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Rare de novo variants associated with autism implicate a large functional network of genes involved in formation and function of synapses.


ABSTRACT: Identification of complex molecular networks underlying common human phenotypes is a major challenge of modern genetics. In this study, we develop a method for network-based analysis of genetic associations (NETBAG). We use NETBAG to identify a large biological network of genes affected by rare de novo CNVs in autism. The genes forming the network are primarily related to synapse development, axon targeting, and neuron motility. The identified network is strongly related to genes previously implicated in autism and intellectual disability phenotypes. Our results are also consistent with the hypothesis that significantly stronger functional perturbations are required to trigger the autistic phenotype in females compared to males. Overall, the presented analysis of de novo variants supports the hypothesis that perturbed synaptogenesis is at the heart of autism. More generally, our study provides proof of the principle that networks underlying complex human phenotypes can be identified by a network-based functional analysis of rare genetic variants.

SUBMITTER: Gilman SR 

PROVIDER: S-EPMC3607702 | biostudies-literature | 2011 Jun

REPOSITORIES: biostudies-literature

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Rare de novo variants associated with autism implicate a large functional network of genes involved in formation and function of synapses.

Gilman Sarah R SR   Iossifov Ivan I   Levy Dan D   Ronemus Michael M   Wigler Michael M   Vitkup Dennis D  

Neuron 20110601 5


Identification of complex molecular networks underlying common human phenotypes is a major challenge of modern genetics. In this study, we develop a method for network-based analysis of genetic associations (NETBAG). We use NETBAG to identify a large biological network of genes affected by rare de novo CNVs in autism. The genes forming the network are primarily related to synapse development, axon targeting, and neuron motility. The identified network is strongly related to genes previously impl  ...[more]

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