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Finding Convergent Biology with Expression Analysis of ASD Risk Genes

Friday, 3 May 2013: 18:15
Meeting Room 1-2 (Kursaal Centre)
A. J. Willsey1, M. Li2, S. J. Sanders1, J. Gockley1, Z. Lin3, Y. Zhu2, B. Devlin4, K. Roeder5, J. P. Noonan1, N. Sestan2 and M. W. State1, (1)Genetics, Yale University School of Medicine, New Haven, CT, (2)Neurobiology, Yale University School of Medicine, New Haven, CT, (3)Computational Biology, Yale University School of Medicine, New Haven, CT, (4)Psychiatry, University of Pittsburgh, Pittsburgh, PA, (5)Statistics, Carnegie Mellon University, Pittsburgh, PA
Background: Recent advances in genomics and transcriptomics are providing unprecedented opportunities to dissect the molecular mechanisms underlying ASD. On the one hand, a series of recent studies have demonstrated that de novo mutation discovery via whole exome sequencing and copy number variation analyses provide a systematic unbiased approach to gene discovery. At the same time, the availability for the first time of a detailed map of gene expression in normal brain is offering the ability to search for points of spatial and temporal convergence among the disparate set of genes clearly identified as carrying ASD risk. Given the high degree of genetic heterogeneity underlying ASD, the combination of reliable gene discovery and expression profiling offers the potential to identify convergent biological processes and reveal novel treatment targets. 

Objectives: To leverage spatiotemporal gene expression data to build expression networks in a developmentally-relevant framework in order to converge ASD risk genes into functional pathways underlying ASD neurobiology. 

Methods: Gene expression networks were constructed using spatiotemporal gene expression data in the developing brain.

Results: Gene expression networks containing ASD risk genes are dynamic across development. Clusters generated from correlation analyses of gene expression in the neocortex highlight the importance of  early and mid-fetal development at a time when early synaptic connections are being formed in these regions.   

Conclusions: Spatiotemporal brain expression analysis makes meaningful and testable predictions about ASD biology. As the number of risk genes increases, we expect far greater resolution to detect the biological processes, brain regions, and developmental periods fundamental to ASD neuropathology.

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