The discovery of integrated gene networks for autism. O. Penn1, F. Hormozdiari1, E. Borenstein1,2,3, E. E. Eichler1,4, SSC Sequencing Consortium 1) Department of Genome Sciences, University of Washington, Seattle, WA; 2) Department of Computer Science and Engineering, University of Washington, Seattle, WA; 3) Santa Fe Institute, Santa Fe, NM; 4) Howard Hughes Medical Institute, Seattle, WA.
Despite extensive genetic heterogeneity underlying disorders such as autism spectrum disorders (ASD) and intellectual disability (ID), there is compelling evidence that risk genes will map to a much smaller number of biologically functional modules. To discover modules enriched for de novo mutations in probands, we developed a novel computational method (MAGI) that simultaneously considers protein-protein interaction and RNAseq expression profiles during brain development. Applying the method to recent exome sequencing data from 1116 ASD and ID patients, we discovered two distinct significant modules (p<0.005) that differ in their properties and associated phenotypes. The first module consists of 80 genes associated with the Wnt and Notch signaling pathways, as well as with the SWI/SNF and NCOR complexes, and exhibits the highest expression early during embryonic development. Probands with truncating mutations in this module are enriched for micro and macrocephaly (KS test p=0.013). The second module consists of 24 genes associated with synaptic function, including long-term potentiation and calcium signaling, and shows higher levels of postnatal expression. Probands with de novo mutations in these modules are found to have lower IQ compared to probands with mutations outside these modules. In addition, missense mutations in both modules are predicted to be more deleterious using the C-scores measurement (p<10-6), providing a useful approach for detecting potentially pathogenic missense. Applying the method independently to epilepsy and schizophrenia exome sequencing cohorts, we found marked overlap among modules suggesting shared common neurodevelopmental pathways. For example, ZMNYND11, CUL3, SMARCC2, and GRIN2A are part of both the ASD/ID and the schizophrenia modules and are indeed mutated in both cohorts. Adding mutations found in the full Simons Simplex Collection (SSC) to the analysis (2661 probands in total) resulted in the identification of more refined and biologically coherent modules. Five significant modules (p<0.05) were discovered, covering a total of 249 genes associated with the SWI/SNF complex, Wnt signaling, long term potentiation, proteasome, and ubiquitin mediated proteolysis pathways. Our approach provides a molecular framework for reducing the genetic heterogeneity of these diseases and a method for identifying de novo missense mutations important in ASD/ID etiology.
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