Comparing Alternative Biometric Models with and without Gene-by-Measured Environment Interaction in Behavior Genetic Designs: Statistical Operating Characteristics.
Comparing Alternative Biometric Models with and without Gene-by-Measured Environment Interaction in Behavior Genetic Designs: Statistical Operating Characteristics.
复制标题
比较行为遗传设计中具有和不具有基因测量环境相互作用的替代生物识别模型：统计操作特征。
DOI：
10.1007/s10519-015-9710-1
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发表时间：
2015
影响因子：
2.6
通讯作者：
Rathouz,PaulJ
中科院分区：
文献类型：
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作者：
Zheng,Hao;VanHulle,CarolA;Rathouz,PaulJ
为了扩展珀塞尔在行为遗传设计中测试基因与测量环境相互作用（GxM）的著名ACE模型，Rathouz等人考虑了用于量化和测试这种相互作用的更广泛的模型类别。由于缺乏封闭形式的可能性，货车Hulle等人仅研究了这些扩展模型的一个子组的统计操作特性。随着估计程序开发使用数值技术在配套文件中，我们研究这些扩展模型的统计运行特性，特别是那些非线性效应。I型误差分析表明，GxM的似然比检验是保守的，从二元Cholesky模型扩展的测试模型，并从二元相关因子模型扩展的模型是自由的。所有模型的参数估计都非常好，大多数模型和参数几乎没有偏倚。在不同的模拟条件下的替代模型之间的比较表明，它是相对更难以确认的存在基因与环境的相互作用，而不是检测非线性效应，排除这种相互作用。
To extend Purcell’s well known ACE model in testing gene by measured environment interactions (GxM) in behavior genetic designs, Rathouz et al. considered a broader class of models for quantifying and testing such interactions. Only a sub-group of these extended models have been investigated for their statistical operating characteristics by Van Hulle et al. due to lack of closed form likelihood. With an estimation procedure developed using numerical techniques in a companion paper, we study statistical operating characteristics of these extended models, especially those with non-linear effects. Type I error analysis shows the likelihood ratio test for GxM to be conservative in testing models extended from the bivariate Cholesky model, and to be liberal for models extended from the bivariate correlated factors model. Parameter estimation for all models is very good, with little bias exhibited for most models and parameters. Comparisons among alternative models under various simulated conditions show that it is relatively more difficult to confirm the existence of gene by environment interactions versus to detect non-linear effects which exclude such interactions.