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Global Genetic Variations Predict Brain Response to Faces

Erin Dickie 1, 2 Amir Tahmasebi 1, 2 Leon French 2, 1 Natasa Kovacevic 2, 1 Tobias Banaschewski 3 Gareth 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 Nichols 15 Mark Lathrop 16 Eva Loth 4 Zdenka Pausova 1 Marcela Rietschel 3 Michal Smolka 17 Andreas Ströhle 10 Roberto Toro 18 Gunter Schumann 4 Tomáš Paus 2, 1, 13, *
* Corresponding author
1 University of Toronto
2 RRI - Rotman Research Institute at the Baycrest Centre
3 University of Heidelberg, Medical Faculty
4 Institute of Psychiatry, Psychology & Neuroscience, King's College London
5 Trinity College Dublin
6 UKE - Universitaetsklinikum Hamburg-Eppendorf = University Medical Center Hamburg-Eppendorf [Hamburg]
7 UdeM - Université de Montréal
8 Universität Mannheim [Mannheim]
9 University of Vermont [Burlington]
10 Charité - Universitätsmedizin Berlin / Charite - University Medicine Berlin
11 Sir Peter Mansfield Magnetic Resonance Centre [Nottingham]
12 PTB - Physikalisch-Technische Bundesanstalt [Berlin]
13 UON - University of Nottingham, UK
14 U1000 - Neuroimagerie en psychiatrie
15 University of Warwick [Coventry]
16 CNG - Centre National de Génotypage
17 TUD - Technische Universität Dresden
18 Génétique Humaine et Fonctions Cognitives
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 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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