A Multi-Omics Analysis of the Autism Brain

Friday, May 13, 2016: 11:00 AM
Hall B (Baltimore Convention Center)
D. Arking, Johns Hopkins University School of Medicine, Baltimore, MD
Background: Despite recent advances in identifying a number of genes involved in autism spectrum disorder (ASD) through identification of large effect de novo mutations, the vast majority of ASD risk remains unexplained. Moreover, while common genetic variation is expected to explain >50% of the variance in liability for ASD, no large/moderate genetic effects have been identified, and current studies are underpowered to identify common variants of small effect.  

Objectives: Understanding the etiology of ASD is critical to identifying potential therapeutic targets. Given the limited success relying solely on genetic studies, we focus on a multi-omics approach, incorporating genome-wide association study (GWAS) results along with transcriptomic and methylation data from the primary affected tissue in ASD, human brain. The goal is to combine these different layers of genome-wide data to identify key pathways in the development of ASD. 

Methods: GWAS results are available through the Psychiatric GWAS Consortium Autism Working Group on ~6,500 parent-affected child trios. Gene expression and methylation data were generated in up to 47 (32 unique individuals) ASD samples and 57 (40 unique individuals) controls, including multiple brain areas for gene expression studies. 

Results: Gene expression studies identify microglial genes robustly dysregulated in ASD cortical brain, pointing to M2-activation as a common feature of ASD brains. Notably, these genes are not enriched for genetic variants associated with ASD (common or rare). Instead, a set of neuronal genes that are not differentially expressed are enriched for the genetic signal. Thus, combining GWAS with transcriptomics suggests a model in which the gene expression changes are secondary to the primary genetic defects, raising the question of whether M2-activation is a common causal pathway for ASD symptoms, or instead, a response to changes occurring during neurodevelopment. The incorporation of genome-wide methylation data is ongoing. 

Conclusions: Combining multiple genome-wide level datasets (GWAS, transcriptomics, methylation) provides key insights into the etiology of ASD that cannot be gleaned from any of the datasets in isolation, and is likely to prove critical in identifying potential therapeutic targets.