New AI model links genetic mutations to specific diseases

Scientists at the Icahn School of Medicine at Mount Sinai have developed a new artificial intelligence (AI) tool that can both identify disease-causing genetic mutations and predict the type of disease those mutations are likely to trigger. The tool could significantly speed up genetic diagnosis and support the development of new treatments for rare and complex conditions.

The method, known as Variant to Phenotype (V2P), addresses a long-standing gap in genetic analysis by linking DNA changes directly to their likely disease outcomes.

Moving beyond harmful-or-not

Current genetic testing tools can estimate whether a genetic variant is harmful, but they typically stop there. Clinicians are often left with long lists of possible mutations without clear guidance on which ones are relevant to a patient’s symptoms.

V2P is designed to go a step further. Using advanced machine learning techniques, it predicts not only whether a variant is pathogenic but also the category of disease it is most likely to cause, such as neurological disorders or cancer. This allows clinicians and researchers to focus on the genetic changes most closely aligned with a patient’s condition.

“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 Dr David Stein. “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 model

The researchers trained V2P using a large dataset of both harmful and benign genetic variants, combined with detailed disease information. This enabled the model to learn patterns linking specific mutations to particular phenotypic outcomes.

When tested on real, de-identified patient data, the tool ranked the true disease-causing mutation among the top ten candidates. According to the team, this performance suggests V2P could streamline the diagnostic process in clinical genetics.

Implications for drug discovery

Beyond diagnostics, the researchers believe the tool could have important applications in biomedical research and drug development.

“Beyond diagnostics, V2P could help researchers and drug developers identify the genes and pathways most closely linked to specific diseases,” says Dr Avner 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 classifies mutations into broad disease categories. The team now plans to refine the system so that it can predict more specific disease outcomes and integrate additional biological data to further support drug discovery.

A step towards precision medicine

The researchers describe V2P as an important advance towards precision medicine, where diagnosis and treatment are tailored to an individual’s genetic profile.

“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 Yuval Itan, co-senior and co-corresponding author and associate professor of artificial intelligence, human health, genetics and genomic sciences 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 prioritise 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.”