  Genetic mutations can cause disease by disrupting normal gene function.
Identifying the disease-causing mutations from millions of genetic variants
within an individual patient is a challenging problem. Computational methods
which can prioritize disease-causing mutations have, therefore, enormous
applications. It is well-known that genes function through a complex regulatory
network. However, existing variant effect prediction models only consider a
variant in isolation. In contrast, we propose VEGN, which models variant effect
prediction using a graph neural network (GNN) that operates on a heterogeneous
graph with genes and variants. The graph is created by assigning variants to
genes and connecting genes with an gene-gene interaction network. In this
context, we explore an approach where a gene-gene graph is given and another
where VEGN learns the gene-gene graph and therefore operates both on given and
learnt edges. The graph neural network is trained to aggregate information
between genes, and between genes and variants. Variants can exchange
information via the genes they connect to. This approach improves the
performance of existing state-of-the-art models.
