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Nat Genet. 2018 Jul;50(7):1032-1040. doi: 10.1038/s41588-018-0130-z. Epub 2018 Jun 11.

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

Chen S1,2,3, Fragoza R1,2,3, Klei L4, Liu Y1,2, Wang J5, Roeder K6,7, Devlin B8, Yu H9,10.

Author information

1
Department of Biological Statistics and Computational Biology, Cornell University, Ithaca, NY, USA.
2
Weill Institute for Cell and Molecular Biology, Cornell University, Ithaca, NY, USA.
3
Department of Molecular Biology and Genetics, Cornell University, Ithaca, NY, USA.
4
Department of Psychiatry, University of Pittsburgh School of Medicine, Pittsburgh, PA, USA.
5
Department of Statistics and Data Science, Carnegie Mellon University, Pittsburgh, PA, USA.
6
Department of Statistics and Data Science, Carnegie Mellon University, Pittsburgh, PA, USA. roeder@andrew.cmu.edu.
7
Computational Biology Department, Carnegie Mellon University, Pittsburgh, PA, USA. roeder@andrew.cmu.edu.
8
Department of Psychiatry, University of Pittsburgh School of Medicine, Pittsburgh, PA, USA. devlinbj@upmc.edu.
9
Department of Biological Statistics and Computational Biology, Cornell University, Ithaca, NY, USA. haiyuan.yu@cornell.edu.
10
Weill Institute for Cell and Molecular Biology, Cornell University, Ithaca, NY, USA. haiyuan.yu@cornell.edu.

Abstract

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.

PMID:
29892012
PMCID:
PMC6314957
[Available on 2019-07-01]
DOI:
10.1038/s41588-018-0130-z

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