Opioid addiction (OA) is a multifactorial disease characterized by aberrant behavior related to obtaining and using opioids. It often arises from treatment of chronic pain patients with prescription opioid (PO) medications and is recognized as a major public health problem. The magnitude of the risk for developing OA remains controversial, because calculated rates suffer from imprecise and poorly defined terminology. This is underscored by the wide range of estimates of PO addiction (POA) in the literature which vary widely from 1% to > 40% of individuals treated long-term with POs for chronic non-progressive musculo-skeletal pain. Notably, there are few data on clinical characteristics and genetic variants that confer risk of POA. This project is focused on identifying the clinical, genetic, and neural characteristics which convey risk for POA. Towards this end, we have assembled a multi-disciplinary team to study a large patient population with a similar history of chronic non-progressive musculoskeletal pain and exposure to PO drugs. We will leverage the central biorepository and electronic health record (EHR) database of the Geisinger Health System to conduct large- scale genomics research and phenotype development. Through a collaboration with Regeneron Pharmaceuticals, the Geisinger biobank currently contains DNA samples on about 110,000 participants and includes both Illumina OmniExpressExome (HOEE) genotyping and whole exome sequence (WES) data, including common and rare variants, from over 60,000 of these subjects. This discovery cohort contains thousands of chronic musculoskeletal pain patients who have been taking greater than 120 mg-equivalents of morphine for more than 3 months and who are considered at high risk for developing POA. Using EHR and self-report tools to develop a case definition and quantitative scoring for POA, we will conduct a robust genome wide association study (GWAS). From the top GWAS candidates, we will identify nearby genes and use the WES data to search for rare variants that could contribute to POA. Using this information, we will derive a clinical/genetic profile of POA. This profile will be enhanced via integration of neuroimaging data. The on-going effort with Regeneron will yield genotype and WES data in a total of 250,000 participants over the next three years, providing additional samples for replication analyses. In the context of long-term opioid therapy, these results will permit identification of chronic non-progressive pain patients at risk for development of POA and provide the basis for preventative measures (non-opioid pharmacotherapy, regular counseling, frequent urine drug screens) and/or alternative pain specialty treatments (acupuncture, stimulators, nerve blocks). This personalized medicine approach will have major clinical impact by minimizing the risk for POA in the chronic pain population.