Multivariate analysis makes use of the information in the cross-twin cross trait correlations to permit us to determine the extent to which two or more measured phenotypes can be explained by common genetic and environmental influences (61). Since our first aim was to determine how much of the covariance between PGD and CU can explained by shared genetic and environmental liabilities, we fitted a Cholesky decomposition to the data (54). Illustrated in Figure 1, this is a method of triangular decomposition where the first variable is assumed to be caused by a latent factor that can also explain some or all of the variance in the remaining variable(s). This pattern continues until the final observed variable is explained by a latent variable, which is uncorrelated with all preceding factors and influences only one variable (i.e. a factor specific to one variable). The same factor structure is repeated for the sources of variance described above (A, C, and E). In order to estimate the variance in CU explained by PGD, we first entered PGD followed by CU.
