- Awarded: 2015
- Award Type: Targeted: Functional Screen of Autism-Associated Variants
- Award #: 367561
The hallmarks of autism spectrum disorder (ASD) are deficits in social communication and interaction, but a coherent underlying etiological mechanism for ASD is yet unknown. New sequencing technologies have revealed thousands of unique mutations in individuals with ASD, but not in their unaffected parents. Dubbed de novo mutations, the majority alter only a single amino acid in the protein the gene encodes. Some of these mutations impact protein function; many others do not. Currently there are no good methods to study the functional relevance of this large set of de novo missense mutations, and for this reason they have yet to reveal much about the underlying etiology of ASD.
The proposed project brings together research groups with complementary skills to tackle this problem. Haiyuan Yu and his team have recently developed a novel high-throughput interactome-scanning pipeline integrating the green fluorescent protein (GFP) and yeast two-hybrid (Y2H) assays for efficient evaluation of the effects of thousands of missense mutations on protein-protein interactions, which are often the key function of proteins1. Indeed, previous studies have shown that dissecting such effects can yield great insight into disease mechanisms, as well as identify genes involved in risk. Bernie Devlin and Kathryn Roeder’s groups have a long history of developing innovative statistical frameworks to interpret genetic data, including tools to sift through de novo mutations to pinpoint genes involved in ASD risk2,3.
The researchers propose to examine the effects of approximately 1,500 de novo missense mutations found in individuals with ASD, selected based on their likelihood to affect ASD risk. They will be queried for their impact on roughly 7,500 protein-protein interactions. Based on these experimental results and other available information, the research team will develop an integrated statistical framework to identify additional risk genes and their interrelationships. This work represents an experimentally and computationally integrated pipeline that should increase both the number of risk genes identified and our knowledge on how they modulate ASD risk.