Using genomics and extensive phenotyping to dissect the relationships between substance use disorders and chronic pain

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Project Abstract Chronic pain is one of the most pressing public health burdens in the United States, affecting up to 20% of the population. Substance use disorders (SUDs) often co-occur with chronic pain. The relationship between chronic pain and opioid use disorder is often attributed to over-use in connection with post-operative pain, but the underlying mechanisms for chronic pain’s comorbidity with other SUDs (alcohol, tobacco, cannabis) are unknown. Depression often co-occurs with both chronic pain and SUDs and could be a mediator of the relationship between pain and SUDs. Socioenvironmental factors, including experiencing discrimination, may also play a role. Given the role of the brain’s reward system in both pain and SUDs, it is also plausible that some of the same genetic risk variants contribute to both chronic pain and SUDs. Both chronic pain and SUDs are moderately heritable and genome-wide association studies have identified loci contributing to their liability. However, these studies have focused on common variants in predominantly European ancestry individuals. This proposal, in response to RFA-PM-23-002, would leverage the multi-ancestral phenotypic and genomic data in All of Us to characterize the relationships between four of the most common SUDs (alcohol, tobacco, cannabis, and opioid use disorders) and chronic pain in a diverse sample. Our first aim will be to curate electronic health records to define a broad measure of chronic pain, as well as more detailed subtypes (e.g., neuropathic vs. nociceptive pain, musculoskeletal vs. visceral pain), and examine how these are related to SUDs. We will test whether a common risk factor, depression, partially mediates the relationship between chronic pain and SUDs. Further, we will estimate the extent to which social determinants of health (e.g., gender, socioeconomic background, experiencing discrimination) are associated with both chronic pain and SUDs. Our second aim will involve whole-genome analyses of chronic pain in multiple ancestries, identifying the genes and pathways that contribute to both chronic pain and SUDs, and employing genetically-informed causal inference models to identify reciprocal relationships. We will use the whole genome sequence data in All of Us to identify genomic factors – common genetic variants, as well as rare variants – that contribute to risk for chronic pain. Next, we will use genomic structural equation modeling and gene network analyses to identify genes and biological pathways that are shared (or distinct) between chronic pain and SUDs. Finally, we will apply multiple causal inference approaches to assess whether there is evidence for causal relationships between chronic pain and SUDs. This proposal will clarify the socioenvironmental and genetic mechanisms associated with chronic pain and SUDs through detailed phenotypic and large-scale genomic analyses on a diverse sample. The findings from these analyses will advance our understanding of why SUDs and chronic pain co-occur, leading to improved treatment and prevention efforts through the identification of shared biological pathways and modifiable psychosocial factors.