We advocate for an integrative long-term perspective on time, noting that culture changes on timescales amenable to behavioral genetic study with appropriate design and modeling extensions. We note the need for replications of behavioral genetic studies to examine model invariance across long-term timescales, which would afford examination of specified as well as unspecified cultural moderators of behavioral genetic effects.

We propose a novel developmentally informed framework to push research beyond a focus on comorbidity between discrete diagnostic categories and to move toward research based on the well-validated dimensional and hierarchical structure of psychopathology. For example, a large body of research speaks to the validity and utility of the internalizing and externalizing spectra as organizing constructs for research on common forms of psychopathology. The internalizing and externalizing spectra act as powerful explanatory variables that channel the psychopathological effects of genetic and environmental risk factors, predict adaptive functioning, and account for the likelihood of disorder-level manifestations of psychopathology. As such, our proposed theoretical framework uses the internalizing and externalizing spectra as central constructs to guide future psychopathology research across the life span. The framework is particularly flexible, because any of the facets or factors from the dimensional and hierarchical structure of psychopathology can form the focus of research. We describe the utility and strengths of this framework for developmental psychopathology in particular and explore avenues for future research.

Nonnormal phenotypic distributions introduce significant problems in the estimation and selection of genetic models. Here, a semiparametric maximum likelihood approach to analyzing non-normal phenotypes is described. In this approach, distributions are explicitly modeled together with genetic and environmental effects. Distributional parameters are introduced through mixture constraints, where the distribution of effects are discretized and freely estimated rather than assumed to be normal. Semiparametric maximum likelihood estimation can be used with a variety of genetic models, can be extended to a variety of pedigree structures, and has various advantages over other approaches to modeling nonnormal data.

Cramer et al. articulate a novel perspective on comorbidity. However, their network models must be compared with more parsimonious latent variable models before conclusions can be drawn about network models as plausible accounts of comorbidity. Latent variable models have proven generative in studying psychopathology and its external correlates, and we doubt network models will prove as useful for psychopathology research.