Genetic and environmental continuity in personality development: a meta-analysis.
- Authors
- Briley, Daniel A; Tucker-Drob, Elliot M
- Year
- 2014
- Journal
- Psychological bulletin
- PMID
- 24956122
- DOI
- 10.1037/a0037091
- PMCID
- PMC4152379
The longitudinal stability of personality is low in childhood but increases substantially into adulthood. Theoretical explanations for this trend differ in the emphasis placed on intrinsic maturation and socializing influences. To what extent does the increasing stability of personality result from the continuity and crystallization of genetically influenced individual differences, and to what extent does the increasing stability of life experiences explain increases in personality trait stability? Behavioral genetic studies, which decompose longitudinal stability into sources associated with genetic and environmental variation, can help to address this question. We aggregated effect sizes from 24 longitudinal behavioral genetic studies containing information on a total of 21,057 sibling pairs from 6 types that varied in terms of genetic relatedness and ranged in age from infancy to old age. A combination of linear and nonlinear meta-analytic regression models were used to evaluate age trends in levels of heritability and environmentality, stabilities of genetic and environmental effects, and the contributions of genetic and environmental effects to overall phenotypic stability. Both the genetic and environmental influences on personality increase in stability with age. The contribution of genetic effects to phenotypic stability is moderate in magnitude and relatively constant with age, in part because of small-to-moderate decreases in the heritability of personality over child development that offset increases in genetic stability. In contrast, the contribution of environmental effects to phenotypic stability increases from near zero in early childhood to moderate in adulthood. The life-span trend of increasing phenotypic stability, therefore, predominantly results from environmental mechanisms.
Univariate behavioral genetic model for single occasion data that decomposes variance in a trait (indicated as a square) into that which is due to latent genetic (A) and environmental (E) components (indicated as circles). The correlation between genetic factors is specified for each group depending on the known genetic association between siblings. The label placed on this parameter in the figure is for each sibling type found in the current study, namely, monozygotic twins (1), dizygotic twins (0.5), half-siblings (0.25), and unrelated siblings (0). When the environmental component is corrected for measurement error, the residual variance of the trait is set to equal 1 – reliability. Parameters that share the same label are constrained to be equal. When the outcome is standardized before analysis, as is the case in the current analysis, the squared a and e parameters represent the proportion of variance in the trait attributable to A and E, respectively.
LLM interpretation
This figure is a path diagram of a univariate behavioral genetic model decomposing the variance of a trait in two siblings into latent genetic (A) and environmental (E) components. Arrows labeled $a$ and $e$ connect these latent factors to the observed traits of Sibling 1 and Sibling 2, with the genetic factors (A) linked by a correlation parameter specified as 1, 0.5, 0.25, or 0 depending on sibling type. The model includes self-loops for variance and a residual variance parameter at the bottom labeled $0 / 1 - \alpha$.
Longitudinal correlated factors model that decomposes variance in repeated assessments of personality into that due to time-specific genetic (A) and environmental (E) components, as well as the temporal stability of the genetic (rA) and environmental (rE) components. Interpretation of the parameters and modifications across sibling groups is the same as in Figure 1 with two notable exceptions. First, that the within-time correlation between genetic factors differs by sibling type has been removed from this figure, but is still essential for the model. Second and relatedly, the cross-time cross-sibling genetic correlation (i.e., the correlation between sibling 1’s A factor at time 1 and sibling 2’s A factor at time 2) is specified to differ by sibling type such that the expected correlation is scaled relative to the amount of shared genetic material between the siblings (i.e., multiplied by 1 for monozygotic twins and .5 for dizygotic twins, etc.). These genetic correlations have been marked with an asterisk. The within-sibling cross-time genetic correlation is equal to rA. Cross-sibling cross-time environmental influences are constrained to zero by definition.
LLM interpretation
This figure is a path diagram of a longitudinal correlated factors model used to decompose the variance of personality assessments over two time points for two siblings. The model illustrates the influence of time-specific genetic (A) and environmental (E) components, with curved double-headed arrows representing temporal stability ($rA$, $rE$) and cross-sibling genetic correlations ($rA^*$). Rectangular nodes represent the observed measurements for Sibling 1 and Sibling 2 at Time 1 and Time 2, with paths labeled $a$ and $e$ indicating the effects of genetic and environmental factors, respectively.
Age-trends in heritability, environmentality, and measurement error corrected environmentality. Circles surrounding data points are scaled by the weighting variable (described in Analytic Approach section) such that larger circles carried more weight in the analysis.
LLM interpretation
This figure consists of three scatter plots with trend lines showing the relationship between age (x-axis, 0–100 years) and three different metrics: Heritability (red), Environmentality (blue), and Corrected Environmentality (blue). The data points are represented by circles scaled by weight, with each plot displaying linear trends (BIC and AIC preferred) and an exponential trend. Heritability shows a general decrease with age, while both Environmentality and Corrected Environmentality show a general increase as age increases.
Age-trends in phenotypic, genetic, environmental, and measurement error corrected environmental stability assuming a 5.56 year time lag between assessments. Circles surrounding data points are scaled by the weighting variable (described in Analytic Approach section) such that larger circles carried more weight in the analysis. Some data points were estimated to be out of bounds of the logical limit of a correlation (i.e., −1 to 1). This likely results from parameter imprecision and slight violations of the traditional assumptions of behavioral genetic models (e.g., monozygotic twins correlated more than twice as strongly as dizygotic twins). A total of 13 such estimates are not displayed on the graph for corrected environmental stability because they were outside of the viewable range.
LLM interpretation
This figure consists of four scatter plots showing age-trends in phenotypic (green), genetic (red), environmental (blue), and corrected environmental stability (blue) against "Age at Baseline (years)." Each plot features data points represented by circles scaled by weight, with both a preferred linear trend line and a dashed exponential trend line. Stability generally increases with age across all four measures, with genetic stability approaching a value of 1.0.
Age-trends in genetic and environmental contributions to phenotypic stability assuming a 5.56 year time lag between assessments. Circles surrounding data points are scaled by the weighting variable (described in Analytic Approach section) such that larger circles carried more weight in the analysis.
LLM interpretation
This figure consists of two scatter plots with overlaid trend lines showing genetic (red, left) and environmental (blue, right) contributions to phenotypic stability across age at baseline (0–100 years). The left plot shows a relatively flat trend for genetic contribution, while the right plot shows a steady increase in environmental contribution as age increases. Both plots include a green curve representing expected phenotypic stability and data points scaled by weight, with various linear and exponential trend lines fitted to the data.
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| Title | Year | PMID |
|---|---|---|
| Meta-analysis of Genome-Wide Association Studies for Extraversion: Findings from the Genetics of Personality Consortium. | 2016 | 26362575 |
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