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Global Genetic Variations Predict Brain Response to Faces

Erin W Dickie 1, 2 Amir Tahmasebi 1, 2 Leon French 2, 1 Natasa Kovacevic 2, 1 Tobias Banaschewski 3 Gareth J Barker 4 Arun Bokde 5 Christian Büchel 6 Patricia Conrod 4, 7 Herta Flor 8, 3 Hugh Garavan 9, 5 Juergen Gallinat 10 Penny Gowland 11 Andreas Heinz 10 Bernd Ittermann 12 Claire Lawrence 13 Karl Mann 3 Jean-Luc Martinot 14 Frauke Nees 3 Thomas E Nichols 15 Mark Lathrop 16 Eva Loth 4 Zdenka Pausova 1 Marcela Rietschel 3 Michal N Smolka 17 Andreas Ströhle 10 Roberto Toro 18 Gunter Schumann 4 Tomáš Paus 2, 1, 13, * 
Abstract : 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.
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Erin W Dickie, Amir Tahmasebi, Leon French, Natasa Kovacevic, Tobias Banaschewski, et al.. Global Genetic Variations Predict Brain Response to Faces. PLoS Genetics, Public Library of Science, 2014, 10 (8), pp.e1004523. ⟨10.1371/journal.pgen.1004523⟩. ⟨pasteur-01967184⟩



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