Integrating large-scale whole-exome data with whole-genome data

  • Awarded: 2015
  • Award Type: Targeted: Whole-Genome Analysis for Autism Risk Variants
  • Award #: 385027

A proportion of risk for autism spectrum disorder (ASD) resides in rare genetic coding variants, which include single nucleotide variation, indels and structural variation, including copy number variants and dosage-neutral balanced structural variation. Analyses of whole-exome sequencing (WES) data from the Simons Simplex Collection (SSC), the Autism Sequencing Consortium (ASC) and other cohorts have demonstrated that key information resides in de novo sequence variation. Information about inherited rare variation can also be gleaned, albeit to a lesser extent than that conveyed by de novo mutation, with intermediate information from case-control variation (which includes both inherited and de novo variation). Non-coding variation is an area that has been relatively unexplored.

Joseph Buxbaum at the Icahn School of Medicine at Mount Sinai, Michael Talkowski at Massachusetts General Hospital and Xin He at the University of Chicago will lead an ASC work group that plans to analyze whole-genome sequence (WGS) data from SSC quads and integrate this with ASC data (currently 22,000 ASD-related exomes, including WES data from the SSC). They propose to enhance approaches to: (a) analyze WGS data and (b) incorporate WGS and WES data into gene discovery, ultimately expanding our knowledge of genes and variants implicated in ASD.

The researchers will carry out comprehensive analysis of genetic variation from SSC WGS data and emerging long-read technologies. They plan to make use of this data together with ASC exome data to (1) refine and implement their existing genetic risk likelihood model called TADA (Transmitted and De novo Association)1 for interpreting WGS data and (2) identify additional ASD-associated genes and gene variants.


1.He X. et al. PLOS Genet. 9, e1003671 (2013) PubMed
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