3.1 Intraclass Correlations and Falconer’s heritability estimates
shows the intraclass correlation computed for local volumes in both identical and fraternal twins (top left: rMZ and top right: rDZ). Red colors indicate a high correlation (r close to 1), whereas blue colors indicate no detectable correlation (r = 0). The significance of the intraclass correlations was assessed by computing p-values corrected for multiple comparisons (bottom left: pICC(MZ), pcorrected = 0.034; bottom right: pICC(DZ), pcorrected = 0.025). A comparison of these two intraclass correlations is given by the maps of Falconer’s heritability statistics (h2, middle right panel). Red colors indicate greater heritability. The left panel in the middle row shows the common anatomical template and the colored lines indicate the different sections exhibited in the color maps. Three features are evident: first, for most subcortical regions, MZ twin volumes are correlated between members of the pair at around r = 0.5, with values much closer to zero for DZ twins. Second, a correction for multiple comparisons reveals that the overall pattern of correlations in the DZ twins is significantly greater than zero; strictly speaking, a higher proportion of the brain has correlations exceeding the p = 0.05 threshold than would be expected by chance if the null hypothesis of no correlation were true. Third, the high values of heritability (right panel in the middle row), with values over 0.5 for the majority of the subcortical regions, are based on twice the difference in the intraclass correlations for the MZ and DZ twins. These give an estimate of the proportion of the variance in those regions that is genetically mediated. As is also implied by the structural equation models below, the heritability maps suggest that the anatomy of the MZ twins resembles each other to a greater degree than the anatomy of the DZ twins in ventricular, callosal, limbic (cingulate gyrus), occipital and anterior temporal regions, while DZ twins resemble each other to a greater degree than randomly chosen individuals of the same age and sex.
Fig. 1 top row: Intraclass correlation maps are shown for the monozygotic twins (rMZ; left panel) and for the dizygotic twins (rDZ; right panel); middle row: An anatomical image (left) shows the sections for which statistics are displayed; maps of Falconer’s (more ...)
3.2 Genetic and Environmental influence on brain structure variability
The influence of additive genetic (A), as well as shared (C) and unique (E) environmental factors on brain structure volumes are mapped in and for the unscaled and scaled data, respectively. The corresponding values are also reported for eight ROIs in (unscaled data) and (scaled data).
Fig. 2 Variance component maps for additive genetic (a2 - top left), common (c2 - top right), and unique environmental (e2 - bottom left) factors for the unscaled data. Bottom right: Color-coded maps representing the model choice at each voxel- Light blue (yellow (more ...)
Fig. 3 Variance component maps for additive genetic (a2 - top left), common (c2 - top right), and unique environmental (e2 - bottom left) factors for the scaled data. Bottom right: Color-coded maps representing the model choice at each voxel- Light blue (yellow (more ...)
Table 1 Measures of the intraclass correlation coefficients (ICC) for the MZ and DZ groups, Falconer’s heritability estimate (h2), the additive genetic (a2), dominant genetic (d2), shared (c2) and unique environmental (e2) variance components, their confidence (more ...)
Table 2 Measures of the intraclass correlation coefficients (ICC) for the MZ and DZ groups, Falconer’s heritability estimate (h2), the additive genetic (a2), dominant genetic (d2), shared (c2) and unique environmental (e2) variance components, their confidence (more ...)
and display voxelwise maps of the ACE variance components. In each map, the proportion of the overall variance is expressed on a scale of 0 (dark blue) to 75% (red). The variance components, a2, c2, e2 are proportions, and vary from 0 to 1, but their contribution to the overall variance is often stated as a percentage). In the bottom left panel, color-coded maps are presented that show the model that provided the best fit. Light blue corresponds to the ACE model, yellow to the AE model and red to the CE model
If we assume that all the regions of the brain have a partly shared genetic influence related to overall scale of the brain, then after adjusting for individual differences in brain scale across subjects, a lesser residual effect of genetic factors should remain (this is based on the fact that overall brain volume is heritable). Therefore, we hypothesized that all brain regions would show a higher heritability prior to the adjustment and we also expected the proportion of variance due to environmental factors to be greater afterwards.
shows that the influence of genetic factors is detectable throughout the brain in the unscaled data (top left): from 20% in the white matter to 75% in subcortical structures such as the corpus callosum and the ventricles, 20–40% in the basal ganglia and the thalamus and 50% in the occipital lobes (d) The effects of the shared environment, as shown by the c2 values (, top right panel), are more prominent than their genetic counterparts in the white matter, such as the internal capsule, the uncinate fasciculus and the superior longitudinal fasciculus and mostly located in the frontal lobes. The unique environment variance (e2) maps demonstrate high variance in the gray matter. As this term not only accounts for the individual environment influence but also measurement errors from all sources, it is not possible to distinguish unique environmental effects from sources of measurement errors that are uncorrelated between the twins. These maps should therefore be interpreted cautiously.
As hypothesized, the scaled maps showed less genetic effects throughout the brain (see - top right). While scaling the data depleted the effects of the common environment on brain structure (only the white matter partly exhibits c2 values equal to 20 – 30%), the genetics influence is still very strong in the limbic lobe, and the subcorticular structures, in particular in the ventricles (60%). Effects are also noticeable in the occipital lobes (20 – 30% - top left - d) The comparison of the top right panels in and shows that overall, genetic influences (a2) are relatively high in the subcortical areas, as well as in the occipital areas, which are the earliest to mature in infancy.
To summarize the effect of the three factors on global structure volumes, ICC, h2
and proportion of variance factors were computed for the five lobes, the ventricles, the thalamus, basal ganglia and the whole brain ( and ). In the unscaled data, the proportion of genetic, shared and unique environmental variance was approximately the same for all the lobes (see ), with around 30 – 40% of the variance being attributable to genetic differences in the cohort. The shared environment also accounted for around half of the variance in these volumes, with the rest of the differences being attributable either to unique environment or measurement errors. Volumes for the basal ganglia and the thalamus were shown to be influenced by genetic factors (A), as well as shared (C) and unique (E) environmental factors (a2
= 40%, and c2
= 50%, for the basal ganglia - a2
= 25% and c2
= 63% for the thalamus). Between 60% and 70% of the variance in ventricular volumes was attributable to dominant genetic factors (the ADE
model resulted in a better fit than the ACE
model, which may be related to the undetectable DZ correlation (ICC
= 0) and to the increased difference between MZ and DZ correlations
This value was still high (50% to 70%) in the scaled data (), where the ADE
model was also proved to be the best fit. The strong influence of genetics was also seen in the thalamus and basal ganglia (where a2
= 58% and d2
= 57%, respectively). Even so, the genetics influence on the whole brain volume was considerably smaller after scaling, whereas the effect of the common environment decreased from 10%. This trend was found for all lobar structures except for the occipital lobes
and the temporal lobes scaled
In the scaled and unscaled data, the A, C, E or A, D, E terms fitted in all cases except for the parietal lobes (where the best p-value = 0.01 was found for the ACE model in the scaled data, and indicates a lack of fit). The full ACE model gave the best fit for all the structures except for the lateral ventricles (where pace = 0.048 and pade = 0.091) in the unscaled data. In comparison, the best fit with ACE was found in the scaled whole brain and frontal lobes only (p = 0.26 and p = 0.54, respectively). The ADE model was the best fit for the most genetically influenced structures, such as the occipital lobes, the ventricles, and the thalamus, whereas the AE model performed better in the basal ganglia (pace = 0.44, pade = 0.40 and pae = 0.61) and in the temporal lobes (pace = 0.41, pade = 0.36 and pae = 0.55).
Overall, when the scaling effect was removed, the explanatory value of the genetic term (A) decreased in all lobar regions. This is in line with expectation, because the variance in substructure volumes obeys an approximate power law relative to the overall size of the brain (Thompson et al., 2003
); in other words, the logarithms of the substructure volumes and overall brain volumes are tightly correlated in normal populations. Because of this dependency, some of the variance in substructure volumes is correlated with variations in overall brain volume, which is also highly heritable (see and Introduction). If some of the same genes influence substructure volumes as influence the overall brain volume (which is likely), then adjusting for overall brain volume is likely to decrease the remaining genetic proportion of variation in substructure volumes; however, if different genes mediate overall brain volume and substructure volumes, adjusting for overall brain volume may (at least in theory) increase the proportion of the remaining variation in sub-structure volumes that is genetically mediated. In our data, even after adjusting for brain volume (see ), the adjusted occipital, limbic lobar volumes were still genetically influenced. Temporal lobes volumes were also controlled by genetics before and after adjustment, but to a lesser extend, which may be explained by a high c2
value in the inferior temporal lobes, and a high a2
value in the anterior temporal area, that persisted after scaling (see - top right
and - Top left
). The environmental (C) component remained high in frontal regions. This effect was not seen for subcortical structures, where the genetic term was still dominant in the lateral ventricles, basal ganglia and thalamus after adjusting for brain scale.