COMPARISON OF ANALYSIS OF VARIANCE AND MAXIMUM-LIKELIHOOD BASED PATH-ANALYSIS OF TWIN DATA - PARTITIONING GENETIC AND ENVIRONMENTAL SOURCESOF COVARIANCE

In order to investigate currently used model fitting strategies for tw in data, analysis of variance (ANOVA) and path-maximum-likelihood (PAT H-ML) methods of analyzing twin data were compared using simulation st udies of 50 monozygotic (MZ) and 50 dizygotic (DZ) twin pairs. Phenoty pic covariance was partitioned into additive genetic effects (A), envi ronmental effects common to cotwins (C), and environmental variance un ique to individuals (E). ANOVA and PATH-ML had identical power to dete ct total covariance. The PATH-ML AE model was much more powerful than ANOVA comparisons of rMZ and rDZ to detect A. However, to he unbiased, the AE model requires the assumption that C = 0.0. To allow use of th e AE model to estimate A, the null hypothesis C = 0.0 is tested by com paring the goodness of fit of the ACE and AE models. Simulation of 50 MZ and 50 DZ pairs revealed that C must be greater than 55% of total V ariance before the null hypothesis would be rejected (P < 0.05) 80% of the time. Several recent publications were reviewed in which the null hypothesis C = 0.0 was accepted and apparently upwardly biased estima tes of A, containing C, were presented with unrealistic P values. It w as concluded that use of the AE model to estimate A gives an inflated view of the power of relatively small twin studies. It was recommended that ANOVA or comparison of the ACE and CE PATH-ML models be used to estimate and test the significance of A as neither requires that C = 0 .0. (C) 1995 Wiley-Liss, Inc.