On Nov. 24, 2025, a new AI model called popEVE developed by Harvard Medical School researchers and colleagues can predict how likely each variant in a patient’s genome is to cause disease.

Every human has tens of thousands of tiny genetic alterations in their DNA, also known as variants, that affect how cells build proteins. Yet in a given human genome, only a few of these changes are likely to modify proteins in ways that cause disease, which raises a key question: How can scientists find the disease-causing needles in the vast haystack of genetic variants?

For years, scientists have been working on genome-wide association studies and artificial intelligence tools to tackle this question. In a paper published in Nature Genetics, the scientists show that popEVE can predict whether variants are benign or pathogenic (disease-causing) and which variants lead to death in childhood versus adulthood.

The model was able to identify more than 100 novel alterations responsible for undiagnosed, rare genetic diseases. The team hopes that popEVE can help clinicians diagnose single-variant genetic diseases — especially rare diseases — more quickly and accurately. The model could also be used to identify new drug targets for genetic conditions. The tool complements efforts across the HMS community to conduct research, build AI tools, and engage in nationwide collaborations to improve the diagnosis and treatment of rare diseases.

The team noted that while popEVE will need to be further verified to ensure its safety and accuracy before it is widely adopted in the clinic, they hope it can eventually increase clinicians’ confidence in using computational models for genetic diagnoses.

The researchers are also integrating popEVE scores into existing variant and protein databases such as ProtVar and UniProt, which will allow scientists worldwide to use the model to compare variants across genes.

By pinpointing the genetic origins of rare or complex diseases, the researchers noted, popEVE may also identify new targets and avenues for drug development.