Leveraging artificial intelligence techniques, researchers have demonstrated that mutations in so-called ‘junk’ DNA can cause autism. The study is the first to functionally link such mutations to the neurodevelopmental condition and the first clear demonstration of non-inherited, noncoding mutations causing any complex human disease or disorder.
The research was led by Olga Troyanskaya in collaboration with Robert Darnell. Troyanskaya is deputy director for genomics at the Flatiron Institute’s Center for Computational Biology (CCB) in New York City and a professor of computer science at Princeton University. Darnell is the Robert and Harriet Heilbrunn Professor of Cancer Biology at Rockefeller University and an investigator at the Howard Hughes Medical Institute.
Their team used machine learning to analyze the whole genomes of 1,790 individuals with autism and their unaffected parents and siblings. These individuals had no family history of autism, meaning the genetic cause of their condition was probably spontaneous mutations rather than inherited mutations.
The analysis predicted the ramifications of genetic mutations in parts of the genome that do not encode proteins, regions often mischaracterized as ‘junk’ DNA. The number of autism cases linked to the noncoding mutations was comparable to the number of cases linked to protein-coding mutations that disable gene function.
The implications of the work extend beyond autism, Troyanskaya says. “This is the first clear demonstration of non-inherited, noncoding mutations causing any complex human disease or disorder.”