Department of Biological Statistics and Computational Biology, Cornell University, Ithaca, NY, USA.
Weill Institute for Cell and Molecular Biology, Cornell University, Ithaca, NY, USA.
Nat Genet. 2018 Jul;50(7):1032-1040. doi: 10.1038/s41588-018-0130-z. Epub 2018 Jun 11.
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.
鉴定与疾病相关的错义突变仍然是一个挑战，特别是在大规模测序研究中。在这里，我们建立了一种实验和计算相结合的方法，来研究在人类互作网络背景下错义突变的功能影响，并通过分析约 2000 个在自闭症患者及其无病兄弟姐妹中发现的新生错义突变来检验我们的方法。在自闭症患者中，破坏互作的新生错义突变更为常见，主要影响枢纽蛋白，并比在无病兄弟姐妹中破坏更多的枢纽互作。此外，它们往往破坏涉及先前与自闭症相关基因的互作，提供了互补的证据，增强了先前确定的关联，并促进了新关联的发现。重要的是，通过分析来自六个疾病的新生错义突变数据，我们证明了我们的互作扰动方法为鉴定和优先考虑导致人类疾病风险的错义突变提供了一个可推广的框架。
