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.
Genes predicted to be disrupted by regulatory mutations in people with autism tended to be involved in brain cell functioning and fell into two categories. One category relates to synapses, communication hubs between neurons, and the other relates to chromatin.
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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.
In the case of autism, the researchers analyzed the genomes of 1,790 families with "simplex" autism spectrum disorder, meaning the condition is apparent in one child but not in other members of the family. (These data were taken from the Simons Simplex Collection of more than 2,000 autism families.) Among this sample, fewer than 30% of the people affected by autism spectrum disorder had a previously identified genetic cause. The newly found mutations are likely to significantly increase that fraction, the researchers said.
The ability to predict the functional effect of each mutation was the key innovation in this new study. Previous studies had found it challenging to detect any difference in the number of regulatory mutations in people with autism compared to unaffected people. The new method, however, looked at mutations predicted to have a high functional impact, and found a significantly higher number of such mutations in affected people.
When the researchers then looked at what genes were affected by these mutations, they turned out to be genes strongly associated with brain functions. These newly discovered mutations affected similar genes and functions as do previously identified mutations.
"Now we open the field to understand all the factors that may be involved in autism," said Theesfeld.
This information also is important to families and their doctors to better diagnose the disorder and to avoid making overly general assumptions how one person's autism might be classified with others. "They say that when you meet one person with autism you have met one person with autism because no cases are alike," said Theesfeld. "Genetically, it seems to be the same way."
With this new method, the team is analyzing the genetic causes of various forms of cancer, heart disease and other disorders.
MEDICA-tradefair.com; Source: Princeton University, Engineering School