Detecting Gene-Environment Interactions Using a Combined Case-Only and Case-Control Approach
Detecting Gene-Environment Interactions Using a Combined Case-Only and Case-Control Approach
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DOI：
10.1093/aje/kwn339
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发表时间：
2009-02-15
影响因子：
5
通讯作者：
Conti, David V.
中科院分区：
文献类型：
--
作者：
Li, Dalin;Conti, David V.
检测基因-环境相互作用的常规方法，即病例对照分析，存在统计功效低的问题。相比之下，仅案例分析/设计在某些情况下可能是强大的，尽管违反遗传和环境因素之间独立性的假设可能会极大地影响结果。作为替代方案，贝叶斯模型平均值可用于联合收割机结合病例对照和仅病例分析。这种方法首先将病例对照和仅病例分析框定为对数线性模型的变体。这两个模型之间的权重是数据和先验信念对2个潜在相互作用因素的独立性的函数。在本文中，作者通过模拟表明，当没有遗传和环境因素的独立性的先验信息时，这种方法往往比病例对照分析更强大。此外，当遗传和环境因素在人群中不是独立的时，偏倚大大减少，与仅病例分析相比，I型错误相应减少。通过更适当的先验规范，可以获得对违反独立性假设的增强的功效或增强的鲁棒性。作者使用一个示例数据分析来证明这种方法的优点。
The conventional method of detecting gene-environment interactions, the case-control analysis, suffers from low statistical power. In contrast, the case-only analysis/design can be powerful in certain scenarios, although violation of the assumption of independence between the genetic and environmental factors can greatly bias the results. As an alternative, Bayes model averaging may be used to combine the case-control and case-only analyses. This approach first frames the case-control and case-only analyses as variations of a log-linear model. The weighting between these 2 models is then a function of the data and prior beliefs on the independence of the 2 potentially interacting factors. In this paper, the authors demonstrate via simulations that when there is no prior information on the independence of the genetic and environmental factors, this approach tends to be more powerful than the case-control analysis. Additionally, when the genetic and environmental factors are not independent in the population, bias is substantially reduced, with a corresponding reduction in type I error in comparison with the case-only analysis. Increased power or increased robustness to violations of the independence assumption may be obtained with more appropriate prior specification. The authors use an example data analysis to demonstrate the advantages of this approach.