@GoodfireAI: 3 months ago, we used interpretability to predict which of 4.2 million genetic variants cause disease. Now, we've valid…
Summary
GoodfireAI used interpretability to predict which genetic variants cause disease, validated predictions with real-world data, and released an open-source database for all variants in the NIH's ClinVar database.
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Cached at: 07/24/26, 09:19 PM
3 months ago, we used interpretability to predict which of 4.2 million genetic variants cause disease.
Now, we’ve validated several of those predictions with real-world datasets, including a national biobank, clinical data, and RNA sequencing data.
4 examples: (1/6)
Do EVEE’s predicted pathogenic variants actually show up in human disease?
@7uomoki checked them against the FinnGen biobank, and found that they were strongly enriched for disease associations across the Finnish population—an independent validation of EVEE’s predictions. (2/6)
We also found evidence that EVEE’s pathogenicity scores track how often a variant causes disease (clinical penetrance) as part of our collaboration with @MayoClinic.
EVEE scores appear to predict the severity of familial hypercholesterolemia (FH) better than other computational predictors. (3/6)
We’ve also further validated EVEE’s mechanistic hypotheses, which predict how a variant affects downstream function.
RNA-seq data confirms many predicted effects from EVEE’s disruption profiles, including specific impacts on splicing at nucleotide-level resolution. (4/6)
Lastly, by combining EVEE’s mechanistic hypotheses with evidence from the literature, we identified 6 variants that may warrant reclassification in ClinVar (the NIH database of clinically interpreted variants).
We’ve submitted supporting evidence which is now under review. (5/6)
We’re now deploying EVEE with collaborators for clinical use cases, as well as continuing to validate more of its predictions.
Read more on how we built EVEE:
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