Global Genetic Variations Predict Brain Response to Faces. Dickie, E., W.; Tahmasebi, A.; French, L.; Kovacevic, N.; Banaschewski, T.; Barker, G., J.; 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, M., N.; Ströhle, A.; Toro, R.; Schumann, G.; Paus, T.; Reed, L.; Williams, S.; Lourdusamy, A.; Costafreda, S.; Cattrell, A.; Nymberg, C.; Topper, L.; Smith, L.; Havatzias, S.; Stueber, K.; Mallik, C.; Clarke, T., K.; Stacey, D.; Peng Wong, C.; Werts, H.; Andrew, C.; Desrivieres, S.; Carvalho, F.; Häke, I.; Ivanov, N.; Klär, A.; Reuter, J.; Palafox, C.; Hohmann, C.; Schilling, C.; Lüdemann, K.; Romanowski, A.; Wolff, E.; Rapp, M.; Brüehl, R.; Ihlenfeld, A.; Walaszek, B.; Schubert, F.; Connolly, C.; Jones, J.; Lalor, E.; McCabe, E.; NíShiothcháin, A.; Whelan, R.; Spanagel, R.; Leonardi-Essmann, F.; Sommer, W.; Struve, M.; Poustka, L.; Steiner, S.; Buehler, M.; Vollstaedt-Klein, S.; Stolzenburg, E.; Schmal, C.; Heym, N.; Newman, C.; Smolka, M.; Huebner, T.; Ripke, S.; Mennigen, E.; Muller, K.; Ziesch, V.; Bromberg, U.; Fadai, T.; Lueken, L.; Yacubian, J.; Finsterbusch, J.; Artiges, E.; Gollier Briand, F.; Massicotte, J.; Bordas, N.; Miranda, R.; Bricaud, Z.; Paillère Martinot, M., L.; Pionne-Dax, N.; Zilbovicius, M.; Boddaert, N.; Cachia, A.; Mangin, J., F.; Poline, J., B.; Barbot, A.; Schwartz, Y.; Lalanne, C.; Frouin, V.; Thyreau, B.; Dalley, J.; Mar, A.; Robbins, T.; Subramaniam, N.; Theobald, D.; Richmond, N.; de Rover, M.; Molander, A.; and Jordan, E. PLoS Genetics, 2014.
abstract   bibtex   
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.
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 title = {Global Genetic Variations Predict Brain Response to Faces},
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 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.},
 bibtype = {article},
 author = {Dickie, Erin W. and Tahmasebi, Amir and French, Leon and Kovacevic, Natasa and Banaschewski, Tobias and Barker, Gareth J. and Bokde, Arun and Büchel, Christian and Conrod, Patricia and Flor, Herta and Garavan, Hugh and Gallinat, Juergen and Gowland, P. and Heinz, Andreas and Ittermann, Bernd and Lawrence, Claire and Mann, Karl and Martinot, Jean Luc and Nees, Frauke and Nichols, Thomas and Lathrop, Mark and Loth, Eva and Pausova, Zdenka and Rietschel, Marcela and Smolka, Michal N. and Ströhle, Andreas and Toro, Roberto and Schumann, Gunter and Paus, Tomáš and Reed, L. and Williams, S. and Lourdusamy, A. and Costafreda, S. and Cattrell, A. and Nymberg, C. and Topper, L. and Smith, L. and Havatzias, S. and Stueber, K. and Mallik, C. and Clarke, T. K. and Stacey, D. and Peng Wong, C. and Werts, H. and Andrew, C. and Desrivieres, S. and Carvalho, F. and Häke, I. and Ivanov, N. and Klär, A. and Reuter, J. and Palafox, C. and Hohmann, C. and Schilling, C. and Lüdemann, K. and Romanowski, A. and Wolff, E. and Rapp, M. and Brüehl, R. and Ihlenfeld, A. and Walaszek, B. and Schubert, F. and Connolly, C. and Jones, J. and Lalor, E. and McCabe, E. and NíShiothcháin, A. and Whelan, R. and Spanagel, R. and Leonardi-Essmann, F. and Sommer, W. and Struve, M. and Poustka, L. and Steiner, S. and Buehler, M. and Vollstaedt-Klein, S. and Stolzenburg, E. and Schmal, C. and Heym, N. and Newman, C. and Smolka, M. and Huebner, T. and Ripke, S. and Mennigen, E. and Muller, K. and Ziesch, V. and Bromberg, U. and Fadai, T. and Lueken, L. and Yacubian, J. and Finsterbusch, J. and Artiges, E. and Gollier Briand, F. and Massicotte, J. and Bordas, N. and Miranda, R. and Bricaud, Z. and Paillère Martinot, Marie L. and Pionne-Dax, N. and Zilbovicius, M. and Boddaert, N. and Cachia, A. and Mangin, J. F. and Poline, J. B. and Barbot, A. and Schwartz, Y. and Lalanne, C. and Frouin, V. and Thyreau, B. and Dalley, J. and Mar, A. and Robbins, T. and Subramaniam, N. and Theobald, D. and Richmond, N. and de Rover, M. and Molander, A. and Jordan, E.},
 journal = {PLoS Genetics},
 number = {8}
}
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