Advancing genetic pathogenicity prediction with three-dimensional proteoform-phenotype analysis
Year: 2026
Type: preprint
Abstract: ABSTRACT Ascertaining the pathogenicity and clinical relevance of genetic mutations is a longstanding challenge across human disease. This is particularly relevant for genes with many variants of unknown significance (VUS), such as PRPH2 -retinopathy, where complex variable phenotype patterns and limited genotype correlations hinder application of precision-medicine therapies. Recent AI-based pathogenicity tools, while powerful, may be limited by insufficient mechanistic or phenotypic training data. We established a three-dimensional proteoform-phenotype analysis framework by combining AI-generated protein structural models with clinical genomics data, in which expert consensus adjudicates model outputs through a human-in-the-loop AI approach, to evaluate 46 curated pathogenic missense variants against a background VUS pool. The framework was prospectively validated using an independently evaluated clinical patient cohort. This analysis resolved spatial biophysical signatures corresponding to distinct clinical phenotypes (i.e., retinitis pigmentosa, macular dystrophy, and pattern dystrophy) as well as complex blended presentations. Leveraging these confirmed structural signatures, we prioritized high-risk VUS. This mechanism-resolved framework can be extended to other therapeutically actionable genes to identify novel causal variants and improve diagnosis, prognosis, and better select rare, eligible patients for emerging molecular gene therapy trials. Plain Language Summary Distinguishing disease-causing mutations from benign ones remains a major barrier to precision medicine. Here, the authors combine AI-generated protein structures with clinical data and expert human adjudication to show that where a PRPH2 variant sits in three-dimensional space predicts the specific retinal disease it causes, enabling variant prioritization for gene-therapy trials
Source: medRxiv
Language: English
Topic: Genomics and Rare Diseases
Subfield: Genetics
Domain: Life Sciences
Open Access status: green
Awards: Phosphoinositide signaling in glaucoma: rescue strategies for Lowe syndrome, The Jackson Laboratory Center for Precision Genetics: From New Models to Novel Therapeutics, G161103358, G2229775135, G2349931395, G3119513115, Core Facilities for Vision Research, Improving rigor and reproducibility in adaptive optics ophthalmoscopy, G4494920243, G5051922681, G5996497050, G6902378652, Therapeutic gene editing and multimodal imaging in juvenile macular degeneration , G7198951180, Translational Gene Therapy for CNGB1 Retinitis Pigmentosa, G8415332196, Precision genome surgery in autologous stem cell transplant, Defining Barriers to Gene Therapy, CRISPR Based Rescue of Glaucoma in Lowe Syndrome, Enhancing cone survival in retinitis pigmentosa through cell-specific therapeutic CRISPR editing of a roxadustat target, Stanford Vision Research Core, Stanford Ophthalmology Advanced Research Program, Gene Editing and Silencing in Phototransduction, Cancer Center Support Grant
OpenAlex ID: W7221075903