Variance in IQ is associated with a wide range of health outcomes, and 1% of the population are affected by intellectual disability. Despite a century of research, the fundamental neural underpinnings of intelligence remain unclear. We integrate results from genome-wide association studies (GWAS) of intelligence with brain tissue and single cell gene expression data to identify tissues and cell types associated with intelligence. GWAS data for IQ (N = 78,308) were meta-analyzed with a study comparing 1247 individuals with mean IQ ~170 to 8185 controls. Genes associated with intelligence implicate pyramidal neurons of the somatosensory cortex and CA1 region of the hippocampus, and midbrain embryonic GABAergic neurons. Tissue-specific analyses find the most significant enrichment for frontal cortex brain expressed genes. These results suggest specific neuronal cell types and genes may be involved in intelligence and provide new hypotheses for neuroscience experiments using model systems.
Subscribe to Journal
Get full journal access for 1 year
only $9.92 per issue
All prices are NET prices.
VAT will be added later in the checkout.
Tax calculation will be finalised during checkout.
Rent or Buy article
Get time limited or full article access on ReadCube.
All prices are NET prices.
Okbay A, Beauchamp JP, Fontana MA, Lee JJ, Pers TH, Rietveld CA, et al. Genome-wide association study identifies 74 loci associated with educational attainment. Nature. 2016;533:539–42.
Lo M-T, Hinds DA, Tung JY, Franz C, Fan C-C, Wang Y, et al. Genome-wide analyses for personality traits identify six genomic loci and show correlations with psychiatric disorders. Nat Genet. 2016;49:152–6. 10.1038/ng.3736.
Sullivan PF, Agrawal A, Bulik C, Andreassen OA, Borglum A, Breen G, et al. Psychiatric genomics: an update and an agenda. 2017;115600. Preprint at http://biorxiv.org/content/early/2017/03/10/115600
Sniekers S, Stringer S, Watanabe K, Jansen PR, Coleman JRI, Krapohl E, et al. Genome-wide association meta-analysis of 78,308 individuals identifies new loci and genes influencing human intelligence. Nat Genet. 2017;49:1558 10.1038/ng.3869.
Davies G, Marioni RE, Liewald DC, Hill WD, Hagenaars SP, Harris SE, et al. Genome-wide association study of cognitive functions and educational attainment in UK Biobank (N=112 151). Mol Psychiatry. 2016;21:758–67.
Benyamin B, Pourcain B, Davis OS, Davies G, Hansell NK, Brion M-J, et al. Childhood intelligence is heritable, highly polygenic and associated with FNBP1L. Mol Psychiatry. 2013;19:253–8.
Deary IJ. Intelligence. Annu Rev Psychol. 2012;63:453–82.
Wray NR, Allele frequencies and the r2 measure of linkage disequilibrium: impact on design and interpretation of association studies. Twin Res Hum Genet. 2005;8:87–94..
Carlson CS, Eberle MA, Rieder MJ, Yi Q, Kruglyak L, Nickerson DA. Selecting a maximally informative set of single-nucleotide polymorphisms for association analyses using linkage disequilibrium. Am J Hum Genet. 2004;74:106–20.
Hormozdiari F, Zhu A, Kichaev G, Ju CJ-T, Segrè AV, Joo JWJ, et al. Widespread allelic heterogeneity in complex traits. Am J Hum Genet. 2017;100:789–802.
Zhu Z, Zhang F, Hu H, Bakshi A, Robinson MR, Powell JE, et al. Integration of summary data from GWAS and eQTL studies predicts complex trait gene targets. Nat Genet. 2016;48:481–7.
Boyle EA, Li YI, Pritchard JK. An expanded view of complex traits: from polygenic to omnigenic. Cell. 2017;169:1177–86.
Shi H, Kichaev G, Pasaniuc B. Contrasting the genetic architecture of 30 complex traits from summary association data. Am J Hum Genet. 2016;99:139–53.
Loh P-R, Bhatia G, Gusev A, Finucane HK, Bulik-Sullivan BK, Pollack SJ, et al. Contrasting genetic architectures of schizophrenia and other complex diseases using fast variance-components analysis. Nat Genet. 2015;47:1385–92.
Breen G, Li Q, Roth BL, O’Donnell P, Didriksen M, Dolmetsch R, et al. Translating genome-wide association findings into new therapeutics for psychiatry. Nat Neurosci. 2016;19:1392–6.
Skene NG, Bryois J, Bakken TE, Breen G, Crowley JJ, Gaspar H, et al. Genetic identification of brain cell types underlying schizophrenia. 2017;145466. Preprint at http://biorxiv.org/content/early/2017/06/02/145466
Zabaneh D, Krapohl E, Gaspar HA, Curtis C, Lee SH, Patel H, et al. A genome-wide association study for extremely high intelligence. Mol Psychiatry. 2017 doi: https://doi.org/10.1038/mp.2017.121.
Consortium G. TEx. Human genomics. The Genotype-Tissue Expression (GTEx) pilot analysis: multitissue gene regulation in humans. Science. 2015;348:648–60.
Deary IJ, Penke L, Johnson W. The neuroscience of human intelligence differences. Nat Rev Neurosci. 2010;11:201–11.
Willer CJ, Li Y, Abecasis GR. METAL: fast and efficient meta-analysis of genomewide association scans. Bioinformatics. 2010;26:2190–1.
Purcell S, Cherny SS, Sham PC. Genetic power calculator: design of linkage and association genetic mapping studies of complex traits. Bioinformatics. 2003;19:149–50.
Peloso GM, Rader DJ, Gabriel S, Kathiresan S, Daly MJ, Neale BM. Phenotypic extremes in rare variant study designs. Eur J Hum Genet. 2016;24:924–30.
de Bakker PIW, Ferreira MAR, Jia X, Neale BM, Raychaudhuri S, Voight BF. Practical aspects of imputation-driven meta-analysis of genome-wide association studies. Hum Mol Genet. 2008;17:R122–R128.
Chang CC, Chow CC, Tellier LC, Vattikuti S, Purcell SM, Lee JJ. Second-generation PLINK: rising to the challenge of larger and richer datasets. Gigascience. 2015;4:7.
Watanabe K, Taskesen E, van Bochoven A, Posthuma D. FUMA: Functional mapping and annotation of genetic associations. 2017;110023. Preprint at http://biorxiv.org/content/early/2017/02/20/110023.abstract
Bulik-Sullivan BK, Loh P-R, Finucane HK, Ripke S, Yang J. Schizophrenia Working Group of the Psychiatric Genomics Consortium. et al. LD score regression distinguishes confounding from polygenicity in genome-wide association studies. Nat Genet. 2015;47:291–5.
Finucane HK, Bulik-Sullivan B, Gusev A, Trynka G, Reshef Y, Loh P-R, et al. Partitioning heritability by functional annotation using genome-wide association summary statistics. Nat Genet. 2015;47:1228–35.
Bryois J, Garrett ME, Song L, Safi A, Giusti-Rodriguez P, Johnson GD, et al. Evaluation of chromatin accessibility in prefrontal cortex of schizophrenia cases and controls. 2017;141986. Preprint at http://biorxiv.org/content/early/2017/05/25/141986
Vernot B, Tucci S, Kelso J, Schraiber JG, Wolf AB, Gittelman RM, et al. Excavating Neandertal and Denisovan DNA from the genomes of Melanesian individuals. Science. 2016;352:235–9.
Buenrostro JD, Giresi PG, Zaba LC, Chang HY, Greenleaf WJ. Transposition of native chromatin for fast and sensitive epigenomic profiling of open chromatin, DNA-binding proteins and nucleosome position. Nat Methods. 2013;10:1213–8.
Sudmant PH, Rausch T, Gardner EJ, Handsaker RE, Abyzov A, Huddleston J, et al. An integrated map of structural variation in 2504 human genomes. Nature. 2015;526:75–81.
de Leeuw CA, Mooij JM, Heskes T, Posthuma D. MAGMA: generalized gene-set analysis of GWAS data. PLoS Comput Biol. 2015;11:e1004219.
de Leeuw CA, Neale BM, Heskes T, Posthuma D. The statistical properties of gene-set analysis. Nat Rev Genet. 2016;17:353–64.
Team RC. R: A language and environment for statistical computing. Vienna, Austria: R Foundation for Statistical Computing; 2013. http://www r-project org.
Barbeira A, Shah KP, Torres JM, Wheeler HE, Torstenson ES, Edwards T, et al. MetaXcan: Summary Statistics Based Gene-Level Association Method Infers Accurate PrediXcan Results. 2016;045260. Preprint at http://biorxiv.org/content/early/2016/03/23/045260.fulltext.pdf+html.
Koscielny G, An P, Carvalho-Silva D, Cham JA, Fumis L, Gasparyan R, et al. Open targets: a platform for therapeutic target identification and validation. Nucleic Acids Res. 2016;45:D985–D994. https://doi.org/10.1093/nar/gkw1055
Liberzon A, Birger C, Thorvaldsdóttir H, Ghandi M, Mesirov JP, Tamayo P. The molecular signatures database (MSigDB) hallmark gene set collection. Cell Syst. 2015;1:417–25.
Bayés A, van de Lagemaat LN, Collins MO, Croning MDR, Whittle IR, Choudhary JS, et al. Characterization of the proteome, diseases and evolution of the human postsynaptic density. Nat Neurosci. 2011;14:19–21.
Wagnon JL, Briese M, Sun W, Mahaffey CL, Curk T, Rot G, et al. CELF4 regulates translation and local abundance of a vast set of mRNAs, including genes associated with regulation of synaptic function. PLoS Genet. 2012;8:e1003067.
Lee JA, Damianov A, Lin CH, Fontes M, Parikshak NN, Anderson ES, et al. Cytoplasmic Rbfox1 regulates the expression of synaptic and autism-related genes. Neuron. 2016;89:113–28.
Hill WD, Davies G, Harris SE, Hagenaars SP, Liewald DC, neuroCHARGE Cognitive Working group. et al. Molecular genetic aetiology of general cognitive function is enriched in evolutionarily conserved regions. Transl Psychiatry. 2016;6:e980.
Zeisel A, Muñoz-Manchado AB, Codeluppi S, Lönnerberg P, La Manno G, Juréus A, et al. Brain structure. Cell types in the mouse cortex and hippocampus revealed by single-cell RNA-seq. Science. 2015;347:1138–42.
Pardiñas AF, Holmans P, Pocklington AJ, Escott-Price V, Ripke S, Carrera N, et al. Common schizophrenia alleles are enriched in mutation-intolerant genes and maintained by background selection. 2016;068593. Preprint at http://biorxiv.org/content/early/2016/08/09/068593
Brawand D, Soumillon M, Necsulea A, Julien P, Csárdi G, Harrigan P, et al. The evolution of gene expression levels in mammalian organs. Nature. 2011;478:343–8.
La Manno G, Gyllborg D, Codeluppi S, Nishimura K, Salto C, Zeisel A, et al. Molecular diversity of midbrain development in mouse, human, and stem cells. Cell. 2016;167:566. e19
Zeng H, Shen EH, Hohmann JG, Oh SW, Bernard A, Royall JJ, et al. Large-scale cellular-resolution gene profiling in human neocortex reveals species-specific molecular signatures. Cell. 2012;149:483–96.
Flinn MV, Geary DC, Ward CV. Ecological dominance, social competition, and coalitionary arms races. Evol Hum Behav. 2005;26:10–46.
Carss KJ, Stevens E, Foley AR, Cirak S, Riemersma M, Torelli S, et al. Mutations in GDP-mannose pyrophosphorylase B cause congenital and limb-girdle muscular dystrophies associated with hypoglycosylation of α-dystroglycan. Am J Hum Genet. 2013;93:29–41.
Jensen BS, Willer T, Saade DN, Cox MO, Mozaffar T, Scavina M, et al. GMPPB-Associated dystroglycanopathy: emerging common variants with phenotype correlation. Hum Mutat. 2015;36:1159–63.
Rodríguez Cruz PM, Belaya K, Basiri K, Sedghi M, Farrugia ME, Holton JL, et al. Clinical features of the myasthenic syndrome arising from mutations in GMPPB. J Neurol Neurosurg Psychiatry. 2016;87:802–9.
Belaya K, Rodríguez Cruz PM, Liu WW, Maxwell S, McGowan S, Farrugia ME, et al. Mutations in GMPPB cause congenital myasthenic syndrome and bridge myasthenic disorders with dystroglycanopathies. Brain. 2015;138:2493–504.
Cabrera-Serrano M, Ghaoui R, Ravenscroft G, Johnsen RD, Davis MR, Corbett A, et al. Expanding the phenotype of GMPPB mutations. Brain. 2015;138:836–44.
Wright KM, Lyon KA, Leung H, Leahy DJ, Ma L, Ginty DD. Dystroglycan organizes axon guidance cue localization and axonal pathfinding. Neuron. 2012;76:931–44.
Roberts JA. The genetics of mental deficiency. Eugen Rev. 1952;44:71–83.
McGue M, Gottesman II. Classical and molecular genetic research on general cognitive ability. Hastings Cent Rep. 2015;45:S25–S31.
LELM Vissers, Gilissen C, Veltman JA. Genetic studies in intellectual disability and related disorders. Nat Rev Genet. 2016;17:9–18.
Network and Pathway Analysis Subgroup of Psychiatric Genomics Consortium. Psychiatric genome-wide association study analyses implicate neuronal, immune and histone pathways. Nat Neurosci. 2015;18:199–209.
Maston GA, Evans SK, Green MR. Transcriptional regulatory elements in the human genome. Annu Rev Genom Hum Genet. 2006;7:29–59.
We gratefully acknowledge the contribution of all of the researchers and participants involved in the collection and analysis of the data included. This study includes data from Sniekers et al. (2017), which made use of the UK Biobank resource under application number 16,406 (as previously acknowledged). Analysis in this paper represents independent research funded by the National Institute for Health Research (NIHR) Biomedical Research Centre at South London and Maudsley NHS Foundation Trust and King’s College London. The views expressed are those of the authors and not necessarily those of the NHS, the NIHR or the Department of Health. Analyses were performed using high performance computing facilities funded with capital equipment grants from the GSTT Charity (TR130505) and Maudsley Charity (980). Analysis from Sniekers et al. (2017) was funded by the Netherlands Organization for Scientific Research (NWO VICI 453-14-005), and carried out on the Genetic Cluster Computer, which is financed by the Netherlands Scientific Organization (NWO:480-05-003), by VU University, Amsterdam, the Netherlands, and by the Dutch Brain Foundation and is hosted by the Dutch National Computing and Networking Services SurfSARA. PRJ is supported by the ‘Stichting Vrienden van Sophia’ (grant nr: 14-27) awarded to DP. Research on the HiQ cohort was supported by a European Research Council Advanced Investigator award (295366) to RP. Collecting DNA from the highest-scoring TIP individuals was supported by an award from the John Templeton Foundation (13575) to RP. JB was supported by the Swiss National Science Foundation. Summary statistics from this analysis have been made available at the NHGRI-EBI GWAS Catalog (http://www.ebi.ac.uk/gwas/).
GB, DP, and PFS conceived the study. JRIC, JB, HAG, PRJ, JS, and NS performed statistical analyses. RP, ABM, SL, GC, and JH acquired data. JRIC and GB wrote the manuscript. All authors reviewed the manuscript.
Conflict of interest
PFS reports the following potentially competing financial interests: Lundbeck (advisory committee), Pfizer (Scientific Advisory Board member), and Roche (grant recipient, speaker reimbursement). GB reports consultancy and speaker fees from Eli Lilly and Illumina and grant funding from Eli Lilly. All remaining authors declare that they have no conflict of interest.
About this article
Cite this article
Coleman, J.R.I., Bryois, J., Gaspar, H.A. et al. Biological annotation of genetic loci associated with intelligence in a meta-analysis of 87,740 individuals. Mol Psychiatry 24, 182–197 (2019). https://doi.org/10.1038/s41380-018-0040-6
Molecular Psychiatry (2021)
Single cell transcriptomics of primate sensory neurons identifies cell types associated with chronic pain
Nature Communications (2021)
Acta Neuropathologica (2021)
Scientific Reports (2020)
Genome-wide association study of MRI markers of cerebral small vessel disease in 42,310 participants
Nature Communications (2020)