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Global genetic variations predict brain response to faces

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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, Vol. 10, No. 8, 01.08.2014, p. e1004523.

Research output: SCORING: Contribution to journal › SCORING: Journal article › Research › peer-review

Harvard

Dickie, EW, Tahmasebi, A, French, L, Kovacevic, N, Banaschewski, T, Barker, GJ, 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, MN, Ströhle, A, Toro, R, Schumann, G, Paus, T & IMAGEN Consortium 2014, 'Global genetic variations predict brain response to faces', PLOS GENET, vol. 10, no. 8, pp. e1004523. https://doi.org/10.1371/journal.pgen.1004523

APA

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., ... IMAGEN Consortium (2014). Global genetic variations predict brain response to faces. PLOS GENET, 10(8), e1004523. https://doi.org/10.1371/journal.pgen.1004523

Vancouver

Dickie EW, Tahmasebi A, French L, Kovacevic N, Banaschewski T, Barker GJ et al. Global genetic variations predict brain response to faces. PLOS GENET. 2014 Aug 1;10(8):e1004523. https://doi.org/10.1371/journal.pgen.1004523

Bibtex

@article{7921b6355faf41cc99c105646aa978d3,
title = "Global genetic variations predict brain response to faces",
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.",
author = "Dickie, {Erin W} and Amir Tahmasebi and Leon French and Natasa Kovacevic and Tobias Banaschewski and Barker, {Gareth J} and Arun Bokde and Christian B{\"u}chel and Patricia Conrod and Herta Flor and Hugh Garavan and Juergen Gallinat and Penny Gowland and Andreas Heinz and Bernd Ittermann and Claire Lawrence and Karl Mann and Jean-Luc Martinot and Frauke Nees and Thomas Nichols and Mark Lathrop and Eva Loth and Zdenka Pausova and Marcela Rietschel and Smolka, {Michal N} and Andreas Str{\"o}hle and Roberto Toro and Gunter Schumann and Tom{\'a}{\v s} Paus and {IMAGEN Consortium} and J{\"u}rgen Finsterbusch",
year = "2014",
month = aug,
day = "1",
doi = "10.1371/journal.pgen.1004523",
language = "English",
volume = "10",
pages = "e1004523",
journal = "PLOS GENET",
issn = "1553-7404",
publisher = "Public Library of Science",
number = "8",

}

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 -