A transcriptome-wide association study based on 27 tissues identifies 106 genes potentially relevant for disease pathology in age-related macular degeneration

Janus Kinase Inhibitor Therapy and Risk of Age-Related Macular Degeneration in Autoimmune Disease

Importance

The involvement of chronic inflammation in the pathogenesis of age-related macular degeneration (AMD) opens therapeutic possibilities to AMD management.

Objective

To determine whether Janus kinase inhibitors (JAKis) are associated with a reduced risk of AMD development in patients with autoimmune diseases.

Design, Setting, and Participants

This retrospective observational cohort study used administrative claims data from Merative MarketScan research databases (Commercial and Medicare Supplemental) and Optum Clinformatics Data Mart databases between January 1, 2010, and January 31, 2022. Patients with autoimmune diseases satisfying study eligibility criteria and who received JAKi treatment (9126 in MarketScan and 5667 in Optum) were propensity score matched (1:1) to identical numbers of study-eligible patients who received non-JAKi-based immunotherapy.

Exposure

Treatment duration of 6 months or longer.

Main Outcomes and Measures

Incidence rates of AMD (exudative and nonexudative) over the first 6 to 18 months of treatment were determined, and bayesian Poisson regression models were used to estimate incidence rate ratios, 95% CIs, and posterior probabilities of AMD.

Results

After matching, female sex represented the majority of the patient population in both MarketScan and Optum (14 019/18 252 [76.6%] and 8563/3364 [75.2%], respectively in the JAKi patient population). More than 60% of the patient population was older than 55 years of age in both cohorts. Over the specified treatment period, a 49% relative reduction in incidence of AMD was observed among patients who received JAKi therapy (10/9126 events; adjusted incidence rate ratio [AIRR], 0.51; 95% CI, 0.19-0.90) vs those who received non-JAKi therapy (43/9126 events; AIRR, 1 [reference]) in MarketScan, and a 73% relative reduction in incidence of AMD was observed among patients who received JAKi therapy (3/5667 events; AIRR, 0.27; 95% CI, 0.03-0.74) vs those who received non-JAKi therapy (21/5667 events; AIRR, 1 [reference]) in Optum. The absolute percentage reductions were 0.36% (MarketScan) and 0.32% (Optum), favoring patients who received JAKi therapy. Posterior probabilities of the adjusted risk being less than unity were 97.6% (MarketScan) and 98.9% (Optum) for those who received JAKi therapy vs those who received non-JAKi therapy in MarketScan and Optum, respectively.

Conclusions and Relevance

JAKi use may be associated with a reduced risk of incident AMD in US adults with major autoimmune diseases. The absolute percentage reduction is consistent with a potential role for JAKi in this population. Future studies with long-term follow-up are recommended to investigate the association between JAKi use and incident AMD in other disease indications. Investigation into the role of systemic inflammation and JAK-signal transducers and activators of transcription signaling in AMD may improve understanding of the pathophysiology of AMD and lead to new treatment options.

Comprehensive functional characterization of complement factor I rare variant genotypes identified in the SCOPE geographic atrophy cohort

Rare variants (RVs) in the gene encoding the regulatory enzyme complement factor I (CFI; FI) that reduce protein function or levels increase age-related macular degeneration risk. A total of 3357 subjects underwent screening in the SCOPE natural history study for geographic atrophy secondary to age-related macular degeneration, including CFI sequencing and serum FI measurement. Eleven CFI RV genotypes that were challenging to categorize as type I (low serum level) or type II (normal serum level, reduced enzymatic function) were characterized in the context of pure FI protein in C3b and C4b fluid phase cleavage assays and a novel bead-based functional assay (BBFA) of C3b cleavage. Four variants predicted or previously characterized as benign were analyzed by BBFA for comparison. In all, three variants (W51S, C67R, and I370T) resulted in low expression. Furthermore, four variants (P64L, R339Q, G527V, and P528T) were identified as being highly deleterious with IC50s for C3b breakdown >1 log increased versus the WT protein, while two variants (K476E and R474Q) were ∼1 log reduced in function. Meanwhile, six variants (P50A, T203I, K441R, E548Q, P553S, and S570T) had IC50s similar to WT. Odds ratios and BBFA IC50s were positively correlated (r = 0.76, p < 0.01), while odds ratios versus combined annotation dependent depletion (CADD) scores were not (r = 0.43, p = 0.16). Overall, 15 CFI RVs were functionally characterized which may aid future patient stratification for complement-targeted therapies. Pure protein in vitro analysis remains the gold standard for determining the functional consequence of CFI RVs.

SR-TWAS: Leveraging Multiple Reference Panels to Improve TWAS Power by Ensemble Machine Learning

Multiple reference panels of a given tissue or multiple tissues often exist, and multiple regression methods could be used for training gene expression imputation models for TWAS. To leverage expression imputation models (i.e., base models) trained with multiple reference panels, regression methods, and tissues, we develop a Stacked Regression based TWAS (SR-TWAS) tool which can obtain optimal linear combinations of base models for a given validation transcriptomic dataset. Both simulation and real studies showed that SR-TWAS improved power, due to increased effective training sample sizes and borrowed strength across multiple regression methods and tissues. Leveraging base models across multiple reference panels, tissues, and regression methods, our real application studies identified 6 independent significant risk genes for Alzheimer's disease (AD) dementia for supplementary motor area tissue and 9 independent significant risk genes for Parkinson's disease (PD) for substantia nigra tissue. Relevant biological interpretations were found for these significant risk genes.

Multi-tissue transcriptome-wide association study identifies novel candidate susceptibility genes for cataract

Introduction

Cataract is the leading cause of blindness among the elderly worldwide. Twin and family studies support an important role for genetic factors in cataract susceptibility with heritability estimates up to 58%. To date, 55 loci for cataract have been identified by genome-wide association studies (GWAS), however, much work remains to identify the causal genes. Here, we conducted a transcriptome-wide association study (TWAS) of cataract to prioritize causal genes and identify novel ones, and examine the impact of their expression.

Methods

We performed tissue-specific and multi-tissue TWAS analyses to assess associations between imputed gene expression from 54 tissues (including 49 from the Genotype Tissue Expression (GTEx) Project v8) with cataract using FUSION software. Meta-analyzed GWAS summary statistics from 59,944 cataract cases and 478,571 controls, all of European ancestry and from two cohorts (GERA and UK Biobank) were used. We then examined the expression of the novel genes in the lens tissue using the iSyTE database.

Results

Across tissue-specific and multi-tissue analyses, we identified 99 genes for which genetically predicted gene expression was associated with cataract after correcting for multiple testing. Of these 99 genes, 20 (AC007773.1, ANKH, ASIP, ATP13A2, CAPZB, CEP95, COQ6, CREB1, CROCC, DDX5, EFEMP1, EIF2S2, ESRRB, GOSR2, HERC4, INSRR, NIPSNAP2, PICALM, SENP3, and SH3YL1) did not overlap with previously reported cataract-associated loci. Tissue-specific analysis identified 202 significant gene-tissue associations for cataract, of which 166 (82.2%), representing 9 unique genes, were attributed to the previously reported 11q13.3 locus. Tissue-enrichment analysis revealed that gastrointestinal tissues represented one of the highest proportions of the Bonferroni-significant gene-tissue associations (21.3%). Moreover, this gastrointestinal tissue type was the only anatomical category significantly enriched in our results, after correcting for the number of tissue donors and imputable genes for each reference panel. Finally, most of the novel cataract genes (e.g., Capzb) were robustly expressed in iSyTE lens data.

Discussion

Our results provide evidence of the utility of imputation-based TWAS approaches to characterize known GWAS risk loci and identify novel candidate genes that may increase our understanding of cataract etiology. Our findings also highlight the fact that expression of genes associated with cataract susceptibility is not necessarily restricted to lens tissue.

Enhancing Readability and Detection of Age-Related Macular Degeneration Using Optical Coherence Tomography Imaging: An AI Approach

Artificial intelligence has been used effectively in medical diagnosis. The objective of this project is to examine the application of a collective AI model using weighted fusion of predicted probabilities from different AI architectures to diagnose various retinal conditions based on optical coherence tomography (OCT). A publicly available Noor dataset, comprising 16,822, images from 554 retinal OCT scans of 441 patients, was used to predict a diverse spectrum of age-related macular degeneration (AMD) stages: normal, drusen, or choroidal neovascularization. These predictions were compared with predictions from ResNet, EfficientNet, and Attention models, respectively, using precision, recall, F1 score, and confusion matric and receiver operating characteristics curves. Our collective model demonstrated superior accuracy in classifying AMD compared to individual ResNet, EfficientNet, and Attention models, showcasing the effectiveness of using trainable weights in the ensemble fusion process, where these weights dynamically adapt during training rather than being fixed values. Specifically, our ensemble model achieved an accuracy of 91.88%, precision of 92.54%, recall of 92.01%, and F1 score of 92.03%, outperforming individual models. Our model also highlights the refinement process undertaken through a thorough examination of initially misclassified cases, leading to significant improvements in the model's accuracy rate to 97%. This study also underscores the potential of AI as a valuable tool in ophthalmology. The proposed ensemble model, combining different mechanisms highlights the benefits of model fusion for complex medical image analysis.

Whole genome sequencing of 4,787 individuals identifies gene-based rare variants in age-related macular degeneration

Genome-wide association studies have contributed extensively to the discovery of disease-associated common variants. However, the genetic contribution to complex traits is still largely difficult to interpret. We report a genome-wide association study of 2394 cases and 2393 controls for age-related macular degeneration (AMD) via whole-genome sequencing, with 46.9 million genetic variants. Our study reveals significant single-variant association signals at four loci and independent gene-based signals in CFH, C2, C3, and NRTN. Using data from the Exome Aggregation Consortium (ExAC) for a gene-based test, we demonstrate an enrichment of predicted rare loss-of-function variants in CFH, CFI, and an as-yet unreported gene in AMD, ORMDL2. Our method of using a large variant list without individual-level genotypes as an external reference provides a flexible and convenient approach to leverage the publicly available variant datasets to augment the search for rare variant associations, which can explain additional disease risk in AMD.

Increased risk of Parkinson's disease amongst patients with age-related macular degeneration and visual disability: A nationwide cohort study

BACKGROUND AND PURPOSE

The association between Parkinson's disease (PD) and age-related macular degeneration (AMD) has been shown in previous reports. However, the association between the severity of AMD and PD development is unknown. The aim was to evaluate the association of AMD with/without visual disability (VD) with the risk of PD occurrence using the National Health Insurance data in South Korea.

METHODS

A total of 4,205,520 individuals, 50 years or older and without a previous diagnosis of PD, participated in the Korean National Health Screening Program in 2009. AMD was verified using diagnostic codes, and participants with VD were defined as those with loss of vision or visual field defect as certified by the Korean Government. The participants were followed up until 31 December 2019, and incident cases of PD were identified using registered diagnostic codes. The hazard ratio was calculated for groups (control and AMD with/without VD) using multivariable adjusted Cox regression analysis.

RESULTS

In total, 37,507 participants (0.89%) were diagnosed with PD. Amongst individuals with AMD, the risk of PD development was higher in individuals with VD (adjusted hazard ratio [aHR] 1.35, 95% confidence interval [CI] 1.09-1.67) than in those without (aHR 1.22, 95% CI 1.15-1.30) compared with controls. Additionally, an increased risk of PD was observed in individuals with AMD compared with controls, regardless of the presence of VD (aHR 1.23, 95% CI 1.16-1.31).

CONCLUSIONS

Visual disability in AMD was associated with the development of PD. This suggests that neurodegeneration in PD and AMD may have common pathways.

Complement factor I: Regulatory nexus, driver of immunopathology, and therapeutic

Complement factor I (FI) is the nexus for classical, lectin and alternative pathway complement regulation. FI is an 88 kDa plasma protein that circulates in an inactive configuration until it forms a trimolecular complex with its cofactor and substrate whereupon a structural reorganization allows the catalytic triad to cleave its substrates, C3b and C4b. In keeping with its role as the master complement regulatory enzyme, deficiency has been linked to immunopathology. In the setting of complete FI deficiency, a consumptive C3 deficiency results in recurrent infections with encapsulated microorganisms. Aseptic cerebral inflammation and vasculitic presentations are also less commonly observed. Heterozygous mutations in the factor I gene (CFI) have been demonstrated to be enriched in atypical haemolytic uraemic syndrome, albeit with a very low penetrance. Haploinsufficiency of CFI has also been associated with decreased retinal thickness and is a strong risk factor for the development of age-related macular degeneration. Supplementation of FI using plasma purified or recombinant protein has long been postulated, however, technical difficulties prevented progression into clinical trials. It is only using gene therapy that CFI supplementation has reached the clinic with GT005 in phase I/II clinical trials for geographic atrophy.

On the interpretation of transcriptome-wide association studies

Transcriptome-wide association studies (TWAS) aim to detect relationships between gene expression and a phenotype, and are commonly used for secondary analysis of genome-wide association study (GWAS) results. Results from TWAS analyses are often interpreted as indicating a genetic relationship between gene expression and a phenotype, but this interpretation is not consistent with the null hypothesis that is evaluated in the traditional TWAS framework. In this study we provide a mathematical outline of this TWAS framework, and elucidate what interpretations are warranted given the null hypothesis it actually tests. We then use both simulations and real data analysis to assess the implications of misinterpreting TWAS results as indicative of a genetic relationship between gene expression and the phenotype. Our simulation results show considerably inflated type 1 error rates for TWAS when interpreted this way, with 41% of significant TWAS associations detected in the real data analysis found to have insufficient statistical evidence to infer such a relationship. This demonstrates that in current implementations, TWAS cannot reliably be used to investigate genetic relationships between gene expression and a phenotype, but that local genetic correlation analysis can serve as a potential alternative.

Transcriptome-wide association study identifies novel candidate susceptibility genes for migraine

Genome-wide association studies (GWASs) have identified more than 130 genetic susceptibility loci for migraine; however, how most of these loci impact migraine development is unknown. To identify novel genes associated with migraine and interpret the transcriptional products of those genes, we conducted a transcriptome-wide association study (TWAS). We performed tissue-specific and multi-tissue TWAS analyses to assess associations between imputed gene expression from 53 tissues and migraine susceptibility using FUSION software. Meta-analyzed GWAS summary statistics from 26,052 migraine cases and 487,214 controls, all of European ancestry and from two cohorts (the Kaiser Permanente GERA and the UK Biobank), were used. We evaluated the associations for genes after conditioning on variant-level effects from GWAS, and we tested for colocalization of GWAS migraine-associated loci and expression quantitative trait loci (eQTLs). Across tissue-specific and multi-tissue analyses, we identified 53 genes for which genetically predicted gene expression was associated with migraine after correcting for multiple testing. Of these 53 genes, 10 (ATF5, CNTNAP1, KTN1-AS1, NEIL1, NEK4, NNT, PNKP, RUFY2, TUBG2, and VAT1) did not overlap known migraine-associated loci identified from GWAS. Tissue-specific analysis identified 45 gene-tissue pairs and cardiovascular tissues represented the highest proportion of the Bonferroni-significant gene-tissue pairs (n = 22 [49%]), followed by brain tissues (n = 6 [13%]), and gastrointestinal tissues (n = 4 [9%]). Colocalization analyses provided evidence of shared genetic variants underlying eQTL and GWAS signals in 18 of the gene-tissue pairs (40%). Our TWAS reports novel genes for migraine and highlights the important contribution of brain, cardiovascular, and gastrointestinal tissues in migraine susceptibility.

Predicting late-stage age-related macular degeneration by integrating marginally weak SNPs in GWA studies

Introduction: Age-related macular degeneration (AMD) is a progressive neurodegenerative disease and the leading cause of blindness in developed countries. Current genome-wide association studies (GWAS) for late-stage age-related macular degeneration are mainly single-marker-based approaches, which investigate one Single-Nucleotide Polymorphism (SNP) at a time and postpone the integration of inter-marker Linkage-disequilibrium (LD) information in the downstream fine mappings. Recent studies showed that directly incorporating inter-marker connection/correlation into variants detection can help discover novel marginally weak single-nucleotide polymorphisms, which are often missed in conventional genome-wide association studies, and can also help improve disease prediction accuracy. Methods: Single-marker analysis is performed first to detect marginally strong single-nucleotide polymorphisms. Then the whole-genome linkage-disequilibrium spectrum is explored and used to search for high-linkage-disequilibrium connected single-nucleotide polymorphism clusters for each strong single-nucleotide polymorphism detected. Marginally weak single-nucleotide polymorphisms are selected via a joint linear discriminant model with the detected single-nucleotide polymorphism clusters. Prediction is made based on the selected strong and weak single-nucleotide polymorphisms. Results: Several previously identified late-stage age-related macular degeneration susceptibility genes, for example, BTBD16, C3, CFH, CFHR3, HTARA1, are confirmed. Novel genes DENND1B, PLK5, ARHGAP45, and BAG6 are discovered as marginally weak signals. Overall prediction accuracy of 76.8% and 73.2% was achieved with and without the inclusion of the identified marginally weak signals, respectively. Conclusion: Marginally weak single-nucleotide polymorphisms, detected from integrating inter-marker linkage-disequilibrium information, may have strong predictive effects on age-related macular degeneration. Detecting and integrating such marginally weak signals can help with a better understanding of the underlying disease-development mechanisms for age-related macular degeneration and more accurate prognostics.

OTTERS: a powerful TWAS framework leveraging summary-level reference data

Most existing TWAS tools require individual-level eQTL reference data and thus are not applicable to summary-level reference eQTL datasets. The development of TWAS methods that can harness summary-level reference data is valuable to enable TWAS in broader settings and enhance power due to increased reference sample size. Thus, we develop a TWAS framework called OTTERS (Omnibus Transcriptome Test using Expression Reference Summary data) that adapts multiple polygenic risk score (PRS) methods to estimate eQTL weights from summary-level eQTL reference data and conducts an omnibus TWAS. We show that OTTERS is a practical and powerful TWAS tool by both simulations and application studies.

Knockout of AMD-associated gene POLDIP2 reduces mitochondrial superoxide in human retinal pigment epithelial cells

Genetic and epidemiologic studies have significantly advanced our understanding of the genetic factors contributing to age-related macular degeneration (AMD). In particular, recent expression quantitative trait loci (eQTL) studies have highlighted POLDIP2 as a significant gene that confers risk of developing AMD. However, the role of POLDIP2 in retinal cells such as retinal pigment epithelium (RPE) and how it contributes to AMD pathology are unknown. Here we report the generation of a stable human RPE cell line ARPE-19 with POLDIP2 knockout using CRISPR/Cas, providing an in vitro model to investigate the functions of POLDIP2. We conducted functional studies on the POLDIP2 knockout cell line and showed that it retained normal levels of cell proliferation, cell viability, phagocytosis and autophagy. Also, we performed RNA sequencing to profile the transcriptome of POLDIP2 knockout cells. Our results highlighted significant changes in genes involved in immune response, complement activation, oxidative damage and vascular development. We showed that loss of POLDIP2 caused a reduction in mitochondrial superoxide levels, which is consistent with the upregulation of the mitochondrial superoxide dismutase SOD2. In conclusion, this study demonstrates a novel link between POLDIP2 and SOD2 in ARPE-19, which supports a potential role of POLDIP2 in regulating oxidative stress in AMD pathology.

Development of a CRISPRi Human Retinal Pigmented Epithelium Model for Functional Study of Age-Related Macular Degeneration Genes

Age-related macular degeneration (AMD) is a blinding disease characterised by dysfunction of the retinal pigmented epithelium (RPE) which culminates in disruption or loss of the neurosensory retina. Genome-wide association studies have identified >60 genetic risk factors for AMD; however, the expression profile and functional role of many of these genes remain elusive in human RPE. To facilitate functional studies of AMD-associated genes, we developed a human RPE model with integrated CRISPR interference (CRISPRi) for gene repression by generating a stable ARPE19 cell line expressing dCas9-KRAB. We performed transcriptomic analysis of the human retina to prioritise AMD-associated genes and selected TMEM97 as a candidate gene for knockdown study. Using specific sgRNAs, we showed that knockdown of TMEM97 in ARPE19 reduced reactive oxygen species (ROS) levels and exerted a protective effect against oxidative stress-induced cell death. This work provides the first functional study of TMEM97 in RPE and supports a potential role of TMEM97 in AMD pathobiology. Our study highlights the potential for using CRISPRi to study AMD genetics, and the CRISPRi RPE platform generated here provided a useful in vitro tool for functional studies of AMD-associated genes.

Using single molecule Molecular Inversion Probes as a cost-effective, high-throughput sequencing approach to target all genes and loci associated with macular diseases

Macular degenerations (MDs) are a subgroup of retinal disorders characterized by central vision loss. Knowledge is still lacking on the extent of genetic and nongenetic factors influencing inherited MD (iMD) and age-related MD (AMD) expression. Single molecule Molecular Inversion Probes (smMIPs) have proven effective in sequencing the ABCA4 gene in patients with Stargardt disease to identify associated coding and noncoding variation, however many MD patients still remain genetically unexplained. We hypothesized that the missing heritability of MDs may be revealed by smMIPs-based sequencing of all MD-associated genes and risk factors. Using 17,394 smMIPs, we sequenced the coding regions of 105 iMD and AMD-associated genes and noncoding or regulatory loci, known pseudo-exons, and the mitochondrial genome in two test cohorts that were previously screened for variants in ABCA4. Following detailed sequencing analysis of 110 probands, a diagnostic yield of 38% was observed. This established an ''MD-smMIPs panel," enabling a genotype-first approach in a high-throughput and cost-effective manner, whilst achieving uniform and high coverage across targets. Further analysis will identify known and novel variants in MD-associated genes to offer an accurate clinical diagnosis to patients. Furthermore, this will reveal new genetic associations for MD and potential genetic overlaps between iMD and AMD.

Systems genomics in age-related macular degeneration

Genomic studies in age-related macular degeneration (AMD) have identified genetic variants that account for the majority of AMD risk. An important next step is to understand the functional consequences and downstream effects of the identified AMD-associated genetic variants. Instrumental for this next step are 'omics' technologies, which enable high-throughput characterization and quantification of biological molecules, and subsequent integration of genomics with these omics datasets, a field referred to as systems genomics. Single cell sequencing studies of the retina and choroid demonstrated that the majority of candidate AMD genes identified through genomic studies are expressed in non-neuronal cells, such as the retinal pigment epithelium (RPE), glia, myeloid and choroidal cells, highlighting that many different retinal and choroidal cell types contribute to the pathogenesis of AMD. Expression quantitative trait locus (eQTL) studies in retinal tissue have identified putative causal genes by demonstrating a genetic overlap between gene regulation and AMD risk. Linking genetic data to complement measurements in the systemic circulation has aided in understanding the effect of AMD-associated genetic variants in the complement system, and supports that protein QTL (pQTL) studies in plasma or serum samples may aid in understanding the effect of genetic variants and pinpointing causal genes in AMD. A recent epigenomic study fine-mapped AMD causal variants by determing regulatory regions in RPE cells differentiated from induced pluripotent stem cells (iPSC-RPE). Another approach that is being employed to pinpoint causal AMD genes is to produce synthetic DNA assemblons representing risk and protective haplotypes, which are then delivered to cellular or animal model systems. Pinpointing causal genes and understanding disease mechanisms is crucial for the next step towards clinical translation. Clinical trials targeting proteins encoded by the AMD-associated genomic loci C3, CFB, CFI, CFH, and ARMS2/HTRA1 are currently ongoing, and a phase III clinical trial for C3 inhibition recently showed a modest reduction of lesion growth in geographic atrophy. The EYERISK consortium recently developed a genetic test for AMD that allows genotyping of common and rare variants in AMD-associated genes. Polygenic risk scores (PRS) were applied to quantify AMD genetic risk, and may aid in predicting AMD progression. In conclusion, genomic studies represent a turning point in our exploration of AMD. The results of those studies now serve as a driving force for several clinical trials. Expanding to omics and systems genomics will further decipher function and causality from the associations that have been reported, and will enable the development of therapies that will lessen the burden of AMD.

An overview of artificial intelligence in diabetic retinopathy and other ocular diseases

Artificial intelligence (AI), also known as machine intelligence, is a branch of science that empowers machines using human intelligence. AI refers to the technology of rendering human intelligence through computer programs. From healthcare to the precise prevention, diagnosis, and management of diseases, AI is progressing rapidly in various interdisciplinary fields, including ophthalmology. Ophthalmology is at the forefront of AI in medicine because the diagnosis of ocular diseases heavy reliance on imaging. Recently, deep learning-based AI screening and prediction models have been applied to the most common visual impairment and blindness diseases, including glaucoma, cataract, age-related macular degeneration (ARMD), and diabetic retinopathy (DR). The success of AI in medicine is primarily attributed to the development of deep learning algorithms, which are computational models composed of multiple layers of simulated neurons. These models can learn the representations of data at multiple levels of abstraction. The Inception-v3 algorithm and transfer learning concept have been applied in DR and ARMD to reuse fundus image features learned from natural images (non-medical images) to train an AI system with a fraction of the commonly used training data (<1%). The trained AI system achieved performance comparable to that of human experts in classifying ARMD and diabetic macular edema on optical coherence tomography images. In this study, we highlight the fundamental concepts of AI and its application in these four major ocular diseases and further discuss the current challenges, as well as the prospects in ophthalmology.

Association between Age-Related Macular Degeneration and the Risk of Diabetes Mellitus: A Nationwide Cohort Study

Age-related macular degeneration (AMD) is a degenerative and progressive disease of the macula, the part of the retina that is responsible for central vision. AMD shares some risk factors with diabetes mellitus (DM), but little is known about the risk of DM in individuals with AMD. With the goal of establishing novel perspectives, this study aimed to investigate the association between AMD and the risk of DM using the Korean Nationwide Health Insurance Database. Individuals aged ≥ 50 years who underwent a national health screening program in 2009 were enrolled. Participants were categorized by the presence of AMD and visual disability (VD). The Cox hazard regression model was used to examine hazard ratios (HRs) of DM with adjustment for potential confounders. Stratified analyses by age, sex, and comorbidities (hypertension or dyslipidemia) were also performed. During a mean follow-up of 8.61 years, there were 403,367 (11.76%) DM incidences among the final 3,430,532 participants. The crude HR (95% confidence interval (CI)) was 1.16 (1.13–1.20) for AMD. After adjusting for potential confounders, AMD was associated with a 3% decreased risk of DM (aHR 0.97, 95% CI 0.95–1.00), but no significant association with the risk of DM was found in AMD with VD (aHR 1.03, 95% CI 0.93–1.14). In summary, we did not find an increased risk of DM in individuals with AMD. A 3% decreased risk of DM in patients with AMD is not clinically meaningful. Our study suggests that the association between AMD and the risk of DM is weak, considering the potential confounders. Further studies examining this association are needed to extend our knowledge.

Evaluating a Causal Relationship between Complement Factor I Protein Level and Advanced Age-Related Macular Degeneration Using Mendelian Randomization

Importance

Risk of advanced age-related macular degeneration (AAMD) is associated with rare genetic variants in the gene encoding Complement factor I (CFI), which is associated with lower circulating CFI protein levels, but the nature of the relationship is unclear.

Objective

Can genetic factors be used to infer whether low circulating CFI is associated with AAMD risk?

Design

Two-sample inverse variance weighted Mendelian Randomisation (MR) was used to evaluate evidence for a relationship between CFI levels and AAMD risk, comparing CFI levels from genetically predefined subsets in AAMD and control cohorts.

Setting

Published genetic and proteomic data was combined with data from cohorts of Geographic Atrophy (GA) patients in a series of MR analyses.

Participants

We derived genetic instruments for systemic CFI level in 3,301 healthy European participants in the INTERVAL study. To evaluate a genetic causal odds ratio (OR) for the effect of CFI levels on AAMD risk, we used results from a genome-wide association study of 12,711 AAMD cases and 14,590 European controls from the International AMD Genomics Consortium (IAMDGC), and CFI levels from patients entered into the research studies SCOPE and SIGHT.

Results

We identified one common CFI variant rs7439493 which was strongly associated with low CFI level, explaining 4.8% of phenotypic variance. Using rs7439493 our MR analysis estimated that AAMD odds increased per standard deviation (SD) decrease in CFI level; OR 1.47 (95% confidence interval (CI) 1.30-1.65, P=2.1×10-10). We identified one rare variant (rs141853578 encoding p.Gly119Arg) which was genome-wide significantly associated with CFI levels after imputation; based on this, a 1 SD decrease in CFI leads to increased AAMD odds of 1.79 (95% CI 1.46-2.19, P=1.9×10-8). The rare variant rs141853578 explained a further 1.7% of phenotypic variance. To benchmark the effect of low CFI levels on AAMD odds using a CFI-specific proteomic assay, we estimated the effect using CFI levels from 24 rs141853578 positive GA patients; each 1 SD (3.5μg/mL) reduction in CFI was associated with 1.67 fold increased odds of AAMD (95% CI 1.40-2.00, P=1.85×10-8).

Conclusion and relevance

Excellent concordance in direction and effect size derived from rare and common variant calculations provide good genetic evidence for a potentially causal role of lower CFI level increasing AAMD risk.

TIGAR-V2: Efficient TWAS tool with nonparametric Bayesian eQTL weights of 49 tissue types from GTEx V8

Standard transcriptome-wide association study (TWAS) methods first train gene expression prediction models using reference transcriptomic data and then test the association between the predicted genetically regulated gene expression and phenotype of interest. Most existing TWAS tools require cumbersome preparation of genotype input files and extra coding to enable parallel computation. To improve the efficiency of TWAS tools, we developed Transcriptome-Integrated Genetic Association Resource V2 (TIGAR-V2), which directly reads Variant Call Format (VCF) files, enables parallel computation, and reduces up to 90% of computation cost (mainly due to loading genotype data) compared to the original version. TIGAR-V2 can train gene expression imputation models using either nonparametric Bayesian Dirichlet process regression (DPR) or Elastic-Net (as used by PrediXcan), perform TWASs using either individual-level or summary-level genome-wide association study (GWAS) data, and implement both burden and variance-component statistics for gene-based association tests. We trained gene expression prediction models by DPR for 49 tissues using Genotype-Tissue Expression (GTEx) V8 by TIGAR-V2 and illustrated the usefulness of these Bayesian cis-expression quantitative trait locus (eQTL) weights through TWASs of breast and ovarian cancer utilizing public GWAS summary statistics. We identified 88 and 37 risk genes, respectively, for breast and ovarian cancer, most of which are either known or near previously identified GWAS (∼95%) or TWAS (∼40%) risk genes and three novel independent TWAS risk genes with known functions in carcinogenesis. These findings suggest that TWASs can provide biological insight into the transcriptional regulation of complex diseases. The TIGAR-V2 tool, trained Bayesian cis-eQTL weights, and linkage disequilibrium (LD) information from GTEx V8 are publicly available, providing a useful resource for mapping risk genes of complex diseases.

Epigenomic signatures in age-related macular degeneration: Focus on their role as disease modifiers and therapeutic targets

Epigenetics is characterized by molecular modifications able to shape gene expression profiles in response to inner and external stimuli. Therefore, epigenetic elements are able to provide intriguing and useful information for the comprehension and management of different human conditions, including aging process, and diseases. On this subject, Age-related Macular Degeneration (AMD) represents one of the most frequent age-related disorders, dramatically affecting the quality of life of older adults worldwide. The etiopathogenesis is characterized by an interplay among multiple genetic and non-genetic factors, which have been extensively studied. Nevertheless, a deeper dissection of molecular machinery associated with risk, onset, progression and effectiveness of therapies is still missing. In this regard, epigenetic signals may be further explored to disentangle disease etiopathogenesis, the possible therapeutic avenues and the differential response to AMD treatment. This review will discuss the epigenomic signatures mostly investigated in AMD, which could be applied to improve the knowledge of disease mechanisms and to set-up novel or modified treatment options.

CoMM-S4: A Collaborative Mixed Model Using Summary-Level eQTL and GWAS Datasets in Transcriptome-Wide Association Studies

Motivation: Genome-wide association studies (GWAS) have achieved remarkable success in identifying SNP-trait associations in the last decade. However, it is challenging to identify the mechanisms that connect the genetic variants with complex traits as the majority of GWAS associations are in non-coding regions. Methods that integrate genomic and transcriptomic data allow us to investigate how genetic variants may affect a trait through their effect on gene expression. These include CoMM and CoMM-S², likelihood-ratio-based methods that integrate GWAS and eQTL studies to assess expression-trait association. However, their reliance on individual-level eQTL data render them inapplicable when only summary-level eQTL results, such as those from large-scale eQTL analyses, are available. Result: We develop an efficient probabilistic model, CoMM-S⁴, to explore the expression-trait association using summary-level eQTL and GWAS datasets. Compared with CoMM-S², which uses individual-level eQTL data, CoMM-S⁴ requires only summary-level eQTL data. To test expression-trait association, an efficient variational Bayesian EM algorithm and a likelihood ratio test were constructed. We applied CoMM-S⁴ to both simulated and real data. The simulation results demonstrate that CoMM-S⁴ can perform as well as CoMM-S² and S-PrediXcan, and analyses using GWAS summary statistics from Biobank Japan and eQTL summary statistics from eQTLGen and GTEx suggest novel susceptibility loci for cardiovascular diseases and osteoporosis. Availability and implementation: The developed R package is available at https://github.com/gordonliu810822/CoMM.

Proteome Landscape of Epithelial-to-Mesenchymal Transition (EMT) of Retinal Pigment Epithelium Shares Commonalities With Malignancy-Associated EMT

Stress and injury to the retinal pigment epithelium (RPE) often lead to dedifferentiation and epithelial-to-mesenchymal transition (EMT). These processes have been implicated in several retinal diseases, including proliferative vitreoretinopathy, diabetic retinopathy, and age-related macular degeneration. Despite the importance of RPE-EMT and the large body of data characterizing malignancy-related EMT, comprehensive proteomic studies to define the protein changes and pathways underlying RPE-EMT have not been reported. This study sought to investigate the temporal protein expression changes that occur in a human-induced pluripotent stem cell–based RPE-EMT model. We utilized multiplexed isobaric tandem mass tag labeling followed by high-resolution tandem MS for precise and in-depth quantification of the RPE-EMT proteome. We have identified and quantified 7937 protein groups in our tandem mass tag–based MS analysis. We observed a total of 532 proteins that are differentially regulated during RPE-EMT. Furthermore, we integrated our proteomic data with prior transcriptomic (RNA-Seq) data to provide additional insights into RPE-EMT mechanisms. To validate these results, we have performed a label-free single-shot data-independent acquisition MS study. Our integrated analysis indicates both the commonality and uniqueness of RPE-EMT compared with malignancy-associated EMT. Our comparative analysis also revealed that multiple age-related macular degeneration–associated risk factors are differentially regulated during RPE-EMT. Together, our integrated dataset provides a comprehensive RPE-EMT atlas and resource for understanding the molecular signaling events and associated biological pathways that underlie RPE-EMT onset. This resource has already facilitated the identification of chemical modulators that could inhibit RPE-EMT, and it will hopefully aid in ongoing efforts to develop EMT inhibition as an approach for the treatment of retinal disease.

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Proteomics data were integrated with prior transcriptomic (RNA-Seq) data on RPE-EMT.

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Dysregulated RPE-EMT proteome shares commonality with malignancy-associated EMT.

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Altered RPE-EMT proteome signatures correlated with known AMD-associated risk factors.

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Protein kinases and phosphatases crosstalk modulate RPE-EMT.

Epistatic interactions of genetic loci associated with age-related macular degeneration

The currently largest genome-wide association study (GWAS) for age-related macular degeneration (AMD) defines disease association with genome-wide significance for 52 independent common and rare genetic variants across 34 chromosomal loci. Overall, these loci contain over 7200 variants and are enriched for genes with functions indicating several shared cellular processes. Still, the precise mechanisms leading to AMD pathology are largely unknown. Here, we exploit the phenomenon of epistatic interaction to identify seemingly independent AMD-associated variants that reveal joint effects on gene expression. We focus on genetic variants associated with lipid metabolism, organization of extracellular structures, and innate immunity, specifically the complement cascade. Multiple combinations of independent variants were used to generate genetic risk scores allowing gene expression in liver to be compared between low and high-risk AMD. We identified genetic variant combinations correlating significantly with expression of 26 genes, of which 19 have not been associated with AMD before. This study defines novel targets and allows prioritizing further functional work into AMD pathobiology.

Retinal layer thicknesses and neurodegeneration in early age-related macular degeneration: insights from the Coimbra Eye Study

PURPOSE

This study aims to analyze the retinal layers and choroidal thickness in a large set of eyes with early age-related macular degeneration (AMD), in order to detect differences by stage suggestive of early neurodegeneration, and to explore biomarkers of different phenotypes.

METHODS

This study is a population-based, cross-sectional study. Patients from the incidence AMD study (NCT02748824) with early AMD (Rotterdam 2a, 2b, 3) were included. All performed spectral-domain optical coherence tomography (SD-OCT) (Spectralis, Heidelberg Engineering, Germany) and automatic segmentation of all retinal layers was obtained with built-in software. Manual correction was performed whenever necessary. The mean thicknesses (ETDRS grid) and volume of each layer were recorded. Subfoveal choroidal thickness was manually measured. Estimates for each layer thickness were calculated with linear mixed models and tested for pairwise differences between stages. Associations between layer thickness and microstructural findings were assessed by multivariate regression analysis.

RESULTS

The final cohort comprised 346 eyes (233 patients): 82.66% (n = 286) in stage 2a, 5.49% (n = 19) in stage 2b, and 11.85% (n = 41) in stage 3. A global tendency for lower/inferior thickness of the neuroretinal layers was found comparing stage 3 to 2a: retinal nerve fiber layer (RNFL), ganglion cell layer (GCL), and inner plexiform layer (IPL) were inferior in the inner/outer ETDRS circles and the outer nuclear layer (ONL) and photoreceptors' segments layer in the central circle (p ≤ 0.002). The retinal pigment epithelium-Bruch's membrane (RPE/BrM) layer was thicker in stage 3 (p ≤ 0.001). Subretinal drusenoid deposits (SDD) were associated with thinner neuroretinal layers and choroid (p < 0.05).

CONCLUSIONS

Our results showed in a large population-based dataset that several inner and outer neuroretinal layers were thinner with a higher stage in early AMD. These findings support the existence of early and progressive neurodegeneration. Neuronal retinal layer thicknesses might thus be used as quantitative biomarkers of disease progression in AMD. The presence of SDD is possibly associated to more prominent and faster neurodegeneration.

Genomic-Metabolomic Associations Support the Role of LIPC and Glycerophospholipids in Age-Related Macular Degeneration

Purpose

Large-scale genome-wide association studies (GWAS) have reported important single nucleotide polymorphisms (SNPs) with significant associations with age-related macular degeneration (AMD). However, their role in disease development remains elusive. This study aimed to assess SNP–metabolite associations (i.e., metabolite quantitative trait loci [met-QTL]) and to provide insights into the biological mechanisms of AMD risk SNPs.

Design

Cross-sectional multicenter study (Boston, Massachusetts, and Coimbra, Portugal).

Participants

Patients with AMD (n = 388) and control participants (n = 98) without any vitreoretinal disease (> 50 years).

Methods

Age-related macular degeneration grading was performed using color fundus photographs according to the Age-Related Eye Disease Study classification scheme. Fasting blood samples were collected and evaluated with mass spectrometry for metabolomic profiling and Illumina OmniExpress for SNPs profiling. Analyses of met-QTL of endogenous metabolites were conducted using linear regression models adjusted for age, gender, smoking, 10 metabolite principal components (PCs), and 10 SNP PCs. Additionally, we analyzed the cumulative effect of AMD risk SNPs on plasma metabolites by generating genetic risk scores and assessing their associations with metabolites using linear regression models, accounting for the same covariates. Modeling was performed first for each cohort, and then combined by meta-analysis. Multiple comparisons were accounted for using the false discovery rate (FDR).

Main Outcome Measures

Plasma metabolite levels associated with AMD risk SNPs.

Results

After quality control, data for 544 plasma metabolites were included. Meta-analysis of data from all individuals (AMD patients and control participants) identified 28 significant met-QTL (β = 0.016–0.083; FDR q-value < 1.14 × 10^–2), which corresponded to 5 metabolites and 2 genes: ASPM and LIPC. Polymorphisms in the LIPC gene were associated with phosphatidylethanolamine metabolites, which are glycerophospholipids, and polymorphisms in the ASPM gene with branched-chain amino acids. Similar results were observed when considering only patients with AMD. Genetic risk score–metabolite associations further supported a global impact of AMD risk SNPs on the plasma metabolome.

Conclusions

This study demonstrated that genomic–metabolomic associations can provide insights into the biological relevance of AMD risk SNPs. In particular, our results support that the LIPC gene and the glycerophospholipid metabolic pathway may play an important role in AMD, thus offering new potential therapeutic targets for this disease.

Implications of genetic variation in the complement system in age-related macular degeneration

Age-related macular degeneration (AMD) is the main cause of vision loss among the elderly in the Western world. While AMD is a multifactorial disease, the complement system was identified as one of the main pathways contributing to disease risk. The strong link between the complement system and AMD was demonstrated by genetic associations, and by elevated complement activation in local eye tissue and in the systemic circulation of AMD patients. Several complement inhibitors have been and are being explored in clinical trials, but thus far with limited success, leaving the majority of AMD patients without treatment options to date. This indicates that there is still a gap of knowledge regarding the functional implications of the complement system in AMD pathogenesis and how to bring these towards clinical translation. Many different experimental set-ups and disease models have been used to study complement activation in vivo and in vitro, and recently emerging patient-derived induced pluripotent stem cells and genome-editing techniques open new opportunities to study AMD disease mechanisms and test new therapeutic strategies in the future. In this review we provide an extensive overview of methods employed to understand the molecular processes of complement activation in AMD pathogenesis. We discuss the findings, advantages and challenges of each approach and conclude with an outlook on how recent, exciting developments can fill in current knowledge gaps and can aid in the development of effective complement-targeting therapeutic strategies in AMD.

New Technologies to Study Functional Genomics of Age-Related Macular Degeneration

Age-related macular degeneration (AMD) is the most common cause of irreversible vision loss in people over 50 years old in developed countries. Currently, we still lack a comprehensive understanding of the genetic factors contributing to AMD, which is critical to identify effective therapeutic targets to improve treatment outcomes for AMD patients. Here we discuss the latest technologies that can facilitate the identification and functional study of putative genes in AMD pathology. We review improved genomic methods to identify novel AMD genes, advances in single cell transcriptomics to profile gene expression in specific retinal cell types, and summarize recent development of in vitro models for studying AMD using induced pluripotent stem cells, organoids and biomaterials, as well as new molecular technologies using CRISPR/Cas that could facilitate functional studies of AMD-associated genes.

Learning from Fifteen Years of Genome-Wide Association Studies in Age-Related Macular Degeneration

Over the last 15 years, genome-wide association studies (GWAS) have greatly advanced our understanding of the genetic landscape of complex phenotypes. Nevertheless, causal interpretations of GWAS data are challenging but crucial to understand underlying mechanisms and pathologies. In this review, we explore to what extend the research community follows up on GWAS data. We have traced the scientific activities responding to the two largest GWAS conducted on age-related macular degeneration (AMD) so far. Altogether 703 articles were manually categorized according to their study type. This demonstrates that follow-up studies mainly involve "Review articles" (33%) or "Genetic association studies" (33%), while 19% of publications report on findings from experimental work. It is striking to note that only three of 16 AMD-associated loci described de novo in 2016 were examined in the four-year follow-up period after publication. A comparative analysis of five studies on gene expression regulation in AMD-associated loci revealed consistent gene candidates for 15 of these loci. Our random survey highlights the fact that functional follow-up studies on GWAS results are still in its early stages hampering a significant refinement of the vast association data and thus a more accurate insight into mechanisms and pathways.

A mega-analysis of expression quantitative trait loci in retinal tissue

Significant association signals from genome-wide association studies (GWAS) point to genomic regions of interest. However, for most loci the causative genetic variant remains undefined. Determining expression quantitative trait loci (eQTL) in a disease relevant tissue is an excellent approach to zoom in on disease- or trait-associated association signals and hitherto on relevant disease mechanisms. To this end, we explored regulation of gene expression in healthy retina (n = 311) and generated the largest cis-eQTL data set available to date. Genotype- and RNA-Seq data underwent rigorous quality control protocols before FastQTL was applied to assess the influence of genetic markers on local (cis) gene expression. Our analysis identified 403,151 significant eQTL variants (eVariants) that regulate 3,007 genes (eGenes) (Q-Value < 0.05). A conditional analysis revealed 744 independent secondary eQTL signals for 598 of the 3,007 eGenes. Interestingly, 99,165 (24.71%) of all unique eVariants regulate the expression of more than one eGene. Filtering the dataset for eVariants regulating three or more eGenes revealed 96 potential regulatory clusters. Of these, 31 harbour 130 genes which are partially regulated by the same genetic signal. To correlate eQTL and association signals, GWAS data from twelve complex eye diseases or traits were included and resulted in identification of 80 eGenes with potential association. Remarkably, expression of 10 genes is regulated by eVariants associated with multiple eye diseases or traits. In conclusion, we generated a unique catalogue of gene expression regulation in healthy retinal tissue and applied this resource to identify potentially pleiotropic effects in highly prevalent human eye diseases. Our study provides an excellent basis to further explore mechanisms of various retinal disease etiologies.

The retina is a multilayered and highly specified neural tissue crucial for high-resolution visual perception and spatial orientation. Environmental and genetic insults to the retina result in many blinding diseases, such as age-related macular degeneration or glaucoma. Commonly, many of these diseases are age-related suggesting that minor changes are accumulating over a life-time, with little or no contribution of strong individual effects. Specifically, this is true for genetic factors known to underlie the etiology of complex diseases including the prevalent eye diseases. In our study, we searched for effects on gene expression due to genetic variation using 311 healthy post-mortem retinal tissue samples. We show that 3,007 of the 16,766 genes investigated are regulated in the retina by genetic variations. Of these, 80 genes are potentially associated to one or more of twelve complex eye diseases or retinal traits tested. Interestingly, 10 genes appear to be involved in the development of several eye traits suggesting that cellular mechanisms may act at a common point in the disease process. Consequently, our study provides the basis to further explore retinal disease pathways and is likely to highlight target molecules for future therapeutic applications.