Expanding the utility of variant effect predictions with phenotype-specific models

Stein, David, Kars, Meltem Ece, Milisavljevic, Baptiste, Mort, Matthew, Stenson, Peter D., Casanova, Jean-Laurent, Cooper, David N. ORCID: https://orcid.org/0000-0002-8943-8484, Boisson, Bertrand, Zhang, Peng, Schlessinger, Avner and Itan, Yuval 2025. Expanding the utility of variant effect predictions with phenotype-specific models. Nature Communications 10.1038/s41467-025-66607-w

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Abstract

Current methods for variant effect prediction do not differentiate between pathogenic variants resulting in different disease outcomes and are restricted in application due to a focus on variants with a single molecular consequence. We have developed Variant-to-Phenotype (V2P), a multi-task, multi-output machine learning model to predict variant pathogenicity conditioned on top-level Human Phenotype Ontology disease phenotypes (n = 23) for single nucleotide variants and insertions/deletions throughout the human genome. V2P leverages a unique approach for the modeling of variant effect that incorporates resultant disease phenotypes as output and during training to improve the quality of variant disease phenotype and effect predictions, simultaneously. We describe the architecture, training strategy, and biological features contributing to V2P’s output, revealing initial characteristics underlying the relationship between disease genotype and phenotype. Moreover, we demonstrate the benefit of incorporating disease phenotypes for variant effect predictions by comparing V2P with several variant effect predictors across various high-quality evaluation datasets from manually curated databases and functional assays. Finally, we examine how V2P’s predictions result in the successful identification of pathogenic variants in real and simulated patient sequencing data, outperforming other tested methods in initial comparisons. V2P offers a complete mapping of human genetic variants to disease-phenotypes, offering a uniquely conditioned set of variant effect characterizations.

Item Type:	Article
Date Type:	Publication
Status:	In Press
Schools:	Schools > Medicine
Publisher:	Nature Research
Date of First Compliant Deposit:	9 December 2025
Date of Acceptance:	11 November 2025
Last Modified:	09 Dec 2025 10:30
URI:	https://orca.cardiff.ac.uk/id/eprint/183017

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