Here we conducted a large-scale genetic association analysis of educational attainment in a sample of approximately 1.1 million individuals and identify 1,271 independent genome-wide-significant SNPs. For the SNPs taken together, we found evidence of heterogeneous effects across environments. The SNPs implicate genes involved in brain-development processes and neuron-to-neuron communication. In a separate analysis of the X chromosome, we identify 10 independent genome-wide-significant SNPs and estimate a SNP heritability of around 0.3% in both men and women, consistent with partial dosage compensation. A joint (multi-phenotype) analysis of educational attainment and three related cognitive phenotypes generates polygenic scores that explain 11–13% of the variance in educational attainment and 7–10% of the variance in cognitive performance. This prediction accuracy substantially increases the utility of polygenic scores as tools in research.
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Branigan, A. R., McCallum, K. J. & Freese, J. Variation in the heritability of educational attainment: an international meta-analysis. Soc. Forces 92, 109–140 (2013).
Conti, G., Heckman, J. & Urzua, S. The education–health gradient. Am. Econ. Rev. 100, 234–238 (2010).
Cutler, D. M. & Lleras-Muney, A. in Making Americans Healthier: Social and Economic Policy as Health Policy (eds House, J. et al.) (Russell Sage Foundation, New York, 2008).
Rietveld, C. A. et al. GWAS of 126,559 individuals identifies genetic variants associated with educational attainment. Science 340, 1467–1471 (2013).
Pickrell, J. K. et al. Detection and interpretation of shared genetic influences on 42 human traits. Nat. Genet. 48, 709–717 (2016).
Belsky, D. W. et al. The genetics of success: how single-nucleotide polymorphisms associated with educational attainment relate to life-course development. Psychol. Sci. 27, 957–972 (2016).
Domingue, B. W., Belsky, D. W., Conley, D., Harris, K. M. & Boardman, J. D. Polygenic influence on educational attainment: new evidence from The National Longitudinal Study of Adolescent to Adult Health. AERA Open 1, 1–13 (2015).
Marioni, R. E. et al. Genetic variants linked to education predict longevity. Proc. Natl Acad. Sci. USA 113, 13366–13371 (2016).
Anttila, A. V. et al. Analysis of shared heritability in common disorders of the brain. Science 360, eaap8757 (2018).
Okbay, A. et al. Genome-wide association study identifies 74 loci associated with educational attainment. Nature 533, 539–542 (2016).
Turley, P. et al. Multi-trait analysis of genome-wide association summary statistics using MTAG. Nat. Genet. 50, 229–237 (2018).
The 1000 Genomes Project Consortium. A global reference for human genetic variation. Nature 526, 68–74 (2015).
Bulik-Sullivan, B. K. et al. LD score regression distinguishes confounding from polygenicity in genome-wide association studies. Nat. Genet. 47, 291–295 (2015).
Wu, Y., Zheng, Z., Visscher, P. M. & Yang, J. Quantifying the mapping precision of genome-wide association studies using whole-genome sequencing data. Genome Biol. 18, 86 (2017).
Yang, J. et al. Conditional and joint multiple-SNP analysis of GWAS summary statistics identifies additional variants influencing complex traits. Nat. Genet. 44, 369–375 (2012).
Kong, A. et al. The nature of nurture: effects of parental genotypes. Science 359, 424–428 (2018).
de Vlaming, R. et al. Meta-GWAS accuracy and power (MetaGAP) calculator shows that hiding heritability is partially due to imperfect genetic correlations across studies. PLoS Genet. 13, e1006495 (2017).
Tropf, F. C. et al. Hidden heritability due to heterogeneity across seven populations. Nat. Hum. Behav. 1, 757–765 (2017).
Johnson, W., Carothers, A. & Deary, I. J. Sex differences in variability in general intelligence: a new look at the old question. Perspect. Psychol. Sci. 3, 518–531 (2008).
Pers, T. H. et al. Biological interpretation of genome-wide association studies using predicted gene functions. Nat. Commun. 6, 5890 (2015).
Azevedo, F. A. C. et al. Equal numbers of neuronal and nonneuronal cells make the human brain an isometrically scaled-up primate brain. J. Comp. Neurol. 513, 532–541 (2009).
Finucane, H. K. et al. Partitioning heritability by functional annotation using genome-wide association summary statistics. Nat. Genet. 47, 1228–1235 (2015).
Reed, T. E. & Jensen, A. R. Arm nerve conduction velocity (NCV), brain NCV, reaction time, and intelligence. Intelligence 15, 33–47 (1991).
Chen, W., McDonnell, S. K., Thibodeau, S. N., Tillmans, L. S. & Schaid, D. J. Incorporating functional annotations for fine-mapping causal variants in a Bayesian framework using summary statistics. Genetics 204, 933–958 (2016).
Wang, G. et al. CaV3.2 calcium channels control NMDA receptor-mediated transmission: a new mechanism for absence epilepsy. Genes Dev. 29, 1535–1551 (2015).
Vilhjálmsson, B. J. et al. Modeling linkage disequilibrium increases accuracy of polygenicrisk scores. Am. J. Hum. Genet. 97, 576–592 (2015).
Martin, A. R. et al. Human demographic history impacts genetic risk prediction across diverse populations. Am. J. Hum. Genet. 100, 635–649 (2017).
Scutari, M., Mackay, I. & Balding, D. Using genetic distance to infer the accuracy of genomic prediction. PLoS Genet. 12, e1006288 (2016).
Trampush, J. W. et al. GWAS meta-analysis reveals novel loci and genetic correlates for general cognitive function: a report from the COGENT consortium. Mol. Psychiatry 22, 336–345 (2017).
Davies, G. et al. Ninety-nine independent genetic loci influencing general cognitive function include genes associated with brain health and structure (n = 280,360). https://doi.org/10.1101/176511 (2017).
Sniekers, S. et al. Genome-wide association meta-analysis of 78,308 individuals identifies new loci and genes influencing human intelligence. Nat. Genet. 49, 1107–1112 (2017).
Savage, J. E. et al. GWAS meta-analysis (n=279,930) identifies new genes and functional links to intelligence. https://doi.org/10.1101/184853 (2017).
Schmitz, L. L. & Conley, D. The effect of Vietnam-era conscription and genetic potential for educational attainment on schooling outcomes. Econ. Educ. Rev. 61, 85–97 (2017).
Heath, A. C. et al. Education policy and the heritability of educational attainment. Nature 314, 734–736 (1985).
Kang, H. J. et al. Spatio-temporal transcriptome of the human brain. Nature 478, 483–489 (2011).
Willer, C. J., Li, Y. & Abecasis, G. R. METAL: fast and efficient meta-analysis of genomewide association scans. Bioinformatics 26, 2190–2191 (2010).
Chang, C. C. et al. Second-generation PLINK: rising to the challenge of larger and richer datasets. Gigascience 4, 1–16 (2015).
Okbay, A. et al. Genetic variants associated with subjective well-being, depressive symptoms, and neuroticism identified through genome-wide analyses. Nat. Genet. 48, 624–633 (2016).
Cochran, W. G. The combination of estimates from different experiments. Biometrics 10, 101–129 (1954).
Bulik-Sullivan, B. et al. An atlas of genetic correlations across human diseases and traits. Nat. Genet. 47, 1236–1241 (2015).
Cameron, A. C. & Miller, D. Robust inference with dyadic data. Winter North American Meetings of the Econometric Society, Boston, January 5, 2015.
The 1000 Genomes Project Consortium. An integrated map of genetic variation from 1,092 human genomes. Nature 491, 56–65 (2012).
Fehrmann, R. S. N. et al. Gene expression analysis identifies global gene dosage sensitivity in cancer. Nat. Genet. 47, 115–125 (2015).
de Leeuw, C. A. et al. MAGMA: generalized gene-set analysis of GWAS data. PLoS Comput. Biol. 11, e1004219 (2015).
Liu, J. Z. et al. A versatile gene-based test for genome-wide association studies. Am. J. Hum. Genet. 87, 139–145 (2010).
Mi, H., Muruganujan, A., Casagrande, J. T. & Thomas, P. D. Large-scale gene function analysis with the PANTHER classification system. Nat. Protoc. 8, 1551–1566 (2013).
Pickrell, J. K. Joint analysis of functional genomic data and genome-wide association studies of 18 human traits. Am. J. Hum. Genet. 94, 559–573 (2014).
Chen, W. et al. Fine mapping causal variants with an approximate Bayesian method using marginal test statistics. Genetics 200, 719–736 (2015).
Henmon, V. A. C. & Nelson, M. J. Henmon–Nelson Tests of Mental Ability, High School Examination—Grades 7 to 12—Forms A, B, and C. Teacher’s Manual. (Houghton-Mifflin, Boston, 1946).
This research was carried out under the auspices of the Social Science Genetic Association Consortium (SSGAC). The research has also been conducted using the UK Biobank Resource under application numbers 11425 and 12512. We acknowledge the Swedish Twin Registry for access to data. The Swedish Twin Registry is managed by the Karolinska Institutet and receives funding through the Swedish Research Council under the grant number 2017-00641. This study was supported by funding from the Ragnar Söderberg Foundation (E9/11, E42/15), the Swedish Research Council (421-2013-1061), The Jan Wallander and Tom Hedelius Foundation, an ERC Consolidator Grant (647648 EdGe), the Pershing Square Fund of the Foundations of Human Behavior, The Open Philanthropy Project (2016-152872), and the NIA/NIH through grants P01-AG005842, P01-AG005842-20S2, P30-AG012810 and T32-AG000186-23 to N.B.E.R. and R01-AG042568 to U.S.C. A full list of acknowledgments is provided in the Supplementary Note.
Anil Malhotra is a consultant for Genomind Inc., Informed DNA, Concert Pharmaceuticals, and Biogen. Nicholas A. Furlotte, Aaron Kleinman and Joyce Tung are employees of 23andMe, Inc.
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Lee, J.J., Wedow, R., Okbay, A. et al. Gene discovery and polygenic prediction from a genome-wide association study of educational attainment in 1.1 million individuals. Nat Genet 50, 1112–1121 (2018). https://doi.org/10.1038/s41588-018-0147-3
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