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Multivariable two-sample Mendelian randomization estimates of the effects of intelligence and education on health
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Intelligence and education are predictive of better physical and mental health, socioeconomic position (SEP), and longevity. However, these associations are insufficient to prove that intelligence and/or education cause these outcomes. Intelligence and education are phenotypically and genetically correlated, which makes it difficult to elucidate causal relationships. We used univariate and multivariable Mendelian randomization to estimate the total and direct effects of intelligence and educational attainment on mental and physical health, measures of socioeconomic position, and longevity. Both intelligence and education had beneficial total effects. Higher intelligence had positive direct effects on income and alcohol consumption, and negative direct effects on moderate and vigorous physical activity. Higher educational attainment had positive direct effects on income, alcohol consumption, and vigorous physical activity, and negative direct effects on smoking, BMI and sedentary behaviour. If the Mendelian randomization assumptions hold, these findings suggest that both intelligence and education affect health.
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“Triangulation of evidence from multiple methodologies — using different methodologies that are subject to different sources and directions of potential bias — can help to identify bias in MR studies.6,7,155 Alignment of results across these different methodologies can improve confidence in an initial causal interpretation.”
From Mendelian randomization (2022) · cites this paper as [155]
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Cites
Specification Tests in Econometrics
Hausman, J. A. (1978) · Econometrica · doi:10.2307/1913827 · Cited by 18,820
Bowden, J., Davey Smith, G., Haycock, P. C., & Burgess, S. (2016) · Genetic Epidemiology · doi:10.1002/gepi.21965 · Cited by 8,922
The MR-Base platform supports systematic causal inference across the human phenome
Hemani, G., Zheng, J., Elsworth, B., Wade, K. H., Haberland, V., Baird, D., Laurin, C., Burgess, S., Bowden, J., Langdon, R., et al. (2018) · eLife · doi:10.7554/elife.34408 · Cited by 7,959
Davey Smith, G., & Ebrahim, S. (2003) · International Journal of Epidemiology · doi:10.1093/ije/dyg070 · Cited by 5,882
Mendelian randomization: genetic anchors for causal inference in epidemiological studies
Davey Smith, G., & Hemani, G. (2014) · Human Molecular Genetics · doi:10.1093/hmg/ddu328 · Cited by 4,677
Reading Mendelian randomisation studies: a guide, glossary, and checklist for clinicians
Davies, N. M., Holmes, M. V., & Davey Smith, G. (2018) · BMJ · doi:10.1136/bmj.k601 · Cited by 4,404
Self-citationFry, A., Littlejohns, T. J., Sudlow, C., Doherty, N., Adamska, L., Sprosen, T., Collins, R., & Allen, N. E. (2017) · American Journal of Epidemiology · doi:10.1093/aje/kwx246 · Cited by 3,979
Robust inference in summary data Mendelian randomization via the zero modal pleiotropy assumption
Hartwig, F. P., Davey Smith, G., & Bowden, J. (2017) · International Journal of Epidemiology · doi:10.1093/ije/dyx102 · Cited by 3,485
The Estimation of Economic Relationships using Instrumental Variables
Sargan, J. D. (1958) · Econometrica · doi:10.2307/1907619 · Cited by 3,377
23andMe Research Team, Lee, J. J., COGENT (Cognitive Genomics Consortium), Social Science Genetic Association Consortium, Wedow, R., Okbay, A., Kong, E., Maghzian, O., Zacher, M., Nguyen-Viet, T. A., et al. (2018) · Nature Genetics · doi:10.1038/s41588-018-0147-3 · Cited by 2,470
Intelligence and educational achievement
Deary, I. J., Strand, S., Smith, P., & Fernandes, C. (2007) · Intelligence · doi:10.1016/j.intell.2006.02.001 · Cited by 1,723
Self-citationBias due to participant overlap in two‐sample Mendelian randomization
Burgess, S., Davies, N. M., & Thompson, S. G. (2016) · Genetic Epidemiology · doi:10.1002/gepi.21998 · Cited by 1,624
Self-citationBowden, J., Fabiola Del Greco, M., Minelli, C., Smith, G. D., Sheehan, N. A., & Thompson, J. R. (2016) · International Journal of Epidemiology · doi:10.1093/ije/dyw220 · Cited by 1,555
Education and Health: Evaluating Theories and Evidence
Cutler, D., & Lleras-Muney, A. (2006) · doi:10.3386/w12352 · Cited by 1,507
Pierce, B. L., & Burgess, S. (2013) · American Journal of Epidemiology · doi:10.1093/aje/kwt084 · Cited by 1,389
Genome-wide association study identifies 74 loci associated with educational attainment
Okbay, A., Beauchamp, J. P., Fontana, M. A., Lee, J. J., Pers, T. H., Rietveld, C. A., Turley, P., Chen, G.-B., Emilsson, V., Meddens, S. F. W., et al. (2016) · Nature · doi:10.1038/nature17671 · Cited by 1,297
Self-citationSanderson, E., Davey Smith, G., Windmeijer, F., & Bowden, J. (2019) · International Journal of Epidemiology · doi:10.1093/ije/dyy262 · Cited by 1,085
Self-citationThe nature of nurture: Effects of parental genotypes
Kong, A., Thorleifsson, G., Frigge, M. L., Vilhjalmsson, B. J., Young, A. I., Thorgeirsson, T. E., Benonisdottir, S., Oddsson, A., Halldorsson, B. V., Masson, G., et al. (2018) · Science · doi:10.1126/science.aan6877 · Cited by 949
Burgess, S., Dudbridge, F., & Thompson, S. G. (2015) · American Journal of Epidemiology · doi:10.1093/aje/kwv017 · Cited by 930
Deary, I. J., Whiteman, M. C., Starr, J. M., Whalley, L. J., & Fox, H. C. (2004) · Journal of Personality and Social Psychology · doi:10.1037/0022-3514.86.1.130 · Cited by 809
Self-citationA weak instrument F -test in linear IV models with multiple endogenous variables
Sanderson, E. C. M., & Windmeijer, F. A. G. (2015) · Journal of Econometrics · doi:10.1016/j.jeconom.2015.06.004 · Cited by 752
Self-citationGenetics and intelligence differences: five special findings
Plomin, R., & Deary, I. J. (2015) · Molecular Psychiatry · doi:10.1038/mp.2014.105 · Cited by 702
Self-citationHow 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
Education and occupational social class: which is the more important indicator of mortality risk?
Smith, G. D., Hart, C., Hole, D. J., MacKinnon, P. L., Gillis, C. R., Watt, G. C. M., Blane, D., & Hawthorne, V. M. (1998) · Journal of Epidemiology & Community Health · doi:10.1136/jech.52.3.153 · Cited by 679
Intelligence Predicts Health and Longevity, but Why?
Gottfredson, L. S., & Deary, I. J. (2004) · Current Directions in Psychological Science · doi:10.1111/j.0963-7214.2004.01301001.x · Cited by 623
Self-citationHoward, D. M., Adams, M. J., Shirali, M., Clarke, T.-K., Marioni, R. E., Davies, G., Coleman, J. R. I., Alloza, C., Shen, X., Barbu, M. C., et al. (2018) · Nature Communications · doi:10.1038/s41467-018-03819-3 · Cited by 612
Self-citationThe Effect of Education on Adult Mortality and Health: Evidence from Britain
Clark, D., & Royer, H. (2013) · American Economic Review · doi:10.1257/aer.103.6.2087 · Cited by 523
Premorbid (early life) IQ and Later Mortality Risk: Systematic Review
Batty, G. D., Deary, I. J., & Gottfredson, L. S. (2006) · Annals of Epidemiology · doi:10.1016/j.annepidem.2006.07.010 · Cited by 491
Self-citationSniekers, S., Stringer, S., Watanabe, K., Jansen, P. R., Coleman, J. R. I., Krapohl, E., Taskesen, E., Hammerschlag, A. R., Okbay, A., Zabaneh, D., et al. (2017) · Nature Genetics · doi:10.1038/ng.3869 · Cited by 490
Deary, I. J. (2012) · Annual Review of Psychology · doi:10.1146/annurev-psych-120710-100353 · Cited by 467
Self-citationSplit-Sample Instrumental Variables Estimates of the Return to Schooling
Angrist, J. D., & Krueger, A. B. (1995) · Journal of Business and Economic Statistics · doi:10.1080/07350015.1995.10524597 · Cited by 417
Brumpton, B., Sanderson, E., Heilbron, K., Hartwig, F. P., Harrison, S., Vie, G. Å., Cho, Y., Howe, L. D., Hughes, A., Boomsma, D. I., et al. (2020) · Nature Communications · doi:10.1038/s41467-020-17117-4 · Cited by 402
Self-citationModifiable pathways in Alzheimer’s disease: Mendelian randomisation analysis
Larsson, S. C., Traylor, M., Malik, R., Dichgans, M., Burgess, S., & Markus, H. S. (2017) · BMJ · doi:10.1136/bmj.j5375 · Cited by 382
Intelligence in youth and all-cause-mortality: systematic review with meta-analysis
Calvin, C. M., Deary, I. J., Fenton, C., Roberts, B. A., Der, G., Leckenby, N., & Batty, G. D. (2010) · International Journal of Epidemiology · doi:10.1093/ije/dyq190 · Cited by 353
Self-citationTwo-Sample Instrumental Variables Estimators
Inoue, A., & Solon, G. (2010) · The Review of Economics and Statistics · doi:10.1162/rest_a_00011 · Cited by 348
Sniekers, S., Stringer, S., Watanabe, K., Jansen, P. R., Coleman, J. R. I., Krapohl, E. M. L., Taskesen, E., Hammerschlag, A. R., Okbay, A., Zabaneh, D., et al. (2017) · Nature Genetics · doi:10.1038/ng1017-1558c · Cited by 333
Apparent latent structure within the UK Biobank sample has implications for epidemiological analysis
Haworth, S., Mitchell, R., Corbin, L., Wade, K. H., Dudding, T., Budu-Aggrey, A., Carslake, D., Hemani, G., Paternoster, L., Smith, G. D., et al. (2019) · Nature Communications · doi:10.1038/s41467-018-08219-1 · Cited by 314
Self-citationHill, W. D., Marioni, R. E., Maghzian, O., Ritchie, S. J., Hagenaars, S. P., McIntosh, A. M., Gale, C. R., Davies, G., & Deary, I. J. (2019) · Molecular Psychiatry · doi:10.1038/s41380-017-0001-5 · Cited by 311
Self-citationThe causal effects of education on health outcomes in the UK Biobank
Davies, N. M., Dickson, M., Davey Smith, G., van den Berg, G. J., & Windmeijer, F. (2018) · Nature Human Behaviour · doi:10.1038/s41562-017-0279-y · Cited by 302
Self-citationIntelligence and education: causal perceptions drive analytic processes and therefore conclusions
Deary, I. J., & Johnson, W. (2010) · International Journal of Epidemiology · doi:10.1093/ije/dyq072 · Cited by 259
Self-citationEducation and coronary heart disease: mendelian randomisation study
Tillmann, T., Vaucher, J., Okbay, A., Pikhart, H., Peasey, A., Kubinova, R., Pajak, A., Tamosiunas, A., Malyutina, S., Hartwig, F. P., et al. (2017) · BMJ · doi:10.1136/bmj.j3542 · Cited by 225
Taylor, A. E., Jones, H. J., Sallis, H., Euesden, J., Stergiakouli, E., Davies, N. M., Zammit, S., Lawlor, D. A., Munafò, M. R., Davey Smith, G., et al. (2018) · International Journal of Epidemiology · doi:10.1093/ije/dyy060 · Cited by 192
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Smithers, L. G., Sawyer, A. C. P., Chittleborough, C. R., Davies, N. M., Davey Smith, G., & Lynch, J. W. (2018) · Nature Human Behaviour · doi:10.1038/s41562-018-0461-x · Cited by 175
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Hill, W. D., Arslan, R. C., Xia, C., Luciano, M., Amador, C., Navarro, P., Hayward, C., Nagy, R., Porteous, D. J., McIntosh, A. M., et al. (2018) · Molecular Psychiatry · doi:10.1038/s41380-017-0005-1 · Cited by 158
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Galama, T. J., Lleras‐Muney, A., & van Kippersluis, H. (2018) · Oxford University Press eBooks · doi:10.1093/acrefore/9780190625979.013.7 · Cited by 140
Davey Smith, G. (2011) · Genes & Nutrition · doi:10.1007/s12263-010-0181-y · Cited by 134
Galama, T. J., Lleras‐Muney, A., & van Kippersluis, H. (2018) · National Bureau of Economic Research · doi:10.3386/w24225 · Cited by 128
Lawlor, D. A., Richmond, R. C., Warrington, N. M., McMahon, G., Smith, G. D., Bowden, J., & Evans, D. M. (2017) · Wellcome Open Research · doi:10.12688/wellcomeopenres.10567.1 · Cited by 122
DiPrete, T. A., Burik, C. A. P., & Koellinger, P. (2018) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1707388115 · Cited by 100
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Brumpton, B., Sanderson, E., Hartwig, F., Harrison, S., Vie, G. Å., Cho, Y., Howe, L., Hughes, A. M., Boomsma, D., Havdahl, A., et al. (2019) · bioRxiv · doi:10.1101/602516 · Cited by 75
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Metadata
- Authors
- Davies, N. M., Hill, W. D., Anderson, E. L., Sanderson, E., Deary, I. J., & Davey Smith, G.
- Affiliations
- University of Bristol (GB)
- University of Edinburgh (GB)
- Medical Research Council Integrative Epidemiology Unit, University of Bristol, Bristol, United Kingdom
- Corresponding author
- Davies, Neil Martin
- ORCID
- Venue
- eLife
- Publisher
- eLife Sciences Publications, Ltd
- Published
- 2019-09-17
- Volume
- 8
- ISSN
- 2050-084X (ISSN-L)
- PMC
- PMC6748790
- OpenAlex
- W2974720922
- Cited by
- 135
- References
- 66
- Citations by year
- 2026: 6 · 2025: 19 · 2024: 26 · 2023: 33 · 2022: 18 · 2021: 17 · 2020: 12 · 2019: 4
- FWCI
- 7.68
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- eLife (publishedVersion, cc-by)
- Europe PMC (PubMed Central) (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Edinburgh Research Explorer (University of Edinburgh) (submittedVersion, cc-by)
- Bristol Research (University of Bristol) (submittedVersion, cc-by)
- Funders
- Economic and Social Research Council (ES/N000757/1)
- Medical Research Council (MC_UU_12013/1, MC_UU_12013/9, MC_UU_00011/1, MR/K026992/1)
- Age UK (Disconnected Mind Grant)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Genetic Associations and Epidemiology
- Social Sciences › Psychology › Experimental and Cognitive Psychology › Cognitive Abilities and Testing
- Social Sciences › Sociology and Political Science › Intergenerational and Educational Inequality Studies
- Keywords
- intelligence, educational attainment, mental health, physical health, socioeconomic position, longevity, income, alcohol consumption, physical activity, smoking, body mass index, sedentary behavior, genetic correlation
- MeSH terms
- Adult, Aged, Education, Female, United Kingdom, Health, Humans, Intelligence, Health Knowledge, Attitudes, Practice, Male, Middle Aged, Biostatistics
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