Study Uses AI to Find Autism Clues in "Junk" DNA

"One man's trash is another man's treasure," is a familiar expression. When it comes to health and genomics, "junk" DNA may turn out to be a goldmine. In a recent study, Princeton University-led researchers used whole-genome sequencing and artificial intelligence (AI) deep learning to identify the contribution of noncoding mutations to autism risk--demonstrating that mutations in "junk" DNA can contribute to a complex disease. The study was led by Princeton professor Olga Troyanskaya, who is also deputy director for genomics at the Flatiron Institute's Center for Computational Biology (CCB) in New York City, along with professor Robert Darnell of The Rockefeller University, also an investigator at the Howard Hughes Medical Institute. Published on May 27 in Nature Genetics, the study presented an AI deep learning framework that "predicts the specific regulatory effects and the deleterious impact of genetic variants," and used it on autism spectrum disorder (ASD).