Genomic Analysis of Avoidance Learning in Addiction

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Abstract: Addictions are among the most heritable of human neuropsychiatric disorders, but human genetic studies have been hampered by the extreme complexity of human genetics, as well as the sheer behavioral complexity of the addictive process, with multiple stages at which humans can exhibit addiction vulnerability – e.g. initial drug exposure, escalation, relapse, etc. We address these shortcomings by studying heterogeneous stock (HS) rats, which have extremely well characterized genetic profiles, and using behavioral models that examine multiple well-defined time periods in the progression of addictive behaviors. Our preliminary findings show that even though addiction is often viewed as aberrant reward learning, much individual variation in addiction propensity is actually due to differences in avoidance learning. For example, in addition to its well-known rewarding properties, cocaine has aversive effects that show greater individual variability than its rewarding effects, and are stronger predictors of cocaine-seeking. Furthermore, we found that rats differ greatly in “punishment resistance”, i.e. propensity to seek rewards despite adverse outcomes, which is one of the defining characteristics of addiction. Both cocaine avoidance and punishment resistance are highly heritable in HS rats (h2 = 0.58 and 0.48, respectively), and both are critically regulated by the rostromedial tegmental nucleus (RMTg), a major GABAergic afferent to midbrain dopamine neurons that plays key roles in avoidance learning. Building on these findings, we seek to identify the genetic differences underlying these two distinct addiction vulnerability phenotypes using a genome-wide association screen (GWAS) to identify candidate genes in HS rats, followed by eQTL analysis on gene expression in the RMTg and afferent circuits that drive these behaviors. This project will identify candidate addiction-related genes using a highly innovative combination of powerful behavioral tests, extensive neural circuitry knowledge, and the Palmer lab's groundbreaking sequencing and analytical approaches.