Scientists at the Icahn School of Medicine at Mount Sinai have recently developed a novel artificial intelligence tool that not only identifies disease-causing genetic mutations but also predicts the type of disease those mutations may trigger.
The method, called V2P (Variant to Phenotype), is designed to accelerate genetic diagnostics and aid in the discovery of new treatments for complex and rare diseases.
Current genetic analysis tools can estimate whether a mutation is harmful, but they cannot determine the type of disease it might cause. V2P fills that gap by using advanced machine learning to link genetic variants with their likely phenotypic outcomes—that is, the diseases or traits a mutation might cause—effectively predicting how a patient’s DNA could influence 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,” lead researcher David Stein says in a research note. “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 tool was trained on a large database of both harmful and benign genetic variants, incorporating disease information to improve prediction accuracy. In tests using real, de-identified patient data, V2P often ranked the true disease-causing variant among the top 10 candidates, highlighting its potential to streamline genetic diagnostics.
V2P gives a clearer window into how genetic changes translate into disease, which has important implications for both research and patient care. By connecting specific variants to the types of diseases they are most likely to cause, scientists can better prioritize which genes and pathways warrant deeper investigation. This aids with identifying potential therapeutic approaches and, ultimately, tailoring interventions to an individual’s specific genomic profile.
Hence, beyond diagnostics, V2P could help researchers and drug developers identify the genes and pathways most closely linked to specific diseases. This has the potential to guide the development of therapies that are genetically tailored to the mechanisms of disease, particularly in rare and complex conditions.
While V2P currently classifies mutations into broad categories such as nervous system disorders or cancers, the researchers aim to refine the tool to predict more specific disease outcomes and integrate it with additional data sources to support drug discovery.
This innovation represents a step toward precision medicine, in which treatments can be matched to a patient’s genetic profile. By connecting genetic variants to their likely disease effects, V2P may help clinicians diagnose more efficiently and help scientists identify new therapeutic targets, say the investigators.
The research appears in the journal Nature Communications, titled “Expanding the utility of variant effect predictions with phenotype-specific models.”
