Department of Environmental Genomics, Jiangsu Key Laboratory of Cancer Biomarkers, Prevention and Treatment, Collaborative Innovation Center for Cancer Personalized Medicine, Nanjing Medical University, Nanjing, China; Department of Genetic Toxicology, The Key Laboratory of Modern Toxicology of Ministry of Education, School of Public Health, Nanjing Medical University, Nanjing, China.
Department of Biostatistics, School of Public Health, Nanjing Medical University, Nanjing, China; China International Cooperation Center for Environment and Human Health, Nanjing Medical University, Nanjing, China.
Gene. 2018 Oct 5;673:174-180. doi: 10.1016/j.gene.2018.06.035. Epub 2018 Jun 14.
Currently, genetic risk score (GRS) model has been a widely used method to evaluate the genetic effect of cancer risk prediction, but seldom studies investigated their discriminatory power, especially for colorectal cancer (CRC) risk prediction. In this study, we applied both simulation and real data to comprehensively compare the discriminability of different GRS models. The GRS models were fitted by logistic regression with three scenarios, including simple count GRS (SC-GRS), logistic regression weighted GRS (LR-GRS, including DL-GRS and OR-GRS) and explained variance weighted GRS (EV-GRS, including EV_DL-GRS and EV_OR-GRS) models. The model performance was evaluated by receiver operating characteristic (ROC) curves and area under curves (AUC) metric, net reclassification improvement (NRI) and integrated discrimination improvement (IDI). In real data analysis, as DL-GRS and EV_DL-GRS models were carried with serious over-fitting, the other three models were kept for further comparison. Compared to unweighted SC-GRS model, reclassification was significantly decreased in OR-GRS model (NRI = -0.082, IDI = -0.002, P < 0.05), while EV_OR-GRS model showed negative NRI and IDI (NRI = -0.077, IDI = -5.54E-04, P < 0.05) compared to OR-GRS model. Besides, traditional model with smoking status (AUC = 0.523) performed lower discriminability compared to the combined model (AUC = 0.607) including genetic (i.e., SC-GRS) and smoking factors. Similarly, the findings from simulation were all consistent to real data results. It is plausible that SC-GRS model could be optimal for predicting genetic risk of CRC. Moreover, the addition of more significant genetic variants to traditional model could further improve predictive power on CRC risk prediction.
目前，遗传风险评分（GRS）模型已被广泛用于评估癌症风险预测的遗传效应，但很少有研究探讨其判别能力，特别是针对结直肠癌（CRC）风险预测。在这项研究中，我们应用模拟和真实数据综合比较了不同 GRS 模型的判别能力。使用逻辑回归拟合 GRS 模型，包括简单计数 GRS（SC-GRS）、逻辑回归加权 GRS（LR-GRS，包括 DL-GRS 和 OR-GRS）和解释方差加权 GRS（EV-GRS，包括 EV_DL-GRS 和 EV_OR-GRS）模型。通过接收者操作特征（ROC）曲线和曲线下面积（AUC）度量、净重新分类改善（NRI）和综合判别改善（IDI）评估模型性能。在真实数据分析中，由于 DL-GRS 和 EV_DL-GRS 模型存在严重的过度拟合，因此保留了其他三个模型进行进一步比较。与未加权的 SC-GRS 模型相比，OR-GRS 模型的再分类显著降低（NRI= -0.082，IDI= -0.002，P<0.05），而 EV_OR-GRS 模型与 OR-GRS 模型相比，NRI 和 IDI 为负值（NRI= -0.077，IDI= -5.54E-04，P<0.05）。此外，包含遗传因素（即 SC-GRS）和吸烟因素的综合模型（AUC=0.607）的判别能力高于仅包含吸烟状况的传统模型（AUC=0.523）。同样，模拟结果与真实数据结果一致。因此，SC-GRS 模型可能是预测 CRC 遗传风险的最佳模型。此外，将更多显著的遗传变异添加到传统模型中，可以进一步提高 CRC 风险预测的预测能力。
