Published Genome Wide Association Studies (GWAS), reporting the presence of alleles exhibiting significant and replicable associations with IQ, are reviewed. The average between-population frequency (polygenic score) of nine alleles positively and significantly associated with intelligence is strongly correlated to country-level IQ (r = .91). Factor analysis of allele frequencies furthermore identified a metagene with a similar correlation to country IQ (r = .86). The majority of the alleles (seven out of nine) loaded positively on this metagene. Allele frequencies varied by continent in a way that corresponds with observed population differences in average phenotypic intelligence. Average allele frequencies for intelligence GWAS hits exhibited higher inter-population variability than random SNPs matched to the GWAS hits or GWAS hits for height. This indicates stronger directional polygenic selection for intelligence relative to height. Random sets of SNPs and Fst distances were employed to deal with the issue of autocorrelation due to population structure. GWAS hits were much stronger predictors of IQ than random SNPs. Regressing IQ on Fst distances did not significantly alter the results nonetheless it demonstrated that, whilst population structure due to genetic drift and migrations is indeed related to IQ differences between populations, the GWAS hit frequencies are independent predictors of aggregate IQ differences.
Genetic variants identified by three large genome-wide association studies (GWAS) of educational attainment (EA) were used to test a polygenic selection model. Weighted and unweighted polygenic scores (PGS) were calculated and compared across populations using data from the 1000 Genomes (n = 26), HGDP-CEPH (n = 52) and gnomAD (n = 8) datasets. The PGS from the largest EA GWAS was highly correlated to two previously published PGSs (r = 0.96–0.97, N = 26). These factors are both highly predictive of average population IQ (r = 0.9, N = 23) and Learning index (r = 0.8, N = 22) and are robust to tests of spatial autocorrelation. Monte Carlo simulations yielded highly significant p values. In the gnomAD samples, the correlation between PGS and IQ was almost perfect (r = 0.98, N = 8), and ANOVA showed significant population differences in allele frequencies with positive effect. Socioeconomic variables slightly improved the prediction accuracy of the model (from 78–80% to 85–89%), but the PGS explained twice as much of the variance in IQ compared to socioeconomic variables. In both 1000 Genomes and gnomAD, there was a weak trend for lower GWAS significance SNPs to be less predictive of population IQ. Additionally, a subset of SNPs were found in the HGDP-CEPH sample (N = 127). The analysis of this sample yielded a positive correlation with latitude and a low negative correlation with distance from East Africa. This study provides robust results after accounting for spatial autocorrelation with Fst distances and random noise via an empirical Monte Carlo simulation using null SNPs.
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