Mutations that disrupt the function of proteins are widely recognized as a risk source for developmental disorders such as intellectual disability, congenital heart defects and autism spectrum disorder (ASD). A new study published in Nature Genetics established a computationally integrated approach to investigate the functional impact of missense mutations
The team, which includes Carnegie Mellon University's Kathyrn Roeder, tested the approach by analyzing genetic structures of individuals with ASD who also had mutations as well as their siblings who did not have the mutations. They found that the framework successfully identified and prioritized missense mutations that contribute to disease or disorder risk.
Identifying disease-associated missense mutations remains a challenge, especially in large-scale sequencing studies. Here we establish an experimentally and computationally integrated approach to investigate the functional impact of missense mutations in the context of the human interactome network and test our approach by analyzing ~2,000 de novo missense mutations found in autism subjects and their unaffected siblings.
Interaction-disrupting de novo missense mutations are more common in autism probands, principally affect hub proteins, and disrupt a significantly higher fraction of hub interactions than in unaffected siblings.
Moreover, they tend to disrupt interactions involving genes previously implicated in autism, providing complementary evidence that strengthens previously identified associations and enhances the discovery of new ones.
Importantly, by analyzing de novo missense mutation data from six disorders, we demonstrate that our interactome perturbation approach offers a generalizable framework for identifying and prioritizing missense mutations that contribute to the risk of human disease.
"Identifying genetic mutations that increase the likelihood of disease is a major challenge to progress for personalized medicine. Using a machine learning model that predicts which mutations are likely to perturb the human interactome network, we showed that these mutations are much more likely to occur in autistic children than their siblings," said Roeder, the UPMC Professor of Statistics and Life Sciences in the Dietrich College of Humanities and Social Sciences. "This result extends to several other mental disorders suggesting that our finding may have even broader applicability."