How nonshared environmental factors come to correlate with heredity

Abstract

Conventional longitudinal behavioral genetic models estimate the relative contribution of genetic and environmental factors to stability and change of traits and behaviors. Longitudinal models rarely explain the processes that generate observed differences between genetically and socially related individuals. We propose that exchanges between individuals and their environments (i.e., phenotype-environment effects) can explain the emergence of observed differences over time. Phenotype-environment models, however, would require violation of the independence assumption of standard behavioral genetic models; that is, uncorrelated genetic and environmental factors. We review how specification of phenotype-environment effects contributes to understanding observed changes in genetic variability over time and longitudinal correlations among nonshared environmental factors. We then provide an example using 30 days of positive and negative affect scores from an all-female sample of twins. Results demonstrate that the phenotype-environment effects explain how heritability estimates fluctuate as well as how nonshared environmental factors persist over time. We discuss possible mechanisms underlying change in gene-environment correlation over time, the advantages and challenges of including gene-environment correlation in longitudinal twin models, and recommendations for future research.

Keywords: affect; developmental behavioral genetics; gene–environment interplay; longitudinal modeling; mood.

Figure 1.

Phenotype–environment (P→E) model. Biometric components of phenotypic scores for Twin i at Time t, P_it, are estimated between- and within-families; $A_t^b$ = between-family genetic effect at time t; $E_t^b$ = between-family (common) environmental effect at time t; $A_t^w$ = within-family genetic effect at time t; $E_t^w$ = within-family (nonshared) environmental effect at time t; $u{A}_b^t$ = unique between-family genetic effect at time t; $u{E}_b^t$ = unique between-family environmental effect at time t; $u{A}_t^w$ = unique within-family genetic effect at time t; $u{E}_t^w$ = unique within-family environmental effect at time t; a_ar, c_ar, and e_ar = autoregressive coefficient between adjacent components. The between-family and within-family genetic loadings for the monozygotic (MZ) twins are 1 and 0, respectively, to meet the assumption that MZ twins share 100% of their genes. The between-family and within-family genetic loadings for the dizygotic (DZ) twins are both ⎷.5 to meet the assumption that and DZ twins share 50%, on average, of their segregating genes. The red line represents the P→E parameter, b_PE, which was only estimated at the within-family level in the DZ group.

Figure 2.

Daily twin correlations of positive and negative affect scores. LOESS lines (in blue) are overlaid to illustrate general trends in twin similarity for each phenotype. On average, differences between monozygotic (MZ) and dizygotic (DZ) twin correlations are statistically significant across the 30 days (positive affect: t = 6.10, df = 58, p < .001; negative affect: t = 4.04, df = 58, p < .001).

Figure 3.

Heritability and environment estimates of positive affect (top panel) and negative affect (bottom panel) by day. h² = heritability, which is the proportion of total variance in daily affect scores attributed to genetic variance; c² = shared environment, which is the proportion of total variance in daily affect scores attributed to shared environmental variance; e² = nonshared environment, which is the proportion of total variance in daily affect scores attributed to nonshared (unique) environmental variance. All estimates are based on classical univariate ACE models (genetic [A], shared environmental [C], and nonshared environmental [E]).

Figure 4.

Model estimated within-family rGE over 30 days for positive affect (PA) and negative affect (NA). rGE was re-estimated in P→E models where days were randomly ordered within twins to illustrate that rGE systematically changes across the 30 days.

Figure 5.

Heritability estimates of positive affect (top) and negative affect (bottom) from phenotype–environment (P→E) model (red) and genetic simplex model (blue) across the 30 days.

Figure 6.

Longitudinal correlations among nonshared environmental correlations across 30 days for positive affect (PA) and negative affect (NA). Model estimated MZ and DZ correlations are taken from phenotype–environment (P→E) models.