Skip to main page content
U.S. flag

An official website of the United States government

Dot gov

The .gov means it’s official.
Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

Https

The site is secure.
The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

Access keys NCBI Homepage MyNCBI Homepage Main Content Main Navigation
. 2021 Nov;599(7883):91-95.
doi: 10.1038/s41586-021-04043-8. Epub 2021 Oct 27.

Disease variant prediction with deep generative models of evolutionary data

Jonathan Frazer #  1 , Pascal Notin #  2 , Mafalda Dias #  1 , Aidan Gomez  2 , Joseph K Min  1 , Kelly Brock  1 , Yarin Gal  3 , Debora S Marks  4   5
Affiliations

Disease variant prediction with deep generative models of evolutionary data

Jonathan Frazer et al. Nature. 2021 Nov.

Erratum in

Abstract

Quantifying the pathogenicity of protein variants in human disease-related genes would have a marked effect on clinical decisions, yet the overwhelming majority (over 98%) of these variants still have unknown consequences1-3. In principle, computational methods could support the large-scale interpretation of genetic variants. However, state-of-the-art methods4-10 have relied on training machine learning models on known disease labels. As these labels are sparse, biased and of variable quality, the resulting models have been considered insufficiently reliable11. Here we propose an approach that leverages deep generative models to predict variant pathogenicity without relying on labels. By modelling the distribution of sequence variation across organisms, we implicitly capture constraints on the protein sequences that maintain fitness. Our model EVE (evolutionary model of variant effect) not only outperforms computational approaches that rely on labelled data but also performs on par with, if not better than, predictions from high-throughput experiments, which are increasingly used as evidence for variant classification12-16. We predict the pathogenicity of more than 36 million variants across 3,219 disease genes and provide evidence for the classification of more than 256,000 variants of unknown significance. Our work suggests that models of evolutionary information can provide valuable independent evidence for variant interpretation that will be widely useful in research and clinical settings.

PubMed Disclaimer

Comment in

Similar articles

See all similar articles

Cited by

See all "Cited by" articles

References

    1. Van Hout, C. V. et al. Exome sequencing and characterization of 49,960 individuals in the UK Biobank. Nature 586, 749–756 (2020). - PubMed - PMC - DOI
    1. Karczewski, K. J. et al. The mutational constraint spectrum quantified from variation in 141,456 humans. Nature 581, 434–443 (2020). - PubMed - PMC - DOI
    1. Landrum, M. J. & Kattman, B. L. ClinVar at five years: delivering on the promise. Hum. Mutat. 39, 1623–1630 (2018). - PubMed - DOI
    1. Raimondi, D. et al. DEOGEN2: prediction and interactive visualization of single amino acid variant deleteriousness in human proteins. Nucleic Acids Res. 45, W201-W206 (2017). - PubMed - PMC - DOI
    1. Feng, B. J. PERCH: a unified framework for disease gene prioritization. Hum. Mutat. 38, 243–251 (2017). - PubMed - PMC - DOI

Publication types

LinkOut - more resources