Exome sequencing and copy number variant (CNV) analyses have contributed significantly to our understanding of the genetic etiology of autism spectrum disorder (ASD). In particular, de novo and private likely-gene-disruptive (LGD) mutations are major risk factors, contributing to 30 percent and 7 percent of simplex autism, respectively. Despite these successes, roughly 60 percent of the genetic etiology of ASD remains unexplained.
Evan Eichler and his colleagues at the University of Washington propose to significantly increase the yield of high-impact ASD mutations by focusing on the discovery of smaller and more complex structural variation within 2,000 genomes from 500 families in the Simons Simplex Collection (SSC). They hypothesize that smaller structural variants (SVs), including CNVs, will contribute disproportionately to dosage imbalance when compared with single nucleotide variants, because the former will be more likely to disrupt noncoding regulatory regions and gene expression. As small SVs are among the most difficult to detect and require extensive validation, a large fraction of SVs are missed by routine variant-calling algorithms.
The researchers will specifically focus on the application of novel methods to discover indels and SVs, including CNVs, and their validation and integration with other forms of genetic variation, in order to develop a more sophisticated model to explain the genetic architecture of ASD. As part of this effort, the team will quantify and compare the risk of different classes of mutations for ASD and investigate transmission disequilibrium differences depending on the parent of origin and gender of the affected individual.
Successful completion of this project will help uncover a new class of mutations contributing to at least another 15 percent of ASD risk, providing targets for future functional characterization and insight into improving the diagnosis of idiopathic ASD.
