We conducted twin modeling in Mx (Neale et al., 2006) using full information maximum likelihood raw ordinal data methods. The use of ordinal data assumes that the observed categories representan underlying normal distribution of liability, such that the proportions of the population lying between adjacent thresholds exactly matches the observed proportion of the sample in each category. In twin modeling, liability to phenotypes such as depression or AUD can be decomposed to three common latent sources of variance: additive genetic factors (A), shared environment (C), and unique environment (E). The C component represents environmental exposures and experiences that are shared by both members of a twin pair and contribute to twins’ similarity irrespective of zygosity in a given phenotype. Environmental factors that are unique to one twin are accounted for by the E component; these factors reduce twin similarity for a given phenotype. The E component also includes random measurement error. Estimates of each of these variance components are calculated by comparing the phenotypic correlation between monozygotic twins, who share all their genes, to dizygotic twins, who share half of their genes on average identical by descent.
