Network-based representation learning enables the identification of risk genes and latent functional pathways in primary open-angle glaucoma

Abstract

Despite the identification of hundreds of risk genes for primary open-angle glaucoma (POAG), a significant portion of the POAG genetic risk landscape remains unexplored. We hypothesized that unsupervised learning on large protein-protein interaction (PPI) networks could enable comprehensive characterization of the genetic pathways that underlie POAG risk. We used graph representation learning on a proteome-scale PPI network to generate embeddings capturing complex features of each proteins interactions. Using these embeddings, we trained a model with POAG-associated genes from the DisGeNET database to output an inferred POAG risk score for over 12,000 gene products, which identified known POAG risk genes with an area under the receiver operating characteristic curve of 0.739 (95% CI 0.686-0.792). These included well-known POAG risk genes such as RHOA and MMP3, as well as genes with significant contributions to other ocular diseases. Pathway analysis on the proteome-wide risk scores implicated 20 biological processes in POAG pathogenesis. Furthermore, cluster analysis of embeddings for POAG risk genes revealed 5 distinct functional neighborhoods, including cytokine signaling, coagulation response, collagen biosynthesis, extracellular matrix development, and fatty acid metabolism. Our results suggest that representation learning can recognize important patterns of protein interaction that allow in silico prioritization of POAG risk genes and pathways.