A global team of scientists has discovered how to make more accurate predictions about disease-causing genetic mutations in humans after applying artificial intelligence techniques to an expanded primate DNA database.
The project combined the genetic information of around 800 primates belonging to 233 species of monkeys, apes and lemurs. An AI algorithm based on the genomic database was then used to analyze the DNA of 454,000 human participants in the UK Biobank project, with results showing greatly improved genetic risk prediction, the researchers said.
We have shown that the more we learn about genetic variation in nonhuman primates, the better we can make predictions about which mutations can cause disease in humans, said Jeffrey Rogers of Baylor College of Medicine, one of the consortium leaders.
The consortia’s work will increase knowledge about human genetics and aid health research, particularly for groups that are not well covered by previous medical studies, while improving guidance for conservationists seeking to protect dwindling primate populations . The findings were published Thursday in the journal Science.
Academic researchers collaborated with Illumina, the US company that manufactures DNA sequencing equipment, to identify 4.3 million common genetic variants found in the genomes of 233 primate species. To predict their health effects, they trained an AI algorithm called PrimateAI-3D with data about these mutations and the three-dimensional structures of the proteins they produce.
You can train a generative language model like ChatGPT on existing text from Wikipedia and elsewhere, said Kyle Farh, Illumina’s vice president for AI. We used a similar deep learning architecture, but our data comes from millions of years of natural selection.
The scientists then applied PrimateAI-3D to identify potentially harmful human mutations, using the DNA and medical data of 454,000 volunteers who donated samples to the UK Biobank.
The findings were particularly effective at finding rare genetic variants that confer a high risk of joint disease. Farh said PrimateAI-3D was overall 12% more accurate than any previous method of assessing genetic risks of developing health problems such as cardiovascular disease and type 2 diabetes.
One benefit of the new technique, he added, was that it applied equally well across humanity, overcoming the biases against populations of white European ancestry inherent in existing assessments of genetic risks, which rely primarily on data from these groups.
It is a step towards implementing genetic medicine for diverse non-European populations, Farh said.
Genomic research also has important implications for primates themselves.
For Rogers, the biggest surprise was learning that the level of genetic variation in primate species is typically two, three, or even four times that of humans. This gives us a perspective on human genetic variation that is very low, even among people in Africa, by the standards of other primates.
Ancestral humans are thought to have lost genetic diversity as populations dwindled to very low numbers tens or hundreds of thousands of years ago.
Primate genetic diversity, found even in very rare and endangered species, could also benefit the animals’ conservation, Rogers added: If we can save the habitats, there’s enough genetic variation in the surviving populations.
Jean Boubli, professor of tropical ecology and conservation at the University of Salford and a leading member of the consortium, called his work a game changer in the study of many aspects of primate evolution. Many of these species are under threat and the findings here could help with conservation efforts, he said.
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