While many eye disorders are linked through defects in vascularization and optic nerve degeneration, genetic correlation studies have yielded variable results despite shared features. For example, glaucoma and myopia both share optic neuropathy as a feature, but genetic correlation studies demonstrated minimal overlap. By leveraging electronic health record (EHR) resources that contain genetic variables such as genetically predicted gene expression (GPGE), researchers have the potential to improve the identification of shared genetic drivers of disease by incorporating knowledge of shared features to identify disease-causing mechanisms. In this study, we examined shared genetic architecture across eye diseases. Our gene-based approach used transcriptome-wide association methods to identify shared transcriptomic profiles across eye diseases within BioVU, Vanderbilt University Medical Center's (VUMC's) EHR-linked biobank. Our phenome-based approach leveraged phenome-wide association studies (PheWASs) to identify eye disease comorbidities. Using the beta estimates from the significantly associated comorbidities, we constructed a phenotypic risk score (PheRS) representing a weighted sum of an individual's eye disease comorbidities. This PheRS is predictive of eye disease status and associated with the altered GPGE of significant genes in an independent population. The implementation of both gene- and phenome-based approaches can expand genetic associations and shed greater insight into the underlying mechanisms of shared genetic architecture across eye diseases.