Pigmentation varies widely across humans and is shaped by melanin quantity, type, and spatial distribution. Retinal pigmentation protects against light-induced damage, yet its genetic and evolutionary bases remain unclear. We developed a deep learning framework (DeepGRP) to quantify retinal pigmentation from high-resolution fundus images and conducted a genome-wide association study (GWAS), identifying 42 signals, including 26 previously unidentified loci, with single-nucleotide polymorphism-based heritability of 21.4%. Single-nucleus assay for transposase-accessible chromatin by sequencing and RNA sequencing of human fetal retinal tissues revealed key cellular contributors, including retinal pigment epithelium and photoreceptor cells. Among candidate genes, ARHGAP18 emerged as a previously unrecognized regulator of melanogenesis. Evidence of polygenic adaptation in Europeans suggests selection driven by snow-reflected light at high latitudes. A polygenic risk score for retinal pigmentation correlated with a 4.8-fold higher risk of myopia and a 1.5-fold lower risk of skin cancer. These findings demonstrate the power of deep learning for large-scale ocular phenotyping and reveal insights into the genetic and evolutionary architecture of retinal pigmentation.

BACKGROUND:Cutaneous melanoma is a common cancer, for which risk stratification has been proposed to aid early detection. OBJECTIVES:To assess the performance of a polygenic risk score (PRS) in predicting the risk of invasive cutaneous melanoma, alone and combined with clinical risk factors. METHODS:The PRS was derived from the most recent genome-wide association study meta-analysis of cutaneous melanoma, which included 28 849 patients with melanoma and 78 922 control participants from 20 studies from the UK, the USA, Australia and Europe. Then, it was tuned in the Canadian Longitudinal Studying on Aging cohort, which included 528 patients with melanoma and 17 787 control participants. The PRS was then tested independently against 14 self-reported clinical factors and a clinical prediction model (the MP16 model) in the QSkin prospective cohort, which included 16 282 participants with genetic data aged 40-69 years at baseline, among whom 359 were identified with new invasive melanomas during 10 years of follow-up. RESULTS:The PRS outperformed any other single clinical risk factor in QSkin (c-index 0.643). The baseline risk model (age, sex, first 10 principal components) had a c-index of 0.603; adding PRS to the baseline model increased the c-index to 0.670 [likelihood ratio test (LRT) P = 1.68 × 10-21]. Adding PRS to the MP16 model significantly enhanced discrimination [MP16 c-index: 0.713; MP16 + PRS c-index: 0.729 (median LRT P = 5.56 × 10-11)] and predicted more true cases in the first and second top deciles (111 for MP16 + PRS vs. 104 for MP16 in the first decile, and 72 for MP16 + PRS vs. 63 for MP16 in the second decile). Across various screening thresholds (10-50%), the sensitivity and/or specificity is higher and the net reclassification improvements were in the range of 0.01-0.05, comparing the MP16 + PRS model with the MP16 model. CONCLUSIONS:Incorporating genetic risk information into existing clinical risk tools significantly improves prediction performance for melanoma.

BACKGROUND:Neurodegeneration in Alzheimer's disease (AD) is thought to be driven by amyloid-beta and tau deposition in the cerebral vasculature and brain. As the eye is an extension of the central nervous system, this study aimed to determine which neurovascular and neuroretinal changes in the eye are caused by AD rather than associations of the disease. METHODS:Bidirectional two-sample univariable and multivariable Mendelian randomization (MR) methods were applied. Instrumental variables were derived from genome-wide association studies (GWAS) of AD and the following ocular features: thickness measurements of central macula (MT), retinal nerve fibre layer (mRNFL), ganglion cell-inner plexiform layer (mGCIPL), outer nuclear layer (ONL), inner segment layer (IS), and outer segment (OS) from macular region OCT scans; arteriolar tortuosity (AT), venular tortuosity (VT), venular width (VW), fractal dimension (FD), vertical cup-to-disc ratio (VCDR), optic cup area (OCA), and optic disc area (ODA) derived from other imaging methods. RESULTS:There was strong evidence that genetic liability to AD affected the retinal vasculature by specifically increasing AT (β = 0.007;95%CI=0.002,0.011;p-value=0.005) in UK Biobank participants (n=52,798). AD may influence the mRNFL (β=-0.047,95%CI=-0.119,0.023,p-value=0.18) and mGCIPL (β=-0.061;95%CI=-0.14,0.025,p-value=0.16) of the inner retina and OS layer (β = 0.044;95%CI=-0.0001,0.08;p-value=0.05) but the evidence was weak. Multivariable MR analysis showed that a causal relationship between optic disc area and AD (OR=0.76;95%CI=0.62,0.93,p-value=0.009) was probably mediated by refractive error. CONCLUSION:Early cerebrovascular signs of AD may be detected by examination of the eye. Further investigation is required to determine the clinical utility of eye screening for dementia.

Objective:To identify risk loci for Fuchs endothelial corneal dystrophy (FECD) and improve a genetic risk prediction model. Design:Genome-wide association study (GWAS), polygenic risk score (PRS) construction, and TCF4 CTG18.1 short tandem repeat (STR) length inference. Participants:The study included 7,316 Europeans (EUR) with FECD or related corneal dystrophy phenotypes and 1,588,467 controls from the UK Biobank, All of Us, FinnGen, and the Million Veteran Program. Two independent EUR FECD cohorts were used for PRS validation (1,851/2,679 cases/controls and 124/257 cases/controls). African (AFR) ancestry analyses included 455 cases and 121,154 controls to build PRS. A subset of All of Us participants was used for joint PRS and STR modelling. Methods:GWAS meta-analyses were performed using FECD diagnoses or corneal dystrophy proxies where necessary, with validity assessed via genetic correlation. Risk loci were identified, and ancestry-specific PRSs were constructed using SBayesRC. PRS performance was evaluated across ancestries with and without TCF4 STR data. Main Outcome:We identified novel loci for corneal dystrophy and constructed PRS-based and STR-based prediction models. Results:The GWAS meta-analysis identified 24 risk loci associated with corneal dystrophy, including 12 novel loci, doubling previous FECD studies. The optimised PRS outperformed existing models in two independent FECD validation cohorts (AUC = 0.83, 95% CI: 0.82-0.84; DeLong's P = 7.04 × 10-19), with individuals in the top PRS decile showing 14-fold and 19-fold increased risk in the two validation sets, respectivelyIn All of Us, STR expansion (>40 repeats) was the key predictor of FECD risk, yielding excellent discrimination (AUC = 0.89; OR = 54) with minimal improvement from PRS. Consistent with this, STR expansion remained the primary driver of risk across ancestries, while PRS provided modest independent value for broader corneal dystrophy phenotypes in EUR and admixed American populations.Among participants without large STR expansion, overall predictive performance was modest; PRS was the only significant genetic contributor (OR = 1.37) for broader corneal dystrophy in Europeans, whereas analyses in FECD non-expansion carriers were underpowered. Conclusions:These findings refine the genetic architecture of FECD, enhance risk prediction, and support a tiered strategy integrating STR expansion testing with PRS.

Background: The visual field (VF) test results of many eyes with glaucoma progress despite treatment. This suggests that some eyes are either untreated or that the management of intraocular pressure (IOP) does not influence the outcome. In this work, we explore whether future VF parameters can be predicted from a baseline optical coherence retinal nerve fibre layer (OCT-RNFL) scan using a deep learning model. Methods: The model was developed using 1792 eyes from 1610 patients, and externally validated on 151 eyes from a second centre using the same Zeiss Cirrus machine and 281 eyes from a third centre using scans obtained from a different (Heidelberg Spectralis) machine. The Vision Transformers (ViT)-based regression model was trained on baseline OCT-RNFL scans to predict three key VF indices (follow-up interval: 4.74 with a standard deviation of 2.59 years). Model performance was evaluated using Mean Absolute Error (MAE) and Root Mean Square Error (RMSE), with 95% confidence intervals (CI). Results: The model achieved an overall MAE of 2.07 (95% CI: 1.91-2.22) and RMSE of 2.87 (95% CI: 2.60-3.14) on the internal validation set. On external validation, the model showed comparable performance with an MAE of 2.07 (95% CI: 1.8-2.35) for the external validation (Zeiss OCT) cohort and 2.11 (95% CI: 1.93-2.31) for the external validation (Heidelberg OCT) cohort. Saliency maps revealed that the inner and outer RNFL layers were key structures in driving the model's predictions. Conclusions: Our ViT-based regression model effectively predicts key VF indices objectively from a single OCT-RNFL scan, with strong performance across two OCT devices, offering a novel tool for predicting glaucoma progression. ### Competing Interest Statement The authors have declared no competing interest. ### Funding Statement This work was supported by an Australian National Health and Medical Research Council Leadership Award (A.W.H.). ### Author Declarations I confirm all relevant ethical guidelines have been followed, and any necessary IRB and/or ethics committee approvals have been obtained. Yes The details of the IRB/oversight body that provided approval or exemption for the research described are given below: University of Tasmania Human Research Ethics Committee (HREC) (29775), and the Royal Victorian Eye and Ear Hospital HREC (24/1597HL). I confirm that all necessary patient/participant consent has been obtained and the appropriate institutional forms have been archived, and that any patient/participant/sample identifiers included were not known to anyone (e.g., hospital staff, patients or participants themselves) outside the research group so cannot be used to identify individuals. Yes I understand that all clinical trials and any other prospective interventional studies must be registered with an ICMJE-approved registry, such as ClinicalTrials.gov. I confirm that any such study reported in the manuscript has been registered and the trial registration ID is provided (note: if posting a prospective study registered retrospectively, please provide a statement in the trial ID field explaining why the study was not registered in advance). Yes I have followed all appropriate research reporting guidelines, such as any relevant EQUATOR Network research reporting checklist(s) and other pertinent material, if applicable. Yes All data produced in the present study are available upon reasonable request to the authors

Abstract Cannabis use is widespread, with genetic differences partly explaining variation in individual patterns of use. We performed the largest-to-date genome-wide association study (GWAS) meta-analysis of cannabis ever-use (N=736,322, 76% European ancestry) and various measures of frequency of use (N=269,160 cannabis users, 84% European ancestry). We identified 54 independent genome-wide significant loci for ever-use and 6 for frequency and show that the genetic architecture of ever-use, frequency, and cannabis use disorder (CUD) are overlapping but distinguishable. We identified 63 loci that were associated with common liability (‘ All-cannabis ’) to different cannabis use traits in European-ancestry individuals. Across analyses, we identified 75 unique loci that had not previously been implicated in cannabis use. Gene prioritization analyses identified 349 genes for ever-use, 5 genes for frequency of use, and 429 for All-cannabis , including previously identified and novel genes. We found enrichment of genetic signals for cannabis use in biologically meaningful categories and relevant human brain cell types, including excitatory neuronal populations. There were substantial genetic correlations between cannabis use and a range of psychiatric disorders and substance use traits, while cannabis polygenic scores were associated with increased risk of psychiatric disorders. Mendelian Randomization showed evidence for (bidirectional) causal associations between cannabis use and ADHD, bipolar disorder, schizophrenia and PTSD.

Abstract We performed a new melanoma GWAS meta-analysis, roughly doubling the sample size to ∼70,000 cases compared to the most recent study by Landi and colleagues. In the new meta-analysis, we identify 116 independent risk loci, replicating 52/54 loci previously reported. Post-GWAS variant-to-gene mapping remains critical to functionally interpret new loci discovered by this analysis. Most loci contain many candidate causal variants in linkage disequilibrium, with very few altering protein-coding sequence, suggesting cis-regulatory function underlying the majority of loci. Quantitative trait locus (QTL) colocalization methods can effectively prioritize target genes at susceptibility loci but many loci remain without assigned targets, perhaps reflecting context-specific variant effects not well-reflected in QTL datasets. To complement QTLs and more comprehensively identify potential causal genes, including those beyond 1 Mb, we applied H3K27ac-HiChIP in human primary melanocytes, the cell type of origin for melanoma. H3K27ac-HiChIP combines a Hi-C approach with a H3K27ac ChIP step to detect enhancer-promoter interactions at the risk loci. Statistically significant chromatin interactions were identified using the FitHiChIP pipeline. We subsequently performed variant-to-gene (V2G) mapping at all genome-wide significant melanoma loci, nominating target genes where fine-mapped variants overlapped or physically interacted with their respective promoters. HiChIP-based V2G mapping approach identified target genes at 94% loci (779 genes nominated at 109/116 loci), outperforming other gene nomination approaches (QTL colocalization, protein-coding variants), which nominated 102 candidate genes at 57% of risk loci (67/116 loci). Among genes identified by melanocyte or melanoma eQTL colocalization, a majority (19 of 32) were also nominated by V2G mapping. Likewise, 3 of 5 splice QTL genes and 21 of 34 meQTL genes overlapped with the V2G gene set. Next, we compared gene sets including the V2G mapping-identified candidates to those identified solely by QTLs and protein-coding variants. The gene set incorporating V2G candidates showed significant enrichment for oncogenic signaling pathways, including WNT/β-catenin signaling (FDR: Pwith V2G gene set= 6.3 × 10-05 vs Pwithout V2G gene set= 0.2), aryl hydrocarbon receptor signaling (P= 1.6 × 10-4 vs 0.2), and signaling by NOTCH1 (P=0.002 vs0.5). Notably, V2G mapping linked risk-associated variants to known cancer drivers (e.g. PIK3CA, NOTCH2, MDM4etc.) at 46% of loci (53/116; nominated 76 cancer drivers), with several located >1Mb away. Strikingly, we detected a highly significant long-range interaction (∼2 Mb) connecting fine-mapped variants near the locus 8q24.21 to cancer driver MYC. Overall, H3K27ac-HiChIP-based V2G mapping greatly improves the interpretation of melanoma susceptibility loci by identifying distant susceptibility genes, highlighting known cancer drivers as potential targets, and revealing that these loci converge on key oncogenic signaling pathways. Citation Format: Rohit Thakur, G J M Shanika R Jayasinghe, Mai Xu, Linh Bui-Raborn, Jianxin Shi, Diptavo Dutta, Phuc H. Hoang, Mathias Seviiri, Christopher I. Amos, Andrew Bakshi, Anne E. Cust, Florence Demenais, David L. Duffy, Lars G. Fritsche, Jiali Han, Nicholas K. Hayward, Kiarash Khosrotehrani, Rajiv Kumar, John F. Thompson, Stuart MacGregor, Miguel Renteria, Diane T. Smelser, Sarah V. Ward, Maria Concetta Fargnoli, Paola Ghiorzo, Alisa M. Goldstein, Chiara Menin, David Millan-Esteban, Eduardo Nagore, Cristina Pellegrini, Susana Puig, Alex Stratigos, David C. Whiteman, Melanoma Meta-Analysis Consortium, Lee E. Whelees, Rebecca I. Hartman, Maria Teresa Landi, Matthew H. Law, Kevin M. Brown. H3K27ac-HiChIP variant-to-gene mapping confirms the importance of cancer drivers and oncogenic signaling pathways in melanoma risk [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2026; Part 2 (Late-Breaking, Clinical Trial, and Invited Abstracts); 2026 Apr 17-22; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2026;86(8_Suppl):Abstract nr LB384.

OBJECTIVE:The aim of this research is to identify germline genetic variants that predispose to uveal melanoma (UM) using data from nine studies involving 5839 individuals with UM (3853 novel) and 349,863 healthy controls. METHODS:Five novel UM genome-wide association studies (GWAS) were performed and included for meta-analysis with four previously published UM GWAS. A fixed-effects inverse-variance weighted (IVW) meta-analysis was performed by combining data from these nine UM case-control cohorts. A follow-up transcriptome-wide association study (TWAS) was conducted to identify candidate target genes at UM risk loci. Genetic correlations with melanoma-related phenotypes were measured to elucidate UM's genetic architecture. RESULTS:We identify nine linkage disequilibrium (LD)-independent loci (three novel) with an IVW P value of less than 5 × 10-8. TWAS analysis indicates five potential target genes, including MOB3B, RBAK, and MTSS1, which have established links to multiple cancer types. We note a significant genetic correlation (rg = 0.31, P = 0.01) between UM and cutaneous melanoma (CM), and a non-significant but consistent correlation with naevus count (rg = 0.25, P = 0.08). CONCLUSIONS:This meta-analysis offers new insights into the genetic architecture of UM, highlights potential therapeutic targets, and explores the genetic relationship with CM and skin pigmentation.

Background Polygenic risk scores are increasingly being used in cancer care to inform population screening programs, personalise risk estimates in familial cancer clinics, and inform therapeutic decision-making. However, lack of clear guidelines for PRS laboratory accreditation restricts the ability of researchers, clinicians and regulatory bodies to evaluate test quality. Via stakeholder consultation, this study aimed to describe the unique aspects of PRS laboratory accreditation to support safe test implementation in cancer care. Methods Semi-structured interviews explored the unique challenges and considerations of PRS testing. Eligible participants were stakeholders with experience in ordering, performing, reporting, and accrediting PRS. Interviews were transcribed verbatim, and deductive content analysis conducted. Results Participants were clinicians, laboratory scientists, researchers, and industry experts with experience in PRS laboratory accreditation (n = 13). Five themes were developed covering the entire PRS testing pipeline: i) laboratory methods, ii) PRS algorithm, iii) clinical context, and iv) reporting results, and a fifth theme, impact of ancestry that affected all aspects of the PRS pipeline. Laboratory methods for sample collection, genotyping, and quality control were identified as well-established and transferable to PRS testing. Use of imputation was identified as a unique consideration for PRS accreditation, which impacts transparency and quality assessments of the result. Imputation is particularly relevant in cancer applications where accuracy may affect risk classification and subsequent clinical management decisions. PRS algorithm updates were considered manageable within existing accreditation frameworks for test improvements. Additional considerations included the need to develop quality standards for the management of missing data, defined validation processes, protocols for collecting additional risk factors if delivering integrated risk (e.g. breast density and family history), and education of laboratory pathologists. Finally, the importance of clear reporting of risk assessment was explored. Based on findings, a conceptual overview and checklists were developed to aid providers and accreditation bodies alike assessing PRS testing. Conclusions Findings highlight the unique considerations for PRS laboratory accreditation and offer a practical framework to guide accreditation guidelines. These considerations are particularly urgent in the cancer setting, where PRS is being most rapidly implemented, and where robust accreditation frameworks are essential to ensure accurate and equitable risk assessments.

Abstract A greater understanding of the biology of nevi will provide insights into the etiology of melanoma. Our large-scale meta-analysis of 14 nevus genome-wide association studies (GWAS) includes 85,965 individuals of European ancestry. We identify 29 nevus-associated loci (p < 5 × 10-8), of which 24 have not been previously reported in a GWAS conducted for nevus count alone. We further identify 255 candidate genes for nevus loci, including SIKE1 which is involved in immune response regulation. This is of interest because immune response regulation influences the formation of nevi and melanoma susceptibility. Gene-set enrichment analyses prioritise immune response-related pathways and cancers that do not have a pigmentation component (e.g. breast, prostate, and glioma). This suggests that the biology underlying nevus count captures risk pathways beyond pigmentation that are relevant to melanoma. In sex-specific analyses, we observe higher total-body nevus count in females than in males, however the genetic architecture is largely shared (genetic correlation = 0.863, 95% CI = 0.453 – 1.273), indicating the difference may be influenced by environmental and behavioural factors rather than genetics. A nevus polygenic risk score explains 5% of the variance in nevus count, indicating its potential to enhance melanoma risk prediction.

BACKGROUND:Although genome-wide association studies (GWAS) have identified numerous common genetic risk variants for Parkinson's disease (PD), the underlying biological mechanisms remain largely unresolved. OBJECTIVES:We aimed to identify circulating proteins causally associated with PD risk in European and East Asian populations and determine whether these associations are shared or ancestry-specific. METHODS:We employed a two-sample Mendelian randomization (MR) approach, integrating large-scale proteomic and genetic data, with validation using summary-data-based MR (SMR). European analyses used UK Biobank Pharma Proteomics Project (UKB-PPP; n = 54,219; 2,392 proteins) and a large PD GWAS (37,688 cases, 18,618 proxy cases, 1.4 million controls). East Asian analyses combined Han Chinese and UKB-PPP data (n = 3,220; 229 proteins) with a PD GWAS (6,724 cases, 24,851 controls). False discovery rate (FDR) < 0.05 determined significance. Sensitivity analyses addressed instrument heterogeneity, pleiotropy, and sample overlap. RESULTS:MR analyses identified 21 proteins causally associated with PD in Europeans and 8 in East Asians, all directionally concordant in SMR validation. Notably, BST1 emerged as a shared causal protein, increasing PD risk in both Europeans (odds ratio [OR] = 1.04, 95% confidence interval [CI]: 1.02-1.06) and East Asians (OR = 1.18, 95% CI: 1.10-1.27), remaining robust after excluding UK Biobank participants. Several ancestry-specific proteins were detected, including TXNDC15 in Europeans and PM20D1 in East Asians, both targets of existing or investigational drugs. CONCLUSIONS:This cross-ancestry proteogenomic analysis reveals shared and ancestry-specific proteomic signatures causally linked to PD, underscoring the importance of using ancestry-aware analytical frameworks to discover robust biomarkers and novel therapeutic targets. © 2026 International Parkinson and Movement Disorder Society.

Germline genetic variation can influence the number of nevi on the skin. However, nevi can be classified in many ways (e.g., by shape, size or evolution); little is known of how genetics influences the density of different nevus morphologies on the body. Here, we explore how germline genetics influences the density of flat, raised and atypical nevi, and identify which classification correlates most with genetic risk of melanoma. We estimate heritability and perform genome-wide association analysis of flat, raised, and atypical nevus count within the Brisbane Twin Nevus Study (N = 3862). We compare the effect estimates of genetic loci associated with each morphology. We also assess how well each morphology correlates with the genetic risk of melanoma using a polygenic risk score of melanoma. Heritability estimates revealed both unique and shared genetic effects between morphologies. We identified two loci near IRF4 and MC1R showing opposing effects on flat and raised nevus counts. Variation in raised nevus count showed the strongest correlation with melanoma risk using polygenic risk scores. Although some genetic effects are shared between nevus morphologies, they appear to be genetically distinct phenotypes. Given the opposing effects of IRF4 and MC1R on flat and raised nevus counts, future studies of nevus count should consider, where possible, analysing each nevus morphology separately as well as combined.

Thyroid diseases are common and highly heritable. We performed a meta-analysis of genome-wide association studies from 19 biobanks for five thyroid diseases: thyroid cancer (ThC), benign nodular goiter, Graves’ disease, lymphocytic thyroiditis and primary hypothyroidism. We analyzed genetic association data from ~2.9 million genomes and identified 313 known and 570 new independent loci linked to thyroid diseases. We discovered genetic correlations between ThC, benign nodular goiter and autoimmune thyroid diseases ( rg = 0.16–0.97). Telomere maintenance genes contributed to benign and malignant thyroid nodular disease risk, whereas cell cycle, DNA repair and damage response genes were associated with ThC. We propose a paradigm that explains genetic predisposition to benign and malignant thyroid nodules. We found polygenic risk score associations with ThC risk of structural disease recurrence, tumor size, multifocality, lymph node metastases and extranodal extension. Polygenic risk scores identified individuals with aggressive ThC in a biobank, creating an opportunity for genetically informed population screening.

Myopia is an increasing global health concern and a leading cause of visual impairment. Genetic factors play a major role, and polygenic risk scores (PRSs) may help identify children at high risk of developing myopia. However, most PRSs are based on European populations, and accurately predicting risk across ancestries remains a challenge. We developed and evaluated PRSs for spherical equivalent refractive error (SER) and myopia using multitrait and multi‑ancestry genomewide association study data. A multitrait analysis of SER‑correlated traits identified 709 genomewide significant loci. PRSs were generated with SBayesRC for each ancestry group and for a combined multi‑ancestry model, and validated in the Australian Twins Eye Study and non‑European participants from the UK Biobank. The European PRSs explained approximately 20% of SER variance in Europeans and 18% in admixed Europeans and showed good transferability to South Asian (14%), East Asian (13%), and African (8%) groups. A multi‑ancestry PRS further improved prediction in Africans, explaining 9% of the variance. Predictive accuracy for high myopia was strong in the admixed group (AUC = 0.82, 95% CI [0.78, 0.87]), with all ancestry groups achieving AUCs of at least 0.70; European ancestry data were not available. PRS also predicted axial length in children, particularly those aged 5-8 years, where individuals in the lowest 10% of the PRS distribution had significantly longer axial lengths (β = 0.81 mm, p = 5.71 × 10-3). These findings enhance genetic prediction of SER and myopia, showing the potential of multitrait, multi-ancestry PRS for early, equitable risk stratification.

Transplant recipients have an elevated risk of actinic keratosis (AK), a precursor of squamous cell carcinoma. We assessed whether an AK polygenic risk score (PRS) can identify transplant-naive individuals and transplant recipients at risk of multiple AKs who may benefit from early prevention. Using genome-wide association data (140 339 cases; 1 430 776 controls), we developed an AK PRS and evaluated it in cohort studies of European descent individuals in the QSkin Sun and Health Study (QSkin) (N = 12 843), Skin Tumors in Allograft Recipients Study (STAR) (N = 357), UK Biobank (N = 963), and All of Us (N = 1859). The PRS was strongly associated with higher AK burden in transplant-naive individuals, conferring a 5-fold increased risk of developing ≥20 AKs (odds ratio [OR] per standard deviation [SD] increase in the PRS = 5.06, 95% confidence interval [CI] = 4.69-5.46). In transplant recipients, it was associated with up to a 2.5-fold increased risk of developing ≥1 AK (United Kingdom Biobank [UKB] OR per SD = 2.49, 95% CI = 1.91-3.23; STAR OR per SD = 1.79, 95% CI = 1.10-2.91; All of Us OR per SD = 1.52, 95% CI = 1.35-1.70). The PRS improved prediction of ≥20 AKs by 13% beyond age and sex (AUC 0.90 vs 0.77) and identified the highest-risk 20% with a 9-fold increased risk in transplant-naive individuals (OR = 8.61, 95% CI = 7.26-10.22) and a 2-fold increased risk in transplant recipients (OR = 2.23, 95% CI = 1.26-4.20), compared to the middle-risk 60%. Incorporating PRS into pretransplant and posttransplant care could enable earlier, personalized prevention.

The choroid is critical for maintaining vision and implicated in several ocular diseases, being the sole source of nutrients and waste removal for the outer retina. Genetic discovery can help elucidate the pathways through which choroidal features influence disease risk. Our meta-analysis of genome-wide association studies (n= 78,682 participants) identified 30 genomic regions, including 20 novel loci, associated with choroidal thickness. Findings suggest inflammatory and vascular processes drive choroidal thickness, with overlapping mechanisms shared with refractive error. Genome-wide independently significant SNPs accounted for 18.7% of the genetic variance in choroidal thickness. Mendelian randomisation analyses showed a causal effect of age-related macular degeneration on choroidal thickness, and suggest a bidirectional causal effect between choroidal thickness and primary angle-closure glaucoma. These findings provide insight into the shared genetic architecture and biological pathways linking choroidal thickness and related diseases.

We conducted the first genome-wide association meta-analyses of global and sectoral peripapillary retinal nerve fibre layer (pRNFL) thickness and Bruch's membrane opening-minimum rim width (BMO-MRW), the major optic nerve head structural and neurodegeneration biomarkers, including up to 25,942 and 12,080 participants, respectively, from the International Glaucoma Genetics Consortium. We identified 9 global pRNFL thickness and 9 global BMO-MRW loci, along with 28 and 19 loci for pRNFL and BMO-MRW sectors, respectively, comprising both shared and sector-specific loci. To identify intraocular pressure (IOP)-independent drug targets, global pRNFL thickness and BMO-MRW were conditioned on IOP. IOP-independent loci were then prioritised to identify candidate causal genes using transcriptome-wide association study and colocalization analysis. Several genes, such as NMNAT2 and TRIOBP, had robust associations with both phenotypes, with potential IOP-independent therapeutic translation for glaucoma. Overall, we identified novel loci for pRNFL thickness and BMO-MRW, highlighting potential drug-target genes acting independently from IOP, and elucidating genetic differences among pRNFL sectors.

Glaucoma is the leading cause of irreversible blindness; vision loss is preventable with timely treatment, but early detection is challenging, leaving ∼50% undiagnosed, highlighting the need for improved risk assessment tools. We developed a polygenic risk score (PRS) using data from >6 million individuals. PRS performance was exceptional in European ancestries; top 10% PRS individuals had 10-fold increased risk (OR=10.0) relative to the remainder. Performance remained good across all major ancestry groups; high-PRS individuals were at high absolute risk, especially Africans. High-risk individuals (top 10% PRS) developed glaucoma up to 25 years earlier than those in the lowest 10% and were at 100 times the risk. The PRS also predicted need for treatment escalation in early glaucoma and both prevalent and incident surgery. Risk profiling with this PRS which is clinically available, enables earlier identification and more timely treatment of high-risk individuals for preventable vision loss, with a reduced screening and monitoring burden for those at low-risk. Glaucoma is the leading cause of irreversible blindness worldwide. Early detection is essential because current treatments cannot restore lost vision. Population-wide screening using conventional risk factors for glaucoma (such as elevated intraocular pressure) is not currently cost-effective, though a better stratification method could improve early diagnosis rates and reduce over-monitoring of lower risk cases. Glaucoma is one of the most heritable common complex diseases, suggesting that genetics-based approaches could revolutionize risk stratification. Previous polygenic risk scores (PRS) indexing glaucoma genetic risk were limited by restricted applicability outside European ancestry populations, small discovery sample sizes, modest predictive power, poor clinical availability. The inability to cover a broad range of ancestries limited the practical clinical application of earlier versions of glaucoma PRS for risk stratification and patient management. A novel PRS, trained on >6 million individuals, has significantly improved glaucoma risk prediction across major ancestry groups, identifying high-risk individuals (top 10% of risk versus remainder) with odds ratios up to 10 in Europeans and 3.4–7.5 in non-Europeans (larger than for any other common complex disease). The excellent PRS risk stratification remained after adjusting for intraocular pressure; adding PRS to a prediction model with age, sex and intraocular pressure increased the Area Under the Curve for predicting glaucoma status from 0.63 to 0.82. High-risk individuals (top 10% PRS) developed glaucoma up to 25 years earlier than those in the lowest 10% and were at 100 times greater risk. The PRS also predicted structural progression, the need for treatment initiation and escalation in early glaucoma, both prevalent and incident incisional surgery for glaucoma, and the likelihood of glaucoma being present in first-degree relatives. These improvements impart strong clinical utility for population risk stratification, and the personalised management of individuals with clinical features suggestive of early stage glaucoma. This clinically available, enhanced PRS enables accurate identification of both high- and low-risk individuals across diverse populations, supporting earlier diagnosis, targeted monitoring, and timely treatment. It provides a clinical tool which can be applied for precision prevention and management of glaucoma, enabling cost-effective, equitable, genetically-informed glaucoma risk stratification across the entire population.