Broad Bandwidth or High Fidelity? Evidence from the Structure of Genetic and Environmental Effects on the Facets of the Five Factor Model

Abstract

The Five Factor Model of personality is well-established at the phenotypic level, but much less is known about the coherence of the genetic and environmental influences within each personality domain. Univariate behavioral genetic analyses have consistently found the influence of additive genes and nonshared environment on multiple personality facets, but the extent to which genetic and environmental influences on specific facets reflect more general influences on higher order factors is less clear. We applied a multivariate quantitative-genetic approach to scores on the CPI-Big Five facets for 490 monozygotic and 317 dizygotic twins who took part in the National Merit Twin Study. Our results revealed a complex genetic structure for facets composing all five factors, with both domain-general and facet-specific genetic and environmental influences. For three of the Big Five domains, models that required common genetic and environmental influences on each facet to occur by way of effects on a higher order trait did not fit as well as models allowing for common genetic and environmental effects to act directly on the facets. These results add to the growing body of literature indicating that important variation in personality occurs at the facet level which may be overshadowed by aggregating to the trait level. Research at the facet level, rather than the factor level, is likely to have pragmatic advantages in future research on the genetics of personality.

Appendix

The independent pathways model specifies that each facet has separate genetic, shared environmental, and nonshared environmental loadings on common (domain-general) genetic, shared environmental, and nonshared environmental factors. In comparison, the common pathways model specifies that each facet has a single phenotypic loading on a common phenotypic factor, which in turn has loadings on a single set of trait-level genetic, shared environmental, and nonshared environmental factors. The following parameter constraints can be applied to the common factor loadings from the independent pathways model to function as a common pathways model

$$ \lambda \left[ {\begin{array}{l} {{\mathbf{A}}_{{\mathbf{f}}} } \\ {{\mathbf{C}}_{{\mathbf{f}}} } \\ {{\mathbf{E}}_{{\mathbf{f}}} } \\ \end{array} } \right] = \lambda_{\text{f}} \times \lambda \left[ {\begin{array}{l} {{\mathbf{A}}_{{\mathbf{t}}} } \\ {{\mathbf{C}}_{{\mathbf{t}}} } \\ {{\text{E}}_{{\mathbf{t}}} } \\ \end{array} } \right], $$

where λ represents common factor loadings, the subscript f indicates that a parameter is allowed to vary across facets, and the subscript t indicates that a parameter is constant across all facets belonging to the same trait. Note that facet-specific genetic and environmental loadings are not represented above; λ[A_f], λ[C_f], and λ[E_f] only represent loadings on the common genetic and environmental variance components.

To illustrate how these constraints function, we can apply the above constraints to the facets of

Agreeableness yielding:

$$ \begin{aligned} {{\uplambda}}\left[ {\begin{array}{l} {{\mathbf{A}}_{\text{trust}} } \\ {{\mathbf{C}}_{\text{trust}} } \\ {{\mathbf{E}}_{\text{trust}} } \\ \end{array} } \right]=&\,1 \times \lambda \left[ {\begin{array}{l} {{\mathbf{A}}_{\text{Agreeablness}} } \\ {{\mathbf{C}}_{\text{Agreeablness}} } \\ {{\mathbf{E}}_{\text{Agreeablness}} } \\ \end{array} } \right] ,\\ {{\uplambda}}\left[ {\begin{array}{l} {{\mathbf{A}}_{\text{compassion}} } \\ {{\mathbf{C}}_{\text{compassion}} } \\ {{\mathbf{E}}_{\text{compassion}} } \\ \end{array} } \right]=&\,{{\uplambda}}_{\text{compassion}} \times \lambda \left[ {\begin{array}{l} {{\mathbf{A}}_{\text{Agreeablness}} } \\ {{\mathbf{C}}_{\text{Agreeablness}} } \\ {{\mathbf{E}}_{\text{Agreeablness}} } \\ \end{array} } \right] ,\;{\text{and}} \\ {{\uplambda}}\left[ {\begin{array}{l} {{\mathbf{A}}_{\text{humility}} } \\ {{\mathbf{C}}_{\text{humility}} } \\ {{\mathbf{E}}_{\text{humility}} } \\ \end{array} } \right]=&\,{{\uplambda}}_{\text{humility}} \times \lambda \left[ {\begin{array}{l} {{\mathbf{A}}_{\text{Agreeablness}} } \\ {{\mathbf{C}}_{\text{Agreeablness}} } \\ {{\mathbf{E}}_{\text{Agreeablness}} } \\ \end{array} } \right]. \\ \end{aligned} $$

Note that the first phenotypic loading (what would ordinarily be λ_trust) is fixed to 1 to set the metric of the latent phenotype. It can be seen that without the constraints on the right hand side of the equations, there are 9 free parameters that would be individually estimated, whereas with the constraints, 5 free parameters are estimated (three trait-level biometric components and k − 1 phenotypic loadings, where k is equal to the number of facets).