Global genetic variations predict brain response to faces.
Dickie EW., Tahmasebi A., French L., Kovacevic N., Banaschewski T., Barker GJ., Bokde A., Büchel C., Conrod P., Flor H., Garavan H., Gallinat J., Gowland P., Heinz A., Ittermann B., Lawrence C., Mann K., Martinot J-L., Nees F., Nichols T., Lathrop M., Loth E., Pausova Z., Rietschel M., Smolka MN., Ströhle A., Toro R., Schumann G., Paus T., IMAGEN consortium None., IMAGEN consortium None., IMAGEN consortium None.
Face expressions are a rich source of social signals. Here we estimated the proportion of phenotypic variance in the brain response to facial expressions explained by common genetic variance captured by ∼ 500,000 single nucleotide polymorphisms. Using genomic-relationship-matrix restricted maximum likelihood (GREML), we related this global genetic variance to that in the brain response to facial expressions, as assessed with functional magnetic resonance imaging (fMRI) in a community-based sample of adolescents (n = 1,620). Brain response to facial expressions was measured in 25 regions constituting a face network, as defined previously. In 9 out of these 25 regions, common genetic variance explained a significant proportion of phenotypic variance (40-50%) in their response to ambiguous facial expressions; this was not the case for angry facial expressions. Across the network, the strength of the genotype-phenotype relationship varied as a function of the inter-individual variability in the number of functional connections possessed by a given region (R(2) = 0.38, p<0.001). Furthermore, this variability showed an inverted U relationship with both the number of observed connections (R2 = 0.48, p<0.001) and the magnitude of brain response (R(2) = 0.32, p<0.001). Thus, a significant proportion of the brain response to facial expressions is predicted by common genetic variance in a subset of regions constituting the face network. These regions show the highest inter-individual variability in the number of connections with other network nodes, suggesting that the genetic model captures variations across the adolescent brains in co-opting these regions into the face network.