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Genetic mutations can cause disease by disrupting normal gene function.Identifying the disease-causing mutations from millions of genetic variantswithin an individual patient is a challenging problem. Computational methodswhich can prioritize disease-causing mutations have, therefore, enormousapplications. It is well-known that genes function through a complex regulatorynetwork. However, existing variant effect prediction models only consider avariant in isolation. In contrast, we propose VEGN, which models variant effectprediction using a graph neural network (GNN) that operates on a heterogeneousgraph with genes and variants. The graph is created by assigning variants togenes and connecting genes with an gene-gene interaction network. In thiscontext, we explore an approach where a gene-gene graph is given and anotherwhere VEGN learns the gene-gene graph and therefore operates both on given andlearnt edges. The graph neural network is trained to aggregate informationbetween genes, and between genes and variants. Variants can exchangeinformation via the genes they connect to. This approach improves theperformance of existing state-of-the-art models.

VEGN: Variant Effect Prediction with Graph Neural Networks