Many mutations in DNA that contribute to disease are not in actual genes but instead lie in the 99% of the genome once considered “junk.” Even though scientists have recently come to understand that these vast stretches of DNA do in fact play critical roles, deciphering these effects on a wide scale has been impossible until now.

Using artificial intelligence, a Princeton University-led team has decoded the functional impact of such mutations in people with autism. The researchers believe this powerful method is generally applicable to discovering such genetic contributions to any disease.

Publishing May 27 in the journal Nature Genetics, the researchers analyzed the genomes of 1,790 families in which one child has autism spectrum disorder but other members do not. The method sorted among 120,000 mutations to find those that affect the behavior of genes in people with autism. Although the results do not reveal exact causes of cases of autism, they reveal thousands of possible contributors for researchers to study.

Much previous research has focused on identifying mutations in genes themselves. Genes are essentially instructions for making the many proteins that build and control the body. Mutations in genes result in mutated proteins whose functions are disrupted. Other types of mutations, however, disrupt how genes are regulated. Mutations in these areas affect not what genes make but when and how much they make.

Until now, it was not possible to look across the entire genome for snippets of DNA that regulate genes and to predict how mutations in this regulatory DNA are likely to contribute to complex disease, the researchers said.  This study is the first proof that mutations in regulatory DNA can cause a complex disease.

“This method provides a framework for doing this analysis with any disease,” said Olga Troyanskaya, professor of computer science and genomics and a senior author of the study. The approach could be particularly helpful for neurological disorders, cancer, heart disease and many other conditions that have eluded efforts to identify genetic causes.