- Awarded: 2013
- Award Type: Research
- Award #: 274624
Among the many interesting findings emerging from studies of de novo mutations in the Simons Simplex Collection has been the extreme degree of genetic heterogeneity that underlies autism spectrum disorders. In addition, one mutation may be associated with a wide range of distinct psychiatric and neurodevelopmental outcomes. Many of the genes that contribute to autism also demonstrate considerable pleiotropy, playing diverse biological roles at different points in brain development. These factors present important obstacles to translating the rapidly growing understanding of autism genomics into a deeper understanding of autism pathophysiology.
Against this backdrop, a collaborative group has formed that includes Matthew State’s lab at the University of California, San Francisco, Nenad Sestan’s and James Noonan’s labs at Yale University, Kathryn Roeder’s lab at Carnegie Mellon University in Pittsburgh and Bernie Devlin’s lab at the University of Pittsburgh Medical Center.
Their goal is to determine when, where and in which cell types specific mutations are acting during brain development to contribute to autism features. This work is founded on the assumption that although there may be hundreds of different genes that contribute to autism, these will point to a much smaller number of biological processes. Understanding when and where in the brain multiple mutations converge may serve as an important key to the development of novel treatments.
The State and Sestan labs are conducting analyses of gene co-expression using data from the BrainSpan project, a comprehensive map of gene expression in the developing human brain. Both the Noonan and Sestan labs are studying gene regulation to gain deeper insight into the relationships observed among autism-related mutations. The Devlin and Roeder labs are developing statistical methods to integrate these divergent datasets. Finally, the Noonan, Sestan and State labs are conducting in vitro and in vivo studies to test the biological relevance of the gene networks identified in these studies.