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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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    Citations

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    Cited by:

    1. von Stumm, Sophie & Plomin, Robert, 2021. "Using DNA to predict intelligence," Intelligence, Elsevier, vol. 86(C).
    2. 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.
    3. Lasker, Jordan, 2022. "Are Piagetian scales just intelligence tests?," Intelligence, Elsevier, vol. 95(C).
    4. Mengge Liu & Lu Wang & Yujie Zhang & Haoyang Dong & Caihong Wang & Yayuan Chen & Qian Qian & Nannan Zhang & Shaoying Wang & Guoshu Zhao & Zhihui Zhang & Minghuan Lei & Sijia Wang & Qiyu Zhao & Feng Li, 2024. "Investigating the shared genetic architecture between depression and subcortical volumes," Nature Communications, Nature, vol. 15(1), pages 1-12, December.
    5. Mingyang Li & Xixi Dang & Yiwei Chen & Zhifan Chen & Xinyi Xu & Zhiyong Zhao & Dan Wu, 2024. "Cognitive processing speed and accuracy are intrinsically different in genetic architecture and brain phenotypes," Nature Communications, Nature, vol. 15(1), pages 1-11, December.
    6. 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.
    7. Knyspel, Jacob & Plomin, Robert, 2024. "Comparing factor and network models of cognitive abilities using twin data," Intelligence, Elsevier, vol. 104(C).
    8. Bingxin Zhao & Yujue Li & Zirui Fan & Zhenyi Wu & Juan Shu & Xiaochen Yang & Yilin Yang & Xifeng Wang & Bingxuan Li & Xiyao Wang & Carlos Copana & Yue Yang & Jinjie Lin & Yun Li & Jason L. Stein & Joa, 2024. "Eye-brain connections revealed by multimodal retinal and brain imaging genetics," Nature Communications, Nature, vol. 15(1), pages 1-19, December.

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