An emerging multi-omic understanding of the genetics of opioid addiction

Abstract

Opioid misuse, addiction, and associated overdose deaths remain global public health crises. Despite the tremendous need for pharmacological treatments, current options are limited in number, use, and effectiveness. Fundamental leaps forward in our understanding of the biology driving opioid addiction are needed to guide development of more effective medication-assisted therapies. This Review focuses on the omics-identified biological features associated with opioid addiction. Recent GWAS have begun to identify robust genetic associations, including variants in OPRM1, FURIN, and the gene cluster SCAI/PPP6C/RABEPK. An increasing number of omics studies of postmortem human brain tissue examining biological features (e.g., histone modification and gene expression) across different brain regions have identified broad gene dysregulation associated with overdose death among opioid misusers. Drawn together by meta-analysis and multi-omic systems biology, and informed by model organism studies, key biological pathways enriched for opioid addiction-associated genes are emerging, which include specific receptors (e.g., GABAB receptors, GPCR, and Trk) linked to signaling pathways (e.g., Trk, ERK/MAPK, orexin) that are associated with synaptic plasticity and neuronal signaling. Studies leveraging the agnostic discovery power of omics and placing it within the context of functional neurobiology will propel us toward much-needed, field-changing breakthroughs, including identification of actionable targets for drug development to treat this devastating brain disease.

Figure 1. Studies of gene regulation associated with opioid addiction phenotypes in postmortem human brain.

OFC, orbitofrontal cortex.

Figure 2. Cross-species analysis of independent datasets has the potential to nominate novel opioid addition–associated genes.

In this example analysis, we identified overlap between a human opioid addiction–associated gene network (Sullivan et al., ref. and 45 independent GeneSets collected from model organism experiments associated with opioid exposure (see Supplemental Table 1) within Geneweaver. We used a threshold requiring a gene nominated by Sullivan et al. (56) to be associated with an opioid exposure phenotype in at least three model organism GeneSet results. This analysis identified 12 such genes from among the 205 network genes examined (shown in column 1). (Of the 211 genes in the network identified in Sullivan et al., ref. , two were not present in Geneweaver and four did not have homologs in mice or rats, leaving 205 genes to be examined.) Note that candidate genes identified by this and related analyses would require functional validation in a controlled, repeatable environment.