A Comprehensive Quantitative Genetic Analysis of Cerebral Surface Area in Youth

Abstract

The genetics of cortical arealization in youth is not well understood. In this study, we use a genetically informative sample of 677 typically developing children and adolescents (mean age 12.72 years), high-resolution MRI, and quantitative genetic methodology to address several fundamental questions on the genetics of cerebral surface area. We estimate that >85% of the phenotypic variance in total brain surface area in youth is attributable to additive genetic factors. We also observed pronounced regional variability in the genetic influences on surface area, with the most heritable areas seen in primary visual and visual association cortex. A shared global genetic factor strongly influenced large areas of the frontal and temporal cortex, mirroring regions that are the most evolutionarily novel in humans relative to other primates. In contrast to studies on older populations, we observed statistically significant genetic correlations between measures of surface area and cortical thickness (r_G = 0.63), suggestive of overlapping genetic influences between these endophenotypes early in life. Finally, we identified strong and highly asymmetric genetically mediated associations between Full-Scale Intelligence Quotient and left perisylvian surface area, particularly receptive language centers. Our findings suggest that spatially complex and temporally dynamic genetic factors are influencing cerebral surface area in our species.SIGNIFICANCE STATEMENT Over evolution, the human cortex has undergone massive expansion. In humans, patterns of neurodevelopmental expansion mirror evolutionary changes. However, there is a sparsity of information on how genetics impacts surface area maturation. Here, we present a systematic analysis of the genetics of cerebral surface area in youth. We confirm prior research that implicates genetics as the dominant force influencing individual differences in global surface area. We also find evidence that evolutionarily novel brain regions share common genetics, that overlapping genetic factors influence both area and thickness in youth, and the presence of strong genetically mediated associations between intelligence and surface area in language centers. These findings further elucidate the complex role that genetics plays in brain development and function.

Keywords: MRI; cortical thickness; genetics; intelligence; neurodevelopment; surface area.

Figure 1.

The heritability of cerebral SA in children and adolescents. Maximum likelihood estimates for additive genetic (a²), shared environmental (c²), and unique environmental (e²) variance in vertex-level cerebral SA are shown for multiple views. Probability maps identifying regions with statistically significant variation are also shown. Gray represents nonsignificant vertices. There were no statistically significant shared environmental effects after correction for multiple testing. Probability maps for familial (a² + c²) covariance also are provided. Because the power to identify familial effects is greater than for individual variance components, a logarithmic scale is use to better visualize regional differences.

Figure 2.

Global effects on areal expansion. A, Results from univariate variance decomposition after including total cerebral SA as a covariate. The shared environment was not significant in these models. a_dif² plots regional differences in heritability relative to the original model presented in Figure 1; negative values indicate regions where heritability decreased after including the global covariate. B, Results from bivariate analyses directly examining the relationship between areal expansion and total SA. Regional phenotypic (r_P), genetic (r_G), and unique environmental (r_E) correlations are provided, as well as tests assessing the statistical significance of genetic and environmental covariance.

Figure 3.

Global genetic influences on SA compared to evolutionary and neurodevelopmental expansion. Evolutionary (top, Evo) and neurodevelopmental (bottom, Devo) maps of cortical expansion from Hill et al. (2010) compared with genetic correlations between total SA and vertex-level areal expansion (r_G). Standardized (Z-transformed) maps are shown for all measures (along figure margins). Concordance maps (middle) indicate vertices where values were greater than the 50th (green) or 75th (red) centile for both r_G and either Evo or Devo. Histograms from spatial permutation analysis for both Evo-Genetic (top) and Devo-Genetic (bottom) correspondence are also provided.

Figure 4.

Interhemispheric correlations in areal expansion. Results of bivariate models examining correlations between vertex-level homologs in the contralateral cortex projected onto the left hemisphere.

Figure 5.

Shared genetic relationships between areal expansion and CT. Regional phenotypic (r_P), genetic (r_G), and environmental (r_E) correlations are shown, as well as tests assessing the statistical significance of genetic and environmental covariance.

Figure 6.

Genetically mediated correlations with intelligence. Vertex-level phenotypic (r_P), genetic (r_G), and environmental (r_E) correlations are shown (top) along with a probability map of statistically significant shared genetic influences. Environmental covariance between SA and FSIQ was not statistically significant at any vertex. Because genetic correlations were much stronger than phenotypic correlations, r_G is plotted a second time with a wider scale (bottom).

Figure 7.

Heritability of cerebral SA for 308 sub-ROIs based on the Desikan–Killany atlas. Maximum likelihood estimates and FDR-corrected probability maps for genetic and familial variance are also shown. Similar to vertex-level measures, there were no statistically significant shared environmental effects after correction for multiple testing.

Figure 8.

Genetic effects of global SA on regional parcellations. A, Results from univariate ACE models after including total cerebral SA as a covariate. a_dif² plots ROI-level differences in heritability relative to the original model without a global covariate. B, Results from bivariate analyses that directly model the relationship between regional SA and total SA.

Figure 9.

Shared genetic relationships between areal expansion and CT at the ROI level. Regional phenotypic (r_P), genetic (r_G), and environmental (r_E) correlations are shown, as well as tests assessing the statistical significance of genetic and environmental covariance.

Figure 10.

Genetically mediated correlations between intelligence and regional parcellations of cerebral SA. Phenotypic (r_P), genetic (r_G), and environmental (r_E) correlations are shown (top) along with a probability map of statistically significant shared genetic influences. Environmental correlations were not statistically significant after correction for multiple testing.