Translating Osteoarthritis Genetic Risk Into Biomarkers: Opportunities, Pitfalls, and Implementation Considerations

Osteoarthritis (OA) is a heterogeneous joint disease in which patients differ widely in onset, progression, pain burden, and inflammatory activity, yet clinical tools for early risk stratification and mechanism-informed subtyping remain limited. Human genetic studies have identified many OA-associated loci, but translation into deployable biomarkers has been slow because most signals are polygenic, largely noncoding, and dependent on tissue and cell state. Despite rapid progress in OA genomics, a major remaining challenge is how to systematically convert genetic discoveries into clinically actionable biomarkers that can support risk prediction, biological stratification, and therapeutic development. This review discusses how OA genetic risk can be converted into practical biomarker strategies by combining statistical variant interpretation with joint-resolved biology. We summarize approaches that prioritize likely effector genes and regulatory modes using fine-mapping and molecular QTL evidence, and then place these signals into the correct anatomical and cellular contexts using single-cell and spatial atlases of cartilage, synovium, and subchondral bone. We highlight three classes of outputs that are most likely to be clinically useful: polygenic risk-informed stratification for early monitoring and trial enrichment; compact molecular panels reflecting genetically supported programs in accessible biospecimens; and imaging-omics models that connect structural phenotypes to mechanism-linked biology. We also review common reasons biomarker pipelines fail in OA, including uncertain variant-to-gene assignment, limited portability across populations, tissue accessibility and stage bias, and technical variation across omics and imaging platforms. Finally, we outline what is needed for responsible deployment-standardized assays, clinically meaningful evaluation, external replication in diverse cohorts, and clear governance for privacy, consent, and model accountability.