Exploring the Germline Genetics of In Situ and Invasive Cutaneous Melanoma: A Genome-Wide Association Study Meta-Analysis.

Importance: It is unknown whether germline genetic factors influence in situ melanoma risk differently than invasive melanoma risk. Objective: To determine whether differences in risk of in situ melanoma and invasive melanoma are heritable. Design, Setting, and Participants: Three genome-wide association study meta-analyses were conducted of in situ melanoma vs controls, invasive melanoma vs controls, and in situ vs invasive melanoma (case-case) using 4 population-based genetic cohorts: the UK Biobank, the FinnGen cohort, the QSkin Sun and Health Study, and the Queensland Study of Melanoma: Environmental and Genetic Associations (Q-MEGA). Melanoma status was determined using International Statistical Classification of Diseases and Related Health Problems codes from cancer registry data. Data were collected from 1987 to 2022, and data were analyzed from September 2022 to June 2023. Exposure: In situ and invasive cutaneous melanoma. Main Outcomes and Measures: To test whether in situ and invasive melanoma have independent heritable components, genetic effect estimates were calculated for single-nucleotide variants (SNV; formerly single-nucleotide polymorphisms) throughout the genome for each melanoma. Then, SNV-based heritability was estimated, the genetic correlation between melanoma subtypes was assessed, and polygenic risk scores (PRS) were generated for in situ vs invasive status in Q-MEGA participants. Results: A total of 6 genome-wide significant loci associated with in situ melanoma and 18 loci with invasive melanoma were identified. A strong genetic correlation (genetic r = 0.96; 95% CI, 0.76-1.15) was observed between the 2 classifications. Notably, loci near IRF4, KLF4, and HULC had significantly larger effects for in situ melanoma compared with invasive melanoma, while MC1R had a significantly larger effect on invasive melanoma compared with in situ melanoma. Heritability estimates were consistent for both, with in situ melanoma heritability of 6.7% (95% CI, 4.1-9.3) and invasive melanoma heritability of 4.9% (95% CI, 2.8-7.2). Finally, a PRS, derived from comparing invasive melanoma with in situ melanoma genetic risk, was on average significantly higher in participants with invasive melanoma (odds ratio per 1-SD increase in PRS, 1.43; 95% CI, 1.16-1.77). Conclusions and Relevance: There is much shared genetic architecture between in situ melanoma and invasive melanoma. Despite indistinguishable heritability estimates between the melanoma classifications, PRS suggest germline genetics may influence whether a person gets in situ melanoma or invasive melanoma. PRS could potentially help stratify populations based on invasive melanoma risk, informing future screening programs without exacerbating the current burden of melanoma overdiagnosis.

Macular structural integrity estimates are associated with Parkinson's disease genetic risk.

BACKGROUND: Optical coherence tomography (OCT) is a non-invasive technique to measure retinal layer thickness, providing insights into retinal ganglion cell integrity. Studies have shown reduced retinal nerve fibre layer (RNFL) and ganglion cell inner plexiform layer (GCIPL) thickness in Parkinson's disease (PD) patients. However, it is unclear if there is a common genetic overlap between the macula and peripapillary estimates with PD and if the genetic risk of PD is associated with changes in ganglion cell integrity estimates in young adults. METHOD: Western Australian young adults underwent OCT imaging. Their pRNFL, GCIPL, and overall retinal thicknesses were recorded, as well as their longitudinal changes between ages 20 and 28. Polygenic risk scores (PRS) were estimated for each participant based on genome-wide summary data from the largest PD genome-wide association study conducted to date. We further evaluated whether PD PRS was associated with changes in thickness at a younger age. To evaluate the overlap between retinal integrity estimates and PD, we annotated and prioritised genes using mBAT-combo and performed colocalisation through the GWAS pairwise method and HyPrColoc. We used a multi-omic approach and single-cell expression data of the retina and brain through a Mendelian randomisation framework to evaluate the most likely causal genes. Genes prioritised were analysed for missense variants that could have a pathogenic effect using AlphaMissense. RESULTS: We found a significant association between the Parkinson's disease polygenic risk score (PD PRS) and changes in retinal thickness in the macula of young adults assessed at 20 and 28 years of age. Gene-based analysis identified 27 genes common to PD and retinal integrity, with a notable region on chromosome 17. Expression analyses highlighted NSF, CRHR1, and KANSL1 as potential causal genes shared between PD and ganglion cell integrity measures. CRHR1 showed consistent results across multiple omics levels. INTERPRETATION: Our findings suggest that retinal measurements, particularly in young adults, could be a potential marker for PD risk, indicating a genetic overlap between retinal structural integrity and PD. The study highlights specific genes and loci, mainly on chromosome 17, as potential shared etiological factors for PD and retinal changes. Our results highlight the importance of further longitudinal studies to validate retinal structural metrics as early indicators of PD predisposition.

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.

Assessing the Efficacy of Synthetic Optic Disc Images for Detecting Glaucomatous Optic Neuropathy Using Deep Learning.

Purpose: Deep learning architectures can automatically learn complex features and patterns associated with glaucomatous optic neuropathy (GON). However, developing robust algorithms requires a large number of data sets. We sought to train an adversarial model for generating high-quality optic disc images from a large, diverse data set and then assessed the performance of models on generated synthetic images for detecting GON. Methods: A total of 17,060 (6874 glaucomatous and 10,186 healthy) fundus images were used to train deep convolutional generative adversarial networks (DCGANs) for synthesizing disc images for both classes. We then trained two models to detect GON, one solely on these synthetic images and another on a mixed data set (synthetic and real clinical images). Both the models were externally validated on a data set not used for training. The multiple classification metrics were evaluated with 95% confidence intervals. Models' decision-making processes were assessed using gradient-weighted class activation mapping (Grad-CAM) techniques. Results: Following receiver operating characteristic curve analysis, an optimal cup-to-disc ratio threshold for detecting GON from the training data was found to be 0.619. DCGANs generated high-quality synthetic disc images for healthy and glaucomatous eyes. When trained on a mixed data set, the model's area under the receiver operating characteristic curve attained 99.85% on internal validation and 86.45% on external validation. Grad-CAM saliency maps were primarily centered on the optic nerve head, indicating a more precise and clinically relevant attention area of the fundus image. Conclusions: Although our model performed well on synthetic data, training on a mixed data set demonstrated better performance and generalization. Integrating synthetic and real clinical images can optimize the performance of a deep learning model in glaucoma detection. Translational Relevance: Optimizing deep learning models for glaucoma detection through integrating DCGAN-generated synthetic and real-world clinical data can be improved and generalized in clinical practice.

Catalyst Design for Rh-Catalyzed Arene and Alkane C-H Borylation: The NHC Affects the Induction Period, and Indenyl is Superior to Cp.

In order to establish design criteria for Rh C-H borylation catalysts, analogues of the successful catalyst [Rh(Ind)(SIDipp)(COE)] (Ind = η5-indenyl, SIDipp = 1,3-bis(2,6-diisopropylphenyl)-4,5-dihydroimidazol-2-ylidene, and COE = cis-cyclooctene) were synthesized by changing the indenyl and carbene ligands. [RhCp(SIDipp)(COE)] (1) formed alongside the C-C activated, cyclometalated byproduct [RhCp(κ2CAr,Ccarbene-SIDipp')(iPr)] (rac-2; SIDipp' = 1-(6-isopropylphenyl)-3-(2,6-diisopropylphenyl)-4,5-dihydroimidazol-2-ylidene). Computational modeling of COE dissociation showed that both C-C and C-H activation of the SIDipp aryl group is thermally attainable and reversible under experimental conditions, with the C-C activation products being the more thermodynamically stable species. Oxidative addition of 1 with SiH(OEt)3 gave the Rh silyl hydride [RhCp(H){Si(OEt)3}(SIDipp)] (rac-3). [Rh(Ind)(IDipp)(COE)] (4; IDipp = 1,3-bis(2,6-diisopropylphenyl)-imidazole-2-ylidene), the carbonyl analogue [Rh(Ind)(IDipp)(CO)] (5; νCO = 1940 cm-1, cf. 1944 cm-1 for [Rh(Ind)(SIDipp)(COE)]), and [Rh(Ind)(IMe4)(COE)] (6; IMe4 = 1,3,4,5-tetramethylimidazol-2-ylidene) were also characterized, but attempts to synthesize Rh carbene complexes with fluorenyl or 1,2,3,4-tetrahydrofluorenyl ligands were not successful. For the catalytic C-H borylation of benzene using B2pin2, 1 was inactive at 80 °C, and [Rh(Ind)(SIDipp)(COE)] was superior to all other complexes tested due to the shortest induction period. However, the addition of HBpin to precatalyst 4 eliminated the induction period. Catalytic n-alkane C-H borylation using [Rh(Ind)(NHC)(COE)] gave yields of up to 21% alkylBpin, but [RhCp*(C2H4)2] was the better catalyst.

Polygenic Prediction of Keratoconus and its Measures: Cross-Sectional and Longitudinal Analyses in Community-Based Young Adults.

AIM: This study evaluates the performance of a multitrait polygenic risk score (PRS) in an independent cohort to predict incident or progression of keratoconus. DESIGN: Prospective cross-sectional and cohort study METHODS: Setting: Single-center; Study population: 1478 community-based young adults (18-30 years; 51% female), including 609 (52% female) who returned for an 8-year follow-up; Observation procedures: Scheimpflug imaging (Pentacam, Oculus), genotyping and development of a multitrait PRS previously validated to predict keratoconus in older adults.; Main outcome measure: Belin/Ambrόsio enhanced ectasia display (BAD-D) score and keratoconus, defined as BAD-D ≥2.6, were each analyzed against the PRS using linear and logistic regression, respectively. RESULTS: Prevalence of keratoconus was 2.5% (95% confidence interval [CI] = 1.9-3.6) in the cross-sectional cohort. Each z-score increase in PRS was associated with worse BAD-D z-score by 0.13 (95%CI = 0.08-0.18) and 1.6 increased odds of keratoconus. The 8-year keratoconus incidence was 2.6% (95%CI = 1.3-4.0). Participants in the highest PRS decile were more likely to have incident keratoconus compared to the rest of the cohort (odds ratio = 3.85, 95%CI = 1.21-12.22). For each z-score increase in PRS, 8-year change in BAD-D z-score worsened by 0.11 (95%CI = 0.04-0.17). CONCLUSIONS: A PRS for keratoconus could be useful in predicting incident keratoconus and progression, demonstrating its potential utility in clinical settings to identify patients at high risk of postsurgery ectasia or those who may benefit most from keratoconus intervention.

A Gold(I)-Acetylene Complex Synthesised using Single-Crystal Reactivity.

Using single-crystal to single-crystal solid/gas reactivity the gold(I) acetylene complex [Au(L1)(η2-HC≡CH)][BArF 4] is cleanly synthesized by addition of acetylene gas to single crystals of [Au(L1)(CO)][BArF 4] [L1=tris-2-(4,4'-di-tert-butylbiphenyl)phosphine, ArF=3,5-(CF3)2C6H3]. This simplest gold-alkyne complex has been characterized by single crystal X-ray diffraction, solution and solid-state NMR spectroscopy and periodic DFT. Bonding of HC≡CH with [Au(L1)]+ comprises both σ-donation and π-backdonation with additional dispersion interactions within the cavity-shaped phosphine.

Genetic predisposition to childhood obesity does not influence the risk of developing skin cancer in adulthood.

The relationship between body mass index (BMI) and melanoma and other skin cancers remains unclear. The objective of this study was to employ the Mendelian randomization (MR) approach to evaluate the effects of genetically predicted childhood adiposity on the risk of developing skin cancer later in life. Two-sample MR analyses were conducted using summary data from genome-wide association study (GWAS) meta-analyses of childhood BMI, melanoma, cutaneous squamous cell carcinoma (cSCC), and basal cell carcinoma (BCC). We used the inverse-variance-weighted (IVW) methods to obtain a pooled estimate across all genetic variants for childhood BMI. We performed multiple sensitivity analyses to evaluate the potential influence of various assumptions on our findings. We found no evidence that genetically predicted childhood BMI was associated with risks of developing melanoma, cSCC, or BCC in adulthood (OR, 95% CI: melanoma: 1.02 (0.93-1.13), cSCC 0.94 (0.79-1.11), BCC 0.97 (0.84-1.12)). Our findings do not support the conclusions from observational studies that childhood BMI is associated with increased risks of melanoma, cSCC, or BCC in adulthood. Intervening on childhood adiposity will not reduce the risk of common skin cancers later in life.

Transition Metal-Free Catalytic C-H Zincation and Alumination.

C-H metalation is the most efficient method to prepare aryl-zinc and -aluminium complexes that are ubiquitous nucleophiles. Virtually all C-H metalation routes to form Al/Zn organometallics require stoichiometric, strong Brønsted bases with no base-catalyzed reactions reported. Herein we present a catalytic in amine/ammonium salt (Et3N/[(Et3N)H]+) C-H metalation process to form aryl-zinc and aryl-aluminium complexes. Key to this approach is coupling an endergonic C-H metalation step with a sufficiently exergonic dehydrocoupling step between the ammonium salt by-product of C-H metalation ([(Et3N)H]+) and a Zn-H or Al-Me containing complex. This step, forming H2/MeH, makes the overall cycle exergonic while generating more of the reactive metal electrophile. Mechanistic studies supported by DFT calculations revealed metal-specific dehydrocoupling pathways, with the divergent reactivity due to the different metal valency (which impacts the accessibility of amine-free cationic metal complexes) and steric environment. Notably, dehydrocoupling in the zinc system proceeds through a ligand-mediated pathway involving protonation of the ß-diketiminate Cγ position. Given this process is applicable to two disparate metals (Zn and Al), other main group metals and ligand sets are expected to be amenable to this transition metal-free, catalytic C-H metalation.

Deep Learning-Based Identification of Intraocular Pressure-Associated Genes Influencing Trabecular Meshwork Cell Morphology.

Purpose: Genome-wide association studies have recently uncovered many loci associated with variation in intraocular pressure (IOP). Artificial intelligence (AI) can be used to interrogate the effect of specific genetic knockouts on the morphology of trabecular meshwork cells (TMCs) and thus, IOP regulation. Design: Experimental study. Subjects: Primary TMCs collected from human donors. Methods: Sixty-two genes at 55 loci associated with IOP variation were knocked out in primary TMC lines. All cells underwent high-throughput microscopy imaging after being stained with a 5-channel fluorescent cell staining protocol. A convolutional neural network was trained to distinguish between gene knockout and normal control cell images. The area under the receiver operator curve (AUC) metric was used to quantify morphological variation in gene knockouts to identify potential pathological perturbations. Main Outcome Measures: Degree of morphological variation as measured by deep learning algorithm accuracy of differentiation from normal controls. Results: Cells where LTBP2 or BCAS3 had been perturbed demonstrated the greatest morphological variation from normal TMCs (AUC 0.851, standard deviation [SD] 0.030; and AUC 0.845, SD 0.020, respectively). Of 7 multigene loci, 5 had statistically significant differences in AUC (P < 0.05) between genes, allowing for pathological gene prioritization. The mitochondrial channel most frequently showed the greatest degree of morphological variation (33.9% of cell lines). Conclusions: We demonstrate a robust method for functionally interrogating genome-wide association signals using high-throughput microscopy and AI. Genetic variations inducing marked morphological variation can be readily identified, allowing for the gene-based dissection of loci associated with complex traits. Financial Disclosures: Proprietary or commercial disclosure may be found in the Footnotes and Disclosures at the end of this article.

Polygenic Risk Scores and Genetically Complex Eye Disease.

The success of genome-wide association studies (GWASs) in uncovering genetic variants associated with complex eye diseases has paved the way for the development of risk prediction approaches based on disease genetics. Derived from GWAS data, polygenic risk scores (PRSs) have been emerging as a promising indicator of an individual's genetic liability to disease. In this review, we recap the current progress of PRS development and utility across a range of common eye diseases. While illustrating the prediction accuracy of PRSs and their valuable role in risk stratification for certain eye diseases, we also address PRSs' uncertain implementation in clinical settings at this stage, particularly in circumstances where limited treatment options are available. Finally, we discuss obstacles in translating PRSs into practice, including barriers to clinical impact, issues when working with different ancestry groups, and communicating risk scores, as well as projections for future improvements.

Genetic Analysis of Perceived Youthfulness Reveals Differences in How Men's and Women's Age Is Assessed.

Skin aging is a natural process that occurs over time but can be accelerated by sun exposure. Measuring skin age in a large population can provide insight into the extent of skin damage from sun exposure and skin cancer risk. Understanding the genetics of skin aging, within and across sexes (males and females), could improve our understanding of the genetic drivers of both skin aging and skin cancer. We used UK Biobank data to examine the genetic overlap between perceived youthfulness and traits relevant to actinic photoaging. Our GWAS identified 22 genome-wide significant loci for women and 43 for men. The genetic correlation (rg) between perceived youthfulness in men and women was significantly less than unity (rg = 0.75, 95% confidence interval = 0.69-0.80), suggesting a gene-by-sex interaction. In women, perceived youthfulness was modestly correlated with keratinocyte cancer (rg = -0.19) and skin tanning (rg = 0.18). In men, perceived youthfulness was correlated with male-pattern baldness (rg = -0.23). This suggests that the genetic architecture of perceived youthfulness may differ between sexes, with genes influencing skin tanning and skin cancer susceptibility driving the difference in women, whereas genes influencing male-pattern baldness and other puberty-related traits drive the difference in men. We recommend that future genetic analysis of skin aging include a sex-stratified component.

Predictive Performance of an Updated Polygenic Risk Score for Age-Related Macular Degeneration.

PURPOSE: A recent genome-wide association study of age-related macular degeneration (AMD) identified new AMD-associated risk variants. These variants now can be incorporated into an updated polygenic risk score (PRS). This study aimed to assess the performance of an updated PRS, PRS2023, in an independent cohort of older individuals with retinal imaging data and to compare performance with an older PRS, PRS2016. DESIGN: Cross-sectional study. PARTICIPANTS: A total of 4175 participants of European ancestry, 70 years of age or older, with genotype and retinal imaging data. METHODS: We used logistic regression models and area under the receiver operating characteristic curve (AUC) to assess the performance of PRS2023 compared with PRS2016. AMD status and severity were graded using color fundus photography. MAIN OUTCOME MEASURES: Association of PRS2023 and PRS2016 with AMD risk at baseline. RESULTS: At enrollment among 4175 participants, 2605 participants (62.4%) had no AMD and 853 participants (20.4%), 671 participants (16.1%), and 46 participants (1.1%) had early, intermediate, and late-stage AMD, respectively. More than 27% of the participants with a high PRS2023 (top quartile) had intermediate or late-stage AMD, compared with < 15% for those in the middle 2 quartiles and less than 13% for those in the lowest quartile. Both PRS2023 and PRS2016 were associated significantly with AMD after adjustment for age, sex, smoking status, and lipid levels, with increasing odds ratios (ORs) for worsening AMD grades. PRS2023 outperformed PRS2016 (P = 0.03 for all AMD and P = 0.03 for late AMD, DeLong test comparing AUC). PRS2023 was associated with late-stage AMD with an adjusted OR of 5.05 (95% confidence interval [CI], 3.41-7.47) per standard deviation. The AUC of a model containing conventional or nongenetic risk factors and PRS2023 was 91% (95% CI, 87%-95%) for predicting late-stage AMD, which improved 12% over the model without the PRS (AUC, 79%; P < 0.001 for difference). CONCLUSIONS: A new PRS, PRS2023, for AMD outperforms a previous PRS and predicts increasing risk for late-stage AMD (with stronger association for more severe imaging-confirmed AMD grades). Our findings have clinical implications for the improved prediction and risk stratification of AMD. FINANCIAL DISCLOSURE(S): Proprietary or commercial disclosure may be found in the Footnotes and Disclosures at the end of this article.

Novel ancestry-specific primary open-angle glaucoma loci and shared biology with vascular mechanisms and cell proliferation.

Primary open-angle glaucoma (POAG), a leading cause of irreversible blindness globally, shows disparity in prevalence and manifestations across ancestries. We perform meta-analysis across 15 biobanks (of the Global Biobank Meta-analysis Initiative) (n = 1,487,441: cases = 26,848) and merge with previous multi-ancestry studies, with the combined dataset representing the largest and most diverse POAG study to date (n = 1,478,037: cases = 46,325) and identify 17 novel significant loci, 5 of which were ancestry specific. Gene-enrichment and transcriptome-wide association analyses implicate vascular and cancer genes, a fifth of which are primary ciliary related. We perform an extensive statistical analysis of SIX6 and CDKN2B-AS1 loci in human GTEx data and across large electronic health records showing interaction between SIX6 gene and causal variants in the chr9p21.3 locus, with expression effect on CDKN2A/B. Our results suggest that some POAG risk variants may be ancestry specific, sex specific, or both, and support the contribution of genes involved in programmed cell death in POAG pathogenesis.

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.

High throughput functional profiling of genes at intraocular pressure loci reveals distinct networks for glaucoma.

INTRODUCTION: Primary open angle glaucoma (POAG) is a leading cause of blindness globally. Characterized by progressive retinal ganglion cell degeneration, the precise pathogenesis remains unknown. Genome-wide association studies (GWAS) have uncovered many genetic variants associated with elevated intraocular pressure (IOP), one of the key risk factors for POAG. We aimed to identify genetic and morphological variation that can be attributed to trabecular meshwork cell (TMC) dysfunction and raised IOP in POAG. METHODS: 62 genes across 55 loci were knocked-out in a primary human TMC line. Each knockout group, including five non-targeting control groups, underwent single-cell RNA-sequencing (scRNA-seq) for differentially-expressed gene (DEG) analysis. Multiplexed fluorescence coupled with CellProfiler image analysis allowed for single-cell morphological profiling. RESULTS: Many gene knockouts invoked DEGs relating to matrix metalloproteinases and interferon-induced proteins. We have prioritized genes at four loci of interest to identify gene knockouts that may contribute to the pathogenesis of POAG, including ANGPTL2, LMX1B, CAV1, and KREMEN1. Three genetic networks of gene knockouts with similar transcriptomic profiles were identified, suggesting a synergistic function in trabecular meshwork cell physiology. TEK knockout caused significant upregulation of nuclear granularity on morphological analysis, while knockout of TRIOBP, TMCO1 and PLEKHA7 increased granularity and intensity of actin and the cell-membrane. CONCLUSION: High-throughput analysis of cellular structure and function through multiplex fluorescent single-cell analysis and scRNA-seq assays enabled the direct study of genetic perturbations at the single-cell resolution. This work provides a framework for investigating the role of genes in the pathogenesis of glaucoma and heterogenous diseases with a strong genetic basis.

Contrasting reactivity of B-Cl and B-H bonds at [Ni(IMes)2] to form unsupported Ni-boryls.

[Ni(IMes)2] reacts with chloroboranes via oxidative addition to form rare unsupported Ni-boryls. In contrast, the oxidative addition of hydridoboranes is not observed and products from competing reaction pathways are identified. Computational studies relate these differences to the mechanism of oxidative addition: B-Cl activation proceeds via nucleophilic displacement of Cl-, while B-H activation would entail high energy concerted bond cleavage.

Genetic risk of glaucoma is associated with vascular and retinal nerve fibre wedge defects.

PURPOSE: To evaluate the association between localised vascular and retinal nerve fibre layer (RNFL) loss and genetic risk for glaucoma and cardiovascular disease using polygenic risk scores (PRS). METHODS: 858 eyes were included from 455 individuals with suspect and early manifest primary open angle glaucoma. Eyes were characterised as having localised vascular and/or RNFL wedge-shaped defects by scrutiny of optical coherence tomography angiography (OCTA) and OCT images, respectively. Investigations included associations with pre-established scores for genetic risk of glaucoma and cardiovascular disease in the context of glaucoma risk factors and systemic vascular disease outcomes. RESULTS: Higher genetic risk for glaucoma was associated with both vascular wedge defects and RNFL defects (p < 0.001 and p = 0.020, respectively). A greater genetic risk of glaucoma was associated with the presence of multiple vascular wedges per eye (p = 0.005). Glaucoma progression based on global RNFL loss was associated with vascular and RNFL wedge defects (p ≤ 0.001 and p = 0.008, respectively). The glaucoma PRS was significantly associated with vascular, but not RNFL, wedge defects after controlling for disc haemorrhage (p = 0.007 and p = 0.070, respectively). Vascular wedge defects were not related to the cardiovascular PRS. CONCLUSION: Individuals with a higher genetic risk of glaucoma based on the PRS were more likely to have retinal vascular defects, as well as structural glaucomatous loss, but this did not relate to systemic cardiovascular risk. This possibly implies a local pathophysiology for the vascular defects in some cases, which may have clinical relevance in the early stages of glaucoma and in individuals at high genetic risk.

Genome-Wide Meta-analysis Identifies Risk Loci and Improves Disease Prediction of Age-Related Macular Degeneration.

PURPOSE: To identify age-related macular degeneration (AMD) risk loci and to establish a polygenic prediction model. DESIGN: Genome-wide association study (GWAS) and polygenic risk score (PRS) construction. PARTICIPANTS: We included 64 885 European patients with AMD and 568 740 control participants (with overlapped samples) in the UK Biobank, Genetic Epidemiology Research on Aging (GERA), International AMD Consortium, FinnGen, and published early AMD GWASs in meta-analyses, as well as 733 European patients with AMD and 20 487 control participants from the Canadian Longitudinal Study on Aging (CLSA) and non-Europeans from the UK Biobank and GERA for polygenic risk score validation. METHODS: A multitrait meta-analysis of GWASs comprised 64 885 patients with AMD and 568 740 control participants; the multitrait approach accounted for sample overlap. We constructed a PRS for AMD based on both previously reported as well as unreported AMD loci. We applied the PRS to nonoverlapping data from the CLSA. MAIN OUTCOME MEASURES: We identified several single nucleotide polymorphisms associated with AMD and established a PRS for AMD risk prediction. RESULTS: We identified 63 AMD risk loci alongside the well-established AMD loci CFH and ARMS2, including 9 loci that were not reported in previous GWASs, some of which previously were linked to other eye diseases such as glaucoma (e.g., HIC1). We applied our PRS to nonoverlapping data from the CLSA. A new PRS was constructed using the PRS method, PRS-CS, and significantly improved the prediction accuracy of AMD risk compared with PRSs from previously published datasets. We further showed that even people who carry all the well-known AMD risk alleles at CFH and ARMS2 vary considerably in their AMD risk (ranging from close to 0 in individuals with low PRS to > 50% in individuals with high PRS). Although our PRS was derived in individuals of European ancestry, the PRS shows potential for predicting risk in people of East Asian, South Asian, and Latino ancestry. CONCLUSIONS: Our findings improve the knowledge of the genetic architecture of AMD and help achieve better accuracy in AMD prediction. FINANCIAL DISCLOSURE(S): Proprietary or commercial disclosure may be found in the Footnotes and Disclosures at the end of this article.

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.