Integrative analysis of common variation associated with autism

  • Awarded: 2018
  • Award Type: Targeted: Genomic Analysis for Autism Risk Variants in SPARK
  • Award #: 605259

Common variation in aggregate contributes to a substantial proportion of risk for developing autism spectrum disorder (ASD). Yet only a few individual variants reaching genome-wide significance have been detected so far. Increasing sample size enabled the first identification of genome-wide significant loci1, suggesting that a further increase in the sample size boosted by the availability of SPARK genomic-wide genotyping data will give rise to robust common variants associated with ASD risk. The next step is to interpret the biological impact of genetic associations. This is a challenge given that the majority of the signals reside in noncoding regions of the genome and that the function of any given variant may only be observable within a certain developmental time period, cell type or brain region.

Hyejung Won and Jason Stein at The University of North Carolina at Chapel Hill hypothesized that the biological interpretation can be improved with better maps of regulatory elements and chromatin interactions in the human brain. Therefore, they seek to exploit a large corpus of functional genomics resources on the human brain in (1) two major developmental epochs, (2) two major brain cell types and (3) different brain regions to create a comprehensive gene regulatory landscape. These data sets include expression quantitative trait loci (eQTL), physical chromatin interactions (Hi-C), regulatory elements (ATAC-seq) and single-cell expression profiles. Leveraging these resources, they will develop a framework to map risk factors to the genes based on the functional annotation and identify developmental, cellular and regional context of ASD pathogenesis.

The successful completion of this project will provide mechanistic insights that connect common variants in ASD with the cellular, regional and temporal substrates that can be used to prioritize further functional validation experiments.


1.Grove J. et al. bioRxiv (2017) Preprint
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