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Predicting functional effect of missense variants using graph attention neural networks

Nature Machine Intelligence volume 4, pages 1017–1028 (2022)Cite this article

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Abstract

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 neurodevelopmental 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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Fig. 1: An overview of gMVP model.
Fig. 2: Evaluating gMVP and published methods using cancer somatic mutation hotspots and random variants in population.
Fig. 3: Evaluating gMVP and published methods in identifying damaging variants in known disease genes such as TP53, PTEN, BRCA1 and MSH2.
Fig. 4: Evaluating gMVP and published methods in distinguishing rare DNMs in cases with neurodevelopmental disorders from those in controls.
Fig. 5: Evaluating gMVP and other published methods in classifying pathogenetic and neutral variants, and in predicting GOF and LOF variants in ion-channel genes.
Fig. 6: Interpreting gMVP predictions with conservation, protein structure and genetic coding constraints.

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Data availability

Pre-computed gMVP scores for all possible missense variants in canonical transcripts on human hg38 can be downloaded from https://www.dropbox.com/s/nce1jhg3i7jw1hx/gMVP.2021-02-28.csv.gz?dl=0. The training data of the main model were downloaded from http://www.discovehrshare.com/downloads (DiscovEHR), http://www.hgmd.cf.ac.uk/ac/index.php (HGMD), https://www.uniprot.org/docs/humpvar (UniProt) and https://ftp.ncbi.nlm.nih.gov/pub/clinvar/vcf_GRCh37/ (ClinVar). Other datasets supporting the findings of this study are available in the paper and the Supplementary Information.

Code availability

The codes for the model design and training and testing procedure are available on GitHub (https://github.com/ShenLab/gMVP/) and Zenodo81.

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Acknowledgements

This work was supported by NIH grants (nos. R01GM120609, R03HL147197, U01HG008680 and K99HG011490) and the Columbia University Precision Medicine Joint Pilot Grants Program. We thank Y. Zhao, G. Zhong, M. AlQuraishi and D. Knowles for helpful discussions.

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Authors and Affiliations

  1. Department of Systems Biology, Columbia University, New York, NY, USA

    Haicang Zhang, Xiao Fan & Yufeng Shen

  2. Columbia College, Columbia University, New York, USA

    Michelle S. Xu

  3. Department of Pediatrics, Columbia University, New York, NY, USA

    Xiao Fan & Wendy K. Chung

  4. Department of Medicine, Columbia University, New York, NY, USA

    Wendy K. Chung

  5. Department of Biomedical Informatics, Columbia University, New York, NY, USA

    Yufeng Shen

  6. JP Sulzberger Columbia Genome Center, Columbia University, New York, NY, USA

    Yufeng Shen

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Contributions

Y.S. conceived and guided the study. H.Z. implemented the algorithms and performed the main analyses. All authors contributed to data analysis, interpretation and manuscript writing.

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Correspondence to Yufeng Shen.

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Nature Machine Intelligence thanks Xiaoming Liu, Wim Vranken, Amit R Majithia and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

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Zhang, H., Xu, M.S., Fan, X. et al. Predicting functional effect of missense variants using graph attention neural networks. Nat Mach Intell 4, 1017–1028 (2022). https://doi.org/10.1038/s42256-022-00561-w

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