Division of Epidemiology, Department of Medicine, Vanderbilt-Ingram Cancer Center, Vanderbilt Epidemiology Center, Vanderbilt University School of Medicine, Nashville, Tennessee, USA.
Department of Preventive Medicine, Chonnam National University Medical School, Gwangju, South Korea.
Int J Cancer. 2022 Nov 15;151(10):1726-1736. doi: 10.1002/ijc.34194. Epub 2022 Jul 21.
Several polygenic risk scores (PRSs) have been developed to predict the risk of colorectal cancer (CRC) in European descendants. We used genome-wide association study (GWAS) data from 22 702 cases and 212 486 controls of Asian ancestry to develop PRSs and validated them in two case-control studies (1454 Korean and 1736 Chinese). Eleven PRSs were derived using three approaches: GWAS-identified CRC risk SNPs, CRC risk variants identified through fine-mapping of known risk loci and genome-wide risk prediction algorithms. Logistic regression was used to estimate odds ratios (ORs) and area under the curve (AUC). PRS , a PRS with 115 GWAS-reported risk variants derived from East-Asian data, validated significantly better than PRS derived from European descendants. In the Korea validation set, OR per SD increase of PRS was 1.63 (95% CI = 1.46-1.82; AUC = 0.63), compared with OR of 1.44 (95% CI = 1.29-1.60, AUC = 0.60) for PRS . PRS derived using meta-analysis results of both populations slightly improved the AUC to 0.64. Similar but weaker associations were found in the China validation set. Individuals among the highest 5% of PRS have a 2.52-fold elevated CRC risk compared with the medium (41-60th) risk group and have a 12% to 20% risk of developing CRC by age 85. PRSs constructed using results from fine-mapping and genome-wide algorithms did not perform as well as PRS and PRS in risk prediction, possibly due to a small sample size. Our results indicate that CRC PRSs are promising in predicting CRC risk in East Asians and highlights the importance of using population-specific data to build CRC risk prediction models.
几种多基因风险评分(PRSs)已被开发用于预测欧洲后裔的结直肠癌(CRC)风险。我们使用来自亚洲血统的 22702 例病例和 212486 例对照的全基因组关联研究(GWAS)数据来开发 PRS,并在两个病例对照研究(1454 例韩国人和 1736 例中国人)中进行了验证。使用三种方法得出了 11 个 PRS:GWAS 确定的 CRC 风险 SNP、通过已知风险位点精细映射确定的 CRC 风险变体以及全基因组风险预测算法。使用逻辑回归估计比值比(ORs)和曲线下面积(AUC)。PRS ,一个使用来自东亚数据的 115 个 GWAS 报告风险变体得出的 PRS,验证效果明显优于源自欧洲后裔的 PRS。在韩国验证集中,PRS 每增加一个标准差的 OR 为 1.63(95%CI=1.46-1.82;AUC=0.63),而 PRS 的 OR 为 1.44(95%CI=1.29-1.60,AUC=0.60)。使用两个人群的荟萃分析结果得出的 PRS 略微提高了 AUC 至 0.64。在中国验证集中也发现了类似但较弱的关联。与中等(41-60 分)风险组相比,PRS 最高的 5%个体 CRC 风险升高 2.52 倍,到 85 岁时 CRC 的风险增加 12%至 20%。使用精细映射和全基因组算法的结果构建的 PRS 在风险预测中的表现不如 PRS 和 PRS ,这可能是由于样本量小。我们的结果表明,CRC PRS 有望预测东亚人群的 CRC 风险,并强调使用特定人群的数据构建 CRC 风险预测模型的重要性。