Genome-wide Association Studies of Retinal Vessel Tortuosity Identify Numerous Novel Loci Revealing Genes and Pathways Associated With Ocular and Cardiometabolic Diseases

Tomasoni, Mattia; Beyeler, Michael Johannes; Vela, Sofia Ortin; Mounier, Ninon; Porcu, Eleonora; Corre, Tanguy; Krefl, Daniel; Button, Alexander Luke; Abouzeid, Hana; Konstantinidis, Lazaros; Bochud, Murielle; Schlingemann, Reinier O.; Bergin, Ciara; Bergmann, Sven

doi:10.1016/j.xops.2023.100288

Cited by 13 publications

(15 citation statements)

References 122 publications

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“…76 It was also suggestively associated with vascular FD (β, −0.069 SD; P =1.06×10 − 7 ). Further sensitivity analyses and comparison with variants identified in a previous GWAS of vascular tortuosity 24 are provided in the Supplemental Results (Figure S13 and S14 and Table S12).…”

Section: Resultsmentioning

confidence: 99%

“…Previous work on retinal vascular tortuosity in the UK Biobank also identified genetic loci linked to cardiometabolic diseases and cancer. 24 Genetic contributors to microvascular indices may provide insights for therapeutic targets with pleiotropic effects for retinopathy, cancer, and microvascular disease in other tissues.…”

Section: Discussionmentioning

confidence: 99%

“…18–20 However, associations with coronary artery disease and renal disease are less consistent. 21–23 Current approaches are now able to extract geometric and branching patterns of the retinal microvasculature such as retinal vascular tortuosity, 24 fractal dimension (FD), 25–31 and vascular density. 32–36 Such indices are more consistently linked to stroke, coronary artery disease, and renal disease in small cross-sectional analyses of ≈1000 individuals.…”

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confidence: 99%

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Deep Learning of the Retina Enables Phenome- and Genome-Wide Analyses of the Microvasculature

et al. 2022

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Background: The microvasculature, the smallest blood vessels in the body, has key roles in maintenance of organ health as well as tumorigenesis. The retinal fundus is a window for human in vivo non-invasive assessment of the microvasculature. Large-scale complementary machine learning-based assessment of the retinal vasculature with phenome-wide and genome-wide analyses may yield new insights into human health and disease. Methods: We utilized 97,895 retinal fundus images from 54,813 UK Biobank participants. Using convolutional neural networks to segment the retinal microvasculature, we calculated fractal dimension (FD) as a measure of vascular branching complexity, and vascular density. We associated these indices with 1,866 incident ICD-based conditions (median 10y follow-up) and 88 quantitative traits, adjusting for age, sex, smoking status, and ethnicity. Results: Low retinal vascular FD and density were significantly associated with higher risks for incident mortality, hypertension, congestive heart failure, renal failure, type 2 diabetes, sleep apnea, anemia, and multiple ocular conditions, as well as corresponding quantitative traits. Genome-wide association of vascular FD and density identified 7 and 13 novel loci respectively, which were enriched for pathways linked to angiogenesis (e.g., VEGF, PDGFR, angiopoietin, and WNT signaling pathways) and inflammation (e.g., interleukin, cytokine signaling). Conclusions: Our results indicate that the retinal vasculature may serve as a biomarker for future cardiometabolic and ocular disease and provide insights on genes and biological pathways influencing microvascular indices. Moreover, such a framework highlights how deep learning of images can quantify an interpretable phenotype for integration with electronic health records, biomarker, and genetic data to inform risk prediction and risk modification.

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Section: Resultsmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

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confidence: 99%

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Deep Learning of the Retina Enables Phenome- and Genome-Wide Analyses of the Microvasculature

et al. 2022

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“…To analyze the retinal vasculature, we developed an automated pipeline building on our previous work on retinal vessel tortuosity [47]. This pipeline enabled us to segment and annotate the retinal vasculature and OD from 130 361 color fundus images (CFIs) of 71 494 subjects after Quality Control (QC).…”

Section: Automated Pipeline For Phenotyping Of the Retinal Vasculaturementioning

confidence: 99%

“…Beyond their value for assessing ocular or systemic health, retinal phenotypes have also been used in Genome-Wide Association Studies (GWAS) to identify genetic variants modulating these phenotypes. How-ever, previous GWAS have focused on a limited set of vascular properties, such as vessel diameter [43][44][45][46], tortuosity [47,48], vascular density, fractal dimension [49], and certain deep latent variables [39] (see Suppl. Text "Comparison with previous GWAS").…”

Section: Introductionmentioning

confidence: 99%

Phenotypic and Genetic Characteristics of Retinal Vascular Parameters and their Association with Diseases

Vela

Beyeler

Trofimova

et al. 2023

Preprint

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Fundus images allow for non-invasive assessment of the retinal vasculature whose features provide important information on health. Using a fully automated image processing pipeline, we extracted 17 different morphological vascular phenotypes, including median vessels diameter, diameter variability, main temporal angles, vascular density, central retinal equivalents, the number of bifurcations, and tortuosity, from over 130k fundus images of close to 72k UK BioBank subjects. We performed genome-wide association studies (GWAS) of these parameters. From this, we estimated their individual heritabilities, ranging between 5 and 25%, and genetic cross-trait correlations, which mostly mirrored the corresponding phenotypic correlations, but tended to be slightly larger. Projecting our genetic association signals onto genes and pathways revealed remarkably low overlap suggesting largely decoupled mechanisms modulating the different traits. Our disease trait associations confirmed some previously known findings and revealed many novel connections. Notably, diameter variability, especially for the veins, seems to have many new and interesting associations with multiple disease traits, including age of death, pulmonary embolism, and heart attack. Our analyses provide evidence that large-scale analysis of image-derived vascular traits has sufficient power for obtaining functional, as well as some initial causal insights into the processes modulating the retinal vasculature.

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

Zhao,

Li,

Fan

et al. 2024

Nat Commun

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

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Genome-wide Association Studies of Retinal Vessel Tortuosity Identify Numerous Novel Loci Revealing Genes and Pathways Associated With Ocular and Cardiometabolic Diseases

Cited by 13 publications

References 122 publications

Deep Learning of the Retina Enables Phenome- and Genome-Wide Analyses of the Microvasculature

Deep Learning of the Retina Enables Phenome- and Genome-Wide Analyses of the Microvasculature

Phenotypic and Genetic Characteristics of Retinal Vascular Parameters and their Association with Diseases

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

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