Global Genetic Variations Predict Brain Response to Faces

Erin W. Dickie, Amir Tahmasebi, Leon French, Natasa Kovacevic, Tobias Banaschewski, Gareth J. Barker, Arun Bokde, Christian Büchel, Patricia Conrod, Herta Flor, Hugh Garavan, Juergen Gallinat, Penny Gowland, Andreas Heinz, Bernd Ittermann, Claire Lawrence, Karl Mann, Jean Luc Martinot, Frauke Nees, Thomas NicholsMark Lathrop, Eva Loth, Zdenka Pausova, Marcela Rietschel, Michal N. Smolka, Andreas Ströhle, Roberto Toro, Gunter Schumann, Tomáš Paus, L. Reed, S. Williams, A. Lourdusamy, S. Costafreda, A. Cattrell, C. Nymberg, L. Topper, L. Smith, S. Havatzias, K. Stueber, C. Mallik, T. K. Clarke, D. Stacey, C. Peng Wong, H. Werts, C. Andrew, S. Desrivieres, F. Carvalho, I. Häke, N. Ivanov, A. Klär, J. Reuter, C. Palafox, C. Hohmann, C. Schilling, K. Lüdemann, A. Romanowski, E. Wolff, M. Rapp, R. Brüehl, A. Ihlenfeld, B. Walaszek, F. Schubert, C. Connolly, J. Jones, E. Lalor, E. McCabe, A. NíShiothcháin, R. Whelan, R. Spanagel, F. Leonardi-Essmann, W. Sommer, M. Struve, L. Poustka, S. Steiner, M. Buehler, S. Vollstaedt-Klein, E. Stolzenburg, C. Schmal, N. Heym, C. Newman, M. Smolka, T. Huebner, S. Ripke, E. Mennigen, K. Muller, V. Ziesch, U. Bromberg, T. Fadai, L. Lueken, J. Yacubian, J. Finsterbusch, E. Artiges, F. Gollier Briand, J. Massicotte, N. Bordas, R. Miranda, Z. Bricaud, Marie L. Paillère Martinot, N. Pionne-Dax, M. Zilbovicius, N. Boddaert, A. Cachia, J. F. Mangin, J. B. Poline, A. Barbot, Y. Schwartz, C. Lalanne, V. Frouin, B. Thyreau, J. Dalley, A. Mar, T. Robbins, N. Subramaniam, D. Theobald, N. Richmond, M. de Rover, A. Molander, E. Jordan

Research output: Contribution to journalArticlepeer-review

16 Citations (Scopus)

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 (R2 = 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 (R2 = 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.

Original languageEnglish
Article numbere1004523
JournalPLoS Genetics
Volume10
Issue number8
DOIs
Publication statusPublished - 2014 Aug 14
Externally publishedYes

ASJC Scopus subject areas

  • Ecology, Evolution, Behavior and Systematics
  • Molecular Biology
  • Genetics
  • Genetics(clinical)
  • Cancer Research

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