Integrating polygenic risk scores and constructing a predictive model to assess the risk of knee osteoarthritis in Taiwanese population

Objective: Knee osteoarthritis (OA) is a prevalent degenerative joint disease that can lead to severe limitations in daily activities and function. Genetic components contribute significantly to knee OA and it always coexists with other comorbid conditions. Our goal is to identify the most effective PGS panel that can be applied in the Taiwanese population. We also aim to develop a predictive model for the risk of knee OA by integrating the polygenic risk scores(PGS) and comorbidity information. Method We analyzed a total of 43,686 patients from the Taiwan Precision Medicine Initiative (TPMI) database and 1,363 cases of knee OA were identified. We validated which of the four published PGS panels was the most appropriate for predicting the risk of knee OA in the Taiwanese population. We then integrate the validated PGS panel and common comorbidities with knee OA to evaluate the predictive performance of the model. Results We found that PGS002767 showed the strongest association with our knee OA patients (OR: 1.59 at rank 5, OR: 1.46 at rank 4 compared to rank 1). GERD (OR:2.17), insomnia (OR:1.57), dyslipidemia (OR:1.49), and hypertension (OR:1.47) were retained in the final model. The AUC of our final model which included PGS002767, BMI and comorbidities was 0.72. Conclusion: To the best of our knowledge, this is the first study to validate the applicability of PGS panels in knee OA patient of the Taiwanese population. We create a predictive model for knee OA allowing clinicians to quickly and cost-effectively screen patients at risk for developing knee OA.