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Predicting glaucoma-causing MYOC gene mutations is hard. A new AI tool, GOLF, accurately assesses and explains the pathogenicity of olfactomedin domain variants, aiding genetic risk stratification.

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Area of Science:

  • Genetics
  • Ophthalmology
  • Artificial Intelligence

Background:

  • Missense mutations in the MYOC gene, especially in the olfactomedin (OLF) domain, are linked to open-angle glaucoma.
  • Accurate prediction of mutation pathogenicity is difficult due to complex variant effects and limited clinical data.

Purpose of the Study:

  • To develop and validate GOLF, a generative AI framework for assessing and explaining the pathogenicity of OLF domain variants.
  • To improve early genetic risk stratification for glaucoma.

Main Methods:

  • Curated a comprehensive dataset of OLF homologs.
  • Trained generative AI models to predict the effect of missense mutations.
  • Employed a sparse autoencoder (SAE) to interpret model decision mechanisms and identify biochemical features.

Main Results:

  • The generative models collectively achieved accurate classification of known pathogenic and benign variants.
  • The SAE revealed biochemical features used by the models for pathogenicity prediction.
  • GOLF demonstrated effective evaluation of disease-causing mutations.

Conclusions:

  • GOLF provides an accurate and interpretable method for assessing OLF domain variant pathogenicity in MYOC.
  • This framework supports genetic risk stratification for glaucoma and deepens understanding of pathogenic variant mechanisms.