Study Reveals Mutations in “Junk” DNA Causes Autism and Opens New Perspectives In Genomics
Researchers in New York, leveraging on artificial intelligence techniques, have demonstrated that mutations in so-called 'junk' DNA can cause autism .This study is the first to functionally link such mutations to the neurodevelopmental condition.
Led by Olga Troyanskaya , 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, the study was also supported by Robert B Darnell ,Professor of Cancer Biology at Rockefeller University and an investigator at the Howard Hughes Medical Institute.
Their team used machine learning and AI to analyze the whole genomes of 1,790 individuals with autism and their unaffected relations. 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 revealed 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 in a telephone interview with Thailand Medical News said, "This is the first clear demonstration of non-inherited, noncoding mutations causing any complex human disease or disorder."
Of the total human genome,only 1 to 2 percent is made up of genes that encode the blueprints for making proteins. Those proteins carry out essential tasks throughout our bodies, such as regulating blood sugar levels, fighting infections and sending communications between cells. The other 98 percent of our genome isn't genetic dead weight, though. The noncoding regions help regulate when and where genes make proteins.
Mutations in protein-coding regions resulted for at most 30 percent of autism cases in individuals without a family history of autism. Evidence suggested that autism-causing mutations must happen elsewhere in the genome as well.
Uncovering which noncoding mutations may cause autism is not an easy task.. A single individual may have dozens of noncoding mutations, most of which will be unique to the individual. This make the traditional approach of identifying common mutations among affected populations nonviable.
Troyanskaya and her colleagues took a novel approach. They trained a machine learning model to predict how a given sequence would affect gene expression.
"This is a shift in thinking about genetic studies that we're introducing with this analysis," says Chandra Theesfeld, a research scientist in Troyanskaya's lab at Princeton. "In addition to scientists studying shared genetic mutations across large groups of individuals, here we're applying a set of smart, sophisticated tools that can even predict and tell us what any specific mutation is going to do, even those that are rare or never observed before."
Noncoding mutations in many of the children with autism altered gene regulation, the analysis suggested. Moreover, the results suggested that the mutations affected gene expression in the brain and genes already linked to autism, such as those responsible for neuron migration and development. "This is consistent with how a
utism most likely manifests in the brain," says study co-author Christopher Park, a research scientist at CCB. "It's not just the number of mutations occurring, but what kind of specific mutations are occurring."
The researchers tested the effects of some of the noncoding mutations in laboratory experiments and studies. They inserted predicted high-impact mutations found in children with autism into cells and observed the resulting changes in gene expression. These cellular changes affirmed the model's predictions.
Troyanskaya says she and her colleagues will continue improving and expanding their method. , She hopes the work will eventually improve how genetic data are used for diagnosing and treating diseases and disorders. "Right now, 98 percent of the genome is usually being thrown away," she says. "Our work allows you to think about what we can do with the 98 percent."
Scientists can apply the same techniques used in the new study to explore the role noncoding mutations play in diseases such as cancer and heart disease, says study co-author Jian Zhou of CCB and Princeton. "This opens a new door and enables a new perspective on the cause of not just autism, but many human diseases."
Reference :Jian Zhou, Christopher Y. Park, Chandra L. Theesfeld, Aaron K. Wong, Yuan Yuan, Claudia Scheckel, John J. Fak, Julien Funk, Kevin Yao, Yoko Tajima, Alan Packer, Robert B. Darnell, Olga G. Troyanskaya. Whole-genome deep-learning analysis identifies contribution of noncoding mutations to autism risk. Nature Genetics, May 27, 2019; DOI: 10.1038/s41588-019-0420-0