Evidence map›Paper›PMID 24975275›Full record

ArticleGenes, brain, and behavior2014

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 and 14 more

Abstract read
In one paragraph

Article in Genes, brain, and behavior, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.

0numbers the graph read from it
0cells of the map it votes in
14citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

14 citing papers in PubMed.

  1. Article
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  5. Article
  6. Article
  7. Article
  8. Article
  9. Article
  10. Review
  11. Article
  12. Autism As a Disorder of High Intelligence.Frontiers in neuroscience · 2016
    Article
  13. Article
  14. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

24 authors.

A ChristoforouK.G. Jebsen Centre for Psychosis Research, Norwegian Centre for Mental Disorders Research (NORMENT), Department of Clinical Science, University of Bergen; Dr. Einar Martens Research Group for Biological Psychiatry, Centre for Medical Genetics and Molecular Medicine, Haukeland University Hospital, Bergen.
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
I Reinvang
V M Steen
I J Deary
S Le Hellard

Funding

Biotechnology and Biological Sciences Research Council BB/F019394/1Biotechnology and Biological Sciences Research Council BB/F022441/1Chief Scientist Office CZB/4/505Chief Scientist Office ETM/55Medical Research Council G0100594Medical Research Council G0600237Medical Research Council G0700704Medical Research Council G0900753Medical Research Council G0901461Medical Research Council G1001375Medical Research Council MR/K002279/1Medical Research Council MR/K026992/1Wellcome Trust
6 · The paper itself

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.

Indexed as

CognitionGenome, HumanAdolescentAdultAgedAged, 80 and overFemaleGenetic MarkersGenome-Wide Association StudyHumansIntelligenceLong-Term Synaptic DepressionMaleMetabolic Networks and PathwaysMiddle AgedPolymorphism, Single NucleotideGenetic MarkersCrystallized intelligencefluid intelligencegene-based analysisGWASpathway analysis

Identifiers

PMID24975275
PMCPMC4261989

What OpenQuestion holds

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Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.