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A general dimension of genetic sharing across diverse cognitive traits inferred from molecular data

Author

Listed:
  • Javier de la Fuente

    (University of Texas at Austin
    University of Texas at Austin)

  • Gail Davies

    (University of Edinburgh
    University of Edinburgh)

  • Andrew D. Grotzinger

    (University of Texas at Austin)

  • Elliot M. Tucker-Drob

    (University of Texas at Austin
    University of Texas at Austin)

  • Ian J. Deary

    (University of Edinburgh
    University of Edinburgh)

Abstract

It has been known since 1904 that, in humans, diverse cognitive traits are positively intercorrelated. This forms the basis for the general factor of intelligence (g). Here, we directly test whether there is a partial genetic basis for individual differences in g using data from seven different cognitive tests (n = 11,263–331,679) and genome-wide autosomal single-nucleotide polymorphisms. A genetic g factor accounts for an average of 58.4% (s.e. = 4.8%) of the genetic variance in the cognitive traits considered, with the proportion varying widely across traits (range, 9–95%). We distil genetic loci that are broadly relevant for many cognitive traits (g) from loci associated specifically with individual cognitive traits. These results contribute to elucidating the aetiology of a long-known yet poorly understood phenomenon, revealing a fundamental dimension of genetic sharing across diverse cognitive traits.

Suggested Citation

  • Javier de la Fuente & Gail Davies & Andrew D. Grotzinger & Elliot M. Tucker-Drob & Ian J. Deary, 2021. "A general dimension of genetic sharing across diverse cognitive traits inferred from molecular data," Nature Human Behaviour, Nature, vol. 5(1), pages 49-58, January.
    • Handle: RePEc:nat:nathum:v:5:y:2021:i:1:d:10.1038_s41562-020-00936-2
    DOI: 10.1038/s41562-020-00936-2
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    Cited by:

    1. von Stumm, Sophie & Plomin, Robert, 2021. "Using DNA to predict intelligence," Intelligence, Elsevier, vol. 86(C).
    2. Lasker, Jordan, 2022. "Are Piagetian scales just intelligence tests?," Intelligence, Elsevier, vol. 95(C).
    3. Andrew D. Grotzinger & Travis T. Mallard & Zhaowen Liu & Jakob Seidlitz & Tian Ge & Jordan W. Smoller, 2023. "Multivariate genomic architecture of cortical thickness and surface area at multiple levels of analysis," Nature Communications, Nature, vol. 14(1), pages 1-13, December.
    4. Andrew D. Grotzinger & Javier de la Fuente & Gail Davies & Michel G. Nivard & Elliot M. Tucker-Drob, 2022. "Transcriptome-wide and stratified genomic structural equation modeling identify neurobiological pathways shared across diverse cognitive traits," Nature Communications, Nature, vol. 13(1), pages 1-15, December.

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