GWAS-based pathway analysis differentiates between fluid and crystallized intelligence

A. Christoforou, T. Espeseth, G. Davies, C. P. D. Fernandes, S. Giddaluru, M. Mattheisen, A. Tenesa, S. E. Harris, D. C. Liewald, A. Payton, W. Ollier, M. Horan, N. Pendleton, P. Haggarty, S. Djurovic, S. Herms, P. Hoffman, S. Cichon, J. M. Starr, A. Lundervold & 4 others I. Reinvang, V. M. Steen, I. J. Deary, S. Le Hellard

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Abstract

Cognitive abilities vary among people. About 40-50% of this variability is due to general intelligence (g), which reflects the positive correlation among individuals' scores on diverse cognitive ability tests. g is positively correlated with many life outcomes, such as education, occupational status, and health, motivating the investigation of its underlying biology. In psychometric research, a distinction is made between general fluid intelligence (gF) - the ability to reason in novel situations - and general crystallized intelligence (gC) - the ability to apply acquired knowledge. This distinction is supported by developmental and cognitive neuroscience studies. Classical epidemiological studies and recent genome-wide association studies (GWASs) have established that these cognitive traits have a large genetic component. However, no robust genetic associations have been published thus far due largely to the known polygenic nature of these traits and insufficient sample sizes. Here, using two GWAS datasets, in which the polygenicity of gF and gC traits was previously confirmed, a gene- and pathway-based approach was undertaken with the aim of characterizing and differentiating their genetic architecture. Pathway analysis, using genes selected on the basis of relaxed criteria, revealed notable differences between these two traits. gF appeared to be characterized by genes affecting the quantity and quality of neurons and therefore neuronal efficiency, whereas long term depression (LTD) seemed to underlie gC. Thus, this study supports the gF-gC distinction at the genetic level and identifies functional annotations and pathways worthy of further investigation.

Original languageEnglish
Pages (from-to)663-674
Number of pages12
JournalGenes, Brain, and Behavior
Volume13
Issue number7
Early online date8 Aug 2014
DOIs
Publication statusPublished - Sep 2014

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Genome-Wide Association Study
Intelligence
Aptitude
Multifactorial Inheritance
Genes
Occupational Health
Psychometrics
Sample Size
Epidemiologic Studies
Depression
Efficiency
Education
Neurons

Keywords

  • crystallized intelligence
  • fluid intelligence
  • gene-based analysis
  • GWAS
  • pathway analysis

Cite this

Christoforou, A., Espeseth, T., Davies, G., Fernandes, C. P. D., Giddaluru, S., Mattheisen, M., ... Le Hellard, S. (2014). GWAS-based pathway analysis differentiates between fluid and crystallized intelligence. Genes, Brain, and Behavior, 13(7), 663-674. https://doi.org/10.1111/gbb.12152

GWAS-based pathway analysis differentiates between fluid and crystallized intelligence. / Christoforou, A.; Espeseth, T.; Davies, G.; Fernandes, C. P. D.; Giddaluru, S.; Mattheisen, M.; Tenesa, A.; Harris, S. E.; Liewald, D. C.; Payton, A.; Ollier, W.; Horan, M.; Pendleton, N.; Haggarty, P.; Djurovic, S.; Herms, S.; Hoffman, P.; Cichon, S.; Starr, J. M.; Lundervold, A.; Reinvang, I.; Steen, V. M.; Deary, I. J.; Le Hellard, S.

In: Genes, Brain, and Behavior, Vol. 13, No. 7, 09.2014, p. 663-674.

Research output: Contribution to journalArticle

Christoforou, A, Espeseth, T, Davies, G, Fernandes, CPD, Giddaluru, S, Mattheisen, M, Tenesa, A, Harris, SE, Liewald, DC, Payton, A, Ollier, W, Horan, M, Pendleton, N, Haggarty, P, Djurovic, S, Herms, S, Hoffman, P, Cichon, S, Starr, JM, Lundervold, A, Reinvang, I, Steen, VM, Deary, IJ & Le Hellard, S 2014, 'GWAS-based pathway analysis differentiates between fluid and crystallized intelligence', Genes, Brain, and Behavior, vol. 13, no. 7, pp. 663-674. https://doi.org/10.1111/gbb.12152
Christoforou A, Espeseth T, Davies G, Fernandes CPD, Giddaluru S, Mattheisen M et al. GWAS-based pathway analysis differentiates between fluid and crystallized intelligence. Genes, Brain, and Behavior. 2014 Sep;13(7):663-674. https://doi.org/10.1111/gbb.12152
Christoforou, A. ; Espeseth, T. ; Davies, G. ; Fernandes, C. P. D. ; Giddaluru, S. ; Mattheisen, M. ; Tenesa, A. ; Harris, S. E. ; Liewald, D. C. ; Payton, A. ; Ollier, W. ; Horan, M. ; Pendleton, N. ; Haggarty, P. ; Djurovic, S. ; Herms, S. ; Hoffman, P. ; Cichon, S. ; Starr, J. M. ; Lundervold, A. ; Reinvang, I. ; Steen, V. M. ; Deary, I. J. ; Le Hellard, S. / GWAS-based pathway analysis differentiates between fluid and crystallized intelligence. In: Genes, Brain, and Behavior. 2014 ; Vol. 13, No. 7. pp. 663-674.
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AU - Christoforou, A.

AU - Espeseth, T.

AU - Davies, G.

AU - Fernandes, C. P. D.

AU - Giddaluru, S.

AU - Mattheisen, M.

AU - Tenesa, A.

AU - Harris, S. E.

AU - Liewald, D. C.

AU - Payton, A.

AU - Ollier, W.

AU - Horan, M.

AU - Pendleton, N.

AU - Haggarty, P.

AU - Djurovic, S.

AU - Herms, S.

AU - Hoffman, P.

AU - Cichon, S.

AU - Starr, J. M.

AU - Lundervold, A.

AU - Reinvang, I.

AU - Steen, V. M.

AU - Deary, I. J.

AU - Le Hellard, S.

N1 - This article is protected by copyright. All rights reserved.

PY - 2014/9

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N2 - Cognitive abilities vary among people. About 40-50% of this variability is due to general intelligence (g), which reflects the positive correlation among individuals' scores on diverse cognitive ability tests. g is positively correlated with many life outcomes, such as education, occupational status, and health, motivating the investigation of its underlying biology. In psychometric research, a distinction is made between general fluid intelligence (gF) - the ability to reason in novel situations - and general crystallized intelligence (gC) - the ability to apply acquired knowledge. This distinction is supported by developmental and cognitive neuroscience studies. Classical epidemiological studies and recent genome-wide association studies (GWASs) have established that these cognitive traits have a large genetic component. However, no robust genetic associations have been published thus far due largely to the known polygenic nature of these traits and insufficient sample sizes. Here, using two GWAS datasets, in which the polygenicity of gF and gC traits was previously confirmed, a gene- and pathway-based approach was undertaken with the aim of characterizing and differentiating their genetic architecture. Pathway analysis, using genes selected on the basis of relaxed criteria, revealed notable differences between these two traits. gF appeared to be characterized by genes affecting the quantity and quality of neurons and therefore neuronal efficiency, whereas long term depression (LTD) seemed to underlie gC. Thus, this study supports the gF-gC distinction at the genetic level and identifies functional annotations and pathways worthy of further investigation.

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