DAWN: A framework to identify autism genes and subnetworks using gene expression and genetics

Li Liu, Jing Lei, Stephan J. Sanders, Arthur Jeremy Willsey, Yan Kou, Abdullah Ercument Cicek, Lambertus Klei, Cong Lu, Xin He, Mingfeng Li, Rebecca A. Muhle, Avi Ma'Ayan, James P. Noonan, Nenad Šestan, Kathryn A. McFadden, Matthew W. State, Joseph D. Buxbaum, Bernie Devlin, Kathryn Roeder

Research output: Contribution to journalArticlepeer-review

96 Scopus citations


Background: De novo loss-of-function (dnLoF) mutations are found twofold more often in autism spectrum disorder (ASD) probands than their unaffected siblings. Multiple independent dnLoF mutations in the same gene implicate the gene in risk and hence provide a systematic, albeit arduous, path forward for ASD genetics. It is likely that using additional non-genetic data will enhance the ability to identify ASD genes. Methods. To accelerate the search for ASD genes, we developed a novel algorithm, DAWN, to model two kinds of data: rare variations from exome sequencing and gene co-expression in the mid-fetal prefrontal and motor-somatosensory neocortex, a critical nexus for risk. The algorithm casts the ensemble data as a hidden Markov random field in which the graph structure is determined by gene co-expression and it combines these interrelationships with node-specific observations, namely gene identity, expression, genetic data and the estimated effect on risk. Results: Using currently available genetic data and a specific developmental time period for gene co-expression, DAWN identified 127 genes that plausibly affect risk, and a set of likely ASD subnetworks. Validation experiments making use of published targeted resequencing results demonstrate its efficacy in reliably predicting ASD genes. DAWN also successfully predicts known ASD genes, not included in the genetic data used to create the model. Conclusions: Validation studies demonstrate that DAWN is effective in predicting ASD genes and subnetworks by leveraging genetic and gene expression data. The findings reported here implicate neurite extension and neuronal arborization as risks for ASD. Using DAWN on emerging ASD sequence data and gene expression data from other brain regions and tissues would likely identify novel ASD genes. DAWN can also be used for other complex disorders to identify genes and subnetworks in those disorders.

Original languageEnglish
Article number22
JournalMolecular Autism
Issue number1
StatePublished - 6 Mar 2014


  • Autism
  • Gene discovery
  • Hidden Markov random field
  • Network
  • Neurite extension
  • Neuronal arborization
  • Risk prediction
  • Weighted gene co-expression network analysis


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