Born to be happy? The etiology of subjective well-being

Abstract

Subjective Wellbeing (SWB) can be assessed with distinct measures that have been hypothesized to represent different domains of SWB. The current study assessed SWB with four different measures in a genetically informative sample of adolescent twins and their siblings aged 13-28 years (N = 5,024 subjects from 2,157 families). Multivariate genetic modeling was applied to the data to explore the etiology of individual differences in SWB measures and the association among them. Developmental trends and sex differences were examined for mean levels and the variance-covariance structure. Mean SWB levels were equal in men and women. A small negative effect of age on mean levels of SWB was found. Individual differences in SWB were accounted for by additive and non-additive genetic influences, and non-shared environment. The broad-sense heritabilities were estimated between 40 and 50%. The clustering of the four different measures (quality of life in general, satisfaction with life, quality of life at present, and subjective happiness) was explained by an underlying additive genetic factor and an underlying non-additive genetic factor. The effect of these latent genetic factors on the phenotypes was not moderated by either age or sex.

Fig. 1

The moderator model. Squares represent measured variables, and circles represent latent, unobserved factors. The triangle represents the mean. The additive genetic, non-additive genetic and nonshared environmental value is a linear function of the moderator M ₁ and M ₂, for A represented by the equation a + β_x M ₁ + β_y M ₂, where β_x and β_y are the unknown parameters to be estimated from the data, representing the magnitude of the sex and age effects

Fig. 2

Cholesky Decomposition for the four measures of SWB for a single person Squares represent measured variables, and circles represent latent, unobserved factors. A represents additive genetic effects, and E is unique environment. Their influence on the phenotype is given by path coefficients a and e. For sake of clarity, the D component is left out of the picture. QL_g = Quality of Life in General; SAT = Satisfaction with life; QL_p = Quality of life at present; HAP = Subjective Happiness

Fig. 3

The correlated two factor model for the four measures of SWB for a single person. Squares represent measured variables, and circles represent latent, unobserved factors. A₁ and A₂ represent the two underlying correlated latent factors. A_s1 to A_s4 represent measure specific additive genetic effects. E is unique environment. Their influence on the phenotype is given by path coefficients a and e. For sake of clarity, the D component is left out of the picture, but could also be represented as a correlated factor model. QL_g = Quality of Life in General; SAT = Satisfaction with life; QL_p = Quality of life at present; HAP = Subjective Happiness

Fig. 4

The Common Pathway model for the four measures of SWB for a single person. Squares represent measured variables, and circles represent latent, unobserved factors. The bigger circle with ‘SWB; in the middle represents the latent ‘phenotypic factor. A, D, and E represent the Additive genetic, dominant genetic, and nonshared environmental influences on this latent factors. Their influence on the phenotypic latent factor is given by path coefficients a, d, and e. Residual variance is depicted with the measurement specific latent factors (A_s, D_s, and E_s). QL_g = Quality of Life in General; SAT = Satisfaction with life; QL_p = Quality of life at present; HAP = Subjective Happiness

Fig. 5

The Independent Pathway model for the four measures of SWB for a single person. Squares represent measured variables, and circles represent latent, unobserved factors. A₁ represent the underlying latent additive genetic factors. A_s1 to A_s4 represent measure specific additive genetic effects. E is unique environment. Their influence on the phenotype is given by path coefficients a and e. For sake of clarity, the D component is left out of the picture, but could also be represented as an independent pathway model. QL_g = Quality of Life in General; SAT = Satisfaction with life; QL_p = Quality of life at present; HAP = Subjective Happiness

Fig. 6

The best fitting model, with an independent pathway specification for both A and D, and a cholesky decomposition for E. The only significant residual variance is found for dominant genetic influences on QL_g,and HAP. Unstandardized path coefficients are depicted in the figure