Deep generative modeling of the human proteome reveals over a hundred novel genes involved in rare genetic disorders.

ABSTRACT: Identifying causal mutations accelerates genetic disease diagnosis, and therapeutic development. Missense variants present a bottleneck in genetic diagnoses as their effects are less straightforward than truncations or nonsense mutations. While computational prediction methods are increasingly successful at prediction for variants in known disease genes, they do not generalize well to other genes as the scores are not calibrated across the proteome^1-6. To address this, we developed a deep generative model, popEVE, that combines evolutionary information with population sequence data⁷ and achieves state-of-the-art performance at ranking variants by severity to distinguish patients with severe developmental disorders⁸ from potentially healthy individual

SUBMITTER: Orenbuch R 

PROVIDER: S-EPMC10802723 | biostudies-literature | 2024 Jan

REPOSITORIES: biostudies-literature