New AI tool identifies not just genetic mutations, but the diseases they may cause


 



Scientists at the Icahn School of Medicine at Mount Sinai have 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. The findings were reported in the December 15 online issue of Nature Communications.

How V2P advances genetic diagnostics

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," says first author David Stein, Ph.D., who recently completed his doctoral training in the labs of Yuval Itan, Ph.D., and Avner Schlessinger, Ph.D. "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.

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