Analytic approaches to twin data using structural equation models.
Analytic approaches to twin data using structural equation models.
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DOI：
10.1093/bib/3.2.119
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发表时间：
2002-06-01
影响因子：
9.5
通讯作者：
Sham, Pak C
中科院分区：
文献类型：
--
作者：
Rijsdijk, Fruhling V;Sham, Pak C
经典的双胞胎研究是行为遗传学中最受欢迎的设计。它在生物识别遗传理论中具有很强的根源，该理论可以在基本的遗传和环境成分方面对观察到的相同和兄弟双胞胎的特征之间的相关性进行预测。可以通过两种双类型的观察到的协方差的结构方程建模（SEM）来推断这些“潜在”因子（模型参数）的相对重要性。 SEM程序通过最大程度地减少观察到的协方差矩阵和预测协方差矩阵之间的拟合优点来估算模型参数。似然比统计数据还可以比较不同竞争模型的拟合度。该程序MX，专门为建模遗传敏感数据而开发的程序，现在广泛用于双分析中。 MX的灵活性允许对多元数据进行建模，以检查两个或多个表型之间的遗传和环境关系以及在责任 - 阈值模型下对分类性状的建模。
The classical twin study is the most popular design in behavioural genetics. It has strong roots in biometrical genetic theory, which allows predictions to be made about the correlations between observed traits of identical and fraternal twins in terms of underlying genetic and environmental components. One can infer the relative importance of these 'latent' factors (model parameters) by structural equation modelling (SEM) of observed covariances of both twin types. SEM programs estimate model parameters by minimising a goodness-of-fit function between observed and predicted covariance matrices, usually by the maximum-likelihood criterion. Likelihood ratio statistics also allow the comparison of fit of different competing models. The program Mx, specifically developed to model genetically sensitive data, is now widely used in twin analyses. The flexibility of Mx allows the modelling of multivariate data to examine the genetic and environmental relations between two or more phenotypes and the modelling to categorical traits under liability-threshold models.