Department of Epidemiology and Biostatistics, Michigan State University, East Lansing, Michigan.
Genet Epidemiol. 2013 Nov;37(7):715-25. doi: 10.1002/gepi.21751. Epub 2013 Aug 11.
The translation of human genome discoveries into health practice is one of the major challenges in the coming decades. The use of emerging genetic knowledge for early disease prediction, prevention, and pharmacogenetics will advance genome medicine and lead to more effective prevention/treatment strategies. For this reason, studies to assess the combined role of genetic and environmental discoveries in early disease prediction represent high priority research projects, as manifested in the multiple risk prediction studies now underway. However, the risk prediction models formed to date lack sufficient accuracy for clinical use. Converging evidence suggests that diseases with the same or similar clinical manifestations could have different pathophysiological and etiological processes. When heterogeneous subphenotypes are treated as a single entity, the effect size of predictors can be reduced substantially, leading to a low-accuracy risk prediction model. The use of more refined subphenotypes facilitates the identification of new predictors and leads to improved risk prediction models. To account for the phenotypic heterogeneity, we have developed a multiclass likelihood-ratio approach, which simultaneously determines the optimum number of subphenotype groups and builds a risk prediction model for each group. Simulation results demonstrated that the new approach had more accurate and robust performance than existing approaches under various underlying disease models. The empirical study of type II diabetes (T2D) by using data from the Genes and Environment Initiatives suggested heterogeneous etiology underlying obese and nonobese T2D patients. Considering phenotypic heterogeneity in the analysis leads to improved risk prediction models for both obese and nonobese T2D subjects.
将人类基因组发现转化为健康实践是未来几十年的主要挑战之一。利用新兴的遗传知识进行早期疾病预测、预防和药物遗传学将推动基因组医学的发展，并导致更有效的预防/治疗策略。出于这个原因，评估遗传和环境发现在早期疾病预测中的综合作用的研究代表了高度优先的研究项目，这体现在目前正在进行的多项风险预测研究中。然而，迄今为止形成的风险预测模型准确性不足，无法用于临床。越来越多的证据表明，具有相同或相似临床表现的疾病可能具有不同的病理生理和病因过程。当异质亚表型被视为单一实体时，预测因子的效应大小会大大降低，导致风险预测模型准确性较低。使用更精细的亚表型有助于识别新的预测因子，并导致改进的风险预测模型。为了解决表型异质性问题，我们开发了一种多类似然比方法，该方法可以同时确定亚表型组的最佳数量，并为每个组构建风险预测模型。模拟结果表明，该新方法在各种潜在疾病模型下的性能比现有方法更准确和稳健。通过使用来自基因和环境倡议的数据对 II 型糖尿病 (T2D) 的实证研究表明，肥胖和非肥胖 T2D 患者的病因存在异质性。在分析中考虑表型异质性会导致肥胖和非肥胖 T2D 患者的风险预测模型得到改进。
