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Scientists have built an artificial intelligence model to flag if previously unknown human genetic mutations are likely to cause disease, potentially transforming possibilities for the treatment of rare conditions.
The technique draws on evolutionary information from hundreds of thousands of mainly animal species and outperforms rivals including Google DeepMind’s AlphaMissense, the researchers said.
The innovation promises to offer doctors extra data to tackle medical problems they have never seen and may even be genetically unique in their origins. Rare diseases are estimated to affect hundreds of millions of people worldwide in aggregate, but many sufferers are never diagnosed.
“There’s many ways in which single genetic variants can give rise to disease — and for this very large number of patients there’s often a terrible scarcity of information out there,” said Jonathan Frazer, a researcher at the Centre for Genomic Regulation in Barcelona.
“It’s hard to diagnose the disease, it’s hard to understand how to treat the disease. We’re hoping that we’ve just provided a new very general tool to help guide this process.”
The Barcelona-based scientists worked with colleagues from Harvard Medical School to build the AI model, known as popEVE. The technology, which is described in a paper published in Nature Genetics on Monday, draws on a 2021 algorithm called EVE — standing for Evolutionary model of Variant Effect.
Jonathan Frazer, a researcher at the Centre for Genomic Regulation in Barcelona © Ivan Marti
The scientists examined how changes in genes affect the instructions they give the body to produce proteins, the building blocks of life. In particular, they focused on “missense” mutations — changes that result in a switch in the identity of a single amino acid, the basic subunit of proteins.
The researchers looked at the evolutionary diversity of genetic sequences in different species to gauge whether possible mutations were likely to be harmful. If genetic changes are absent from the records it may mean they are destructive, since the organisms suffering from them would have had poor survival prospects.
The scientists combined these patterns with readings from the UK Biobank and gnomAD human genetic databases. This allowed them to calibrate the data from animals and other organisms with information on what kinds of mutations healthy people can tolerate.
The researchers tested the model on genetic data from 31,000 families with children who suffered severe developmental disorders. In 513 cases where the youngsters showed a completely new genetic mutation, popEVE correctly identified it as the most damaging variant in their bodies 98 per cent of the time.
The model further revealed 123 genes that are in many cases active in the growing brain and interact physically with known disease-causing proteins, but have never before been linked to developmental disorders.
PopEVE worked better than models developed by Google DeepMind and others at predicting the severity of diseases and in catering to populations of non-European ancestries, the researchers said. Google DeepMind did not respond to a request for comment.
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The popEVE model does not require large amounts of energy to run and so might be well suited to low- and middle-income countries, the researchers suggested. It had already been successfully used on patients in Senegal, including to help treat a muscular atrophy sufferer with a vitamin B2 boost, they added.
PopEVE significantly expanded the capabilities of the original EVE and would be particularly helpful for patients for whom parental genetic samples were not available, said Damian Smedley, computational genomics professor at Queen Mary University of London.
“This latest work will allow scalable application to every gene,” Smedley said. “Being able to systematically assess the impact of all variants in a patient’s genome is key to fully delivering the promise of genomic sequencing in healthcare.”