The paper describes the development and validation of popEVE, a deep generative model designed to interpret missense genetic variants on a proteome-wide scale. By integrating deep evolutionary data with human population variation, the model creates a calibrated scoring system that allows researchers to compare the severity of mutations across different proteins. This approach addresses a major limitation in clinical genomics where previous tools often overpredicted pathogenicity or failed to distinguish between mild and life-threatening conditions. In practical applications, popEVE identified 123 novel candidate genes for severe developmental disorders and successfully prioritized causal mutations using only a patient's exome. The researchers demonstrate that high-scoring variants frequently cluster at critical 3D interaction sites, such as ligand-binding pockets and protein interfaces. Ultimately, the framework improves diagnostic yields for rare diseases, particularly in cases where parental sequencing data is unavailable.

References:

• Orenbuch R, Shearer C A, Kollasch A W, et al. Proteome-wide model for human disease genetics[J]. Nature Genetics, 2025: 1-10.