Global genetic variations predict brain response to faces
Standard
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; IMAGEN Consortium.
in: PLOS GENET, Jahrgang 10, Nr. 8, 01.08.2014, S. e1004523.Publikationen: SCORING: Beitrag in Fachzeitschrift/Zeitung › SCORING: Zeitschriftenaufsatz › Forschung › Begutachtung
Harvard
APA
Vancouver
Bibtex
}
RIS
TY - JOUR
T1 - Global genetic variations predict brain response to faces
AU - Dickie, Erin W
AU - Tahmasebi, Amir
AU - French, Leon
AU - Kovacevic, Natasa
AU - Banaschewski, Tobias
AU - Barker, Gareth J
AU - Bokde, Arun
AU - Büchel, Christian
AU - Conrod, Patricia
AU - Flor, Herta
AU - Garavan, Hugh
AU - Gallinat, Juergen
AU - Gowland, Penny
AU - Heinz, Andreas
AU - Ittermann, Bernd
AU - Lawrence, Claire
AU - Mann, Karl
AU - Martinot, Jean-Luc
AU - Nees, Frauke
AU - Nichols, Thomas
AU - Lathrop, Mark
AU - Loth, Eva
AU - Pausova, Zdenka
AU - Rietschel, Marcela
AU - Smolka, Michal N
AU - Ströhle, Andreas
AU - Toro, Roberto
AU - Schumann, Gunter
AU - Paus, Tomáš
AU - IMAGEN Consortium
AU - Finsterbusch, Jürgen
PY - 2014/8/1
Y1 - 2014/8/1
N2 - 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.
AB - 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.
U2 - 10.1371/journal.pgen.1004523
DO - 10.1371/journal.pgen.1004523
M3 - SCORING: Journal article
C2 - 25122193
VL - 10
SP - e1004523
JO - PLOS GENET
JF - PLOS GENET
SN - 1553-7404
IS - 8
ER -