Abstract
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.
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Change history
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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Acknowledgements
This work was funded by the Netherlands Organization for Scientific Research (NWO VICI 453-14-005). The analyses were 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. This research has been conducted using the UK Biobank resource under application number 16406. We thank the participants and researchers who collected and contributed to the data.
Summary statistics have been made available for download from http://ctg.cncr.nl/software/summary_statistics.
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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.
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Integrated supplementary information
Supplementary Figure 1 Quantile–quantile plots.
Quantile–quantile plot for SNP-based P values (top) and gene-based P values (bottom).
Supplementary Figure 2 Regional chromatin state plots for SNPs with P < 5 × 10−8 in four genomic loci.
(a–d) 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.
Supplementary Figure 3 Regional chromatin state plots for SNPs with P < 5 × 10−8 in six genomic loci.
(a–f) Chromatin state plots are included for 6 of the 18 genome-wide significant loci.
Supplementary Figure 4 Regional chromatin state plots for SNPs with P < 5 × 10−8 in six genomic loci.
(a–f) Chromatin state plots are included for 6 of the 18 genome-wide significant loci.
Supplementary Figure 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).
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Supplementary Figures 1–6 and Supplementary Note. (PDF 2684 kb)
Supplementary Tables 1–18
Supplementary Tables 1–18. (XLSX 2999 kb)
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Sniekers, S., Stringer, S., Watanabe, K. 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). https://doi.org/10.1038/ng.3869
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DOI: https://doi.org/10.1038/ng.3869
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