Cherny S S, DeFries J C, Fulker D W
Institute for Behavioral Genetics, University of Colorado, Boulder 80309-0447.
Behav Genet. 1992 Jul;22(4):489-97. doi: 10.1007/BF01066617.
The multiple regression methodology proposed by DeFries and Fulker (DF; 1985, 1988) for the analysis of twin data is compared with maximum-likelihood estimation of genetic and environmental parameters from covariance structure. Expectations for the regression coefficients from submodels omitting the h2 and c2 terms are derived. Model comparisons similar to those conducted using maximum-likelihood estimation procedures are illustrated using multiple regression. Submodels of the augmented DF model are shown to yield parameter estimates highly similar to those obtained from the traditional latent variable model. While maximum-likelihood estimation of covariance structure may be the optimal statistical method of estimating genetic and environmental parameters, the model-fitting approach we propose is a useful extension to the highly flexible and conceptually simple DF methodology.
将DeFries和Fulker（DF；1985年、1988年）提出的用于分析双胞胎数据的多元回归方法，与从协方差结构对遗传和环境参数进行最大似然估计的方法进行了比较。推导了省略h2和c2项的子模型回归系数的期望值。使用多元回归说明了类似于使用最大似然估计程序进行的模型比较。结果表明，扩展DF模型的子模型产生的参数估计与从传统潜变量模型获得的估计高度相似。虽然协方差结构的最大似然估计可能是估计遗传和环境参数的最佳统计方法，但我们提出的模型拟合方法是对高度灵活且概念简单的DF方法的有益扩展。
