Fine-mapping of retinal vascular complexity loci identifies Notch regulation as a shared mechanism with myocardial infarction outcomes

视网膜血管复杂性基因位点的精细定位表明,Notch调控与心肌梗死结局存在共同机制。

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Abstract

There is increasing evidence that the complexity of the retinal vasculature measured as fractal dimension, D(f), might offer earlier insights into the progression of coronary artery disease (CAD) before traditional biomarkers can be detected. This association could be partly explained by a common genetic basis; however, the genetic component of D(f) is poorly understood. We present a genome-wide association study (GWAS) of 38,000 individuals with white British ancestry from the UK Biobank aimed to comprehensively study the genetic component of D(f) and analyse its relationship with CAD. We replicated 5 D(f) loci and found 4 additional loci with suggestive significance (P < 1e-05) to contribute to D(f) variation, which previously were reported in retinal tortuosity and complexity, hypertension, and CAD studies. Significant negative genetic correlation estimates support the inverse relationship between D(f) and CAD, and between D(f) and myocardial infarction (MI), one of CAD's fatal outcomes. Fine-mapping of D(f) loci revealed Notch signalling regulatory variants supporting a shared mechanism with MI outcomes. We developed a predictive model for MI incident cases, recorded over a 10-year period following clinical and ophthalmic evaluation, combining clinical information, D(f), and a CAD polygenic risk score. Internal cross-validation demonstrated a considerable improvement in the area under the curve (AUC) of our predictive model (AUC = 0.770 ± 0.001) when comparing with an established risk model, SCORE, (AUC = 0.741 ± 0.002) and extensions thereof leveraging the PRS (AUC = 0.728 ± 0.001). This evidences that D(f) provides risk information beyond demographic, lifestyle, and genetic risk factors. Our findings shed new light on the genetic basis of D(f), unveiling a common control with MI, and highlighting the benefits of its application in individualised MI risk prediction.

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