出版日期：2024-07-02 00:00:00

著者：Hsieh, Ai-ru

著錄名稱、卷期、頁數：BMC Urology 24(1),　136

摘要：Background In recent years, Genome-Wide Association Studies (GWAS) has identified risk variants related to complex diseases, but most genetic variants have less impact on phenotypes. To solve the above problems, methods that can use variants with low genetic effects, such as genetic risk score (GRS), have been developed to predict disease risk. Methods As the GRS model with the most incredible prediction power for complex diseases has not been determined, our study used simulation data and prostate cancer data to explore the disease prediction power of three GRS models, including the simple count genetic risk score (SC-GRS), the direct logistic regression genetic risk score (DL-GRS), and the explained variance weighted GRS based on directed logistic regression (EVDL-GRS). Results and Conclusions We used 26 SNPs to establish GRS models to predict the risk of biochemical recurrence (BCR) after radical prostatectomy. Combining clinical variables such as age at diagnosis, body mass index, prostate-specific antigen, Gleason score, pathologic T stage, and surgical margin and GRS models has better predictive power for BCR. The results of simulation data (statistical power = 0.707) and prostate cancer data (area under curve = 0.8462) show that DL-GRS has the best prediction performance. The rs455192 was the most relevant locus for BCR (p = 2.496 × 10–6) in our study.

關鍵字：Genetic risk score;Genome-Wide Association Study;Prostate cancer

語言：en_US

ISSN：1471-2490

期刊性質：國外

收錄於：SCI

通訊作者：Ai-Ru Hsieh(謝璦如)

審稿制度：是

國別：USA

出版型式：電子版