A multiclass likelihood ratio approach for genetic risk prediction allowing for phenotypic heterogeneity.
A multiclass likelihood ratio approach for genetic risk prediction allowing for phenotypic heterogeneity.
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DOI：
10.1002/gepi.21751
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发表时间：
2013-11
影响因子：
2.1
通讯作者：
Lu, Qing
中科院分区：
文献类型：
--
作者：
Wen, Yalu;Lu, Qing
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，并带来更有效的预防/治疗策略。因此，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，正如目前正在进行的多重风险预测研究所表明的那样。然而，迄今为止形成的风险预测模型缺乏足够的临床使用准确性。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学和病因学过程。当异质亚表型被视为单个实体时，预测因子的效应大小可能会大大降低，从而导致风险预测模型的准确性较低。使用更精细的亚表型有助于识别新的预测因子，并改进风险预测模型。为了解释表型异质性，我们开发了一种多类似然比方法，该方法同时确定亚表型组的最佳数量并为每个组建立风险预测模型。模拟结果表明，在各种基础疾病模型下，新方法比现有方法具有更准确、更稳健的性能。使用基因与环境倡议的数据对 II 型糖尿病 (T2D) 进行的实证研究表明，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可以改进肥胖和非肥胖 T2D 受试者的风险预测模型。
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.
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影响因子：
30.8
作者：
Marchini, Jonathan;Howie, Bryan;Donnelly, Peter
通讯作者：
Donnelly, Peter
评估18种常见遗传变异的综合遗传变异对2型糖尿病风险的综合影响。
影响因子：
7.7
作者：
Lango H;UK Type 2 Diabetes Genetics Consortium;Palmer CN;Morris AD;Zeggini E;Hattersley AT;McCarthy MI;Frayling TM;Weedon MN
通讯作者：
Weedon MN
DOI：
10.1016/s0140-6736(20)32205-4
发表时间：
2021-04-24
期刊：
Lancet (London, England)
影响因子：
--
作者：
Scheltens P;De Strooper B;Kivipelto M;Holstege H;Chételat G;Teunissen CE;Cummings J;van der Flier WM
通讯作者：
van der Flier WM
影响因子：
158.5
作者：
Hu, FB;Manson, JE;Willett, WC
通讯作者：
Willett, WC
影响因子：
4.1
作者：
Kilpelainen, Tuomas O.;Lakka, Timo A.;Laakso, Markku
通讯作者：
Laakso, Markku