Cited by 50Cognitive Abilities and TestingGenetic Associations and EpidemiologyGenetics and Physical Performance

A Polygenic Score for Higher Educational Attainment is Associated with Larger Brains

Open access

Published version of a preprint

Abstract

People who score higher on intelligence tests tend to have larger brains. Twin studies suggest the same genetic factors influence both brain size and intelligence. This has led to the hypothesis that genetics influence intelligence partly by contributing to the development of larger brains. We tested this hypothesis using four large imaging genetics studies (combined N = 7965) with polygenic scores derived from a genome-wide association study (GWAS) of educational attainment, a correlate of intelligence. We conducted meta-analysis to test associations among participants’ genetics, total brain volume (i.e., brain size), and cognitive test performance. Consistent with previous findings, participants with higher polygenic scores achieved higher scores on cognitive tests, as did participants with larger brains. Participants with higher polygenic scores also had larger brains. We found some evidence that brain size partly mediated associations between participants’ education polygenic scores and their cognitive test performance. Effect sizes were larger in the population-based samples than in the convenience-based samples. Recruitment and retention of population-representative samples should be a priority for neuroscience research. Findings suggest promise for studies integrating GWAS discoveries with brain imaging to understand neurobiology linking genetics with cognitive performance.

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Integrity signals

Registry

Crossref · 10.1093/cercor/bhy219

Cites

13 of 13 references verified0 citation errors · 0 not checkable

Statistics

Claims the paper makes

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

  • EduYears shows phenotypic correlations and shares genetic components with multiple traits and diseases relevant to medical research, including cognitive function, neurodegeneration and psychiatric disorders.

    “As previous studies and our results suggest, EduYears shows phenotypic correlations and shares genetic components with multiple traits and diseases relevant to medical research, including cognitive function, neurodegeneration and psychiatric disorders43–46”

    From Shared genetic architectures of educational attainment in East Asian and European populations (2024) · cites this paper as [44]

    The Introduction and Discussion report associations between educational-attainment genetics and cognitive performance, supporting the cognitive-function part of the claim. The passages do not establish phenotypic correlations or shared genetic components with neurodegeneration or psychiatric disorders. (LLM, conf=0.86)

    Experimental — accuracy not yet measured

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

The self-check needs the paper’s full text.

Cites

Flagged first, then by citations

Research works printed but not linked: 42

  1. Abecasis, 2012, An integrated map of genetic variation from 1,092 human genomes, Nature, 491, 56, 10.1038/nature11632 · DOI · Paper page
  2. Adams, 2016, Novel genetic loci underlying human intracranial volume identified through genome-wide association, Nat Neurosci, 19, 1569, 10.1038/nn.4398 · DOI
  3. Baron, 1986, The moderator-mediator variable distinction in social psychological-research: conceptual, strategic, and statistical considerations, J Pers Soc Psychol, 51, 1173, 10.1037/0022-3514.51.6.1173 · DOI · Paper page
  4. Belsky, 2016, The genetics of success: how single-nucleotide polymorphisms associated with educational attainment relate to life-course development, Psychol Sci, 27, 957, 10.1177/0956797616643070 · DOI · Paper page
  5. Bland, 2011, Statistics notes: correlation in restricted ranges of data, BMJ, 343, 577
  6. Burgess, 2015, Using published data in Mendelian randomization: a blueprint for efficient identification of causal risk factors, Eur J Epidemiol, 30, 543, 10.1007/s10654-015-0011-z · DOI · Paper page
  7. Calvet, 2016, Detection and sequencing of Zika virus from amniotic fluid of fetuses with microcephaly in Brazil: a case study, Lancet Infect Dis, 16, 653, 10.1016/S1473-3099(16)00095-5 · DOI
  8. Cesarini, 2017, Genetics and educational attainment, NPJ Sci Learn, 2, 4, 10.1038/s41539-017-0005-6 · DOI
  9. DerSimonian, 1986, Meta-analysis in clinical trials, Control Clin Trials, 7, 177, 10.1016/0197-2456(86)90046-2 · DOI · Paper page
  10. Domingue, 2015, Polygenic influence on educational attainment, AERA Open, 1, 1, 10.1177/2332858415599972 · DOI · Paper page
  11. Elliott, 2018, A connectome-wide functional signature of transdiagnostic risk for mental illness, Biol Psychiatry, 452, 10.1016/j.biopsych.2018.03.012 · DOI
  12. Falk, 2013, What is a representative brain? Neuroscience meets population science, Proc Natl Acad Sci, 110, 17615, 10.1073/pnas.1310134110 · DOI · Paper page
  13. Fischl, 2012, FreeSurfer, Neuroimage, 62, 774, 10.1016/j.neuroimage.2012.01.021 · DOI · Paper page
  14. Gignac, 2017, Brain volume and intelligence: the moderating role of intelligence measurement quality, Intelligence, 64, 18, 10.1016/j.intell.2017.06.004 · DOI
  15. Haier, 2004, Structural brain variation and general intelligence, Neuroimage, 23, 425, 10.1016/j.neuroimage.2004.04.025 · DOI
  16. Hair, 2015, Association of child poverty, brain development, and academic achievement, JAMA Pediatr, 169, 822, 10.1001/jamapediatrics.2015.1475 · DOI
  17. Heckman, 1979, Sample selection bias as a specification error, Econometrica, 47, 153, 10.2307/1912352 · DOI · Paper page
  18. Holmes, 2015, Brain genomics superstruct project initial data release with structural, functional, and behavioral measures, Sci Data, 2, 150031, 10.1038/sdata.2015.31 · DOI
  19. Lewinn, 2017, Sample composition alters associations between age and brain structure, Nat Commun, 8, 874, 10.1038/s41467-017-00908-7 · DOI
  20. Lyall, 2016, Cognitive test scores in UK biobank: data reduction in 480,416 participants and longitudinal stability in 20,346 participants, PLoS One, 11, e0156366, 10.1371/journal.pone.0156366 · DOI
  21. McDaniel, 2005, Big-brained people are smarter: a meta-analysis of the relationship between in vivo brain volume and intelligence, Intelligence, 33, 337, 10.1016/j.intell.2004.11.005 · DOI
  22. Mendoza, 1987, Corrections for Attenuation and Range Restriction on the Predictor, J Educ Stat, 12, 282, 10.3102/10769986012003282 · DOI
  23. Peterson, 2014, Convenience samples of college students and research reproducibility, J Bus Res, 67, 1035, 10.1016/j.jbusres.2013.08.010 · DOI · Paper page
  24. Pietschnig, 2015, Meta-analysis of associations between human brain volume and intelligence differences: How strong are they and what do they mean?, Neurosci Biobehav Rev, 57, 411, 10.1016/j.neubiorev.2015.09.017 · DOI · Paper page
  25. Plomin, 2018, The new genetics of intelligence, Nat Rev Genet, 19, 148, 10.1038/nrg.2017.104 · DOI
  26. Posthuma, 2003, Genetic correlations between brain volumes and the WAIS-III dimensions of verbal comprehension, working memory, perceptual organization, and processing speed, Twin Res, 6, 131, 10.1375/136905203321536254 · DOI
  27. Posthuma, 2002, The association between brain volume and intelligence is of genetic origin, Nat Neurosci, 5, 83, 10.1038/nn0202-83 · DOI · Paper page
  28. Poulton, 2015, The Dunedin multidisciplinary health and development study: overview of the first 40 years, with an eye to the future, Soc Psychiatry Psychiatr Epidemiol, 50, 679, 10.1007/s00127-015-1048-8 · DOI
  29. Preacher, 2008, Asymptotic and resampling strategies for assessing and comparing indirect effects in multiple mediator models, Behav Res Methods, 40, 879, 10.3758/BRM.40.3.879 · DOI · Paper page
  30. Price, 2006, Principal components analysis corrects for stratification in genome-wide association studies, Nat Genet, 38, 904, 10.1038/ng1847 · DOI · Paper page
  31. Rietveld, 2014, Common genetic variants associated with cognitive performance identified using the proxy-phenotype method, Proc Natl Acad Sci, 111, 13790, 10.1073/pnas.1404623111 · DOI · Paper page
  32. Sears, 1986, College sophomores in the laboratory. influences of a narrow data base on social psychology’s view of human nature, J Pers Soc Psychol, 51, 515, 10.1037/0022-3514.51.3.515 · DOI
  33. Selzam, 2017, Predicting educational achievement from DNA, Mol Psychiatry, 22, 267, 10.1038/mp.2016.107 · DOI
  34. Smith, 2002, Accurate, robust, and automated longitudinal and cross-sectional brain change analysis, Neuroimage, 17, 479, 10.1006/nimg.2002.1040 · DOI
  35. Sobel, 2007, Identification of causal parameters in randomized studies with mediating variables, J Educ Behav Stat, 33, 230, 10.3102/1076998607307239 · DOI
  36. Stauffer, 2001, The proper sequence for correcting correlation coefficients for range restriction and unreliability, Psychometrika, 66, 63, 10.1007/BF02295732 · DOI
  37. Sudlow, 2015, UK Biobank: an open access resource for identifying the causes of a wide range of complex diseases of middle and old age, PLoS Med, 12, e1001779, 10.1371/journal.pmed.1001779 · DOI · Paper page
  38. Tingley, 2014, Mediation: R package for causal mediation analysis, J Stat Softw, 59, 1, 10.18637/jss.v059.i05 · DOI
  39. Toga, 2005, Genetics of brain structure and intelligence, Annu Rev Neurosci, 28, 1, 10.1146/annurev.neuro.28.061604.135655 · DOI
  40. van Valen, 1974, Brain size and intelligence in man, Am J Phys Anthropol, 40, 417, 10.1002/ajpa.1330400314 · DOI
  41. Wechsler, 1997, WAIS‐III administration and scoring manual
  42. Wray, 2007, Prediction of individual genetic risk to disease from genome-wide association studies, Genome Res, 17, 1520, 10.1101/gr.6665407 · DOI

Cited by

Sorted by citations

Authors
Elliott, M. L., Belsky, D. W., Anderson, K., Corcoran, D. L., Ge, T., Knodt, A., Prinz, J. A., Sugden, K., Williams, B., Ireland, D., Poulton, R., Caspi, A., Holmes, A., Moffitt, T., & Hariri, A. R.
Affiliations
  • Duke University (US)
  • Yale University (US)
  • Broad Institute (US)
  • University of Otago (NZ)
Corresponding author
Elliott, Maxwell L
Venue
Cerebral Cortex
Published
2018-09-12
Volume
29
Issue
8
Pages
3496-3504
ISSN
  • 1047-3211 (ISSN-L)
  • 1460-2199
OpenAlex
W2949631323
Cited by
50
References
13
Citations by year
2026: 4 · 2025: 4 · 2024: 2 · 2023: 8 · 2022: 8 · 2021: 8 · 2020: 8 · 2019: 5 · 2018: 3
FWCI
0.96
Type
journal article
Language
en
Open access
Locations
  • Cerebral Cortex (publishedVersion, other-oa)
  • bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion, cc-by)
  • PubMed Central (publishedVersion)
  • PubMed (publishedVersion)
  • Research Portal (King's College London) (publishedVersion, other-oa)
Funders
  • NZ HRC
  • NZ MBIE
  • National Institute on Aging (R01AG032282, R01AG049789)
  • UK Medical Research Council (MR/P005918/1)
  • National Institutes of Health (R01DA033369, R01DA031579, K99AG054573)
  • National Science Foundation Graduate Research (DGE-1644868)
  • National Institute of Mental Health (K01MH099232)
Topics
  • Social Sciences › Psychology › Experimental and Cognitive Psychology › Cognitive Abilities and Testing
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Genetic Associations and Epidemiology
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Genetics and Physical Performance
Keywords
polygenic risk score, educational attainment, brain size, total brain volume, intelligence, cognitive performance, genetic mediation, neuroimaging genetics, population-based sample, recruitment and retention
MeSH terms
Adolescent, Adult, Aged, Brain, Cognition, Educational Status, Female, United Kingdom, Humans, Intelligence, Magnetic Resonance Imaging, Male, Middle Aged, New Zealand, Organ Size, United States, Multifactorial Inheritance, Genome-Wide Association Study, Young Adult

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review