Genome-wide association meta-analysis of 78,308 individuals identifies new loci and genes influencing human intelligence

Journal name:
Nature Genetics
Volume:
49,
Pages:
1107–1112
Year published:
DOI:
doi:10.1038/ng.3869
Received
Accepted
Published online
Corrected online

Intelligence is associated with important economic and health-related life outcomes1. Despite intelligence having substantial heritability2 (0.54) and a confirmed polygenic nature, initial genetic studies were mostly underpowered3, 4, 5. Here we report a meta-analysis for intelligence of 78,308 individuals. We identify 336 associated SNPs (METAL P < 5 × 10−8) in 18 genomic loci, of which 15 are new. Around half of the SNPs are located inside a gene, implicating 22 genes, of which 11 are new findings. Gene-based analyses identified an additional 30 genes (MAGMA P < 2.73 × 10−6), of which all but one had not been implicated previously. We show that the identified genes are predominantly expressed in brain tissue, and pathway analysis indicates the involvement of genes regulating cell development (MAGMA competitive P = 3.5 × 10−6). Despite the well-known difference in twin-based heritability2 for intelligence in childhood (0.45) and adulthood (0.80), we show substantial genetic correlation (rg = 0.89, LD score regression P = 5.4 × 10−29). These findings provide new insight into the genetic architecture of intelligence.

At a glance

Figures

  1. Regional association and linkage disequilibrium plots for 18 genome-wide significant loci.
    Figure 1: Regional association and linkage disequilibrium plots for 18 genome-wide significant loci.

    The y axis represents the negative logarithm (base 10) of the SNP P value and the x axis represents the position on the chromosome, with the name and location of genes in the UCSC Genome Browser shown in the bottom panel. The SNP with the lowest P value in the region is marked by a purple diamond. The colors of the other SNPs indicate the r2 of these SNPs with the lead SNP. Plots were generated with LocusZoom34.

  2. Results of SNP-based meta-analysis for intelligence based on 78,308 individuals.
    Figure 2: Results of SNP-based meta-analysis for intelligence based on 78,308 individuals.

    Association results from the GWAS meta-analysis pertaining to individuals of European descent. (a) Negative log10-transformed P values for each SNP (y axis) are plotted by chromosomal position (x axis). The red and blue lines represent the thresholds for genome-wide statistically significant associations (P = 5 × 10−8) and suggestive associations (P = 1 × 10−5), respectively. Green dots represent the independent hits. (b) Functional categories for the 336 genome-wide significant SNPs. (c) The minimum (most active) chromatin state across 127 tissues for the 336 genome-wide significant SNPs. (d) The Regulome database score for the 336 genome-wide significant SNPs. The lower the score, the more likely it is that a SNP has a regulatory function. For bd, the numbers in parentheses in the legends refer to the number of lead SNPs for that category. ncRNA, noncoding RNA; TSS, transcription start site; TF, transcription factor.

  3. Gene-based genome-wide analysis for intelligence and genetic overlap with other traits.
    Figure 3: Gene-based genome-wide analysis for intelligence and genetic overlap with other traits.

    (a) Negative log10-transformed P values for each gene are plotted. Green dots represent significantly associated genes from GWGAS. The threshold for gene-wide statistical significant associations was set at the Bonferroni threshold of P = 2.73 × 10−6; the suggestive threshold was set at P = 2.73 × 10−5. (b) Heat map of gene expression levels of genes for intelligence in 45 tissue types (see Supplementary Table 18 for n values per tissue). A value above zero (red) depicts a relatively high expression level with respect to the mean expression level of the gene over all tissues, whereas a value below zero (blue) depicts a relatively low expression level. (c) Epigenetic states of genes. The bars denote the proportions of epigenetic states across 127 tissue types. (d) Genetic correlations between intelligence and 32 health-related outcomes. Error bars show 95% confidence intervals for estimates of rg. Red bars represent the traits that showed a significant genetic correlation after correction for multiple testing (P < 1.56 × 10−3), pink bars represent the traits that showed a nominally significant correlation (P < 0.05) and blue bars represent the traits that did not show a genetic correlation significantly different from zero. Note, as Alzheimer's disease is an age-related disorder, we calculated the rg with this phenotype across three age groups and found no difference in the rg values (Supplementary Note). TSS, transcription start site.

  4. Quantile-quantile plots.
    Supplementary Fig. 1: Quantile–quantile plots.

    Quantile–quantile plot for SNP-based P values (top) and gene-based P values (bottom).

  5. Regional chromatin state plots for SNPs with P < 5 [times] 10-8 in four genomic loci.
    Supplementary Fig. 2: Regional chromatin state plots for SNPs with P < 5 × 10−8 in four genomic loci.

    (ad) Chromatin state plots are included for 4 of the 18 genome-wide significant loci. The 1p31.1 and 20q13.13 loci are not included because the lead SNPs in these regions (rs66495454 and rs113315451) are indels. In each picture, the top panel shows the lead SNP (purple) and all other SNPs reaching genome-wide significance in the region. The colors represent r2 with the lead SNP. The bottom panel shows chromatin states for 127 tissue types (y axis) across the whole region. Different colors represent the different states, varying from “active TSS” (state 1) to “quiescent/low” (state 15). This information can be used to determine which SNPs to study in a functional follow-up.

  6. Regional chromatin state plots for SNPs with P < 5 [times] 10-8 in six genomic loci.
    Supplementary Fig. 3: Regional chromatin state plots for SNPs with P < 5 × 10−8 in six genomic loci.

    (af) Chromatin state plots are included for 6 of the 18 genome-wide significant loci.

  7. Regional chromatin state plots for SNPs with P < 5 [times] 10-8 in six genomic loci.
    Supplementary Fig. 4: Regional chromatin state plots for SNPs with P < 5 × 10−8 in six genomic loci.

    (af) Chromatin state plots are included for 6 of the 18 genome-wide significant loci.

  8. Predictive power (R2) of the polygenic score based on different intelligence discovery GWAS studies in four independent hold-out samples.
    Supplementary Fig. 5: Predictive power (R2) of the polygenic score based on different intelligence discovery GWAS studies in four independent hold-out samples.

    Comparisons of the explained variance (R2) in cognitive ability between polygenic scores based on the current meta-analysis and previous GWAS studies. The error bars represent the standard error. Cohorts: HIQ: High IQ sample; RS: Rotterdam Study; TEDS: Twins Early Development Study; ACPRC: Age and Cognitive Performance Research Centre; Discovery GWAS: Benyamin et al. 2014: childhood IQ; Davies et al. 2016: UK Biobank cognitive test (touchscreen). The R2 for HIQ is reported on the liability scale (assuming a population prevalence of 3x10-4).

  9. Epigenetic states of genes.
    Supplementary Fig. 6: Epigenetic states of genes.

Change history

Corrected online 31 May 2017
In the version of this article initially published online, heritability was misspelled in the penultimate sentence of the abstract. The error has been corrected in the print, PDF and HTML versions of this article.

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Author information

  1. Present address: Geisinger Health System, Danville, Pennsylvania, USA.

    • Christopher F Chabris

Affiliations

  1. Department of Complex Trait Genetics, Center for Neurogenomics and Cognitive Research, Amsterdam Neuroscience, VU University Amsterdam, Amsterdam, the Netherlands.

    • Suzanne Sniekers,
    • Sven Stringer,
    • Kyoko Watanabe,
    • Philip R Jansen,
    • Erdogan Taskesen,
    • Anke R Hammerschlag,
    • Aysu Okbay,
    • Philipp Koellinger &
    • Danielle Posthuma
  2. Department of Child and Adolescent Psychiatry, Erasmus Medical Center, Rotterdam, the Netherlands.

    • Philip R Jansen &
    • Henning Tiemeier
  3. MRC Social, Genetic and Developmental Psychiatry Centre, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK.

    • Jonathan R I Coleman,
    • Eva Krapohl,
    • Delilah Zabaneh,
    • Gerome Breen &
    • Robert Plomin
  4. NIHR Biomedical Research Centre for Mental Health, South London and Maudsley NHS Trust, London, UK.

    • Jonathan R I Coleman &
    • Gerome Breen
  5. Alzheimer Centrum, Amsterdam Neuroscience, VU University Medical Center, Amsterdam, the Netherlands.

    • Erdogan Taskesen
  6. Erasmus University Rotterdam Institute for Behavior and Biology, Rotterdam, the Netherlands.

    • Aysu Okbay,
    • Philipp Koellinger &
    • Cornelius A Rietveld
  7. Genetic Epidemiology Unit, Department of Epidemiology, Erasmus Medical Center, Rotterdam, the Netherlands.

    • Najaf Amin &
    • Cornelia M van Duijn
  8. Center for Experimental Social Science, Department of Economics, New York University, New York, New York, USA.

    • David Cesarini
  9. Department of Psychology, Union College, Schenectady, New York, USA.

    • Christopher F Chabris
  10. Department of Psychology, University of Minnesota, Minneapolis, Minnesota, USA.

    • William G Iacono,
    • James J Lee,
    • Matt McGue &
    • Mike B Miller
  11. Department of Epidemiology, Erasmus Medical Center, Rotterdam, the Netherlands.

    • M Arfan Ikram &
    • Henning Tiemeier
  12. Department of Economics, Stockholm School of Economics, Stockholm, Sweden.

    • Magnus Johannesson
  13. Department of Psychology, Harvard University, Cambridge, Massachusetts, USA.

    • James J Lee
  14. Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden.

    • Patrik K E Magnusson
  15. Centre for Epidemiology, Division of Population Health, Health Services Research and Primary Care, University of Manchester, Manchester, UK.

    • William E R Ollier &
    • Antony Payton
  16. Division of Neuroscience and Experimental Psychology, School of Biological Sciences, Faculty of Biology, Medicine and Health, University of Manchester, Manchester Academic Health Science Centre, Manchester, UK.

    • Neil Pendleton
  17. Department of Applied Economics, Erasmus School of Economics, Erasmus University Rotterdam, Rotterdam, the Netherlands.

    • Cornelius A Rietveld
  18. Department of Psychiatry, Erasmus Medical Center, Rotterdam, the Netherlands.

    • Henning Tiemeier
  19. Translational Epidemiology, Faculty Science, Leiden University, Leiden, the Netherlands.

    • Cornelia M van Duijn
  20. Department of Clinical Genetics, Amsterdam Neuroscience, VU University Medical Center, Amsterdam, the Netherlands.

    • Danielle Posthuma

Contributions

S. Sniekers performed the analyses. D.P. conceived the study. S. Stringer performed quality control on the UK Biobank data. K.W. and E.T. conducted in silico follow-up analyses. P.R.J., E.K. and J.R.I.C. conducted polygenic risk score analyses. P.K., C.A.R., D.Z., H.T., C.M.v.D., N.A., P.M., D.C., M.J., M.M., M.B.M., W.G.I., J.J.L., G.B., R.P., N.P., A.P., W.E.R.O., M.A.I. and C.F.C. contributed data. A.R.H. provided scripts for the pathway analyses. A.O. performed the educational attainment meta-analysis. S. Sniekers and D.P. wrote the manuscript. All authors discussed the results and commented on the manuscript.

Competing financial interests

The authors declare no competing financial interests.

Corresponding author

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Author details

Supplementary information

Supplementary Figures

  1. Supplementary Figure 1: Quantile–quantile plots. (93 KB)

    Quantile–quantile plot for SNP-based P values (top) and gene-based P values (bottom).

  2. Supplementary Figure 2: Regional chromatin state plots for SNPs with P < 5 × 10−8 in four genomic loci. (201 KB)

    (ad) Chromatin state plots are included for 4 of the 18 genome-wide significant loci. The 1p31.1 and 20q13.13 loci are not included because the lead SNPs in these regions (rs66495454 and rs113315451) are indels. In each picture, the top panel shows the lead SNP (purple) and all other SNPs reaching genome-wide significance in the region. The colors represent r2 with the lead SNP. The bottom panel shows chromatin states for 127 tissue types (y axis) across the whole region. Different colors represent the different states, varying from “active TSS” (state 1) to “quiescent/low” (state 15). This information can be used to determine which SNPs to study in a functional follow-up.

  3. Supplementary Figure 3: Regional chromatin state plots for SNPs with P < 5 × 10−8 in six genomic loci. (95 KB)

    (af) Chromatin state plots are included for 6 of the 18 genome-wide significant loci.

  4. Supplementary Figure 4: Regional chromatin state plots for SNPs with P < 5 × 10−8 in six genomic loci. (121 KB)

    (af) Chromatin state plots are included for 6 of the 18 genome-wide significant loci.

  5. Supplementary Figure 5: Predictive power (R2) of the polygenic score based on different intelligence discovery GWAS studies in four independent hold-out samples. (70 KB)

    Comparisons of the explained variance (R2) in cognitive ability between polygenic scores based on the current meta-analysis and previous GWAS studies. The error bars represent the standard error. Cohorts: HIQ: High IQ sample; RS: Rotterdam Study; TEDS: Twins Early Development Study; ACPRC: Age and Cognitive Performance Research Centre; Discovery GWAS: Benyamin et al. 2014: childhood IQ; Davies et al. 2016: UK Biobank cognitive test (touchscreen). The R2 for HIQ is reported on the liability scale (assuming a population prevalence of 3x10-4).

  6. Supplementary Figure 6: Epigenetic states of genes. (461 KB)

PDF files

  1. Supplementary Text and Figures (2,749 KB)

    Supplementary Figures 1–6 and Supplementary Note.

Excel files

  1. Supplementary Tables 1–18 (3,071 KB)

    Supplementary Tables 1–18.

Additional data