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Global Genetic Variations Predict Brain Response to Faces

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 li...

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Autores principales: Dickie, Erin W., Tahmasebi, Amir, French, Leon, Kovacevic, Natasa, Banaschewski, Tobias, Barker, Gareth J., Bokde, Arun, Büchel, Christian, Conrod, Patricia, Flor, Herta, Garavan, Hugh, Gallinat, Juergen, Gowland, Penny, Heinz, Andreas, Ittermann, Bernd, Lawrence, Claire, Mann, Karl, Martinot, Jean-Luc, Nees, Frauke, Nichols, Thomas, Lathrop, Mark, Loth, Eva, Pausova, Zdenka, Rietschel, Marcela, Smolka, Michal N., Ströhle, Andreas, Toro, Roberto, Schumann, Gunter, Paus, Tomáš
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2014
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4133042/
https://www.ncbi.nlm.nih.gov/pubmed/25122193
http://dx.doi.org/10.1371/journal.pgen.1004523
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author Dickie, Erin W.
Tahmasebi, Amir
French, Leon
Kovacevic, Natasa
Banaschewski, Tobias
Barker, Gareth J.
Bokde, Arun
Büchel, Christian
Conrod, Patricia
Flor, Herta
Garavan, Hugh
Gallinat, Juergen
Gowland, Penny
Heinz, Andreas
Ittermann, Bernd
Lawrence, Claire
Mann, Karl
Martinot, Jean-Luc
Nees, Frauke
Nichols, Thomas
Lathrop, Mark
Loth, Eva
Pausova, Zdenka
Rietschel, Marcela
Smolka, Michal N.
Ströhle, Andreas
Toro, Roberto
Schumann, Gunter
Paus, Tomáš
author_facet Dickie, Erin W.
Tahmasebi, Amir
French, Leon
Kovacevic, Natasa
Banaschewski, Tobias
Barker, Gareth J.
Bokde, Arun
Büchel, Christian
Conrod, Patricia
Flor, Herta
Garavan, Hugh
Gallinat, Juergen
Gowland, Penny
Heinz, Andreas
Ittermann, Bernd
Lawrence, Claire
Mann, Karl
Martinot, Jean-Luc
Nees, Frauke
Nichols, Thomas
Lathrop, Mark
Loth, Eva
Pausova, Zdenka
Rietschel, Marcela
Smolka, Michal N.
Ströhle, Andreas
Toro, Roberto
Schumann, Gunter
Paus, Tomáš
author_sort Dickie, Erin W.
collection PubMed
description 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 (R(2) = 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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spelling pubmed-41330422014-08-19 Global Genetic Variations Predict Brain Response to Faces Dickie, Erin W. Tahmasebi, Amir French, Leon Kovacevic, Natasa Banaschewski, Tobias Barker, Gareth J. Bokde, Arun Büchel, Christian Conrod, Patricia Flor, Herta Garavan, Hugh Gallinat, Juergen Gowland, Penny Heinz, Andreas Ittermann, Bernd Lawrence, Claire Mann, Karl Martinot, Jean-Luc Nees, Frauke Nichols, Thomas Lathrop, Mark Loth, Eva Pausova, Zdenka Rietschel, Marcela Smolka, Michal N. Ströhle, Andreas Toro, Roberto Schumann, Gunter Paus, Tomáš PLoS Genet Research Article 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 (R(2) = 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. Public Library of Science 2014-08-14 /pmc/articles/PMC4133042/ /pubmed/25122193 http://dx.doi.org/10.1371/journal.pgen.1004523 Text en © 2014 Dickie et al http://creativecommons.org/licenses/by/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are properly credited.
spellingShingle Research Article
Dickie, Erin W.
Tahmasebi, Amir
French, Leon
Kovacevic, Natasa
Banaschewski, Tobias
Barker, Gareth J.
Bokde, Arun
Büchel, Christian
Conrod, Patricia
Flor, Herta
Garavan, Hugh
Gallinat, Juergen
Gowland, Penny
Heinz, Andreas
Ittermann, Bernd
Lawrence, Claire
Mann, Karl
Martinot, Jean-Luc
Nees, Frauke
Nichols, Thomas
Lathrop, Mark
Loth, Eva
Pausova, Zdenka
Rietschel, Marcela
Smolka, Michal N.
Ströhle, Andreas
Toro, Roberto
Schumann, Gunter
Paus, Tomáš
Global Genetic Variations Predict Brain Response to Faces
title Global Genetic Variations Predict Brain Response to Faces
title_full Global Genetic Variations Predict Brain Response to Faces
title_fullStr Global Genetic Variations Predict Brain Response to Faces
title_full_unstemmed Global Genetic Variations Predict Brain Response to Faces
title_short Global Genetic Variations Predict Brain Response to Faces
title_sort global genetic variations predict brain response to faces
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4133042/
https://www.ncbi.nlm.nih.gov/pubmed/25122193
http://dx.doi.org/10.1371/journal.pgen.1004523
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