An interactome perturbation framework prioritizes damaging missense mutations for developmental disorders

• Published in: Nature genetics Vol. 50; no. 7; pp. 1032 - 1040
• Main Authors: Chen, Siwei, Fragoza, Robert, Klei, Lambertus, Liu, Yuan, Wang, Jiebiao, Roeder, Kathryn, Devlin, Bernie, Yu, Haiyuan
• Format: Journal Article
• Language: English
• Published: New York Nature Publishing Group US 01.07.2018; Nature Publishing Group
• Subjects: 45; 45/70; 631/114; 631/208/366; 692/699; 82/1; 82/111; 82/47; 82/51; Acids; Agriculture; Animal Genetics and Genomics; Autism; Autism Spectrum Disorder - genetics; Autistic Disorder - genetics; Bioinformatics; Biomedical and Life Sciences; Biomedicine; Cancer Research; Child; Cloning; Developmental disabilities; Developmental Disabilities - genetics; Disease; Disorders; Female; Gene Function; Gene mutation; Genes; Genetic Predisposition to Disease - genetics; Genomes; Health risk assessment; Human Genetics; Humans; Integrated approach; Male; Missense mutation; Mutagenesis; Mutation; Mutation, Missense - genetics; Neurodevelopmental disorders; Protein-protein interactions; Proteins; Siblings
• ISSN: 1061-4036; 1546-1718; 1546-1718
• DOI: 10.1038/s41588-018-0130-z

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.