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Predicting functional effect of missense variants using graph attention neural networks.

Accurate prediction of damaging missense variants is critically important
for interpreting a genome sequence. Although many methods have been
developed, their performance has been limited. Recent advances in machine
learning and the availability of large-scale population genomic sequencing
data provide new opportunities to considerably improve computational
predictions. Here we describe the graphical missense variant pathogenicity
predictor (gMVP), a new method based on graph attention neural networks.
Its main component is a graph with nodes that capture predictive features
of amino acids and edges weighted by co-evolution strength, enabling
effective pooling of information from the local protein context and
functionally correlated distal positions. Evaluation of deep mutational
scan data shows that gMVP outperforms other published methods in
identifying damaging variants in TP53, PTEN, BRCA1 and MSH2. Furthermore, it achieves the best separation of de novo missense variants
in neuro developmental disorder cases from those in controls. Finally, the
model supports transfer learning to optimize gain- and loss-of-function
predictions in sodium and calcium channels. In summary, we demonstrate that
gMVP can improve interpretation of missense variants in clinical testing
and genetic studies.

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