Ramos, Yolande F. M.Rice, Sarah J.Ali, Shabana AmandaPastrello, ChiaraJurisica, IgorFarooq Rai, MuhammadCollins, Kelsey H.Lang, AnnemarieMaerz, TristanGeurts, JeroenRuiz Romero, CristinaJune, Ronald K.Appleton, C. ThomasRockel, Jason S.Kapoor, Mohit2024-05-062024-05-062024-02Ramos, Yolande FM, Sarah J. Rice, Shabana Amanda Ali, Chiara Pastrello, Igor Jurisica, Muhammad Farooq Rai, Kelsey H. Collins et al. "Evolution and advancements in genomics and epigenomics in OA research: How far we have come." Osteoarthritis and Cartilage (2024).1063-4584https://scholarworks.montana.edu/handle/1/18464cc-by-nc-nd ; © This manuscript version is made available under the CC-BY-NC-ND 4.0 license https://creativecommons.org/licenses/by-nc-nd/4.0/Objective. Osteoarthritis (OA) is the most prevalent musculoskeletal disease affecting articulating joint tissues, resulting in local and systemic changes that contribute to increased pain and reduced function. Diverse technological advancements have culminated in the advent of high throughput “omic” technologies, enabling identification of comprehensive changes in molecular mediators associated with the disease. Amongst these technologies, genomics and epigenomics – including methylomics and miRNomics, have emerged as important tools to aid our biological understanding of disease. Design. In this narrative review, we selected articles discussing advancements and applications of these technologies to OA biology and pathology. We discuss how genomics, deoxyribonucleic acid (DNA) methylomics, and miRNomics have uncovered disease-related molecular markers in the local and systemic tissues or fluids of OA patients. Results. Genomics investigations into the genetic links of OA, including using genome-wide association studies, have evolved to identify 100+ genetic susceptibility markers of OA. Epigenomic investigations of gene methylation status have identified the importance of methylation to OA-related catabolic gene expression. Furthermore, miRNomic studies have identified key microRNA signatures in various tissues and fluids related to OA disease. Conclusions. Sharing of standardized, well-annotated omic datasets in curated repositories will be key to enhancing statistical power to detect smaller and targetable changes in the biological signatures underlying OA pathogenesis. Additionally, continued technological developments and analysis methods, including using computational molecular and regulatory networks, are likely to facilitate improved detection of disease-relevant targets, in-turn, supporting precision medicine approaches and new treatment strategies for OA.en-USosteoarthritisgenomicsepigenomicsmethylomicsmiRNomicsdata sharingArticle10.1016/j.joca.2024.02.656