ABSTRACT
Recent large genome-wide association studies (GWAS) have identified multiple confident risk loci linked to addiction-associated behavioral traits. Genetic variants linked to addiction-associated traits lie largely in non-coding regions of the genome, likely disrupting cis-regulatory element (CRE) function. CREs tend to be highly cell type-specific and may contribute to the functional development of the neural circuits underlying addiction. Yet, a systematic approach for predicting the impact of risk variants on the CREs of specific cell populations is lacking. To dissect the cell types and brain regions underlying addiction-associated traits, we applied LD score regression to compare GWAS to genomic regions collected from human and mouse assays for open chromatin, which is associated with CRE activity. We found enrichment of addiction-associated variants in putative regulatory elements marked by open chromatin in neuronal (NeuN+) nuclei collected from multiple prefrontal cortical areas and striatal regions known to play major roles in reward and addiction. To further dissect the cell type-specific basis of addiction-associated traits, we also identified enrichments in human orthologs of open chromatin regions of mouse neuron subtypes: cortical excitatory, PV, D1, and D2. Lastly, we developed machine learning models from mouse cell type-specific regions of open chromatin to further dissect human NeuN+ open chromatin regions into cortical excitatory or striatal D1 and D2 neurons and predict the functional impact of addiction-associated genetic variants. Our results suggest that different neuron subtypes within the reward system play distinct roles in the variety of traits that contribute to addiction.
Significance Statement Our study on cell types and brain regions contributing to heritability of addiction-associated traits suggests that the conserved non-coding regions within cortical excitatory and striatal medium spiny neurons contribute to genetic predisposition for nicotine, alcohol, and cannabis use behaviors. This computational framework can flexibly integrate epigenomic data across species to screen for putative causal variants in a cell type- and tissue-specific manner across numerous complex traits.
Competing Interest Statement
AJL, ER, and ARP are inventors of the cSNAIL patent. Other authors do not declare any conflict of interest.
Footnotes
CONFLICT OF INTEREST: AJL, ER, and ARP are inventors of the cSNAIL patent. Other authors do not declare any conflict of interest.
Funding: National Institute of General Medical Sciences training grant T32GM008208 (BNP), Sloan Foundation Fellowship (ARP), National Institute on Drug Abuse Avenir Award 1DP1DA046585 (ARP), National Science Foundation Graduate Student Research Fellowship DGE1745016 (AJL), Carnegie Mellon Brainhub Presidential Fellowship (ER), Carnegie Mellon Computational Biology Department Lane Postdoctoral Fellowship (IMK)