)-nitric oxide associated 1 (also known as C4orf14)-RNA polymerase II subunit B (POLR2B)-insulin-like growth factor binding protein 7 (IGFBP7).44 In other GWAS, which were mainly conducted in whites, new loci were identified for AMD (Table 1), including TIMP metallopeptidase inhibitor 3 (TIMP3),45Lipase C, Hepatic type (LIPC),46Collagen Type VIII Alpha 1 Chain (COL8A1)- Filamin A Interacting Protein 1 Like (FILIP1L), ADAM Metallopeptidase With Thrombospondin Type 1 Motif 9 (ADAMTS9), and beta 3-glucosyltransferase (B3GLCT, also known as B3GALTL).47 In a GWAS of AMD progression, age-related maculopathy susceptibility 2 (ARMS2)-HTRA1, CFH, C2-CFB-SKIV2L, C3, LIPC, and Chymotrypsinogen B2 (CTRB2)-Chymotrypsinogen B1 (CTRB1) were associated with AMD progression, and SNPs rs58978565 near tenascin R (TNR), rs28368872 near activating transcription factor 7 interacting protein 2 (ATF7IP2), and rs142450006 near matrix metallopeptidase 9 (MMP9) with progression to nAMD but not geographic atrophy.48

In a large-scale GWAS that involved over 16,000 AMD patients and 17,000 controls, 52 independently associated common and rare variants distributed across 34 loci were identified.19 A locus near MMP9 was found specifically for nAMD, not dry AMD. Very rare coding variants (frequency <0.1%) in CFH, CFI, TIMP3, and solute carrier family 16 member 8 (SLC16A8) were also found with a significant association.19 These findings suggested that both common and rare variants contribute to the genetic architecture of AMD.

Rare disease-associated variants are mostly located in exonic regions and are more likely to affect protein structure and function. They are usually of larger effect sizes than common SNPs. High-throughput sequencing of the CFH gene identified a strong association of a rare variant, R1210C, with AMD and a 6-year-earlier onset of disease.49 Subsequently, a WGS study reported the association of a rare nonsynonymous SNP (K155Q, MAF=0.55%, OR=3.45) in the C3 gene with AMD.20 Later, WES in 9 families with early-onset AMD identified 2 rare and functional CFH variants (R53C and D90G).50 The first EWAS on AMD identified an East Asian–specific mutation, cholesteryl ester transfer protein (CETP) D442G, which increased the risk of AMD, along with another 3 novels AMD loci: long intergenic nonprotein coding RNA 3040 (LINC03040, also known as C6orf223), solute carrier family 44 member 4 (SLC44A4) and FYVE, RhoGEF and PH domain containing 6 (FGD6).51 Rare variants in other genes have also been identified for AMD by using regional targeted deep sequencing and WES (Table 1), such as complement C9 (C9),52CFI,52ubiquitin protein ligase E3D (UBE3D),53 and collagen type VIII alpha 1 chain (COL8A1).54

Apart from GWAS, WGS, and WES, the candidate gene approach has also identified new candidate genes for AMD. Fine-mapping a candidate gene can pinpoint the causal variant and SNP analysis and help identify rare disease-associated coding variants. For example, the placental growth factor (PGF), angiopoietin 2 (ANGPT2), and TEK receptor tyrosine kinase (TEK, also known as TIE2) genes in the angiogenesis pathway have been identified as candidate genes for nAMD in Chinese,55–57 so were the CETP and Adenosine triphosphate binding cassette subfamily G member 1 (ABCG1) genes in the high-density lipoprotein metabolic pathway.58 By sequencing analysis of PGF, a novel 18-base-pair deletion mutation in the promoter was found to confer over 5-fold of risk to nAMD.59 It is noteworthy that many candidate genes have been reported just once in specific study cohorts (Table 1). Further replications in other study populations are warranted.

Molecular Genetics of Polypoidal Choroidal Vasculopathy

As for AMD, candidate gene analysis is a major approach used to identify susceptibility genes for PCV. In view of the clinical similarities between nAMD and PCV, some major genes for nAMD have been tested for their associations with PCV. In a white cohort, SNPs in 3 major AMD loci, CFH, CFB/C2, and ARMS2-HTRA1, were associated with PCV.60 In 2007, the ARMS2-HTRA1 variants were associated with both PCV and wet AMD in a Japanese population.61 In a Singaporean Chinese cohort, SNPs rs3753394 and rs800292 of CFH and rs11200638 of HTRA1 were associated with PCV.62 In Chinese cohorts from different regions of China, SNPs in CFH and ARMS2-HTRA1 have also been associated with PCV. In Hong Kong Chinese, ARMS2 rs10490924, HTRA1 rs11200638, CFH rs800292, and CETP rs3764261 were associated with PCV,58,63 and ABCG1 rs225396, ANGPT2 rs13269021, and TIE2 rs625767 were first identified as genetic factors for PCV.56,57,64 In a systematic review and meta-analysis, 31 polymorphisms in 10 AMD-associated genes/loci: ARMS2, HTRA1, CFH, C2, CFB, RDBP, SKIV2L, CETP, 8p21, and 4q12, were confirmed to be associated with PCV. However, 12 polymorphisms at the ARMS2-HTRA1 locus showed significant differences between PCV and nAMD.65 Later in a study in multiple East Asian cohorts, SNPs at 8 out of 34 known AMD loci were associated with PCV, including ARMS2- HTRA1, CFH, C2-CFB-SKIV2L, CETP, vascular endothelial growth factor A (VEGFA), ADAMTS9-AS2, transforming growth factor beta receptor 1 (TGFBR1), and collagen type IV alpha 3 chain (COL4A3). Weaker association for PCV was observed at ARMS2-HTRA1 and lysine methyltransferase 2E (KMT2E)- SRSF protein kinase 2 (SRPK2), compared with nAMD.66 In contrast, WES analysis showed an association of rare coding variants c.986A>G in FGD6,67 and c.6196A>G in immunoglobulin-like and fibronectin type III domain containing 1 (IGFN1),68 with PCV but not with nAMD. These findings together suggested that PCV and nAMD have shared and distinct genetic components, and certain gene variants that could affect the phenotypic expressions of PCV and nAMD.

Molecular Genetics of Central Serous Choroid Retinopathy

In CSCR, CFH was the first candidate gene assessed. A significant association has been reported between Japanese69 and Europeans.70,71 In a GWAS, two SNPs, CFH rs800292 and vasoactive intestinal peptide receptor 2 (VIPR2) rs3793217, were associated with CSCR in Japanese.72 The involvement of the complement system in cCSCR was also confirmed by a GWAS in Europeans.73 In addition, a GWAS in Japanese and European cohorts identified 2 loci for CSCR, rs13278062 in TNFRSF10A-LOC389641 and rs6061548 near GATA binding protein 5 (GATA5).74 In a meta-analysis study, significant associations of ARMS2, CFH, and TNFRSF10A with CSCR were confirmed.75 Notably, SNP rs13278062 in TNFRSF10A had the same trend of effect on CSCR, nAMD, and PCV, whereas SNPs in ARMS2 and CFH showed an opposite effect on CSCR as compared with nAMD and PCV.75 Of note, the same trend of effect means that a certain allele of an SNP has a risk or protective effect on one disease and a consistent effect on another disease. On the contrary, an opposite effect means a specific allele shows a risk effect on one disease but a protective effect on another disease. In a genome-wide survival analysis of macular neovascularization (MNV) development in CSCR, 4 known AMD/PCV genes, ARMS2, CFH, COL4A3, and B3GALTL, were found of association with MNV development.76 Of note, COL4A3 and B3GALTL were associated stronger with PCV, suggesting certain shared genetic susceptibility between PCV and MNV in the central serous choroid.

AMD, PCV, and CSCR showed both similarities and differences in genetic associations with ethnic diversities. Further larger-scale genomic studies are warranted to identify more susceptibility genes for PCV and CSCR, and differentiate the genetic architectures among nAMD, PCV, and CSCR. Moreover, these diseases are multifactorial. Single gene only explains a small part of the disease heritability. Thus, studying the interactions among the genes and other factors, such as inherent systemic conditions, lifestyle, environmental exposures, and interventional modalities, will provide further insights into the roles of the genes in the pathogeneses and management of the diseases.


AMD, PCV, and CSCR are complex diseases that resulted from the additive and interactive effects of multiple environmental, inherent, and genetic risk factors. While aging, smoking, higher body mass index, and higher high-density lipoprotein-cholesterol pose risks for AMD and PCV,3,77 risk factors for CSCR include exogenous glucocorticoid usage, endogenous hypercortisolism, psychological stress, and type A personality.78 How they act on the pathogeneses of these maculopathies is complicated and not exactly known.

The joint effects of 2 or more genes, most frequently between CFH and ARMS2-HTRA1, have been reported in nAMD and PCV, but not in CSCR. In study subjects from the United States, significant gene-gene interaction was detected between CFH rs10801575 and HTRA1 rs2014307.79 Subjects homozygous for both risk alleles of CFH Y402H and ARMS2 A69S had a 50-fold increased risk of AMD.80 Individuals homozygous for both the G allele of RORA rs12900948 and ARMS2 A69S had a 40.8-fold increased risk of nAMD.81 In the International AMD Genomics Consortium data set, intronic and intergenic nuclear SNPs in abhydrolase domain containing 2, acylglycerol lipase (ABHD2)/retinaldehyde binding protein 1 (RLBP1) demonstrated significant joint effects and nominally significant interaction effects with the synonymous mitochondrial SNP MT-ND5 G12771A.82 In a Finnish cohort, carriers of at least 1 risk allele in each of the 3 susceptibility loci (ARMS2, CFH, and C3) had an 18-fold risk of AMD when compared with homozygote noncarriers in all 3 loci.83 In a Korean cohort, joint effects for CFH and ARMS2 variants were shown on nAMD, with the ORs for individuals carrying 1, 2, and 3-copy risk alleles being 1.08, 3.49, and 3.64, respectively.84 In a Hong Kong Chinese cohort, combined CFH rs800292 and HTRA1 rs11200638 caused a 23.3-fold increased risk of nAMD.85 Moreover, CFH rs800292 had a significant interaction with ANGPT2 rs13269021 on nAMD and PCV.56 Polygenic risk score, which summarizes the estimated effect of multiple variants on a phenotype, would be an excellent tool to further study the multi-SNP effects of more than 2 SNPs on nAMD and PCV, including particularly the significant SNPs identified in GWAS.

Although the genetic architecture of nAMD and PCV involve gene-gene interactions, there are also gene-environment interactions. A dominant environmental risk factor is smoking. A study in the United States has detected significant interaction between ARMS2 A69S and cigarette smoking history. ARMS2, CFH, and smoking together account for 61% of the population-attributable risk of AMD, with a population-attributable risk percentage of 20% for smoking, 36% for ARMS2, and 43% for CFH.86 In a genome-wide gene-environment interaction analysis, an intergenic SNP rs17073641 between serpin family B member 8 (SERPINB8) and cadherin 7 (CDH7) associated strongly with AMD in nonsmokers (OR=0.57) but inversely among smokers (OR=1.42), indicating genetic modifying effects of smoking.87 In another cohort from the United States, a significant interaction between nitric oxide synthase 2 (NOS2, also known as NOS2A) rs2248814 and smoking was detected on AMD risk, with the stronger association between AMD and smoking in carriers of AA genotypes (OR=35.98) than in carriers of the AG genotype (OR=3.05) or GG genotype (OR=2.1), suggesting that NOS2A might modulate the effect of smoking on AMD.88 In an Australian cohort, the association of the apolipoprotein E (APOE) with early AMD was varied by smoking status, with the ε2-containing genotypes positively associated with early AMD for never and previous smokers but not for current smokers, whereas the ε4-containing genotype group (ε3ε4/ε4ε4) had an inverse association with early AMD among current smokers only.89 In a Hong Kong Chinese cohort, combined HTRA1 rs11200638 and smoking caused a 15.7-fold increased risk to exudate AMD.85 In a Japanese cohort, there was a significant interaction between CFH Y402H and smoking on PCV, with a synergy index of 2.41.90 These findings together provided strong evidence for the involvement of gene-smoking interaction in the risk of AMD and PCV.

Certain inherent factors, such as sex, also interact with genetic factors and expresses a specific association with AMD, PCV, and/or CSCR. A joint analysis of 3229 cases and 2835 controls from Germany, UK, and United States, revealed the association of a synonymous SNP rs17810398 in death-associated protein-like 1 (DAPL1) with AMD in females (P = 2.62×10-8, OR = 1.54) but not in males (P = 0.38, OR = 1.08).91 In an Australian cohort, a significant association of the ε2 genotype of APOE was found only in females who progressed with AMD.92 Apart from gene variants, the relative telomere length was also associated with AMD, with significantly shorter relative telomere length in AMD patients compared with controls in a European cohort, and this association occurred only in women (OR = 1.14; P < 0.001), not in men (OR = 1.01; P = 0.76).93 These findings indicate certain sex-differential pathways for AMD, especially in females.

An interaction between C3 rs17030 and sex were identified in PCV in a Hong Kong Chinese cohort, with the rs17030-G allele conferring an increased risk for PCV in males (P = 0.010, OR = 1.56) but not in females. C3 may cast an epistatic effect with sex in the pathogenesis of PCV.39 As for CSCR, the number of carriers in a Netherlands cohort of rare variants in the phosphatidylinositol glycan anchor biosynthesis class Z (PIGZ), dual oxidase 1 (DUOX1), radical S-adenosyl methionine domain containing 1 (RSAD1), and laminin subunit beta 3 (LAMB3) genes was significantly higher in the female chronic CSCR patients compared with female controls, whereas no associations were identified in the male patients.94 Of note, such sex-differential associations of gene variants with AMD, PCV, and cCSCR were reported in single studies and required replications.


Different responses to treatments, mainly the anti-VEGF therapy and PDT, have been observed in nAMD, PCV, and CSCR patients. There is a putative genetic background underlying individuals’ response to the treatments. In fact, some gene variants have been reported to cast pharmacogenetic effects on AMD, PCV, and CSCR.

PDT has been a major treatment for nAMD and PCV, especially before the advent of anti-VEGF therapy. Half-dose PDT is the major treatment for chronic CSCR. The effects of PDT treatments for nAMD, PCV, and CSCR have been investigated in patients carrying different genotypes. Pharmacogenetic studies of PDT for CSCR are limited. In a Chinese cohort, none of the 7 SNPs examined in CFH were associated with 1-month outcomes after PDT in patients with CSCR.95 In contrast, in a Japanese cohort with a 12-month follow-up, the minor T allele of ARMS2 A69S was associated with poor response to PDT in patients with chronic CSCR.96

In nAMD, pharmacogenetic studies on PDT have been focused on the ARMS2-HTRA1 and CFH genes, with variable results. In a Japanese cohort of wet AMD, the best-corrected visual acuity (BCVA) 1 year after PDT was significantly increased in patients with HTRA1 rs11200638 GG as compared with patients with GA or AA.97 In another Japanese cohort, ARMS2 rs10490924 was associated with the 3-year outcomes of PDT in patients with wet AMD, with the G allele associated with greater improvement in BCVA at 36 months after the first PDT.98 In contrast, in a wet AMD cohort from the United States, ARMS2 A69S (rs10490924) was not associated with the response to PDT treatment.99 In a cohort from Israel, ARMS2 rs10490924 and HTRA1 rs11200638 were also not associated with the response to PDT in nAMD.100 Therefore, the pharmacogenetic association of ARMS2-HTRA1 with PDT in nAMD seemed to be population specific. Further studies in more populations are warranted.

In a wet AMD cohort from the United States, CFH Y402H was associated with the response to PDT, with patients with the TT genotype having worse outcomes than those with TC and CC.99 In a UK cohort, wet AMD patients homozygous for CFH Y402H also had worse visual acuity after PDT.101 However, in a Finland cohort of nAMD, CFH Y402H did not affect the outcome of PDT.102 Also, in an Australian cohort of nAMD, CFH Y402H had no significant association with the response to PDT;103 instead, 2 SNPs in C-reactive protein, rs2808635, and rs876538, were associated with a positive response to PDT.103 Further studies in more cohorts are needed to elucidate the effects of these candidate genes in the pharmacogenetics of nAMD.

Pharmacogenetic studies on PDT for PCV were mainly reported in Asian populations. In a Korean cohort, PCV patients with the T allele of ARMS2 A69S had poorer BCVA than those with the G allele after combined PDT and intravitreal bevacizumab treatment at 12 months.104 In Japanese patients, a pharmacogenetic association was found between ARMS2 A69S and the long-term results after PDT in eyes with PCV, with patients carrying the T allele having poorer visual acuity at the 12-month visit.105 Also, the G allele of ARMS2 A69S was associated with a lower chance of retreatment after intravitreal aflibercept combined with PDT in PCV patients.106 Besides, the serpin family F member 1 (SERPINF1) rs12603825 was shown to influence the initial response and visual prognosis after PDT for PCV. Patients with the AA genotype received additional treatment after PDT within a significantly shorter time required more retreatment within 3 months, and showed worse visual prognosis after PDT.107

Anti-VEGF therapy is the mainstay treatment for nAMD and PCV, and chronic CSCR with secondary choroidal neovascularization. The response of nAMD patients to anti-VEGF treatments has been associated with polymorphisms in different genes, mainly CFH, ARMS2-HTRA, and VEGFA. In study cohorts from the United States, wet AMD patients with the CC genotype of CFH Y402H had worse visual outcomes with intravitreal bevacizumab than those with TC and TT.108 Wet AMD patients homozygous for the CFH Y402H risk allele had a 37% higher risk of requiring additional ranibizumab injections over 9 months.109 In a cohort from Austria, the CFH 402HH genotype was correlated with lower visual acuity outcomes after treatment with bevacizumab.110 For other CFH polymorphisms, the CFH rs800292 (I62V) was associated with a poor response to anti-VEGF treatment for nAMD.111 These findings showed that the response to anti-VEGF treatment differed according to CFH genotypes in nAMD patients.

There are ethnic differences in pharmacogenetic associations for the AMRS2-HTRA1 locus. In an Australian cohort of nAMD, the homozygote risk genotypes AA at HTRA1 rs11200638 and GG at ARMS2 A69S were associated with poorer VA outcomes at 12 months after ranibizumab or bevacizumab treatment.112 In a Spanish cohort of nAMD, patients homozygous for the risk T allele of ARMS2 A69S required more retreatments of anti-VEGF drugs over the 48-month follow-up.113 Notably, however, in a Korean cohort of nAMD, patients with the risk alleles of ARMS2-HTRA1 had improved treatment response and less need for additional injections.114

There is also evidence of pharmacogenetic responses to the VEGFA gene in anti-VEGF treatment for nAMD. In a United States cohort of nAMD, the poor-responders to anti-VEGF therapy had a higher frequency of the risk T allele of VEGFA rs943080.115 In an Australian cohort of nAMD, patients with the T allele of VEGFA rs3025000 had better visual outcomes at 6 months and a higher likelihood to respond to anti-VEGF treatment at 3, 6, and 12 months.116 In a Spanish cohort, the VEGFA rs699947 polymorphism predisposed nAMD patients to a good response to ranibizumab treatment.111

Other genes have also been shown with pharmacogenetic associations with anti-VEGF treatment effects in nAMD. In a Spanish cohort, genetic variants in CFB and fms-related receptor tyrosine kinase 1 (FLT1, also known as VEGFR1) predisposed patients to good response, whereas variants in SERPINF1 were associated with poor response.111 In another Spanish cohort, the BCVA response after 52-week aflibercept treatment in nAMD patients was associated with vascular endothelial growth factor B (VEGFB) rs12366035 and complement C5 (C5) rs12366035.117 In a cohort of nAMD from Finland, the interleukin 8 (IL8) promoter polymorphism 251A/T was associated with anatomic nonresponse to bevacizumab treatment.118 Moreover, in a cohort of European descendants, rare variants in the chromosome 10 open reading frame 88 (C10orf88) and unc-93 homolog B1 (UNC93B1) genes had a worse response to anti-VEGF therapy in nAMD patients.119 These findings suggested that both rare and common gene variants affect the pharmacogenetic responses of nAMD patients to anti-VEGF treatment.

Of note, however, negative findings have been reported. In a United States cohort of nAMD, SNPs CFH Y402H, ARMS2 A69S, and HTRA1 rs11200638 were not associated with the response to anti-VEGF therapy.120 In another United States cohort, the CFH, ARMS2-HTRA1, C3, VEGFA, and kinase insert domain receptor (KDR, also known as VEGFR2) gene variants were not associated with the response to anti-VEGF therapy in nAMD patients.121–123 In a randomized controlled trial from the UK, no significant pharmacogenetic association was detected for nAMD with ARMS2 A69S.124

Pharmacogenetic reports of anti-VEGF treatment for PCV are mainly on Asian populations. In a Korean cohort, PCV patients with the ARMS2 A69S-TT and HTRA1 rs11200638-AA genotypes had poorer anatomic and visual outcomes at 12 months after combined PDT with intravitreal bevacizumab treatment.104 In another Korean cohort, a significant pharmacogenetic association was found between ARMS2 A69S and the regression of retinal pigment epithelial detachment in PCV patients who completed the 12-month anti-VEGF monotherapy.125 In a Japanese cohort of PCV patients receiving PDT combined with intravitreal ranibizumab or aflibercept, the absence of retreatment over 24 months was associated with the nonrisk genotype of ARMS2 A69S.126

As of to-date, genetic polymorphisms have been related to variable responses to anti-VEGF therapy in nAMD and PCV patients. Further studies in more study cohorts are needed, taking into account of the differences in disease subtypes, treatments, follow-up time, selection of candidate genes and variants, genetic models, and the combined effects of different genetic variants.

The gene-gene, gene-environment, and pharmacogenetic interaction analyses, especially at a genome-wide scale, will involve and generate large data sets. Advanced and automatic analytical technologies are needed. Machine learning has been used to study epistasis, especially to identify gene-gene interactions from GWAS data.127 Artificial intelligence (AI) with deep learning will play an important role in analyzing big data.


In recent years, AI has been actively applied to investigations of retinal diseases, advancing phenotyping, and refining structural features. In nAMD, deep learning has provided reliable algorithms in objective quantification of the microvasculature in the retina.128 Optical coherence tomography (OCT) images have been analyzed by AI technology for automated segmentation, extraction, and quantification. In nAMD, such analysis can give predictions of disease progression. Applications have been attempted for AI systems in clinical practice utilizing OCT by automated whole-volume segmentation for differential diagnosis of AMD and diabetic macular edema.129 Fundus photos can be analyzed by deep learning algorithms with deep convolution and network architectures to predict disease severity130 and differentiate a multitude of retinal complications.131 However, responses to treatment have not been reported.132

AI investigations on OCT images and fundus photos of retinal diseases have advanced phenotyping and refined structural features including minute temporal and microvasculature changes. Attempts have also been to correlate to genotypes. GWAS data sets involving 32,215 whites aged over 50 years from the International AMD Genomics Consortium have been tested by the neural network, lasso regression, support vector machine, and random forest to develop risk and prediction models for AMD.133 These machine learning-based methods are capable to predict AMD at all stages with acceptable areas under the curve and Brier scores. A study has involved a huge amount of data for developing an automatic and comprehensive prediction model for AMD by a machine learning algorithm.134 The data analyzed contained detailed demographic and ophthalmic information including age, drusen, hyper-pigmentation, and fundus photos. Lifestyle factors included smoking, diet, and education. The genetic information was 49 AMD-associated SNPs. Notably, an acceptable estimation of the area under the curve at around 0.92 was obtained.134 Meanwhile, a deep learning approach of a combination of convolution neural networks has been used for the automatic classification of AMD in different stages, which were analyzed with for over more than 170,000 photographs with 10 genetic loci obtained by GWAS to reveal the detailed links of genetic variants with advancements of AMD.135 The results from such big studies prove that the utilization of genetic information to establish genetic risk scores by advanced AI methodologies should provide a robust platform for reliable presymptomatic diagnosis and for prognostic prediction.136 A recent bibliometric analysis of genetic retinal diseases in India has revealed available complex and non-Mendelian genetic data of AMD.137 Combination of such large amount of genetic data with refined phenotyping can lead to the establishment of disease prediction models by advanced AI technologies, which should pave the way to precision medicine for AMD.


Complex retinal diseases are difficult to cure. Their clinical presentations and course are complicated especially with the involvements of choroidopathy and microvasculature. Early diagnosis is crucial for management to prevent or inhibit disease progression. Prediction of prognosis is vital too. There is also a need to differentiate nAMD, PCV, and CSCR for effective management. They overlap in some features but are different in clinical course, prognosis, and responses to treatment.3 Clinical assessments, ophthalmoscopic examinations, OCT imaging, and fundus photography have been exhaustively used and advanced for diagnostic confirmation and prognostic prediction. There can be prediction scores, but individualized assessments are required. Such individualized precision medicine is made possible with the advent and application of AI technologies to deal with the extremely larger volume of data and complicated data analysis. AI in recent years has been developed for the reliable and meticulous analysis of genetic data with clinical implications.138 Genetic data, with big advancements in recent years particularly by GWAS, has been proven useful for clinical prediction, as well exemplified in a recent report of the association of 30 genetic variants with responses to anti-VEGF treatment in a big study cohort of over 15,000 study subjects.139 In a study of GWAS variants in totally 2058 study subjects in discovery and validation cohorts, SNP rs12138564 in the chaperonin containing TCP1 subunit 3 (CCT3) gene has a mild association with anti-VEGF treatment outcome.119

Accurate and informative prediction of risk, progression, and prognosis of AMD, PCV, and CSCR required individual information for the clinical management to be tailored made. Such individualized precision medicine should be possible for general applications in most populations in the near future with the advancements of AI technologies and genetic information.


Neovascular AMD, PCV, and CSCR represent a major group of complex maculopathies and a leading cause of central visual impairment. They are multifactorial in etiology, resulted from joint and interactive effects of multiple risk factors. In recent years, the application of different strategies of genetic approaches has led to the identification of a large number of candidate genes for these macular diseases, especially for nAMD. Also, the shared and distinct genetic components and profiles among the 3 maculopathies have been recognized, indicating the importance of comparative genetics in further understanding of their pathogeneses. Apart from the main effect of single genes, gene-gene, and gene-environment interactions also play a role in the genetic architectures of maculopathies. In addition, pharmacogenetic interactions between gene variants and treatment responses will make personalized treatment possible. Analysis of these interactions, in the context of genome-wide data and a complete set of refined phenotyping ocular parameters, requires advanced statistical programs and computer power. AI with deep learning will thus play an essential role in automating these analyses in a more efficient manner. These, in the long run, will facilitate the identification of more genetic biomarkers with translational values, leading to advanced and more precise diagnostics, subgrouping, treatment options, and prognostic assessment and prediction, eventually toward precision medicine for complex retinal diseases.


1. Wong WL, Su X, Li X, et al. Global prevalence of age-related macular degeneration and disease burden projection for 2020 and 2040: a systematic review and meta-analysis. Lancet Glob Health. 2014;2:106–116.
2. Wong CW, Yanagi Y, Lee WK, et al. Age-related macular degeneration and polypoidal choroidal vasculopathy in Asians. Prog Retin Eye Res. 2016;53:107–139.
3. Laude A, Cackett PD, Vithana EN, et al. Polypoidal choroidal vasculopathy and neovascular age-related macular degeneration: same or different disease? Prog Retin Eye Res. 2010;29:19–29.
4. Yannuzzi LA, Freund KB, Goldbaum M, et al. Polypoidal choroidal vasculopathy masquerading as central serous chorioretinopathy. Ophthalmology. 2000;107:767–777.
5. Kawasaki R, Yasuda M, Song SJ, et al. The prevalence of age-related macular degeneration in Asians: a systematic review and meta-analysis. Ophthalmology. 2010;117:921–927.
6. Seddon JM, Ajani UA, Mitchell BD. Familial aggregation of age-related maculopathy. Am J Ophthalmol. 1997;123:199–206.
7. Klaver CC, Wolfs RC, Assink JJ, et al. Genetic risk of age-related maculopathy. Population-based familial aggregation study. Arch Ophthalmol. 1998;116:1646–1651.
8. Meyers SM, Greene T, Gutman FA. A twin study of age-related macular degeneration. Am J Ophthalmol. 1995;120:757–766.
9. Klein ML, Schultz DW, Edwards A, et al. Age-related macular degeneration. Clinical features in a large family and linkage to chromosome 1q. Arch Ophthalmol. 1998;116:1082–1088.
10. Mold C, Kingzette M, Gewurz H. C-reactive protein inhibits pneumococcal activation of the alternative pathway by increasing the interaction between factor H and C3b. J Immunol. 1984;133:882–885.
11. Hong K, Kinoshita T, Takeda J, et al. Inhibition of the alternative C3 convertase and classical C5 convertase of complement by group A streptococcal M protein. Infect Immun. 1990;58:2535–2541.
12. Merle H, Donnio A, Richer R, et al. Familial bilateral polypoidal choroidal vasculopathy. Retin Cases Brief Rep. 2008;2:181–183.
13. Machida S, Takahashi T, Gotoh N, et al. Monozygotic twins with polypoidal choroidal vasuculopathy. Clin Ophthalmol. 2010;4:793–800.
14. Park DW, Schatz H, Gaffney MM, et al. Central serous chorioretinopathy in two families. Eur J Ophthalmol. 1998;8:42–47.
15. Weenink AC, Borsje RA, Oosterhuis JA. Familial chronic central serous chorioretinopathy. Ophthalmologica. 2001;215:183–187.
16. van Dijk EHC, Schellevis RL, Breukink MB, et al. Familial central serous chorioretinopathy. Retina. 2019;39:398–407.
17. International HapMap C. The International HapMap Project. Nature. 2003;426:789–796.
18. Klein RJ, Zeiss C, Chew EY, et al. Complement factor H polymorphism in age-related macular degeneration. Science. 2005;308:385–389.
19. Fritsche LG, Igl W, Bailey JN, et al. A large genome-wide association study of age-related macular degeneration highlights contributions of rare and common variants. Nat Genet. 2016;48:134–143.
20. Helgason H, Sulem P, Duvvari MR, et al. A rare nonsynonymous sequence variant in C3 is associated with high risk of age-related macular degeneration. Nat Genet. 2013;45:1371–1374.
21. Allikmets R, Shroyer NF, Singh N, et al. Mutation of the Stargardt disease gene (ABCR) in age-related macular degeneration. Science. 1997;277:1805–1807.
22. Allikmets R. Further evidence for an association of ABCR alleles with age-related macular degeneration. The International ABCR Screening Consortium. Am J Hum Genet. 2000;67:487–491.
23. De La Paz MA, Guy VK, Abou-Donia S, et al. Analysis of the Stargardt disease gene (ABCR) in age-related macular degeneration. Ophthalmology. 1999;106:1531–1536.
24. Rivera A, White K, Stöhr H, et al. A comprehensive survey of sequence variation in the ABCA4 (ABCR) gene in Stargardt disease and age-related macular degeneration. Am J Hum Genet. 2000;67:800–813.
25. Conley YP, Thalamuthu A, Jakobsdottir J, et al. Candidate gene analysis suggests a role for fatty acid biosynthesis and regulation of the complement system in the etiology of age-related maculopathy. Hum Mol Genet. 2005;14:1991–2002.
26. Ayyagari R, Zhang K, Hutchinson A, et al. Evaluation of the ELOVL4 gene in patients with age-related macular degeneration. Ophthalmic Genet. 2001;22:233–239.
27. Edwards AO, Ritter R III, Abel KJ, et al. Complement factor H polymorphism and age-related macular degeneration. Science. 2005;308:421–424.
28. Haines JL, Hauser MA, Schmidt S, et al. Complement factor H variant increases the risk of age-related macular degeneration. Science. 2005;308:419–421.
29. Seddon JM, Francis PJ, George S, et al. Association of CFH Y402H and LOC387715 A69S with progression of age-related macular degeneration. JAMA. 2007;297:1793–1800.
30. Gotoh N, Yamada R, Hiratani H, et al. No association between complement factor H gene polymorphism and exudative age-related macular degeneration in Japanese. Hum Genet. 2006;120:139–143.
31. Chen LJ, Liu DT, Tam PO, et al. Association of complement factor H polymorphisms with exudative age-related macular degeneration. Mol Vis. 2006;12:1536–1542.
32. Ng TK, Chen LJ, Liu DT, et al. Multiple gene polymorphisms in the complement factor h gene are associated with exudative age-related macular degeneration in chinese. Invest Ophthalmol Vis Sci. 2008;49:3312–3317.
33. Mori K, Gehlbach PL, Kabasawa S, et al. Coding and noncoding variants in the CFH gene and cigarette smoking influence the risk of age-related macular degeneration in a Japanese population. Invest Ophthalmol Vis Sci. 2007;48:5315–5319.
34. Gold B, Merriam JE, Zernant J, et al. Variation in factor B (BF) and complement component 2 (C2) genes is associated with age-related macular degeneration. Nat Genet. 2006;38:458–462.
35. Yates JR, Sepp T, Matharu BK, et al. Complement C3 variant and the risk of age-related macular degeneration. N Engl J Med. 2007;357:553–561.
36. Fagerness JA, Maller JB, Neale BM, et al. Variation near complement factor I is associated with risk of advanced AMD. Eur J Hum Genet. 2009;17:100–104.
37. Francis PJ, Hamon SC, Ott J, et al. Polymorphisms in C2, CFB and C3 are associated with progression to advanced age related macular degeneration associated with visual loss. J Med Genet. 2009;46:300–307.
38. Liu K, Chen LJ, Tam PO, et al. Associations of the C2-CFB-RDBP-SKIV2L locus with age-related macular degeneration and polypoidal choroidal vasculopathy. Ophthalmology. 2013;120:837–843.
39. Liu K, Lai TY, Chiang SW, et al. Gender specific association of a complement component 3 polymorphism with polypoidal choroidal vasculopathy. Sci Rep. 2014;4:7018.
40. Yang F, Sun Y, Jin Z, et al. Complement factor I polymorphism is not associated with neovascular age-related macular degeneration and polypoidal choroidal vasculopathy in a chinese population. Ophthalmologica. 2014;232:37–45.
41. Dewan A, Liu M, Hartman S, et al. HTRA1 promoter polymorphism in wet age-related macular degeneration. Science. 2006;314:989–992.
42. Yang Z, Camp NJ, Sun H, et al. A variant of the HTRA1 gene increases susceptibility to age-related macular degeneration. Science. 2006;314:992–993.
43. Mori K, Horie-Inoue K, Kohda M, et al. Association of the HTRA1 gene variant with age-related macular degeneration in the Japanese population. J Hum Genet. 2007;52:636–641.
44. Arakawa S, Takahashi A, Ashikawa K, et al. Genome-wide association study identifies two susceptibility loci for exudative age-related macular degeneration in the Japanese population. Nat Genet. 2011;43:1001–1004.
45. Chen W, Stambolian D, Edwards AO, et al. Genetic variants near TIMP3 and high-density lipoprotein-associated loci influence susceptibility to age-related macular degeneration. Proc Natl Acad Sci USA. 2010;107:7401–7406.
46. Neale BM, Fagerness J, Reynolds R, et al. Genome-wide association study of advanced age-related macular degeneration identifies a role of the hepatic lipase gene (LIPC). Proc Natl Acad Sci U S A. 2010;107:7395–7400.
47. Fritsche LG, Chen W, Schu M, et al. Seven new loci associated with age-related macular degeneration. Nat Genet. 2013;45:433–439.
48. Yan Q, Ding Y, Liu Y, et al. Genome-wide analysis of disease progression in age-related macular degeneration. Hum Mol Genet. 2018;27:929–940.
49. Raychaudhuri S, Iartchouk O, Chin K, et al. A rare penetrant mutation in CFH confers high risk of age-related macular degeneration. Nat Genet. 2011;43:1232–1236.
50. Yu Y, Triebwasser MP, Wong EK, et al. Whole-exome sequencing identifies rare, functional CFH variants in families with macular degeneration. Hum Mol Genet. 2014;23:5283–5293.
51. Cheng CY, Yamashiro K, Chen LJ, et al. New loci and coding variants confer risk for age-related macular degeneration in East Asians. Nat Commun. 2015;6:6063.
52. Seddon JM, Yu Y, Miller EC, et al. Rare variants in CFI, C3 and C9 are associated with high risk of advanced age-related macular degeneration. Nat Genet. 2013;45:1366–1370.
53. Huang LZ, Li YJ, Xie XF, et al. Whole-exome sequencing implicates UBE3D in age-related macular degeneration in East Asian populations. Nat Commun. 2015;6:6687.
54. Corominas J, Colijn JM, Geerlings MJ, et al. Whole-Exome sequencing in age-related macular degeneration identifies rare variants in COL8A1, a component of Bruch’s Membrane. Ophthalmology. 2018;125:1433–1443.
55. Chen LJ, Ma L, Chu WK, et al. Identification of PGF as a new gene for neovascular age-related macular degeneration in a chinese population. Invest Ophthalmol Vis Sci. 2016;57:1714–1720.
56. Ma L, Brelen ME, Tsujikawa M, et al. Identification of ANGPT2 as a New gene for neovascular age-related macular degeneration and polypoidal choroidal vasculopathy in the Chinese and Japanese populations. Invest Ophthalmol Vis Sci. 2017;58:1076–1083.
57. Chen ZJ, Ma L, Brelen ME, et al. Identification of TIE2 as a susceptibility gene for neovascular age-related macular degeneration and polypoidal choroidal vasculopathy. Br J Ophthalmol. 2021;105:1035–1040.
58. Liu K, Chen LJ, Lai TY, et al. Genes in the high-density lipoprotein metabolic pathway in age-related macular degeneration and polypoidal choroidal vasculopathy. Ophthalmology. 2014;121:911–916.
59. Ma L, Ng TK, Chen H, et al. Identification and characterization of a novel promoter variant in placental growth factor for neovascular age-related macular degeneration. Exp Eye Res. 2019;187:107748.
60. Lima LH, Schubert C, Ferrara DC, et al. Three major loci involved in age-related macular degeneration are also associated with polypoidal choroidal vasculopathy. Ophthalmology. 2010;117:1567–1570.
61. Kondo N, Honda S, Ishibashi K, et al. LOC387715/HTRA1 variants in polypoidal choroidal vasculopathy and age-related macular degeneration in a Japanese population. Am J Ophthalmol. 2007;144:608–612.
62. Lee KY, Vithana EN, Mathur R, et al. Association analysis of CFH, C2, BF, and HTRA1 gene polymorphisms in Chinese patients with polypoidal choroidal vasculopathy. Invest Ophthalmol Vis Sci. 2008;49:2613–2619.
63. Liang XY, Lai TY, Liu DT, et al. Differentiation of exudative age-related macular degeneration and polypoidal choroidal vasculopathy in the ARMS2/HTRA1 locus. Invest Ophthalmol Vis Sci. 2012;53:3175–3182.
64. Ma L, Liu K, Tsujikawa M, et al. Association of ABCG1 with neovascular age-related macular degeneration and polypoidal choroidal vasculopathy in Chinese and Japanese. Invest Ophthalmol Vis Sci. 2016;57:5758–5763.
65. Ma L, Li Z, Liu K, et al. Association of genetic variants with polypoidal choroidal vasculopathy: a systematic review and updated meta-analysis. Ophthalmology. 2015;122:1854–1865.
66. Fan Q, Cheung CMG, Chen LJ, et al. Shared genetic variants for polypoidal choroidal vasculopathy and typical neovascular age-related macular degeneration in East Asians. J Hum Genet. 2017;62:1049–1055.
67. Huang L, Zhang H, Cheng CY, et al. A missense variant in FGD6 confers increased risk of polypoidal choroidal vasculopathy. Nat Genet. 2016;48:640–647.
68. Wen X, Liu Y, Yan Q, et al. Association of IGFN1 variant with polypoidal choroidal vasculopathy. J Gene Med. 2018;20:3007.
69. Miki A, Kondo N, Yanagisawa S, et al. Common variants in the complement factor H gene confer genetic susceptibility to central serous chorioretinopathy. Ophthalmology. 2014;121:1067–1072.
70. de Jong EK, Breukink MB, Schellevis RL, et al. Chronic central serous chorioretinopathy is associated with genetic variants implicated in age-related macular degeneration. Ophthalmology. 2015;122:562–570.
71. Mohabati D, Schellevis RL, van Dijk EHC, et al. Genetic risk factors in severe, nonsevere and acute phenotypes of central serous chorioretinopathy. Retina. 2020;40:1734–1741.
72. Hosoda Y, Yoshikawa M, Miyake M, et al. CFH and VIPR2 as susceptibility loci in choroidal thickness and pachychoroid disease central serous chorioretinopathy. Proc Natl Acad Sci U S A. 2018;115:6261–6266.
73. Schellevis RL, van Dijk EHC, Breukink MB, et al. Role of the complement system in chronic central serous chorioretinopathy: a genome-wide association study. JAMA Ophthalmol. 2018;136:1128–1136.
74. Hosoda Y, Miyake M, Schellevis RL, et al. Genome-wide association analyses identify two susceptibility loci for pachychoroid disease central serous chorioretinopathy. Commun Biol. 2019;2:468.
75. Chen ZJ, Lu SY, Rong SS, et al. Genetic associations of central serous chorioretinopathy: a systematic review and meta-analysis. Br J Ophthalmol. 2022;106:1542–1548.
76. Mori Y, Miyake M, Hosoda Y, et al. Genome-wide survival analysis for macular neovascularization development in central serous chorioretinopathy revealed shared genetic susceptibility with polypoidal choroidal vasculopathy. Ophthalmology. 2022;129:1034–1042.
77. Cheung CM, Laude A, Yeo I, et al. Systemic, ocular and genetic risk factors for age-related macular degeneration and polypoidal choroidal vasculopathy in Singaporeans. Sci Rep. 2017;7:41386.
78. Kaye R, Chandra S, Sheth J, et al. Central serous chorioretinopathy: An update on risk factors, pathophysiology and imaging modalities. Prog Retin Eye Res. 2020;79:100865.
79. Zhang H, Morrison MA, Dewan A, et al. The NEI/NCBI dbGAP database: genotypes and haplotypes that may specifically predispose to risk of neovascular age-related macular degeneration. BMC Med Genet. 2008;9:51.
80. Schaumberg DA, Hankinson SE, Guo Q, et al. A prospective study of 2 major age-related macular degeneration susceptibility alleles and interactions with modifiable risk factors. Arch Ophthalmol. 2007;125:55–62.
81. Schaumberg DA, Chasman D, Morrison MA, et al. Prospective study of common variants in the retinoic acid receptor-related orphan receptor alpha gene and risk of neovascular age-related macular degeneration. Arch Ophthalmol. 2010;128:1462–1471.
82. Persad PJ, Heid IM, Weeks DE, et al. Joint Analysis of Nuclear and Mitochondrial Variants in Age-Related Macular Degeneration Identifies Novel Loci TRPM1 and ABHD2/RLBP1. Invest Ophthalmol Vis Sci. 2017;58:4027–4038.
83. Seitsonen SP, Onkamo P, Peng G, et al. Multifactor effects and evidence of potential interaction between complement factor H Y402H and LOC387715 A69S in age-related macular degeneration. PLoS One. 2008;3:e3833.
84. Lee SJ, Kim NR, Chin HS. LOC387715/HTRA1 polymorphisms, smoking and combined effects on exudative age-related macular degeneration in a Korean population. Clin Exp Ophthalmol. 2010;38:698–704.
85. Tam PO, Ng TK, Liu DT, et al. HTRA1 variants in exudative age-related macular degeneration and interactions with smoking and CFH. Invest Ophthalmol Vis Sci. 2008;49:2357–2365.
86. Schmidt S, Hauser MA, Scott WK, et al. Cigarette smoking strongly modifies the association of LOC387715 and age-related macular degeneration. Am J Hum Genet. 2006;78:852–864.
87. Naj AC, Scott WK, Courtenay MD, et al. Genetic factors in nonsmokers with age-related macular degeneration revealed through genome-wide gene-environment interaction analysis. Ann Hum Genet. 2013;77:215–231.
88. Ayala-Haedo JA, Gallins PJ, Whitehead PL, et al. Analysis of single nucleotide polymorphisms in the NOS2A gene and interaction with smoking in age-related macular degeneration. Ann Hum Genet. 2010;74:195–201.
89. Adams MK, Simpson JA, Richardson AJ, et al. Apolipoprotein E gene associations in age-related macular degeneration: the Melbourne Collaborative Cohort Study. Am J Epidemiol. 2012;175:511–518.
90. Nakanishi H, Yamashiro K, Yamada R, et al. Joint effect of cigarette smoking and CFH and LOC387715/HTRA1 polymorphisms on polypoidal choroidal vasculopathy. Invest Ophthalmol Vis Sci. 2010;51:6183–6187.
91. Grassmann F, Friedrich U, Fauser S, et al. A candidate gene association study identifies DAPL1 as a female-specific susceptibility locus for age-related macular degeneration (AMD). Neuromolecular Med. 2015;17:111–120.
92. Baird PN, Richardson AJ, Robman LD, et al. Apolipoprotein (APOE) gene is associated with progression of age-related macular degeneration (AMD). Hum Mutat. 2006;27:337–342.
93. Koller A, Brandl C, Lamina C, et al. Relative telomere length is associated with age-related macular degeneration in women. Invest Ophthalmol Vis Sci. 2022;63:30.
94. Schellevis RL, Breukink MB, Gilissen C, et al. Exome sequencing in patients with chronic central serous chorioretinopathy. Sci Rep. 2019;9:6598.
95. Linghu D, Xu H, Liang Z, et al. Association between CFH single nucleotide polymorphisms and response to photodynamic therapy in patients with central serous chorioretinopathy. Int Ophthalmol. 2020;40:951–956.
96. Hayashida M, Miki A, Nakai S, et al. Genetic factors associated with treatment response to reduced-fluence photodynamic therapy for chronic central serous chorioretinopathy. Mol Vis. 2020;26:505–509.
97. Tsuchihashi T, Mori K, Horie-Inoue K, et al. Complement factor H and high-temperature requirement A-1 genotypes and treatment response of age-related macular degeneration. Ophthalmology. 2011;118:93–100.
98. Nakai S, Honda S, Matsumiya W, et al. ARMS2 variants may predict the 3-year outcome of photodynamic therapy for wet age-related macular degeneration. Mol Vis. 2017;23:514–519.
99. Brantley MA Jr, Edelstein SL, King JM, et al. Association of complement factor H and LOC387715 genotypes with response of exudative age-related macular degeneration to photodynamic therapy. Eye (Lond). 2009;23:626–631.
100. Chowers I, Meir T, Lederman M, et al. Sequence variants in HTRA1 and LOC387715/ARMS2 and phenotype and response to photodynamic therapy in neovascular age-related macular degeneration in populations from Israel. Mol Vis. 2008;14:2263–2271.
101. Goverdhan SV, Hannan S, Newsom RB, et al. An analysis of the CFH Y402H genotype in AMD patients and controls from the UK, and response to PDT treatment. Eye (Lond). 2008;22:849–854.
102. Seitsonen SP, Jarvela IE, Meri S, et al. The effect of complement factor H Y402H polymorphism on the outcome of photodynamic therapy in age-related macular degeneration. Eur J Ophthalmol. 2007;17:943–949.
103. Feng X, Xiao J, Longville B, et al. Complement factor H Y402H and C-reactive protein polymorphism and photodynamic therapy response in age-related macular degeneration. Ophthalmology. 2009;116:1908–1912.
104. Park DH, Kim IT. LOC387715/HTRA1 variants and the response to combined photodynamic therapy with intravitreal bevacizumab for polypoidal choroidal vasculopathy. Retina. 2012;32:299–307.
105. Sakurada Y, Kubota T, Imasawa M, et al. Association of LOC387715 A69S genotype with visual prognosis after photodynamic therapy for polypoidal choroidal vasculopathy. Retina. 2010;30:1616–1621.
106. Nakai S, Matsumiya W, Miki A, et al. Association of an age-related maculopathy susceptibility 2 gene variant with the 12-month outcomes of intravitreal aflibercept combined with photodynamic therapy for polypoidal choroidal vasculopathy. Jpn J Ophthalmol. 2019;63:389–395.
107. Nakata I, Yamashiro K, Yamada R, et al. Genetic variants in pigment epithelium-derived factor influence response of polypoidal choroidal vasculopathy to photodynamic therapy. Ophthalmology. 2011;118:1408–1415.
108. Brantley MA Jr, Fang AM, King JM, et al. Association of complement factor H and LOC387715 genotypes with response of exudative age-related macular degeneration to intravitreal bevacizumab. Ophthalmology. 2007;114:2168–2173.
109. Lee AY, Raya AK, Kymes SM, et al. Pharmacogenetics of complement factor H (Y402H) and treatment of exudative age-related macular degeneration with ranibizumab. Br J Ophthalmol. 2009;93:610–613.
110. Nischler C, Oberkofler H, Ortner C, et al. Complement factor H Y402H gene polymorphism and response to intravitreal bevacizumab in exudative age-related macular degeneration. Acta Ophthalmol. 2011;89:344–349.
111. Cobos E, Recalde S, Anter J, et al. Association between CFH, CFB, ARMS2, SERPINF1, VEGFR1 and VEGF polymorphisms and anatomical and functional response to ranibizumab treatment in neovascular age-related macular degeneration. Acta Ophthalmol. 2018;96:201–212.
112. Abedi F, Wickremasinghe S, Richardson AJ, et al. Genetic influences on the outcome of anti-vascular endothelial growth factor treatment in neovascular age-related macular degeneration. Ophthalmology. 2013;120:1641–1648.
113. Valverde-Megias A, Veganzones-de-Castro S, Donate-Lopez J, et al. ARMS2 A69S polymorphism is associated with the number of ranibizumab injections needed for exudative age-related macular degeneration in a pro re nata regimen during 4 years of follow-up. Graefes Arch Clin Exp Ophthalmol. 2017;255:2091–2098.
114. Kang HK, Yoon MH, Lee DH, et al. Pharmacogenetic influence of LOC387715/HTRA1 on the efficacy of bevacizumab treatment for age-related macular degeneration in a Korean population. Korean J Ophthalmol. 2012;26:414–422.
115. Zhao L, Grob S, Avery R, et al. Common variant in VEGFA and response to anti-VEGF therapy for neovascular age-related macular degeneration. Curr Mol Med. 2013;13:929–934.
116. Abedi F, Wickremasinghe S, Richardson AJ, et al. Variants in the VEGFA gene and treatment outcome after anti-VEGF treatment for neovascular age-related macular degeneration. Ophthalmology. 2013;120:115–121.
117. Bures Jelstrup A, Pomares E, Navarro R. on behalf of the BSG. Relationship between Aflibercept efficacy and genetic variants of genes associated with neovascular age-related macular degeneration: the BIOIMAGE trial. Ophthalmologica. 2020;243:461–470.
118. Hautamaki A, Kivioja J, Vavuli S, et al. Interleukin 8 promoter polymorphism predicts the initial response to bevacizumab treatment for exudative age-related macular degeneration. Retina. 2013;33:1815–1827.
119. Lorés-Motta L, Riaz M, Grunin M, et al. Association of Genetic variants with response to anti-vascular endothelial growth factor therapy in age-related macular degeneration. JAMA Ophthalmol. 2018;136:875–884.
120. Orlin A, Hadley D, Chang W, et al. Association between high-risk disease loci and response to anti-vascular endothelial growth factor treatment for wet age-related macular degeneration. Retina. 2012;32:4–9.
121. Hagstrom SA, Ying GS, Pauer GJT, et al. Pharmacogenetics for genes associated with age-related macular degeneration in the Comparison of AMD Treatments Trials (CATT). Ophthalmology. 2013;120:593–599.
122. Hagstrom SA, Ying GS, Pauer GJ, et al. VEGFA and VEGFR2 gene polymorphisms and response to anti-vascular endothelial growth factor therapy: comparison of age-related macular degeneration treatments trials (CATT). JAMA Ophthalmol. 2014;132:521–527.
123. Hagstrom SA, Ying GS, Maguire MG, et al. VEGFR2 gene polymorphisms and response to anti-vascular endothelial growth factor therapy in age-related macular degeneration. Ophthalmology. 2015;122:1563–1568.
124. Lotery AJ, Gibson J, Cree AJ, et al. Pharmacogenetic associations with vascular endothelial growth factor inhibition in participants with neovascular age-related macular degeneration in the IVAN Study. Ophthalmology. 2013;120:2637–2643.
125. Park UC, Shin JY, Chung H, et al. Association of ARMS2 genotype with response to anti-vascular endothelial growth factor treatment in polypoidal choroidal vasculopathy. BMC Ophthalmol. 2017;17:241.
126. Kikushima W, Sakurada Y, Sugiyama A, et al. Comparison of two-year outcomes after photodynamic therapy with ranibizumab or aflibercept for polypoidal choroidal vasculopathy. Sci Rep. 2017;7:16461.
127. Chicco D, Faultless T. Brief Survey on machine learning in epistasis. Methods Mol Biol. 2021;2212:169–179.
128. Moraes G, Fu DJ, Wilson M, et al. Quantitative analysis of OCT for neovascular age-related macular degeneration using deep learning. Ophthalmology. 2021;128:693–705.
129. Wilson M, Chopra R, Wilson MZ, et al. Validation and clinical applicability of whole-volume automated segmentation of optical coherence tomography in retinal disease using deep learning. JAMA Ophthalmol. 2021;139:964–973.
130. Grassmann F, Mengelkamp J, Brandl C, et al. A deep learning algorithm for prediction of age-related eye disease study severity scale for age-related macular degeneration from color fundus photography. Ophthalmology. 2018;125:1410–1420.
131. Cen LP, Ji J, Lin JW, et al. Automatic detection of 39 fundus diseases and conditions in retinal photographs using deep neural networks. Nat Commun. 2021;12:4828.
132. Ferrara D, Newton EM, Lee AY. Artificial intelligence-based predictions in neovascular age-related macular degeneration. Curr Opin Ophthalmol. 2021;32:389–396.
133. Yan Q, Jiang Y, Huang H, et al. Genome-wide association studies-based machine learning for prediction of age-related macular degeneration risk. Transl Vis Sci Technol. 2021;10:29.
134. Ajana S, Cougnard-Grégoire A, Colijn JM, et al. Predicting progression to advanced age-related macular degeneration from clinical, genetic, and lifestyle factors using machine learning. Ophthalmology. 2021;128:587–597.
135. Winkler TW, Grassmann F, Brandl C, et al. Genome-wide association meta-analysis for early age-related macular degeneration highlights novel loci and insights for advanced disease. BMC Med Genomics. 2020;13:120.
136. Topol EJ. High-performance medicine: the convergence of human and artificial intelligence. Nat Med. 2019;25:44–56.
137. Kumaragurupari R, Mishra C. A bibliometric analysis of research on genetic retinal diseases done in India. Indian J Ophthalmol. 2022;70:2546–2550.
138. Ji Y, Chen N, Liu S, et al. Research progress of artificial intelligence image analysis in systemic disease-related ophthalmopathy. Dis Markers. 2022;2022:3406890.
139. Strunz T, Pöllmann M, Gamulescu MA, et al. Genetic association analysis of anti-VEGF treatment response in neovascular age-related macular degeneration. Int J Mol Sci. 2022;23:6094.

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