An interactome perturbation framework prioritizes damaging missense mutations for developmental disorders

By Siwei Chen, Robert Fragoza, L. Klei, Yuan Liu, Jiebiao Wang, Kathryn Roeder, B. Devlin, Haiyuan Yu. 2018

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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. An integrated experimental-computational approach evaluates the impact of de novo missense mutations on protein–protein interactions. This interactome-based framework can be used to identify and prioritize disease-associated missense mutations.

Subfields: Cognitive Neuroscience, Genetics, Molecular Biology