Discovery sample.

The current study was based on 78,308 individuals. The origin of the samples is as follows:

UK Biobank web-based measure (UKB-wb; n = 17,862). GWAS results have not yet been published; raw genotypic data were available for the present study. UK Biobank touchscreen measure (UKB-ts; n = 36,257, non-overlapping with UKB-wb). Results have been published before6; raw genotypic data were available for the present study. CHIC consortium5 (n = 12,441). Results have been published before; meta-analysis summary statistics were available for the present study. Five additional cohorts (n = 11,748). For these, 69 SNP associations with IQ have previously been published as part of a lookup effort7, but full GWAS results have not been published previously. Per-cohort full GWAS summary statistics were available for the present study.

We describe these data sets in more detail below.

UK Biobank samples (UKB-wb, UKB-ts). We used the data provided by the UK Biobank Study35 resource (see URLs), which is a major national health resource including >500,000 participants. All participants provided written informed consent; the UK Biobank received ethical approval from the National Research Ethics Service Committee North West–Haydock (reference11/NW/0382), and all study procedures were performed in accordance with the World Medical Association Declaration of Helsinki ethical principles for medical research. The current study was conducted under UK Biobank application number 16406.

The study design of the UK Biobank has been described in detail elsewhere35,36. Briefly, invitation letters were sent out in 2006–2010 to ∼9.2 million individuals, including all people aged 40–69 years who were registered with the National Health Service and living up to ∼25 miles from one of the 22 study assessment centers. A total of 503,325 participants were subsequently recruited into the study35. Apart from registry-based phenotypic information, extensive self-reported baseline data have been collected by questionnaire, in addition to anthropometric assessments and DNA collection. For the present study, we used imputed data obtained from UK Biobank (May 2015 release) including ∼73 million genetic variants in 152,249 individuals. Details on the data are provided elsewhere (see URLs). In summary, the first ∼50,000 samples were genotyped on the UK BiLEVE Axiom array, and the remaining ∼100,000 samples were genotyped on the UK Biobank Axiom array. After standard quality control of the SNPs and samples, which was centrally performed by UK Biobank, the data set comprised 641,018 autosomal SNPs in 152,256 samples for phasing and imputation. Imputation was performed with a reference panel that included the UK10K haplotype panel and the 1000 Genomes Project Phase 3 reference panel.

We used two fluid intelligence phenotypes from the Biobank data set. These are based on questionnaires that were taken either in the assessment center at the initial intake ('touchscreen', field 20016) or at a later moment at home ('web-based', field 20191). The measures indicate the number of correct answers out of 13 fluid intelligence questions. The data distribution roughly approximates a normal distribution.

For the analyses in our study, we only included individuals of European descent. After removal of related individuals and those with discordant sex, who withdrew consent or had missing phenotype data, 36,257 individuals remained for analysis for the fluid intelligence touchscreen measure and 28,846 remained for the web-based version. As 10,984 individuals had taken both the touchscreen and web-based test, we only included the data from the touchscreen test for these individuals. This resulted in 54,119 individuals with a score on either the fluid intelligence web-based (UKB-wb) or touchscreen (UKB-ts) version (Supplementary Table 1). At the time of taking the test, the age of the participants ranged between 40 and 78 years. Half of the participants were between 40 and 60 years old, 44% were between 60 and 70 years old and 6% were older than 70 years. The mean age was 58.98 years with a standard deviation of 8.19.

Summary statistics from the CHIC consortium. We downloaded the publicly available combined GWAS results from the meta-analyses as reported by CHIC5 (see URLs). Details on the included cohorts and performed analyses are reported in the original publication5. Briefly, CHIC includes six cohorts totaling 12,441 individuals: the Avon Longitudinal Study of Parents and Children (ALSPAC, n = 5,517), the Lothian Birth Cohorts of 1921 and 1936 (LBC1921, n = 464; LBC1936, n = 947), the Brisbane Adolescent Twin Study subsample of the Queensland Institute of Medical Research (QIMR, n = 1,752), the Western Australian Pregnancy Cohort Study (Raine, n = 936) and the Twins Early Development Study (TEDS, n = 2,825). All individuals are children aged from 6–18 years. Within each cohort, the cognitive performance measure was adjusted for sex and age and principal components were included to adjust for population stratification. See also Supplementary Table 1.

Full GWAS data from additional cohorts. We used the same additional (non-CHIC) cohorts as described in detail in ref. 7, which included 11,748 individuals from five cohorts. In ref. 7, results were only reported for 69 SNPs, as these served as a secondary analysis for a lookup effort. In the current study, we used the full genome-wide results from these cohorts. GWAS were conducted in 2013, and summary statistics were obtained from the PIs of the five cohorts. The quality control protocol entailed excluding SNPs with MAF <0.01, imputation quality score <0.4, Hardy–Weinberg P value <1 × 10−6 and call rate <0.957. The five cohorts included the Erasmus Rucphen Family Study (ERF, n = 1,076), the Generation R Study (GenR, n = 3,701), the Harvard/Union Study (HU, n = 389), the Minnesota Center for Twin and Family Research Study (MCTFR, n = 3,367) and the Swedish Twin Registry Study (STR, n = 3,215). Detailed descriptions of these cohorts are provided in ref. 7 and summarized in Supplementary Table 1. Within each cohort, the cognitive performance measure was adjusted for sex and age and principal components were included to adjust for population stratification.

SNP analysis in the UK Biobank sample.

Association tests were performed in SNPTEST37 (see URLs), using linear regression. Both phenotypes were corrected for a number of covariates, including age, sex and a minimum of five genetically determined principal components, depending on how many were associated with the phenotype (5 for the web-based test and 15 for the touchscreen version, tested by linear regression). Additionally, we included the Townsend deprivation index as a covariate, which is based on postal code and measures material deprivation. The touchscreen version of the phenotype was also corrected for assessment center and genotyping array. SNPs with imputation quality <0.8 and MAF <0.001 (based on all Europeans present in the total sample) were excluded after the association analysis, resulting in 12,573,858 and 12,595,966 SNPs for the touchscreen and web-based test, respectively.

Gene analysis.

The SNP-based P values from the meta-analysis were used as input for the gene-based analysis. We used all 19,427 protein-coding genes from the NCBI 37.3 gene definitions as the basis for a genome-wide gene association analysis (GWGAS) in MAGMA (see URLs). After SNP annotation, there were 18,338 genes that were covered by at least one SNP. Gene association tests were performed taking LD between SNPs into account. We applied a stringent Bonferroni correction to account for multiple testing, setting the genome-wide threshold for significance at 2.73 × 10−6.

Pathway analysis.

We used MAGMA to test for association of predefined gene sets with intelligence. A total of 6,166 GO and 674 Reactome gene sets were obtained (see URLs). We computed competitive P values, which are less likely to be below the threshold of significance than self-contained P values. Competitive P values are the outcomes of the test that the combined effect of genes in a gene set is significantly larger than the combined effect of all other genes, whereas self-contained P values are informative when testing against the null hypothesis of no association. Self-contained P values are not interpreted and not reported by us. Competitive P values were corrected for multiple testing using MAGMA's built-in empirical multiple-testing correction with 10,000 permutations.

Meta-analysis.

Meta-analysis of the results of the 13 cohorts was performed in METAL11 (see URLs). We did not include SNPs that were not present in the UK Biobank sample. The analysis was based on P values, taking sample size and direction of effect into account using the sample size scheme.

Genetic correlations.

Genetic correlations (r g ) were calculated between intelligence and 32 other traits for which summary statistics from GWAS were publicly available, using LD score regression (see URLs). This method corrects for sample overlap, by estimating the intercept of the bivariate regression. A conservative Bonferroni-corrected threshold of 1.56 × 10−3 was used to determine significant correlations.

Functional annotation.

We identified all SNPs that had an r2 value of 0.1 or higher with the 18 independent lead SNPs and were included in the METAL output. We used the 1000 Genomes Project Phase 3 reference panel to calculate r2. We further filtered on SNPs with P < 0.05. In addition, we only annotated SNPs with MAF >0.01.

Positional annotations for all lead SNPs and SNPs in LD with the lead SNPs were obtained by performing ANNOVAR gene-based annotation using RefSeq genes. In addition, CADD scores38 and RegulomeDB15 scores were annotated to SNPs by matching chromosome, position, reference and alternative alleles. For each SNP, eQTLs were extracted from GTEx (44 tissue types)39, the Blood eQTL browser40 and BIOS gene-level eQTLs41. The eQTLs obtained from GTEx were filtered on gene P < 0.05, and eQTLs obtained from the other two databases were filtered on FDR < 0.05. The FDR values were provided by GTEx, BIOS and the Blood eQTL browser. For GTEx eQTLs, there is one FDR value available per gene–tissue pair. As such, the FDR is identical for all eQTLs belonging to the same gene–tissue pair. For BIOS and the Blood eQTL browser, an FDR value was computed for each SNP.

To test whether the SNPs were functionally active by means of histone modifications, we obtained epigenetic data from the NIH Roadmap Epigenomics Mapping Consortium42 and ENCODE43. For every 200 bp of the genome, a 15-core chromatin state was predicted by a hidden Markov model based on five histone marks (H3K4me3, H3K4me1, H3K27me3, H3K9me3 and H3K36me3) for 127 tissue and cell types44. We annotated chromatin states (15 states in total) to SNPs by matching chromosome and position for every tissue or cell type. We computed the minimum state (1, the most active state) and the consensus state (majority of states) across 127 tissue and cell types for each SNP.

Chromatin states were also determined for the 52 genes (47 from the gene-based test + 5 additional genes implicated by single-SNP GWAS). For each gene and tissue, the chromatin state was obtained per 200-bp interval in the gene. We then annotated the genes by means of a consensus decision when multiple states were present for a single gene; that is, the state of the gene was defined as the modus of all states present in the gene.

Tissue expression of genes.

RNA sequencing data from 1,641 tissue samples with 45 unique tissue labels were derived from the GTEx consortium39. This set includes 313 brain samples over 13 unique brain regions (see Supplementary Table 18 for sample size per tissue). Of the 52 genes implicated by either the GWAS or the GWGWAS, 44 were included in the GTEx data. Normalization of the data was performed as described previously45. Briefly, genes with RPKM value smaller than 0.1 in at least 80% of the samples were removed. The remaining genes were log 2 transformed (after using a pseudocount of 1), and finally a zero-mean normalization was applied.

Proxy replication in educational attainment.

For the replication analysis, we used a subset of the data from ref. 21. In particular, we excluded the Erasmus Rucphen Family Study, the Minnesota Center for Twin and Family Research Study, the Swedish Twin Registry Study, the 23andMe data and all individuals from UK Biobank, to make sure that there was no sample overlap with our IQ data set. Genetic correlation between intelligence and educational attainment in this non-overlapping subsample was r g = 0.73, s.e.m. = 0.03, P = 1.4 × 10−163. The replication analysis was based on the phenotype EduYears, which measures the number of years of schooling completed. A total of 306 of our 336 top SNPs (and 16 of 18 independent lead SNPs) were available in the educational attainment sample. We performed a sign concordance analysis for the 16 independent lead SNPs, using the exact binomial test. For each independent signal we determined whether either the lead SNP had a P value smaller than 0.05/16 in the educational attainment analysis or another (correlated) top SNP in the same locus had such a P value, if this was not the case for the lead SNP. All 47 genes implicated in the GWGAS for intelligence were available for lookup in the educational attainment sample. For each gene, we determined whether it had a P value smaller than 0.05/47 in the educational attainment analysis.

Polygenic risk score analysis.

We used LDpred16 to calculate the variance explained in intelligence in independent samples by a polygenic risk score based on our discovery analysis, as well as two previous GWAS for intelligence5,6. LDpred adjusts GWAS summary statistics for the effects of LD by using an approximate Gibbs sampler that calculates the posterior means of effects, conditional on LD information, when calculating polygenic risk scores. We used varying priors for the fraction of SNPs with nonzero effects (priors: 0.01, 0.05, 0.1, 0.5, 1 and an infinitesimal prior). Independent data sets available for polygenic risk score analyses are described in the Supplementary Note.

Data availability.

Summary statistics have been made available for download from http://ctg.cncr.nl/software/summary_statistics. Genotype data that underlie the findings of this study are available from UK Biobank but restrictions apply to the availability of these data, which were used under license for the current study (application number 16406) and so are not publicly available. Summary statistics from the CHIC consortium are available from http://ssgac.org/documents/CHIC_Summary_Benyamin2014.txt.gz. Additional supporting data are provided in the supplementary material.

URLs.

UK Biobank, http://www.ukbiobank.ac.uk; genotyping and quality control of UK Biobank, http://biobank.ctsu.ox.ac.uk/crystal/refer.cgi?id=155580; CHIC summary statistics http://ssgac.org/documents/CHIC_Summary_Benyamin2014.txt.gz; SNPTEST, https://mathgen.stats.ox.ac.uk/genetics_software/snptest/snptest.html; MAGMA, http://ctg.cncr.nl/software/magma; MSigDB, http://software.broadinstitute.org/gsea/msigdb/collections.jsp; METAL, http://genome.sph.umich.edu/wiki/METAL_Program; LD score regression (LDSC), https://github.com/bulik/ldsc.