Sporadic point mutations and large copy number variants (CNVs) contribute significantly to the etiology of autism but most of the genetic architecture has not yet been understood. Most CNVs associated with autism are large and the majority of pathogenic genes have not been proven. The goal of this proposal is to significantly increase the yield of high-impact autism mutations by focusing on the discovery of understudied classes of rare variants from whole-genome sequence data being generated from 35,000 samples from autism families. This proposal focuses on rare, gene-disruptive mutations and leverages the additional sensitivity afforded by whole-genome shotgun sequencing data, novel CNV discovery methods, and particular patterns of gene-disruptive and missense mutation to increase yield of pathogenic mutations. Our target will include the discovery and validation of smaller and more complex structural variants (including CNVs), clustered de novo missense mutations, and private, gene-disruptive mutations transmitted preferentially from mothers to sons. We will test these candidates by rapid targeted sequencing in 15,000 additional cases where patient recontact and familial follow-up is possible. We propose to select 10 genes with the highest burden of mutation for further clinical evaluation, phenotypic variability, and comprehensive genetic characterization. This proposal specifically focuses on application of novel genomic methods, recurrent mutations, and inheritance patterns to discover pathogenic variants in order to develop a more sophisticated model to explain the genetic architecture of autism. As part of this effort, we will quantify and compare the risk of different classes of mutation for autism and investigate transmission disequilibrium differences depending on the parent of origin and gender of proband. The end product of this analysis will be the identification and characterization of new classes of highly penetrant genic mutations that contribute significantly to etiology of autism, providing targets for clinical diagnostics and future therapeutics.
This study integrates two genetic risk factors (copy number variation and single-nucleotide mutation) of autism to pinpoint likely disease-causing genes. We will test the significance of rare, gene-disruptive mutations by applying cutting-edge genomic technologies to examine large numbers of families with autism. We will select a subset of genes and families for clinical follow-up to determine if there are common features and whether the mutations are necessary and sufficient to cause disease.
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