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To characterize the phenotype observed in a case series with macular disease and determine the cause.Multicenter case series.Six families (7 patients) with sporadic or multiplex macular disease with onset at 20 to 78 years, and 1 patient with age-related macular degeneration.Patients underwent ophthalmic examination; exome, genome, or targeted sequencing; and/or polymerase chain reaction (PCR) amplification of the breakpoint, followed by cloning and Sanger sequencing or direct Sanger sequencing.Clinical phenotypes, genomic findings, and a hypothesis explaining the mechanism underlying disease in these patients.All 8 cases carried the same deletion encompassing the genes TPRX1, CRX, and SULT2A1, which was absent from 382 control individuals screened by breakpoint PCR and 13 096 Clinical Genetics patients with a range of other inherited conditions screened by array comparative genomic hybridization. Microsatellite genotypes showed that these 7 families are not closely related, but genotypes immediately adjacent to the deletion breakpoints suggest they may share a distant common ancestor.Previous studies had found that carriers for a single defective CRX allele that was predicted to produce no functional CRX protein had a normal ocular phenotype. Here, we show that CRX whole-gene deletion in fact does cause a dominant late-onset macular disease.

To generate a prognostic model to predict keratoconus progression to corneal crosslinking (CXL).Retrospective cohort study.We recruited 5025 patients (9341 eyes) with early keratoconus between January 2011 and November 2020. Genetic data from 926 patients were available. We investigated both keratometry or CXL as end points for progression and used the Royston-Parmar method on the proportional hazards scale to generate a prognostic model. We calculated hazard ratios (HRs) for each significant covariate, with explained variation and discrimination, and performed internal-external cross validation by geographic regions.After exclusions, model fitting comprised 8701 eyes, of which 3232 underwent CXL. For early keratoconus, CXL provided a more robust prognostic model than keratometric progression. The final model explained 33% of the variation in time to event: age HR (95% CI) 0.9 (0.90-0.91), maximum anterior keratometry 1.08 (1.07-1.09), and minimum corneal thickness 0.95 (0.93-0.96) as significant covariates. Single-nucleotide polymorphisms (SNPs) associated with keratoconus (n=28) did not significantly contribute to the model. The predicted time-to-event curves closely followed the observed curves during internal-external validation. Differences in discrimination between geographic regions was low, suggesting the model maintained its predictive ability.A prognostic model to predict keratoconus progression could aid patient empowerment, triage, and service provision. Age at presentation is the most significant predictor of progression risk. Candidate SNPs associated with keratoconus do not contribute to progression risk.

Retinal diseases are a leading cause of blindness in developed countries, accounting for the largest share of visually impaired children, working-age adults (inherited retinal disease), and elderly individuals (age-related macular degeneration). These conditions need specialised clinicians to interpret multimodal retinal imaging, with diagnosis and intervention potentially delayed. With an increasing and ageing population, this is becoming a global health priority. One solution is the development of artificial intelligence (AI) software to facilitate rapid data processing. Herein, we review research offering decision support for the diagnosis, classification, monitoring, and treatment of retinal disease using AI. We have prioritised diabetic retinopathy, age-related macular degeneration, inherited retinal disease, and retinopathy of prematurity. There is cautious optimism that these algorithms will be integrated into routine clinical practice to facilitate access to vision-saving treatments, improve efficiency of healthcare systems, and assist clinicians in processing the ever-increasing volume of multimodal data, thereby also liberating time for doctor-patient interaction and co-development of personalised management plans.

ImportanceThe potential association of schizophrenia with distinct retinal changes is of clinical interest but has been challenging to investigate because of a lack of sufficiently large and detailed cohorts.ObjectiveTo investigate the association between retinal biomarkers from multimodal imaging (oculomics) and schizophrenia in a large real-world population.Design, Setting, and ParticipantsThis cross-sectional analysis used data from a retrospective cohort of 154 830 patients 40 years and older from the AlzEye study, which linked ophthalmic data with hospital admission data across England. Patients attended Moorfields Eye Hospital, a secondary care ophthalmic hospital with a principal central site, 4 district hubs, and 5 satellite clinics in and around London, United Kingdom, and had retinal imaging during the study period (January 2008 and April 2018). Data were analyzed from January 2022 to July 2022.Main Outcomes and MeasuresRetinovascular and optic nerve indices were computed from color fundus photography. Macular retinal nerve fiber layer (RNFL) and ganglion cell–inner plexiform layer (mGC-IPL) thicknesses were extracted from optical coherence tomography. Linear mixed-effects models were used to examine the association between schizophrenia and retinal biomarkers.ResultsA total of 485 individuals (747 eyes) with schizophrenia (mean [SD] age, 64.9 years [12.2]; 258 [53.2%] female) and 100 931 individuals (165 400 eyes) without schizophrenia (mean age, 65.9 years [13.7]; 53 253 [52.8%] female) were included after images underwent quality control and potentially confounding conditions were excluded. Individuals with schizophrenia were more likely to have hypertension (407 [83.9%] vs 49 971 [48.0%]) and diabetes (364 [75.1%] vs 28 762 [27.6%]). The schizophrenia group had thinner mGC-IPL (−4.05 μm, 95% CI, −5.40 to −2.69; P = 5.4 × 10−9), which persisted when investigating only patients without diabetes (−3.99 μm; 95% CI, −6.67 to −1.30; P = .004) or just those 55 years and younger (−2.90 μm; 95% CI, −5.55 to −0.24; P = .03). On adjusted analysis, retinal fractal dimension among vascular variables was reduced in individuals with schizophrenia (−0.14 units; 95% CI, −0.22 to −0.05; P = .001), although this was not present when excluding patients with diabetes.Conclusions and RelevanceIn this study, patients with schizophrenia had measurable differences in neural and vascular integrity of the retina. Differences in retinal vasculature were mostly secondary to the higher prevalence of diabetes and hypertension in patients with schizophrenia. The role of retinal features as adjunct outcomes in patients with schizophrenia warrants further investigation.

Contains fulltext : 292919.pdf (Publisher’s version ) (Open Access) BACKGROUND: High myopia (HM), defined as a spherical equivalent refractive error (SER) ≤ -6.00 diopters (D), is a leading cause of sight impairment, through myopic macular degeneration (MMD). We aimed to derive an improved polygenic score (PGS) for predicting children at risk of HM and to test if a PGS is predictive of MMD after accounting for SER. METHODS: The PGS was derived from genome-wide association studies in participants of UK Biobank, CREAM Consortium, and Genetic Epidemiology Research on Adult Health and Aging. MMD severity was quantified by a deep learning algorithm. Prediction of HM was quantified as the area under the receiver operating curve (AUROC). Prediction of severe MMD was assessed by logistic regression. FINDINGS: In independent samples of European, African, South Asian and East Asian ancestry, the PGS explained 19% (95% confidence interval 17-21%), 2% (1-3%), 8% (7-10%) and 6% (3-9%) of the variation in SER, respectively. The AUROC for HM in these samples was 0.78 (0.75-0.81), 0.58 (0.53-0.64), 0.71 (0.69-0.74) and 0.67 (0.62-0.72), respectively. The PGS was not associated with the risk of MMD after accounting for SER: OR = 1.07 (0.92-1.24). INTERPRETATION: Performance of the PGS approached the level required for clinical utility in Europeans but not in other ancestries. A PGS for refractive error was not predictive of MMD risk once SER was accounted for. FUNDING: Supported by the Welsh Government and Fight for Sight (24WG201).

The predominant cause of Fuchs endothelial corneal dystrophy (FECD) is a CTG repeat expansion (termed CTG18.1) situated within an intron of the transcription factor encoding gene,TCF4. Here we use a primary FECD case-derived corneal endothelial cell (CEC) system to enhance our understanding of multiple pathogenic processes underlying CTG18.1-mediated FECD. We define differential gene expression and alternative splice events using long- and short-read RNA-seq datasets generated from 15 biologically independent primary CEC lines, comprehensively characterizing aberrant splicing-related aspects of the disease. Further targeted analysis of these data, alongside a complementary spatial transcriptomics approach, reveals a unique and distinctive pattern ofTCF4-specific dysregulation that underpins expansion-positive FECD and isolates downstream consequences of this shift as an important pathogenic component of disease. To explore whetherTCF4dysregulation, irrespective of CTG18.1 expansions, could cause FECD we also interrogated exome data generated from a large (n=141) genetically refined cohort of CTG18.1 expansion-negative FECD cases. We identify four rare and predicted deleterious (minor allele frequency 15)TCF4variants all encompassed by only a small fraction (10/93) of totalTCF4transcripts suggesting that, in rare instances, disruption of a specific subset of TCF4 isoforms may also confer CEC-specific disease independent of CTG18.1 expansions. In summary, our study supports the hypothesis that at least two distinct pathogenic mechanisms, RNA toxicity andTCF4isoform-specific dysregulation, underpin the pathophysiology of FECD. We anticipate these data will inform and guide the development of translational interventions for this common triplet-repeat mediated disease.Graphical abstract

Introduction Smoking lapses after the quit date often lead to full relapse. To inform the development of real time, tailored lapse prevention support, we used observational data from a popular smoking cessation app to develop supervised machine learning algorithms to distinguish lapse from non-lapse reports. Aims and Methods We used data from app users with ≥20 unprompted data entries, which included information about craving severity, mood, activity, social context, and lapse incidence. A series of group-level supervised machine learning algorithms (eg, Random Forest, XGBoost) were trained and tested. Their ability to classify lapses for out-of-sample (1) observations and (2) individuals were evaluated. Next, a series of individual-level and hybrid algorithms were trained and tested. Results Participants (N = 791) provided 37 002 data entries (7.6% lapses). The best-performing group-level algorithm had an area under the receiver operating characteristic curve (AUC) of 0.969 (95% confidence interval [CI] = 0.961 to 0.978). Its ability to classify lapses for out-of-sample individuals ranged from poor to excellent (AUC = 0.482–1.000). Individual-level algorithms could be constructed for 39/791 participants with sufficient data, with a median AUC of 0.938 (range: 0.518–1.000). Hybrid algorithms could be constructed for 184/791 participants and had a median AUC of 0.825 (range: 0.375–1.000). Conclusions Using unprompted app data appeared feasible for constructing a high-performing group-level lapse classification algorithm but its performance was variable when applied to unseen individuals. Algorithms trained on each individual’s dataset, in addition to hybrid algorithms trained on the group plus a proportion of each individual’s data, had improved performance but could only be constructed for a minority of participants. Implications This study used routinely collected data from a popular smartphone app to train and test a series of supervised machine learning algorithms to distinguish lapse from non-lapse events. Although a high-performing group-level algorithm was developed, it had variable performance when applied to new, unseen individuals. Individual-level and hybrid algorithms had somewhat greater performance but could not be constructed for all participants because of the lack of variability in the outcome measure. Triangulation of results with those from a prompted study design is recommended prior to intervention development, with real-world lapse prediction likely requiring a balance between unprompted and prompted app data.

BACKGROUND: The U.K. 100,000 Genomes Project is in the process of investigating the role of genome sequencing in patients with undiagnosed rare diseases after usual care and the alignment of this research with health care implementation in the U.K. National Health Service. Other parts of this project focus on patients with cancer and infection.METHODS: We conducted a pilot study involving 4660 participants from 2183 families, among whom 161 disorders covering a broad spectrum of rare diseases were present. We collected data on clinical features with the use of Human Phenotype Ontology terms, undertook genome sequencing, applied automated variant prioritization on the basis of applied virtual gene panels and phenotypes, and identified novel pathogenic variants through research analysis.RESULTS: Diagnostic yields varied among family structures and were highest in family trios (both parents and a proband) and families with larger pedigrees. Diagnostic yields were much higher for disorders likely to have a monogenic cause (35%) than for disorders likely to have a complex cause (11%). Diagnostic yields for intellectual disability, hearing disorders, and vision disorders ranged from 40 to 55%. We made genetic diagnoses in 25% of the probands. A total of 14% of the diagnoses were made by means of the combination of research and automated approaches, which was critical for cases in which we found etiologic noncoding, structural, and mitochondrial genome variants and coding variants poorly covered by exome sequencing. Cohortwide burden testing across 57,000 genomes enabled the discovery of three new disease genes and 19 new associations. Of the genetic diagnoses that we made, 25% had immediate ramifications for clinical decision making for the patients or their relatives.CONCLUSIONS: Our pilot study of genome sequencing in a national health care system showed an increase in diagnostic yield across a range of rare diseases. (Funded by the National Institute for Health Research and others.).

Background: Congenital cataracts are the most common cause of visual impairment worldwide. Inherited cataract is a clinically and genetically heterogeneous disease. Here we report disease-causing variants in a novel gene, CYP21A2, causing autosomal dominant posterior polar cataract. Variants in this gene are known to cause autosomal recessive congenital adrenal hyperplasia (CAH). Methods: Using whole-exome sequencing (WES), we have identified disease-causing sequence variants in two families of British and Irish origin, and in two isolated cases of Asian-Indian and British origin. Bioinformatics analysis confirmed these variants as rare with damaging pathogenicity scores. Segregation was tested within the families using direct Sanger sequencing. Results: A nonsense variant NM_000500.9 c.955 C > T; p.Q319* was identified in CYP21A2 in two families with posterior polar cataract and in an isolated case with unspecified congenital cataract phenotype. This is the same variant previously linked to CAH and identified as Q318* in the literature. We have also identified a rare missense variant NM_000500.9 c.770 T > C; p.M257T in an isolated case with unspecified congenital cataract phenotype. Conclusion: This is the first report of separate sequence variants in CYP21A2 associated with congenital cataract. Our findings extend the genetic basis for congenital cataract and add to the phenotypic spectrum of CYP21A2 variants and particularly the CAH associated Q318* variant. CYP21A2 has a significant role in mineralo- and gluco-corticoid biosynthesis. These findings suggest that CYP21A2 may be important for extra-adrenal biosynthesis of aldosterone and cortisol in the eye lens.

Highlights • KCNV2-associated retinopathy is a slowly progressive disease with early retinal changes, which are predominantly symmetric between eyes. • Disease course can be unpredictable and may severely affect children and young adults. • Findings suggest a potential window for intervention until 40 years of age, albeit with variability between patients due to macular atrophy., Purpose To describe the detailed retinal phenotype of KCNV2-associated retinopathy. Study design Multicenter international retrospective case series. Methods Review of retinal imaging including fundus autofluorescence (FAF) and optical coherence tomography (OCT), including qualitative and quantitative analyses. Results Three distinct macular FAF features were identified: (1) centrally increased signal (n = 35, 41.7%), (2) decreased autofluorescence (n = 27, 31.1%), and (3) ring of increased signal (n = 37, 44.0%). Five distinct FAF groups were identified based on combinations of those features, with 23.5% of patients changing the FAF group over a mean (range) follow-up of 5.9 years (1.9-13.1 years). Qualitative assessment was performed by grading OCT into 5 grades: (1) continuous ellipsoid zone (EZ) (20.5%); (2) EZ disruption (26.1%); (3) EZ absence, without optical gap and with preserved retinal pigment epithelium complex (21.6%); (4) loss of EZ and a hyporeflective zone at the foveola (6.8%); and (5) outer retina and retinal pigment epithelium complex loss (25.0%). Eighty-six patients had scans available from both eyes, with 83 (96.5%) having the same grade in both eyes, and 36.1% changed OCT grade over a mean follow-up of 5.5 years. The annual rate of outer nuclear layer thickness change was similar for right and left eyes. Conclusions KCNV2-associated retinopathy is a slowly progressive disease with early retinal changes, which are predominantly symmetric between eyes. The identification of a single OCT or FAF measurement as an endpoint to determine progression that applies to all patients may be challenging, although outer nuclear layer thickness is a potential biomarker. Findings suggest a potential window for intervention until 40 years of age.

Summary Background Geographic atrophy is a major vision-threatening manifestation of age-related macular degeneration, one of the leading causes of blindness globally. Geographic atrophy has no proven treatment or method for easy detection. Rapid, reliable, and objective detection and quantification of geographic atrophy from optical coherence tomography (OCT) retinal scans is necessary for disease monitoring, prognostic research, and to serve as clinical endpoints for therapy development. To this end, we aimed to develop and validate a fully automated method to detect and quantify geographic atrophy from OCT. Methods We did a deep-learning model development and external validation study on OCT retinal scans at Moorfields Eye Hospital Reading Centre and Clinical AI Hub (London, UK). A modified U-Net architecture was used to develop four distinct deep-learning models for segmentation of geographic atrophy and its constituent retinal features from OCT scans acquired with Heidelberg Spectralis. A manually segmented clinical dataset for model development comprised 5049 B-scans from 984 OCT volumes selected randomly from 399 eyes of 200 patients with geographic atrophy secondary to age-related macular degeneration, enrolled in a prospective, multicentre, phase 2 clinical trial for the treatment of geographic atrophy (FILLY study). Performance was externally validated on an independently recruited dataset from patients receiving routine care at Moorfields Eye Hospital (London, UK). The primary outcome was segmentation and classification agreement between deep-learning model geographic atrophy prediction and consensus of two independent expert graders on the external validation dataset. Findings The external validation cohort included 884 B-scans from 192 OCT volumes taken from 192 eyes of 110 patients as part of real-life clinical care at Moorfields Eye Hospital between Jan 1, 2016, and Dec, 31, 2019 (mean age 78·3 years [SD 11·1], 58 [53%] women). The resultant geographic atrophy deep-learning model produced predictions similar to consensus human specialist grading on the external validation dataset (median Dice similarity coefficient [DSC] 0·96 [IQR 0·10]; intraclass correlation coefficient [ICC] 0·93) and outperformed agreement between human graders (DSC 0·80 [0·28]; ICC 0·79). Similarly, the three independent feature-specific deep-learning models could accurately segment each of the three constituent features of geographic atrophy: retinal pigment epithelium loss (median DSC 0·95 [IQR 0·15]), overlying photoreceptor degeneration (0·96 [0·12]), and hypertransmission (0·97 [0·07]) in the external validation dataset versus consensus grading. Interpretation We present a fully developed and validated deep-learning composite model for segmentation of geographic atrophy and its subtypes that achieves performance at a similar level to manual specialist assessment. Fully automated analysis of retinal OCT from routine clinical practice could provide a promising horizon for diagnosis and prognosis in both research and real-life patient care, following further clinical validation Funding Apellis Pharmaceuticals.

Aim: To familiarize clinicians with clinical genomics, and to describe the potential of cloud computing for enabling the future routine use of genomics in eye hospital settings.Design: Review article exploring the potential for cloud-based genomic pipelines in eye hospitals.Methods: Narrative review of the literature relevant to clinical genomics and cloud computing, using PubMed and Google Scholar. A broad overview of these fields is provided, followed by key examples of their integration.Results: Cloud computing could benefit clinical genomics due to scalability of resources, potentially lower costs, and ease of data sharing between multiple institutions. Challenges include complex pricing of services, costs from mistakes or experimentation, data security, and privacy concerns.Conclusions and future perspectives: Clinical genomics is likely to become more routinely used in clinical practice. Currently this is delivered in highly specialist centers. In the future, cloud computing could enable delivery of clinical genomics services in non-specialist hospital settings, in a fast, cost-effective way, whilst enhancing collaboration between clinical and research teams.

Background Lens development is orchestrated by transcription factors. Disease-causing variants in transcription factors and their developmental target genes are associated with congenital cataracts and other eye anomalies. Methods Using whole exome sequencing, we identified disease-causing variants in two large British families and one isolated case with autosomal dominant congenital cataract. Bioinformatics analysis confirmed these disease-causing mutations as rare or novel variants, with a moderate to damaging pathogenicity score, with testing for segregation within the families using direct Sanger sequencing. Results Family A had a missense variant (c.184 G>A; p.V62M) in PAX6 and affected individuals presented with nuclear cataract. Family B had a frameshift variant (c.470–477dup; p.A160R*) in PITX3 that was also associated with nuclear cataract. A recurrent missense variant in HSF4 (c.341 T>C; p.L114P) was associated with congenital cataract in a single isolated case. Conclusions We have therefore identified novel variants in PAX6 and PITX3 that cause autosomal dominant congenital cataract.

Rare eye diseases such as inherited retinal diseases (IRDs) are challenging to diagnose genetically. IRDs are typically monogenic disorders and represent a leading cause of blindness in children and working-age adults worldwide. A growing number are now being targeted in clinical trials, with approved treatments increasingly available. However, access requires a genetic diagnosis to be established sufficiently early. Critically, the timely identification of a genetic cause remains challenging. We demonstrate that a deep-learning algorithm, Eye2Gene, trained on the largest imaging dataset of patients with IRDs currently available, provides expert-level accuracy for genetic diagnosis for the 36 most common molecular causes (top-5 accuracy = 85.6%). This algorithm has been deployed online (app.eye2gene.com) and externally validated on data provided by four different clinical centers. Eye2Gene can facilitate access to diagnostic expertise, only currently available in a limited number of specialist centers globally, and thereby dramatically accelerate the genetic diagnostic odyssey.

Biallelic pathogenic variants in ABCA4 are the commonest cause of monogenic retinal disease. The full-field electroretinogram (ERG) quantifies severity of retinal dysfunction. We explored application of machine learning in ERG interpretation and in genotype-phenotype correlations.International standard ERGs in 597 cases of ABCA4 retinopathy were classified into three functional phenotypes by human experts: macular dysfunction alone (group 1), or with additional generalized cone dysfunction (group 2), or both cone and rod dysfunction (group 3). Algorithms were developed for automatic selection and measurement of ERG components and for classification of ERG phenotype. Elastic-net regression was used to quantify severity of specific ABCA4 variants based on effect on retinal function.Of the cohort, 57.6%, 7.4%, and 35.0% fell into groups 1, 2, and 3 respectively. Compared with human experts, automated classification showed overall accuracy of 91.8% (SE, 0.169), and 96.7%, 39.3%, and 93.8% for groups 1, 2, and 3. When groups 2 and 3 were combined, the average holdout group accuracy was 93.6% (SE, 0.142). A regression model yielded phenotypic severity scores for the 47 commonest ABCA4 variants.This study quantifies prevalence of phenotypic groups based on retinal function in a uniquely large single-center cohort of patients with electrophysiologically characterized ABCA4 retinopathy and shows applicability of machine learning. Novel regression-based analyses of ABCA4 variant severity could identify individuals predisposed to severe disease.Machine learning can yield meaningful classifications of ERG data, and data-driven scoring of genetic variants can identify patients likely to benefit most from future therapies.

To review and describe in detail the clinical course, functional and anatomical characteristics of RP2-associated retinal degeneration.Retrospective case series.Males with disease-causing variants in the RP2 gene.Review of all case notes and results of molecular genetic testing, retinal imaging (fundus autofluorescence (FAF) imaging, optical coherence tomography (OCT)) and electrophysiology assessment.Molecular genetic testing, clinical findings including best-corrected visual acuity (BCVA), qualitative and quantitative retinal imaging analysis, and electrophysiology parameters.Fifty-four molecularly confirmed patients were identified, from 38 pedigrees. Twenty-eight disease-causing variants were identified; with 20 not previously clinically characterized. Fifty-three patients (98.1%) presented with retinitis pigmentosa. The mean age of onset (range, ±SD) was 9.6 years of age (1-57 years, ± 9.2 years). Forty-four patients (91.7%) had childhood-onset disease, with mean age of onset of 7.6 years. The commonest first symptom was night blindness (68.8%). Mean BCVA (range, ±SD) was 0.91 LogMAR (0-2.7, ±0.80) and 0.94 LogMAR (0-2.7, ±0.78) for right and left eyes respectively. Based on the WHO visual impairment criteria, 18 patients (34%) had low vision. The majority (17/22) showed ERG evidence of a rod-cone dystrophy. Pattern ERG P50 was undetectable in all but 2 patients. A range of FAF findings was observed, from normal to advanced atrophy. There were no statistically significant differences between right and left eyes for ellipsoid zone (EZ) width and outer nuclear layer (ONL) thickness. The mean annual rate of EZ width loss was 219 μm/year and the mean annual decrease in ONL thickness was 4.93 μm/year. No patient with childhood-onset disease had identifiable EZ after the age of 26 years at baseline or follow-up. Four patients had adulthood-onset disease and a less severe phenotype.This study details the clinical phenotype of RP2 retinopathy in a large cohort. The majority presented with early-onset severe retinal degeneration, with early macular involvement and complete loss of the foveal photoreceptor layer by the third decade of life. Full-field ERGs revealed rod-cone dystrophy in the vast majority, but with generalised (peripheral) cone system involvement of widely varying severity in the first two decades of life.

Purpose To investigate genetics, electrophysiology, and clinical course of KCNV2-associated retinopathy in a cohort of children and adults. Study design This was a multicenter international clinical cohort study. Methods Review of clinical notes and molecular genetic testing. Full-field electroretinography (ERG) recordings, incorporating the international standards, were reviewed and quantified and compared with age and recordings from control subjects. Results In total, 230 disease-associated alleles were identified from 117 patients, corresponding to 75 different KCNV2 variants, with 28 being novel. The mean age of onset was 3.9 years old. All patients were symptomatic before 12 years of age (range, 0-11 years). Decreased visual acuity was present in all patients, and 4 other symptoms were common: reduced color vision (78.6%), photophobia (53.5%), nyctalopia (43.6%), and nystagmus (38.6%). After a mean follow-up of 8.4 years, the mean best-corrected visual acuity (BCVA ± SD) decreased from 0.81 ± 0.27 to 0.90 ± 0.31 logarithm of minimal angle of resolution. Full-field ERGs showed pathognomonic waveform features. Quantitative assessment revealed a wide range of ERG amplitudes and peak times, with a mean rate of age-associated reduction indistinguishable from the control group. Mean amplitude reductions for the dark-adapted 0.01 ERG, dark-adapted 10 ERG a-wave, and LA 3.0 30 Hz and LA3 ERG b-waves were 55%, 21%, 48%, and 74%, respectively compared with control values. Peak times showed stability across 6 decades. Conclusion In KCNV2-associated retinopathy, full-field ERGs are diagnostic and consistent with largely stable peripheral retinal dysfunction. Report 1 highlights the severity of the clinical phenotype and established a large cohort of patients, emphasizing the unmet need for trials of novel therapeutics., Highlights • The current study established the largest and most characterized cohort of molecularly confirmed patients with KCNV2-associated retinopathy. • Report 1 highlights the genetic background, evidence of electroretinography stability over a broad age range, and the severe phenotype of the disease.

Genetically determined cataract is both clinically and molecularly highly heterogeneous. Here, we have identified a heterozygous variant in the lens integral membrane protein LIM2, the second most abundant protein in the lens, responsible for congenital sutural/lamellar cataract in a three-generation Japanese family.Whole exome sequencing (WES) was undertaken in one affected and one unaffected individual from a family with autosomal dominant congenital cataract to establish the underlying genetic basis.A recurrent missense variant LIM2: c.388CT; p.R130C was identified and found to co-segregate with disease. In addition, one variant COL11A1:c.3788CT of unknown significance (VUS) was also identified.We report a variant in LIM2 causing an isolated autosomal-dominant congenital sutural/lamellar cataract in a Japanese family. This is the first report of a LIM2 variant in the Japanese population. Hence, we expand the mutation spectrum of LIM2 variants in different ethnic groups.

Myopia is the commonest visual impairment. Several genetic loci confer risk, but mechanisms by which they do this are unknown. Retinal signals drive eye growth, and myopia usually results from an excessively long eye. The common variant most strongly associated with myopia is near the GJD2 gene, encoding connexin-36, which forms retinal gap junctions. Light-evoked responses of retinal neurons can be recorded noninvasively as the electroretinogram (ERG). We analyzed these responses from 186 adult twin volunteers who had been genotyped at this locus. Participants underwent detailed ERG recordings incorporating international standard stimuli as well as experimental protocols aiming to separate dark-adapted rod- and cone-driven responses. A mixed linear model was used to explore association between allelic dosage at the locus and international standard ERG parameters after adjustment for age, sex, and family structure. Significant associations were found for parameters of light-adapted, but not dark-adapted, responses. Further investigation of isolated rod- and cone-driven ERGs confirmed associations with cone-driven, but not rod-driven, a-wave amplitudes. Comparison with responses to similar experimental stimuli from a patient with a prior central retinal artery occlusion, and from two patients with selective loss of ON-bipolar cell signals, was consistent with the associated parameters being derived from signals from cone-driven OFF-bipolar cells. Analysis of single-cell transcriptome data revealed strongest GJD2 expression in cone photoreceptors; bipolar cell expression appeared strongest in OFF-bipolar cells and weakest in rod-driven ON-bipolar cells. Our findings support a potential role for altered signaling in cone-driven OFF pathways in myopia development.

Background/AimsTo investigate the utility of a data-driven deep learning approach in patients with inherited retinal disorder (IRD) and to predict the causative genes based on fundus photography and fundus autofluorescence (FAF) imaging.MethodsClinical and genetic data from 1302 subjects from 729 genetically confirmed families with IRD registered with the Japan Eye Genetics Consortium were reviewed. Three categories of genetic diagnosis were selected, based on the high prevalence of their causative genes: Stargardt disease (ABCA4), retinitis pigmentosa (EYS) and occult macular dystrophy (RP1L1). Fundus photographs and FAF images were cropped in a standardised manner with a macro algorithm. Images for training/testing were selected using a randomised, fourfold cross-validation method. The application program interface was established to reach the learning accuracy of concordance (target: >80%) between the genetic diagnosis and the machine diagnosis (ABCA4, EYS, RP1L1 and normal).ResultsA total of 417 images from 156 Japanese subjects were examined, including 115 genetically confirmed patients caused by the three prevalent causative genes and 41 normal subjects. The mean overall test accuracy for fundus photographs and FAF images was 88.2% and 81.3%, respectively. The mean overall sensitivity/specificity values for fundus photographs and FAF images were 88.3%/97.4% and 81.8%/95.5%, respectively.ConclusionA novel application of deep neural networks in the prediction of the causative IRD genes from fundus photographs and FAF, with a high prediction accuracy of over 80%, was highlighted. These achievements will extensively promote the quality of medical care by facilitating early diagnosis, especially by non-specialists, access to care, reducing the cost of referrals, and preventing unnecessary clinical and genetic testing.

A number of large technology companies have created code-free cloud-based platforms that allow researchers and clinicians without coding experience to create deep learning algorithms. In this study, we comprehensively analyse the performance and featureset of six platforms, using four representative cross-sectional and en-face medical imaging datasets to create image classification models. The mean (s.d.) F1 scores across platforms for all model–dataset pairs were as follows: Amazon, 93.9 (5.4); Apple, 72.0 (13.6); Clarifai, 74.2 (7.1); Google, 92.0 (5.4); MedicMind, 90.7 (9.6); Microsoft, 88.6 (5.3). The platforms demonstrated uniformly higher classification performance with the optical coherence tomography modality. Potential use cases given proper validation include research dataset curation, mobile ‘edge models’ for regions without internet access, and baseline models against which to compare and iterate bespoke deep learning approaches. Several technology companies offer platforms for users without coding experience to develop deep learning algorithms. This Analysis compares the performance of six ‘code-free deep learning’ platforms (from Amazon, Apple, Clarifai, Google, MedicMind and Microsoft) in creating medical image classification models.

Purpose To present the detailed retinal phenotype of patients with Leber Congenital Amaurosis/Early-Onset Severe Retinal Dystrophy (LCA/EOSRD) caused by sequence variants in four genes, either not (n = 1) or very rarely (n = 3) previously associated with the disease. Methods Retrospective case series of LCA/EOSRD from four pedigrees. Chart review of clinical notes, multimodal retinal imaging, electrophysiology, and molecular genetic testing at a single tertiary referral center (Moorfields Eye Hospital, London, UK). Results The mean age of presentation was 3 months of age, with disease onset in the first year of life in all cases. Molecular genetic testing revealed the following disease-causing variants: PRPF8 (heterozygous c.5804G > A), PRPH2 (homozygous c.620_627delinsTA, novel variant), RP1 (homozygous c.4147_4151delGGATT, novel variant) and RPGR (heterozygous c.1894_1897delGACA). PRPF8, PRPH2, and RP1 variants have very rarely been reported, either as unique cases or case reports, with limited clinical data presented. RPGR variants have not previously been associated with LCA/EOSRD. Clinical history and detailed retinal imaging are presented. Conclusions The reported cases extend the phenotypic spectrum of PRPF8-, PRPH2-, RP1-, and RPGR-associated disease, and the genotypic spectrum of LCA/EOSRD. The study highlights the importance of retinal and functional phenotyping, and the importance of specific genetic diagnosis to potential future therapy.

ObjectiveTo evaluate the role of automated optical coherence tomography (OCT) segmentation, using a validated deep-learning model, for assessing the effect of C3 inhibition on the area of geographic atrophy (GA); the constituent features of GA on OCT (photoreceptor degeneration (PRD), retinal pigment epithelium (RPE) loss and hypertransmission); and the area of unaffected healthy macula.To identify OCT predictive biomarkers for GA growth.MethodsPost hoc analysis of the FILLY trial using a deep-learning model for spectral domain OCT (SD-OCT) autosegmentation. 246 patients were randomised 1:1:1 into pegcetacoplan monthly (PM), pegcetacoplan every other month (PEOM) and sham treatment (pooled) for 12 months of treatment and 6 months of therapy-free monitoring. Only participants with Heidelberg SD-OCT were included (n=197, single eye per participant).The primary efficacy endpoint was the square root transformed change in area of GA as complete RPE and outer retinal atrophy (cRORA) in each treatment arm at 12 months, with secondary endpoints including RPE loss, hypertransmission, PRD and intact macular area.ResultsEyes treated PM showed significantly slower mean change of cRORA progression at 12 and 18 months (0.151 and 0.277 mm, p=0.0039; 0.251 and 0.396 mm, p=0.039, respectively) and RPE loss (0.147 and 0.287 mm, p=0.0008; 0.242 and 0.410 mm, p=0.00809). PEOM showed significantly slower mean change of RPE loss compared with sham at 12 months (p=0.0313). Intact macular areas were preserved in PM compared with sham at 12 and 18 months (p=0.0095 and p=0.044). PRD in isolation and intact macula areas was predictive of reduced cRORA growth at 12 months (coefficient 0.0195, p=0.01 and 0.00752, p=0.02, respectively)ConclusionThe OCT evidence suggests that pegcetacoplan slows progression of cRORA overall and RPE loss specifically while protecting the remaining photoreceptors and slowing the progression of healthy retina to iRORA.

IntroductionInherited retinal diseases (IRD) are a leading cause of visual impairment and blindness in the working age population. Mutations in over 300 genes have been found to be associated with IRDs and identifying the affected gene in patients by molecular genetic testing is the first step towards effective care and patient management. However, genetic diagnosis is currently slow, expensive and not widely accessible. The aim of the current project is to address the evidence gap in IRD diagnosis with an AI algorithm, Eye2Gene, to accelerate and democratise the IRD diagnosis service.Methods and analysisThe data-only retrospective cohort study involves a target sample size of 10 000 participants, which has been derived based on the number of participants with IRD at three leading UK eye hospitals: Moorfields Eye Hospital (MEH), Oxford University Hospital (OUH) and Liverpool University Hospital (LUH), as well as a Japanese hospital, the Tokyo Medical Centre (TMC). Eye2Gene aims to predict causative genes from retinal images of patients with a diagnosis of IRD. For this purpose, 36 most common causative IRD genes have been selected to develop a training dataset for the software to have enough examples for training and validation for detection of each gene. The Eye2Gene algorithm is composed of multiple deep convolutional neural networks, which will be trained on MEH IRD datasets, and externally validated on OUH, LUH and TMC.Ethics and disseminationThis research was approved by the IRB and the UK Health Research Authority (Research Ethics Committee reference 22/WA/0049) ‘Eye2Gene: accelerating the diagnosis of IRDs’ Integrated Research Application System (IRAS) project ID: 242050. All research adhered to the tenets of the Declaration of Helsinki. Findings will be reported in an open-access format.

Purpose To investigate the clinical course, genetic findings, and phenotypic spectrum of autosomal recessive bestrophinopathy (ARB) in a large cohort of children and adults. Design Retrospective case series. Participants Patients with a detailed clinical phenotype consistent with ARB, biallelic likely disease-causing sequence variants in the BEST1 gene, or both identified at a single tertiary referral center. Methods Review of case notes, retinal imaging (color fundus photography, fundus autofluorescence, OCT), electrophysiologic assessment, and molecular genetic testing. Main Outcome Measures Visual acuity (VA), retinal imaging, and electrophysiologic changes over time. Results Fifty-six eyes of 28 unrelated patients were included. Compound heterozygous variants were detected in most patients (19/27), with 6 alleles recurring in apparently unrelated individuals, the most common of which was c.422G→A, p.(Arg141His; n = 4 patients). Mean presenting VA was 0.52 ± 0.36 logarithm of the minimum angle of resolution (logMAR), and final VA was 0.81 ± 0.75 logMAR (P = 0.06). The mean rate of change in VA was 0.05 ± 0.13 logMAR/year. A significant change in VA was detected in patients with a follow-up of 5 years or more (n = 18) compared with patients with a follow-up of 5 years or less (n = 10; P = 0.001). Presence of subretinal fluid and vitelliform material were early findings in most patients, and this did not change substantially over time. A reduction in central retinal thickness was detected in most eyes (80.4%) over the course of follow-up. Many patients (10/26) showed evidence of generalized rod and cone system dysfunction. These patients were older (P < 0.001) and had worse VA (P = 0.02) than those with normal full-field electroretinography results. Conclusions Although patients with ARB are presumed to have no functioning bestrophin channels, significant phenotypic heterogeneity is evident. The clinical course is characterized by a progressive loss of vision with a slow rate of decline, providing a wide therapeutic window for anticipated future treatment strategies.

Dyskeratosis congenita (DC) is an inherited bone-marrow-failure disorder characterized by a triad of mucocutaneous features that include abnormal skin pigmentation, nail dystrophy, and oral leucoplakia. Despite the identification of several genetic variants that cause DC, a significant proportion of probands remain without a molecular diagnosis. In a cohort of eight independent DC-affected families, we have identified a remarkable series of heterozygous germline variants in the gene encoding thymidylate synthase (TYMS). Although the inheritance appeared to be autosomal recessive, one parent in each family had a wild-type TYMS coding sequence. Targeted genomic sequencing identified a specific haplotype and rare variants in the naturally occurring TYMS antisense regulator ENOSF1 (enolase super family 1) inherited from the other parent. Lymphoblastoid cells from affected probands have severe TYMS deficiency, altered cellular deoxyribonucleotide triphosphate pools, and hypersensitivity to the TYMS-specific inhibitor 5-fluorouracil. These defects in the nucleotide metabolism pathway resulted in genotoxic stress, defective transcription, and abnormal telomere maintenance. Gene-rescue studies in cells from affected probands revealed that post-transcriptional epistatic silencing of TYMS is occurring via elevated ENOSF1. These cell and molecular abnormalities generated by the combination of germline digenic variants at the TYMS-ENOSF1 locus represent a unique pathogenetic pathway for DC causation in these affected individuals, whereas the parents who are carriers of either of these variants in a singular fashion remain unaffected.

A molecular diagnosis from the analysis of sequencing data in rare Mendelian diseases has a huge impact on the management of patients and their families. Numerous patient phenotype-aware variant prioritisation (VP) tools have been developed to help automate this process, and shorten the diagnostic odyssey, but performance statistics on real patient data are limited. Here we identify, assess, and compare the performance of all up-to-date, freely available, and programmatically accessible tools using a whole-exome, retinal disease dataset from 134 individuals with a molecular diagnosis. All tools were able to identify around two-thirds of the genetic diagnoses as the top-ranked candidate, with LIRICAL performing best overall. Finally, we discuss the challenges to overcome most cases remaining undiagnosed after current, state-of-the-art practices.

PurposeRetinal signatures of systemic disease (‘oculomics’) are increasingly being revealed through a combination of high-resolution ophthalmic imaging and sophisticated modelling strategies. Progress is currently limited not mainly by technical issues, but by the lack of large labelled datasets, a sine qua non for deep learning. Such data are derived from prospective epidemiological studies, in which retinal imaging is typically unimodal, cross-sectional, of modest number and relates to cohorts, which are not enriched with subpopulations of interest, such as those with systemic disease. We thus linked longitudinal multimodal retinal imaging from routinely collected National Health Service (NHS) data with systemic disease data from hospital admissions using a privacy-by-design third-party linkage approach.ParticipantsBetween 1 January 2008 and 1 April 2018, 353 157 participants aged 40 years or older, who attended Moorfields Eye Hospital NHS Foundation Trust, a tertiary ophthalmic institution incorporating a principal central site, four district hubs and five satellite clinics in and around London, UK serving a catchment population of approximately six million people.Findings to dateAmong the 353 157 individuals, 186 651 had a total of 1 337 711 Hospital Episode Statistics admitted patient care episodes. Systemic diagnoses recorded at these episodes include 12 022 patients with myocardial infarction, 11 735 with all-cause stroke and 13 363 with all-cause dementia. A total of 6 261 931 retinal images of seven different modalities and across three manufacturers were acquired from 1 54 830 patients. The majority of retinal images were retinal photographs (n=1 874 175) followed by optical coherence tomography (n=1 567 358).Future plansAlzEye combines the world’s largest single institution retinal imaging database with nationally collected systemic data to create an exceptional large-scale, enriched cohort that reflects the diversity of the population served. First analyses will address cardiovascular diseases and dementia, with a view to identifying hidden retinal signatures that may lead to earlier detection and risk management of these life-threatening conditions.

Posterior corneal vesicles (PCVs) have clinical features that are similar to posterior polymorphous corneal dystrophy (PPCD). To help determine whether there is a shared genetic basis, we screened 38 individuals with PCVs for changes in the three genes identified as causative for PPCD.We prospectively recruited patients for this study. We examined all individuals clinically, with their first-degree relatives when available. We used a combination of Sanger and exome sequencing to screen regulatory regions of OVOL2 and GRHL2, and the entire ZEB1 coding sequence.The median age at examination was 37.5 years (range 4.7-84.0 years), 20 (53%) were male and in 19 (50%) the PCVs were unilateral. Most individuals were discharged to optometric review, but five had follow-up for a median of 12 years (range 5-13 years) with no evidence of progression. In cases with unilateral PCVs, there was statistically significant evidence that the change in the affected eye was associated with a lower endothelial cell density (p = 0.0003), greater central corneal thickness (p = 0.0277) and a steeper mean keratometry (p = 0.0034), but not with a higher keratometric astigmatism or a reduced LogMAR visual acuity. First-degree relatives of 13 individuals were available for examination, and in 3 (23%), PCVs were identified. No possibly pathogenic variants were identified in the PPCD-associated genes screened.We found no evidence that PCVs share the same genetic background as PPCD. In contrast to PPCD, we confirm that PCVs is a mild, non-progressive condition with no requirement for long-term review. However, subsequent cataract surgery can lead to corneal oedema.

Variants in the PROM1 gene are associated with cone (-rod) dystrophy, macular dystrophy, and other phenotypes. We describe the clinical and genetic characteristics of 10 patients from eight Japanese families with PROM1-associated retinal disorder (PROM1-RD) in a nationwide cohort. A literature review of PROM1-RD in the Japanese population was also performed. The median age at onset/examination of 10 patients was 31.0 (range, 10-45)/44.5 (22-73) years. All 10 patients showed atrophic macular changes. Seven patients (70.0%) had spared fovea to various degrees, approximately half of whom had maintained visual acuity. Generalized cone (-rod) dysfunction was demonstrated in all nine subjects with available electrophysiological data. Three PROM1 variants were identified in this study: one recurrent disease-causing variant (p.Arg373Cys), one novel putative disease-causing variant (p.Cys112Arg), and one novel variant of uncertain significance (VUS; p.Gly53Asp). Characteristic features of macular atrophy with generalized cone-dominated retinal dysfunction were shared among all 10 subjects with PROM1-RD, and the presence of foveal sparing was crucial in maintaining visual acuity. Together with the three previously reported variants [p.R373C, c.1551+1G>A (pathogenic), p.Asn580His (likely benign)] in the literature of Japanese patients, one prevalent missense variant (p.Arg373Cys, 6/9 families, 66.7%) detected in multiple studies was determined in the Japanese population, which was also frequently detected in the European population.

Significance Bone marrow failure (BMF) syndromes are inherited life-threatening conditions characterized by low blood cell production and predisposition to cancer. In this study we report a germ line frameshift variant in the Sp1 transcription factor in a family of patients with BMF and acute leukemia. Sp1 is ubiquitously expressed in human tissues and regulates transcription for blood cell lineage specification. Dissecting the molecular function of this SP1 variant revealed a hypermorphic effect, triggering superactivation of Sp1-mediated transcription in the patients’ blood. To our knowledge, this is the first report of a naturally occurring germ line variant in SP1 that alters transcriptional networks and disrupts hematopoiesis in humans., Inherited bone marrow failure (BMF) syndromes are a heterogeneous group of diseases characterized by defective hematopoiesis and often predisposing to myelodysplastic syndrome (MDS) and acute myelogenous leukemia. We have studied a large family consisting of several affected individuals with hematologic abnormalities, including one family member who died of acute leukemia. By whole-exome sequencing, we identified a novel frameshift variant in the ubiquitously expressed transcription factor specificity protein 1 (SP1). This heterozygous variant (c.1995delA) truncates the canonical Sp1 molecule in the highly conserved C-terminal DNA-binding zinc finger domains. Transcriptomic analysis and gene promoter characterization in patients’ blood revealed a hypermorphic effect of this Sp1 variant, triggering superactivation of Sp1-mediated transcription and driving significant up-regulation of Sp1 target genes. This familial genetic study indicates a central role for Sp1 in causing autosomal dominant transmission of BMF, thereby confirming its critical role in hematopoiesis in humans.

Purpose To describe the natural history of Leber congenital amaurosis (LCA) associated with GUCY2D variants (GUCY2D-LCA) in a cohort of children and adults, in preparation for trials of novel therapies. Design Retrospective case series. Methods Participants: Patients with GUCY2D-LCA at a single referral center. Procedures: Review of clinical notes, retinal imaging including fundus autofluorescence (FAF) and optical coherence tomography (OCT), electroretinography (ERG), and molecular genetic testing. Main Outcome Measures: Demographic data, symptoms at presentation, visual acuity, evidence of progression, OCT and FAF findings, ERG assessment, and molecular genetics. Results Twenty-one subjects with GUCY2D-LCA were included, with a mean follow-up ± standard deviation (SD) of 10 ± 11.85 years. Marked reduction in visual acuity (VA) and nystagmus was documented in all patients within the first 3 years of life. Fifty-seven percent (n = 12) exhibited photophobia and 38% (n = 8) had nyctalopia. VA was worse than hand motion in 71% of the patients (n = 15). Longitudinal assessment of VA showed stability in all patients, except 1 patient who experienced deterioration over a follow-up of 44 years. Hyperopia was reported in 13 of the 17 subjects (71%) with available refraction data. Eighteen subjects had either normal fundus appearance (n = 14) or a blond fundus (n = 3), while only 4 of the eldest subjects had mild retinal pigment epithelium (RPE) atrophy (mean, 49 years; range 40-54 years). OCT data were available for 11 subjects and 4 different grades of ellipsoid zone (EZ) integrity were identified: (1) continuous/intact EZ (n = 6), (2) focally disrupted EZ (n = 2), (3) focally disrupted with RPE changes (n = 2), and (4) diffuse EZ disruption with RPE changes (n = 1). All examined subjects had stable OCT findings over the long follow-up period. Full-field ERGs showed evidence of a severe cone-rod dystrophy in 5 of 6 patients and undetectable ERGs in 1 subject. Novel genotype-phenotype correlations are also reported. Conclusion GUCY2D-LCA is a severe early-onset retinal dystrophy associated with very poor VA from birth. Despite the severely affected photoreceptor function, the relatively preserved photoreceptor structure based on EZ integrity until late in the disease in the majority of subjects suggests a wide therapeutic window for gene therapy trials., Highlights • GUCY2D-associated Leber congenital amaurosis is a severe early-onset retinal dystrophy. • There is severe cone and rod dysfunction but with preserved photoreceptor structure evident on optical coherence tomography. • Stable natural history suggests a wide therapeutic window for intervention.

© 2020 The Authors. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/), The cause of autosomal-dominant retinitis pigmentosa (adRP), which leads to loss of vision and blindness, was investigated in families lacking a molecular diagnosis. A refined locus for adRP on Chr17q22 (RP17) was delineated through genotyping and genome sequencing, leading to the identification of structural variants (SVs) that segregate with disease. Eight different complex SVs were characterized in 22 adRP-affected families with >300 affected individuals. All RP17 SVs had breakpoints within a genomic region spanning YPEL2 to LINC01476. To investigate the mechanism of disease, we reprogrammed fibroblasts from affected individuals and controls into induced pluripotent stem cells (iPSCs) and differentiated them into photoreceptor precursor cells (PPCs) or retinal organoids (ROs). Hi-C was performed on ROs, and differential expression of regional genes and a retinal enhancer RNA at this locus was assessed by qPCR. The epigenetic landscape of the region, and Hi-C RO data, showed that YPEL2 sits within its own topologically associating domain (TAD), rich in enhancers with binding sites for retinal transcription factors. The Hi-C map of RP17 ROs revealed creation of a neo-TAD with ectopic contacts between GDPD1 and retinal enhancers, and modeling of all RP17 SVs was consistent with neo-TADs leading to ectopic retinal-specific enhancer-GDPD1 accessibility. qPCR confirmed increased expression of GDPD1 and increased expression of the retinal enhancer that enters the neo-TAD. Altered TAD structure resulting in increased retinal expression of GDPD1 is the likely convergent mechanism of disease, consistent with a dominant gain of function. Our study highlights the importance of SVs as a genomic mechanism in unsolved Mendelian diseases.

Purpose To characterize the retinal phenotype of juvenile neuronal ceroid lipofuscinosis (JNCL), highlight delayed and mistaken diagnosis, and propose an algorithm for early identification. Design Retrospective case series. Subjects Eight children (5 females) with JNCL. Methods Review of clinical notes, retinal imaging including fundus autofluorescence (FAF) and optical coherence tomography (OCT), electroretinography (ERG), and both microscopy and molecular genetic testing. Main Outcome Measurements Demographic data, signs and symptoms, visual acuity, FAF and OCT findings, ERG phenotype, and microscopy/molecular genetics. Results Subjects presented with rapid bilateral vision loss over one to eighteen months, with mean visual acuity deteriorating from 0.44 LogMAR (range: 0.20 - 1.78 LogMAR) at baseline, to 1.34 LogMAR (0.30 LogMAR - light perception) at last follow-up. Age of onset ranged from 3 to 7 years (mean 5.3 years). The age at diagnosis of JNCL ranged from 7 to 10 years (mean 8.3 years). Six children displayed eccentric fixation, and six had cognitive or neurological signs at time of diagnosis (75%). Seven patients had bilateral bull’s-eye maculopathy at presentation. Coats-like exudative vasculopathy, not previously reported in JNCL, was observed in one patient. OCT imaging revealed near complete loss of outer retinal layers, and marked atrophy of the nerve fibre and ganglion cell layers, at the central macula. An ‘electronegative’ ERG was present in four patients (50%), but with additional a-wave reduction; there was an undetectable ERG in the remaining four. Blood film microscopy revealed vacuolated lymphocytes and electron microscopy showed lysosomal (fingerprint) inclusions, in all eight patients. Conclusions In a young child with bilateral rapidly progressive vision loss and macular disturbance, blood film microscopy to detect vacuolated lymphocytes is a rapid, readily accessible, and sensitive screening test for JNCL. Early suspicion of JNCL can be aided by detailed directed history and high-resolution retinal imaging, with subsequent targeted microscopy/genetic testing. Early diagnosis is critical to ensure appropriate management, counselling, support and social care for children and their families. Furthermore, although potential therapies for this group of disorders are in early phase clinical trial, realistic expectations are that successful intervention will be most effective when initiated at the earliest stage of disease.

Objective: Predict visual function with machine-learning applied to automatically acquired quantitative imaging biomarkers in geographic atrophyDesign: Post-hoc analysis of data from a clinical trial and routine clinical care.Methods: Automated segmentation of OCT scans from 476 eyes (325 patients) with geographic atrophy. Machine learning modelling of resultant quantitative OCT (qOCT) biomarkers to predict cross-sectional visual acuity under both standard luminance (VA) and low luminance (LLVA) conditions.Main Outcome Measure: Correlation coefficient (R2) and mean absolute error (MAE) for cross-sectional VA and LLVA in Early Treatment Diabetic Retinopathy Study (ETDRS) letters.Results: Best-corrected VA under both standard luminance (R2 0.46 MAE 10.2 ETDRS letters) and low-luminance conditions (R2 0.25 MAE 12.1) could be predicted. The foveal region contributed the most (46.5%) toward model performance, with retinal pigment epithelium loss and outer retinal atrophy contributing the most (31.1%). For LLVA, however, features in the non-foveal regions were most important (74.5%), led by photoreceptor degeneration (38.9%).Conclusions: Our method of automatic qOCT segmentation demonstrates functional significance for vision in geographic atrophy, including LLVA. LLVA is itself predictive of geographic atrophy progression, implying that the predictive qOCT biomarkers provided by our model are also prognostic.

Motivation During the past decade, genome-wide association studies (GWAS) have been used to map quantitative trait loci (QTLs) underlying complex traits. However, most GWAS focus on additive genetic effects while ignoring non-additive effects, on the assumption that most QTL act additively. Consequently, QTLs driven by dominance and other non-additive effects could be overlooked. Results We developed ADDO, a highly efficient tool to detect, classify and visualize QTLs with additive and non-additive effects. ADDO implements a mixed-model transformation to control for population structure and unequal relatedness that accounts for both additive and dominant genetic covariance among individuals, and decomposes single-nucleotide polymorphism effects as either additive, partial dominant, dominant or over-dominant. A matrix multiplication approach is used to accelerate the computation: a genome scan on 13 million markers from 900 individuals takes about 5 h with 10 CPUs. Analysis of simulated data confirms ADDO’s performance on traits with different additive and dominance genetic variance components. We showed two real examples in outbred rat where ADDO identified significant dominant QTL that were not detectable by an additive model. ADDO provides a systematic pipeline to characterize additive and non-additive QTL in whole genome sequence data, which complements current mainstream GWAS software for additive genetic effects. Availability and implementation ADDO is customizable and convenient to install and provides extensive analytics and visualizations. The package is freely available online at https://github.com/LeileiCui/ADDO. Supplementary information Supplementary data are available at Bioinformatics online.

Background This case series reports the performance of a next‐generation sequencing (NGS) panel of 176 retinal genes (NGS 176) in patients with inherited retinal disease (IRD). Methods Subjects are patients who underwent genetic testing between 1 August 2016 and 1 January 2018 at Moorfields Eye Hospital, London, UK. Panel‐based genetic testing was performed unless a specific gene (e.g., RS1) or small group of genes (e.g., ABCA4, PRPH2) were suspected. If a novel variant was identified, a further comment on their predicted pathogenicity and evolutionary conservation was offered and segregation studies performed. The main outcome measure is the likelihood of obtaining a genetic diagnosis using NGS 176. Results 488 patients were included. A molecular diagnosis was obtained for 59.4% of patients. Younger patients were more likely to receive a molecular diagnosis; with 92% of children under the age of 6 years receiving a conclusive result. There was a change in their initially assigned inheritance pattern in 8.4% of patients following genetic testing. Selected IRD diagnoses (e.g., achromatopsia, congenital stationary night blindness) were associated with high diagnostic yields. Conclusion This study confirms that NGS 176 is a useful first‐tier genetic test for most IRD patients. Age and initial clinical diagnosis were strongly associated with diagnostic yield., A retrospective study of 488 patients with inherited retinal dystrophy confirms that NGS 176 is a useful first tier genetic test, achieving a molecular diagnosis in 59.4% of those tested. Age and initial clinical diagnosis were strongly associated with diagnostic yield.

Key Points Question What are the enablers and barriers of patient engagement for app-based home vision monitoring at scale? Findings In this cohort and survey study including 417 adults, 258 patients were active users (61.9%) of whom 166 patients (64.3%) were compliant users. Engagement was positively associated with higher comfort with technology, White British ethnicity, visual acuity, neovascular age-related macular degeneration diagnosis, and the number of intravitreal injections and was negatively associated with increased age. Meaning These findings suggest effective smartphone app-based home vision monitoring should address the risk factors for low engagement and digital exclusion during clinical practice setting deployment., This cohort study examines the associations between patient characteristics and clinical measures with vision monitoring app uptake and engagement., Importance Telemedicine is accelerating the remote detection and monitoring of medical conditions, such as vision-threatening diseases. Meaningful deployment of smartphone apps for home vision monitoring should consider the barriers to patient uptake and engagement and address issues around digital exclusion in vulnerable patient populations. Objective To quantify the associations between patient characteristics and clinical measures with vision monitoring app uptake and engagement. Design, Setting, and Participants In this cohort and survey study, consecutive adult patients attending Moorfields Eye Hospital receiving intravitreal injections for retinal disease between May 2020 and February 2021 were included. Exposures Patients were offered the Home Vision Monitor (HVM) smartphone app to self-test their vision. A patient survey was conducted to capture their experience. App data, demographic characteristics, survey results, and clinical data from the electronic health record were analyzed via regression and machine learning. Main Outcomes and Measures Associations of patient uptake, compliance, and use rate measured in odds ratios (ORs). Results Of 417 included patients, 236 (56.6%) were female, and the mean (SD) age was 72.8 (12.8) years. A total of 258 patients (61.9%) were active users. Uptake was negatively associated with age (OR, 0.98; 95% CI, 0.97-0.998; P = .02) and positively associated with both visual acuity in the better-seeing eye (OR, 1.02; 95% CI, 1.00-1.03; P = .01) and baseline number of intravitreal injections (OR, 1.01; 95% CI, 1.00-1.02; P = .02). Of 258 active patients, 166 (64.3%) fulfilled the definition of compliance. Compliance was associated with patients diagnosed with neovascular age-related macular degeneration (OR, 1.94; 95% CI, 1.07-3.53; P = .002), White British ethnicity (OR, 1.69; 95% CI, 0.96-3.01; P = .02), and visual acuity in the better-seeing eye at baseline (OR, 1.02; 95% CI, 1.01-1.04; P = .04). Use rate was higher with increasing levels of comfort with use of modern technologies (β = 0.031; 95% CI, 0.007-0.055; P = .02). A total of 119 patients (98.4%) found the app either easy or very easy to use, while 96 (82.1%) experienced increased reassurance from using the app. Conclusions and Relevance This evaluation of home vision monitoring for patients with common vision-threatening disease within a clinical practice setting revealed demographic, clinical, and patient-related factors associated with patient uptake and engagement. These insights inform targeted interventions to address risks of digital exclusion with smartphone-based medical devices.

Despite great strides in the development and wide acceptance of standards for exchanging structured information about genomic variants, there is no corresponding standard for exchanging phenotypic data, and this has impeded the sharing of phenotypic information for computational analysis. Here, we introduce the Global Alliance for Genomics and Health (GA4GH) Phenopacket schema, which supports exchange of computable longitudinal case-level phenotypic information for diagnosis and research of all types of disease including Mendelian and complex genetic diseases, cancer, and infectious diseases. To support translational research, diagnostics, and personalized healthcare, phenopackets are designed to be used across a comprehensive landscape of applications including biobanks, databases and registries, clinical information systems such as Electronic Health Records, genomic matchmaking, diagnostic laboratories, and computational tools. The Phenopacket schema is a freely available, community-driven standard that streamlines exchange and systematic use of phenotypic data and will facilitate sophisticated computational analysis of both clinical and genomic information to help improve our understanding of diseases and our ability to manage them.

Congenital cataracts are the most common cause of visual impairment worldwide. Inherited cataract is a clinically and genetically heterogeneous disease. Here we report disease-causing variants in a novel gene,Using whole-exome sequencing (WES), we have identified disease-causing sequence variants in two families of British and Irish origin, and in two isolated cases of Asian-Indian and British origin. Bioinformatics analysis confirmed these variants as rare with damaging pathogenicity scores. Segregation was tested within the families using direct Sanger sequencing.A nonsense variant NM_000500.9 c.955 C T; p.Q319* was identified inThis is the first report of separate sequence variants in

Discovery of disease-causing structural variants (dcSV) from whole genome sequencing data is difficult due to high number of false positives and a lack of efficient way to estimate allele frequency. Here we introduce SVRare, an application that aggregates structural variants (SV) called by other tools, and efficiently annotates rare SVs to aid dcSVs discovery.Applied in the Genomics England (GEL) research environment to data from the 100K Genomes Project, SVRare aggregated 554,060,126 SVs called by Manta and Canvas in all the 71,408 participants in the rare-disease arm. From a pilot study of 4313 families, SVRare identified 36 novel protein-coding disrupting SVs on diagnostic grade genes that may explain proband’s phenotype. It is estimated that SVRare can increase SV-based diagnosis yield by at least 4-fold. We also performed a genome-wide association study, and uncovered clusters of dcSVs in genes with known pathogenicity, such as PKD1/2 - cystic kidney diseases and LDLR - familial hypercholesterolaemia.

Genome-wide association studies (GWAS) have identified hundreds of loci associated with Crohns disease (CD), however, as with all complex diseases, deriving pathogenic mechanisms from these non-coding GWAS discoveries has been challenging. To complement GWAS and better define actionable biological targets, we analysed sequenced data from more than 30,000 CD patients and 80,000 population controls. We observe rare coding variants in established CD susceptibility genes as well as ten genes where coding variation directly implicates the gene in disease risk for the first time.

Purpose To characterize the molecular mechanism underpinning early-onset macular drusen (EOMD), a phenotypically severe subtype of age-related macular degeneration (AMD), in a subgroup of patients. Design Multicenter case series, in vitro experimentation, and retrospective analysis of previously reported variants. Participants Seven families with apparently autosomal dominant EOMD. Methods Patients underwent a comprehensive ophthalmic assessment. Affected individuals from families A, B, and E underwent whole exome sequencing. The probands from families C, D, F, and G underwent Sanger sequencing analysis of the complement factor H (CFH) gene. Mutant recombinant factor H like-1 (FHL-1) proteins were expressed in HEK293 cells to assess the impact on FHL-1 expression and function. Previously reported EOMD-causing variants in CFH were reviewed. Main Outcome Measures Detailed clinical phenotypes, genomic findings, in vitro characterization of mutation effect on protein function, and postulation of the pathomechanism underpinning EOMD. Results All affected participants demonstrated bilateral drusen. The earliest reported age of onset was 16 years (median, 46 years). Ultra-rare (minor allele frequency [MAF], ≤0.0001) CFH variants were identified as the cause of disease in each family: CFH c.1243del, p.(Ala415ProfsTer39) het; c.350+1G→T het; c.619+1G→A het, c.380G→A, p.(Arg127His) het; c.694C→T p.(Arg232Ter) het (identified in 2 unrelated families in this cohort); and c.1291T→A, p.(Cys431Ser). All mutations affect complement control protein domains 2 through 7, and thus are predicted to impact both FHL-1, the predominant isoform in Bruch’s membrane (BrM) of the macula, and factor H (FH). In vitro analysis of recombinant proteins FHL-1R127H, FHL-1A415f/s, and FHL-1C431S demonstrated that they are not secreted, and thus are loss-of-function proteins. Review of 29 previously reported EOMD-causing mutations found that 75.8% (22/29) impact FHL-1 and FH. In total, 86.2% (25/29) of EOMD-associated variants cause haploinsufficiency of FH or FHL-1. Conclusions Early-onset macular drusen is an underrecognized, phenotypically severe subtype of AMD. We propose that haploinsufficiency of FHL-1, the main regulator of the complement pathway in BrM, where drusen develop, is an important mechanism underpinning the development of EOMD in a number of cases. Understanding the molecular basis of EOMD will shed light on AMD pathogenesis given their pathologic similarities.