Testing Models for the Contributions of Genes and Environment to Developmental Change in Adolescent Depression

Abstract

We tested two models to identify the genetic and environmental processes underlying longitudinal changes in depression among adolescents. The first assumes that observed changes in covariance structure result from the unfolding of inherent, random individual differences in the overall levels and rates of change in depression over time (random growth curves). The second assumes that observed changes are due to time-specific random effects (innovations) accumulating over time (autoregressive effects). We found little evidence of age-specific genetic effects or persistent genetic innovations. Instead, genetic effects are consistent with a gradual unfolding in the liability to depression and rates of change with increasing age. Likewise, the environment also creates significant individual differences in overall levels of depression and rates of change. However, there are also time-specific environmental experiences that persist with fidelity. The implications of these differing genetic and environmental mechanisms in the etiology of depression are considered.

Depressive vulnerability, stressful life events and episode onset of major depression: a longitudinal model

“…Seventh, consistent with a recent developmental twin study (Gillespie et al 2015), our model contained two different kinds of environmental contributants to MD: (i) early adversities such as a disturbed family environment and poor parental warmth which, by influencing DV, produced a long-lasting effect and (ii) concurrent SLEs which had a substantial but quite short-lived impact on risk.…”

The gene–environmental architecture of the development of adolescent substance use

“…In the first step, we estimated a phenotypic dual change score model (Gillespie et al, 2015, Long et al, 2017 to describe the phenotypic development of substance use from age 13 to age 17. Dual change score models are well suited for segregating autoregressive effects from overall levels of a variable, and from the linear and non-linear inter-individual changes in a variable over time.…”

“…Adopting the recommendations of Gillespie and colleagues (Gillespie et al, 2015, Long et al, 2017, the present study combined a latent growth curve approach with an autoregressive approach, in order to disentangle different mechanisms of change. A latent growth model assumes that the level (i.e., intercept) and rates of change (i.e., linear or non-linear slope) in substance use results from the unfolding of inherent, random effects over time that can be decomposed into genetic and environmental sources of variance.…”

“…Adopting the recommendations of Gillespie et al . (Gillespie et al 2015; Long et al 2017), the present study combined a latent growth curve approach with an autoregressive approach, in order to disentangle different mechanisms of change. A latent growth model assumes that the level (i.e.…”

Determining the stability of genome-wide factors in BMI between ages 40 to 69 years

“…Illustrated in Fig 1 , this approach predicts a causal process of inertial effects, whereby BMI genetics at one time causally affect BMI at the next. We have applied this validated approach to personality [ 34 ], anxiety and depression [ 35 , 36 ], substance use [ 37 ] and brain aging [ 38 ]. We are aware of two reports that have tested autoregression models with respect to BMI data [ 9 , 18 ].…”