Longitudinal Change in Retinal Nerve Fiber Layer Thickness and Intraocular Pressure in Young Adults.Translational vision science & technology • April 01, 2025
Samantha Lee, Gareth Lingham, Alex Hewitt, David Mackey
Age-related changes in glaucoma endophenotypes have been described thoroughly, yet, there are limited data on the normal age-related changes in young adults. This study profiles the 8-year longitudinal change in peripapillary retinal nerve fiber layer (pRNFL), intraocular pressure (IOP), and central corneal thickness (CCT) in young adults. A community-based cohort of young adults from the Raine Study underwent eye examinations that included optical coherence tomography of the optic disc, tonometry, and pachymetry when they were 20 and 28 years old. The main outcome measures were the changes in pRNFL thickness, IOP, and CCT over 8 years, adjusted for sex, ethnicity, and other potential confounders. A total of 693, 712, and 680 participants were included in the pRNFL, IOP, and CCT analyses, respectively. Over the 8 years, the global pRNFL reduced from a mean of 100.6 ± 9.3 to 97.9 ± 9.4 µm, at an average rate of 0.27 µm/year (95% confidence interval [CI], 0.24-0.30). Sectoral pRNFL similarly thinned by 0.06 to 0.38 µm/year, but this thinning was not statistically significant at the superotemporal and inferonasal sectors. IOP decreased and CCT increased between 20 and 28 years old, at an average rate of 0.18 mm Hg/year (95% CI, 0.15-0.20) and 0.18 µm/year (95% CI, 0.10-0.27), respectively. During the third decade of life, there is a decrease in pRNFL thickness and IOP in healthy adults. The current study findings will enable clinicians to differentiate potential pathological change from normal age-related variations in these measures.
Polymorphic tandem repeats influence cell type-specific gene expression across the human immune landscape.BioRxiv : The Preprint Server For Biology • April 28, 2025
Hope Tanudisastro, Anna S Cuomo, Ben Weisburd, Matthew Welland, Eleanor Spenceley, Michael Franklin, Angli Xue, Blake Bowen, Kristof Wing, Owen Tang, Michael Gray, Andre L Reis, Jonathan Margoliash, Nehir Kurtas, Jeffrey Pullin, Arthur Lee, Harrison Brand, Michael Harper, Katalina Bobowik, Michael Silk, John Marshall, Vivian Bakiris, Bindu Madala, Caitlin Uren, Caitlin Bartie, Anne Senabouth, Harriet Dashnow, Liam Fearnley, Alejandro Martin Trujillo, Egor Dolzhenko, Zhen Qiao, Stuart Grieve, Tung Nguyen, Eyal Ben David, Ling Chen, Kyle Farh, Michael Talkowski, Stephen Alexander, Owen Siggs, Leonhard Gruenschloss, Hannah Nicholas, Jennifer Piscionere, Cas Simons, Chris Wallace, Melissa Gymrek, Ira Deveson, Alex Hewitt, Gemma Figtree, Katrina De Lange, Joseph Powell, Daniel Macarthur
Tandem repeats (TRs) - highly polymorphic, repetitive sequences dispersed across the human genome - are crucial regulators of gene expression and diverse biological processes, but have remained underexplored relative to other classes of genetic variation due to historical challenges in their accurate calling and analysis. Here, we leverage whole genome and single-cell RNA sequencing from over 5.4 million blood-derived cells from 1,925 individuals to explore the impact of variation in over 1.7 million polymorphic TR loci on blood cell type-specific gene expression. We identify over 62,000 single-cell expression quantitative trait TR loci (sc-eTRs), 16.6% of which are specific to one of 28 distinct immune cell types. Further fine-mapping uncovers 4,283 sc-eTRs as candidate causal drivers of gene expression in 13.6% of genes tested genome-wide. We show through colocalization that TRs are likely mediators of genetic associations with immune-mediated and hematological traits in over 700 genes, and further identify novel TRs warranting investigation in rare disease cohorts. TRs are critical, yet long-overlooked, contributors to cell type-specific gene expression, with implications for understanding rare disease pathogenesis and the genetic architecture of complex traits.
Genome-Wide Association Study to Identify Genetic Variants Associated With Diabetic Maculopathy. Investigative Ophthalmology & Visual Science • March 26, 2025
Rajya Gurung, Charvi Nangia, Tengda Cai, Liesel Fitzgerald, Bennet Mccomish, Ebony Liu, Georgia Kaidonis, Bronwyn Ridge, Alex Hewitt, Brendan Vote, Nitin Verma, Jamie Craig, Colin N Palmer, Kathryn Burdon, Weihua Meng
Diabetic maculopathy (including diabetic macular edema [DME]) is the leading cause of vision loss in people with diabetes. We aimed to identify the genetic determinants of diabetic maculopathy. We conducted a genome-wide association study (GWAS) in two cohorts with a meta-analysis. The Australian cohort comprised 551 cases of DME and 599 controls recruited from the states of South Australia and Tasmania. The Scottish cohort comprised 1951 cases of diabetic maculopathy and 6541 controls from the Genetics of Diabetes Audit and Research in Tayside Scotland study (GoDARTS). Genotyping, imputation, and association analysis using logistic regression were conducted in each cohort, before combining summary statistics in a meta-analysis using the GWAMA package. A locus on chromosome 7 reached genome-wide significance in GoDARTS but showed the opposite direction of effect in the Australian cohort. The meta-analysis identified two suggestive associations (P < 5 Ă— 10-6) for diabetic maculopathy risk with similar effect direction; one at chromosome 1 close to the RNU5E-1 gene and one at chromosome 13 upstream of the ERICH6B gene. The two loci were evaluated in silico for potential functional links to diabetic maculopathy. Both are located in regulatory regions and have annotations indicating regulatory functions. They are also expression quantitative trait locus (eQTLs) for genes plausibly involved in diabetic maculopathy pathogenesis, with links to folate metabolism and the regulation of VEGF. The study suggests several promising SNPs and genes related to diabetic maculopathy risk. Despite being the largest genetic study of diabetic maculopathy to date, larger, homogeneous cohorts will be required to identify robust genetic risk loci for the disease.
A generalised vision transformer-based self-supervised model for diagnosing and grading prostate cancer using histological images.Prostate Cancer And Prostatic Diseases • November 24, 2024
Abadh Chaurasia, Helen Harris, Patrick Toohey, Alex Hewitt
Background: Gleason grading remains the gold standard for prostate cancer histological classification and prognosis, yet its subjectivity leads to grade variability between pathologists, potentially impacting clinical decision-making. Herein, we trained and validated a generalised AI-driven system for diagnosing prostate cancer using diverse datasets from tissue microarray (TMA) core and whole slide images (WSIs) with Haematoxylin and Eosin staining.
Methods: We analysed eight prostate cancer datasets, which included 12,711 histological images from 3648 patients, incorporating TMA core images and WSIs. The Macenko method was used to normalise colours for consistency across diverse images. Subsequently, we trained a multi-resolution (5x, 10x, 20x, and 40x) binary classifier to identify benign and malignant tissue. We then implemented a multi-class classifier for Gleason patterns (GP) sub-categorisation from malignant tissue. Finally, the models were externally validated on 11,132 histology images from 2176 patients to determine the International Society of Urological Pathology (ISUP) grade. Models were assessed using various classification metrics, and the agreement between the model's predictions and the ground truth was quantified using the quadratic weighted Cohen's Kappa (Îş) score.
Results: Our multi-resolution binary classifier demonstrated robust performance in distinguishing malignant from benign tissue with Îş scores of 0.967 on internal validation. The model achieved Îş scores ranging from 0.876 to 0.995 across four unseen testing datasets. The multi-class classifier also distinguished GP3, GP4, and GPs with an overall Îş score of 0.841. This model was further tested across four datasets, obtaining Îş scores ranging from 0.774 to 0.888. The models' performance was compared against an independent pathologist's annotation on an external dataset, achieving a Îş score of 0.752 for four classes.
Conclusions: The self-supervised ViT-based model effectively diagnoses and grades prostate cancer using histological images, distinguishing benign and malignant tissues and classifying malignancies by aggressiveness. External validation highlights its robustness and clinical applicability in digital pathology.
Predictive Power of Polygenic Risk Scores for Intraocular Pressure or Vertical Cup-Disc Ratio. JAMA Ophthalmology • November 21, 2024
Weixiong He, Samantha Lee, Santiago Diaz Torres, Xikun Han, Puya Gharahkhani, Michael Hunter, Chandrakumar Balartnasingam, Jamie Craig, Alex Hewitt, David Mackey, Stuart Macgregor
Early detection of glaucoma is essential to timely monitoring and treatment, and primary open-angle glaucoma risk can be assessed by measuring intraocular pressure (IOP) or optic nerve head vertical cup-disc ratio (VCDR). Polygenic risk scores (PRSs) could provide a link between genetic effects estimated from genome-wide association studies (GWASs) and clinical applications to provide estimates of an individual's genetic risk by combining many identified variants into a score. To construct IOP and VCDR PRSs with clinically relevant predictive power. This genetic association study evaluated the PRSs for 6959 of 51 338 individuals in the Canadian Longitudinal Study on Aging (CLSA; 2010 to 2015 with data from 11 centers in Canada) and 4960 of 5107 individuals the community-based Busselton Healthy Aging Study (BHAS; 2010 to 2015 in Busselton, Western Australia) with an artificial intelligence grading approach used to obtain precise VCDR estimates for the CLSA dataset. Data for approximately 500 000 individuals in UK Biobank from 2006 to 2010 were used to validate the power of the PRS. Data were analyzed from June to November 2023. IOP and VCDR PRSs and phenotypic variance (R2) explained by each PRS. Participants in CLSA were aged 45 to 85 years; those in BHAS, 46 to 64 years; and those in UK Biobank, 40 to 69 years. The VCDR PRS explained 22.0% (95% CI, 20.1-23.9) and 19.7% (95% CI, 16.3-23.3) of the phenotypic variance in VCDR in CLSA and BHAS, respectively, while the IOP PRS explained 12.9% (95% CI, 11.3-14.6) and 9.6% (95% CI, 8.1-11.2) of phenotypic variance in CLSA and BHAS IOP measurements. The VCDR PRS variance explained 5.2% (95% CI, 3.6-7.1), 12.1% (95% CI, 7.5-17.5), and 14.3% (95% CI, 9.3-19.9), and the IOP PRS variance explained 2.3% (95% CI, 1.5-3.3), 3.2% (95% CI, 1.3-5.8), and 7.5% (95% CI, 6.2-8.9) (P < .001) across African, East Asian, and South Asian populations, respectively. VCDR and IOP PRSs derived using a large recently published multitrait GWAS exhibited validity across independent cohorts. The findings suggest that an IOP PRS has the potential to identify individuals who may benefit from more intensive IOP-lowering treatments, which could be crucial in managing glaucoma risk more effectively. Individuals with a high VCDR PRS may be at risk of developing glaucoma even if their IOP measures fall within the normal range, suggesting that these PRSs could help in early detection and intervention, particularly among those who might otherwise be considered at low risk based on IOP alone.
