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Genome-wide association meta-analysis in 269,867 individuals identifies new genetic and functional links to intelligence

Abstract

Intelligence is highly heritable1 and a major determinant of human health and well-being2. Recent genome-wide meta-analyses have identified 24 genomic loci linked to variation in intelligence3,4,5,6,7, but much about its genetic underpinnings remains to be discovered. Here, we present a large-scale genetic association study of intelligence (n = 269,867), identifying 205 associated genomic loci (190 new) and 1,016 genes (939 new) via positional mapping, expression quantitative trait locus (eQTL) mapping, chromatin interaction mapping, and gene-based association analysis. We find enrichment of genetic effects in conserved and coding regions and associations with 146 nonsynonymous exonic variants. Associated genes are strongly expressed in the brain, specifically in striatal medium spiny neurons and hippocampal pyramidal neurons. Gene set analyses implicate pathways related to nervous system development and synaptic structure. We confirm previous strong genetic correlations with multiple health-related outcomes, and Mendelian randomization analysis results suggest protective effects of intelligence for Alzheimer’s disease and ADHD and bidirectional causation with pleiotropic effects for schizophrenia. These results are a major step forward in understanding the neurobiology of cognitive function as well as genetically related neurological and psychiatric disorders.

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Fig. 1: SNP-based associations with intelligence in the GWAS meta-analysis of n = 269,867 independent individuals.
Fig. 2: Cross-locus interactions for genomic regions associated with intelligence in 269,867 independent individuals.
Fig. 3: Implicated genes, pathways, and tissue and cell expression profiles for intelligence in 269,867 independent individuals.

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References

  1. Polderman, T. J. et al. Meta-analysis of the heritability of human traits based on fifty years of twin studies. Nat. Genet. 47, 702–709 (2015).

    Article  PubMed  CAS  Google Scholar 

  2. Wraw, C., Deary, I. J., Gale, C. R. & Der, G. Intelligence in youth and health at age 50. Intelligence 53, 23–32 (2015).

    Article  PubMed  PubMed Central  Google Scholar 

  3. Davies, G. et al. Genetic contributions to variation in general cognitive function: a meta-analysis of genome-wide association studies in the CHARGE consortium (N = 53949). Mol. Psychiatry 20, 183–192 (2015).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  4. Davies, G. et al. Genome-wide association study of cognitive functions and educational attainment in UK Biobank (N = 112 151). Mol. Psychiatry 21, 758–767 (2016).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  5. 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).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  6. 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).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  7. Zabaneh, D. et al. A genome-wide association study for extremely high intelligence. Mol. Psychiatry 23, 1226–1232 (2018).

    Article  PubMed  CAS  Google Scholar 

  8. Jensen, A.R. The G Factor: The Science of Mental Ability (Praeger, Westport, CT, US, 1998).

  9. Carroll, J. B. Human Cognitive Abilities: A Survey of Factor-Analytic Studies. (Cambridge University Press, Cambridge, UK, 1993).

    Book  Google Scholar 

  10. Spearman, C. “General intelligence,” objectively determined and measured. Am. J. Psychol. 15, 201–292 (1904).

    Article  Google Scholar 

  11. Plomin, R. & Kovas, Y. Generalist genes and learning disabilities. Psychol. Bull. 131, 592–617 (2005).

    Article  PubMed  Google Scholar 

  12. Plomin, R. & von Stumm, S. The new genetics of intelligence. Nat. Rev. Genet. 19, 148–159 (2018).

    Article  PubMed  CAS  PubMed Central  Google Scholar 

  13. Johnson, W., Bouchard, T. J., Krueger, R. F., McGue, M. & Gottesman, I. I. Just one g: consistent results from three test batteries. Intelligence 32, 95–107 (2004).

    Article  Google Scholar 

  14. Johnson, W., Nijenhuis, Jt & Bouchard, T. J. Still just 1 g: consistent results from five test batteries. Intelligence 36, 81–95 (2008).

    Article  Google Scholar 

  15. Deary, I. J., Penke, L. & Johnson, W. The neuroscience of human intelligence differences. Nat. Rev. Neurosci. 11, 201–211 (2010).

    Article  PubMed  CAS  Google Scholar 

  16. Deary, I. J. Intelligence. Annu. Rev. Psychol. 63, 453–482 (2012).

    Article  PubMed  Google Scholar 

  17. Bulik-Sullivan, B. K. et al. LD Score regression distinguishes confounding from polygenicity in genome-wide association studies. Nat. Genet. 47, 291–295 (2015).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  18. Deary, I. J., Strand, S., Smith, P. & Fernandes, C. Intelligence and educational achievement. Intelligence 35, 13–21 (2007).

    Article  Google Scholar 

  19. Rietveld, C. A. et al. GWAS of 126,559 individuals identifies genetic variants associated with educational attainment. Science 340, 1467–1471 (2013).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  20. Euesden, J., Lewis, C. M. & O’Reilly, P. F. PRSice: Polygenic Risk Score software. Bioinformatics 31, 1466–1468 (2015).

    Article  PubMed  CAS  Google Scholar 

  21. Vilhjálmsson, B. J. et al. Modeling linkage disequilibrium increases accuracy of polygenic risk scores. Am. J. Hum. Genet. 97, 576–592 (2015).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  22. Hill, W. D. et al. Molecular genetic aetiology of general cognitive function is enriched in evolutionarily conserved regions. Transl. Psychiatry 6, e980 (2016).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  23. Kircher, M. et al. A general framework for estimating the relative pathogenicity of human genetic variants. Nat. Genet. 46, 310–315 (2014).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  24. Watanabe, K., Taskesen, E., van Bochoven, A. & Posthuma, D. Functional mapping and annotation of genetic associations with FUMA. Nat. Commun. 8, 1826 (2017).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  25. de Leeuw, C. A., Mooij, J. M., Heskes, T. & Posthuma, D. MAGMA: generalized gene-set analysis of GWAS data. PLoS Comput. Biol. 11, e1004219 (2015).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  26. Ashburner, M. et al. Gene ontology: tool for the unification of biology. Nat. Genet. 25, 25–29 (2000).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  27. Posthuma, D. et al. The association between brain volume and intelligence is of genetic origin. Nat. Neurosci. 5, 83–84 (2002).

    Article  PubMed  CAS  Google Scholar 

  28. MacArthur, J. et al. The new NHGRI-EBI Catalog of published genome-wide association studies (GWAS Catalog). Nucleic Acids Res. 45(D1), D896–D901 (2017).

    Article  PubMed  CAS  Google Scholar 

  29. Zhu, Z. et al. Causal associations between risk factors and common diseases inferred from GWAS summary data. Nat. Commun. 9, 224 (2018).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  30. Johnson, W., Deary, I. J. & Iacono, W. G. Genetic and environmental transactions underlying educational attainment. Intelligence 37, 466–478 (2009).

    Article  PubMed  PubMed Central  Google Scholar 

  31. Richards, M. & Sacker, A. Is education causal? Yes. Int. J. Epidemiol. 40, 516–518 (2011).

    Article  PubMed  Google Scholar 

  32. Kendler, K. S., Ohlsson, H., Sundquist, J. & Sundquist, K. IQ and schizophrenia in a Swedish national sample: their causal relationship and the interaction of IQ with genetic risk. Am. J. Psychiatry 172, 259–265 (2015).

    Article  PubMed  Google Scholar 

  33. Le Hellard, S. et al. Identification of gene loci that overlap between schizophrenia and educational attainment. Schizophr. Bull. 43, 654–664 (2017).

    PubMed  Google Scholar 

  34. Willer, C. J., Li, Y. & Abecasis, G. R. METAL: fast and efficient meta-analysis of genomewide association scans. Bioinformatics 26, 2190–2191 (2010).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  35. Peloso, G. M. et al. Phenotypic extremes in rare variant study designs. Eur. J. Hum. Genet. 24, 924–930 (2016).

    Article  PubMed  Google Scholar 

  36. Purcell, S., Cherny, S. S. & Sham, P. C. Genetic Power Calculator: design of linkage and association genetic mapping studies of complex traits. Bioinformatics 19, 149–150 (2003).

    Article  PubMed  CAS  Google Scholar 

  37. Coleman, J.R.I. et al. Biological annotation of genetic loci associated with intelligence in a meta-analysis of 87,740 individuals. Mol. Psychiatry https://doi.org/10.1038/s41380-018-0040-6 (2018).

  38. König, I. R., Loley, C., Erdmann, J. & Ziegler, A. How to include chromosome X in your genome-wide association study. Genet. Epidemiol. 38, 97–103 (2014).

    Article  PubMed  Google Scholar 

  39. Bulik-Sullivan, B. et al. An atlas of genetic correlations across human diseases and traits. Nat. Genet. 47, 1236–1241 (2015).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  40. Okbay, A. et al. Genome-wide association study identifies 74 loci associated with educational attainment. Nature 533, 539–542 (2016).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  41. Chang, C. C. et al. Second-generation PLINK: rising to the challenge of larger and richer datasets. Gigascience 4, 7 (2015).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  42. Finucane, H. K. et al. Partitioning heritability by functional annotation using genome-wide association summary statistics. Nat. Genet. 47, 1228–1235 (2015).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  43. Wang, K., Li, M. & Hakonarson, H. ANNOVAR: functional annotation of genetic variants from high-throughput sequencing data. Nucleic Acids Res. 38, e164 (2010).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  44. Boyle, A. P. et al. Annotation of functional variation in personal genomes using RegulomeDB. Genome Res. 22, 1790–1797 (2012).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  45. Ernst, J. & Kellis, M. ChromHMM: automating chromatin-state discovery and characterization. Nat. Methods 9, 215–216 (2012).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  46. Kundaje, A. et al. Integrative analysis of 111 reference human epigenomes. Nature 518, 317–330 (2015).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  47. Zhu, Z. et al. Integration of summary data from GWAS and eQTL studies predicts complex trait gene targets. Nat. Genet. 48, 481–487 (2016).

    Article  PubMed  CAS  Google Scholar 

  48. GTEx Consortium. The Genotype-Tissue Expression (GTEx) pilot analysis: multitissue gene regulation in humans. Science 348, 648–660 (2015).

    Article  PubMed Central  CAS  Google Scholar 

  49. Westra, H. J. et al. Systematic identification of trans eQTLs as putative drivers of known disease associations. Nat. Genet. 45, 1238–1243 (2013).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  50. Zhernakova, D. V. et al. Identification of context-dependent expression quantitative trait loci in whole blood. Nat. Genet. 49, 139–145 (2017).

    Article  PubMed  CAS  Google Scholar 

  51. Schmitt, A. D. et al. A compendium of chromatin contact maps reveals spatially active regions in the human genome. Cell Rep. 17, 2042–2059 (2016).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  52. Liberzon, A. et al. Molecular signatures database (MSigDB) 3.0. Bioinformatics 27, 1739–1740 (2011).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  53. Skene, N. G. et al. Genetic identification of brain cell types underlying schizophrenia. Nat. Genet. 50, 825–833 (2018).

    Article  PubMed  CAS  PubMed Central  Google Scholar 

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Acknowledgements

This work was funded by the Netherlands Organization for Scientific Research through the following grants: NWO Brain and Cognition 433­-09-228 (D.P.), NWO MagW VIDI 452­-12-­014 (S.v.d.S.), NWO VICI 453-13-005 (D.P.), and 645­-000-­003 (D.P.). P.R.J. was funded by the Sophia Foundation for Scientific Research (SSWO, grant S14-27 to P.R.J.). The analyses were carried out on the Genetic Cluster Computer, which is financed by the Netherlands Scientific Organization (NWO: 480-05-003 to D.P.), Vrije Universiteit, Amsterdam, The Netherlands, and the Dutch Brain Foundation, and is hosted by the Dutch National Computing and Networking Services, SurfSARA. J.H.-­L. was funded by the Swedish Research Council (Vetenskapsrådet, award 2014­3863), StratNeuro, the Wellcome Trust (108726/Z/15/Z), and the Swedish Brain Foundation (Hjärnfonden). N.G.S. was supported by the Wellcome Trust (108726/Z/15/Z). J.B. was funded by the Swiss National Science Foundation. This research has been conducted using the UK Biobank resource under application 16406. We thank the numerous participants, researchers, and staff from many studies who collected and contributed to the data. Additional acknowledgements can be found in the Supplementary Information.

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Authors

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D.P., J.E.S., and P.R.J. performed the analyses. D.P. conceived the idea of the study and supervised analyses. S. Stringer performed quality control on the UK Biobank data and wrote the analysis pipeline. K.W. constructed and applied the FUMA pipeline for performing follow-up analyses. J.B. conducted the single-cell enrichment analyses. C.A.d.L., M. Nagel, A.R. Hammerschlag, T.J.C.P., and S.v.d.S. assisted with pipeline development and data analysis. S.A., P.B.B., J.R.I.C., K.L.G., J.A.K., R.K., E. Krapohl, M.L., M. Nygaard, C.A.R., J.W.T., H.Y., D.Z., S.H., N.K.H., I.K.K., S.L., G.W.M., A.B.M.-M., E.B.Q., G.S., N.G.S., B.T.W., D.E.A., D.K., D.A., R.M.B., P.B., K.E.B., T.D.C., O.C.-F., A. Christoforou, E.T.C., E.C., A. Corvin, G. Davies, I.J.D., P.D., D.D., S.D., G. Donohoe, E.D.C., J.G.E., T.E., N.A.F., D.C.G., I.G., M.G., S.G., A.R. Hariri, A. Hatzimanolis, M.C.K., E. Knowles, B.K., J.L., S.L.-H., T.L., D.C.L., E.L., A.J.L., A.K.M., I.M., D.M., A.C.N., W.O., A. Palotie, A. Payton, N.P., R.A.P., K.R., I.R., P.R., D.R., F.W.S., M.A.S., O.B.S., N.S., J.M.S., V.M.S., N.C.S., R.E.S., K.S., A.N.V., D.R.W., E.W., J.Y., G.A., O.A.A., G.B., L.C., B.D., D.M.D., A. Heinz, J.H.-L., M.A.I., K.S.K., N.G.M., S.E.M., N.L.P., R.P., T.J.C.P., S.R., P.F.S., H.T., S.I.V., and M.J.W. contributed data. T.W. read and commented on the paper. D.P., J.E.S., and P.R.J. wrote the paper. All authors critically reviewed the paper.

Corresponding author

Correspondence to Danielle Posthuma.

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Competing interests

P.F.S. reports the following potentially competing financial interests: Lundbeck (advisory committee), Pfizer (scientific advisory board member), and Roche (grant recipient, speaker reimbursement). G.B. reports consultancy and speaker fees from Eli Lilly and Illumina and grant funding from Eli Lilly. J.H.-L. reports interests from Cartana (scientific advisor) and Roche (grant recipient). T.D.C. is a consultant to Boehringer Ingelheim Pharmaceuticals and Lundbeck. All other authors declare no financial interests or potential conflicts of interest.

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Savage, J.E., Jansen, P.R., Stringer, S. et al. Genome-wide association meta-analysis in 269,867 individuals identifies new genetic and functional links to intelligence. Nat Genet 50, 912–919 (2018). https://doi.org/10.1038/s41588-018-0152-6

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