AI program predicts key disease-associated genetic mutations for hundreds of complex diseases

From KurzweilAI:

DNA-modelA decade of work at Johns Hopkins has yielded a computer program that predicts, with far more accuracy than current methods, which mutations are likely to have the largest effect on the activity of the “dimmer switches” (which alter the cell’s gene activity) in DNA — suggesting new targets for diagnosis and treatment of many diseases. A summary of the research was published online today (June 15) in the journal Nature Genetics. “Our computer program can comb through the genetic information from a specific cell type and predict which ‘dimmer switch’ mutations are most likely to alter the cell’s gene activity, and therefore its function,” says Michael Beer, Ph.D., associate professor of biomedical engineering at the Johns Hopkins University School of Medicine.

Which genetic mutations matter?

“The plan is to continually improve the formula as we learn more about these regulatory regions,” he says, “but already it can narrow down a list of disease-associated mutations by a factor of 20, allowing researchers to focus on the ones that are most likely to matter.” Researchers have sequenced the genomes of many patients suffering from common multigene diseases, looking for shared mutations in their control regions. The trouble is, Beer says, that these studies yield hundreds of mutations, most of them benign. So he and his team of researchers designed a computer program that could learn the difference between mutations that are likely to affect gene activity levels and those that likely won’t. “There are a lot of common diseases, like diabetes, that are probably the result of several different mutations in control regions. The mutations don’t directly cause a change in the proteins [that are] made, but they impact their abundance,” he says, and sorting out which ones matter most in diseases is key to advancing treatments.

More here.