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

Abstract

Intelligence is associated with important economic and health-related life outcomes. Despite intelligence having substantial heritability (0.54) and a confirmed polygenic nature, initial genetic studies were mostly underpowered. 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 heritability for intelligence in childhood (0.45) and adulthood (0.80), we show substantial genetic correlation (r_g = 0.89, LD score regression P = 5.4 × 10^-29). These findings provide new insight into the genetic architecture of intelligence.

Fig. 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 the position on the chromosome, with the name and location of genes in the UCSC Genome Browser 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 are generated with LocusZoom.

Fig. 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 statistical significant associations (P=5×10⁻⁸) and suggestive associations (P=1×10⁻⁵) respectively. Green dots represent the independent hits. (b) Functional categories for 336 genome-wide significant SNPs. (c) The minimum (most active) chromatine state across 127 tissues for 336 genome-wide significant SNPs. (d) The Regulome database score for 336 genome-wide significant SNPs. The lower the score the more likely it is that a SNP has a regulatory function. For b–d the numbers in brackets in the legends refer to the number of lead SNPs for that category.

Fig. 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) Heatmap of gene-expression levels of genes for intelligence in 45 tissue types (see Supplementary Table 18 for N 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 r_g. Red bars represent the traits that showed a significant genetic correlation after correction for multiple testing (P<1.56×10⁻³), pink bars the traits that showed a nominal significant correlation (P<0.05), and blue bars 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 r_g with this phenotype across three age groups and found no difference in r_g’s (Supplementary Note).