A Polygenic Score for Higher Educational Attainment is Associated with Larger Brains
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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.
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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)
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Multimodal population brain imaging in the UK Biobank prospective epidemiological study
Miller, K. L., Alfaro-Almagro, F., Bangerter, N. K., Thomas, D. L., Yacoub, E., Xu, J., Bartsch, A. J., Jbabdi, S., Sotiropoulos, S. N., Andersson, J. L. R., et al. (2016) · Nature Neuroscience · doi:10.1038/nn.4393 · Cited by 2,020 · see citing works
Limitations of the Application of Fourfold Table Analysis to Hospital Data
Berkson, J. (1946) · Biometrics Bulletin · doi:10.2307/3002000 · Cited by 1,810 · see citing works
Power and predictive accuracy of polygenic risk scores
Dudbridge, F. (2013) · PLoS Genetics · doi:10.1371/journal.pgen.1003348 · Cited by 1,509 · see citing works
Human Demographic History Impacts Genetic Risk Prediction across Diverse Populations
Martin, A. R., Gignoux, C. R., Walters, R. K., Wojcik, G. L., Neale, B. M., Gravel, S., Daly, M. J., Bustamante, C. D., & Kenny, E. E. (2017) · The American Journal of Human Genetics · doi:10.1016/j.ajhg.2017.03.004 · Cited by 1,391 · see citing works
Wechsler Abbreviated Scale of Intelligence--Second Edition
Wechsler, D. (2018) · PsycTESTS Dataset · doi:10.1037/t15171-000 · Cited by 1,153 · see citing works
Luby, J., Belden, A., Botteron, K., Marrus, N., Harms, M. P., Babb, C., Nishino, T., & Barch, D. (2013) · JAMA Pediatrics · doi:10.1001/jamapediatrics.2013.3139 · Cited by 733 · see citing works
Genetics and intelligence differences: five special findings
Plomin, R., & Deary, I. J. (2015) · Molecular Psychiatry · doi:10.1038/mp.2014.105 · Cited by 702 · see citing works
How Much Does Education Improve Intelligence? A Meta-Analysis
Ritchie, S. J., & Tucker-Drob, E. M. (2018) · Psychological Science · doi:10.1177/0956797618774253 · Cited by 680 · see citing works
Genetic analysis of social-class mobility in five longitudinal studies
Belsky, D. W., Domingue, B. W., Wedow, R., Arseneault, L., Boardman, J. D., Caspi, A., Conley, D., Fletcher, J. M., Freese, J., Herd, P., et al. (2018) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1801238115 · Cited by 315 · see citing works
Self-citationReview of the <i>Wechsler Abbreviated Scale of Intelligence, Second Edition (WASI-II)</i>
McCrimmon, A. W., & Smith, A. D. (2013) · Journal of Psychoeducational Assessment · doi:10.1177/0734282912467756 · Cited by 286 · see citing works
Protective Prevention Effects on the Association of Poverty With Brain Development
Brody, G. H., Gray, J. C., Yu, T., Barton, A. W., Beach, S. R. H., Galván, A., MacKillop, J., Windle, M., Chen, E., Miller, G. E., et al. (2017) · JAMA Pediatrics · doi:10.1001/jamapediatrics.2016.2988 · Cited by 135 · see citing works
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Research works printed but not linked: 4
- Grotzinger AD, Rhemtulla M, Vlaming R, de, Ritchie SJ, Mallard TT, Hill WD, Ip HF, McIntosh AM, Deary IJ, Koellinger PD, et al 2018. Genomic SEM Provides Insights into the Multivariate Genetic Architecture of Complex Traits. bioRxiv. 305029.
- Selzam S, Krapohl E, von Stumm S, O'Reilly PF, Rimfeld K, Kovas Y, Dale PS, Lee JJ, Plomin R. 2017. Predicting educa- tional achievement from DNA. Mol Psychiatry. 22:267-272.
- Tucker-drob EM 2017. Measurement Error Correction of Genome- Wide Polygenic Scores in Prediction Samples. bioRxiv.
- Ware EB, Schmitz LL, Faul JD, Gard A, Mitchell C, Smith JA, Zhao W, Weir D, Kardia SL 2017. Heterogeneity in polygenic scores for common human traits. bioRxiv. 106062.
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Elliott, M. L., Knodt, A. R., Ireland, D., Morris, M. L., Poulton, R., Ramrakha, S., Sison, M. L., Moffitt, T. E., Caspi, A., & Hariri, A. R. (2020) · Psychological Science · doi:10.1177/0956797620916786 · Cited by 714 · see citing works
Self-citationJudd, N., Sauce, B., Wiedenhoeft, J., Tromp, J., Chaarani, B., Schliep, A., van Noort, B., Penttilä, J., Grimmer, Y., Insensee, C., et al. (2020) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2001228117 · Cited by 105 · see citing works
Shared genetic architectures of educational attainment in East Asian and European populations
Chen, T.-T., Kim, J., Lam, M., Chuang, Y.-F., Chiu, Y.-L., Lin, S.-C., Jung, S.-H., Kim, B., Kim, S., Cho, C., et al. (2024) · Nature Human Behaviour · doi:10.1038/s41562-023-01781-9 · Cited by 18 · see citing works
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- 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
- ORCID
- Venue
- Cerebral Cortex
- Publisher
- Oxford University Press (OUP)
- Published
- 2018-09-12
- Volume
- 29
- Issue
- 8
- Pages
- 3496-3504
- ISSN
- 1047-3211 (ISSN-L)
- 1460-2199
- PMC
- PMC6645179
- OpenAlex
- W2949631323
- Cited by
- 50
- References
- 11
- 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
- hybrid
- doi.org
- Licence
- 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
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