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Integrative genetic brain analysis in autism

Dan Arking, Ph.D.

Johns Hopkins University School of Medicine

A major goal of autism research is to identify the underlying genetic variants that contribute to risk of the disorder. Knowledge of these genetic variants may yield insight into how and why autism symptoms develop, and ultimately provide a list of potential therapeutic targets.

To help achieve this goal, Dan Arking and his group at Johns Hopkins University in Baltimore attempted to characterize all publicly available postmortem autism and control brains. They looked at gene expression from three different brain regions, along with methyl marks on the DNA (which regulate gene expression) and genetic variation.

Working with archived brain tissue, the researchers met a significant number of technical challenges, both in obtaining high-quality data1 and in analyzing the data. This led to the development of an optimized pipeline for RNA gene expression analysis2. Arking’s group has also developed a new software package for analyzing genetic data that focuses on the gene as a whole, rather than using the standard approach of looking at single genetic variants3, 4.

Arking’s analyses have also begun to shed light on some of the risk factors for developing autism. Genetic analyses as part of the Psychiatric GWAS Consortium have revealed a clear overlap between genetic risk factors for autism and schizophrenia5, 6, implicating a shared biology between the two disorders. Direct comparison of brains of people with autism and controls shows a pattern of altered methylation in autism, suggesting an early developmental defect.

Finally, Arking’s group has identified a set of 1,117 genes whose expression levels are different in people with autism. Their ongoing analyses focus on identifying specific pathways and genes with altered methylation or gene expression, with the goal of identifying potential therapeutic targets.

References:

  1. Gupta S. et al. BMC Genomics 13, 26 (2012) PubMed
  2. Ellis S.E. et al. BMC Genomics (In review)
  3. Huang H. et al. PLoS Genet. 7, e1002177 (2011) PubMed
  4. Chanda P. et al. PLoS One 8, e68585 (2013) PubMed
  5. Cross-Disorder Group of the Psychiatric Genomics Consortium et al. Lancet 381, 1371-1379 (2013) PubMed
  6. Cross-Disorder Group of the Psychiatric Genomics Consortium et al. Nat Genet. 45, 984-994 (2013) PubMed

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