is the case with data from unrelated individuals. The data to identify a larger number of factors come from the cross-relative, cross-phenotype correlations. Third, given a genetically informative design, it is possible to partition variation in the latent factors into genetic and environmental components. For example, Figure 1 shows a path diagram of a model with three latent factors and seven observed variables, comprising three endophenotype measurements (End1 to End3, e.g., Actigraph data) and four behavioral measurements (Beh1 to Beh4). Application of this model to such a dataset might yield a general factor (F1) with substantial loadings on all measures, a factor F2 with large loadings on the endophenotypes but little effect on the behavioral measurements, and a third factor F3, which has the opposite pattern to F2. Under these circumstances, interest might center on F1 because both domains of measurement are influenced by it. Factor scores for all persons in the sample could be derived by maximum likelihood, and this could be used in, e.g., GWAS to identify genetic factors that contribute to the endophenotype and the behavioral components of ADHD. Without data from twins or other genetically informative studies of relatives, it is not possible to know in
