New AI Can Predict Which Diseases Your DNA Might Spark

Scientists at the Icahn School of Medicine at Mount Sinai have created a new artificial intelligence tool that can both find genetic mutations that cause disease and predict what kinds of illnesses those mutations are likely to lead to.

The method, called V2P (Variant to Phenotype), is intended to speed up genetic testing and support the search for new treatments for complex and rare conditions. The work is described in the December 15 online issue of Nature Communications.

AI links genetic mutations to disease outcomes

Most current genetic analysis tools can tell whether a mutation appears harmful, but they usually cannot identify the specific type of disease it may cause. V2P addresses this limitation by using advanced machine learning to connect genetic variants with their likely phenotypic outcomes—that is, the diseases or traits a mutation might cause—effectively forecasting how a person’s DNA could shape 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.”

The team trained V2P on a large collection of genetic variants that included both harmful and harmless changes, along with detailed information about associated diseases. When tested on real, de-identified patient data, the system frequently placed the true disease-causing mutation within the top 10 candidates, suggesting it could significantly reduce the time and effort required for genetic diagnosis.

Guiding drug discovery with AI-powered gene insights

“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.”

At present, V2P groups mutations into broad disease categories such as nervous system disorders or cancers. The researchers plan to make the tool more precise so that it can predict more narrowly defined disease outcomes and combine it with additional data sources to further support drug discovery efforts.

Toward more precise, genetics-based medicine

This development moves the field closer to precision medicine, where therapies are chosen to match an individual’s genetic profile. By connecting specific genetic variants to their likely health effects, V2P could help clinicians reach diagnoses more quickly and help researchers uncover new targets for treatment, according to the investigators.

“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.”

Reference: “Expanding the utility of variant effect predictions with phenotype-specific models” by 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, 28 November 2025, Nature Communications.
DOI: 10.1038/s41467-025-66607-w

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 work was supported by the National Institutes of Health (NIH) grants R24AI167802 and P01AI186771, the Fondation Leducq, and the Leona M. and Harry B. Helmsley Charitable Trust grant 2209-05535. Additional financial support was provided by NIH grants R01CA277794, R01HD107528, and R01NS145483. This work was supported in part by Clinical and Translational Science Awards (CTSA) grant UL1TR004419 from the National Center for Advancing Translational Sciences. Research reported in this publication was also supported by the Office of Research Infrastructure of the NIH under award number S10OD026880 and S10OD030463.

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