GWAS of retinal vessel tortuosity identifies 85 novel loci recovering variants associated with disease

Fundus pictures of the eye allow for non-invasive inspection of the microvasculature system of the retina which is informative on cardiovascular health. Automated image processing enables the extraction of morphometric properties of this system as quantitative features that can be used for modelling disease risks. Here we report the results of the largest genome-wide association study (GWAS) of retinal vessel tortuosity conducted to date using data from the UK Biobank (N=63,899). We identified 87 loci associated with this trait (85 of which are novel). The heritability of the trait was h2=0.23 (0.02). We carried out a replication study on a small independent population-based cohort, SKIPOGH (N=436). While the power of this study was too small to replicate individual hits, the effect size estimates correlated significantly between the two studies (Pearson correlation r=0.55, p=4.6E-6). We showed that the alleles associated with retinal vessel tortuosity point to a common genetic architecture of this trait with CVD and related traits. Our results shed new light on the genetics of cardiovascular risk factors and disease

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

Purpose: To identify novel susceptibility loci for retinal vascular tortuosity, to better understand the molecular mechanisms modulating this trait, and reveal causal relationships with diseases and their risk factors. Design: Genome-wide Association Studies (GWAS) of vascular tortuosity of retinal arteries and veins followed by replication meta-analysis and Mendelian randomization (MR). Participants: We analyzed 116 639 fundus images of suitable quality from 63 662 participants from 3 cohorts, namely the UK Biobank (n = 62 751), the Swiss Kidney Project on Genes in Hypertension (n = 397), and OphtalmoLaus (n = 512). Methods: Using a fully automated retina image processing pipeline to annotate vessels and a deep learning algorithm to determine the vessel type, we computed the median arterial, venous and combined vessel tortuosity measured by the distance factor (the length of a vessel segment over its chord length), as well as by 6 alternative measures that integrate over vessel curvature. We then performed the largest GWAS of these traits to date and assessed gene set enrichment using the novel high-precision statistical method PascalX. Main Outcome Measure: We evaluated the genetic association of retinal tortuosity, measured by the distance factor. Results: Higher retinal tortuosity was significantly associated with higher incidence of angina, myocardial infarction, stroke, deep vein thrombosis, and hypertension. We identified 175 significantly associated genetic loci in the UK Biobank; 173 of these were novel and 4 replicated in our second, much smaller, metacohort. We estimated heritability at ∼25% using linkage disequilibrium score regression. Vessel type specific GWAS revealed 116 loci for arteries and 63 for veins. Genes with significant association signals included COL4A2, ACTN4, LGALS4, LGALS7, LGALS7B, TNS1, MAP4K1, EIF3K, CAPN12, ECH1, and SYNPO2. These tortuosity genes were overexpressed in arteries and heart muscle and linked to pathways related to the structural properties of the vasculature. We demonstrated that retinal tortuosity loci served pleiotropic functions as cardiometabolic disease variants and risk factors. Concordantly, MR revealed causal effects between tortuosity, body mass index, and low-density lipoprotein. Conclusions: Several alleles associated with retinal vessel tortuosity suggest a common genetic architecture of this trait with ocular diseases (glaucoma, myopia), cardiovascular diseases, and metabolic syndrome. Our results shed new light on the genetics of vascular diseases and their pathomechanisms and highlight how GWASs and heritability can be used to improve phenotype extraction from high-dimensional data, such as images. Financial Disclosure(s): The author(s) have no proprietary or commercial interest in any materials discussed in this article