An interactome perturbation framework prioritizes damaging missense mutations for developmental disorders.

Area of Science:

• Genetics
• Computational Biology
• Systems Biology

Background:

• Identifying disease-associated missense mutations is difficult, particularly in large genetic studies.
• Missense mutations can alter protein function and disrupt biological networks.

Purpose of the Study:

• To develop and validate an integrated approach for assessing the functional impact of missense mutations using the human interactome network.
• To identify and prioritize disease-associated missense mutations, specifically in autism spectrum disorder (ASD).

Main Methods:

• Integrated experimental and computational analysis of missense mutations within the human interactome network.
• Analysis of approximately 2,000 de novo missense mutations from autism subjects and unaffected siblings.
• Evaluation of mutation impact on protein-protein interaction networks, focusing on hub proteins.

Main Results:

• Interaction-disrupting de novo missense mutations were more prevalent in autism probands compared to controls.
• These mutations disproportionately affected hub proteins and disrupted a greater fraction of their interactions.
• The approach strengthened existing autism gene associations and facilitated new discoveries, showing generalizability across six disorders.

Conclusions:

• The interactome perturbation approach is a powerful framework for identifying and prioritizing missense mutations contributing to human disease risk.
• This method enhances the discovery of novel disease-associated genes and strengthens evidence for known ones.
• The study provides a generalizable strategy for analyzing missense mutations in large-scale sequencing studies across various disorders.