Automated AI labeling of optic nerve head enables insights into cross-ancestry glaucoma risk and genetic discovery in >280,000 images from UKB and CLSA

Dysfunctional one-carbon metabolism identifies vitamins B6, B9, B12, and choline as neuroprotective in glaucoma

Glaucoma, characterized by the loss of retinal ganglion cells (RGCs), is a leading cause of blindness for which there are no neuroprotective therapies. To explore observations of elevated homocysteine in glaucoma, we elevate vitreous homocysteine, which increases RGC death by 6% following ocular hypertension. Genetic association with higher homocysteine does not affect glaucoma-associated outcomes from the UK Biobank and serum homocysteine levels have no effect on glaucomatous visual field progression. This supports a hypothesis in which elevated homocysteine is a pathogenic, rather than causative, feature of glaucoma. Further exploration of homocysteine metabolism in glaucoma animal models demonstrates early and sustained dysregulation of genes involved in one-carbon metabolism and the interaction of essential cofactors and precursors (B6, B9, B12, and choline) in whole retina and optic nerve head and RGCs. Supplementing these provides neuroprotection in an acute model and prevents neurodegeneration and protects visual function in a chronic model of glaucoma.

Artificial Intelligence in Glaucoma: Advances in Diagnosis, Progression Forecasting, and Surgical Outcome Prediction

Glaucoma is a leading cause of irreversible blindness, with challenges persisting in early diagnosis, disease progression, and surgical outcome prediction. Recent advances in artificial intelligence have enabled significant progress by extracting clinically relevant patterns from structural, functional, and molecular data. This review outlines the current applications of artificial intelligence in glaucoma care, including early detection using fundus photography and OCT and disease progression prediction using deep learning architectures such as convolutional neural networks, recurrent neural networks, transformer models, generative adversarial networks, and autoencoders. Surgical outcome forecasting has been enhanced through multimodal models that integrate electronic health records and imaging data. We also highlight emerging AI applications in omics analysis, including transcriptomics and metabolomics, for biomarker discovery and individualized risk stratification. Despite these advances, key challenges remain in interpretability, integration of heterogeneous data, and the lack of personalized surgical timing guidance. Future work should focus on transparent, generalizable, and multimodal AI models, supported by large, well-curated datasets, to advance precision medicine in glaucoma.

Association between leukocyte telomere length and incident glaucoma: A prospective UK biobank study

BACKGROUND

Leukocyte telomere length (LTL) has been associated with various diseases, including age-related eye diseases such as cataract and age-related macular degeneration. However, the role of LTL in the longitudinal development of glaucoma is still unknown. Here we prospectively evaluate the association of LTL with glaucoma incidence and related traits, in the UK Biobank cohort.

METHODS

The study cohort included 419,603 participants with complete baseline data for glaucoma analyses. Multivariable Cox proportional hazards models were used to evaluate the association between LTL and the risk of glaucoma incidence, and multivariable linear regression was employed to test the association between LTL and glaucoma-related traits.

RESULTS

During a 13.58-year follow-up period, 7385 (1.76%) participants developed glaucoma. No association between LTL and incident glaucoma was found in either Model 1 (adjusted for age, sex, ethnicity and the ancestry components; HR = 1.011, 95% CI: 0.990-1.033; P = 0.311), or Model 2 (additionally adjusted for smoking status, alcohol consumption, body mass index, systolic blood pressure, education level, Townsend Deprivation Index, polygenic risk score for glaucoma, and history of diabetes and cardiovascular diseases; HR = 1.010, 95% CI: 0.988-1.032; P = 0.367). Non-significant associations were also observed for glaucoma-related traits, including the retinal nerve fibre layer, ganglion cell-inner plexiform layer, and intraocular pressure with LTL (all P-values > 0.05), but LTL was associated with a slightly increased vertical cup-to-disc ratio (P = 0.009).

CONCLUSIONS

This study suggested that LTL is not a major biomarker for incident glaucoma in the UK Biobank population. Further studies in different populations are warranted.

A scoping review of advancements in machine learning for glaucoma: current trends and future direction

Introduction

Machine learning technology has demonstrated significant potential in glaucoma research, particularly in early diagnosis, predicting disease progression, evaluating treatment responses, and developing personalized treatment strategies. The application of machine learning not only enhances the understanding of the pathological mechanism of glaucoma and optimizes the diagnostic process but also provides patients with accurate medical services.

Methods

This study aimed to describe the current state of research, highlight directions for further development, and identify potential trends for improvement. This review was conducted following the scoping review of the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) extension to showcase advancements in the application of machine learning in glaucoma research and treatment.

Results

We employed a comprehensive search strategy to retrieve literature from the Web of Science Core Collection database, ultimately including 3,581 articles in the analysis. Through data analysis, we identified current research hotspots, noted differences in researchers' attitudes and opinions, and predicted potential future development trends.

Discussion

We divided the research topics into six categories, clearly identifying "eye diseases", "retinal fundus imaging" and "risk factors" as the key terms for the development of this field. These findings signify the promising prospects of machine learning, particularly when integrated with multimodal technologies and large language models, to enhance the diagnosis and treatment of glaucoma.

Research Trends and Hotspots of Big Data in Ophthalmology: A Bibliometric Analysis and Visualization

PURPOSE

The burst of modern information has significantly promoted the development of global medicine into a new era of big data healthcare. Ophthalmology is one of the most prominent medical specialties driven by big data analytics. This study aimed to describe the development status and research hotspots of big data in ophthalmology.

METHODS

English articles and reviews related to big data in ophthalmology published from January 1, 1999, to April 30, 2024, were retrieved from the Web of Science Core Collection. The relevant information was analyzed and visualized using VOSviewer and CiteSpace software.

RESULTS

A total of 406 qualified documents were included in the analysis. The annual number of publications on big data in ophthalmology reached a rapidly increasing stage since 2019. The United States (n = 147) led in the number of publications, followed by India (n = 77) and China (n = 69). The L.V. Prasad Eye Institute in India was the most productive institution (n = 50), and Anthony Vipin Das was the most influential author with the most relevant literature (n = 45). The electronic medical records were the primary source of ophthalmic big data, and artificial intelligence served as the principal analytics tool. Diabetic retinopathy, glaucoma, and myopia are currently the main topics of interest in this field.

CONCLUSIONS

The application of big data in ophthalmology has experienced rapid growth in recent years. Big data is expected to play an increasingly significant role in shaping the future of research and clinical practice in ophthalmology.

Progress in Translating Glaucoma Genetics Into the Clinic: A Review

Precision medicine is paving the way for personalised risk assessment, and its translation into glaucoma clinics holds potential to change current management paradigms. Our understanding of glaucoma's genetic architecture has expanded in recent years, recognising both monogenic and polygenic contributions. Genetic testing within glaucoma populations can provide additional information for clinicians to support decision-making. Here, we review the evidence base for genetic variants strongly associated with glaucoma and outline a vision for translating these learnings into the clinic. Integrating clinical and genetic information will provide clinicians and patients with the strongest evidence to deliver personalised glaucoma management.

Genetic risk factors associated with ocular perfusion pressure in primary open-angle glaucoma

BACKGROUND

Primary open-angle glaucoma (POAG) is the leading cause of irreversible vision loss. However, its genetic risk factors, such as the vascular hypothesis of POAG, remain unclear. Here, we aimed to explore the genetic associations between mean ocular perfusion pressure (MOPP) and POAG. We performed genome-wide analysis with gene-based analysis from the UK Biobank (N = 459,195), which includes genetic data and ocular phenotypes. Bidirectional two-sample Mendelian randomisation (MR), multivariable MR, and mediation analysis were conducted using summary statistics from a previous meta-analysis of genome-wide association studies (N = 216,257).

RESULTS

CEP85L, GRIA4, GRIN2A, LRFN5, MAGI1, POU6F2, RBFOX1, RBMS1, RBMS3, RBPMS, TRHDE, TUBB3, ZFHX3, and ZMAT4 were significantly correlated with various ocular phenotypes. Furthermore, POAG shared strong genetic associations with corneal resistance factor (CRF), intraocular pressure (IOP), refractive error (RE), and MOPP but none with corneal hysteresis (CH). Univariable MR showed a negative causal effect of CH, CRF, and MOPP and a positive causal effect of IOP on POAG occurrence. In multivariable MR, MOPP exhibited a direct causal effect on POAG, which was supported by the mediation analysis results.

CONCLUSIONS

We successfully determined 14 genetic loci related to CH, CRF, IOP, RE, and MOPP. In univariable and multivaribale MR analyses, a causal effect of MOPP on POAG were observed. In addition, the mediation analysis supported that MOPP exerted direct and indirect causal effects on POAG. This finding indicates that MOPP may serve as a potential causal factor in POAG, providing valuable insights into the pathophysiology of POAG as vascular theory.

Genome-wide association studies, Polygenic Risk Scores and Mendelian randomisation: an overview of common genetic epidemiology methods for ophthalmic clinicians

Genetic information will be increasingly integrated into clinical eye care within the current generation of ophthalmologists. For monogenic diseases such as retinoblastoma, genetic studies have been relatively straightforward as these conditions result from pathogenic variants in a single gene resulting in large physiological effects. However, most eye diseases result from the cumulative effects of multiple genetic variants and environmental factors. In such diseases, because each variant usually has an individually small effect, genetic studies for complex diseases are comparatively more challenging. This article aims to provide an overview of three genetic epidemiology methods for polygenic (or complex) diseases: genome-wide association studies (GWAS), Polygenic Risk Scores (PRS) and Mendelian randomisation (MR). A GWAS systematically conducts association analyses of a trait of interest against millions of genetic variants, usually in the form of single nucleotide polymorphisms, across the genome. GWAS findings can then be used for PRS construction and MR analyses. To construct a PRS, the cumulative effect of many genetic variants associated with a trait from a prior GWAS is calculated and taken as a quantitative representation of an individual's genetic risk of a complex disease. MR studies analyse an outcome measure against the genetic variants of an exposure, and are particularly useful in investigating causal relations between two traits where randomised controlled trials are not possible or ethical. In addition to explaining the principles of these three genetic epidemiology concepts, this article provides a minimally technical description of their basic methodology that is accessible to the non-expert reader.

Applications of Machine Learning-Driven Molecular Models for Advancing Ophthalmic Precision Medicine

Ophthalmic diseases such as glaucoma, age-related macular degeneration (ARMD), and optic neuritis involve complex molecular and cellular disruptions that challenge current diagnostic and therapeutic approaches. Advanced artificial intelligence (AI) and machine learning (ML) models offer a novel lens to analyze these diseases by integrating diverse datasets, identifying patterns, and enabling precision medicine strategies. Over the past decade, applications of AI in ophthalmology have expanded from imaging-based diagnostics to molecular-level modeling, bridging critical gaps in understanding disease mechanisms. This paper systematically reviews the application of AI-driven methods, including reinforcement learning (RL), graph neural networks (GNNs), Bayesian inference, and generative adversarial networks (GANs), in the context of these ophthalmic conditions. RL models simulate transcription factor dynamics in hypoxic or inflammatory environments, offering insights into disrupted molecular pathways. GNNs map intricate molecular networks within affected tissues, identifying key inflammatory or degenerative drivers. Bayesian inference provides probabilistic models for predicting disease progression and response to therapies, while GANs generate synthetic datasets to explore therapeutic interventions. By contextualizing these AI tools within the broader framework of ophthalmic disease management, this review highlights their potential to transform diagnostic precision and therapeutic outcomes. Ultimately, this work underscores the need for continued interdisciplinary collaboration to harness AI's potential in advancing the field of ophthalmology and improving patient care.

Structural framework to address variant-gene relationship in primary open-angle glaucoma

Primary open-angle glaucoma (POAG) is a complex, multifactorial disease leading to progressive optic neuropathy and irreversible vision loss. Genome-Wide Association Studies (GWAS) have significantly advanced our understanding of the genetic loci associated with POAG. Expanding on these findings, Exome-Wide Association Studies (ExWAS) refine the genetic landscape by identifying rare coding variants with potential functional relevance. Post-GWAS in silico analyses, including fine-mapping, gene-based association testing, and pathway analysis, offer insights into target genes and biological mechanisms underlying POAG. This review aims to provide a comprehensive roadmap for the post-GWAS characterization of POAG genes. We integrate current knowledge from GWAS, ExWAS, and post-GWAS analyses, highlighting key genetic variants and pathways implicated in POAG. Recent advancements in genomics, such as ATAC-seq, CUT&RUN, and Hi-C, are crucial for identifying disease-relevant gene regulatory elements by profiling chromatin accessibility, histone modifications, and three-dimensional chromatin architecture. These approaches help pinpoint regulatory elements that influence gene expression in POAG. Expression Quantitative Trait Loci (eQTL) analysis and Transcriptome-Wide Association Studies (TWAS) elucidate the impact of these elements on gene expression and disease risk, while functional validations like enhancer reporter assays confirm their relevance. The integration of high-resolution genomics with functional assays and the characterization of genes in vivo using animal models provides a robust framework for unraveling the complex genetic architecture of POAG. This roadmap is essential for advancing our understanding and identification of genes and regulatory networks involved in POAG pathogenesis.

Predictive Power of Polygenic Risk Scores for Intraocular Pressure or Vertical Cup-Disc Ratio

Importance

Early detection of glaucoma is essential to timely monitoring and treatment, and primary open-angle glaucoma risk can be assessed by measuring intraocular pressure (IOP) or optic nerve head vertical cup-disc ratio (VCDR). Polygenic risk scores (PRSs) could provide a link between genetic effects estimated from genome-wide association studies (GWASs) and clinical applications to provide estimates of an individual's genetic risk by combining many identified variants into a score.

Objective

To construct IOP and VCDR PRSs with clinically relevant predictive power.

Design, Setting, and Participants

This genetic association study evaluated the PRSs for 6959 of 51 338 individuals in the Canadian Longitudinal Study on Aging (CLSA; 2010 to 2015 with data from 11 centers in Canada) and 4960 of 5107 individuals the community-based Busselton Healthy Aging Study (BHAS; 2010 to 2015 in Busselton, Western Australia) with an artificial intelligence grading approach used to obtain precise VCDR estimates for the CLSA dataset. Data for approximately 500 000 individuals in UK Biobank from 2006 to 2010 were used to validate the power of the PRS. Data were analyzed from June to November 2023.

Main Outcomes and Measures

IOP and VCDR PRSs and phenotypic variance (R2) explained by each PRS.

Results

Participants in CLSA were aged 45 to 85 years; those in BHAS, 46 to 64 years; and those in UK Biobank, 40 to 69 years. The VCDR PRS explained 22.0% (95% CI, 20.1-23.9) and 19.7% (95% CI, 16.3-23.3) of the phenotypic variance in VCDR in CLSA and BHAS, respectively, while the IOP PRS explained 12.9% (95% CI, 11.3-14.6) and 9.6% (95% CI, 8.1-11.2) of phenotypic variance in CLSA and BHAS IOP measurements. The VCDR PRS variance explained 5.2% (95% CI, 3.6-7.1), 12.1% (95% CI, 7.5-17.5), and 14.3% (95% CI, 9.3-19.9), and the IOP PRS variance explained 2.3% (95% CI, 1.5-3.3), 3.2% (95% CI, 1.3-5.8), and 7.5% (95% CI, 6.2-8.9) (P < .001) across African, East Asian, and South Asian populations, respectively.

Conclusions and Relevance

VCDR and IOP PRSs derived using a large recently published multitrait GWAS exhibited validity across independent cohorts. The findings suggest that an IOP PRS has the potential to identify individuals who may benefit from more intensive IOP-lowering treatments, which could be crucial in managing glaucoma risk more effectively. Individuals with a high VCDR PRS may be at risk of developing glaucoma even if their IOP measures fall within the normal range, suggesting that these PRSs could help in early detection and intervention, particularly among those who might otherwise be considered at low risk based on IOP alone.

Genome-wide meta-analysis identifies 22 loci for normal tension glaucoma with significant overlap with high tension glaucoma

Primary open-angle glaucoma typically presents as two subtypes. This study aimed to elucidate the shared and distinct genetic architectures of normal-tension (NTG) and high-tension glaucoma (HTG), motivated by the need to develop intraocular pressure (IOP)-independent drug targets for the disease. We conducted a comprehensive multi-ethnic meta-analysis, prioritized variants based on functional annotation, and explored drug-gene interactions. We further assessed the genetic overlap between NTG and HTG using pairwise GWAS analysis. We identified 22 risk loci associated with NTG, 17 of which have not previously been reported for NTG. Two loci, BMP4 and TBKBP1, have not previously been associated with glaucoma at the genome-wide significance level. Our results indicate that while there is a significant overlap in risk loci between tension subtypes, the magnitude of the effect tends to be lower in NTG compared to HTG, particularly for IOP-related loci. Additionally, we identified a potential role for biologic immunomodulatory treatments as neuroprotective agents.

The AI revolution in glaucoma: Bridging challenges with opportunities

Recent advancements in artificial intelligence (AI) herald transformative potentials for reshaping glaucoma clinical management, improving screening efficacy, sharpening diagnosis precision, and refining the detection of disease progression. However, incorporating AI into healthcare usages faces significant hurdles in terms of developing algorithms and putting them into practice. When creating algorithms, issues arise due to the intensive effort required to label data, inconsistent diagnostic standards, and a lack of thorough testing, which often limits the algorithms' widespread applicability. Additionally, the "black box" nature of AI algorithms may cause doctors to be wary or skeptical. When it comes to using these tools, challenges include dealing with lower-quality images in real situations and the systems' limited ability to work well with diverse ethnic groups and different diagnostic equipment. Looking ahead, new developments aim to protect data privacy through federated learning paradigms, improving algorithm generalizability by diversifying input data modalities, and augmenting datasets with synthetic imagery. The integration of smartphones appears promising for using AI algorithms in both clinical and non-clinical settings. Furthermore, bringing in large language models (LLMs) to act as interactive tool in medicine may signify a significant change in how healthcare will be delivered in the future. By navigating through these challenges and leveraging on these as opportunities, the field of glaucoma AI will not only have improved algorithmic accuracy and optimized data integration but also a paradigmatic shift towards enhanced clinical acceptance and a transformative improvement in glaucoma care.

Genetic Risk Stratification of Primary Open-Angle Glaucoma in Japanese Individuals

PURPOSE

To assess the impact of genetic risk estimation for primary open-angle glaucoma (POAG) in Japanese individuals.

DESIGN

Cross-sectional analysis.

PARTICIPANTS

Genetic risk scores (GRSs) were constructed based on a genome-wide association study (GWAS) of POAG in Japanese people. A total of 3625 Japanese individuals, including 1191 patients and 2434 controls (Japanese Tohoku), were used for the model selection. We also evaluated the discriminative accuracy of constructed GRSs in a dataset comprising 1034 patients and 1147 controls (the Japan Glaucoma Society Omics Group [JGS-OG] and the Genomic Research Committee of the Japanese Ophthalmological Society [GRC-JOS]) and 1900 participants from a population-based study (Hisayama Study).

METHODS

We evaluated 2 types of GRSs: polygenic risk scores using the pruning and thresholding procedure and a GRS using variants associated with POAG in the GWAS of the International Glaucoma Genetics Consortium (IGGC). We selected the model with the highest areas under the receiver operating characteristic curve (AUC). In the population-based study, we evaluated the correlations between GRS and ocular measurements.

MAIN OUTCOME MEASURE

Proportion of patients with POAG after stratification according to the GRS.

RESULTS

We found that a GRS using 98 variants, which showed genome-wide significance in the IGGC, showed the best discriminative accuracy (AUC, 0.65). In the Japanese Tohoku, the proportion of patients with POAG in the top 10% individuals was significantly higher than that in the lowest 10% (odds ratio [OR], 6.15; 95% confidence interval [CI], 4.35-8.71). In the JGS-OG and GRC-JOS, we confirmed similar impact of POAG GRS (AUC, 0.64; OR [top vs. bottom decile], 5.81; 95% CI, 3.79-9.01). In the population-based study, POAG prevalence was significantly higher in the top 20% individuals of the GRS compared with the bottom 20% (9.2% vs. 5.0%). However, the discriminative accuracy was low (AUC, 0.56). The POAG GRS was correlated positively with intraocular pressure (r = 0.08: P = 4.0 × 10-4) and vertical cup-to-disc ratio (r = 0.11; P = 4.0 × 10-6).

CONCLUSIONS

The GRS showed moderate discriminative accuracy for POAG in the Japanese population. However, risk stratification in the general population showed relatively weak discriminative performance.

FINANCIAL DISCLOSURE(S)

Proprietary or commercial disclosure may be found in the Footnotes and Disclosures at the end of this article.

GWAS-by-subtraction reveals an IOP-independent component of primary open angle glaucoma

The etiology of primary open angle glaucoma is constituted by both intraocular pressure-dependent and intraocular pressure-independent mechanisms. However, GWASs of traits affecting primary open angle glaucoma through mechanisms independent of intraocular pressure remains limited. Here, we address this gap by subtracting the genetic effects of a GWAS for intraocular pressure from a GWAS for primary open angle glaucoma to reveal the genetic contribution to primary open angle glaucoma via intraocular pressure-independent mechanisms. Seventeen independent genome-wide significant SNPs were associated with the intraocular pressure-independent component of primary open angle glaucoma. Of these, 7 are located outside known normal tension glaucoma loci, 11 are located outside known intraocular pressure loci, and 2 are novel primary open angle glaucoma loci. The intraocular pressure-independent genetic component of primary open angle glaucoma is associated with glaucoma endophenotypes, while the intraocular pressure-dependent component is associated with blood pressure and vascular permeability. A genetic risk score for the intraocular pressure-independent component of primary open angle glaucoma is associated with 26 different retinal micro-vascular features, which contrasts with the genetic risk score for the intraocular pressure-dependent component. Increased understanding of these intraocular pressure-dependent and intraocular pressure-independent components provides insights into the pathogenesis of glaucoma.

Highly Accurate and Precise Automated Cup-to-Disc Ratio Quantification for Glaucoma Screening

Objective

An enlarged cup-to-disc ratio (CDR) is a hallmark of glaucomatous optic neuropathy. Manual assessment of the CDR may be less accurate and more time-consuming than automated methods. Here, we sought to develop and validate a deep learning-based algorithm to automatically determine the CDR from fundus images.

Design

Algorithm development for estimating CDR using fundus data from a population-based observational study.

Participants

A total of 181 768 fundus images from the United Kingdom Biobank (UKBB), Drishti_GS, and EyePACS.

Methods

FastAI and PyTorch libraries were used to train a convolutional neural network-based model on fundus images from the UKBB. Models were constructed to determine image gradability (classification analysis) as well as to estimate CDR (regression analysis). The best-performing model was then validated for use in glaucoma screening using a multiethnic dataset from EyePACS and Drishti_GS.

Main Outcome Measures

The area under the receiver operating characteristic curve and coefficient of determination.

Results

Our gradability model vgg19_batch normalization (bn) achieved an accuracy of 97.13% on a validation set of 16 045 images, with 99.26% precision and area under the receiver operating characteristic curve of 96.56%. Using regression analysis, our best-performing model (trained on the vgg19_bn architecture) attained a coefficient of determination of 0.8514 (95% confidence interval [CI]: 0.8459-0.8568), while the mean squared error was 0.0050 (95% CI: 0.0048-0.0051) and mean absolute error was 0.0551 (95% CI: 0.0543-0.0559) on a validation set of 12 183 images for determining CDR. The regression point was converted into classification metrics using a tolerance of 0.2 for 20 classes; the classification metrics achieved an accuracy of 99.20%. The EyePACS dataset (98 172 healthy, 3270 glaucoma) was then used to externally validate the model for glaucoma classification, with an accuracy, sensitivity, and specificity of 82.49%, 72.02%, and 82.83%, respectively.

Conclusions

Our models were precise in determining image gradability and estimating CDR. Although our artificial intelligence-derived CDR estimates achieve high accuracy, the CDR threshold for glaucoma screening will vary depending on other clinical parameters.

Financial Disclosures

Proprietary or commercial disclosure may be found in the Footnotes and Disclosures at the end of this article.

JASPER: Fast, powerful, multitrait association testing in structured samples gives insight on pleiotropy in gene expression

Joint association analysis of multiple traits with multiple genetic variants can provide insight into genetic architecture and pleiotropy, improve trait prediction, and increase power for detecting association. Furthermore, some traits are naturally high-dimensional, e.g., images, networks, or longitudinally measured traits. Assessing significance for multitrait genetic association can be challenging, especially when the sample has population sub-structure and/or related individuals. Failure to adequately adjust for sample structure can lead to power loss and inflated type 1 error, and commonly used methods for assessing significance can work poorly with a large number of traits or be computationally slow. We developed JASPER, a fast, powerful, robust method for assessing significance of multitrait association with a set of genetic variants, in samples that have population sub-structure, admixture, and/or relatedness. In simulations, JASPER has higher power, better type 1 error control, and faster computation than existing methods, with the power and speed advantage of JASPER increasing with the number of traits. JASPER is potentially applicable to a wide range of association testing applications, including for multiple disease traits, expression traits, image-derived traits, and microbiome abundances. It allows for covariates, ascertainment, and rare variants and is robust to phenotype model misspecification. We apply JASPER to analyze gene expression in the Framingham Heart Study, where, compared to alternative approaches, JASPER finds more significant associations, including several that indicate pleiotropic effects, most of which replicate previous results, while others have not previously been reported. Our results demonstrate the promise of JASPER for powerful multitrait analysis in structured samples.

Polygenic Risk Scores Driving Clinical Change in Glaucoma

Glaucoma is a clinically heterogeneous disease and the world's leading cause of irreversible blindness. Therapeutic intervention can prevent blindness but relies on early diagnosis, and current clinical risk factors are limited in their ability to predict who will develop sight-threatening glaucoma. The high heritability of glaucoma makes it an ideal substrate for genetic risk prediction, with the bulk of risk being polygenic in nature. Here, we summarize the foundations of glaucoma genetic risk, the development of polygenic risk prediction instruments, and emerging opportunities for genetic risk stratification. Although challenges remain, genetic risk stratification will significantly improve glaucoma screening and management.

Unsupervised representation learning on high-dimensional clinical data improves genomic discovery and prediction

Although high-dimensional clinical data (HDCD) are increasingly available in biobank-scale datasets, their use for genetic discovery remains challenging. Here we introduce an unsupervised deep learning model, Representation Learning for Genetic Discovery on Low-Dimensional Embeddings (REGLE), for discovering associations between genetic variants and HDCD. REGLE leverages variational autoencoders to compute nonlinear disentangled embeddings of HDCD, which become the inputs to genome-wide association studies (GWAS). REGLE can uncover features not captured by existing expert-defined features and enables the creation of accurate disease-specific polygenic risk scores (PRSs) in datasets with very few labeled data. We apply REGLE to perform GWAS on respiratory and circulatory HDCD-spirograms measuring lung function and photoplethysmograms measuring blood volume changes. REGLE replicates known loci while identifying others not previously detected. REGLE are predictive of overall survival, and PRSs constructed from REGLE loci improve disease prediction across multiple biobanks. Overall, REGLE contain clinically relevant information beyond that captured by existing expert-defined features, leading to improved genetic discovery and disease prediction.

Eye-brain connections revealed by multimodal retinal and brain imaging genetics

The retina, an anatomical extension of the brain, forms physiological connections with the visual cortex of the brain. Although retinal structures offer a unique opportunity to assess brain disorders, their relationship to brain structure and function is not well understood. In this study, we conducted a systematic cross-organ genetic architecture analysis of eye-brain connections using retinal and brain imaging endophenotypes. We identified novel phenotypic and genetic links between retinal imaging biomarkers and brain structure and function measures from multimodal magnetic resonance imaging (MRI), with many associations involving the primary visual cortex and visual pathways. Retinal imaging biomarkers shared genetic influences with brain diseases and complex traits in 65 genomic regions, with 18 showing genetic overlap with brain MRI traits. Mendelian randomization suggests bidirectional genetic causal links between retinal structures and neurological and neuropsychiatric disorders, such as Alzheimer's disease. Overall, our findings reveal the genetic basis for eye-brain connections, suggesting that retinal images can help uncover genetic risk factors for brain disorders and disease-related changes in intracranial structure and function.

Independent Effects of Blood Pressure on Intraocular Pressure and Retinal Ganglion Cell Degeneration: A Mendelian Randomization Study

Purpose

To investigate the causal effect of elevated blood pressure on primary open-angle glaucoma (POAG) and POAG endophenotypes.

Methods

Two-sample Mendelian randomization (MR) was performed to investigate the causal effect of elevated systolic blood pressure (SBP) (N = 757,601) and diastolic blood pressure (DBP) (N = 757,601) on intraocular pressure (IOP) (N = 139,555), macular retinal nerve fiber layer (mRNFL) thickness (N = 33,129), ganglion cell complex (GCC) thickness (N = 33,129), vertical cup-to-disc ratio (VCDR) (N = 111,724), and POAG liability (Ncases = 16,677, Ncontrols = 199,580). The primary analysis was conducted using the inverse-variance weighted approach. Sensitivity analyses were performed to investigate robustness to horizontal pleiotropy, winner's curse, and collider bias. Multivariable MR was performed to investigate whether any effect of blood pressure on retinal ganglion cell degeneration was mediated through increased IOP.

Results

Increased genetically predicted SBP and DBP associated with an increase in IOP (0.17 mm Hg [95% CI = 0.11 to 0.24] per 10 mm Hg higher SBP, P = 5.18 × 10-7, and 0.17 mm Hg [95% CI = 0.05 to 0.28 mm Hg] per 10 mm Hg higher DBP, P = 0.004). Increased genetically predicted SBP associated with a thinner GCC (0.04 µm [95% CI = -0.07 to -0.01 µm], P = 0.018) and a thinner mRNFL (0.04 µm [95% CI = -0.07 to -0.01 µm], P = 0.004), an effect that arises independently of IOP according to our mediation analysis. Neither SBP nor DBP associated with VCDR or POAG liability.

Conclusions

These findings support a causal effect of elevated blood pressure on retinal ganglion cell degeneration that does not require intermediary changes in IOP. Targeted blood pressure control may help preserve vision by lowering IOP and, independently, by preventing retinal ganglion cell degeneration, including in individuals with a normal IOP.

Artificial intelligence for glaucoma: state of the art and future perspectives

PURPOSE OF REVIEW

To address the current role of artificial intelligence (AI) in the field of glaucoma.

RECENT FINDINGS

Current deep learning (DL) models concerning glaucoma diagnosis have shown consistently improving diagnostic capabilities, primarily based on color fundus photography and optical coherence tomography, but also with multimodal strategies. Recent models have also suggested that AI may be helpful in detecting and estimating visual field progression from different input data. Moreover, with the emergence of newer DL architectures and synthetic data, challenges such as model generalizability and explainability have begun to be tackled.

SUMMARY

While some challenges remain before AI is routinely employed in clinical practice, new research has expanded the range in which it can be used in the context of glaucoma management and underlined the relevance of this research avenue.

Lessons From The Glaucoma Foundation Think Tank 2023: A Patient-Centric Approach to Glaucoma

PRCIS

The main takeaways also included that BIG DATA repositories and AI are important combinatory tools to foster novel strategies to prevent and stabilize glaucoma and, in the future, recover vision loss from the disease.

PURPOSE

To summarize the main topics discussed during the 28th Annual Glaucoma Foundation Think Tank Meeting "A Patient-Centric Approach to Glaucoma" held in New York on June 9 and 10, 2023.

METHODS

The highlights of the sessions on BIG DATA, genetics, modifiable lifestyle risk factors, female sex hormones, and neuroprotection in the field of primary open angle glaucoma (POAG) were summarized.

RESULTS

The researchers discussed the importance of BIG DATA repositories available at national and international levels for POAG research, including the United Kingdom Biobank. Combining genotyped large cohorts worldwide, facilitated by artificial intelligence (AI) and machine-learning approaches, led to the milestone discovery of 312 genome-wide significant disease loci for POAG. While these loci could be combined into a polygenic risk score with clinical utility, Think Tank meeting participants also provided analytical epidemiological evidence that behavioral risk factors modify POAG polygenetic risk, citing specific examples related to caffeine and alcohol use. The impact of female sex hormones on POAG pathophysiology was discussed, as was neuroprotection and the potential use of AI to help mitigate specific challenges faced in clinical trials and speed approval of neuroprotective agents.

CONCLUSIONS

The experts agreed on the importance of genetics in defining individual POAG risk and highlighted the additional crucial role of lifestyle, gender, blood pressure, and vascular risk factors. The main takeaways also included that BIG DATA repositories and AI are important combinatory tools to foster novel strategies to prevent and stabilize glaucoma and, in the future, recover vision loss from the disease.

Integrating genetic regulation and single-cell expression with GWAS prioritizes causal genes and cell types for glaucoma

Primary open-angle glaucoma (POAG), characterized by retinal ganglion cell death, is a leading cause of irreversible blindness worldwide. However, its molecular and cellular causes are not well understood. Elevated intraocular pressure (IOP) is a major risk factor, but many patients have normal IOP. Colocalization and Mendelian randomization analysis of >240 POAG and IOP genome-wide association study (GWAS) loci and overlapping expression and splicing quantitative trait loci (e/sQTLs) in 49 GTEx tissues and retina prioritizes causal genes for 60% of loci. These genes are enriched in pathways implicated in extracellular matrix organization, cell adhesion, and vascular development. Analysis of single-nucleus RNA-seq of glaucoma-relevant eye tissues reveals that the POAG and IOP colocalizing genes and genome-wide associations are enriched in specific cell types in the aqueous outflow pathways, retina, optic nerve head, peripapillary sclera, and choroid. This study nominates IOP-dependent and independent regulatory mechanisms, genes, and cell types that may contribute to POAG pathogenesis.

JASPER: fast, powerful, multitrait association testing in structured samples gives insight on pleiotropy in gene expression

Joint association analysis of multiple traits with multiple genetic variants can provide insight into genetic architecture and pleiotropy, improve trait prediction and increase power for detecting association. Furthermore, some traits are naturally high-dimensional, e.g., images, networks or longitudinally measured traits. Assessing significance for multitrait genetic association can be challenging, especially when the sample has population sub-structure and/or related individuals. Failure to adequately adjust for sample structure can lead to power loss and inflated type 1 error, and commonly used methods for assessing significance can work poorly with a large number of traits or be computationally slow. We developed JASPER, a fast, powerful, robust method for assessing significance of multitrait association with a set of genetic variants, in samples that have population sub-structure, admixture and/or relatedness. In simulations, JASPER has higher power, better type 1 error control, and faster computation than existing methods, with the power and speed advantage of JASPER increasing with the number of traits. JASPER is potentially applicable to a wide range of association testing applications, including for multiple disease traits, expression traits, image-derived traits and microbiome abundances. It allows for covariates, ascertainment and rare variants and is robust to phenotype model misspecification. We apply JASPER to analyze gene expression in the Framingham Heart Study, where, compared to alternative approaches, JASPER finds more significant associations, including several that indicate pleiotropic effects, some of which replicate previous results, while others have not previously been reported. Our results demonstrate the promise of JASPER for powerful multitrait analysis in structured samples.

WITHDRAWN: Genome-wide risk prediction of primary open-angle glaucoma across multiple ancestries

This manuscript has been withdrawn by medRxiv following a formal request by the QIMR Berghofer Medical Research Institute Research Integrity Office owing to lack of author consent.

Towards modifying the genetic predisposition for glaucoma: An overview of the contribution and interaction of genetic and environmental factors

Glaucoma, the leading cause of irreversible blindness worldwide, is a complex human disease, with both genetic and environmental determinants. The availability of large-scale, population-based cohorts and biobanks, combining genotyping and detailed phenotyping, has greatly accelerated research into the aetiology of glaucoma in recent years. Hypothesis-free genome-wide association studies have furthered our understanding of the complex genetic architecture underpinning the disease, while epidemiological studies have provided advances in the identification and characterisation of environmental risk factors. It is increasingly recognised that the combined effects of genetic and environmental factors may confer a disease risk that reflects a departure from the simple additive effect of the two. These gene-environment interactions have been implicated in a host of complex human diseases, including glaucoma, and have several important diagnostic and therapeutic implications for future clinical practice. Importantly, the ability to modify the risk associated with a particular genetic makeup promises to lead to personalised recommendations for glaucoma prevention, as well as novel treatment approaches in years to come. Here we provide an overview of genetic and environmental risk factors for glaucoma, as well as reviewing the evidence and discussing the implications of gene-environment interactions for the disease.

The Association of Physical Activity with Glaucoma and Related Traits in the UK Biobank

PURPOSE

To examine the association of physical activity (PA) with glaucoma and related traits, to assess whether genetic predisposition to glaucoma modified these associations, and to probe causal relationships using Mendelian randomization (MR).

DESIGN

Cross-sectional observational and gene-environment interaction analyses in the UK Biobank. Two-sample MR experiments using summary statistics from large genetic consortia.

PARTICIPANTS

UK Biobank participants with data on self-reported or accelerometer-derived PA and intraocular pressure (IOP; n = 94 206 and n = 27 777, respectively), macular inner retinal OCT measurements (n = 36 274 and n = 9991, respectively), and glaucoma status (n = 86 803 and n = 23 556, respectively).

METHODS

We evaluated multivariable-adjusted associations of self-reported (International Physical Activity Questionnaire) and accelerometer-derived PA with IOP and macular inner retinal OCT parameters using linear regression and with glaucoma status using logistic regression. For all outcomes, we examined gene-PA interactions using a polygenic risk score (PRS) that combined the effects of 2673 genetic variants associated with glaucoma.

MAIN OUTCOME MEASURES

Intraocular pressure, macular retinal nerve fiber layer (mRNFL) thickness, macular ganglion cell-inner plexiform layer (mGCIPL) thickness, and glaucoma status.

RESULTS

In multivariable-adjusted regression models, we found no association of PA level or time spent in PA with glaucoma status. Higher overall levels and greater time spent in higher levels of both self-reported and accelerometer-derived PA were associated positively with thicker mGCIPL (P < 0.001 for trend for each). Compared with the lowest quartile of PA, participants in the highest quartiles of accelerometer-derived moderate- and vigorous-intensity PA showed a thicker mGCIPL by +0.57 μm (P < 0.001) and +0.42 μm (P = 0.005). No association was found with mRNFL thickness. High overall level of self-reported PA was associated with a modestly higher IOP of +0.08 mmHg (P = 0.01), but this was not replicated in the accelerometry data. No associations were modified by a glaucoma PRS, and MR analyses did not support a causal relationship between PA and any glaucoma-related outcome.

CONCLUSIONS

Higher overall PA level and greater time spent in moderate and vigorous PA were not associated with glaucoma status but were associated with thicker mGCIPL. Associations with IOP were modest and inconsistent. Despite the well-documented acute reduction in IOP after PA, we found no evidence that high levels of habitual PA are associated with glaucoma status or IOP in the general population.

FINANCIAL DISCLOSURE(S)

Proprietary or commercial disclosure may be found in the Footnotes and Disclosures at the end of this article.

Unsupervised representation learning improves genomic discovery and risk prediction for respiratory and circulatory functions and diseases

High-dimensional clinical data are becoming more accessible in biobank-scale datasets. However, effectively utilizing high-dimensional clinical data for genetic discovery remains challenging. Here we introduce a general deep learning-based framework, REpresentation learning for Genetic discovery on Low-dimensional Embeddings (REGLE), for discovering associations between genetic variants and high-dimensional clinical data. REGLE uses convolutional variational autoencoders to compute a non-linear, low-dimensional, disentangled embedding of the data with highly heritable individual components. REGLE can incorporate expert-defined or clinical features and provides a framework to create accurate disease-specific polygenic risk scores (PRS) in datasets which have minimal expert phenotyping. We apply REGLE to both respiratory and circulatory systems: spirograms which measure lung function and photoplethysmograms (PPG) which measure blood volume changes. Genome-wide association studies on REGLE embeddings identify more genome-wide significant loci than existing methods and replicate known loci for both spirograms and PPG, demonstrating the generality of the framework. Furthermore, these embeddings are associated with overall survival. Finally, we construct a set of PRSs that improve predictive performance of asthma, chronic obstructive pulmonary disease, hypertension, and systolic blood pressure in multiple biobanks. Thus, REGLE embeddings can quantify clinically relevant features that are not currently captured in a standardized or automated way.

Study profile: the Genetics of Glaucoma Study

PURPOSE

Glaucoma, a major cause of irreversible blindness, is a highly heritable human disease. Currently, the majority of the risk genes for glaucoma are unknown. We established the Genetics of Glaucoma Study (GOGS) to identify disease genes and improve genetic prediction of glaucoma risk and response to treatment.

PARTICIPANTS

More than 5700 participants with glaucoma or a family history of glaucoma were recruited through a media campaign and the Australian Government healthcare service provider, Services Australia, making GOGS one of the largest genetic studies of glaucoma globally. The mean age of the participants was 65.30±9.36 years, and 62% were female. Participants completed a questionnaire obtaining information about their glaucoma-related medical history such as family history, glaucoma status and subtypes, surgical procedures, and prescriptions. The questionnaire also obtained information about other eye and systemic diseases. Approximately 80% of the participants provided a DNA sample and ~70% consented to data linkage to their Australian Government Medicare and Pharmaceutical Benefits Scheme schedules.

FINDINGS TO DATE

4336 GOGS participants reported that an optometrist or ophthalmologist has diagnosed them with glaucoma and 3639 participants reported having a family history of glaucoma. The vast majority of the participants (N=4393) had used at least one glaucoma-related medication; latanoprost was the most commonly prescribed drug (54% of the participants who had a glaucoma prescription). A subset of the participants reported a surgical treatment for glaucoma including a laser surgery in 2008 participants and a non-laser operation in 803 participants. Several comorbid eye and systemic diseases were also observed; the most common reports were ocular hypertension (53% of the participants), cataract (48%), hypertension (40%), nearsightedness (31%), astigmatism (22%), farsightedness (16%), diabetes (12%), sleep apnoea (11%) and migraines (10%).

FUTURE PLANS

GOGS will contribute to the global gene-mapping efforts as one of the largest genetic studies for glaucoma. We will also use GOGS to develop or validate genetic risk prediction models to stratify glaucoma risk, particularly in individuals with a family history of glaucoma, and to predict clinical outcomes (eg, which medication works better for an individual and whether glaucoma surgery is required). GOGS will also help us answer various research questions about genetic overlap and causal relationships between glaucoma and its comorbidities.

Integrating genetics and metabolomics from multi-ethnic and multi-fluid data reveals putative mechanisms for age-related macular degeneration

Age-related macular degeneration (AMD) is a leading cause of blindness in older adults. Investigating shared genetic components between metabolites and AMD can enhance our understanding of its pathogenesis. We conduct metabolite genome-wide association studies (mGWASs) using multi-ethnic genetic and metabolomic data from up to 28,000 participants. With bidirectional Mendelian randomization analysis involving 16,144 advanced AMD cases and 17,832 controls, we identify 108 putatively causal relationships between plasma metabolites and advanced AMD. These metabolites are enriched in glycerophospholipid metabolism, lysophospholipid, triradylcglycerol, and long chain polyunsaturated fatty acid pathways. Bayesian genetic colocalization analysis and a customized metabolome-wide association approach prioritize putative causal AMD-associated metabolites. We find limited evidence linking urine metabolites to AMD risk. Our study emphasizes the contribution of plasma metabolites, particularly lipid-related pathways and genes, to AMD risk and uncovers numerous putative causal associations between metabolites and AMD risk.

Large-scale multitrait genome-wide association analyses identify hundreds of glaucoma risk loci

Glaucoma, a leading cause of irreversible blindness, is a highly heritable human disease. Previous genome-wide association studies have identified over 100 loci for the most common form, primary open-angle glaucoma. Two key glaucoma-associated traits also show high heritability: intraocular pressure and optic nerve head excavation damage quantified as the vertical cup-to-disc ratio. Here, since much of glaucoma heritability remains unexplained, we conducted a large-scale multitrait genome-wide association study in participants of European ancestry combining primary open-angle glaucoma and its two associated traits (total sample size over 600,000) to substantially improve genetic discovery power (263 loci). We further increased our power by then employing a multiancestry approach, which increased the number of independent risk loci to 312, with the vast majority replicating in a large independent cohort from 23andMe, Inc. (total sample size over 2.8 million; 296 loci replicated at P < 0.05, 240 after Bonferroni correction). Leveraging multiomics datasets, we identified many potential druggable genes, including neuro-protection targets likely to act via the optic nerve, a key advance for glaucoma because all existing drugs only target intraocular pressure. We further used Mendelian randomization and genetic correlation-based approaches to identify novel links to other complex traits, including immune-related diseases such as multiple sclerosis and systemic lupus erythematosus.

Disentangling the genetic overlap and causal relationships between primary open-angle glaucoma, brain morphology and four major neurodegenerative disorders

BACKGROUND

Primary open-angle glaucoma (POAG) is an optic neuropathy characterized by progressive degeneration of the optic nerve that leads to irreversible visual impairment. Multiple epidemiological studies suggest an association between POAG and major neurodegenerative disorders (Alzheimer's disease, amyotrophic lateral sclerosis, frontotemporal dementia, and Parkinson's disease). However, the nature of the overlap between neurodegenerative disorders, brain morphology and glaucoma remains inconclusive.

METHOD

In this study, we performed a comprehensive assessment of the genetic and causal relationship between POAG and neurodegenerative disorders, leveraging genome-wide association data from studies of magnetic resonance imaging of the brain, POAG, and four major neurodegenerative disorders.

FINDINGS

This study found a genetic overlap and causal relationship between POAG and its related phenotypes (i.e., intraocular pressure and optic nerve morphology traits) and brain morphology in 19 regions. We also identified 11 loci with a significant local genetic correlation and a high probability of sharing the same causal variant between neurodegenerative disorders and POAG or its related phenotypes. Of interest, a region on chromosome 17 corresponding to MAPT, a well-known risk locus for Alzheimer's and Parkinson's disease, was shared between POAG, optic nerve degeneration traits, and Alzheimer's and Parkinson's diseases. Despite these local genetic overlaps, we did not identify strong evidence of a causal association between these neurodegenerative disorders and glaucoma.

INTERPRETATION

Our findings indicate a distinctive and likely independent neurodegenerative process for POAG involving several brain regions although several POAG or optic nerve degeneration risk loci are shared with neurodegenerative disorders, consistent with a pleiotropic effect rather than a causal relationship between these traits.

FUNDING

PG was supported by an NHMRC Investigator Grant (#1173390), SM by an NHMRC Senior Research Fellowship and an NHMRC Program Grant (APP1150144), DM by an NHMRC Fellowship, LP is funded by the NEIEY015473 and EY032559 grants, SS is supported by an NIH-Oxford Cambridge Fellowship and NIH T32 grant (GM136577), APK is supported by a UK Research and Innovation Future Leaders Fellowship, an Alcon Research Institute Young Investigator Award and a Lister Institute for Preventive Medicine Award.

Inference of chronic obstructive pulmonary disease with deep learning on raw spirograms identifies new genetic loci and improves risk models

Chronic obstructive pulmonary disease (COPD), the third leading cause of death worldwide, is highly heritable. While COPD is clinically defined by applying thresholds to summary measures of lung function, a quantitative liability score has more power to identify genetic signals. Here we train a deep convolutional neural network on noisy self-reported and International Classification of Diseases labels to predict COPD case-control status from high-dimensional raw spirograms and use the model's predictions as a liability score. The machine-learning-based (ML-based) liability score accurately discriminates COPD cases and controls, and predicts COPD-related hospitalization without any domain-specific knowledge. Moreover, the ML-based liability score is associated with overall survival and exacerbation events. A genome-wide association study on the ML-based liability score replicates existing COPD and lung function loci and also identifies 67 new loci. Lastly, our method provides a general framework to use ML methods and medical-record-based labels that does not require domain knowledge or expert curation to improve disease prediction and genomic discovery for drug design.

Temporal single-cell atlas of non-neuronal retinal cells reveals dynamic, coordinated multicellular responses to central nervous system injury

Non-neuronal cells are key to the complex cellular interplay that follows central nervous system insult. To understand this interplay, we generated a single-cell atlas of immune, glial and retinal pigment epithelial cells from adult mouse retina before and at multiple time points after axonal transection. We identified rare subsets in naive retina, including interferon (IFN)-response glia and border-associated macrophages, and delineated injury-induced changes in cell composition, expression programs and interactions. Computational analysis charted a three-phase multicellular inflammatory cascade after injury. In the early phase, retinal macroglia and microglia were reactivated, providing chemotactic signals concurrent with infiltration of CCR2+ monocytes from the circulation. These cells differentiated into macrophages in the intermediate phase, while an IFN-response program, likely driven by microglia-derived type I IFN, was activated across resident glia. The late phase indicated inflammatory resolution. Our findings provide a framework to decipher cellular circuitry, spatial relationships and molecular interactions following tissue injury.

Eye-brain connections revealed by multimodal retinal and brain imaging genetics in the UK Biobank

As an anatomical extension of the brain, the retina of the eye is synaptically connected to the visual cortex, establishing physiological connections between the eye and the brain. Despite the unique opportunity retinal structures offer for assessing brain disorders, less is known about their relationship to brain structure and function. Here we present a systematic cross-organ genetic architecture analysis of eye-brain connections using retina and brain imaging endophenotypes. Novel phenotypic and genetic links were identified between retinal imaging biomarkers and brain structure and function measures derived from multimodal magnetic resonance imaging (MRI), many of which were involved in the visual pathways, including the primary visual cortex. In 65 genomic regions, retinal imaging biomarkers shared genetic influences with brain diseases and complex traits, 18 showing more genetic overlaps with brain MRI traits. Mendelian randomization suggests that retinal structures have bidirectional genetic causal links with neurological and neuropsychiatric disorders, such as Alzheimer's disease. Overall, cross-organ imaging genetics reveals a genetic basis for eye-brain connections, suggesting that the retinal images can elucidate genetic risk factors for brain disorders and disease-related changes in intracranial structure and function.

Genetic Associations Between Smoking- and Glaucoma-Related Traits

Purpose

The purpose of this study was to describe the genetic relationship between smoking and glaucoma.

Methods

We used summary-level genetic data for smoking initiation, smoking intensity (cigarettes per day [CPD]), intraocular pressure (IOP), vertical cup-disc ratio, and open-angle glaucoma (OAG) to estimate global genetic correlations (rg) and perform two-sample Mendelian randomization (MR) experiments that explored relations between traits. Finally, we examined associations between smoking genetic risk scores (GRS) and smoking traits with measured IOP and OAG in Rotterdam Study participants.

Results

We identified weak inverse rg between smoking- and glaucoma-related traits that were insignificant after Bonferroni correction. However, MR analysis revealed that genetically predicted smoking initiation was associated with lower IOP (-0.18 mm Hg per SD, 95% confidence interval [CI] = -0.30 to -0.06, P = 0.003). Furthermore, genetically predicted smoking intensity was associated with decreased OAG risk (odds ratio [OR] = 0.74 per SD, 95% CI = 0.61 to 0.90, P = 0.002). In the Rotterdam Study, the smoking initiation GRS was associated with lower IOP (-0.09 mm Hg per SD, 95% CI = -0.17 to -0.01, P = 0.04) and lower odds of OAG (OR = 0.84 per SD, 95% CI = 0.73 to 0.98, P = 0.02) in multivariable-adjusted analyses. In contrast, neither smoking history nor CPD was associated with IOP (P ≥ 0.38) or OAG (P ≥ 0.54). Associations between the smoking intensity GRS and glaucoma traits were null (P ≥ 0.13).

Conclusions

MR experiments and GRS generated from Rotterdam Study participants support an inverse relationship between smoking and glaucoma.

Translational Relevance

Understanding the genetic drivers of the inverse relationship between smoking and glaucoma could yield new insights into glaucoma pathophysiology.

Reverse translation of artificial intelligence in glaucoma: Connecting basic science with clinical applications

Artificial intelligence (AI) has been approved for biomedical research in diverse areas from bedside clinical studies to benchtop basic scientific research. For ophthalmic research, in particular glaucoma, AI applications are rapidly growing for potential clinical translation given the vast data available and the introduction of federated learning. Conversely, AI for basic science remains limited despite its useful power in providing mechanistic insight. In this perspective, we discuss recent progress, opportunities, and challenges in the application of AI in glaucoma for scientific discoveries. Specifically, we focus on the research paradigm of reverse translation, in which clinical data are first used for patient-centered hypothesis generation followed by transitioning into basic science studies for hypothesis validation. We elaborate on several distinctive areas of research opportunities for reverse translation of AI in glaucoma including disease risk and progression prediction, pathology characterization, and sub-phenotype identification. We conclude with current challenges and future opportunities for AI research in basic science for glaucoma such as inter-species diversity, AI model generalizability and explainability, as well as AI applications using advanced ocular imaging and genomic data.

Thrombospondin 1 missense alleles induce extracellular matrix protein aggregation and TM dysfunction in congenital glaucoma

Glaucoma is a highly heritable disease that is a leading cause of blindness worldwide. Here, we identified heterozygous thrombospondin 1 (THBS1) missense alleles altering p.Arg1034, a highly evolutionarily conserved amino acid, in 3 unrelated and ethnically diverse families affected by congenital glaucoma, a severe form of glaucoma affecting children. Thbs1R1034C-mutant mice had elevated intraocular pressure (IOP), reduced ocular fluid outflow, and retinal ganglion cell loss. Histology revealed an abundant, abnormal extracellular accumulation of THBS1 with abnormal morphology of juxtacanalicular trabecular meshwork (TM), an ocular tissue critical for aqueous fluid outflow. Functional characterization showed that the THBS1 missense alleles found in affected individuals destabilized the THBS1 C-terminus, causing protein misfolding and extracellular aggregation. Analysis using a range of amino acid substitutions at position R1034 showed that the extent of aggregation was correlated with the change in protein-folding free energy caused by variations in amino acid structure. Extracellular matrix (ECM) proteins, especially fibronectin, which bind to THBS1, also accumulated within THBS1 deposits. These results show that missense variants altering THBS1 p.Arg1034 can cause elevated IOP through a mechanism involving impaired TM fluid outflow in association with accumulation of aggregated THBS1 in the ECM of juxtacanalicular meshwork with altered morphology.

Obesity-related biomarkers underlie a shared genetic architecture between childhood body mass index and childhood asthma

Obesity and asthma are both common diseases with high population burden worldwide. Recent genetic association studies have shown that obesity is associated with asthma in adults. The relationship between childhood obesity and childhood asthma, and the underlying mechanisms linking obesity to asthma remain to be clarified. In the present study, leveraging large-scale genetic data from UK biobank and several other data sources, we investigated the shared genetic components between body mass index (BMI, n = 39620) in children and childhood asthma (ncase = 10524, ncontrol = 373393). We included GWAS summary statistics for nine obesity-related biomarkers to evaluate potential biological mediators underlying obesity and asthma. We found a genetic correlation (Rg = 0.10, P = 0.02) between childhood BMI and childhood asthma, whereas the genetic correlation between adult BMI (n = 371541) and childhood asthma was null (Rg = -0.03, P = 0.21). Genomic structural equation modeling analysis further provided evidence that the genetic effect of childhood BMI on childhood asthma (standardized effect size 0.17, P = 0.009) was not driven by the genetic component of adult BMI. Bayesian colocalization analysis identified a shared causal variant rs12436181 that was mapped to gene AMN using gene expression data in lung tissue. Mendelian randomization showed that the odds ratio of childhood asthma for one standard deviation higher of childhood BMI was 1.13 (95% confidence interval: 0.96-1.34). A systematic survey of obesity-related biomarkers showed that IL-6 and adiponectin are potential biological mediators linking obesity and asthma in children. This large-scale genetic study provides evidence that unique childhood obesity pathways could lead to childhood asthma. The findings shed light on childhood asthma pathogenic mechanisms and prevention.

Genomics reveal population structure, evolutionary history, and signatures of selection in the northern bottlenose whale, Hyperoodon ampullatus

Information on wildlife population structure, demographic history, and adaptations are fundamental to understanding species evolution and informing conservation strategies. To study this ecological context for a cetacean of conservation concern, we conducted the first genomic assessment of the northern bottlenose whale, Hyperoodon ampullatus, using whole-genome resequencing data (n = 37) from five regions across the North Atlantic Ocean. We found a range-wide pattern of isolation-by-distance with a genetic subdivision distinguishing three subgroups: the Scotian Shelf, western North Atlantic, and Jan Mayen regions. Signals of elevated levels of inbreeding in the Endangered Scotian Shelf population indicate this population may be more vulnerable than the other two subgroups. In addition to signatures of inbreeding, evidence of local adaptation in the Scotian Shelf was detected across the genome. We found a long-term decline in effective population size for the species, which poses risks to their genetic diversity and may be exacerbated by the isolating effects of population subdivision. Protecting important habitat and migratory corridors should be prioritized to rebuild population sizes that were diminished by commercial whaling, strengthen gene flow, and ensure animals can move across regions in response to environmental changes.

The genetic basis for adult onset glaucoma: Recent advances and future directions

Glaucoma, a diverse group of eye disorders that results in the degeneration of retinal ganglion cells, is the world's leading cause of irreversible blindness. Apart from age and ancestry, the major risk factor for glaucoma is increased intraocular pressure (IOP). In primary open-angle glaucoma (POAG), the anterior chamber angle is open but there is resistance to aqueous outflow. In primary angle-closure glaucoma (PACG), crowding of the anterior chamber angle due to anatomical alterations impede aqueous drainage through the angle. In exfoliation syndrome and exfoliation glaucoma, deposition of white flaky material throughout the anterior chamber directly interfere with aqueous outflow. Observational studies have established that there is a strong hereditable component for glaucoma onset and progression. Indeed, a succession of genome wide association studies (GWAS) that were centered upon single nucleotide polymorphisms (SNP) have yielded more than a hundred genetic markers associated with glaucoma risk. However, a shortcoming of GWAS studies is the difficulty in identifying the actual effector genes responsible for disease pathogenesis. Building on the foundation laid by GWAS studies, research groups have recently begun to perform whole exome-sequencing to evaluate the contribution of protein-changing, coding sequence genetic variants to glaucoma risk. The adoption of this technology in both large population-based studies as well as family studies are revealing the presence of novel, protein-changing genetic variants that could enrich our understanding of the pathogenesis of glaucoma. This review will cover recent advances in the genetics of primary open-angle glaucoma, primary angle-closure glaucoma and exfoliation glaucoma, which collectively make up the vast majority of all glaucoma cases in the world today. We will discuss how recent advances in research methodology have uncovered new risk genes, and how follow up biological investigations could be undertaken in order to define how the risk encoded by a genetic sequence variant comes into play in patients. We will also hypothesise how data arising from characterising these genetic variants could be utilized to predict glaucoma risk and the manner in which new therapeutic strategies might be informed.

Novel technical and privacy-preserving technology for artificial intelligence in ophthalmology

PURPOSE OF REVIEW

The application of artificial intelligence (AI) in medicine and ophthalmology has experienced exponential breakthroughs in recent years in diagnosis, prognosis, and aiding clinical decision-making. The use of digital data has also heralded the need for privacy-preserving technology to protect patient confidentiality and to guard against threats such as adversarial attacks. Hence, this review aims to outline novel AI-based systems for ophthalmology use, privacy-preserving measures, potential challenges, and future directions of each.

RECENT FINDINGS

Several key AI algorithms used to improve disease detection and outcomes include: Data-driven, imagedriven, natural language processing (NLP)-driven, genomics-driven, and multimodality algorithms. However, deep learning systems are susceptible to adversarial attacks, and use of data for training models is associated with privacy concerns. Several data protection methods address these concerns in the form of blockchain technology, federated learning, and generative adversarial networks.

SUMMARY

AI-applications have vast potential to meet many eyecare needs, consequently reducing burden on scarce healthcare resources. A pertinent challenge would be to maintain data privacy and confidentiality while supporting AI endeavors, where data protection methods would need to rapidly evolve with AI technology needs. Ultimately, for AI to succeed in medicine and ophthalmology, a balance would need to be found between innovation and privacy.

Cohort profile: genomic data for 26 622 individuals from the Canadian Longitudinal Study on Aging (CLSA)

PURPOSE

The Canadian Longitudinal Study on Aging (CLSA) Comprehensive cohort was established to provide unique opportunities to study the genetic and environmental contributions to human disease as well as ageing process. The aim of this report was to describe the genomic data included in CLSA.

PARTICIPANTS

A total of 26 622 individuals from the CLSA Comprehensive cohort of men and women aged 45-85 recruited between 2010 and 2015 underwent genome-wide genotyping of DNA samples collected from blood. Comprehensive quality control metrics were measured for genetic markers and samples, respectively. The genotypes were imputed to the TOPMed reference panel. Sex chromosome abnormalities were identified by copy number profiling. Classical human leukocyte antigen gene haplotypes were imputed at two-field (four-digit).

FINDINGS TO DATE

Of the 26 622 genotyped participants, 24 655 (92.6%) were identified as having European ancestry. These genomic data were linked to physical, lifestyle, medical, economic, environmental and psychosocial factors collected longitudinally in CLSA. The combined analysis, including CLSA genomic data, uncovered over 100 novel loci associated with key parameters to define glaucoma. The CLSA genomic dataset validated the contribution of a polygenic risk score to screen individuals with high fracture risk. It is also a valuable resource to directly identify common genetic variations associated with conditions related to complex traits. Taking advantage of the comprehensive interview and physical information collected in CLSA, this genomic dataset has been linked to psychosocial factors to investigate both the independent and interactive effects on cardiovascular disease.

FUTURE PLANS

The CLSA overall is ongoing. Follow-up data will continue to be collected from participants in the current genomic subcohort, including the DNA methylation and metabolomic data. Ongoing studies focus on elucidating the role of genetic factors in cognitive decline and cardiovascular diseases. This genomic data resource is available on request through the CLSA data access application process.

[Artificial intelligence and glaucoma: A literature review]

In recent years, research in artificial intelligence (AI) has experienced an unprecedented surge in the field of ophthalmology, in particular glaucoma. The diagnosis and follow-up of glaucoma is complex and relies on a body of clinical evidence and ancillary tests. This large amount of information from structural and functional testing of the optic nerve and macula makes glaucoma a particularly appropriate field for the application of AI. In this paper, we will review work using AI in the field of glaucoma, whether for screening, diagnosis or detection of progression. Many AI strategies have shown promising results for glaucoma detection using fundus photography, optical coherence tomography, or automated perimetry. The combination of these imaging modalities increases the performance of AI algorithms, with results comparable to those of humans. We will discuss potential applications as well as obstacles and limitations to the deployment and validation of such models. While there is no doubt that AI has the potential to revolutionize glaucoma management and screening, research in the coming years will need to address unavoidable questions regarding the clinical significance of such results and the explicability of the predictions.