The approach, known as V2P (Variant to Phenotype), is intended to speed up genetic testing and support the development of new therapies for rare and complex illnesses. The research was published in the December 15 online issue of Nature Communications.

Predicting disease from genetic variation

Most existing genetic analysis tools are able to estimate whether a mutation is potentially damaging, but they typically stop there. They do not explain what kind of disease may result. V2P is designed to overcome this limitation by using advanced machine learning to connect genetic variants with their expected phenotypic outcomes — meaning the diseases or traits a mutation may produce. In this way, the system helps predict how a person’s DNA could affect their health.

“Our approach allows us to pinpoint the genetic changes that are most relevant to a patient’s condition, rather than sifting through thousands of possible variants,” says first author David Stein, PhD, who recently completed his doctoral training in the labs of Yuval Itan, PhD, and Avner Schlessinger, PhD. “By determining not only whether a variant is pathogenic but also the type of disease it is likely to cause, we can improve both the speed and accuracy of genetic interpretation and diagnostics.”

Training the AI to find the right mutation

To build the model, the researchers trained V2P on a large dataset containing both harmful and harmless genetic variants, along with detailed disease information. This training allowed the system to learn patterns linking specific variants to health outcomes. When tested using real, de-identified patient data, V2P frequently ranked the true disease-causing mutation within the top 10 candidates, demonstrating its potential to simplify and accelerate genetic diagnosis.

“Beyond diagnostics, V2P could help researchers and drug developers identify the genes and pathways most closely linked to specific diseases,” says Dr. Schlessinger, co-senior and co-corresponding author, Professor of Pharmacological Sciences, and Director of the AI Small Molecule Drug Discovery Center at the Icahn School of Medicine at Mount Sinai. “This can guide the development of therapies that are genetically tailored to the mechanisms of disease, particularly in rare and complex conditions.”

Expanding precision medicine and drug discovery

At present, V2P sorts mutations into broad disease categories, such as nervous system disorders or cancers. The research team plans to enhance the system so it can make more detailed predictions and combine its results with additional data sources to further assist drug discovery.

The researchers say this advance marks meaningful progress toward precision medicine, where treatments are selected based on an individual’s genetic profile. By linking genetic variants to their likely disease effects, V2P could help clinicians reach diagnoses faster and help scientists uncover new targets for therapy.

“V2P gives us a clearer window into how genetic changes translate into disease, which has important implications for both research and patient care,” says Dr. Itan, co-senior and co-corresponding author, Associate Professor of Artificial Intelligence and Human Health, and Genetics and Genomic Sciences, a core member of The Charles Bronfman Institute for Personalized Medicine, and a member of The Mindich Child Health and Development Institute at the Icahn School of Medicine at Mount Sinai. “By connecting specific variants to the types of diseases they are most likely to cause, we can better prioritize which genes and pathways warrant deeper investigation. This helps us move more efficiently from understanding the biology to identifying potential therapeutic approaches and, ultimately, tailoring interventions to an individual’s specific genomic profile.”

The paper is titled “Expanding the utility of variant effect predictions with phenotype-specific models.”

The study’s authors, as listed in the journal, are David Stein, Meltem Ece Kars, Baptiste Milisavljevic, Matthew Mort, Peter D. Stenson, Jean-Laurent Casanova, David N. Cooper, Bertrand Boisson, Peng Zhang, Avner Schlessinger, and Yuval Itan.

This research was supported by National Institutes of Health (NIH) grants R24AI167802 and P01AI186771, funding from the Fondation Leducq, and the Leona M. and Harry B. Helmsley Charitable Trust grant 2209-05535. Additional support came from NIH grants R01CA277794, R01HD107528, and R01NS145483. The work also received partial support through Clinical and Translational Science Awards (CTSA) grant UL1TR004419 from the National Center for Advancing Translational Sciences, as well as support from the Office of Research Infrastructure of the NIH under award numbers S10OD026880 and S10OD030463.