Background: Prostate specific antigen (PSA) is widely used as a diagnostic biomarker for prostate cancer (PC). However, due to its low predictive performance, many patients without PC suffer from the harms of unnecessary prostate needle biopsies. The present study aims to evaluate the reproducibility and performance of a genetic risk prediction model and estimate its utility as a diagnostic biomarker in a clinical scenario. Methods: We created a logistic regression model incorporating 16 SNPs that were significantly associated with PC in a genome-wide association study of the Japanese. The model was validated by two independent sets of samples comprising 3,294 cases and 6,281 controls. Various cut offs were evaluated to be used in a clinical scenario. Results: The area under a curve (AUC) of the model was 0.679, 0.655, and 0.661 for the samples used to create the model and those used for validation respectively. The AUC of the model was not significantly altered in samples with PSA 1-10 ng/ml. 24.2% and 9.7% of the patients had odds ratio <0.5 (low risk) or >2 (high risk) in the model, and assuming the overall positive rate of prostate needle biopsies to be 20% in PSA gray zone (PSA 2-10 ng/ml), the positive biopsy rates were 10.7% and 42.4% respectively for the two genetic risk groups. Conclusions: The genetic risk prediction model was highly reproducible, and its predictive performance was not influenced by PSA. The model could have a potential to affect clinical decision when it is applied to patients with gray-zone PSA, which should be confirmed in future clinical studies.