Delineating genetic risk to addiction via analysis of 3D chromatin architecture

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Project Summary: Addition to opioids and substance abuse disorders are one of the most urgent public health crises in the US, with drug overdose being the leading cause of accidental death. 20% of individuals who try heroin become addicted to opioids. Genetics plays a major role in defining this variability. Opioid addiction is estimated to be 60% heritable, however the variants and genes that define this heritability have remained elusive. As has been observed with diseases like schizophrenia, increased sample size and power for Genome Wide Association Studies may help to reveal more of the genetic basis of addiction. However, identifying the loci associated with addiction is only the first step. In order to translate these findings into insights that can further treatment or prevention of substance abuse disorders we must also determine the consequence of addiction susceptibility loci. Delineating the genes, pathways, and cellular phenotypes that are altered by a given risk locus is a substantial challenge and has been the major roadblock in the translation of GWAS results to clinically actionable findings. We propose to utilize 3-dimensional architecture of the genome to enable a hypothesis-driven evaluation of the genetic risk to addiction. This approach will entail identifying the cis-regulatory elements that control gene expression in tissues, cell types and contexts that are relevant to the pathophysiology of addiction. We propose to combine post-mortem tissue studies with iPS- derived astrocytes, microglia, oligodendrocytes and dopaminergic neurons to provide deeper resolution of the cell types and brain regions which contribute to addiction susceptibility. Finally, we propose to utilize the iPS-derived cell types to identify loci may be suitable for functional studies and utilize genome editing to investigate the impact of regulatory elements associated with risk to addiction. Rather than analyze each cis-regulatory element and SNP individually, this approach will evaluate the genetic variation across all regulatory elements that control expression of a shared target gene. This approach has the potential identify new risk loci for addiction and streamline the identification of the mechanism by which these variants contribute to addiction susceptibility.