Psychological Science

A previous genome-wide association study (GWAS) of more than 100,000 individuals identified molecular-genetic predictors of educational attainment. We undertook in-depth life-course investigation of the polygenic score derived from this GWAS using the four-decade Dunedin Study (N = 918). There were five main findings. First, polygenic scores predicted adult economic outcomes even after accounting for educational attainments. Second, genes and environments were correlated: Children with higher polygenic scores were born into better-off homes. Third, children’s polygenic scores predicted their adult outcomes even when analyses accounted for their social-class origins; social-mobility analysis showed that children with higher polygenic scores were more upwardly mobile than children with lower scores. Fourth, polygenic scores predicted behavior across the life course, from early acquisition of speech and reading skills through geographic mobility and mate choice and on to financial planning for retirement. Fifth, polygenic-score associations were mediated by psychological characteristics, including intelligence, self-control, and interpersonal skill. Effect sizes were small. Factors connecting DNA sequence with life outcomes may provide targets for interventions to promote population-wide positive development.

1000 Genomes Project. (2016). 1000 Genomes project data. Retrieved from http://www.1000genomes.org/ Google Scholar
Baron R. M., Kenny D. A. (1986). The moderator–mediator variable distinction in social psychological research: Conceptual, strategic, and statistical considerations. Journal of Personality and Social Psychology, 51, 11731182. doi:10.1037/0022-3514.51.6.1173 Google Scholar CrossRef, Medline
Belsky D. W., Moffitt T. E., Caspi A. (2013). Genetics in population health science: Strategies and opportunities. American Journal of Public Health, 103(Suppl. 1), S73S83. doi:10.2105/AJPH.2012.301139 Google Scholar CrossRef, Medline
Breen R., Jonsson J. O. (2005). Inequality of opportunity in comparative perspective: Recent research on educational attainment and social mobility. Annual Review of Sociology, 31, 223243. doi:10.1146/annurev.soc.31.041304.122232 Google Scholar CrossRef
Breen R., Salazar L. (2011). Educational assortative mating and earnings inequality in the United States. American Journal of Sociology, 117, 808843. doi:10.1086/661778 Google Scholar CrossRef
Case A., Fertig A., Paxson C. (2005). The lasting impact of childhood health and circumstance. Journal of Health Economics, 24, 365389. doi:10.1016/j.jhealeco.2004.09.008 Google Scholar CrossRef, Medline
Case A., Paxson C. (2008). Stature and status: Height, ability, and labor market outcomes. Journal of Political Economy, 116, 499532. doi:10.1086/589524 Google Scholar CrossRef, Medline
Chabris C. F., Lee J. J., Cesarini D., Benjamin D. J., Laibson D. I. (2015). The fourth law of behavior genetics. Current Directions in Psychological Science, 24, 304312. doi:10.1177/0963721415580430 Google Scholar Link
Conley D., Domingue B., Cesarini D., Dawes C. T., Rietveld C. A., Boardman J. (2015). Is the effect of parental education on offspring biased or moderated by genotype? Sociological Science, 2, 82105. doi:10.15195/v2.a6 Google Scholar CrossRef
de Zeeuw E. L., van Beijsterveldt C. E. M., Glasner T. J., Bartels M., Ehli E. A., Davies G. E., . . . Boomsma D. I. (2014). Polygenic scores associated with educational attainment in adults predict educational achievement and ADHD symptoms in children. American Journal of Medical Genetics Part B: Neuropsychiatric Genetics, 165B, 510520. doi:10.1002/ajmg.b.32254 Google Scholar CrossRef, Medline
Domingue B. W., Belsky D. W., Conley D., Harris K. M., Boardman J. D. (2015). Polygenic influence on educational attainment. AERA Open, 1(3), 113. doi:10.1177/2332858415599972 Google Scholar Link
Dudbridge F. (2013). Power and predictive accuracy of polygenic risk scores. PLoS Genetics, 9(3), Article e1003348. doi:10.1371/journal.pgen.1003348 Google Scholar CrossRef, Medline
Dunn L. (1965). The Peabody Picture Vocabulary Test. Minneapolis, MN: American Guidance Service. Google Scholar
Euesden J., Lewis C. M., O’Reilly P. F. (2015). PRSice: Polygenic Risk Score software. Bioinformatics, 31, 14661468. doi:10.1093/bioinformatics/btu848 Google Scholar CrossRef, Medline
Firkowska A. N., Ostrowska A., Sokolowska M., Stein Z., Susser M., Wald I. (1978). Cognitive development and social policy. Science, 200, 13571362. doi:10.1126/science.663616 Google Scholar CrossRef, Medline
Heckman J. J. (2006). Skill formation and the economics of investing in disadvantaged children. Science, 312, 19001902. doi:10.1126/science.1128898 Google Scholar CrossRef, Medline
Henig R. M. (2015, December 11). Are there genes for intelligence—And is it racist to ask? Retrieved from http://news.nationalgeographic.com/2015/12/151211-genetics-intelligence-racism-science/ Google Scholar
Howie B. N., Donnelly P., Marchini J. (2009). A flexible and accurate genotype imputation method for the next generation of genome-wide association studies. PLoS Genetics 5(6), Article e1000529. doi:10.1371/journal.pgen.1000529 Google Scholar CrossRef, Medline
Khoury M. J., Evans J. P. (2015). A public health perspective on a national precision medicine cohort: Balancing long-term knowledge generation with early health benefit. The Journal of the American Medical Association, 313, 21172118. doi:10.1001/jama.2015.3382 Google Scholar CrossRef, Medline
Krapohl E., Plomin R. (2016). Genetic link between family socioeconomic status and children’s educational achievement estimated from genome-wide SNPs. Molecular Psychiatry, 21, 437443. doi:10.1038/mp.2015.2 Google Scholar CrossRef, Medline
Krapohl E., Rimfeld K., Shakeshaft N. G., Trzaskowski M., McMillan A., Pingault J.-B., . . . Plomin R. (2014). The high heritability of educational achievement reflects many genetically influenced traits, not just intelligence. Proceedings of the National Academy of Sciences, USA, 111, 1527315278. doi:10.1073/pnas.1408777111 Google Scholar CrossRef, Medline
Lander E. S. (2015). Cutting the Gordian helix—regulating genomic testing in the era of precision medicine. The New England Journal of Medicine, 372, 11851186. doi:10.1056/NEJMp1501964 Google Scholar CrossRef, Medline
Lezak D. M., Howieson D. B., Loring D. W., Hannay H. J., Fischer J. S. (2004). Neuropsychological assessment (4th ed.). New York, NY: Oxford University Press. Google Scholar
Moffitt T. E., Arseneault L., Belsky D., Dickson N., Hancox R. J., Harrington H., . . . Caspi A. (2011). A gradient of childhood self-control predicts health, wealth, and public safety. Proceedings of the National Academy of Sciences, USA, 108, 26932698. doi:10.1073/pnas.1010076108 Google Scholar CrossRef, Medline
Pavot W., Diener E. (1993). Review of the Satisfaction With Life Scale. Psychological Assessment, 5, 164172. Google Scholar CrossRef
Plomin R. (2012). Genetics: How intelligence changes with age. Nature, 482, 165166. doi:10.1038/482165a Google Scholar CrossRef, Medline
Plomin R., Bergeman C. S. (1991). The nature of nurture: Genetic influence on “environmental” measures. Behavioral & Brain Sciences, 14, 414427. doi:10.1017/S0140525X00070588 Google Scholar CrossRef
Poulton R., Moffitt T. E., Silva P. A. (2015). The Dunedin Multidisciplinary Health and Development Study: Overview of the first 40 years, with an eye to the future. Social Psychiatry & Psychiatric Epidemiology, 50, 679693. doi:10.1007/s00127-015-1048-8 Google Scholar CrossRef, Medline
Preacher K. J., Hayes A. F. (2008). Asymptotic and resampling strategies for assessing and comparing indirect effects in multiple mediator models. Behavior Research Methods, 40, 879891. doi:10.3758/BRM.40.3.879 Google Scholar CrossRef, Medline
Preacher K. J., Kelley K. (2011). Effect size measures for mediation models: Quantitative strategies for communicating indirect effects. Psychological Methods, 16, 93115. doi:10.1037/A0022658 Google Scholar CrossRef, Medline
Price A. L., Patterson N. J., Plenge R. M., Weinblatt M. E., Shadick N. A., Reich D. (2006). Principal components analysis corrects for stratification in genome-wide association studies. Nature Genetics, 38, 904909. doi:10.1038/ng1847 Google Scholar CrossRef, Medline
Price A. L., Zaitlen N. A., Reich D., Patterson N. (2010). New approaches to population stratification in genome-wide association studies. Nature Reviews Genetics, 11, 459463. doi:10.1038/nrg2813 Google Scholar CrossRef, Medline
Rietveld C. A., Conley D., Eriksson N., Esko T., Medland S. E., Vinkhuyzen A. A. E., . . . Social Science Genetics Association Consortium. (2014). Replicability and robustness of genome-wide-association studies for behavioral traits. Psychological Science, 25, 19751986. doi:10.1177/0956797614545132 Google Scholar Link
Rietveld C. A., Esko T., Davies G., Pers T. H., Turley P., Benyamin B., . . . Koellinger P. D. (2014). Common genetic variants associated with cognitive performance identified using the proxy-phenotype method. Proceedings of the National Academy of Sciences, USA, 111, 1379013794. doi:10.1073/pnas.1404623111 Google Scholar CrossRef, Medline
Rietveld C. A., Medland S. E., Derringer J., Yang J., Esko T., Martin N. W., . . . Koellinger P. D. (2013). GWAS of 126,559 individuals identifies genetic variants associated with educational attainment. Science, 340, 14671471. doi:10.1126/science.1235488 Google Scholar CrossRef, Medline
Roberts N. J., Vogelstein J. T., Parmigiani G., Kinzler K. W., Vogelstein B., Velculescu V. E. (2012). The predictive capacity of personal genome sequencing. Science Translational Medicine, 4(133), Article 133ra58. doi:10.1126/scitranslmed.3003380 Google Scholar CrossRef
Schwartz C. R. (2013). Trends and variation in assortative mating: Causes and consequences. Annual Review of Sociology, 39, 451470. doi:10.1146/annurev-soc-071312-145544 Google Scholar CrossRef
Scottish Council for Research in Education. (1976). Burt Word Reading Test (rev. ed.). London, England: Hodder & Stoughton. Google Scholar
Sherry S. S., Ward M. M., Kholodov M., Baker J., Phan L., Smigielski E. E., Sirotkin K. (2001). dbSNP: The NCBI database of genetic variation. Nucleic Acids Research, 29, 308311. Google Scholar CrossRef, Medline
Singer J. D., Willett J. B. (2003). Applied longitudinal data analysis. New York, NY: Oxford University Press. Google Scholar CrossRef
Talbot M. (2015, January 12). The talking cure. The New Yorker. Retrieved from http://www.newyorker.com/magazine/2015/01/12/talking-cure Google Scholar
Terman L. M., Merrill M. A. (1960). Stanford-Binet Intelligence Scale: Manual for the third revision. Oxford, England: Houghton Mifflin. Google Scholar
Tucker-Drob E. M., Bates T. C. (2016). Large cross-national differences in gene × socioeconomic status interaction on intelligence. Psychological Science, 27, 138149. doi:10.1177/0956797615612727 Google Scholar Link
Ward M. E., McMahon G., St Pourcain B., Evans D. M., Rietveld C. A., Benjamin D. J., . . . Timpson N. J. (2014). Genetic variation associated with differential educational attainment in adults has anticipated associations with school performance in children. PLoS ONE, 9(7), Article e100248. doi:10.1371/journal.pone.0100248 Google Scholar CrossRef
Wechsler D. (1974). Manual for the Wechsler Intelligence Scale for Children–Revised. New York, NY: Psychological Corporation. Google Scholar
Wikipedia. (2014). Overseas experience. Retrieved from http://en.wikipedia.org/w/index.php?title=Overseas_experience&oldid=633298303 Google Scholar
Wikipedia. (2015). List of countries by income equality. Retrieved from https://en.wikipedia.org/w/index.php?title=List_of_countries_by_income_equality&oldid=686250962 Google Scholar
Wood A. R., Esko T., Yang J., Vedantam S., Pers T. H., Gustafsson S., . . . Frayling T. M. (2014). Defining the role of common variation in the genomic and biological architecture of adult human height. Nature Genetics, 46, 11731186. doi:10.1038/ng.3097 Google Scholar CrossRef, Medline

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Vol 27, Issue 7, 2016

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The Genetics of Success

Daniel W. Belsky1, 2, Terrie E. Moffitt3, 4, 5, 6, David L. Corcoran5, Benjamin Domingue7, HonaLee Harrington3, Sean Hogan8, Renate Houts3, Sandhya Ramrakha8, Karen Sugden3, Benjamin S. Williams3, Richie Poulton8, Avshalom Caspi3, 4, 5, 6Department of Medicine, Duke University School of MedicineSocial Science Research Institute, Duke UniversityDepartment of Psychology & Neuroscience, Duke UniversityDepartment of Psychiatry and Behavioral Sciences, Duke University School of MedicineCenter for Genomic and Computational Biology, Duke UniversityMRC Social, Genetic & Developmental Psychiatry Research Centre, Institute of Psychiatry, Psychology & Neuroscience, King’s College LondonGraduate School of Education, Stanford UniversityDunedin Multidisciplinary Health & Development Research Unit, Department of Psychology, University of Otago


Psychological Science

Vol 27, Issue 7, pp. 957 - 972

First published date: June-01-2016


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Daniel W. Belsky, Terrie E. Moffitt, David L. Corcoran, Benjamin Domingue, HonaLee Harrington, Sean Hogan, Renate Houts, Sandhya Ramrakha, Karen Sugden, Benjamin S. Williams, Richie Poulton, Avshalom Caspi
Psychological Science 2016 27:7, 957-972

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