PROJECT SUMMARY / ABSTRACT Over 100 million surgical procedures are performed in the United States each year, with up to 80% of patients experiencing postoperative pain. Higher levels of pain after surgery are associated with the development chronic post-surgical pain (CPSP) and chronic opioid use, feeding into a pain epidemic that has a greater annual societal cost than that for cancer, heart disease, and diabetes combined. Though there have been multiple phenotypic and environmental factors that have been identified to be predisposing for the development of CPSP, much less data is available regarding genetic predictors of CPSP. Previous studies have been limited by small samples or candidate-gene approaches that are often not reproducible in different cohorts. It therefore remains difficult to predict patients genetically predisposed to severe postoperative pain or those who will progress to develop CPSP, and to tailor antinociceptive therapy to a patient’s specific genetic predisposition to the development of pain. The goal of the proposed work is to overcome these limitations by using the largest and most well genotyped cohort employed thus far in the study of postsurgical pain genetics. Over 3400 patients with both pre/postsurgical self-reported pain and genomic sequencing data will make up this cohort. We hypothesize that 1) identifiable variations in genes and genetic pathways are associated with postsurgical pain, and 2) these variations can contribute to differing responses to pharmacologic therapy. This study will use candidate gene, genome-wide association study, and pathway-based analyses to identify genetic variations that influence postsurgical pain. I will then leverage one of the most granular clinical trials of low back pain (NIH HEAL BACPAC) to study genetic associations of responsiveness for a common pain medication (duloxetine). The candidate for this mentored Patient-Oriented Career Development Award aims to complement his previous training with additional expertise in large-scale genomics, biostatistics, and clinical trial design. In order to facilitate future collaboration with colleagues across multiple disciplines, in concert with the proposed study the candidate with also pursue didactive and experiential translational science training related to his research aims. The University of Michigan has committed abundant resources to support this proposal, and it is also supported by a multidisciplinary mentorship group comprised of authorities in genetics, genomics, biostatistics, perioperative pain, and clinical trial design and execution. The proposed studies will provide novel insights into the genetics of chronic postoperative pain and form the basis for future studies into personalized pain therapy.