Table 3.
| Standardized Variance-Covariance Estimates (on and below Diagonal) and Correlations (above Diagonal in grey) | ||||
|---|---|---|---|---|
| SA | 3DGI | GCA | ||
| Genetic (A) |
SA | .94 (.72; .96) |
.90 (.68; .99) |
1.0 (.12; 1.0) |
| 3DGI | .90 (.68; .99) |
.83 (.68; .88) |
.35 (−1.0; 1.0) |
|
| GCA | 1.0 (.11; >1.0) |
.35 (−1.0; >1.0) |
.51 (.24; .78) |
|
| SA | 3DGI | GCA | ||
| Common Environmental (C) |
SA | .01 (.00; .22) |
−.02 (−1.0; .21) |
−.20 (−1.0; .73) |
| 3DGI | −.02 (−.10; .21) |
−.02 (.10; .21) |
.41 (−.72; 1.0) |
|
| GCA | −.20 (−1.0; .73) |
.02 (.00; .17) |
.41 (.00; >1.0) |
|
| SA | 3DGI | GCA | ||
| Unique Environmental (E) |
SA | .06 (.04; .07) |
.11 (.07; .16) |
.13 (.03; .35) |
| 3DGI | .11 (.08; .16) |
.16 (.12; .21) |
.24 (.03; .79) |
|
| GCA | .13 (.02; .34) |
.24 (.03; .79) |
.23 (.02; .30 |
|
| Trivariate Model Comparisons | ||||||
|---|---|---|---|---|---|---|
| AIC | −2LL | DF | Δ-2LL | ΔDF | P | |
| 1. Full Trivariate Model | 383.08 | 3341.08 | 1479 | |||
| 2. AE-AE-ACE | 374.76 | 3342.76 | 1484 | 1.68 | 5 | 0.892 |
| 3. SA-GCA = 0 | 383.91 | 3353.91 | 1485 | 11.15 | 1 | <0.001 |
| 4. 3DGI-GCA = 0 | 372.88 | 3342.88 | 1485 | 0.11 | 1 | 0.73 |
Note: Three matrices divided by A, C, and E variance components. Values on and below the diagonals are variance and covariance estimates, in standardized form. Above the diagonals, shaded grey items reflect genetic and environmental correlations derived from the A, C, and E covariance matrices. The 95% confidence intervals are shown in parentheses; estimates are significant when the intervals do not include zero.
SA = cortical surface area, 3DGI = gyrification index, GCA = general cognitive ability.
Note: AIC = Akaike’s information criterion; −2LL = Negative 2 log-likelihood; DF = Degrees of freedom; Δ-2LL = Change in negative 2 log-likelihood; ΔDF = Change in degrees of freedom;
Model 1 reflects a full trivariate model in which A, C, and E are estimated for each of the three phenotypes.
Model 2 reflects a model in which A and E components are estimated for all variables, and C is estimated only for GCA (a slight improvement in fit). Models 3 and 4 reflect variations of model 2, in which the genetic correlation (path a) between SA or 3DGI and GCA is set to 0. Model comparison statistics reflect the fit of model 2 compared to model 1, and submodels 3 and 4 tested against of model 2. Model 4 (the best fitting model based on AIC) is also depicted in Figure 1.